Robert and Nigel Moynihan; Tim Delaney; Joe Ramsbottom; John Lowry; Frank ..... Wilson, 1977; Kavanagh, 1990; Nairn and Farrelly, 1991; Carruthers and ...
National University of Ireland University College Dublin
The diversity of birds and butterflies in Irish lowland landscapes with special reference to the effects of set-aside management on birds in the breeding season.
by
FINTAN BRACKEN
A thesis presented in fulfilment of the requirements of the National University of Ireland for the degree of Ph.D.
Department of Zoology, University College Dublin, Ireland.
Supervisor: Prof. T. Bolger September 2004
CONTENTS ACKNOWLEDGEMENTS DECLARATION ABSTRACT
v vii viii
CHAPTER 1: GENERAL INTRODUCTION 1.1 BIODIVERSITY 1.2 IMPORTANCE OF SCALE 1.3 LOWLAND LANDSCAPES IN IRELAND 1.4 THE REGION STUDIED 1.5 STRUCTURE AND AIMS OF CURRENT STUDY
1 1 2 3 7 8
CHAPTER 2: THE DIVERSITY OF BIRDS IN DIFFERENT HABITAT TYPES 2.1 INTRODUCTION 2.1.1 Ireland’s Reduced Bird Fauna 2.1.2 Island Biogeography 2.1.3 Lack of Habitat Suitable for Specialist Species 2.1.4 Competition from Resident Species 2.1.5 Studies of Birds in Irish Habitats 2.1.6 Aims of the Study 2.2 METHODS 2.2.1 Study Sites 2.2.2 Breeding Season Bird Counts 2.2.3 Winter Season Bird Counts 2.2.4 Vegetation Structure 2.2.5 Data Processing 2.2.5.1 Breeding Season Datasets 2.2.5.2 Winter Season Datasets 2.2.6 Statistical Analysis 2.2.6.1 Ordinations 2.2.6.2 Detrended Correspondence Analysis (DCA) 2.2.6.3 Principal Component Analysis (PCA) 2.2.6.4 Canonical Correspondence Analysis (CCA) 2.2.6.5 Redundancy Analysis (RDA) 2.2.6.6 Diversity indices 2.2.6.6.1 Species Richness 2.2.6.6.2 Shannon-Weiner Index (H’) 2.2.6.6.3 Simpson’s Index (D) 2.2.6.6.4 Jaccard Index of Similarity 2.2.6.7 Kruskal Wallis Test, One-way ANOVA and Bonferroni t test 2.3 RESULTS 2.3.1 Birds Recorded During the Breeding Season 2.3.2 Bird Abundances 2.3.2.1 All Species Recorded 2.3.2.2 Breeding Species Only 2.3.3 Species Composition of Samples 2.3.3.1 All Birds 2.3.3.2 Breeding Birds 2.3.4 Comparison between Habitat Types 2.3.4.1 Diversity Indices 2.3.4.2 Assemblage Composition – All Species Recorded 2.3.4.3 Assemblage Composition – Breeding Species Only 2.3.5 Vegetation Effects – All Species Recorded 2.3.6 Birds Recorded During the Winter Season 2.3.7 Comparison between Habitat Types During the Winter Season 2.3.7.1 Diversity Indices 2.3.7.2 Assemblage Composition DISCUSSION 2.4 2.4.1 Farmland Bird Studies
10 10 10 10 13 15 16 18 18 18 24 25 26 27 27 27 28 28 30 31 32 33 34 34 34 35 36 36 37 37 38 38 38 39 39 42 44 44 48 52 55 57 60 60 62 66 66
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2.4.2 2.4.3 2.4.5 2.4.6 2.4.7
Woodland Bird Studies Comparison of Habitats Migrants Vegetation Structure Conclusions
71 75 79 82 82
CHAPTER 3: THE EFFECTS OF SET-ASIDE MANAGEMENT ON BIRDS IN THE BREEDING SEASON 3.1 INTRODUCTION 3.1.1 The Decline of Farmland Birds 3.1.2 Potential Benefit of Set-aside 3.1.3 Studies of Birds on Set-aside in the Breeding Season 3.1.4 Studies of Skylarks on Set-aside 3.1.5 Studies of Winter Birds on Set-aside 3.1.6 Rotational Set-aside Versus Non-Rotational Set-aside 3.1.7 Aims of the Study 3.2 METHODS 3.2.1 Study Sites 3.2.2 Bird Counts 3.2.3 Vegetation Structure 3.2.4 Data Processing 3.2.5 Statistical Analysis 3.2.5.1 Wilcoxon Sign Rank Test RESULTS 3.3 3.3.1 Comparison between Set-aside and Non-Set-aside 3.3.1.1 Diversity Indices 3.3.1.2 Species Composition– All Species Recorded 3.3.1.3 Species Composition – Breeding Species Only 3.3.2 Comparison between Set-aside, Grass and Tillage 3.3.2.1 Diversity Indices 3.3.2.2 Species Composition – All Species Recorded 3.3.2.3 Species Composition – Breeding Species Only 3.3.3 Comparison of Different Types of Set-aside Management 3.3.3.1 Diversity Indices 3.3.3.2 Species Composition– All Species Recorded 3.3.3.2.1 Field Species Only Included 3.3.3.2.2 Boundary Species Only Included 3.3.3.3 Species Composition – Breeding Species Only 3.3.4 Comparison between Grazed and Non-grazed Set-aside 3.3.5 Vegetation and Habitat Effects 3.3.5.1 Vegetation Stratification Profiles of All Sites – All Species Recorded 3.3.5.2 Vegetation Stratification Profiles of All Sites – Breeding Species Only 3.3.5.3 Horizontal Density and Vegetation Density DISCUSSION 3.4
84 84 84 86 86 90 95 96 98 99 99 103 103 104 105 105 105 105 105 106 110 110 110 111 114 116 116 118 121 123 125 127 128 128 130 133 134
CHAPTER 4: THE DIVERSITY OF BUTTERFLIES IN DIFFERENT HABITAT TYPES 4.1 INTRODUCTION 4.1.1 Butterflies 4.1.2 Habitat and Host Plant Affinities of Butterflies 4.1.3 Butterflies as Indicators of Global Climate Change 4.1.4 Aims 4.2 METHODS 4.2.1 Butterfly Sampling 4.2.2 Study 1 4.2.3 Study 2 4.2.4 Study 3 4.2.5 Study 4 4.2.6 Data Processing 4.2.6.1 Study 1 4.2.6.2 Study 2 4.2.6.3 Study 3
147 147 147 150 153 157 157 157 159 159 161 163 164 164 164 165
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4.2.7 Statistical Analysis 4.2.7.1 Ordinations and Diversity Indices 4.3 RESULTS 4.3.1 Study 1 4.3.1.1 Speckled Wood 4.3.1.2 Ringlet 4.3.1.3 Meadow Brown 4.3.1.4 Green-veined White 4.3.1.5 Peacock 4.3.1.6 Species Diversity 4.3.2 Study 2 4.3.3 Study 3 4.3.3.1 2002 4.3.3.2 2001 4.3.4 Study 4 4.4 DISCUSSION 4.4.1 Study 1 4.4.2 Study 2 4.4.3 Study 3 4.4.4 Study 4 4.4.5 Conclusions
166 166 166 166 168 169 170 171 172 173 175 178 178 180 183 185 185 188 190 192 193
CHAPTER 5: THE DIVERSITY OF BIRDS AND BUTTERFLIES IN RELATION TO LANDSCAPE STRUCTURE 195 5.1 INTRODUCTION 195 5.1.1 Landscape Ecology 195 5.1.2 Land-use Intensity Gradient 198 5.1.3 Remote Sensing 198 5.1.4 Types of Landscape Indices 199 5.1.4.1 Area/Density/Edge Metrics 201 5.1.4.2 Shape Metrics 202 5.1.4.3 Core Area Metrics 203 5.1.4.4 Isolation/Proximity Indices 203 5.1.4.5 Contagion/Interspersion Metrics 205 5.1.4.6 Connectivity Metrics 207 5.1.4.7 Diversity Metrics 207 5.1.5 Aims 208 5.2 METHODS 209 5.2.1 Study Sites 209 5.2.2 Breeding Bird Counts 209 5.2.3 Winter Bird Counts 210 5.2.4 Butterfly Sampling 210 5.2.5 Remote Sensing Methodology 210 5.2.6 Data Processing 218 5.2.6.1 Breeding Bird Datasets 218 5.2.6.2 Winter Bird Datasets 219 5.2.6.3 Butterfly Datasets 219 5.2.6.4 Remote Sensing Datasets 219 5.2.7 Statistical Analysis 221 5.2.7.1 Ordinations 221 5.2.7.2 Diversity Indices 221 5.2.7.3 Correlations 221 RESULTS 221 5.3 5.3.1 Remote Sensing Images of the Land-Use Units (LUUs) 221 5.3.2 Distribution of Land-Use Classes across the LUUs 229 5.3.3 Normalised Difference Vegetation Index (NDVI) 232 5.3.4 Species Diversity Indices for the LUUs 233 5.3.5 Level 1 LUU Landscape Metrics 234 5.3.5.1 Principal Component Analysis (PCA) 234 5.3.5.2 Breeding Season Birds 0-100m 235
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5.3.5.2.1 Correlation with Diversity Indices 5.3.5.2.2 Redundancy Analysis 5.3.5.3 Winter Season Birds 0-50m 5.3.5.3.1 Correlation with Diversity Indices 5.3.5.3.2 Redundancy Analysis 5.3.5.4 Butterflies 5.3.5.4.1 Correlation with Diversity Indices 5.3.5.4.2 Redundancy Analysis 5.3.6 Level 1 LUU Class Metrics 5.3.6.1 Breeding Season Birds 0-100m 5.3.6.1.1 Correlation with Diversity Indices 5.3.6.1.2 RDA of the Arable Land Class 5.3.6.1.3 RDA of the Forest Class 5.3.6.1.4 RDA of the Grassland Class 5.3.6.2 Winter Season Birds 0-50m 5.3.6.2.1 Correlation with Diversity Indices 5.3.6.2.2 RDA of the Arable Land Class 5.3.6.2.3 RDA of the Forest Class 5.3.6.2.4 RDA of the Grassland Class 5.3.6.3 Butterflies 5.3.6.3.1 Correlation with Diversity Indices 5.3.6.3.2 RDA of the Arable Land Class 5.3.6.3.3 RDA of the Forest Class 5.3.6.3.4 RDA of the Grassland Class 5.3.7 Level 2 LUU Landscape Metrics 5.3.7.1 Principal Component Analysis (PCA) 5.3.7.2 Breeding Season Birds 0-100m 5.3.7.2.1 Correlation with Diversity Indices 5.3.7.2.2 Redundancy Analysis 5.3.7.3 Winter Season Birds 0-50m 5.3.7.3.1 Correlation with Diversity Indices 5.3.7.3.2 Redundancy Analysis 5.3.7.4 Butterflies 5.3.7.4.1 Correlation with Diversity Indices 5.3.7.4.2 Redundancy Analysis 5.3.8 Level 2 LUU Class Metrics 5.3.8.1 Breeding Season Birds 0-100m 5.3.8.1.1 Correlation with Diversity Indices 5.3.8.1.2 RDA of the Arable Land Class 5.3.8.1.3 RDA of Other Classes 5.3.8.2 Winter Season Birds 0-50m 5.3.8.2.1 Correlation with Diversity Indices 5.3.8.2.2 RDA of the Arable Land Class 5.3.8.3 Butterflies 5.3.8.3.1 Correlation with Diversity Indices 5.3.8.3.2 RDA of the Arable Land Class DISCUSSION 5.4 5.4.1 Land-use Gradient 5.4.2 Birds and Landscape Structure Associations 5.4.3 Butterflies and Landscape Structure Associations 5.4.5 Limitations of Landscape Metrics 5.4.6 Conclusions
235 236 239 239 240 240 240 241 243 243 243 245 246 248 250 250 251 251 253 253 253 255 257 259 261 261 262 262 263 265 265 265 266 266 266 268 268 268 270 270 271 271 273 273 273 276 277 277 279 285 288 289
CHAPTER 6: GENERAL DISCUSSION AND CONCLUSIONS
291
BIBLIOGRAPHY
304
APPENDICES
323
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Acknowledgements First of all I have to sincerely thank Professor Tom Bolger for all his help and patience over the past few years, without his guidance this thesis would never have been completed. I would also like to thank Dr. Mark Rogers and Prof. Bolger for providing the facilities in the department to carry out this research. Thank also to Eric Callaghan, Maeve Flynn and Stephen Heery for getting up at the crack of dawn and assisting me on occasion with the bird sampling fieldwork during the first two years of the project. I am very grateful to all the landowners who kindly allowed me access to their land during the project: Coillte, especially Marie Mannion and Arthur Buckley of Coillte, Portlaoise and Mountrath; Jim Fox, for all his invaluable help; the De Vesci Estate, Abbeyleix, especially Peter Fegan; Matthew Costigan; Phillip Bradley; Mervin McCann; Andrew Bergin; Liam Dunne; Peter Walsh-Kemmis; Bill and Helen Kelly; Robert and Nigel Moynihan; Tim Delaney; Joe Ramsbottom; John Lowry; Frank Handy; Mark Onions; Tom Cushen; Eoghan Miller; Henry Burns; Alan Salter; and Peter Hyland. I would especially like to thank Jim Fox for all his invaluable help and useful comments during fieldwork. Thanks to John Challoner of Teagasc, Portlaoise and Hugh McCreavey of Teagasc, Athy who helped me immensely by providing names and phone numbers of farmers with set-aside in the Portlaoise and Athy areas in 2003. Thanks to Eva Ivits and others at FeLis in the University of Freiburg for their assistance with remote sensing and landscape metrics. Thanks to everyone involved with BioAssess particularly Dan Chamberlain, Rob Fuller and Chris van Swaay, who were always willing to answer any of my queries. I would like to thank Tim Ryle for
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his assistance with the vegetation stratification profiles and butterfly transect descriptions amongst many other things. I would especially like to thank my parents for all their support over all the years. Thanks for all the financial assistance and for putting up with me coming home every year to annoy ye! Thanks also to my father for coming out to Abbeyleix wood to pull my car out of a hole at 7am! Thanks to the rest of my family, flatmates and friends for their support and ‘slagging’ throughout the last four years. Sometimes, I reckon, especially amongst my longest friends in Portlaoise that without this project, nights out and trips to matches would have been a lot less enjoyable without the frequent banter about ‘birds and the bees’, me being the ‘birdman’, driving the ‘birdmobile’, being called ‘Charlie Bird’, having people screeching ‘caw caw’ at me, etc., etc. I’m glad I brought such amusement to everyone. A special thanks to everyone in Research Lab 1, Aoife, Brian, Cróna, Michael, Nuala, Sylvia, Tim, Valerie and all the masters, 4th year students and others who have worked in the lab during my time there, for all their help and support throughout the 4 years and for making the time spent in the lab so enjoyable! The shrieks, screams, bursts of laughter coming from the lab are renowned throughout the department! Thanks also to everyone else in the Zoology Department who has assisted me during the years, particularly Billy Clarke who provided me with some beautiful bird photographs. Finally, I would like to thank the agencies that funded this research: Energy, Environment and Sustainable Development Programme Project No. EVK2-CT199900280; and Enterprise Ireland.
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DECLARATION
I, the undersigned, declare that the work reported in this thesis is original, and has not been published or submitted as part or whole requirement for any other degrees or qualifications.
_______________________ Fintan Bracken
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Abstract This study examined bird and butterfly diversity in agricultural land and woodland in Irish lowland landscapes in Co. Laois and Co. Kildare. Similar studies have been conducted in Britain and other European countries but it was believed that the situation might have been different in Ireland due to the island status, the physical environment and history of landuse of the country, which have resulted in impoverished bird and butterfly faunas. In the breeding season, bird species richness and abundances were significantly different between the habitats studied: broadleaf forest, coniferous forest, pasture, set-aside and tillage. However, in winter this was not the case. The habitats differed in the bird species assemblages they contained both in summer and winter. Farmland habitats contributed more unique species to the total bird diversity, which probably reflects the lack of woodland specialists in Ireland. Farmland and woodland habitats did not differ significantly in terms of butterfly species diversity and abundance. However, this may have been due to the low numbers recorded. Individual species showed preference for different aspects of local habitat and vegetation structure including the presence of larval foodplants. The numbers of butterflies and species recorded were higher from transects placed along hedgerows compared to those in the middle of fields in the same farmland site. In lowland farmland in Ireland, bird species diversity in the breeding season was greater in set-aside compared to neighbouring farmland. The type of management of set-aside was important and determined which species were likely to be found using the set-aside field. The majority of field interior species including skylark,
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meadow pipit, pheasant and snipe showed a preference for non-rotational set-aside over rotational set-aside and the other management types. Landscape structure was also shown to be important for bird and butterfly diversity in lowland Ireland. In the breeding season, area of forest had a negative influence on bird diversity while large unfragmented areas of grassland had positive effects. The presence of areas of grassland and mature mixed forest in the landscape were more important for birds in winter. Butterfly diversity in the landscape was influenced by heterogeneity of habitat patches with grassland and forest patches having positive effects. Large areas of arable land and coniferous forest had negative effects on butterfly diversity. The types of habitats present in the landscape and how they are managed, along with the structure of the landscape, determine species diversity of birds and butterflies in lowland Irish landscapes.
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Chapter 1: General Introduction. 1.1 Biodiversity The extinction of species worldwide over recent decades has led to an increased interest in, and concern about, ‘biodiversity’. However, what exactly does the term ‘biodiversity’ mean? There are several definitions but in its simplest form is a contraction of ‘biological diversity’. One definition is the sum of all biotic variation from the level of genes to ecosystems (Purvis and Hector, 2000). Other definitions include abiotic aspects and the importance of scale. The Convention on Biological Diversity defines biodiversity as follows: ‘biological diversity means the variability among living organisms from all sources, including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within species, between species and of ecosystems’. Noss (1990) states that the biodiversity of an area is made up of the three primary attributes of ecosystems: composition, structure, and function. Composition is the identity and variety of elements in a collection, and includes species lists and measures of species diversity and genetic diversity. Structure is the physical organisation or pattern of a system, from habitat complexity as measured within communities to the pattern of patches and other elements at a landscape scale. Function involves ecological and evolutionary processes, including gene flow, disturbances, and nutrient cycling. If Noss’ definition is applied, then biodiversity is important in maintaining the healthy functioning of ecosystems. The ambiguous nature of the term biodiversity means that the definition used should reflect the purpose or goal in defining it in the first place.
1
As it is clearly impossible to look at all aspects of the biodiversity of an area or region, biological diversity is subdivided into some of its component parts to develop and test hypotheses about the regulation of biodiversity (Huston, 1994). Thus, in this study it was decided that the focus would be the species diversity of birds and butterflies. 1.2 Importance of Scale In the study of biodiversity the issue of scale is of fundamental importance, as species diversity has been shown to generally increase with increasing area (Huston, 1994). This increase of diversity with area could be a result of environmental heterogeneity, as increasing the sample area involves including additional habitat types with groups of different species. Thus, the diversity of birds and butterflies can be studied at various scales from small single habitat patches of tens of square metres, to landscapes that contain many patches of several different habitat types over several square kilometres, to the entire Earth. Heterogeneity on large scales, such as landscape, is contributed by geological processes that help determine the types and amounts of minerals, bedrock and soils in the area; geological processes in conjunction with climate also influence patterns of topography that result from erosion and topography influences the distribution of water, soil nutrients, solar energy, and other factors across a landscape. The vertical structure and complexity produced by the roots, stems, branches and leaves of woody plants, as well as the nonwoody plants growing with them, determines the heterogeneity at smaller spatial scales (Huston, 1994). It is clear that landscape structure is very important in the study of the diversity of both birds and butterflies (Baz and Garcia-Boyero, 1995; McGarigal and McComb, 1995). However, as with biodiversity, landscape is also an ambiguous term, 2
but in ecology, landscape is generally considered to be an area of land containing a mosaic of habitat patches (McGarigal et al., 2002). The species diversity of birds and butterflies is determined by landscape structure and composition as this in turn determines the habitats available to these taxa. Habitat heterogeneity is one of the key determinants of biodiversity at the landscape scale (Benton et al., 2003). This heterogeneity of the landscape is based on the type, size, shape and spatial arrangement of habitat patches in it, which will determine the occurrence and abundance of species in this landscape. Three key questions need to be answered in studies of landscape ecology: 1. What types of habitats are present in the landscape? 2. How are these habitats managed and what are the effects of that management in comparison to other types? For example, are forests managed for timber production different to those left unmanaged, or is a tillage system left in setaside different from an intensively tilled system? 3. What is the structure of the landscape? For example, what size and shape are habitat patches in the landscape and how are they arranged in relation to patches of similar or different habitat? In this study these questions will be addressed in the context of birds and butterflies in lowland Irish landscapes. 1.3 Lowland Landscapes in Ireland Ireland is a small country of only 83,000 km2 and consists of a broad central lowland that is broken up by a number of separate hill and mountain areas and is ringed by a rim of distinct upland areas (Aalen et al., 1997). The Central Lowland is a low-lying plain lying mainly between 60m and 120m above sea level. The bedrock is mainly Carboniferous limestone which lies underneath a mantle of recent glacial 3
deposits and sometimes an additional layer of peat bogs. The climate of the island is markedly oceanic with frequent rain and low annual temperatures due to the prevalent, moist westerly winds from the warm waters of the North Atlantic. The central region of the country receives intermediate levels of sunshine, rain and temperature compared to the wetter and colder western seaboard and the sunnier, drier and warmer east and southeast. The mean annual rainfall for the region is between 800 and 1,600mm with approximately 150 to 175 rain days annually (Aalen et al., 1997). The mean annual sunshine is between 1,300 and 1,400 hours with the mean daily temperature in January of 4.5 to 5ºC and in July of 15.5 to 16ºC. The soils are mainly well-drained and fertile, although some wet mineral soils and low level peat occurs in the area as well. The only upland area in this region is in the north of Co. Laois where the Slieve Bloom Mountains are located. The Slieve Blooms are dominated by coniferous plantation forest and blanket bog. The area selected for study are the lowland landscapes of Co. Laois and Co. Kildare. Like much of Ireland, these are dominated by agricultural land with occasional patches of forest, raised bogs, urban areas and wetlands, such as lakes and rivers. Eighty per cent of the surface of Ireland is devoted to agriculture, which is heavily divided into fields by various types of boundaries (Aalen et al., 1997). It is estimated that field boundaries measure 830,000 km in length and cover 1.5% of the land area, which is a higher proportion than that of deciduous forest (Aalen et al., 1997). Grassland is currently the dominant agricultural land-use in Ireland but this was not always the case as the area under tillage crops has declined drastically in the Republic of Ireland with a 73% decrease between 1851 and 1997 from 1,375,000 ha to 376,000 ha (Taylor and O’Halloran, 1999; 2002). The area of land under barley has increased during this time period (+ 271%) and is now the major cereal crop in the
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country, whereas oats (– 97%) and wheat (– 40%) have declined substantially. Root crops, including potatoes, turnips and sugar beet, come second to cereals in terms of area planted, and peas, beans and other green crops third. Root crops have shown an 86% decline between 1851 and 1997 with the area planted falling from 394,000 ha to 55,000 ha, with areas of 252,000 ha and 119,000 ha lost from potato and turnip planting respectively during this period (Taylor and O’Halloran, 1999). As a result of these changes, Ireland has experienced a polarisation of agriculture, with cereal farming becoming largely confined to southern and eastern counties and grass-based livestock rearing dominating the north, west and midlands (Taylor and O’Halloran, 2002). In the second half of the 19th century all bar three counties, Galway, Kerry and Clare, were considered to have mixed land use (i.e. land under tillage crops making up at least 20% of the total land area devoted to tillage crops, hay and pasture), whereas by the 1990s only Louth, Dublin, Kildare, Carlow and Wexford qualified for this category. However, mixed farming and rotational cropping practices have also declined both in the more intensively farmed east of the country, for example the cereal-growing areas of Kildare, Carlow and Wexford, and in the less intensively managed west, for example on coastal mixed farmland in Donegal, Mayo and Galway (Taylor and O’Halloran, 2002). In the 1970s, silage began to replace hay in Ireland and the proportion of land under silage had increased from 42% of the total area of land under hay or silage in 1981 to 73% in 1997. Substantial amounts of hedgerows have been lost since the 1930s with at least one-fifth removed up to the present day. This decline in hedges has been greater in the tillage areas of the east and southeast than in the pastoral north and west (Taylor and O’Halloran, 2002).
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Livestock and tillage are the main types of farming practised in the study region (Aalen et al., 1997). The landscape of Co. Laois is a mixed agricultural landscape with dairy, cattle and tillage all practised, along with a smaller amount of sheep rearing. Most of the area around Athy in Co. Kildare is given over to intensive tillage with barley, wheat and sugar beet being the main crops and oats and peas sown to a lesser extent. This area consists of many large fields with a high level of hedgerow removal, whereas, by comparison, much of the ancient hedgerows are intact in farmland in Co. Laois. Another component of the Irish landscape is woodland, which exists as small patches scattered throughout the country. Private woodland and state forests only account for 9% of the land area of Ireland, with 84% consisting of non-native coniferous species. Eighty per cent of woodland is state-owned of which over 90% is made up of coniferous tree species, such as Sitka spruce (Picea sitchensis) and lodgepole pine (Pinus contorta) (Hutchinson, 1989; Aalen et al., 1997). Woodland, especially dense forests of oak (Quercus robur and Q. petraea) and elm (Ulmus glabra), covered much of the country in prehistoric times but this changed over the millennia as the Neolithic farmers, the Celts, the early Christians, the Normans and the English cleared vast areas of woodland for farming and timber (Aalen et al., 1997). It is estimated that the extent of the clearance was so great that by 1600 only 2 to 12.5% of the country remained under woodland (Aalen et al., 1997). Oak was the most widespread species at this time, with ash (Fraxinus excelsior), hazel (Corylus avellana) and birch (Betula verrucosa and B. pubescens) also common (Hutchinson, 1989). Many of the remaining woods were felled in the seventeenth century so that the timber could be used for shipbuilding, house building, iron smelting and also as fuel for the local population (Hutchinson, 1989). Some planting of deciduous trees
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occurred on estates from 1700 but declined in the mid 1800s (Hutchinson, 1989). Thus, by 1900 only 0.5% of the country was forested. The area of forest has increased during the last century as the state and private owners began large-scale tree planting. Afforestation has increased steadily with between 20,000 and 25,000 hectares being planted annually during the mid 1990s (Aalen et al., 1997). Bogs are one of the most characteristic landscape features in Ireland and cover about 17% of the total area, which is a higher proportion than any other European country except Finland (Hutchinson, 1989; Aalen et al., 1997). The major concentrations of bogs are in the midlands and the west with only 3% of Co. Wexford being bog covered compared to 62% of west Donegal (Aalen et al., 1997). The main types of bogs are raised and blanket bogs. Blanket bogs are characteristic of mountain areas and the west of Ireland and occur where pine woodland died off and the increasingly wet climate led to the development of bog vegetation (Hutchinson, 1989). Raised bogs cover 314,000 ha over the land surface of Ireland and are typical of the central plain of the country (Aalen et al., 1997). Raised bogs were formed where shallow lakes were overgrown by vegetation, which subsequently led to plant debris accumulating to form peat (Hutchinson, 1989). Peat can be extracted from bogs to be used as fuel called turf. This exploitation of raised bogs and the drainage and reclamation of bogs for agriculture has led to the degradation of this habitat over the last four centuries (Aalen et al., 1997). The semi-state body, Bord na Móna, was established in 1946 and used large machinery to strip a layer of peat off the bog surface of large areas of midlands raised bogs (Aalen et al., 1997). 1.4 The Region Studied In the study region of Co. Laois and southeast Co. Kildare, there are many small areas of woodland that are mainly coniferous plantations of Sitka spruce. Mixed 7
forests of beech (Fagus sylvatica) and Norway spruce (Picea abies) are also present in the area along with some broad-leaved woodland. The best example of broadleaved woodland in the region is the semi-natural oak woodland in the De Vesci Estate in Abbeyleix, Co. Laois. Raised bogs cover a large area of the region also. The Nore and the Barrow Rivers drain the area, but there are no major lakes in this region. Portlaoise in Co. Laois is the main urban area of the region with a population of about 15,000 followed by Athy in Co. Kildare and Abbeyleix, Stradbally and Mountmellick in Co. Laois. 1.5 Structure and Aims of Current Study Many studies of bird and butterfly diversity in agricultural land and woodland have been carried out in Britain and other parts of the Europe, but here I examine the situation in Ireland, which I believe may be different due to the island status, the physical environment and history of landuse of the country. In this thesis, I compare the bird and butterfly fauna of different lowland agricultural and woodland habitats. These results are then used to establish the similarity of the species assemblages in the studied habitats to other bird and butterfly communities in similar habitats from previous studies in Ireland and Britain. I examine the effects of management of a habitat by examining the effects of various types of set-aside management on birds during the breeding season. I did not study the effects of forest management on birds in Ireland as this has been done elsewhere by the BIOFOREST project (http://bioforest.ucc.ie). Finally, I look at bird and butterfly diversity in relation to landscape features in lowland Ireland. An outline of the structure of the thesis is as follows. Chapter 2 deals with the diversity of birds in different habitats, namely broadleaf forest, coniferous forest, pasture, set-aside and tillage in both the breeding and winter seasons. Chapter 3 8
examines the effects of set-aside management on birds in the breeding season. Chapter 4 looks at the diversity of butterflies in the five habitat types studied for birds and Chapter 5 deals with bird and butterfly diversity in relation to the structure of the landscape, by examining the relationships between their species diversity and remote sensing derived landscape indicators. Finally, a general discussion of the important results and conclusions of the previous chapters is given in Chapter 6.
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Chapter 2: The Diversity of Birds in Different Habitat Types. 2.1 INTRODUCTION 2.1.1 Ireland’s Reduced Bird Fauna There is some disagreement in the literature about the actual numbers of bird species breeding in Britain and Ireland but all sources agree that only approximately 67 per cent of the birds that breed in Britain breed in Ireland (Lack, 1976; Reed, 1981; Gibbons et al., 1993). The latest information from The New Atlas of Breeding Birds (Gibbons et al., 1993) shows that 204 species bred in Britain between 1988 and 1991, excluding introduced, re-introduced and feral species, compared to 137 in Ireland. There are three main reasons suggested to explain why Ireland has an impoverished bird fauna in comparison to Britain and continental Europe, its island status, the lack of suitable habitats and the competitive ability of some resident species. 2.1.2 Island Biogeography The absence of sedentary species such as tawny owl (a list of the scientific names of all bird species mentioned is given in Appendix I), woodpeckers, nuthatch, marsh tit and willow tit from Ireland is probably due to their inability to cross the Irish Sea in the first place (Hutchinson, 1989). The great spotted woodpecker and the green woodpecker have been recorded as vagrants in recent decades; however, the great spotted woodpecker was a resident species in Ireland up to the 17th century (Hutchinson, 1989; D’Arcy, 1999). Ireland’s island status also accounts for some of the paucity.
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Ireland’s current isolation from Britain and mainland Europe occurred only 9,000 years ago (Lynch and Hayden, 1993). A number of landbridges may have existed between Ireland and Britain shortly before the complete separation of the islands, but there is still debate on their duration, type and even existence. Devoy (1986) postulated that the landbridge was little more than a series of depositional sedimentary structures, which was partially discontinuous. However, another more recent theory suggests that a ridge of crustal deformation may have acted as a landbridge as it moved through the channel of the Irish Sea as the glaciers retreated (Wingfield, 1995). It has been suggested that our island status is the main reason behind our depauperate bird fauna, as Ireland’s diversity of mammals, reptiles, amphibians and insects is also low relative to Britain (Lack, 1976; Gorman, 1979). The Theory of Island Biogeography (MacArthur and Wilson, 1967) suggests that the equilibrium number of species on an island is controlled by immigration and extinction rates. Thus smaller islands contain fewer species due to the presence of smaller populations with the inherent increased risk of extinction. When the equilibrium is reached the number of species remains relatively constant but the species composition may change. This theory may suggest that Ireland has a species richness appropriate for an island of its size. Remote islands have small numbers of land animals due to the fact that animals reach them from outside only rarely and by chance, and their numbers are balanced by random extinction of species. Lack (1976) disagreed with MacArthur and Wilson’s theory and argued “that the general ecological poverty on remote islands favours the evolution of fewer species of land birds with broader ecological niches, which exclude in competition a greater number of specialised species, and form a stable community from which potential newcomers from outside are continually being excluded”. In his book,
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Island Biology, Lack (1976) tries to explain the absence of many bird species from Ireland. It is smaller than Britain and therefore has fewer extremes with a higher rainfall, milder winters and cooler summers. He argues that the smaller number of breeding species is not due to the sea gap as up to 10 of the 32 missing British breeding land bird species have bred occasionally in Ireland, but have evidently failed to become established due to ecological deficiencies. A further thirteen species have been recorded on passage or as vagrants. Nearly all of the widespread bird species in Britain breed in Ireland also, but of those with a more restricted British range less than half breed here, while none of the 20 species confined to the east and south of Britain breed in Ireland. He argues that dispersal difficulties are not important, as migrant species that regularly cross the sea are proportionately less well represented in Ireland than the resident British species. The coal tit is one of a few species that have evolved broader ecological niches in Ireland. The coal tit is common is Irish broadleaved woods and has evolved a slightly broader bill than the British form, and this may be due to the absence of the broadleaved tit species, marsh tit and willow tit from Ireland (Lack, 1976; Gosler and Carruthers, 1994). Lack (1969; 1976) held the view that dispersal difficulties over the Irish Sea were not responsible for the reduced Irish avifauna, but Hutchinson (1989) argues that the absence from Ireland of this group of birds supports MacArthur and Wilson’s 1967 hypothesis that immigration rates are dependent on distance from the source pool. Hutchinson (1989) hypothesises that ‘if conditions in Ireland are, in fact, suitable for these species, but competition or failure to cross the Irish Sea has prevented colonisation, one would expect reduced species diversity in an Irish habitat together with higher densities of those species which are present’.
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Thus, another theory for Ireland’s current avifauna is that generalist species have occupied the available habitats of the island and competitively exclude specialists from establishing in these areas. Density compensation may also play a role in Ireland, as generalist species, such as wren, robin and chaffinch appear to be more abundant in woodland (Wilson, 1977), forestry plantations (Kavanagh, 1990) and farmland (Lysaght, 1989) than in Britain. The increased proportion of generalists in Ireland compensates for the lack of species in general and specialists in particular. Most of Ireland’s woodland passerines, which are mostly sedentary, would have had difficulty spreading from Britain to Ireland and, thus, inhabit Ireland on the edge of their range (D’Arcy, 1999). At the edge of their range, birds tend to only occupy the best available breeding sites, whereas in the centre of their range they will use even marginal sites (D’Arcy, 1999). Therefore, these species are more vulnerable to extinction when exposed to extraneous pressures, which may help to further explain why fewer species breed in this country compared to Britain and Europe (D’Arcy, 1999). 2.1.3 Lack of Habitat Suitable for Specialist Species Ireland lacks several habitats that occur in Britain. These include the lowland heath and chalk downlands of southern England and the Caledonian Scots pine woodlands of Scotland (Hutchinson, 1989). The absence of these habitats explains the lack of breeding species such as woodlark, Dartford warbler, stone-curlew and crested tit. It has long been noted that Ireland’s avifauna has a lack of terrestrial habitat specialists, especially woodland species (Fuller, 1995). Of the bird species missing in Ireland, twenty use closed canopy scrub and woodland in Britain (Fuller, 1995). Fuller (1995) postulates that several of the missing woodland species may have been 13
present once but became extinct with the clearing of most of these islands’ forests over the centuries. D’Arcy (1999) presents evidence to support this claim as he shows that capercaillie, greater spotted woodpecker and, possibly lesser spotted woodpecker were resident in Irish forests until the 17th or 18th centuries before the decimation of these forests. In 1600, approximately 12% of Ireland was forested but this declined to only 2% by 1800 due to the commercial exploitation of the country’s woodlands (McCracken, 1971 cited in D’Arcy, 1999). Climate and geographical location may also limit bird diversity as Britain and Ireland are at the range limit of many species. Almost all landscapes in Ireland have been heavily influenced by human activity, with broadleaf woodland being a particularly altered habitat (Wilson, 1977; Cabot, 1999). There are no truly broadleaf woodland bird species in Ireland, although the jay may be classified as a broadleaf species but it can also be found in coniferous woodland. Thus, broadleaf and coniferous woodland will be expected to have very similar species. Tawny owl, green woodpecker, greater spotted woodpecker, lesser spotted woodpecker, marsh tit, willow tit and nuthatch are among the British broadleaf woodland species absent from Ireland. These species show an unrestricted or westerly distribution in Britain and an affinity for sessile oakwoods which are present in Ireland (Wilson, 1977). Moore and Hooper (1975) found that there was a relationship between the size of woodland and the number of breeding bird species present and concluded that “most woodland birds are much more likely to occur in a woodland of 100 ha or above than in a smaller wood. Only a very few common species are as likely to occur in a 10 ha wood as in a 100 ha wood”. Wilson (1977) argues that as only three sessile oakwoods, which existed in Ireland in 1977 were at least 100 ha, on a size basis alone many of the broadleaved species listed above would be excluded. Also, the remaining
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areas of sessile oakwoods in the country have been altered by man’s activities leading to the absence of standing dead wood, which is required by woodpeckers, nuthatch, marsh and willow tits and other species (Wilson, 1977). Thus, Wilson (1977) argues that “destruction and fragmentation of native woodlands rather than either the existence of zoo-geographical barriers or operation of the ‘island effect’ is the main reason for Ireland’s depauperate woodland avifauna”. 2.1.4 Competition from Resident Species Only 35% (16 of the 46) of the migrant species which regularly breed in Britain breed in Ireland but 69% of the 125 resident British species breed in Ireland (O’Connor, 1986). Thus it appears that resident species out compete summer migrants, which may explain why many have not colonised the island (Hutchinson, 1989). Resident species have an advantage over migrant species as they are present on the breeding grounds earlier and are more competitive having survived winter adversity compared to migrants who have wintered in warmer climates (O’Connor, 1986). Nairn and Farrelly (1991) suggested that summer migrant species have more specialised habitat requirements than most resident species and this also gives them a competitive disadvantage. The mild climate of the island provides fewer resources for migrants and appears to favour resident species more compared to the greater seasonality of the British climate (Hutchinson, 1989). In conclusion, there is no simple single explanation of Ireland’s depauperate avian fauna.
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2.1.5 Studies of Birds in Irish Habitats Studies of bird populations of terrestrial habitats in Ireland are scarce in comparison to Britain (e.g. Adams and Eddington, 1973; Moore and Hooper, 1975; Moss, Taylor and Easterbee, 1979; Chamberlain et al., 1999b and 2001). The breeding and wintering bird communities of lowland farmland in Co. Down were examined by Moles and Breen (1995), McMahon et al. (2003) studied winter bird assemblages on farmland in Co. Kildare, while Lysaght (1989) studied the breeding bird populations of farmland in mid-west Ireland. There are more studies of woodland bird communities, particularly in deciduous woods (e.g. Batten, 1976; Wilson, 1977; Kavanagh, 1990; Nairn and Farrelly, 1991; Carruthers and Gosler, 1994; O’Halloran et al., 2002). As discussed above, there is a paucity of woodland specialist bird species in Ireland (Fuller, 1995). However, the bird communities of open habitats do not lack species to the same extent as woodland when compared to the situation in Britain. The bird species richness on farmland in Britain and Ireland is very similar as many of the species are generalists, such as wren, chaffinch and robin. However, several species, such as lesser whitethroat, do occur in both countries but it only rarely breeds in Ireland. Despite the dominance of generalist species in the farmland landscape, farmland specialists do exist and most are seed-eating species such as grey partridge, stock dove, turtle dove, woodpigeon, quail, corn bunting, goldfinch, linnet, tree sparrow, skylark and yellowhammer (Taylor and O’Halloran, 1999). In Ireland, the corn bunting is now believed to be extinct and numbers of grey partridge, quail and turtle dove are very low (Taylor and O’Halloran, 1999; 2002). Another possible reason why there is little difference in the farmland bird communities of the two countries may be due to the fact that many species have only adapted to farmland
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relatively recently as this is a much newer habitat than woodland, for instance. Thus, the bird communities of farmland in both Ireland and Britain have developed closely together as agriculture has developed over the last four or five centuries. Another habitat of lowland landscapes in Ireland and Britain is raised bog. Although raised bogs are very important habitats for plants, insects and amphibians, few species of bird breed in this habitat with skylark, meadow pipit, mallard, curlew, snipe, cuckoo and various gull species being the most common (Fuller, 1982). Studies of the bird communities of raised bogs in Ireland and Britain are very scarce but the ones that have been conducted have found species richness and bird densities to be very low (Fuller, 1982; Hutchinson, 1989). Madden (1987) found only 12 species on an intact raised bog in Co. Offaly during ten visits in 1985/86, with only mallard, snipe, skylark and meadow pipit being proven to have bred and curlew was suspected of nesting. Thus, the bird communities of raised bogs in both countries appear to be very similar with very few species recorded in either country. The breeding communities at lakes in Ireland are slightly impoverished compared to Britain. Fuller (1982) lists forty-nine species that breed at lakes in Britain, including species such as red-throated diver, curlew, dipper, mallard, moorhen and little grebe. Forty-four of these species breed in Ireland and another species, goldeneye, is common in winter and may breed here also (Dempsey and O’Clery, 1995; Coombes et al., 2002). The four lake species that do not breed in Ireland are black-throated diver, black-necked grebe, Slavonian grebe and little ringed plover. This synopsis illustrates that although Ireland overall contains less breeding species than Britain, individual habitat types in Ireland hold varying proportions of the species found in the equivalent habitat in Britain.
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2.1.6 Aims of the Study In this chapter, we compared the bird fauna of different lowland agricultural and woodland habitats during both the breeding and winter seasons. The aims of the study were: •
to assess the extent of differences in bird species diversity and abundance between different woodland and agricultural habitats in both seasons. The woodland habitats examined were broadleaf and coniferous forest, with pasture, set-aside and tillage comprising the farmland habitats.
•
to determine how similar the bird species assemblages in the studied habitats are to other communities in similar habitats from other studies in Ireland and Britain.
•
to assess the relationship between vegetation structure and the diversity and abundance of birds in these habitats during the breeding season.
2.2 METHODS 2.2.1 Study Sites Five habitat types were surveyed during the 2002 breeding bird season from the start of April to the end of June with four replicate sites assigned to each habitat type. These were: •
Broadleaf Woodland: These sites were all forested sites in which the dominant tree species are broadleaf species such as oak and beech.
•
Coniferous Woodland: These sites were also forested but are instead dominated by Sitka spruce (Picea sitchensis) a coniferous tree species.
•
Pasture: Cattle grazed all these grassland sites.
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•
Set-aside: These sites were all non-rotational set-aside sites in which the land had been taken out of agricultural production for at least three years.
•
Tillage: Various crops were grown on these sites including winter wheat, spring barley, peas and sugar beet. The tillage and set-aside sites were paired close to each other on the same farms. The sites allocated to each habitat type are shown in Table 2.1. The locations
of the sites and photographs of some of the sites are illustrated in Figures 2.1 to 2.6.
Table 2.1: The sites sampled in 2002 to allow habitat comparisons. Broadleaf 1
2
3
4
Coniferous
Pasture
Set-aside
Tillage
Abbeyleix
Abbeyleix
Dysart
Coursetown
Coursetown
(B1-LUU1)
(C1-LUU1)
(P1-LUU3)
(S1-LUU6)
(T1-LUU6)
Dysart
Dooary
Fallowbeg
Bennetsbridge
Bennetsbridge
(B2-LUU3)
(C2-LUU2)
(P2-LUU5)
(S2-Benn)
(T2-Benn)
Baunreagh
Ballykilcavan 2
Bray
Bray
(B3-LUU4)
(C3-Baun)
(P3-Bal2)
(S3-Bray)
(T3-Bray)
Oughaval
Ballykilcavan
Beladd
Srowland
Srowland
(B4-Ough)
(C4-LUU4)
(P4-Bela)
(S4-Srow)
(T4-Srow)
Ballykilcavan
1
1
The selection of twenty appropriate sites for the study to be based in counties Laois and Kildare involved a lot of preparation and discussions with landowners. When a site was found that might be of interest contact was made with the landowner and permission was sought to allow the use of the site for our studies. Within each site a grid of four sample points, placed 200m apart, was marked out. In some sites, however, it was not possible due to the size and shape of the site but efforts were always made to keep the sampling points as close to 200m apart as possible and laid out in the most regular shape possible. A combination of a handheld GPS, a compass, 30m tapes, large-scale maps and pacing was used to mark each sampling point on the ground as accurately as possible. 19
15
7 27
16
8 23
3 17
22 20 21
18
2
24
10 4 9
26 25
11 6
19
5
29
28
13 12
14
1 Figure 2.1: Map of the study region of Co. Laois and Co. Kildare with the locations of all of the various study sites marked (See legend below for details).
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Figure 2.1 Legend = Land-use Unit (LUU) or 1 km-square site (Chapters 4 and 5). = Replicate Habitat Sites (Chapters 2 and 4). = Set-aside Management Sites (Chapter 3). 1 = LUU1 Abbeyleix, which also includes B1-LUU1 & C1-LUU1 as Replicate Sites (S 415 825). 2 = LUU2 Dooary, which includes C2-LUU2 as a Replicate Site (S 498 886). 3 = LUU3 Dysart, which includes B2-LUU3 & P1-LUU3 as Replicate Sites (S 530 972). 4 = LUU4 Ballykilcavan 1, which includes B3-LUU4 & C4-LUU4 as Replicate Sites, and also a longterm/grazed set-aside site (D) and a tillage site for the Set-aside Study (S 604 965). 5 = LUU5 Fallowbeg, which includes P2-LUU5 as a Replicate Site (S 567 903). 6 = LUU6 Coursetown, which includes S1-LUU6 & T1-LUU6 as Replicate Sites and also as nonrotational set-aside (B) and tillage sites for the Set-aside Study (S 650 948). 7 = Baunreagh (1 km-square), which includes C3-Baun as a Replicate Site (N 290 025). 8 = Beladd (P4-Bela) (S 492 983). 9 = Oughaval (B4-Ough) (S 587 952). 10 = Ballykilcavan 2 (P3-Bal2) (S 594 968). 11 = Srowland (S4-Srow & T4-Srow) and (B-Non-rotational & Tillage) (S 670 961). 12 = Bennetsbridge 1 (S2-Benn & T2-Benn) (S 667 924). 13 = Bray (S3-Bray & T3-Bray) and (B-Non-rotational & Tillage) (S 707 932). 14 = Kyledellig (A-Rotational, C-1st Yr. Pasture & Grass) (S 302 849). 15 = Avoley (C-1st Yr. Pasture Set-aside & Tillage) (N 428 083). 16 = Borris (D-Long-term/Grazed Set-aside & Tillage) (S 476 997). 17 = Sheffield (A-Rotational & Grass) (S 497 961). 18 = Money (E-Other 1st Yr. Set-aside & Tillage) (S 529 944). 19 = Timogue (D-Long-term/Grazed Set-aside & Tillage) (S 551 937). 20 = Ratheniska (A-Rotational & Tillage) (S 539 955). 21 = Killalooghan (B-Non-rotational & Tillage) (S 547 953). 22 = Stradbally (D-Long-term/Grazed Set-aside & Tillage) (S 563 965). 23 = Knocknambraher 2 (C-1st Yr. Pasture & Tillage) (S 574 971). 24 = Knocknambraher 1 (B-Non-rotational Set-aside & Tillage) (S 580 975). 25 = Ballymanus 2 (E-Other 1st Yr. Set-aside & Tillage) (S 613 991). 26 = Ballymanus 1 (D-Long-term/Grazed Set-aside) (S 612 993). 27 = Ratheenaniska (C-1st Yr. Pasture Set-aside & Tillage) (N 600 003). 28 = Bennetsbridge 2 (B-Non-rotational & Tillage) (S 669 926). 29 = Killyganard (F-Wildflower Set-aside & Tillage) (S 657 909). Note: Many of the sites were used for several studies, for example, all of the LUUs were also used as replicate habitat sites for Chapters 2 and 4. Grid references are for the centre of the sites.
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Figure 2.2: The broadleaf woodland site, B1-LUU1, at Abbeyleix in Co. Laois.
Figure 2.3: The coniferous woodland site, C2-LUU2, at Dooary in Co. Laois.
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Figure 2.4: The pasture site, P2-LUU5, at Fallowbeg in Co. Laois.
Figure 2.5: The tillage site, T1-LUU6, at Coursetown in Co. Kildare.
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Figure 2.6: The non-rotational set-aside site at Srowland in Co. Kildare. 2.2.2 Breeding Season Bird Counts Point counts are one of the most widely used methods for estimating numbers of birds and relating them to features of habitat in extensive surveys (Bibby et al., 2000). Four visits to each sample point were made during the breeding season between the start of April and the end of June 2002. Additional data were collected whilst walking between the points. Counts were carried out in the morning (at dawn or shortly afterwards) in dry weather conditions with little wind. All points from the same site were counted on the same morning. Between 4 and 19 points were sampled on a single morning. The route taken between the points was reversed on alternate visits so that particular points were not always counted at the same time of the morning. The recorder stood at each point for 5 minutes during which all birds recorded by sight or sound were noted in the following four distance bands: 0–25m, 25–50m, 50–100m, and >100m. As far as possible counting the same individuals more than 24
once was avoided and if a bird moved, it was recorded in the distance band in which it was first observed or heard. All adult birds seen or heard, considered to be probably nesting within the site or that use the site for feeding or roosting were recorded. Thus, counts of known juvenile birds or family parties were not included. If a bird was heard singing from several points then it was recorded at the point with which it was most strongly associated. Birds recorded singing in this study were considered to be showing evidence of breeding as they were displaying territorial behaviour. Individuals that were seen or recorded calling only were not considered to be breeding. An example of a completed point count recording sheet is given in Appendix II. 2.2.3 Winter Season Bird Counts The winter bird survey took the form of 250m line transects in each of the twenty sites. Thus, each habitat type was represented by four 250m transects. Each site was sampled once a month from November 2002 to February 2003. The transects were walked between late morning and early afternoon (10.00am and 3.30pm). Each transect was divided into five 50m sections. Transects were walked at a steady pace with all birds being identified and counted within each 50m section. Each bird was also recorded as being within 0-25m, 25-50m, 50-100m and >100m to the left or right of the transect line. Birds were recorded in the distance band in which they were first detected. Whenever possible accurate counts of flocks were attempted. However, the flock size was always estimated first in case the birds flew off during the count. Birds flying overhead were not recorded unless they were clearly associated with the habitat. For example: birds that were about to land or just flushed were included – as were raptors hunting over
25
patches, but not, for instance, a flock of fieldfares flying over. Any birds not conclusively identified were ignored. 2.2.4 Vegetation Structure A ‘vegetation stratification profile’, which involved estimating the cover of vegetation within pre-determined height classes, was produced for each sampling point in the summer of 2002. This was undertaken as many species of birds select their habitats on the basis of vegetation structure (MacArthur and MacArthur, 1961). This involved dividing the vegetation into six height classes (or strata) on a geometrical scale. The strata used are given below in Table 2.2.
Table 2.2: The strata used with their geometrical scale. Stratum
Geometrical Scale
St_1
0 to 0.5m
St_2
0.5 to 2m
St_3
2 to 4m
St_4
4 to 8m
St_5
8 to 16m
St_6
> 16m
For each stratum a visual estimation was made, to the nearest 5%, of the cover of all vegetation by a projection on a horizontal plane. Estimation of the cover profile was made for an area of 50 m radius around each point.
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2.2.5 Data Processing 2.2.5.1 Breeding Season Datasets Unless otherwise stated, the datasets used for all of the analyses were processed in the same way. The data for each species were summed for the four visits and the 3 or 4 sampling points in each site were averaged to give site rather than point data (note that the set-aside site S1-LUU6 contained only 3 sampling points – it was the only one of the 20 sites to do so). These values were then divided by 4 to give the average per visit. The final dataset used in the analyses was the average number of birds per visit per site. The maximum number of breeding birds was calculated as the maximum number of each species recorded on a single visit and again averaging the 3 or 4 sampling points in each site. The datasets used for diversity indices were derived from the averages per site (i.e. the average of the 3 or 4 sampling points in each site) for all four visits combined multiplied by 12 (as the computer program required values to be integers). The figures for the vegetation stratification profiles were averages of the 3 or 4 sampling points in each site. 2.2.5.2 Winter Season Datasets The winter datasets used for the majority of analyses were the average number of birds in each site per visit (i.e. all the visits were summed and the totals per site calculated and then divided by 4 to get the average per visit). The datasets used for diversity indices of winter birds were derived from the totals per site from all four visits (i.e. the dataset above multiplied by 4).
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2.2.6 Statistical Analysis Statistical methods, such as multiple regression, especially general linear models (GLM), are often used in the analysis of population size and community structure; however, ordinations were used as the main method of community analysis in this thesis primarily because of the relatively low numbers of birds and butterflies recorded. 2.2.6.1 Ordinations Ordination techniques such as principal components analysis (PCA), correspondence analysis (CA) and detrended correspondence analysis (DCA) are used to reduce the variation in community composition to the scatter of samples and species in an ordination diagram (Ter Braak and Šmilauer, 1998). The ordination diagram or scatter figure expresses relative similarities or affinities of the ordinated species and samples (Gauch, 1977). The relationships of the characteristics that relate the species and samples can then be observed (Gauch, 1977). This type of ordination is a single-space ordination as it represents positions of the entities (species or samples) in a space directly defined by the biological variables (Gauch, 1977). These ordination diagrams can be interpreted by using external data. For example, correlation or multiple regression can be used to relate environmental variables to the scores on the ordination axes (Ter Braak and Šmilauer, 1998). Other types of ordinations may be used to relate samples and species directly to environmental gradients, and to study patterns of communities as related to environmental factors (Gauch, 1977). This is a dual-space ordination as it seeks positions of the entities in a space defined by variables other than those used for ordination measurements (i.e. environmental gradients rather than species scores or similarity measures) (Gauch, 1977). These types of ordinations then allow one to infer 28
relationships between environmental variables and either species or samples. If these ordination techniques are to be successful then they must either assume linear relationships between species responses and environmental factors when gradients are short or accommodate the curvilinearities of ecological data, i.e. the bell-shaped or Gaussian distribution of species populations along environmental gradients, and the nonlinear relationship of similarity values to environmental separation (Gauch, 1977) where the environmental gradients are long and include species turnover. Examples of these types of ordinations include redundancy analysis (RDA) and canonical correspondence analysis (CCA). Sets of species data were traditionally ordinated to generate hypotheses about the relationships between species assemblage composition and the environmental or other factors, which determine it (Greig-Smith, 1983). Greig-Smith (1983) states that ‘the value of ordination is basically in selecting from the infinite number of possible important influencing factors those which it is worth investigating further, though there may also be useful clues from the interaction of different factors as they appear on the ordination diagram’. Canonical ordination techniques such as canonical correspondence analysis (CCA) and redundancy analysis (RDA) provide a solution to one of the difficulties of ordination techniques. This difficulty with ordination techniques, such as PCA and CA, is that the ordination axes are just particular orthogonal directions in the ordination diagram, but other directions may well be better related to the environmental variables (Ter Braak and Šmilauer, 1998). Canonical ordination solves this problem, as it is a combination of ordination and multiple regression, whereby the regression model is inserted in the ordination model (Ter Braak and Šmilauer, 1998). As a result the ordination axes appear in order of variance explained by linear
29
combinations of environmental variables (Ter Braak and Šmilauer, 1998). Therefore, canonical ordination techniques are designed to detect the patterns of variation in the species data that can be ‘best’ explained by the observed environmental variables (Jongman et al., 1987). The ordination, which results from these techniques, expresses not only a pattern of variation in species composition but also the main relations between the species and each of the environmental variables (Jongman et al., 1987). 2.2.6.2 Detrended Correspondence Analysis (DCA) DCA is an indirect gradient analysis technique, i.e. an ordination technique that searches for major gradients in the species data irrespective of any environmental variables (Ter Braak, 1988). DCA assumes a unimodal model for the relationship between the responses of each species and the ordination axes (Ter Braak, 1988). Ordination axes can be envisaged as being theoretical environmental variables or underlying gradients. The unimodal model is fitted by the method of two-way weighted averaging (Ter Braak, 1988). Detrended correspondence analysis (DCA) is derived from correspondence analysis (CA) (also called reciprocal averaging (RA)), which is a simpler method of ordination (Hill, 1979). The arch effect is a fault of several types of ordination, including CA. This is the tendency for the second axis and occasionally higher axes to be strongly related to the first axis (Hill, 1979) and occurs if the sample scores on the second ordination axis are approximately a quadratic function of the sample scores of the first axis. However, DCA avoids the arch effect by ensuring that there shall be no systematic relation of any kind between the higher axes and the first (Hill, 1979). Rescaling in DCA allows those parts of the gradient where the standard deviation is low to be expanded and those where it is high to be contracted (Hill, 1979).
30
The length of the gradient derived from DCA is used to determine which method of direct gradient analysis is most appropriate to use on the dataset. The linear context or redundancy analysis (RDA) is most useful when the gradients are short (4 SD) (Ter Braak and Šmilauer, 1998). Either RDA or CCA may be useful for intermediate gradient lengths (Ter Braak and Šmilauer, 1998). 2.2.6.3 Principal Component Analysis (PCA) Principal Component Analysis (PCA) is a linear method of ordination and is also an indirect gradient analysis technique, like DCA (Ter Braak and Šmilauer, 1998). PCA constructs a theoretical variable that minimises the total residual sum of squares after fitting straight lines to the species data, by choosing the best values for the sites (i.e. the site scores) (Jongman et al., 1987). PCA does not reduce the dimensionality of the original data itself, but by changing the reference axes to a new orthogonal set, it concentrates the variability in the successive axes derived (GreigSmith, 1983). Thus the first few axes account for the information of value in interpreting the data, while the later axes can be ignored (Greig-Smith, 1983). As in the canonical form of linear ordination, RDA, the arrow, in the PCA diagram, points in the direction in which the species’ abundance value increases at the largest rate across the ordination diagram (Ter Braak and Šmilauer, 1998). The major value of PCA is in the identification of groups of related variables, which can then be used to generate hypotheses to explain these groupings, or be used as composite variables whose scores can be mapped as general indices, or be used as orthogonal independent variables for multiple regressions (Johnston, 1978). Usually only a small number of components are interpretable in PCA and those are the ones 31
with the largest eigenvalues, even though as many components as variables can be defined in the rewritten matrix (Johnston, 1978). The other components only account for very small portions of the original variance and are therefore trivial (Johnston, 1978). 2.2.6.4 Canonical Correspondence Analysis (CCA) Canonical ordinations are direct gradient analysis techniques. These techniques attempt to explain the species responses by ordination axes that are constrained to be linear combinations of supplied environmental variables (Ter Braak, 1988). Thus the ordination diagram obtained from a direct gradient analysis has a known environmental basis. In general, the maximum number of constrained ordination axes is equal to the number of environmental variables, unless “detrending is in force” (Ter Braak, 1988). Canonical Correspondence Analysis (CCA) is one method of direct gradient analysis and is the unimodal method of canonical ordination (Ter Braak and Šmilauer, 1998). CCA selects the linear combination of environmental variables that maximises the dispersion of the species scores to give the first canonical axis (Jongman et al., 1987). The second and subsequent CCA axes also select linear combinations of environmental variables that maximise the dispersion of the species scores, however they must be uncorrelated with previous CCA axes (Jongman et al., 1987). Thus, CCA is ‘restricted correspondence analysis’ as the site scores are restricted to be a linear combination of measured environmental variables (Jongman et al., 1987). The eigenvalue measures the amount of variation in the species data explained by that particular axis and, hence, by the environmental variables (Jongman et al., 1987). In the CCA diagram, species and sites are represented by points with sites that have a high value of a species tending to be close to the point for that species 32
(Jongman et al., 1987). Arrows represent the environmental variables. A method of interpreting the diagram is that the inferred weighted average is higher than average if the projection point lies on the same side of the origin as the head of an arrow and is lower than average if the origin lies between the projection point and the head of an arrow (Jongman et al., 1987). Also, “environmental variables with long arrows are more strongly correlated with the ordination axes than those with short arrows, and therefore more closely related to the pattern of variation in the species composition shown in the ordination diagram” (Jongman et al., 1987). Monte Carlo permutation tests can be used to evaluate the statistical significance of the relationship, given the covariables, between the species and the whole set of environmental variables (Jongman et al., 1987). A Monte Carlo permutation test is a test of statistical significance obtained by repeatedly shuffling samples (Jongman et al., 1987). 2.2.6.5 Redundancy Analysis (RDA) Redundancy analysis (RDA) is another method of direct gradient analysis and can be expressed as a constrained form of multiple regression of the species’ responses on the environmental (or explanatory) variables (Ter Braak, 1988). This type of regression is called reduced rank regression (Ter Braak, 1988). In RDA distinction is made between response (species) variables and explanatory (environmental) variables, as in regression analysis (Ter Braak and Šmilauer, 1998). RDA is also the canonical form of principal components analysis (PCA). RDA selects the linear combination of environmental variables that gives the smallest total residual sum of squares (Jongman et al., 1987). The species-environment correlation equals the correlation between the site scores that are weighted sums of the species scores and the site scores that are a linear combination of the environmental variables 33
(Jongman et al., 1987). On the ordination diagram, the cosine of the angle between the arrows of a species and an environmental variable is an approximation of the correlation coefficient between the species and the environmental variable (Jongman et al., 1987). The arrow, in RDA, points in the direction in which the species’ abundance value increases at the largest rate across the ordination diagram (Ter Braak and Šmilauer, 1998). DCA, PCA, CCA and RDA were performed with CANOCO version 4 (Ter Braak and Šmilauer, 1999). 2.2.6.6 Diversity indices Several diversity indices were used to compare bird assemblages based on the proportional abundances of species. These were calculated using a program called ‘Species Diversity and Richness’ version 2.1 (1998) by Henderson and Seaby. The datasets used were derived from the averages per site (i.e. the average of the 3 or 4 sampling points in each site) for all four visits combined multiplied by 12. The indices were calculated for all birds and breeding birds recorded in the 0-25m, 0-50m and 0100m distance bands. 2.2.6.6.1 Species Richness This index is simply a measure of the number of species found in a defined sampling unit. 2.2.6.6.2 Shannon-Weiner Index (H’) The Shannon-Weiner Index was used to measure the amount of order/disorder contained in a system. This index assumes that individuals are randomly sampled from an ‘indefinitely large’ (i.e. an effectively infinite) population and that all species are represented in the sample (Pielou, 1975). Species number and equitability or 34
evenness of the allocation of individuals among the species are the two components of diversity which are combined in this function (Begon et al., 1996). The index is defined by the equation: S obs
H ' = −∑ pi log e pi i =1
where pi = the proportion of individuals in the ith species. The computer program used calculates the Shannon-Wiener Index using the natural logarithm but any log base may be adopted once the choice of the log base is consistent when comparing the diversity between samples (Magurran, 1988). 2.2.6.6.3 Simpson’s Index (D) This index is a measure of dominance because it is weighted towards the abundances of the commonest species rather than providing a measure of species richness. Simpson (1949) used this index to describe the probability that a second individual drawn from a population should be of the same species as the first. As the dominance increases, the diversity decreases and therefore this index is often expressed as 1/D or 1-D (Magurran, 1988). The index, D is given by:
D=
1 S obs
∑p
2 i
i
but, for a finite population pi2 =
N i ( N i − 1) , N T ( N T − 1)
where Ni is the number of individuals in the ith species and NT the total individuals in the sample.
35
2.2.6.6.4 Jaccard Index of Similarity The Jaccard Similarity Index was used to examine the extent to which two sites are similar in terms of their species composition. This index is calculated from a simple presence or absence matrix with a value of 1 if the species is present in the site and 0 if it was absent. The index equals 1 when there is complete similarity between the sites (i.e. where the two sets of species are identical), and equals 0 when the sites are dissimilar and have no species in common (Magurran, 1988). Therefore the index indirectly measures the beta diversity between the sites as the higher the similarity coefficient, the lower the degree of dissimilarity between the sites. The equation for this index is expressed as CJ= j /(a + b – j) where j is the number of species in common to both samples and a and b are the total number of species in each sample (Magurran, 1988). The index was calculated using NTSYSpc Version 2.02g (Adams et al., 1998). The NTSYSpc program produces a similarity matrix in which the Jaccard Similarity Index scores for every combination of the twenty sites is given. However, in this study the only values of interest were the six similarity values between the four sites of each habitat type (for example for the broadleaf habitat the similarity values between the following sites were used: B1 and B2; B1 and B3; B1 and B4; B2 and B3; B2 and B4; B3 and B4). Thus, these 30 scores (6 for each of the 5 habitats) were used for all subsequent analyses with the Jaccard Index of Similarity. 2.2.6.7 Kruskal Wallis Test, One-way ANOVA and Bonferroni t test Differences in Shannon-Weiner (H’), Simpson’s Index (D), species richness and Jaccard Similarity Index between the five habitat types were tested for significance using the Kruskal-Wallis test. Differences between habitat types in terms 36
of bird abundance were assessed using one-way analysis of variance (ANOVA) and the Bonferroni t test. These tests were performed using SAS version 8.2 (SAS, 2001).
2.3 RESULTS 2.3.1 Birds Recorded During the Breeding Season Nine different datasets were available for use in this comparison of birds recorded in the five habitat types during the breeding season. These were all birds recorded within 25m, 50m and 100m of the sampling points, breeding birds recorded within 25m, 50m and 100m of the sampling points and the maximum number of breeding birds recorded within 25m, 50m and 100m of the sampling points. The maximum number of breeding birds was determined as the maximum number of each breeding species recorded on a single visit and averaging the 3 or 4 sampling points in each site. From preliminary analyses, it was shown that all of the datasets yielded similar results therefore not all are included. When the lengths of the gradients produced by detrended correspondence analysis (DCA) were examined it was seen that they were generally longest for the 0-25m and 0-50m distance bands (Table 2.3). This indicated that species turnover was greatest in these datasets. Therefore, it was decided to present only the 0-50m datasets. The results were virtually identical for the breeding birds and maximum breeding birds datasets therefore only the sets which included all birds and the breeding birds dataset were used. However, in some cases other datasets will be used where they provide additional information.
37
Table 2.3: Lengths of the gradients derived from DCA for each of the nine datasets analysed for breeding birds recorded in the five habitat types (* = the gradient length after the outlier species Meadow Pipit was deleted from the dataset). Datasets
0-25m
0-50m
0-100m
All Birds
4.661
4.398
3.148
Breeding Birds
4.809*
4.412
2.639
Maximum Number of Breeding Birds
4.844*
4.557
2.473
2.3.2 Bird Abundances 2.3.2.1 All Species Recorded There were significant differences between the average number of birds recorded per visit in the different habitat types (F1,19 = 13.18, P < 0.0001). The forest habitat sites had over 20 individual birds recorded, on average, per visit compared to a mean less than 10 birds per site in the open habitats (Table 2.4).
Table 2.4: Average number (and standard error) of birds recorded in each site per visit for all birds recorded within 50m of the sampling points for the five different habitat types. Values with the same superscript do not differ significantly (P < 0.05). Habitat Type
No. of Sites
Mean
Standard Error
Broadleaf
4
21.38a
2.73
Coniferous
4
24.13a
2.52
Pasture
4
9.44b
2.34
Set-aside
4
9.94b
2.08
Tillage
4
4.06b
2.04
2.3.2.2 Breeding Species Only There were also significant differences between the average number of breeding birds recorded per visit in the different habitat types (F1,19 = 23.31, P < 38
0.0001). As with all birds recorded, the forest habitats were significantly different from the three open sites. Coniferous sites had an average of 16 breeding birds recorded per visit with a mean of 12 birds present in broadleaf habitat (Table 2.5). Less than 4 individual breeding birds were recorded per site per visit in pasture, setaside and tillage habitats (Table 2.5).
Table 2.5: Mean numbers (and standard errors) of breeding birds recorded in each site per visit within 50m of the sampling points for the five different habitat types. Means with the same superscript do not differ significantly (P < 0.05). Habitat Type
No. of Sites
Mean
Standard Error
Broadleaf
4
12.06a
1.5
Coniferous
4
16.19a
1.86
Pasture
4
3.81b
1.34
Set-aside
4
3.63b
0.98
Tillage
4
1.31b
0.51
2.3.3 Species Composition of Samples 2.3.3.1 All Birds On average 275 individuals were recorded per visit in the five habitat types during spring/summer 2002, representing 37 different species (Table 2.6). Eight species, wren (16%), goldcrest (13%), coal tit (12%), robin (9%), skylark (8%), chaffinch (6%), blackbird (6%) and blue tit (5%) comprised over 75% of the individuals seen (Figure 2.7).
39
Table 2.6: All of the bird species occurring in each of the five habitat types within 50m of the sampling points (+ = Species occurred once in that habitat; * = Species occurred frequently (more than 1 individual recorded) in that habitat). Common Name
Scientific Name
Blackbird Blackcap Blue Tit Bullfinch Buzzard Chaffinch Chiffchaff Coal Tit Collared Dove Dunnock Goldcrest Goldfinch Great Tit Greenfinch Jackdaw Jay Long-tailed Tit Magpie Meadow Pipit Mistle Thrush Pheasant Pied Wagtail Reed Bunting Robin Rook Sedge Warbler Skylark Song Thrush Starling Swallow Tree Sparrow Treecreeper Whitethroat Willow Warbler Woodpigeon Wren Yellowhammer
Turdus merula Sylvia atricapilla Parus caeruleus Pyrrhula pyrrhula Buteo buteo Fringilla coelebs Phylloscopus collybita Parus ater Streptopelia decaocto Prunella modularis Regulus regulus Carduelis carduelis Parus major Carduelis chloris Corvus monedula Garrulus glandarius Aegithalos caudatus Pica pica Anthus pratensis Turdus viscivorus Phasianus colchicus Motacilla alba (yarrelli) Emberiza schoeniclus Erithacus rubecula Corvus frugilegus Acrocephalus schoenobaenus Alauda arvensis Turdus philomelos Sturnus vulgaris Hirundo rustica Passer montanus Certhia familiaris Sylvia communis Phylloscopus trochilus Columba palumbus Troglodytes troglodytes Emberiza citrinella
Broadleaf * * *
Coniferous * * *
Pasture
* * * *
* * *
*
* *
+ *
* * * +
Setaside *
Tillage
*
*
*
*
* * +
+ *
* *
*
* * + *
+
*
+
* * * * * * +
+ +
*
+ *
+
+ + *
*
+ *
*
* +
* * *
+ * + * * +
* * *
*
* *
+ * *
40
+ * *
* * *
* * *
* *
Others 15%
Wren 17%
Dunnock 2% Meadow Pipit 3% Woodpigeon 4%
Goldcrest 13%
Blue Tit 5% Blackbird 6% Chaffinch 6%
Coal Tit 12% Robin 9%
Skylark 8%
Figure 2.7: Species composition of all the birds recorded within 50m of the sampling points during the breeding season in all sites based on the average number of birds recorded in each site per visit.
Two of the species recorded during the breeding season are shown in Figures 2.8 and 2.9.
Figure 2.8: Chaffinch (Fringilla coelebs) (Photo: Billy Clarke). 41
Figure 2.9: Great Tit (Parus major) (Photo: Billy Clarke).
2.3.3.2 Breeding Birds Twenty-four breeding species were recorded (Table 2.7), with 148 individuals seen per visit. Wren was again the dominant species with over 23% of all individuals recorded belonging to this species (Figure 2.10). Eighty-four per cent of the individuals were made up of seven breeding species wren (23%), goldcrest (18%), robin (14%), coal tit (11%), skylark (8%), chaffinch (5%) and woodpigeon (4.5%) (Figure 2.10).
42
Table 2.7: The breeding bird species occurring in each of the five habitat types within 50m of the sampling points (+ = Species occurred once in that habitat; * = Species occurred frequently (more than 1 individual recorded) in that habitat). Common Name
Scientific Name
Broad-
Conif-
leaf
erous *
Pasture
Set-
Tillage
aside
Blackbird
Turdus merula
*
Blackcap
Sylvia atricapilla
*
*
Blue Tit
Parus caeruleus
*
*
Bullfinch
Pyrrhula pyrrhula
Chaffinch
Fringilla coelebs
*
*
Chiffchaff
Phylloscopus collybita
*
*
Coal Tit
Parus ater
*
*
Collared Dove
Streptopelia decaocto
Dunnock
Prunella modularis
*
+
*
Goldcrest
Regulus regulus
*
*
+
Goldfinch
Carduelis carduelis
Great Tit
Parus major
Greenfinch
Carduelis chloris
*
Meadow Pipit
Anthus pratensis
+
+
Mistle Thrush
Turdus viscivorus
Robin
Erithacus rubecula
*
+
Sedge Warbler
Acrocephalus schoenobaenus
*
Skylark
Alauda arvensis
*
Song Thrush
Turdus philomelos
Swallow
Hirundo rustica
Whitethroat
Sylvia communis
Willow Warbler
Phylloscopus trochilus
+
+
Woodpigeon
Columba palumbus
*
*
*
+
Wren
Troglodytes troglodytes
*
*
*
*
*
Yellowhammer
Emberiza citrinella
+
+
* + *
+ +
*
*
*
+
+ *
*
*
*
*
*
+
+ *
43
Song Thrush Blackcap Others 2% 5% 1.5% Chiffchaff Dunnock 2% 3%
Wren 23%
Woodpigeon 4.5% Blue Tit 3% Chaffinch 5% Skylark 8% Goldcrest 18% Robin 14%
Coal Tit 11%
Figure 2.10: Species composition of all the breeding birds recorded within 50m of the sampling points during the breeding season in all sites based on the average number of birds recorded in each site per visit.
2.3.4 Comparison between Habitat Types 2.3.4.1 Diversity Indices Species richness was significantly different between the habitat types for all birds recorded (P = 0.02) and breeding birds recorded (P = 0.03) within 50m of the sampling points (Figure 2.11). The average species richness was high in broadleaf (14.25 species) and declined consistently from coniferous sites (11.75), to pasture (11), to set-aside (8.25) and was lowest in tillage, with an average of only 4.5 species present in each site. Breeding bird species richness followed a very similar pattern (Figure 2.11).
44
16 14
Species Richness
12 10 Breeding 0-50m Total 0-50m
8 6 4 2 0 Broadleaf
Coniferous
Pasture
Set-aside
Tillage
Habitat Type
Figure 2.11: Average species richness per site of breeding species (green bars) and all species recorded (navy bars) for each habitat type within 50m of the sampling points (with standard error bars).
However, a different pattern was observed when the total number of species in all sites of a particular habitat type was examined (Figure 2.12). The total species richness per habitat type was calculated by accumulating the total number of each species present within all the individual sites of each habitat type. Thus for all of the birds recorded within 50m of the sampling points, 20 species were present between the four broadleaf sites, 18 in coniferous, 19 in set-aside and 12 species were found in the tillage sites (Figure 2.12). The four pasture sites contained the highest number of species (24) (Figure 2.12). This showed that the different habitats were more similar in terms of species richness when the four sites of each habitat type are added together. This aspect was more obvious with the breeding bird dataset as there was a difference of only 4 between the number of species recorded in broadleaf, coniferous,
45
pasture and set-aside habitats (Figure 2.12). Tillage had by far the lowest number of breeding species with only 4 being recorded (Figure 2.12).
26 24 22 Breeding 0-50m Total 0-50m
Numbers of Species
20 18 16 14 12 10 8 6 4 2 0 Broadleaf
Coniferous
Pasture
Set-aside
Tillage
Habitat Type
Figure 2.12: Number of all species (green bars) and breeding (navy bars) species recorded in all sites of each habitat type within 50m of the sampling points.
There were significant differences between the five habitat types in relation to the Jaccard Similarity Index values for all birds in the 0-50m distance band (Kruskal Wallis Test, P = 0.0001). Sites in broadleaf and coniferous had very similar bird species compositions with mean Jaccard Similarity Index values of 0.68 and 0.61 respectively (Table 2.8). In contrast, sites in the open habitats, namely pasture (0.26), set-aside (0.24) and tillage (0.11), had low mean similarity scores (Table 2.8). The Jaccard Similarity Index values were also significantly different between habitats for breeding birds recorded within 50m of the sampling points (Kruskal Wallis Test, P =
46
0.0028). Similarly, the forested habitats had high Jaccard values and the open habitats had low values (Table 2.8). This implies that β (Beta) diversity was low in the forest habitats and high in the open habitats as β diversity = 1 – Jaccard Similarity Index value e.g. the broadleaf habitat for all birds recorded would have a β diversity value of 0.32 (as 1 – 0.68 = 0.32). This implies that the species compositions in the individual sites of the two forest habitat types were very similar while there were different species in the different sites of the three open habitat types (i.e. the species composition of the individual sites of pasture).
Table 2.8: Table of means and standard errors of the Jaccard Similarity Index for all birds and breeding birds recorded within 50m of the sampling points for the five different habitat types. Habitat Type
N
All Birds
Breeding Birds
Mean
SE
Mean
SE
Broadleaf
6
0.68
0.03
0.61
0.05
Coniferous
6
0.61
0.03
0.58
0.02
Pasture
6
0.26
0.06
0.24
0.06
Set-aside
6
0.24
0.06
0.17
0.04
Tillage
6
0.11
0.07
0.22
0.12
There were no significant differences between the habitat types for all birds recorded for the Shannon-Weiner and Simpson’s Indices. However, there were significant differences in the breeding bird communities for Shannon-Weiner (P = 0.028) and Simpson’s (P = 0.0296) Indices. Broadleaf and coniferous habitats had high Shannon-Weiner values of 1.86 and 1.72 respectively, compared with 1.0 for setaside and 0.49 for tillage sites (Table 2.9). Simpson’s Index was also high in
47
broadleaf (5.44), coniferous (4.58) and pasture (4.69) sites but low in set-aside (2.45) and tillage (1.45) (Table 2.9).
Table 2.9: Means and standard errors of the Shannon-Weiner Index (H’) and Simpson’s Index (D) for breeding birds recorded within 50m of the sampling points for the five different habitat types. Habitat Type
No. of
H’
D
Sites
Mean
SE
Mean
SE
Broadleaf
4
1.86
0.1
5.44
0.93
Coniferous
4
1.72
0.16
4.58
0.76
Pasture
4
1.46
0.37
4.69
1.6
Set-aside
4
1.00
0.19
2.45
0.37
Tillage
4
0.49
0.28
1.45
0.61
2.3.4.2 Assemblage Composition – All Species Recorded Wren was the dominant species in broadleaf forest where it represented 22% of the individuals sighted, followed by coal tit (17%), robin (11%), blue tit (10%) and chaffinch (9%) (Figure 2.13). Coniferous forest was dominated by goldcrest which made up over 31% of the individuals (Figure 2.13). Coal tit (18%), wren (15%), robin (11%) and chaffinch (6.5%) were also very common in coniferous sites (Figure 2.13). Wren (17%), blackbird (13%), robin (13%), chaffinch (9%) and blue tit (8%) were the main species in the pasture (Figure 2.13). Skylark was clearly the dominant species in set-aside and tillage with 41% and 37% of the individuals respectively (Figure 2.13). Meadow pipit (16%), wren (9.5%) and dunnock (7.5%) were other important set-aside species (Figure 2.13). The tillage community also contained high proportions of yellowhammer (12%) and blackbird (11%) (Figure 2.13).
48
100%
Others Blackcap Treecreeper Yellowhammer Great Tit Dunnock Meadow Pipit Woodpigeon Blue Tit Blackbird Chaffinch Skylark Robin Coal Tit Goldcrest Wren
80%
60%
40%
20%
0%
Broadleaf
Coniferous
Pasture
Set-aside
Tillage
All Habitats
Figure 2.13: Species composition of all the birds recorded within 50m of the sampling points for the five different habitat types and all habitats combined, based on the average number of birds recorded in each site per visit.
The length of the gradient for DCA was 4.398. The subsequent CCA for all birds recorded (Figure 2.14) shows a difference between the five habitat types in bird assemblage composition with the forest habitats being more closely related to each other than the open habitats. The 1st canonical axis was very significant (P = 0.005) and mainly represented the difference between tillage and set-aside on the positive side of the diagram and broadleaf and coniferous sites on the negative side. The second canonical axis divided broadleaf, pasture and tillage on the positive half of the diagram from coniferous and set-aside on the negative half of the diagram. All of the canonical axes were significant (P-value = 0.005) and explained 45.4% of the species inertia and 100% of the variance in the species-environment relation (Table 2.10). Jay, blackcap, great tit, mistle thrush, buzzard and chaffinch showed an affinity for broadleaf habitat. Goldcrest, chiffchaff and coal tit showed a preference for coniferous sites. Pasture showed strong associations with bullfinch, swallow and 49
pied wagtail. Meadow pipit, skylark, pheasant, sedge warbler, whitethroat, collared dove and reed bunting were closely associated with set-aside. Tillage was the preferred habitat of starling, rook and goldfinch. Broadleaf sites contained an average of 2.5 blackcaps over the entire sampling season, compared to 1 for coniferous habitat and 0.5 for pasture. No blackcaps were recorded in set-aside or tillage. An average of 1.88 and 1.56 chaffinches were seen in each site on each visit in broadleaf and coniferous respectively. Fourteen chaffinches were observed in all four visits to the four pasture sites, compared with two in set-aside and none in tillage. An average of 7.63 goldcrests were present in coniferous sites per visit, compared with 1.13 in broadleaf sites, none in set-aside or tillage and only one in all of the pasture sites over the whole sampling season. Pasture contained one pied wagtail and four swallows over all four visits, with none of these birds recorded in any other habitat. Over 6 meadow pipits were recorded per set-aside site over all four visits combined, compared with 1 in pasture sites. Tillage had a single meadow pipit per site over the entire sampling season. The only two goldfinches recorded during the study were found in tillage.
50
+1.0
GO TS RO
Pasture
SG
BF PW SL
JD
Tillage
GR
Broadleaf
GT LT BZ M. BT J. CH ST BC WR R. WP TC WW CC CT
Y.
MG B. D.
S. GC MP RB
PHWH CD SW
Set-aside
-1.0
Coniferous
-1.0
+1.0
Figure 2.14: CCA of the total birds recorded within 50m of the sampling points in all habitat types. P-value of 1st canonical axis = 0.005. P-value of all canonical axes = 0.005.
Table 2.10: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the CCA of the total birds recorded within 50m of the sampling points in all habitat types. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total inertia .682 .221 .174 .090 2.569 .948 .829 .870 .843 26.5 35.1 41.9 45.4 58.5 77.4 92.3 100.0
51
2.3.4.3 Assemblage Composition – Breeding Species Only Wren was also the dominant breeding species in broadleaf forest as it represented 34% of the community (Figure 2.15). Robin (17%), coal tit (16%), goldcrest (8%), and chaffinch (6%) commonly bred in the broadleaf sites also (Figure 2.15). The breeding community of coniferous forest was also dominated by goldcrest (38%), followed by wren (19%), robin (15.5%) and coal tit (14%) (Figure 2.15). Wren (31%), and robin (26%) were the main species in pasture (Figure 2.15). As with all birds recorded during the breeding season, skylark was by far the dominant breeding species in set-aside and tillage with 54% and 71% of the communities respectively (Figure 2.15). Wren and dunnock were also important breeding species in these habitats, with 17.5% and 10.5% respectively in set-aside and 9.5% and 14% respectively in tillage (Figure 2.15).
100%
Others Greenfinch Song Thrush Chiffchaff Blackcap Great Tit Dunnock Woodpigeon Blue Tit Blackbird Chaffinch Skylark Robin Coal Tit Goldcrest Wren
80%
60%
40%
20%
0%
Broadleaf
Coniferous
Pasture
Set-aside
Tillage
All Habitats
Figure 2.15: Species composition of the breeding birds recorded within 50m of the sampling points for the five different habitat types and all habitats combined, based on the average number of birds recorded in each site per visit.
52
DCA showed that the length of the gradient was 4.412 for breeding birds within 50m of the sampling points and CCA showed similar patterns of species composition in the different habitat types to the previous CCA (Figure 2.16). However, set-aside and tillage were very closely associated in terms of their breeding bird communities. The 1st canonical axis represented the difference between set-aside and tillage on the positive side of the ordination and broadleaf, pasture and coniferous on the negative side. The second canonical axis separated broadleaf and pasture in the positive half of the diagram from coniferous on the negative half of the diagram. The 1st canonical axis and all canonical axes were significant (P = 0.005 and 0.005 respectively). The 1st canonical axis accounted for 30.1% of the species inertia and 68.2% of the inertia between the species and the environment (Table 2.11). Species inertia for all canonical axes was 44.1% and species-environment inertia was 100% (Table 2.11). Broadleaf was the preferred habitat of breeding great tit, blue tit, robin and blackbird. Goldcrest, chiffchaff, coal tit, willow warbler and mistle thrush were closely associated with coniferous sites. Bullfinch, swallow and greenfinch showed a preference for pasture. Meadow pipit, dunnock and sedge warbler showed a preference for set-aside, while skylark, whitethroat and yellowhammer seemed to prefer both set-aside and tillage. Four breeding great tits were recorded in all of the broadleaf sites combined, compared to 1 in the coniferous sites. An average of 2.19 singing coal tits were present in each coniferous site on each visit, compared with 1.81 per broadleaf site per visit, none in set-aside or tillage and only one in all of the pasture sites over the whole sampling season. A single breeding bullfinch was recorded in a pasture site, with no others recorded in any other habitat. All three breeding sedge warblers were recorded
53
in set-aside. An average of nearly two singing skylarks was recorded per set-aside site per visit with approximately 1 recorded per tillage sites. Tillage and set-aside both
+1.0
contained one breeding yellowhammer over the entire breeding season.
Pasture
BF
Broadleaf
GR
SL
GT
MP
BT ST BC B. R. WR WP CH CT WW
SW
Tillage
CC GC
D. WH CD S. Y.
Set-aside
M.
-1.0
Coniferous
-1.0
+1.0
Figure 2.16: CCA of the breeding birds recorded within 50m of the sampling points in all habitat types. P-value of 1st canonical axis = 0.005. P-value of all canonical axes = 0.005. Table 2.11: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the CCA of the breeding birds recorded within 50m of the sampling points in all habitat types. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data :
1 2 3 4 Total inertia .741 .200 .105 .042 2.462 .980 .898 .898 .508 30.1 38.2 42.5 44.1 54
of species-environment relation: 68.2 86.5 96.2 100.0 2.3.5 Vegetation Effects – All Species Recorded CCA was used to analyse the distribution of species and sites in relation to the vegetation stratification profiles of the individual sites (Figures 2.17 and 2.18). The first canonical axis divided the species and sites preferring strata 1 (0-0.5m) and 2 (0.5-2m) on the positive side of the diagram from those showing a preference for strata 3 (2-4m), 4 (4-8m), 5 (8-16m) and 6 (>16m) on the negative side (Figures 2.17 and 2.18). Thus all of the open habitat sites were on the positive side of the diagram and the forested sites were on the negative side (Figure 2.17). The 1st canonical axis was significant (P = 0.04) and accounted for 21.1% of the species inertia and 54.7% of the inertia between the species and the environment (Table 2.12). The second canonical axis primarily divided stratum 2 on the positive half of the diagram from stratum 1 on the negative half. All of the canonical axes were found to be nonsignificant (P-value = 0.09) and explained 36% of the species inertia and 93.3% of the species-environment inertia (Table 2.12). Collared dove, goldfinch, dunnock, greenfinch and pied wagtail were among the species that were primarily associated with vegetation under 0.5m (Figure 2.18). Meadow pipit, starling, magpie, jackdaw and sedge warbler showed a preference for vegetation between 0.5 and 2m whilst treecreeper, buzzard, great tit, wren and others exhibited a preference for taller vegetation between 2 and 8m (Figure 2.18). Coal tit, goldcrest, willow warbler and chiffchaff showed a preference for habitats with trees between 8 and 16m (Figure 2.18). Jay was associated with vegetation over 16m (Figure 2.18).
55
+1.0
T4- Srow
St_2
T3- Bray
S3- Bray
B1- LUU1 P2- LUU5
St_4 B3- LUU4
St_3
C4- LUU4 C2- LUU2
P3- Bal2
St_6 St_5
B2- LUU3
S1- LUU6
B4- Ough S4- Srow C1- LUU1 T2- Benn
S2- Benn P4- Bela
C3- Baun
P1- LUU3
-1.0
St_1
-1.0
+1.0
Figure 2.17: CCA of all birds recorded within 50m of the sampling points in relation to sites and the different strata of the vegetation stratification profiles. P-value of 1st canonical axis = 0.04. P-value of all canonical axes = 0.09.
Table 2.12: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the CCA of the total birds recorded within 50m of the sampling points and vegetation stratification profiles. Axes 1 2 3 4 Total inertia Eigenvalues : .542 .220 .101 .061 2.569 Species-environment correlations: .869 .841 .822 .719 Cumulative percentage variance of species data : 21.1 29.7 33.6 36.0 of species-environment relation: 54.7 77.0 87.2 93.3
56
+1.0
GO TS
St_2 CD
D.
St_4
GR PW
BT BZ
St_3
GT M. WR WP BC R. CH ST
LT
SL
TC J.
Y.
MG
B.
BF
St_6 CT CC
St_5
S.
GC WW
SW JD MP PH RO WH
RB
SG
-1.0
St_1
-1.0
+1.0
Figure 2.18: CCA of all birds recorded within 50m of the sampling points in relation to species and the different strata of the vegetation stratification profiles. P-value of 1st canonical axis = 0.04. P-value of all canonical axes = 0.09.
The CCA of breeding birds recorded within 50m of the sampling points were non-significant with the P-value of 1st canonical axis equalling 0.185 and the P-value of all canonical axes being 0.645. 2.3.6 Birds Recorded During the Winter Season Four datasets were available for the analysis of winter birds, i.e. all birds recorded within 25m, 50m and 100m and over 100m of the transect line. Again, the gradient lengths from DCA were generally longest in the 0-25m distance band (Table
57
2.13). Thus indicating that species turnover was greatest for this dataset. The gradient lengths decreased steadily from 0-50m to 0-100m and finally to over 100m. It was decided to use the 0-50m distance band for analyses of the winter bird data to keep a consistent pattern throughout the results for this chapter. The 236 birds recorded on average per visit to the five habitat types during winter 2002/2003 were represented by 36 different species. Both blackbird and coal tit each made up 12% of the population in winter, with goldcrest and redwing each containing over 10% of the community (Figure 2.19). Wren was only the sixth commonest species seen in winter with five per cent of the individuals recorded (Figure 2.19). Blackbird, coal tit, goldcrest, redwing, robin (9%), wren (5%), meadow pipit (5%), starling (4%), chaffinch (4%) and rook (3%) were the ten commonest winter species with 75% of all birds recorded (Figure 2.19). The occurrence of the species in each habitat is given in Table 2.14.
Others 19%
Wren 5% Goldcrest 11%
Fieldfare 3%
Coal Tit 12%
Rook 3% Starling 4%
Robin 9%
Redwing 10%
Chaffinch 4%
Meadow Pipit 5% Blue Tit 3%
Blackbird 12%
Figure 2.19: Species composition of all the birds recorded within 50m of the sampling points during the winter season in all sites based on the average number of birds recorded in each site per visit.
58
Table 2.13: Lengths of the gradients derived from DCA for winter birds recorded in the five habitat types. Datasets
0-25m
0-50m
0-100m
0- >100m
Winter Birds
4.498
4.155
4.033
4.061
Table 2.14: All of the bird species recorded during the winter survey in each of the five habitat types within 50m of the transect line (+ = Species occurred once in that habitat; * = Species occurred frequently (more than 1 individual recorded) in that habitat). Common Name Blackbird Blue Tit Bullfinch Chaffinch Coal Tit Dunnock Fieldfare Goldcrest Goldfinch Golden Plover Great Tit Greenfinch House Sparrow Jackdaw Jay Linnet Long-tailed Tit Magpie Meadow Pipit Mistle Thrush Pheasant Pied Wagtail Redwing Reed Bunting Robin Rook Skylark Snipe Song Thrush Sparrowhawk Starling Stonechat Treecreeper Woodpigeon Wren Yellowhammer
Scientific Name Turdus merula Parus caeruleus Pyrrhula pyrrhula Fringilla coelebs Parus ater Prunella modularis Turdus pilaris Regulus regulus Carduelis carduelis Pluvialis apricaria Parus major Carduelis chloris Passer domesticus Corvus monedula Garrulus glandarius Carduelis cannabina Aegithalos caudatus Pica pica Anthus pratensis Turdus viscivorus Phasianus colchicus Motacilla alba (yarrelli) Turdus iliacus Emberiza schoeniclus Erithacus rubecula Corvus frugilegus Alauda arvensis Gallinago gallinago Turdus philomelos Accipiter nisus Sturnus vulgaris Saxicola torquata Certhia familiaris Columba palumbus Troglodytes troglodytes Emberiza citrinella
Broadleaf * * * * * + *
Conifer -ous * *
* * *
*
+
Pasture * * + * * * * + * *
Setaside * +
*
* *
+ * + *
+ * *
*
Tillage
* *
+ +
*
* * * *
* * +
* +
*
*
*
* *
* + * * *
*
+
+ * * * * + +
* * * * *
* *
59
* * +
* +
* *
2.3.7 Comparison between Habitat Types During the Winter Season Broadleaf habitat had the highest abundance of winter birds with an average of over 14 birds recorded per site per visit (Table 2.15). In comparison, approximately 5 individuals were recorded, on average, in set-aside sites during a winter visit (Table 2.15). However, these differences were not significant.
Table 2.15: Average number (and standard error) of birds recorded in each site per visit in winter within 50m of the transect line for the five different habitat types. Habitat Type
No. of Sites
Mean
Standard Error
Broadleaf
4
14.38
2.32
Coniferous
4
10.06
1.85
Pasture
4
13.38
4.92
Set-aside
4
5.13
1.86
Tillage
4
8.81
3.99
2.3.7.1 Diversity Indices No significant differences were found between habitat types in species richness, Simpson’s Index (D) and Shannon-Weiner Index (H’). The average species richness per site was high in broadleaf and pasture (10.25 species each), intermediate in tillage (8.5 species) and was lowest in coniferous and set-aside, which both had an average of 7 species present per site (Table 2.16). A similar pattern was observed when the number of species in all sites of each habitat type was examined (Table 2.16). The four pasture sites and the four tillage sites contained the highest number of species with 20 recorded (Table 2.16). Seventeen species were present between the four broadleaf sites, 15 in set-aside and 12 species were found in the coniferous sites (Table 2.16).
60
There were similarly no significant differences in the Shannon-Weiner and Simpson’s Indices. The mean Shannon-Weiner Index and Simpson’s Index values were very similar between broadleaf, pasture, set-aside and tillage habitats with the exception of coniferous, which had lower values than the rest of the habitats (Table 2.16).
Table 2.16: The number of winter species in all sites for each habitat type, and the mean winter species richness per site, Shannon-Weiner Index (H’) and Simpson’s Index (D) (with standard errors) for each habitat type within 50m of the transect line. Habitat
No. of
No. of Spp.
Sp. Rich
H’
D
Type
Sites
in All Sites
Mean
SE
Mean
SE
Mean
SE
Broadleaf
4
17
10.25
1.65
1.88
0.16
5.93
0.88
Coniferous
4
12
7
1.35
1.43
0.18
3.66
0.81
Pasture
4
20
10.25
1.55
1.83
0.14
5.38
0.91
Set-aside
4
15
7
0.82
1.53
0.19
5.00
1.11
Tillage
4
20
8.5
1.71
1.68
0.09
5.45
0.83
There were significant differences between the five habitat types in terms of the Jaccard Similarity Index for winter birds (Kruskal Wallis Test, P = 0.0158). Broadleaf and coniferous sites had very similar bird species compositions with mean Jaccard Similarity Index values of 0.53 and 0.52 respectively (Table 2.17). However, pasture (0.35), set-aside (0.36) and tillage (0.28), had low mean similarity scores (Table 2.17). Therefore, as during the breeding season, β diversity was low in the forest habitats and high in the open habitats.
61
Table 2.17: Mean Jaccard Similarity Index (with standard error) for winter birds recorded within 50m of the transect line for the five different habitat types. Habitat Type
N
Mean
Standard Error
Broadleaf
6
0.53
0.05
Coniferous
6
0.52
0.05
Pasture
6
0.35
0.06
Set-aside
6
0.36
0.03
Tillage
6
0.28
0.05
2.3.7.2 Assemblage Composition In winter, the wren (7%) was replaced as the commonest species in broadleaf forest by the coal tit (22%) (Figure 2.20). Blackbird (17%), goldcrest (13%) and robin (10%) were also common winter species in broadleaf forest sites (Figure 2.20). Goldcrest was still the dominant species in coniferous forest in winter with 36% of the birds recorded belonging to this species, but coal tit was also present in large numbers with 29% of the community (Figure 2.20). The winter migrant thrush species, redwing (25%) and fieldfare (7.5%), constituted a large proportion of the winter community in pasture sites (Figure 2.20). Starling (16.5%), robin (11.5%) and blackbird (11%) were other common pasture species (Figure 2.20). In contrast to the breeding season, meadow pipit was the dominant species on set-aside with over 42% of the population, whereas skylark was only ranked third with 13% (Figure 2.20). Blackbird was the second commonest set-aside species with 15.5% of the winter community (Figure 2.20). The commonest winter tillage species were redwing (20.5%), rook (14%), blackbird (12%) and house sparrow (12%) (Figure 2.20).
62
100%
Others Redwing Starling House Sparrow Long-tailed Tit Fieldfare Rook Dunnock Meadow Pipit Blue Tit Blackbird Chaffinch Skylark Robin Coal Tit Goldcrest Wren
80%
60%
40%
20%
0%
Broadleaf
Coniferous
Pasture
Set-aside
Tillage
All Habitats
Figure 2.20: Species composition of all of the winter birds recorded within 50m of the sampling points for the five different habitat types and all habitats combined, based on the average number of birds recorded in each site per visit.
The length of the gradient for DCA was 4.155 and CCA illustrates a difference between the five habitat types in bird composition especially with the species assemblage of set-aside being far different than the other habitats (Figure 2.21). Coniferous and broadleaf sites were closely related to each other and pasture and tillage contained very similar species. The 1st canonical axis was very significant (P = 0.005) and mainly represented the difference between tillage, pasture and set-aside on the positive side of the diagram and broadleaf and coniferous on the negative side. The second canonical axis divided broadleaf, coniferous and set-aside on the positive half of the diagram from tillage and pasture on the negative half of the diagram. All of the canonical axes were significant (P-value = 0.005) and explained 34.1% of the species inertia and 100% of the variance in the species-environment relation (Table 2.18).
63
Skylark, meadow pipit, reed bunting, greenfinch, snipe and linnet were closely associated with set-aside. Goldcrest, treecreeper, long-tailed tit, wren, song thrush, pheasant and coal tit were some of the species, which exhibited a preference for coniferous or broadleaf forest habitat. Pasture and tillage were preferred habitats of redwing, stonechat, house sparrow, pied wagtail, mistle thrush, goldfinch, starling, rook, fieldfare and golden plover. Approximately 10 meadow pipits were recorded per set-aside site over all four visits combined, compared with 1.25 in pasture sites over the winter season. All four snipe recorded during the survey were present in set-aside. An average of 3.81 and 2.06 redwings were recorded in pasture and tillage per site per visit respectively. Two redwings were observed in all four visits in all set-aside sites, compared with one in broadleaf and none in coniferous. Tillage harboured exactly twice as many rooks as pasture with 5.5 recorded per site over the recording season. All house sparrows were recorded in tillage, with an average of 1.25 per site per visit. Of the seven golden plovers recorded, four were in tillage with the remainder in pasture. An average of 3.75 goldcrests were present in coniferous sites per visit, compared with 2.31 per broadleaf site, none in set-aside, three in all pasture sites between all visits and only one in all tillage sites over the whole sampling season. Long-tailed tits were present in coniferous and broadleaf forests with an average of 3.25 and 2 birds recorded per site over all four visits combined. Broadleaf sites contained an average of 3.81 coal tits per site per visit, compared to 3.06 for coniferous habitat. An average of 5 wrens were present in broadleaf sites per visit, compared with 2.75 per coniferous site per visit, 2 in tillage, 1.75 in pasture and only an average of 0.5 wrens per set-aside site per visit.
64
+1.0
LI RB GR SN MP S.
Set-aside
JD
Coniferous Broadleaf
LT GC J. CT TC PH
Y.
BT
GT
ST WR
B. D. R.
WP
CH GP RE BF SG SC GO FF HS M. Tillage RO SH PW MG
-1.0
Pasture
-1.0
+1.0
Figure 2.21: CCA of the winter birds recorded within 50m of the transect line in all habitat types. P-value of 1st canonical axis = 0.005. P-value of all canonical axes = 0.005.
Table 2.18: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the CCA of the winter birds recorded within 50m of the transect line in all habitat types. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total inertia .649 .573 .228 .112 4.586 .973 .958 .766 .627 14.2 26.7 31.6 34.1 41.6 78.3 92.8 100.0
65
2.4 DISCUSSION Many studies have been conducted in Britain and other parts of Europe comparing bird diversity in different agricultural and woodland habitats. However, I believed that the situation in Ireland was worthy of close examination because its island status, physical environment and history of land use appear to have resulted in bird communities which differ from those in other European countries. 2.4.1 Farmland Bird Studies Comparison of the bird assemblages recorded in this study with the bird species most commonly recorded on farmland in previous Irish studies and a list of the commonest British farmland species (c.f. Table 2.19) suggests that the sites examined here may be more similar to those studies in Britain than to the sites used in previous Irish studies. For example, goldcrest was not among the top ten common species in farmland in the present study but in the other Irish farmland studies it was one of the commonest species, with a ranking of sixth in studies of Holt (1996) and Flynn (2002). However, goldcrest was absent from the fifteen commonest farmland bird species in Britain (O’Connor and Shrubb, 1986). In addition, skylark was the commonest farmland species recorded in this study during the breeding season as it represented 24% of all birds recorded and 34% of the breeding birds. Yet it was not a predominant species in the other Irish studies although it was ranked as number six in the British list for the period 1962-1981. This may be due to the fact that skylark is mainly associated with tillage farming and set-aside in particular and the previous Irish studies dealt mainly with mixed or pasture farms that may not have provided suitable habitat for skylark. Skylark, wren, blackbird, meadow pipit, robin, dunnock, yellowhammer, blue tit, chaffinch and woodpigeon were the most abundant of all the birds recorded on 66
farmland in this study and accounted for 82% of the community. Eighty per cent of the breeding farmland bird community was composed of skylark, wren, robin, dunnock and chaffinch. In winter, the migrant redwing was the most abundant species with 19% of the population. The ten commonest species, redwing, blackbird, meadow pipit, robin, starling, rook, fieldfare, chaffinch, house sparrow and wren accounted for 80% of the winter community. Redwing and fieldfare are entirely winter migrants in Britain and Ireland and return to Scandinavia, Iceland, Finland and northern Europe to breed in summer (Lack, 1986). Chaffinch and blackbird are partial migrants as the resident Irish populations increase in winter due to immigration from the rest of Europe. Migrant chaffinches are believed to arrive from Scandinavia, Finland and continental Europe (Lack, 1986). Large numbers of blackbirds arrive each winter from Scandinavia, the Low Countries, Germany and the Baltic states (Lack, 1986). Moles and Breen (1995) surveyed a mixture of arable and grass fields in Co. Down and found that dunnock was by far the commonest species recorded during the breeding season with 22% of the community with wren (11%), robin (10%), chaffinch (7%), song thrush (5%), blue tit (4%), woodpigeon (4%), linnet (2%), yellowhammer (2%) and willow warbler (2%) being the next most abundant species. Meadow pipit was the commonest winter species representing 17% of the individuals recorded. Fieldfare (12%), starling (10%), rook (9%), woodpigeon (7%), blackbird (7%), blackheaded gull (7%), dunnock (4%), house sparrow (4%) and chaffinch (3%) were other common winter species. Wren, robin, blackbird, dunnock and chaffinch were the dominant species in five farmland plots in mid-west Ireland near Limerick city and together comprised between 61% and 72% of the total populations of the plots (Lysaght, 1989). Only four
67
summer migrant species were recorded in the plots: willow warbler, chiffchaff, spotted flycatcher and swallow, and these accounted for only 8% of the total population. Wren and robin dominated the bird community of these plots which is in contrast to the situation in Britain where blackbird, skylark or chaffinch are the most abundant species in the majority of farmland plots. Lysaght, (1989) postulates that, as wren and robin show a marked east-west increase in breeding density in Britain, the results of his study indicate that this trend extends further westward into Ireland. This trend may be explained by the milder climate in the west along with the higher density of hedgerows, which provide more suitable breeding habitats for both species. However, climate and habitat would be more suitable for many other species also that do not show increased breeding densities compared to Britain. As both wren and robin are generalist species in Ireland’s depauperate bird community, it is hypothesised that they are able to breed at higher densities due to the lack of competition from specialist species. It has been suggested that hedgerows on farmland are sub-optimal habitats for wrens and maybe robins as Williamson (1969; 1971) and Benson and Williamson (1972) showed that in years when populations were low the majority of wren territories were located in well-wooded habitats, while hedgerows and other suboptimal habitats were only exploited when populations were high. However, Fuller et al. (2001) argue that there is not enough evidence to conclude that hedgerows in general are sub-optimal habitats for wren and other species because if they were, then they would be expected to be occupied at higher rates in years when population sizes were relatively high but this has not been found to be the case (Chamberlain and Fuller, 1999; Fuller et al., 2001). Lysaght (1989) suggests ‘that the principal reason for the prominence of the wren and robin in mid-west Ireland is their ability to exploit
68
more successfully the available resources of farmland, due primarily to their adaptability’. Holt (1996) investigated the passerine community of hedgerows on farmland adjacent to Kilcolman National Nature Reserve in north Co. Cork from November 1994 to October 1995. In winter, Holt (1996) found that, in north Co. Cork, and with the exception of blackbird and blue tit, the winter density of the commonest 15 species was high in comparison to Britain. The commonest species were redwing (14%), chaffinch (13%), robin (10.5%), wren (9%), song thrush (7.5%), fieldfare (7.5%), blackbird (7%), goldcrest (6%), dunnock (6%) and blue tit (3%). By contrast during the breeding season the commonest species were wren (28%), robin (15%), chaffinch (13%), dunnock (10.5%), blackbird (8%), goldcrest (5%), song thrush (4%), reed bunting (3%), woodpigeon (2%) and blue tit (2%), with the first five present at higher densities than in Britain. Flynn (2002) found very similar results for six farm sites in Co. Wexford and Co. Offaly during the 1999 breeding season. Wren, robin, blackbird, chaffinch and dunnock comprised 72% of the breeding bird community, with wren accounting for approximately 25% alone. Goldcrest, song thrush, blue tit, coal tit, willow warbler and great tit were the next commonest species recorded.
69
Table 2.19: Comparison of the ranking of the commonest farmland species in the breeding season as recorded in previous studies. Rank
Laois/Kildare 2002 (All birds)
–
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Skylark Wren Blackbird Meadow Pipit Robin Dunnock Yellowhammer Blue Tit Chaffinch Woodpigeon Starling Jackdaw Long-tailed Tit Sedge Warbler
15
Greenfinch, Great Tit, Magpie, Swallow, Tree Sparrow (equal 15th)
Laois/Kildare – 2002 (Breeding only)
Lysaght (1989) Limerick - 1987
Wren Robin Blackbird Dunnock Chaffinch Willow Warbler Blue Tit Goldcrest Song Thrush Starling Jackdaw Great Tit Greenfinch Coal Tit
Moles & Breen (1995) Co. Down – 1972 & 1992 Dunnock Wren Robin Chaffinch Song Thrush Blue Tit Woodpigeon Linnet Yellowhammer Willow Warbler Goldcrest Moorhen Coal Tit Magpie
Skylark Wren Robin Dunnock Chaffinch Blackbird Blue Tit Greenfinch Song Thrush Sedge Warbler Meadow Pipit Whitethroat Yellowhammer Bullfinch, Collared Dove, Coal Tit, Goldcrest, Great Tit, Swallow (equal 14th)
Wren Robin Chaffinch Dunnock Blackbird Goldcrest Song Thrush Reed Bunting Woodpigeon Blue Tit Coal Tit Great Tit Bullfinch Pied Wagtail
Wren Blackbird Robin Chaffinch Dunnock Goldcrest Song Thrush Blue Tit Coal Tit Willow Warbler Great Tit Greenfinch Meadow Pipit Swallow
British Farmland CBC (1962-1981) (O’Connor & Shrubb, 1986) Blackbird Chaffinch Dunnock Robin Wren Skylark Blue Tit Song Thrush Yellowhammer Willow Warbler Great Tit Linnet Greenfinch Starling
Chiffchaff
Meadow Pipit
Swallow
Yellowhammer
Whitethroat
70
Holt (1996) Kilcolman Cork - 1995
CBC,
Flynn (2002) Offaly/ Wexford - 1999
2.4.2 Woodland Bird Studies In contrast to the above, comparison of the most commonly recorded bird species in woodland in this study and in previous studies from Irish and British woods (c.f. Table 2.20) shows that the bird assemblages of woodland sites were similar in all regions. Although their ranking varied, the ten commonest species, were identical in the sites studied here and in other Irish studies, with the exception of siskin and great tit (Table 2.20). The data for the ten commonest species recorded in woodland in Ireland were taken from Whelan (1995), who calculated the rankings from ten woodland sites studied by Batten (1976), Wilson (1977), MacLochlainn (1984), Kavanagh (1990), Nairn and Farrelly (1991), and McCarthy (1992). Starling was the only common British woodland species that was not recorded in woodland in this study. In general, the bird assemblages of woodland in Ireland and Britain are very similar in terms of the 10 or 15 most commonly recorded species with the exception of the order of these species in the ranking (Table 2.20). Therefore, the real difference between the assemblages in Ireland and Britain is not the abundant species but the less common woodland specialist species like woodpeckers, nuthatch, tawny owl and willow tit that occur in Britain but are absent from Ireland. In this study, goldcrest was the commonest species in the forest sites (broadleaf and coniferous) during the breeding season as 19% of all recorded birds and 25% of the breeding community were individuals of this species. Wren also accounted for a quarter of the breeding birds recorded. Goldcrest, wren, coal tit, robin, chaffinch, blue tit and blackbird comprised 84% of all birds recorded. Ninety-three percent of the breeding population was accounted for by six species: goldcrest, wren, robin, coal tit, chaffinch, blue tit and chiffchaff.
71
In winter, goldcrest (22%) was displaced by coal tit (25%) as the most abundant bird species in woodland. Coal tit, goldcrest, blackbird, robin, wren, blue tit, log-tailed tit, chaffinch, song thrush and pheasant were the commonest winter forest species, which together made up 93% of the winter bird community. The breeding goldcrest population in Ireland is thought to be sedentary but its population may be boosted in winter by birds from the continent (Hutchinson, 1989). Chaffinch, robin, blue tit, goldcrest, coal tit, wren, blackbird, siskin, woodpigeon and treecreeper were the ten most abundant breeding species recorded from ten sites in Irish forests (Whelan, 1995). Chiffchaff and willow warbler seem to prefer conifer plantations for breeding (Whelan, 1995). In comparison, the eleven most common breeding birds in studies of 240 British woods were wren, robin, blackbird, song thrush, willow warbler, blue tit, great tit, woodpigeon, dunnock and starling (Fuller, 1982). Batten (1976) studied the bird communities of several woodlands in Killarney, Co. Kerry and found that one third of the 180 territories per ten hectares recorded in a Norway spruce plot were of goldcrest. Diversity was highest in a native coniferous yew forest compared to European Norway spruce and lower again in American Sitka spruce. Only eight species were recorded holding territory in the Sitka spruce plantation studied with a density of only 100 birds per 10 hectares (Batten, 1976). Goldcrest (40% of territories), chaffinch (19%) and robin (13%) dominated the community with siskin (9%), coal tit (8%), wren (5%), blackbird (4%) and chiffchaff (1.3%) also present. Batten (1976) suggested that bird populations in coniferous forests are most diverse when the trees are native, which may also be explained in part by the number of arthropod species associated with native trees. Goldcrest occurred at higher densities in Irish coniferous woodland than in British forests. Species richness
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was between 15 and 21 in the three oak woods studied with densities between 100 and 158 territories per 10 hectares (Batten, 1976). Goldcrests were present in much larger numbers than warblers in these oak woods, which led Batten to believe that the absence of the willow warbler from Irish oak woods is due to the occupation of its niche by the goldcrest. Goldcrest also seems to partially depend on ivy as a nest site so low numbers of this species in a forest may be related to a lack of ivy (Batten, 1976). The yew woods near Muckross in Killarney were studied by Batten (1976) and Carruthers and Gosler (1994), with Batten (1976) finding that they were dominated by goldcrest with densities of 90.4 territories/10 ha. Carruthers and Gosler (1994) noted that the only similarity in community structure between English and Irish yew forest sites was an under-representation of robins, which was probably due to the lack of invertebrate prey associated with yew trees. Wilson (1977) investigated the breeding bird communities of five sessile oak woodland plots in Killarney, Co. Kerry and in the Wicklow mountains and found that between 14 and 21 species were recorded per plot with five or six species making up 76-85% of the communities. The commonest species were chaffinch, robin, goldcrest, blue tit, coal tit and wren with the chaffinch being most abundant in the three Kerry plots and goldcrest or wren in the two Wicklow ones (Wilson, 1977). The bird assemblages of mixed tree short rotation deciduous plantation were dominated by willow warbler (24% of the recorded territories), chaffinch (11.5%), reed bunting, robin and wren (10% each) (Kavanagh, 1990). The commonest species in a pure willow plantation were sedge warbler (18%), willow warbler (16%), reed bunting (14.5%), chaffinch, dunnock and blackbird (8% each). The dominance of
73
warblers and buntings was due to the presence of permanent early successional stages due to a short rotation coppicing regime in these plantations (Kavanagh, 1990). Nairn and Farrelly (1991) found 21 breeding species in the broadleaved woodland, dominated by oak forest, in the Glen of the Downs in Co. Wicklow with densities of 128.7 pairs per 10 hectares. The five most abundant species were wren, robin, blue tit, chaffinch and great tit (Nairn and Farrelly, 1991).
Table 2.20: Comparison of the ranking of the commonest breeding species in woodland found in this study and various studies in Ireland and Britain. Rank
Laois/Kildare
–
2002 (All birds)
Laois/Kildare 2002
–
(Breeding
only)
Irish (10
Woodland study
(Whelan,
British
Woodland
sites)
CBC
(240
1995)
(Fuller, 1982)
sites)
(10 most abundant) 1
Goldcrest
Goldcrest
Chaffinch
Wren
2
Wren
Wren
Robin
Blue Tit
3
Coal Tit
Robin
Blue Tit
Blackbird
4
Robin
Coal Tit
Goldcrest
Woodpigeon
5
Chaffinch
Chaffinch
Coal Tit
Willow Warbler
6
Blue Tit
Blue Tit
Wren
Robin
7
Blackbird
Chiffchaff
Blackbird
Great Tit
8
Woodpigeon
Blackcap
Siskin
Dunnock
9
Treecreeper
Song Thrush
Woodpigeon
Chaffinch
10
Great Tit
Blackbird
Treecreeper
Starling
11
Blackcap
Great Tit
Song Thrush
12
Chiffchaff
Dunnock
Goldcrest
13
Song Thrush
Willow Warbler
Chiffchaff
14
Long-tailed Tit
Mistle Thrush
Blackcap
15
Dunnock,
Mistle
Coal Tit
th
Thrush (equal 15 )
74
2.4.3 Comparison of Habitats A potential bias could have arisen in the comparisons of the birds in the different habitats because of the potential variability in detectability of particular species when point counts are used. For example, it is far easier to detect a singing blackbird 150m away than a singing treecreeper at a similar distance. This problem is often addressed through the use of a detection function or by using Distance software (Bibby et al., 2000). This was not generally possible in this study because the numbers detected of many species were too small to allow meaningful analysis. However, as the main focus of this chapter was species composition and not density, the statistical methods used are adequate. In this study, the average species richness per site was significantly different between the habitat types during the breeding season, with all broadleaf forest sites being most species rich followed by coniferous sites, pasture, set-aside and tillage (Figure 2.11). However, when beta diversity was taken into account the effect of habitat was less significant (Figure 2.12). Nevertheless, species richness and diversity were not significantly different between habitat types in winter. Beta (β) diversity was low in the forest habitats and high in the open, agricultural habitats during both the breeding and winter seasons (Tables 2.8 and 2.17). There was a difference between the five habitat types in bird assemblage composition with the forest habitats being more closely related to each other than the open habitats, however, set-aside and tillage had very similar breeding bird communities (Figures 2.14 and 2.16). However, in winter, the bird assemblages of set-aside were very different to the other habitats due to the presence of skylark, meadow pipit, and linnet, amongst others (Figure 2.21). Both forest types had very similar species compositions, pasture and tillage also shared a very similar winter bird community (Figure 2.21).
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Abundances of birds were significantly different between the habitat types during the breeding season but not in winter. Densities of birds were at least twice as high in forest habitats compared to open, agricultural habitats during the breeding season (Table 2.4 and Table 2.5). In this study, wren, blackbird, robin, chaffinch and blue tit dominated the pasture habitat bird community during the breeding season (Figure 2.13). Wren (31%), and robin (26%) were the main breeding species in pasture grassland (Figure 2.15). The winter migrant thrush species, redwing and fieldfare, constituted approximately one third of the winter community in pasture sites, with starling, robin and blackbird other common species (Figure 2.20). Skylark was, by far, the dominant species in set-aside and tillage during the breeding season (Figure 2.13). In the breeding season, meadow pipit, wren and dunnock were abundant in set-aside also, with tillage containing high proportions of yellowhammer and blackbird (Figure 2.13). In contrast to the breeding season, meadow pipit was the dominant species on set-aside in winter with over 42% of the population, blackbird was the second commonest set-aside species while skylark was only ranked third (Figure 2.20). The most abundant winter tillage species were redwing (20.5%), rook (14%), blackbird (12%) and house sparrow (12%) (Figure 2.20). McMahon et al. (2003) studied the over-wintering bird populations using stubble, set-aside, winter wheat and improved grassland fields and field boundaries in Co. Kildare in the winter of 2001/2002. Over-winter stubble had the greatest species richness and diversity, while improved grassland supported the lowest species richness and diversity. Skylark (14% of the bird population) was the dominant species in stubble followed by robin (10%), chaffinch (9%), rook (7%) and blackbird (7%)
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(McMahon et al., 2003). The commonest species in the set-aside community were woodpigeon (29%), robin (12%), fieldfare (8%) and blackbird (8%). Grassland was dominated by six species, fieldfare (20%), redwing (11%), robin (10%), blackbird (9%), woodpigeon (8%), and meadow pipit (7%). Woodpigeon (23%), chaffinch (17%), robin (11%) and blackbird (8%) were the commonest species recorded in winter wheat (McMahon et al., 2003). Wren was also the dominant species in broadleaf forest during the breeding season with 22% of all birds recorded and 34% of breeding birds, followed by coal tit, robin, blue tit, chaffinch and goldcrest (Figures 2.13 and 2.15). However, wren only comprised 7% of all birds recorded in broadleaf woodland in winter. Coal tit was the most abundant species in winter in broadleaf forest with 22% of the community (Figure 2.21). Blackbird, goldcrest and robin were also common winter species in broadleaf forest sites (Figure 2.21). Approximately one third of the coniferous forest bird community was goldcrest during the breeding and winter seasons (Figures 2.13, 2.15 and 2.20). Coal tit, wren and robin were also very abundant species in coniferous sites during the breeding season (Figures 2.13 and 2.15). Coal tit was also present in large numbers in winter with 29% of the community (Figure 2.20). Fuller et al. (2001) looked at how distinctive bird communities of hedgerows and woodland are in lowland agricultural landscapes in England and Wales and found similar results to this study. Species number was significantly greater in woodland plots than farmland plots and the species richness on farmland plots increased linearly with cover of farm woodland and non-linearly with density of total hedgerow. Woodland and farmland plots had different bird communities but there was considerable overlap between both communities in terms of species composition 77
(Fuller et al., 2001). They showed that tawny owl, marsh tit, coal tit, goldcrest, spotted flycatcher, treecreeper, nuthatch, great spotted woodpecker, chiffchaff and blackcap are woodland specialist species. In contrast, dunnock, whitethroat, lesser whitethroat, linnet, goldfinch, greenfinch and yellowhammer showed clear preferences for hedgerow (Fuller et al., 2001). Hedgerows, and thus farmland, provide habitats for a large number of generalists or widely distributed species that cannot be classified as habitat specialists (Fuller et al., 2001). The breeding and winter bird communities of the farmland and woodland sites from this study are very similar to those recorded in other similar studies in Ireland and Britain. However, species composition in this study may be more similar to certain studies rather than others. For instance, the breeding bird farmland community of this study may be more similar to the communities of British studies than previous Irish studies due to the low numbers of goldcrest in this study and the high abundance of skylark (O’Connor and Shrubb, 1986; Holt, 1996; Flynn, 2002). In total during the breeding season, thirty-seven species were recorded in the five habitats with 24 of these breeding. However, their individual contributions to species richness varied. Broadleaf forest harboured two species, buzzard and jay, which did not occur elsewhere. There were no species which occurred exclusively in coniferous forest but one species, mistle thrush, appeared to only breed there. Pasture contributed three unique species, bullfinch, swallow and pied wagtail, and three species, bullfinch, swallow and greenfinch, were also found to breed only in this habitat. Collared dove, sedge warbler and whitethroat only bred in set-aside while pheasant was also only seen in this habitat. Goldfinch and tree sparrow were only seen in tillage but no breeding species occurred there exclusively.
78
In winter, thirty-six species in total were recorded in the five habitats. Pheasant occurred exclusively in broadleaf forest, there was no species seen exclusively in conifers, goldfinch and starling were only seen in pasture but set-aside and tillage both had four species which were not seen elsewhere. Greenfinch, linnet, reed bunting and snipe occurred in set-aside and house sparrow, pied wagtail, stonechat and sparrowhawk in tillage. The contribution of each habitat to the overall species richness in both the breeding and winter seasons illustrates that farmland habitats contribute far more unique species to the totals than forest habitats. If woodland habitats were removed in the UK the loss of species would be expected to be greater than in this study, as woodpeckers and other specialist woodland species would be lost. This emphasizes that point that Ireland has more farmland specialist bird species than woodland specialists. However, it is clear that the vast majority of the species recorded were generalists and occurred in several habitats. Even though certain species were unique to specific habitats in this study, all of these species could possibly be found in several of the studied habitats had a larger scale study been undertaken. 2.4.5 Migrants The species richness and abundance of summer migrants in Ireland are low compared with Britain (Lack, 1976; O’Connor, 1986; Hutchinson, 1989; Fuller and Crick, 1992). The summer migrant community in this study constituted blackcap, chiffchaff, swallow, whitethroat, sedge warbler and willow warbler. These species make up 3.8% of all species recorded in all twenty study sites and 5% of breeding species.
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Only 3.7% of all the birds recorded during the breeding season on the farmland sites were migrants. When only the farmland breeding bird community was examined, migrants comprised 4.4%. Summer migrants made up 4% of all birds recorded on pasture but only 1.7% of the breeding community. A larger proportion of the set-aside community was comprised of migrant bird species as 5% of all birds recorded were migrants and 8.8% of breeding birds. No summer migrants were recorded in the tillage sites. Similarly low proportions of summer migrants have been found in the other studies of farmland bird communities in Ireland. In the studies of Holt (1996) in Co. Cork and Flynn (2002) in Co. Wexford and Co. Offaly, the summer migrant communities were both less than five per cent. Lysaght (1989) found that summer migrants formed 4 – 12% of the community, with an average of 5.8% in the five farmland plots of his Co. Limerick study. Moles and Breen (1995) found summer migrants accounted for 3% of the community in 1972 and 4% in 1992. Summer migrants on farmland in Britain comprise 12.5% of the community on average (Benson and Williamson, 1972). In forest sites 3.9% of all birds recorded were migrants in the summer with blackcap comprising half of the migrant community. The forest breeding community was comprised of 5.6% migrants. A larger proportion of the broadleaf community was comprised of migrants (4.7% of all birds recorded and 7.7% of the breeding community) compared to coniferous forest sites (3.1% and 4.1% respectively). Nairn and Farrelly (1991) compared the percentage of migrant species in the breeding communities of Irish and British broadleaved woodlands by using data from their study and the studies of Batten (1976), Wilson (1977) and Fuller (1982). They showed that there was an average of 1.83 migrant species present in each of six Irish
80
sites, which accounted for 9.4% of the community. The studies from 240 British woodland sites had an average of 8.05 migrant species, which represented 21.6% of the breeding population (Nairn and Farrelly, 1991). Kavanagh (1990) found that 34.3% of the population of two short rotation forestry plantations on cutover bog in Co. Offaly consisted of summer migrants. This high proportion of summer migrants was probably mainly due to the scrubby nature of the plantations, which were suitable breeding habitat for warblers (Kavanagh, 1990). The evidence from studies of Irish breeding bird communities, including this study, gives some support to the theory that summer migrant species may be at a competitive disadvantage to resident species in most habitats and may be one of the main reasons why they only occur in low numbers in Ireland (O’Connor, 1986; Hutchinson, 1989; Nairn and Farrelly, 1991). However, distance from source populations must also be taken into account. Redwing and fieldfare were the only pure winter migrant species recorded in the study and comprised 13.5% of the winter community in all sites. Winter migrants accounted for just over 25% of birds recorded in farmland sites and only 0.5% in forest sites. Large proportions of the winter communities of pasture (32.7%) and tillage (28%) were migrants, but they comprised only 2.1% of the set-aside community. Holt (1996) found that 20.7% of the winter community on farmland in Co. Cork were pure migrants, while in farmland in Co. Down they represented 21.7% of the population in 1972 and 33.3% in 1992 (Moles and Breen, 1995). The broadleaf winter migrant bird community was only 0.7% of the total winter birds recorded and no winter migrants were recorded in coniferous sites. Thus the winter community had a far greater percentage of migrants than the summer.
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2.4.6 Vegetation Structure Vegetation structure has previously been shown to be a very important determinant of bird species composition in habitats. For example MacArthur and MacArthur (1961) showed that bird species diversity was predicted by the height profile of foliage density in deciduous forests. In this study during the breeding season, treecreeper, great tit, wren and others exhibited a preference for taller vegetation between 2 and 8m, whilst jay was associated with sites, which had tall trees over 16m (Figure 2.18). Goldcrest, chiffchaff and coal tit were more abundant in coniferous sites as these sites contained trees between 8 and 16m (Figures 2.14, 2.16 and 2.18). Meadow pipit, skylark, pheasant and other species were closely associated with vegetation under 2m, which was common in set-aside sites (Figures 2.14, 2.16 and 2.18). Thus, the different strata of vegetation are important for determining which species will occur in a habitat during the breeding season, as some prefer short vegetation to feed and breed, whilst others need tall trees. Moles and Breen (1995) found that in general, for the most common birds, feeding grounds were most important during winter and tall cover during the breeding season. 2.4.7 Conclusions The essential question that was to be answered in this chapter was, on an island that has a depauperate bird fauna and where many of the habitat specialists are absent, do bird assemblages vary between terrestrial habitat types? In other words, due to low number of specialists in Ireland is there a difference in bird species composition between habitats and to what extent do bird communities differ in species richness. It has been postulated that terrestrial habitats, such as broadleaf and coniferous woodland, pasture, set-aside and tillage, are similar to each other in terms
82
of species richness but the habitats vary in the actual bird species that make up their communities. The results of this chapter show that the bird communities of these lowland Irish habitats are distinct. Species richness was different between the habitats during the breeding season, which contradicts the hypothesis stated above. However during winter, species richness was not different between the habitat types. In conclusion, the primary aim of this study was to assess the differences in bird species diversity and abundance between different woodland and agricultural habitats in spring/summer and winter. The results show that there were significant differences between broadleaf forest, coniferous forest, pasture, set-aside and tillage habitats in the breeding and winter seasons in lowland Ireland. The habitats all differed in the species assemblages they contained in summer and winter, with several species being closely associated with each of the individual habitat types. Species richness and abundances were also significantly different between the various farmland and woodland habitats in the breeding season but not in winter. Very few species unique to either of the forest habitats were found in the breeding or winter seasons but each of the farmland habitats, in contrast, had several species unique to their habitat. Thus, despite the lack of habitat specialist bird species, particularly woodland specialists, in Ireland there are differences between the bird communities of different terrestrial habitats.
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Chapter 3: The Effects of Set-aside Management on Birds in the Breeding Season. 3.1 INTRODUCTION 3.1.1 The Decline of Farmland Birds The decline in farmland birds across Europe is well documented (Pain and Pienkowski, 1997; Krebs et al., 1999; Donald et al., 2001), with many studies focussing on the declines in the UK (Fuller et al., 1995; Siriwardena et al., 1998). In the last 40 years the major changes in agriculture have resulted in the greatest declines in farmland specialist birds (Siriwardena et al., 1998). Fuller et al. (1995) found that 24 out of 28 farmland bird species had undergone range contractions between 1970 and 1990 in the UK and 15 of the 18 species, whose populations it was possible to assess, had declined (7 by over 50%). In addition, the extent of the range contractions and farmland bird population declines were not matched by bird assemblages in other habitats (Wilson et al., 1997). With the exception of the grey partridge and the corncrake, the causes of population decline of most bird species are not completely understood (Fuller et al., 1995). Apart from changes in agriculture causing the population declines, natural factors such as weather, climate change, disease and predation may be important factors (Wilson et al., 1997; Mason and MacDonald, 2000). The population dynamics of the grey partridge have been intensively studied and it has been shown to be particularly badly affected by agricultural intensification with declines in Europe estimated at 83% (Potts, 1986; Tucker and Heath, 1994 cited in Sotherton, 1998). The use of pesticides and the decline in the practice of
84
establishing temporary grassland within a mixed arable/grass ley rotation by undersowing appeared to be the major causes of the reduced populations of the preferred chick food insects. The reduced food for chicks led to increased chick mortality, which in turn reduced the population, as recruitment was lower than the natural death rates (Sotherton, 1998). Similarly, the corncrake has suffered huge range contractions and population declines across Europe in recent times (Sutherland, 1994). The Irish population of corncrakes has decreased most, with a 70% reduction in its range between 1972 and 1991 and an overall population decline of over 80% between 1988 and 1993 (Gibbons et al., 1988; O’Connell, 1999). Corncrakes breed in hay and silage meadows and other tall vegetation (Green, 1996). Nests, chicks and adults are lost during mowing of meadows (Green, 1996; O’Connell, 1999). The switch from hay-making to silage production contributed to the decline of the corncrake as this resulted in the mechanisation of mowing, earlier mowing and the loss of habitats with tall vegetation (Green, 1996; O’Connell, 1999). The use of herbicides and chemical fertilisers are secondary reasons for the decline of the corncrake as these resulted in the vigorous growth of agricultural grasses and also poisoned the corncrake’s food source, the soil invertebrate fauna (O’Connell, 1999). In Ireland, agricultural practices are largely determined by policies such as the Common Agricultural Policy, which have over the years changed in their emphasis. Croton (2003) recommends that payments should be made through the Rural Development pillar of the CAP, and through “National Envelopes,” to farmers that farm in a sustainable manner, deliver biodiversity, landscape and other environmental benefits and provide the basis for sustainable development of rural communities. Setaside involves removing land from production and is one of the measures which have 85
been suggested to deliver such benefits. In this study I will investigate whether different forms of set-aside management have differing effects on bird populations. A history of the Common Agricultural Policy and Set-aside practices are given in Appendix III. 3.1.2 Potential Benefit of Set-aside Even though set-aside was originally introduced as an agronomic measure, it is clear that it has potential environmental benefits. Rotational set-aside, which is derived from naturally regenerated stubble fields, might lead to increased availability of winter food due to the recreation of stubbles. Foraging opportunities could be provided in exclusively arable landscapes by the introduction of grassland in the form of sown set-aside (Evans, 1997). This might help increase invertebrate prey in the spring/summer and also provide nest sites for the ground-nesting species. 3.1.3 Studies of Birds on Set-aside in the Breeding Season Several studies have been conducted on farmland birds on set-aside in Britain including Henderson et al. (2000a; 2000b; 2001) and Buckingham et al. (1999). In southern and eastern England set-aside permanent fallow was found to contain three times as many birds per hectare and had more species using them than arable fields (Sears, 1992). Overall, twelve bird species (buzzard, goldfinch, grey partridge, linnet, magpie, mistle thrush, red-legged partridge, rook, skylark, starling, stock dove and woodpigeon) showed a significant preference for set-aside fields compared with only three for arable (chaffinch, house sparrow and pheasant). Similarly, Berg and Part (1994) show that skylarks, whinchats, whitethroats and linnets were found in significantly higher numbers on set-aside fields at forest edges than on cereal fields at forest edges in Sweden.
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In Scotland, first-summer set-aside fields were found to contain more breeding birds species and higher wader densities than in the previous summer and than in crop fields (Watson and Rae, 1997). Grey partridges and skylarks were also present in higher densities in set-aside than in crops. However, mowing of set-aside destroyed many breeding attempts. Wader and grey partridge densities were also higher in first-summer set-aside compared with those in the first year of resumed cropping. The reason first-summer set-aside had high densities of birds may be due to the larger numbers of invertebrates present in these fields. Dense tussock grass became dominant in later summers on non-rotational set-aside and this led to the presence of little short vegetation and bare ground. Thus non-rotational set-aside in later years held fewer skylarks than in the first summer. Another reason why birds may be scarcer in later years of set-aside is that mowing greatly reduces the number and abundance of most species of arable weed with each successive year. Henderson et al. (2000a) compared bird abundance between set-aside and nearby crops or grassland across England. Field type preferences of several bird functional groups were examined; namely gamebirds, pigeons, crows, skylark, thrushes (Turdidae) and granivorous passerines (Passeridae, Fringillidae and Emberizidae). It was shown that bird abundances were significantly higher in setaside than on winter cereals for all six functional groups. Rotational set-aside held the highest numbers of birds of all groups with the exception of crows, which preferred grassland. Winter cereals or grassland were the least preferred habitat in most cases. This study also showed that, on farms which included both rotational and nonrotational set-aside, all functional groups, except crows, preferred rotational over nonrotational set-aside and the authors claim that this illustrates the importance of setaside sward composition in influencing bird abundance. These results imply that set87
aside is utilised by birds as a source of food which is why it is favoured over tillage and grassland by a wide range of species. It also appears that the nature of the sward that develops on set-aside land is crucial if it is to maximally benefit birds. Thus, most birds favoured natural regeneration rotational set-aside rather than the more structurally uniform non-rotational set-aside. In a second more intensive and smaller scaled study, Henderson et al. (2000b) compared bird abundance and distribution on paired tillage and set-aside fields. Bird abundance and diversity was higher on set-aside than on fields of wheat, brassicas, root crops and seed rye. A range of species, including waders, gamebirds, pigeons and passerines showed this response, with a stronger preference for rotational set-aside over non-rotational. Most species recorded within the field itself would have been feeding there rather than nesting, and therefore, set-aside probably contains greater food abundance than arable crop fields. Henderson et al. (2000b) suggest that as the majority of birds used the outer 20m of the field that many of the benefits of the whole field set-aside approach may be derived from marginal strips. Also, these strips should be managed to maintain a patchy, relatively diverse sward of arable plants like rotational set-aside. Mason and MacDonald (2000) examined whether landscape features or landuse were important determinants in the distribution of eight breeding bird species in farmland in eastern England. Only skylarks and yellow wagtails established territories within crops. Over half of skylark territories were in autumn-sown cereals. However, skylark densities were highest in set-aside (24 km-2) followed by spring-sown crops and lastly autumn-sown cereals. Skylarks showed no evidence of switching away from autumn-sown crops as the breeding season progressed. Skylarks showed preferences for set-aside but other grassland was avoided. Skylark numbers were
88
negatively correlated with hedgerow length. Mason and MacDonald (2000) suggest that the data of Wilson et al. (1997) and Poulsen et al. (1998) indicate that winter cereal is a sink habitat for skylark populations as they are unlikely to be able to replace themselves. The authors postulate that 64% of skylark territories were in habitats in which breeding was unlikely to be successful. The majority of yellow wagtails nested in spring-sown crops with a strong preference for potatoes and an avoidance of autumn-sown crops and grassland. Most yellow wagtail territories were in the largest fields. Eybert et al. (1995) showed that fallow land (set-aside) was a preferred feeding habitat of linnets. Yellowhammer territories were strongly associated with tall hedges, but preferences for crops were weak. Set-aside and broadleaved crops seemed to be important foraging habitats for yellowhammers early in the breeding season (Stoate et al., 1998). The effects of agricultural intensification on the breeding success of corn buntings on the South Downs in west Sussex in England were studied by Brickle et al. (2000). They found that the corn buntings that were feeding nestlings foraged mostly in grassy margins with spring-sown barley, unintensified grass and set-aside used more than expected. The set-aside in the study was non-rotational and had a fairly uniform sward. Foraging was less than expected on winter-sown wheat and intensively managed grassland. Sawfly larvae and other invertebrates fed to chicks were more abundant in the foraging habitats than elsewhere and these were the areas that had received fewer pesticide applications. The length of foraging trips by parents both in terms of distance and time increased the lower the abundance of chick-food invertebrates close to nests. The weights of nestlings increased with greater chickfood invertebrate abundance. The probability of nest survival decreased with the
89
abundance of chick-food invertebrates close to the nest, which may be due to the increased risk of predation. In southern England significantly higher densities of breeding pheasants were found where woodland edges bordered set-aside compared with edges adjacent to cereal fields and grassland (Sotherton et al., 1994). Fifteen radio-tagged hens were followed during April and the nests of those that had used set-aside earlier in the season were more likely to be successful regardless of where their nests were sited and they had a higher survival rate until the end of May. However, no nests were located in rotational set-aside fields as the low sparse cover provided early in the nesting season deemed them to be unsuitable as pheasant nesting habitat. Undisturbed set-aside could increase the numbers of sawflies (Hymenoptera: Tenthredinidae) in the arable landscape as their larvae enter the soil to pupate in July, over winter there and emerge as adults in May (Sotherton, 1998). However, cultivation to prepare seedbeds for following crops destroys many pupae (Sotherton, 1998). Sawfly larvae are important in the diet of the insect-eating chicks of birds such as gamebirds and skylarks (Sotherton, 1998). Poulsen (1996) states that ‘important skylark chick food like sawflies and lepidopteran larvae are typically found in lower density in cereals compared with natural grassland and other less intensively managed areas.’ 3.1.4 Studies of Skylarks on Set-aside Due to the huge declines in their populations, skylarks have been the focus of many studies in Europe over the past 15 years or so. For example, skylark numbers declined by 51% between 1968 and 1995 on UK lowland farmland, which was an absolute loss of approximately 3 million breeding birds (Siriwardena et al., 1998). Since 1975, skylark numbers in pastoral areas of Britain have declined almost as 90
sharply as those associated with arable areas, partly due to the intensification of grassland management (Browne et al., 2000). Despite the decline in skylark numbers, their range has been reduced by less than 5%, and they are still widespread in most open country habitats throughout Britain and Ireland (Browne et al., 2000). Skylarks nest on the ground in open habitats with short, grassy or herbaceous vegetation cover (Snow et al., 1998). They take a wide variety of invertebrate prey during the breeding season, particularly when feeding their chicks (Wilson et al., 1997). Poulsen (1996) studied the behaviour and parental care of skylark chicks in three different crop types: spring barley, grass and sown non-rotational set-aside and showed that parental care differed between broods from different crop types. During the last four days of the nestling period and the first four days after fledging, feeding frequencies and feeding distances were greater in set-aside than spring barley or grass. The load size of the feeding trips was significantly higher in set-aside than in spring barley and grass. Fledglings, on average, dispersed greater distances in fields of spring barley and grass compared to set-aside. This may be due to the fledglings’ own experience of food availability influencing their foraging behaviour. Also, different crop types allow differing chick mobilities depending on the vegetation density. The higher the predation pressures the faster and wider the chicks will spread. To test whether the changes in agricultural land-use and intensity of management have contributed to the population decline, Wilson and colleagues (1997) examined whether distribution and breeding success varied between organically and intensively managed crop fields in southern England and found that density was lowest on fields with tall boundary structures or adjacent to unsuitable habitat and also those fields with tall, dense vegetation cover. Set-aside and organically cropped fields had significantly higher densities of skylarks throughout
91
the breeding season compared to intensively cropped fields or grazed pasture (Wilson et al., 1997). Nests were usually built in crops between 20 and 50cm tall, and were rarely present in crops over 60cm. Breeding success was higher on set-aside than on intensively managed cereals. Most nest failures were the result of predation but many failed nests on grass fields were caused by silage cutting or trampling. Brood starvation occurred only in cereal fields. Skylarks made nesting attempts in set-aside and silage fields throughout the breeding season as the cutting regime meant that some fields always had suitable vegetation. However, cutting operations would have destroyed some nests and also led to territory abandonment on these fields. Wilson et al. (1997) suggest that skylark pairs must make two or three nesting attempts per breeding season in order for populations to be self-sustaining. As a single crop type rarely provides suitable habitat for nesting throughout the season, skylarks require mixed farms with a high diversity of crop types to make multiple successful nesting attempts without territory enlargement or abandonment. Nest success on set-aside fields and organic cereals was high and this may reflect the fact that a more diverse vegetation structure and reduced pesticide input during the breeding season results in these fields having relatively abundant invertebrate food resources (Wilson et al., 1997). Set-aside is also strongly favoured by foraging skylarks outside the breeding season (Wilson et al., 1996). Poulsen et al. (1998) found that territory density was 2-3 times greater in setaside and permanent pasture fields, than in winter and spring-sown cereals. The setaside fields were in their fourth year and had been sown with creeping red fescue (Festuca rubra), perennial rye-grass (Lolium perenne) and white clover (Trifolium repens). Territory size was 1.7 ha for skylarks in set-aside compared with 4.5 ha for winter cereals and 2.5 ha for other crop types. In set-aside and permanent pasture,
92
nesting began in April and peaked in late May. This contrasted with spring barley where nesting was first detected in late May and with silage grass in early June. Setaside fields contained more than double the density of successful nests than any of the arable crop types. Average clutch size at hatching was over 15% larger in set-aside fields than in silage grass and spring barley. This study suggests that the reasons for larger clutch sizes in set-aside are the shorter distances travelled by parents on feeding trips compared with spring barley or silage grass, and the greater abundance of nestling insect food in set-aside. Fledging success was similar in all crop types. However, the number of fledglings produced per hectare varied between 0.5 in setaside, 0.13 in silage grass, and 0.21 in spring barley. Winter cereals contained no nests with chicks. The authors suggest that the causes of chick death were predation in setaside fields, farming practices in silage grass fields, and suspected starvation in spring cereals. This study therefore shows that there is potentially high nesting success for skylarks in set-aside. Chamberlain et al. (1999a) showed that set-aside had consistently high rates of occupancy and high densities of singing male skylarks across the breeding season at both national and local scales. In the second half of the breeding season (mid-May to July), skylark density declined significantly on winter cereals compared with spring cereals, legumes, moorland and other habitats. Skylarks have been shown to prefer open habitats and in this study tended to be absent from fields bounded by hedgerows, particularly those containing trees (Chamberlain et al., 1999a). This may be a mechanism of predator avoidance. The authors found that lowland crops greater than 30 cm in height had relatively low occupancy by skylarks. Winter cereals reach this height significantly earlier than other crops such as spring cereals. This means that skylarks cannot make as many breeding attempts per season in winter cereals as in
93
set-aside, spring cereals or other habitats. Therefore increases in winter cereals, combined with the corresponding loss of spring cereals, have had a negative effect on skylark populations. Browne et al. (2000) present the results of the British Trust for Ornithology’s (BTO) national survey of breeding skylarks in Britain and report that arable areas supported the highest densities of skylarks, with 46-49% of the British breeding population associated with arable areas. Marginal and upland areas supported lower densities but still held about 34% of the breeding population. From the more intensive finer scaled study, set-aside and other various types of ungrazed grassland held the highest densities of skylarks and these habitats were most preferred by the species. The highest proportion of the English and Welsh skylark farmland population was held on winter cereal, improved grassland and set-aside compared with grazed pasture, winter cereals and spring cereals in Scotland. Another study by Henderson et al. (2001) looked at the breeding season responses of skylarks to vegetation structure in set-aside and showed that sward structure comprising a mixture of non-vegetated ground and vegetation of medium height (around 17 cm) was significantly associated with an increase in skylark abundance on whole-field set-aside. Skylark densities increased until at least 30% unvegetated ground was available. However, a very high proportion of bare ground was also unsuitable for skylarks. Thus, the presence of patchy swards will increase skylark foraging and breeding abundance compared with dense grassland. The authors of the study found that the preferred structure was mainly characteristic of rotational set-aside with at least half of the rotational fields sampled potentially suitable in contrast to only 10% of non-rotational set-aside fields.
94
Henderson and Evans (2000) show that granivorous birds and gamebirds significantly prefer set-aside with optimally 17% and 39% bare ground/straw or litter respectively. The increased need for bare ground in gamebirds is probably due to their requiring a larger area of unvegetated ground from which to obtain food and also to allow easier initial access to the field. Pigeons were also associated with bare ground but not significantly. As with the study above, younger set-aside (i.e. natural regeneration rotational set-aside or early non-rotational set-aside) was the most likely to contain this patchiness of bare ground/straw/litter mixed with vegetation cover. 3.1.5 Studies of Winter Birds on Set-aside The study by Buckingham et al. (1999) examined the use of set-aside land by farmland bird species in the UK during winter. The study found that five of the six declining farmland bird species recorded in the study occurred in significantly greater numbers on rotational set-aside in winter than would be expected if the birds were randomly distributed over the farmland landscape. Song thrush showed no preference for rotational set-aside (stubble fields that are left fallow over-winter) whereas grey partridge, skylark, linnet, yellowhammer and cirl bunting preferred set-aside land. There was an almost universal avoidance of winter cereals and ploughed land by farmland birds due to the lack of seed and invertebrate food in these field types. Species varied in the type of set-aside selected, with seed-eating passerines preferring naturally regenerated vegetation while grey partridge showed a preference for a more established grass cover. Barley stubbles were significantly selected most commonly by birds out of the other previous crops of first year naturally regenerated set-aside. Buckingham et al. (1999) state that ‘grazing of first year naturally regenerated fallows makes them less attractive to seed-eating birds’. The authors show that ‘if the right
95
management is followed, set-aside land can produce a source of winter food for several declining farmland bird species’. Wilson et al. (1996) analysed field type preferences of farmland birds in winter using resampling methods. The authors also concluded that naturally regenerated set-aside from stubbles is more likely to benefit wintering seed-eating birds than sown grass set-aside due to the greater abundance of seeds. Invertebratefeeding birds prefer grazed grass fields in winter compared to ungrazed set-aside fields as these fields may already have a depleted soil invertebrate population due to recent cultivations. Henderson and Evans (2000) comment on studies of cirl and corn buntings in which both species showed a preference for weed-rich stubble fields as winter foraging habitat. Corn buntings were present in approximately double the density on weedy stubbles compared to “clean” stubbles. 3.1.6 Rotational Set-aside Versus Non-Rotational Set-aside Several of the studies mentioned above have shown that many farmland bird species exhibit a preference for rotational set-aside fields over non-rotational set-aside (Henderson et al., 2000a, b and 2001; Watson and Rae, 1997). The differences in the management of these two types of set-aside have resulted in the differences in their usefulness as feeding and breeding habitats for farmland birds. Naturally regenerated rotational set-aside results in important feeding areas for birds, such as corn bunting, cirl bunting, linnet, house sparrow and chaffinch, as some areas that were previously cropped are just left untouched after harvest as winter stubbles each year (Buckingham et al., 1999). In the UK, at least, a broad-spectrum herbicide is sprayed on this type of set-aside to control weeds in the following crops (Henderson et al., 2000a, b). The plant diversity is dependent on the soil type and the 96
seed bank and this will in turn determine the invertebrate diversity of the set-aside (Vickery et al., 2002). Naturally regenerated rotational set-aside contains important invertebrate food sources for insectivorous birds in the summer that are unavailable in cereal fields, and also has a patchy sward which allows foraging at the sward base or on the ground (Vickery et al., 2002). Several farmland bird species such as grey partridge, tree sparrow, goldfinch, greenfinch and linnet are likely to benefit from the increased abundance of weed seeds on set-aside, as crops have a limited numbers of weeds present due to intensive management (Vickery et al., 2002). Rotational set-aside has been found to contain three times the density of insects than conventional cereal, and hold especially high numbers of plant hoppers and coleopteran families (Carabidae, Staphylinidae and Chrysomelidae) (Sotherton, 1998). As mentioned previously, the usefulness to birds of winter stubble created under rotational set-aside is increased by the amount and quality of regeneration of broad-leaved weeds (Henderson and Evans, 2000). Soil type and previous management of the field, especially herbicide usage, affects the weeds present in the set-aside. Non-rotational set-aside provides better foraging opportunities for birds of prey such as kestrel and barn owl than rotational set-aside, as the denser vegetation favours small mammals (Vickery et al., 2002). Firbank et al. (2003) examined the impacts of set-aside management on agronomy and ecology by using a questionnaire for farmers and field studies of plants, invertebrates and breeding birds on up to 200 set-aside fields, half rotational and half non-rotational. Winter cereals were the least preferred habitat for all groups of birds studied, with the majority preferring rotational set-aside. Set-aside
97
management didn’t seem to affect bird abundance except that skylark densities were low in fields that had been sprayed with non-selective herbicide in May (Firbank, 1998 cited in Firbank et al., 2003). This contrasts with evidence from experiments from the earlier 5-year set-aside scheme that management practices can greatly influence the ecology of set-aside. Henderson and Evans (2000) and Henderson et al. (2001) show that seed-eating passerines, gamebirds and skylarks required bare ground or litter. Early naturally regenerated set-aside is especially beneficial for breeding birds due to the combination of habitat structure, management and diversity of plants and invertebrates acting as food resources (Firbank et al., 2003). Neither rotational nor non-rotational set-aside appeared to cause agronomic problems in adjacent crops. Early agronomic and environmental problems during the formative years of set-aside were eliminated by changes to the rules (Sotherton, 1998). Both the vegetation of sown grass and naturally regenerated non-rotational set-aside becomes more typical of grassland over the years, and consequently, they become more similar over time. Therefore as non-rotational set-aside vegetation develops it becomes less suitable habitat for the range of bird species, which utilise it in the early stages (Firbank et al., 2003). 3.1.7 Aims of the Study The aims of this study were: •
to investigate whether farmland bird species showed preferences for set-aside over tillage and grassland fields in Ireland.
•
to establish whether some of the main set-aside management regimes in Ireland had different effects on the bird species assemblages. These management strategies included both rotational and non-rotational set-asides, which are mentioned extensively in the literature from the UK (Henderson et 98
al., 2000a, b and 2001; Watson and Rae, 1997). It was hypothesised that more species, especially skylark, would show a preference for rotational set-aside over non-rotational. 3.2 METHODS 3.2.1 Study Sites The study sites comprised of 21 set-aside fields paired with 19 adjacent tillage or grassland fields. All fields were located on farms in the Portlaoise and Athy areas of Co. Laois and Co. Kildare. Four main types of set-aside management were chosen for study along with several fields that were managed in more unusual ways. These were: •
A: Rotational set-aside regenerated from stubble. In this case the cereal crop had been harvested during the previous summer and the land was left fallow over winter and the vegetation allowed to regenerate naturally (3 replicates).
•
B:
Non-rotational set-aside (3 years or older). These lands had been left in
set-aside for at least 3 years. The vegetation was either originally sown grass or natural regenerated vegetation. One site is a ninth year set-aside that was burnt off using pesticides in September 2003. The old grass was dead and yellow with vegetation beginning to regenerate. (6 replicates). •
C: First year pasture set-aside. This type of set-aside was productive grassland in the previous year and was used either to graze animals, cut silage, or both (4 replicates).
•
D: Long-term set-aside, which is grazed by animals in winter. This set-aside land has been in the scheme for 3 or more years and the farmers graze their own animals from 1 September to 14 January (4 replicates).
99
•
E:
Other types of first year set-aside. In one field, sugar beet had been
harvested in the previous autumn and the field had been left fallow to naturally regenerate. The other site was winter wheat in the previous year. It was cultivated in the autumn and volunteer wheat was becoming established in the spring. (2 sites). •
F:
Wildflower set-aside. This category consisted of a single site, which was
sown with wild flower seeds, and later in the year when these flowers have set seed the crop of seeds is harvested. (1 sites).
The sites were selected and marked over a 6-week period from the start of April 2003. It was originally hoped to have four replicate fields of each set-aside type A to D in the Laois/Kildare area. However, this proved impossible, as for example, it was not possible to find a fourth 1st year set-aside stubble field (Type A). Approximately 35 farmers were contacted during the set-up period to enquire about the management method of their set-aside fields and their willingness to allow access to their land. When the selection was finalised, the total of 40 fields were split between 18 landowners. The locations and details of these sites are given in Figure 2.1 and Appendix IV, with photographs of four sites shown in Figures 3.1 – 3.4. Each set-aside field was paired with an adjacent field of tillage or grassland (in two cases 2 set-aside fields were paired with the same agricultural field). Ideally, each field contained four sampling points laid out in a 200m square. However, this was sometimes not possible due to the size and/or shape of the field. All tilled fields contained four sampling points while set-aside and grassland fields contained either three or four points. Within each field, sampling points were between 110 and 200m apart and were marked with a 4-ft bamboo.
100
Figure 3.1: The rotational set-aside (Type A) site at Sheffield in Co. Laois.
Figure 3.2: The non-rotational set-aside (Type B) site at Coursetown in Co. Kildare.
101
Figure 3.3: The first year pasture set-aside (Type C) site at Raheenaniska in Co. Laois.
Figure 3.4: The long-term/grazed set-aside (Type D) site at Ballykilcavan in Co. Laois.
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3.2.2 Bird Counts Point counts were used to sample the birds in the breeding season following the method described in Chapter 2, section 2.2.2. Between mid-April and early July 2003, four visits were made to each sampling site. Three or four fields (i.e. 12 to 16 points) were sampled each morning, starting at dawn. The order in which the points were sampled was reversed on alternate visits so that particular points were not always counted at the same time of the morning. 3.2.3 Vegetation Structure Measures of vegetation structure were taken for each field between 10th and 23rd June 2003. As in the previous study, vegetation stratification profiles were estimated for each sampling point. Three additional habitat variables were measured in each field. Sampling points in the fields were selected by measuring distances of between 0 and 100m, selected randomly, from a central point in 5 directions: 72, 144, 216, 288 and 360º. If any of the sampling locations fell outside of the chosen field, a new random distance was selected which fell within this field. At each of the five sampling points, the vertical height of vegetation, the horizontal density of the vegetation and vegetation density were measured. These measures were selected as previous studies had found them important for grassland bird communities (Cody, 1968; Bibby et al., 2000). The vertical height of the vegetation was defined as the height of the vegetation where the highest stem or piece of vegetation leaves the sward (i.e. the height of the second highest piece of vegetation). The percentage cover of vegetation in a 50cm square quadrat was used to estimate the horizontal density. 103
Vegetation density was measured as the height at which 90% of the width of a chequered board was obscured. A 10cm wide by 130cm long board was divided into 5cm black and white squares and placed upright in the vegetation using a hinged piece of wood. Then looking from a distance of 5m the height on the board where 90 % of the squares are obscured by vegetation was estimated. The method follows Bibby et al. (2000). 3.2.4 Data Processing The data for the four visits for all bird species recorded and breeding species were summed and an average of the 3 or 4 sampling points in each site calculated to get site rather than point data. These values were then divided by 4 to get the average per visit. Thus the dataset used in the analyses represented the average number of individuals of each bird species per visit per site. The maximum number of breeding birds was determined as the maximum number of each species recorded on a single visit and this was again averaged over the 3 or 4 sampling points in each site. The datasets for the comparisons between set-aside and non-set-aside and between set-aside, grass and tillage for (a) all species recorded, (b) breeding species only and (c) the maximum number of breeding birds recorded on any one visit used data from all 21 set-aside sites and the 21 adjacent tillage or grassland sites with the Ballymanus tillage site and the Kyledellig grass site included twice. The datasets for the comparisons between set-aside and tillage used data from the 19 paired set-aside and tillage sites. The effects of set-aside management were analysed using only data from setaside types A, B, C and D sites. The comparison between grazed and non-grazed setaside used the standard dataset (average number of birds per visit per site) for single 104
species. The datasets used for diversity indices were derived from the averages per site (i.e. the average of the 3 or 4 sampling points in each site) for all four visits combined multiplied by 12. The figures for the vegetation stratification profiles were averages of the 3 or 4 sampling points in each site. The species data used in the vegetation stratification profiles ordinations were from the 21 set-aside sites, the 2 grass sites and the 17 tillage sites. The other measures of vegetation structure used in analyses (vertical height, horizontal density and vegetation density) were averages of the five sets of measurements taken at each site. 3.2.5 Statistical Analysis The majority of the techniques used have been described in Chapter 2. 3.2.5.1 Wilcoxon Sign Rank Test The Wilcoxon Sign Rank Test was used to test if there was a significant difference in species numbers between paired set-aside and grass/tillage sites and was performed using SAS version 8.2 (SAS, 2001). 3.3 RESULTS As in the previous chapter, all analyses were performed with the datasets for all birds and breeding birds in the 0–50m distance band. However, in some cases other datasets will be used and these will be noted in the text. 3.3.1 Comparison between Set-aside and Non-Set-aside 3.3.1.1 Diversity Indices There was a significantly higher number of bird species in the twenty-one setaside sites compared with their paired grass or tillage site for all birds and breeding
105
birds recorded in the 0-50m distance band (Table 3.1). The values for the ShannonWeiner Index (H’) and Simpson’s Index (D) for all birds and breeding birds recorded were significantly higher in set-aside sites than non-set-aside sites (Table 3.1).
Table 3.1: Table of S statistics and P-values for Wilcoxon Sign Rank Test on diversity indices in paired set-aside and non-set-aside (grass or tillage) sites. Species Richness Dataset
S
P value
statistic All Birds 0-50m Breeding Birds 0-50m
H’ S
D
P value
statistic
S
P value
statistic
73.5
0.007
64.5
0.02
58.5
0.04
72
0.008
74.5
0.006
75.5
0.005
3.3.1.2 Species Composition– All Species Recorded DCA of all recorded birds within 50m of the sampling points showed that the length of the gradient was 3.522. Therefore, RDA was used to analyse the distribution of species in set-aside and/or grass/tillage habitats (Figure 3.5). The RDA ordination shows that the vast majority of the bird species recorded had a preference for set-aside sites over productive agricultural sites (grass/tillage). The first canonical axis divided the birds preferring grass or tillage on the positive side of the diagram (right-hand side) from those showing a preference for set-aside on the negative side (left-hand side). The significance of the first axis was tested using a Monte Carlo test constrained for paired sites defined by covariables and it was found to be significant (P = 0.015). The covariables explained 1 – 0.387 or 62.3% of the variance in the species data (Table 3.2). The current environmental variables, set-aside and non-setaside, explained only 5.7% of the variance in the species data (Table 3.2). The remaining 32% of the total variance was unexplained. The variance in the species data 106
after fitting the covariables was 0.387, with the 1st canonical axis accounting for 14.7% of this residual variance and all axes explained 74.4% (Table 3.2). Only six species were seen more frequently in grass or tillage: whitethroat, goldcrest, blackcap, stonechat, tree sparrow and treecreeper. Fifteen goldcrests and 8 whitethroats were recorded in all non-set-aside sites over the four visits combined, compared to 11 and 2 respectively for set-aside. Meadow pipit, skylark, pheasant and pied wagtail were just some of the species that were recorded in greater numbers on set-aside. On average 2.25 meadow pipits were recorded per set-aside site per visit, compared with 0.73 in non-set-aside sites. Set-aside contained an average of 2.90 skylarks per site over all four visits combined, while only an average 1.19 individuals were present in each grass/tillage site. Pied wagtails were only found on set-aside with 9 individuals recorded in all sites over all visits.
107
+1.0
BF JD CC
PW
Setaside
Y.M. GT WW HC RB GO CT BT
WH GC SC BC
CD SW MG CH WP RO
Grass or Tillage
TC
R.
TS
SL PH WR S. SG SN D. B. ST HS BZ LI GR
-1.0
MP
-1.0
+1.0
Figure 3.5: Redundancy analysis (RDA) of all birds recorded within 50m of the sampling points in Set-aside or Grass/Tillage. All set-aside sites were paired with a grass or tillage site. P-value of all canonical axes = 0.015.
Table 3.2: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of all birds recorded within 50m of the sampling points in Set-aside or Non-set-aside. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total variance .057 .146 .050 .035 1.000 .576 .000 .000 .000 14.7 52.4 65.4 74.4 100.0 .0 .0 .0
Sum of all unconstrained eigenvalues Sum of all canonical eigenvalues
.387 .057
108
Skylark and meadow pipit are illustrated in Figures 3.6 and 3.7.
Figure 3.6: Skylark (Alauda arvensis) (Photo: Billy Clarke).
Figure 3.7: Meadow Pipit (Anthus pratensis) (Photo: Billy Clarke). 109
3.3.1.3 Species Composition – Breeding Species Only The RDA of breeding birds recorded within 0-50m of the sampling points showed no significant effects of habitat type. 3.3.2 Comparison between Set-aside, Grass and Tillage 3.3.2.1 Diversity Indices The mean species richness, Shannon-Weiner Index (H’) values and Simpson’s Index (D) values were calculated per site for the 18 set-aside sites adjacent to tillage, the three set-aside sites adjacent to grass sites, the two grass sites and the 17 tillage sites (Table 3.3). The mean species richness was greater in set-aside-tillage sites compared to tillage sites and likewise set-aside-grass had a higher species richness than grass sites for all and breeding birds recorded (Table 3.3). The mean ShannonWeiner Index (H’) and Simpson’s Index (D) values were also greater in set-asidetillage sites compared to tillage sites and in set-aside-grass compared to grass sites for all and breeding birds recorded within 50m of the sampling points (Table 3.3). However, there was an exception as set-aside-grass sites for all birds within 50m of the sampling sites had a slightly lower mean H’ value than grass sites (Table 3.3). The 18 paired set-aside and tillage sites were also compared in relation to the three diversity indices. The Wilcoxon Sign Rank Test was used to analyse the data to see if the set-aside sites had significantly higher bird diversity than their paired tillage site. This statistical test examined each pair of sites in turn and then calculated whether it is significantly more likely to have had a higher diversity of birds in a setaside site in comparison to its tillage site or vice versa. There was a significantly higher number of bird species in the eighteen setaside sites compared with their paired tillage site for all birds and breeding birds
110
recorded within 50m of the sampling points (Table 3.4). The values for the ShannonWeiner Index (H’) and Simpson’s Index (D) for all birds and breeding birds recorded were significantly higher in set-aside sites than tillage sites (Table 3.4).
Table 3.3: Table of means and standard errors for species richness (S), ShannonWeiner Index (H’) and Simpson’s Index (D) for all birds recorded within 50m of the sampling points for set-aside, grass and tillage sites. Habitat Type
Set-aside –
Set-aside –
Tillage
Grass
Tillage
Grass
(17 sites)
(2 sites)
(18 sites)
(3 sites)
Mean
SE
Mean
SE
Mean
SE
Mean
SE
S - All Birds
12.5
1.12
15.67
1.33
8.53
1.05
15.5
3.5
S - Breeding
7.61
0.75
9
1.0
5.47
0.61
8
2.0
H' - All Birds
2.07
0.12
2.33
0.15
1.74
0.14
2.35
0.13
H' - Breeding
1.68
0.11
1.87
0.08
1.3
0.15
1.69
0.24
D - All Birds
7.37
0.79
8.34
2.16
5.9
0.69
8.3
0.73
D - Breeding
5.32
0.54
5.2
0.41
3.79
0.42
4.28
0.96
Table 3.4: Table of S statistics and P-values for Wilcoxon Sign Rank Test on diversity indices in the paired set-aside and tillage sites (18 pairs only). Species Richness Dataset
S
P value
statistic All Birds 0-50m Breeding Birds 0-50m
H’ S
D
P value
statistic
S
P value
statistic
62.5
0.004
54.5
0.016
54.5
0.016
61
0.005
54.5
0.016
58.5
0.016
3.3.2.2 Species Composition – All Species Recorded As expected from the comparison of set-aside and non-set-aside sites (Figure 3.5), the ordination diagram produced from RDA illustrated that the majority of the bird species recorded showed a preference for set-aside sites (Figure 3.8). Several 111
species, such as wren, willow warbler and blackbird were predominantly in set-aside or grass but had no clear preference for either. Grass sites contained an average of 3.58 wrens each per visit compared with 1.80 wrens per set-aside and 1.51 per tillage site. On average 0.81 willow warblers and 4.01 blackbirds were recorded per set-aside site over all visits, compared with 2 and 9 respectively in grass sites, and 0.17 and 1.89 respectively in tillage sites. Tillage was preferred by only a small number of species including blackcap and tree sparrow. Three blackcaps were observed using all tillage sites over the 4 visits combined, while none utilised grass and 1 bird was seen in set-aside within 50m of the sampling points. Both tree sparrows recorded in this distance band occurred in tillage. As in the previous RDAs, a Monte Carlo test constrained for paired sites defined by covariables, was used to test for significance in the differences between the habitats. The 1st canonical axis, which was significant (P = 0.0015), mainly divided the birds preferring tillage on the positive side of the diagram (right-hand side) from those birds showing a preference for set-aside and grass on the negative side (left-hand side). The covariables explained 62.3% of the variance in the species data (Table 3.5). The environmental variables explained only 8.3% of the variance in the species data (Table 3.5). The variance in the species data after fitting the covariables was 0.387, with the 1st canonical axis accounting for 16.4% of this residual variance and all axes explained 76.5% (Table 3.5).
112
+1.0
BF JD
Setaside PW
GO RB WP MP
CD GT
CH CT HS Y. CC RO SW SG M.HC PH BZ S. SN BT D.GR LI MG R. WW WR SL
SC WH BC TS
Tillage
GC
ST B.
TC
-1.0
Grass
-1.0
+1.0
Figure 3.8: Redundancy analysis (RDA) of all birds recorded within 50m of the sampling points in Set-aside or Grass or Tillage. All set-aside sites were paired with a grass or tillage site. P-value of 1st canonical axis = 0.015. P-value of all canonical axes = 0.01.
Table 3.5: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of all birds recorded within 50m of the sampling points in Set-aside or Grass or Tillage. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total variance .064 .020 .139 .045 1.000 .600 .695 .000 .000 16.4 21.5 57.2 68.8 76.5 100.0 .0 .0
Sum of all unconstrained eigenvalues Sum of all canonical eigenvalues
.387 .083 113
3.3.2.3 Species Composition – Breeding Species Only The ordination diagram for the RDA of the breeding birds recorded within the 0-50m distance band (Figure 3.9) showed a similar pattern to all birds recorded within 50m of the sampling point (Figure 3.8). The length of the gradient was 3.337. The Pvalue of the 1st canonical axis and all axes was 0.015 for both. The covariables explained 64.5% of the variance in the species data (Table 3.6). The current environmental variables, set-aside and non-set-aside, explained only 5.1% of the variance in the species data (Table 3.6). The remaining 30.4% of the total variance was unexplained. The variance in the species data after fitting the covariables was 0.355, with the 1st canonical axis accounting for 13% of this residual variance (Table 3.6). The first two axes explained 100% of the residual variance of the speciesenvironment relation after fitting covariables (Table 3.6). The ordination diagram shows that the majority of the breeding bird species recorded showed a preference for set-aside sites. The 1st canonical axis mainly divided the birds preferring tillage on the positive side of the diagram from those birds showing a preference for set-aside and grass on the negative side. Nine blue tits were recorded in set-aside sites over the sampling season compared to one in tillage and none in grass sites (Figure 3.9). The only four chiffchaffs and the only two linnets singing during the sampling season were recorded in set-aside. Dunnocks were very evenly distributed throughout the three habitat types with between 2.57 and 2.33 individuals recorded per site per visit. Six whitethroats were recorded in tillage sites over all four visits, with two in set-aside and the species being absent from grassland.
114
+1.0
Setaside
LI
BT RB
WP
R.
CH CD
CC ST B. MP S. GT SW GR D. WR WW SL
GC
Y.
WH BC
CT
Tillage GO
-1.0
Grass
-1.0
+1.0
Figure 3.9: Redundancy analysis (RDA) of the breeding birds recorded within 50m of the sampling points in Set-aside or Grass or Tillage. All set-aside sites were paired with a grass or tillage site. P-value of 1st canonical axis = 0.015. P-value of all canonical axes = 0.015.
Table 3.6: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of the breeding birds recorded within 50m of the sampling points in Set-aside or Grass or Tillage. Axes 1 2 3 4 Total variance Eigenvalues : .046 .005 .100 .087 1.000 Species-environment correlations: .618 .360 .000 .000 Cumulative percentage variance of species data : 13.0 14.5 42.7 67.2 of species-environment relation: 89.8 100.0 .0 .0 Sum of all unconstrained eigenvalues Sum of all canonical eigenvalues
.355 .051
115
3.3.3 Comparison of Different Types of Set-aside Management 3.3.3.1 Diversity Indices There were no significant differences between set-aside types A, B, C and D in any of Shannon-Weiner (H’), Simpson’s Index (D) or species richness for all birds or breeding birds recorded in the 0-50m distance bands. However, first year pasture setaside had the highest species richness with an average of 9.5 species compared with 5.47 species as the lowest in tillage for breeding birds (Figure 3.10 and Table 3.7). Species richness was on average 15.5 for grass sites for all birds recorded within 50m of the sampling point (Figure 3.10 and Table 3.7). Non-rotational setaside had the second highest species richness (14.67 species) followed by 1st year pasture set-aside (13.25 species). Tillage was the most species poor with an average of only 8.53 species. The total species richness for each set-aside or agriculture type was calculated by summing the total number of different species present within all the individual sites of each habitat type. Thus 30 species were present between the 6 sites of nonrotational set-aside and 32 species were also found in the 17 tillage sites (Table 3.7). The order was reversed for the breeding species with non-rotational set-aside sites holding more species (21 species) than tillage sites (19) (Tables 3.7). Only 21 different species were found in the two grassland sites and the three rotational sites for all birds recorded (Table 3.7).
116
20 18
Species Richness
16 14 12 Breeding 0-50m Total 0-50m
10 8 6 4 2 0 A: Rot
B: Non-rot
C: 1st Pas
D: Longterm
Grass
Tillage
Set-aside or Agricultural Type
Figure 3.10: Mean species richness of breeding species (green bars) and all species recorded (navy bars) per set-aside or agricultural type within 50m of the sampling points (with standard error bars).
Table 3.7: The average (and standard error) species richness and breeding species richness per site and the number of species and breeding species in all sites for each habitat type within 50m of the sampling points. Habitat Type
No. of
Average Species Richness per
Number of Species in
Sites
Site
All Sites
All Birds
Breeding
Mean
SE
Mean
SE
All Birds
Breeding
A: Rotational
3
11.33
3.84
6.33
1.67
21
13
B: Non-rotational
6
14.67
2.22
8.67
1.48
30
21
C: 1 Yr. Pasture
4
13.25
1.44
9.5
0.96
23
18
D: Long-term
5
11
1.61
6.6
1.12
23
17
Grass
2
15.5
3.5
8
2.0
21
12
Tillage
17
8.53
1.05
5.47
0.61
32
19
st
(Grazed)
117
3.3.3.2 Species Composition– All Species Recorded The length of the gradient for DCA was 3.41. The subsequent RDA diagram for all birds in the 0-50m distance (Figure 3.11) illustrates the presence of a difference between the four main set-aside types in bird assemblage composition. The 1st canonical axis was very significant (P = 0.005) and mainly represented the difference between non-rotational set-aside on the positive side of the diagram and 1st year pasture set-aside and long-term/grazed set-aside on the negative side. The second canonical axis divided 1st year pasture set-aside on the positive half of the diagram from long-term/grazed set-aside on the negative half of the diagram. All of the canonical axes were significant (P-value = 0.005) and explained 65.1% of the variance of the species data and 100% of the variance of the species-environment relation (Table 3.8). Meadow pipit, skylark, pheasant, magpie, house sparrow, snipe, linnet and starling were closely associated with non-rotational set-aside (Figure 3.11). Chaffinch and sedge warbler showed an affinity for rotational set-aside (type A). Hooded crow showed a preference for long-term/grazed set-aside. Goldcrest was strongly associated with 1st year pasture set-aside. First year pasture set-aside showed strong associations with robin, blackcap and willow warbler. Over 6 meadow pipits were recorded per non-rotational set-aside site per visit, compared with 2 in rotational set-aside sites. Types C and D had an average of a single meadow pipit per site over the entire sampling season. Non-rotational set-aside contained an average of 2 skylarks per site per visit. No skylarks were recorded in 1st year pasture set-aside and only 4 were observed in all four visits in all longterm/grazed set-aside sites. Rotational set-aside (type A) contained 3 skylarks per site over all four visits combined. On average five house sparrows were recorded in non-
118
rotational sites and one in rotational sites over all four visits. No house sparrows were observed in the other set-aside types. The only hooded crow observed on set-aside was on long-term/grazed set-aside. An average of 2.25 goldcrests were present in 1st year pasture set-aside (type C) sites over all visits, compared with none in types A and D sites and only one in all non-rotational sites over the whole sampling season. First year pasture set-aside and rotational set-aside contained an average of 1.3 and 1 robins respectively per site per visit. Non-rotational and long-term/grazed set-aside contained 1.8 and 1.6 robins per site respectively over all four visits combined. It was thought that a reason for the difference between first year pasture setaside and other set-aside types was the high number of woodland species in one of the type C sites. Therefore the RDA ordination was repeated with blackcap, bullfinch, goldcrest and willow warbler removed. However, when these woodland species were removed the relationship between the set-aside types remained the same as in Figure 3.11.
119
+1.0
C: 1st Yr. Pasture GC
R. BF
BC CC
B.
RB JD CT
WW GO WR
WP CD
MG SG BZ LI SN PH HS
MP
B: Non-rotational
PW
S.
RO
Y.
BT
GT
GR
WH HC M.
SW CH
D.
SL ST
A: Rotational
-1.0
D: Long-term (Grazed)
-1.0
+1.0
Figure 3.11: Redundancy analysis (RDA) of all birds recorded within 50m of the sampling points in Set-aside Types A, B, C and D. P-value of 1st canonical axis = 0.005 and P-value of all canonical axes = 0.005.
Table 3.8: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of all birds recorded within 50m of the sampling points in Set-aside Types A, B, C and D. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total variance .339 .028 .010 .274 1.000 .783 .607 .411 .000 33.9 36.7 37.6 65.1 90.0 97.4 100.0 .0
120
3.3.3.2.1 Field Species Only Included The ordinations of set-aside management were repeated for all birds recorded within 50m of the sampling points for field bird species only. These species were hooded crow, jackdaw, kestrel, magpie, rook, starling, woodpigeon, meadow pipit, snipe, pheasant and skylark. The habitats utilised by bird species in the breeding season were taken from Chamberlain et al. (1999b) with additional species information from Snow et al. (1998) and expert opinion. The length of gradient obtained from DCA was 2.901. The RDA diagram for field species only (Figure 3.12) is very similar to the RDA of all birds recorded within 50m of the sampling points (Figure 3.11). All of the environmental variable arrows are in the same positions in Figure 3.11, indicating that it was the field species, which were determining the differences between the set-aside types in relation to bird species composition. The 1st canonical axis was again very significant (P = 0.005) and represented the difference between non-rotational set-aside on the positive side of the diagram and rotational, 1st year pasture set-aside and long-term/grazed set-aside on the negative side. The second canonical axis divided 1st year pasture set-aside on the positive half of the diagram from long-term/grazed set-aside and rotational set-aside on the negative half of the diagram. All of the canonical axes were significant (Pvalue = 0.005) and explained 81.2% of the species variance and 100% of the speciesenvironment variance (Table 3.9). As in the previous section, meadow pipit, skylark, pheasant, magpie, snipe, and starling were closely associated with non-rotational set-aside (Figure 3.12). Hooded crow and rook showed a preference for long-term/grazed set-aside. Jackdaw showed some association with 1st year pasture set-aside.
121
+1.0
C: 1st Yr. Pasture
JD
WP
SG
B: Non-rotational SN
RO
PH MG
MP
S.
HC
A: Rotational
-1.0
D: Long-term (Grazed)
-1.0
+1.0
Figure 3.12: Graph of RDA of all field species (11) birds recorded within 50m of the sampling points in the four main set-aside types. P-value of 1st canonical axis = 0.005. P-value of all canonical axes = 0.005.
Table 3.9: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of all field species (11) birds recorded within 50m of the sampling points in the four main set-aside types. Axes 1 2 3 4 Total variance Eigenvalues : .443 .021 .000 .347 1.000 Species-environment correlations: .758 .409 .109 .000 Cumulative percentage variance of species data : 44.3 46.5 46.5 81.2 of species-environment relation: 95.3 99.9 100.0 .0
122
3.3.3.2.2 Boundary Species Only Included The set-aside management ordinations were then repeated for field boundary bird species only (see Appendix V). Again, the habitats utilised by bird species in the breeding season were taken from Chamberlain et al. (1999b) with additional species information from Snow et al. (1998) and expert opinion. The DCA of these 36 boundary species produced a gradient length of 3.333. The subsequent RDA diagram for field boundary species only, shows a similar relationship between the set-aside management types as in Figure 3.11 (Figure 3.13). However, none of the canonical axes were significant, with the P-value of the 1st canonical axis equalling 0.845 and the P-value of all canonical axes being 0.73. This indicates that the field boundary species did not differ between the set-aside types. All of the canonical axes explained only 47.3% of the variance in the species data and 100% of the species-environment variance (Table 3.10).
123
+1.0
D: Long-term (Grazed)
A: Rotational
HC M.
Y.
SW CH D. ST
WH
BT RO
GO
BF BC CC R.
GT WP CD
GR
WW
WR
CT
BZ
C: 1st Yr. Pasture
RB JD
GC
LI B.
HS SG MG
-1.0
B: Non-rotational
-1.0
+1.0
Figure 3.13: Graph of RDA of all field boundary species (36) birds recorded within 50m of the sampling points in the four main set-aside types. P-value of 1st canonical axis = 0.845. P-value of all canonical axes = 0.73.
Table 3.10: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of all field boundary species (36) birds recorded within 50m of the sampling points in the four main set-aside types. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total variance .076 .057 .024 .316 1.000 .675 .571 .416 .000 7.6 13.3 15.7 47.3 48.5 84.8 100.0 .0
124
3.3.3.3 Species Composition – Breeding Species Only In the case of the breeding birds the maximum number of birds recorded on any one visit within 50m was used. The ordination diagram for the RDA (Figure 3.14) shows a similar pattern to all birds in the 0-50m distance band (Figure 3.11). The length of the gradient was 2.909. The P-value of the 1st canonical axis was 0.04 and 0.045 for all canonical axes. All of the canonical axes explained 64.2% of the species variance and 100% of the variance in the species-environment relation (Table 3.11). The main difference from Figure 3.11 is that the second canonical axis represented the difference between 1st year pasture set-aside in the positive half and rotational and long-term/grazed in the negative half. Other differences were that wren and yellowhammer were strongly associated with type C set-aside (1st year pasture) and dunnock was more closely associated with types A and D in the RDA of maximum breeding birds with the 0-50m distance (Figure 3.14).
125
+1.0
C: 1st Yr. Pasture GC
Y.
CH
CT
CC BC WR GO
LI
CD
WW
B: Non-rotational
R. MP
BT
RB S.
WP GR
B. WH SW
A: Rotational
ST
SL
D.
-1.0
D: Long-term (Grazed)
-1.0
+1.0
Figure 3.14: Redundancy analysis (RDA) of the maximum number of breeding birds recorded on any one visit within 50m of the sampling points in Set-aside Types A, B, C and D. P-value of 1st canonical axis = 0.04 and P-value of all canonical axes = 0.045.
Table 3.11: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of the maximum number of breeding birds recorded on any one visit within 50m of the sampling points in Set-aside Types A, B, C and D. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total variance .251 .061 .016 .315 1.000 .709 .771 .451 .000 25.1 31.2 32.7 64.2 76.6 95.2 100.0 .0
126
3.3.4 Comparison between Grazed and Non-grazed Set-aside The RDAs of set-aside management effects above illustrate a difference between non-rotational set-aside (type B) on the positive side of the diagrams and first year pasture (type C) and long-term/grazed set-aside (type D) on the negative side of the diagrams. As there seemed to be a difference between recently grazed set-aside (types C and D) and set-aside which had not been grazed for at least 3 years, the data was analysed with the Kruskal-Wallis test to determine whether certain species preferred grazed or non-grazed set-aside. Meadow pipit significantly preferred nongrazed set-aside to grazed set-aside for all birds and breeding birds (Table 3.12). The differences were almost significant for skylark, magpie and pheasant which tended to be more abundant in non-grazed set-aside (Table 3.12). However, goldcrest significantly preferred grazed set-aside (Table 3.12). No other species showed significant responses.
Table 3.12: Table of Chi-Square values for several species for grazed versus nongrazed set-aside for the 0-50m datasets. Species
Dataset
Chi-Square
Pr > Chi-Square
Meadow pipit
All 0-50m
10.3525
0.0056
Meadow pipit
Breeding 0-50m
10.0554
0.0068
Skylark
All 0-50m
5.2216
0.0735
Skylark
Breeding 0-50m
5.2216
0.0735
Magpie
All 0-50m
5.1923
0.0746
Pheasant
All 0-50m
5.1734
0.0753
Goldcrest
All 0-50m
11.8586
0.0027
Goldcrest
Breeding 0-50m
7.4118
0.0246
127
3.3.5 Vegetation and Habitat Effects 3.3.5.1 Vegetation Stratification Profiles of All Sites – All Species Recorded The DCA of all recorded birds within 50m of the sampling points showed that the length of the gradient was 3.39. RDA was used to analyse the distribution of species and sites in relation to the vegetation stratification profiles of the individual sites (Figures 3.15 and 3.16). The first canonical axis divided the species and sites preferring strata 1 (00.5m) and 2 (0.5-2m) on the positive side of the diagram from those showing a preference for strata 3 (2-4m), 4 (4-8m), 5 (8-16m) and 6 (>16m) on the negative side (Figures 3.15 and 3.16). Meadow pipit, skylark and sedge warbler were among the species which preferred vegetation under 2m, whilst chaffinch, treecreeper, yellowhammer and others exhibited a preference for vegetation over 4m in height (Figure 3.16). The 1st canonical axis was found to be non-significant (P = 0.15). The 1st canonical axis accounted for 14.1% of the species variance and 56.3% of the variance between the species and the environment (Table 3.13). The second canonical axis divided strata 3, 4 and 5 on the positive half of the diagram from stratum 6 on the negative half. All of the canonical axes were significant (P-value = 0.045) and explained 24.5% of the species variance and 97.8% of the species-environment variance (Table 3.13).
128
+1.0
B-Killa
St_1
St_3
G-Kyle2
C-Kyle2
T-Killa C-Rahee C-Knoc2
D-Strad
St_5
St_4
G-Sheff
T-Knoc2
F-Benne
F-Killy
T-Strad
B-Bray
T-Killy
D-Borri
B-Cours
T-Timog D-Balm1 A-Kyle1
B-Srowl
B-Knoc1 C-Avole
T-Borri
T-Rahee
D-Timog
St_2
A-Sheff T-Knoc1
T-Benne
St_6 T-Avole T-Money
T-Srowl T-Rathe
A-Rathe T-Balm2 T-Bray T-Cours
T-Balyk
E-Money
D-Balyk
-1.0
E-Balm2
-1.0
+1.0
Figure 3.15: Redundancy analysis (RDA) of all birds recorded within 50m of the sampling points in relation to sites and the different strata of the vegetation stratification profiles. P-value of 1st canonical axis = 0.15. P-value of all canonical axes = 0.045.
Table 3.13: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of all birds recorded within 50m of the sampling points in relation to the different strata of the vegetation stratification profiles. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total variance .141 .080 .015 .008 1.000 .568 .624 .566 .466 14.1 22.1 23.6 24.5 56.3 88.3 94.4 97.8
129
+1.0
St_1
St_3 WR D.
B. R. BT WP CH
St_5
CD BF HS WW GR LI JD PH GC
SG
ST CT SL RO GT BC CC TS GO
St_4
TC Y.
MP WH RB SN HC
BZ S. SW
PW
MG
St_2 M.
-1.0
St_6
SC
-1.0
+1.0
Figure 3.16: Redundancy analysis (RDA) of all birds recorded within 50m of the sampling points in relation to species and the different strata of the vegetation stratification profiles. P-value of 1st canonical axis = 0.15. P-value of all canonical axes = 0.045. 3.3.5.2 Vegetation Stratification Profiles of All Sites – Breeding Species Only The DCA of breeding birds showed that the gradient length was 3.326. RDA was used to analyse the distribution of species and sites in relation to the vegetation stratification profiles of the individual sites (Figures 3.17 and 3.18). The first canonical axis for the RDA ordination divided the species and sites preferring stratum 2 (0.5-2m) on the negative side of the diagram from those showing a preference for strata 1 (0-0.5m), 3 (2-4m), 4 (4-8m), 5 (8-16m) and 6 (>16m) on the
130
positive side (Figures 3.17 and 3.18). Meadow pipit, skylark, reed bunting and sedge warbler preferred stratum 2 (Figure 3.18). The 1st canonical axis was significant for breeding birds (P = 0.025) and accounted for 18.2% of the variance in the species data and 62.1% of the variance between the species and the environment for breeding birds (Table 3.14). The second canonical axis divided strata 4, 5 and 6 on the positive half of the diagram from strata 1 and 3 on the negative half (Figures 3.17 and 3.18). Wren, woodpigeon and willow warbler exhibited a preference for vegetation between 2m and 4m in height (Figure 3.18). All of the canonical axes were significant for breeding birds (P-value = 0.005) and explained 28.7% of the species variance and 97.9% of the species-environment variance (Table 3.14).
131
+1.0
E-Money
T-Rahee E-Balm2
T-Avole
St_6 B-Bray
St_4
T-Balyk T-Bray T-Money
St_2
A-Sheff
T-Knoc2
St_5
T-Knoc1 B-Knoc1
T-Balm2 D-Balyk T-Benne T-Rathe T-Cours A-Rathe
A-Kyle1
G-Sheff F-Killy
T-Killy C-Avole
D-Strad
T-Borri T-Srowl
T-Strad D-Balm1
C-Kyle2
F-Benne D-Borri C-Knoc2
D-Timog
G-Kyle2
St_3
T-Timog C-Rahee T-Killa
B-Srowl B-Cours
B-Killa
-1.0
St_1
-1.0
+1.0
Figure 3.17: Redundancy analysis (RDA) of breeding birds recorded within 50m of the sampling points in relation to sites and the different strata of the vegetation stratification profiles. P-value of 1st canonical axis = 0.025. P-value of all canonical axes = 0.005.
Table 3.14: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of breeding birds recorded within 50m of the sampling points in relation to the different strata of the vegetation stratification profiles. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total variance .182 .076 .022 .007 1.000 .662 .529 .654 .512 18.2 25.8 28.0 28.7 62.1 88.1 95.6 97.9
132
+1.0
St_6 St_4 St_5 CH
St_2
RB
LI BC BT R. GT CT GC Y. SL B. CC GO ST WW WP WH GR
SW S.
CD
WR
St_3
D.
MP
-1.0
St_1
-1.0
+1.0
Figure 3.18: Redundancy analysis (RDA) of breeding birds recorded within 50m of the sampling points in relation to species and the different strata of the vegetation stratification profiles. P-value of 1st canonical axis = 0.025. P-value of all canonical axes = 0.005. 3.3.5.3 Horizontal Density and Vegetation Density Variance/mean ratios for horizontal density and vegetation density were calculated and the vegetation patchiness in the different set-aside types compared. The difference was nearly significant (Kruskal-Wallis test, P = 0.0594). The mean variance/mean ratio was 4.12 for rotational set-aside compared with 0.03 for 1st year pasture set-aside (Table 3.41).
133
Table 3.15: Table of means and standard errors from analysis of variance of variance/mean ratios of horizontal density in set-aside types A-D. Set-aside Type
No. of Sites
Mean
Standard Error
A: Rotational
3
4.12
1.36
B: Non-rotational
6
1.26
0.86
C: 1st Yr. Pasture
4
0.03
0.02
D: Long-term (Grazed)
5
1.38
0.93
3.4 DISCUSSION Studies published from the UK, Sweden, Denmark and Switzerland show that, in summer, many bird species prefer set-aside over neighbouring agricultural fields (Henderson and Evans, 2000). Diversity and abundance of many farmland bird species are greater in set-aside than neighbouring productive agricultural fields (Sears, 1992; Berg and Part, 1994; Watson and Rae, 1997; Henderson et al., 2000a, b). Pheasants, linnets and skylarks have been shown to prefer set-aside as a feeding and/or breeding habitat (Sotherton et al., 1994; Eybert et al., 1995; Poulsen, 1996; Wilson et al, 1997; Poulsen et al., 1998; Chamberlain et al., 1999a; Browne et al., 2000; Henderson et al., 2001). This arises because set-aside contains higher densities of indigenous weed seeds, plants and invertebrates than crops, which results in increased foraging opportunities (Henderson and Evans, 2000). However, not all setaside fields are equally utilised by birds, as those with a heterogeneous mix of vegetation types and ground cover are preferred. This is due to there being a trade-off between the presence of a potential food resource (e.g. invertebrates) and access to the resource (i.e. amount of bare ground), which may limit the usefulness of some fields to various bird species (Henderson and Evans, 2000). The results of this study show that the vast majority of the bird species recorded exhibit a preference for set-aside fields over non-set-aside fields (i.e. grass 134
and tillage sites combined) (Figure 3.5) Set-aside sites also contained a significantly higher number of bird species and had significantly higher Shannon-Weiner Index (H’) values and Simpson’s Index (D) values. Thus, the primary hypothesis of this study that farmland bird species would show preferences for set-aside over tillage and grassland fields in Ireland is upheld. This result also supports all the other set-aside studies cited as skylark, meadow pipit, linnet, pheasant and snipe were among the species showing preference for set-aside sites. The results also show that the type of management of set-aside is important. The four main set-aside management regimes (rotational, non-rotational, 1st year pasture and long-term/grazed set-asides) considered in this study all contained characteristic bird species assemblages (Figures 3.11 to 3.14). The main difference in management types was found between non-rotational set-aside and 1st year pasture (type C) and long-term/grazed (type D) set-asides combined. The other major division was between first year pasture and long-term/grazed set-asides. Thus there was a difference between recently grazed set-aside (types C and D) and set-aside which had not be grazed for at least 3 years (i.e. non-rotational set-aside). Meadow pipit preferred non-grazed set-aside to grazed set-aside (Table 3.12). Skylark, magpie and pheasant also showed some preference for non-grazed set-aside but these associations were not significant (Table 3.12). Non-grazed set-aside had taller and less patchy cover than recently grazed set-aside and therefore was more suitable as nesting sites for meadow pipit and skylark early in the breeding season (i.e. early April). In contrast, goldcrest significantly preferred grazed set-aside (Table 3.12). Watson and Rae (1997) claim that grazing of set-aside is beneficial as it reduces germination by opening swards and thus makes seeds more available to birds. Wilson et al. (1996) show that in winter, invertebrate-feeding birds prefer grazed grass fields to ungrazed
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set-aside fields, as in these fields the soil invertebrate fauna may already have been depleted due to recent cultivations. Meadow pipit, skylark, pheasant, magpie, house sparrow, snipe, linnet and starling were closely associated with non-rotational set-aside (Figure 3.11). Hooded crow showed a preference for long-term/grazed set-aside (Figure 3.11). First year pasture set-aside showed strong associations with robin, blackcap and willow warbler (Figure 3.11). When set-aside management types were examined in relation to field species only (Figure 3.12), the same significant pattern emerged as when all species were included (Figure 3.11). The species associated with non-rotational set-aside seem to be benefiting truly from the set-aside as many of these species, such as meadow pipit, pheasant, snipe and skylark are field species, as they utilise open habitat or grassland. However, there were also species primarily associated with other habitat features such as trees, hedgerows or rivers. The non-significant effect when only field boundary species were included (Figure 3.13) emphasises that it is the field species that are truly distinguishing the different management types. Goldcrest was strongly associated with 1st year pasture set-aside with birds recorded within 50m and 100m whereas it was strongly associated with non-rotational set-aside for birds recorded in the 0-25m distance band (Figures 3.11 and 3.14; RDA ordinations for 0-25m and 0-100m not included). The reason why goldcrest changed preference for set-aside type between distance bands is because the 1st year pasture sites contained trees with goldcrests over 25m from the sampling points. Therefore within the 0-25m distance band the birds in these trees weren’t recorded and more goldcrests were recorded within 25m of the sampling points in non-rotational set136
aside sites compared to 1st year pasture sites. Thus the effects of set-aside management are ‘local’ and depend on the scale of observation. The 0-50m distance band appeared to be the most discriminatory scale as it showed more significant effects than either the 0-25m or the 0-100m distance bands. Generally, the lengths of the gradient increased from 0-25m to 0-50m and then decreased from 0-50m to 0100m. This implies that the species turnover increased initially as the scale was increased and more species were observed. However, it then decreased as more species, which were not necessarily living within the particular habitat type, were added. At a distance of 25m from the sampling point many important habitat features like hedgerows and trees may not be included. However, between 0 and 100m overlap in the observations was beginning to occur so differences between sites were not as acute. On the other hand, the 0-50m distance band contained many of the important habitat features whilst still containing high species turnover and no overlap of observations. An important point is that both skylark and meadow pipit were very closely associated with non-rotational set-aside (Figures 3.11 and 3.14). Meadow pipit and skylark are ground-nesting birds and as such actually utilise set-aside fields themselves as both foraging and breeding habitats. This result is in contrast to most of the other studies cited earlier where skylark was reported to prefer rotational set-aside to non-rotational (Henderson et al., 2000a, b and 2001; Watson and Rae, 1997). However, Poulsen et al. (1998) found that non-rotational sown set-aside in its fourth year was preferred by skylarks to winter cereals, spring barley or silage grass. The dense vegetation structure of the set-aside did not seem to deter breeding or reduce fledging success unless the vegetation collapsed onto nests during breeding (Poulsen et al., 1998). Thus, the literature also suggests that non-rotational set-aside, like that in
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this study, is a preferred habitat of skylarks. Species richness was greater in nonrotational set-aside compared to the other set-aside types, including rotational, for all birds recorded, however for breeding species 1st year pasture was marginally more species rich than non-rotational set-aside (Figure 3.10 and Table 3.7). Thus the hypothesis that more species, especially skylark, would show a preference for rotational set-aside over non-rotational is rejected. The results suggest that instead, non-rotational set-aside is the type of set-aside in Ireland that is most beneficial to birdlife. One reason why the rotational set-aside sites in this study might be less preferred by farmland bird species than the non-rotational set-aside sites is that there were only three rotational sites and twice as many non-rotational sites. Two of the rotational sites, Kyledellig and Sheffield, had an average vertical height of vegetation of 58.4 cm with a horizontal vegetation density average of 61.5% cover and 18.2 cm vertical density average. The third site, Ratheniska, had little vegetation during the field season as the field was sprayed with herbicide in autumn 2002. Ratheniska had an average vertical height of only 3.6 cm with an average of 15% cover and 0 cm for vertical density. However, skylarks were present in Ratheniska but absent from the other two rotational sites, which seems to conflict with what one would expect to find. Thus, the result that non-rotational set-aside is more species rich than rotational setaside in Ireland seems to be upheld, but further studies around the country may be required to verify these results. The ordinations with the vegetation stratification profiles for all set-aside, grass and tillage sites show that skylark, meadow pipit, reed bunting and sedge warbler prefer vegetation below 2m in height (Figures 3.16 and 3.18). However, none of the measures of vegetation structure including vegetation stratification profiles, the
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vertical height of vegetation, the horizontal density of the vegetation and vegetation density showed any significant relationship with the bird community data gathered in set-aside types A to D. This may be due to the timing of the collection of these data (10th to 23rd June 2003) as some of the set-aside sites had already had their first cut and some sites had patchier vegetation earlier in the season. Thus it would have been optimal to record the vegetation structure earlier in the season, e.g. in May. However, Wilson et al. (1997) found that neither vegetation height nor cover were significant predictors of skylark abundance in set-aside, which implies that the lack of significant interactions in this study may be a true result. Current farming practices on tillage fields, such as insecticide and herbicide applications, could have reduced the arthropod fauna especially sawfly and lepidopteran larvae and Hemiptera, which are important skylark chick-food items (Poulsen et al., 1998). Firbank et al. (2003) found significantly more invertebrates (including evidence of more carabids) in pitfall traps in set-aside than in the adjacent crop. This is not in agreement with data from pitfall traps on similar sites within counties Laois and Kildare, where substantially more carabids were captured in tillage than in set-aside (unpublished data). However, in contrast, more spiders were found in the same study sites in set-aside compared with adjacent tillage sites (unpublished data). Other soil macrofauna were twice as abundant in set-aside than in tillage from the same sites (unpublished data). All the set-aside invertebrate data were obtained from non-rotational set-aside sites and no data is available for other set-aside management options. So it appears that Irish non-rotational set-aside also contains more chick-food than tillage. Carabids are an exception but these are known not to be preferred chick-food for most bird species. The high arthropod densities in the nonrotational set-aside sites may be as a result of them not being farmed intensively for
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several years. Moreby and Aebischer (1992) showed that densities of certain invertebrate groups could be considerably higher on set-aside land than in winter cereals. Also, naturally regenerated set-aside fields contained more varied insect abundances than sown set-aside, which is probably due to the far more varied vegetation structure and composition found on the former (Clarke, 1992). An important consideration of this study, is that most of the set-aside fields were small and irregular in shape compared to the large and more uniformly shaped tillage and grass fields. Efforts were made to avoid or reduce the confounding effects which would result if most of the set-aside points were placed near field boundaries and most of the tillage points were in the middle of large fields. However, more of the set-aside sampling points were located nearer field boundaries such as hedgerows and trees than the tillage and grass points. The arable landscape in Ireland may be more heterogeneous than in England as areas, which are predominantly arable, still have pockets of grassland mixed in the habitat mosaic, while in England vast areas are devoted almost totally to tillage. Also, the switch from spring-sown to autumn-sown cereals in England is far more pronounced than in Ireland with five times the Irish proportion of winter to spring barley area in England. This may help explain why rotational set-aside is not as important in Ireland as in England. Because in parts of England the cereals are virtually all autumn sown there is an absence of over winter stubbles, which are important feeding areas for birds (Buckingham et al., 1999; Wilson et al., 1996). Thus, rotational set-aside reintroduces winter stubbles into the farmland landscape in these regions of England. However, in Ireland much of our cereal production remains spring sown (61% in 1997 from Taylor and O’Halloran, 2002) so there is still a substantial amount of winter stubbles available to birds without the introduction of
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stubbles as rotational set-aside, even if this area of winter stubbles is smaller than anticipated as in many areas the stubbles are burnt or ploughed back into the soil during the winter (Taylor and O’Halloran, 2002). Therefore as more winter food may be available in Ireland without rotational set-aside, many birds may prefer the established non-rotational set-aside, as it is a more permanent habitat and they do not have to seek out new areas of rotational set-aside each breeding season. Skylark feed on cereal grain and weed seeds in autumn, winter and spring whilst meadow pipit will take some plant seeds in autumn and winter (Snow et al., 1998). Thus, these species may be able to remain on their established territories on non-rotational set-aside during the winter in Ireland and feed in neighbouring stubble fields. However, according to the literature, the nature of the sward is the most important characteristic of set-aside, as this will determine its usefulness to birds regardless of whether it was sown or naturally regenerated set-aside. If set-aside is too densely vegetated then this can lead to accessibility problems for certain species when foraging. Sown set-aside is always densely vegetated while naturally regenerated setaside can be. Sotherton (1998) and Poulsen et al. (1998) claim that if natural regenerating set-aside is left unmanaged the developing sward consisting of cereal volunteers from the previous crop and annual weeds from the newly disturbed seed bank will succeed to a grassy sward dominated by biennials and perennials within three years. Sotherton (1998) also claims that the resulting grassland-like habitat is of little benefit to game or to many other species, which require the habitat structure, floral composition and invertebrate fauna provided by cereal fields. Both the vegetation of sown grass and naturally regenerated non-rotational set-aside becomes more typical of grassland over the years, and consequently, they become more similar over time (Firbank et al., 2003). Therefore as non-rotational set-aside vegetation
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develops it becomes less suitable habitat for the range of bird species, which utilise it in the early stages (Firbank et al., 2003). Despite set-aside being preferred by a wide range of species and evidence of improved breeding success for some species such as skylarks and grey partridges, there is an absence of direct evidence that their population sizes at the farm or regional scales have increased as a result of set-aside (Firbank, 1998 cited in Firbank et al., 2003; Fuller, 2000). Even though set-aside has covered a large area of the agricultural landscape in recent years up to half of the set-aside in the UK may have been uniform grass-dominated set-aside, which is not as useful to birds as set-aside with a heterogeneous sward (Henderson and Evans, 2000). This may be a reason why set-aside has not had the expected effect on declining farmland bird populations. Firbank et al. (2003) suggest that set-aside may have only resulted in a redistribution of birds without affecting population sizes, or maybe its presence compensated for other damaging changes in the farmland landscape. The continued decline of the skylark population in the UK, despite the widespread introduction of set-aside is a major concern. However, the rate of decrease has declined in recent years but this could be attributable to the slowdown in agricultural intensification within the same period. It is also postulated that the population decline may have been more rapid except for the presence of set-aside in the landscape. Browne et al. (2000) estimate that set-aside holds approximately 10% of the lowland farmland population, despite covering only 2-3% of farmland area. Set-aside supported skylark densities almost three times that on other crops. Higher skylark breeding densities and an extended breeding season on set-aside, may allow skylarks to fledge up to twice the number of chicks per given area of set-aside compared to cereals, despite higher predation rates
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in set-aside (Henderson and Evans, 2000; Donald and Vickery, 2000; Poulsen et al., 1998). It has been suggested that instead of large-scale measures such as set-aside, smaller-scale and more cost-effective methods should be introduced to the agricultural landscape (Brickle et al., 2000; Vickery et al., 2002). Brickle et al. (2000) suggests that initiatives such as the provision of grassy margins or beetle banks, selective spraying of headlands and under-sowing of cereals with grass would be beneficial to corn buntings as invertebrate availability on the whole farm would be increased. Vickery et al. (2002) argues that field margins would be a more cost-effective method of providing food resources for birds in arable landscapes than whole field options. The abundance of plant and invertebrate food decreases generally with increasing distance from the field boundary. Many birds nest in hedgerows and they tend to forage close to their nests which results in field margins being utilised more than field centres. Henderson et al. (2000b) showed that the majority of species occurred more frequently in set-aside field margins rather than their centres. Several species such as yellowhammer and tree sparrow also seem to prefer field margins in winter (Robinson and Sutherland, 1999; Vickery et al., 2002). For other bird species the location of foraging within fields in winter has not been studied so preferences for margins or centres are unknown (Vickery et al., 2002). Henderson and Evans (2000) recommend that natural regeneration or sown wildbird cover set-aside land should be placed near a hedgerow if it is to be used to produce winter bird food. Some species such as skylark and lapwing avoid field margins and whole field options such as the current set-aside scheme are required. Alternatively, grassy set-aside strips could be placed in the open, away from any cover, if the function of the set-aside is to produce nesting sites for skylarks (Browne et al., 2000). However, if the set-aside scheme is replaced
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in the future any new scheme should be over large enough total areas so that the benefits to farmland birds across landscapes are not reduced (Firbank et al., 2003). A recent review of studies of plants and animals on set-aside found that setaside unequivocally enhances farmland biodiversity in North America and Europe based on a meta-analysis of 127 published studies (Van Buskirk and Willi, 2004). The number of species of birds, insects, spiders, and plants is 1 – 1.5 standard deviation units higher on set-aside land compared to conventional agricultural land, and population densities increase by 0.5 – 1 standard deviation unit. Species richness was greater in set-aside areas that consisted of naturally regenerated vegetation rather than sown cover. Set-aside land was also more effective at increasing species richness when the conventional agricultural field used for comparison contained crops rather than grasses. North American bird species that have suffered large declines benefited more from set-aside, while European set-aside land was beneficial for most of the studied bird species, of which almost all were declining. Larger and older areas of setaside contain more species and higher densities of these taxa (i.e. birds, insects, spiders and plants), except in the case of bird species richness which declined significantly with age of set-aside. Set-aside land also seems to be more beneficial to farmland biodiversity in countries with less intensive agricultural practices and higher proportions of land under agroenvironment agreements. The authors recommend that landscapes should contain heterogeneous mixtures of rotational and non-rotational (long-term) set-aside lands as different ages are preferred by different taxa, and also that, larger areas of land should be set aside at the regional scale to ensure the availability and accessibility of colonisation sources. Pain and Pienkowski (1997) discussed the various mechanisms that may be introduced to reverse the trend of decline in farmland bird species. Extensification of
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agricultural practices is the main measure proposed with less produced per area of land with less input of fertilisers, pesticides, etc. Diverse crop rotations have a beneficial effect on skylark populations as different crop types in a small area allows multiple nesting attempts in the same year and also increases the amount and diversity of chick-food available within this area (Poulsen et al., 1998). Sustainable agriculture may maintain or increase overall farm profitability, and may also increase the natural or social capital of the countryside (Pretty, 1998). However, the alternative argument is that high input and high yield agriculture is necessary to meet the demands for food and that high productivity protects uncultivated land from being brought into agricultural production and may free surplus agricultural land for conservation purposes (Mason and MacDonald, 2000). Unless the move to more extensive agriculture is accompanied by significant improvements to the amount and quality of semi-natural habitats and hedgerows within the farm landscape, then the benefits to birds may not be very substantial (Mason and MacDonald, 2000). Mason and MacDonald (2000) suggest that ‘the targeting of agricultural subsidies to land managed in precise ways to benefit wildlife may be the best way forward in the short term, while waiting for the concept of large-scale, low-input sustainable agriculture to gain acceptability within the political and farming communities’. The purpose of the methods mentioned above is to increase habitat heterogeneity in the farmland landscape at various spatial scales. Benton et al. (2003) argue that ‘the loss of ecological heterogeneity at multiple spatial and temporal scales is a universal consequence of multivariate agricultural intensification and, therefore, that future research should develop cross-cutting policy frameworks and management solutions that recreate that heterogeneity as the key to restoring and sustaining biodiversity in temperate agricultural systems’.
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In conclusion, in lowland farmland in Ireland bird species diversity in the breeding season was greater in set-aside compared to neighbouring grass and tillage sites, with most bird species also exhibiting a preference for set-aside over non-setaside sites. The type of management of set-aside was important and determined which species were likely to be found utilising the set-aside field. Non-rotational set-aside seems to be the most beneficial type of management in lowland Ireland for farmland birds as many of the species that preferred this set-aside type were field interior species such as skylark, meadow pipit, pheasant and snipe. This is in contrast to many studies in the UK that found that rotational set-aside was preferred by more farmland bird species, including skylark, than non-rotational set-aside.
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Chapter 4: The Diversity of Butterflies in Different Habitat Types. 4.1 INTRODUCTION 4.1.1 Butterflies Butterflies are most unusual among insects as they have a high popular appeal. Watching and studying butterflies arguably ranks only second to ornithology as a pastime for naturalists interested in animal life (New, 1997). Thus, the charisma of this group of insects, along with their ease of recognition in the field, has led to an abundance of data on the distribution and ecology of butterflies (e.g. Baynes, 1960; Crichton and Ní Lamhna, 1975; Heath et al., 1984; Feltwell, 1986; Novák, 1990; Shackleton et al., 1999; Asher et al., 2001). Twenty-eight species of butterflies are resident in Ireland compared to 59 in Britain and approximately 560 in Europe (Figures 4.1-4.3; Appendix VI) (Asher et al., 2001). Three migrant species, the Clouded Yellow (Colias crocea), Red Admiral (Vanessa atalanta) and Painted Lady (Vanessa cardui), also regularly occur and breed in Ireland. Of these species only 16 can be considered to occur throughout most of Britain and Ireland. Nearly 40% of the resident Irish species have a range boundary in Ireland as Britain and Ireland represent the north-western limits of the range of these species in Europe. Asher et al. (2001) divide the butterfly fauna into two groups: wider countryside species and habitat specialist species (see Appendix VI for the classification of the Irish resident species). Wider countryside species are those that are habitat generalists or those that use habitats that are very widespread. These
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species tend to be mobile species and the larvae often use several species of foodplants (Asher et al., 2001). Green-veined White (Pieris napi), Small Tortoiseshell (Aglais urticae) and Speckled Wood (Parage aegeria) are examples of wider countryside species. Habitat specialists are confined to specific, discrete habitat ‘islands’ that are localised or patchy in the modern landscape (e.g. lowland heath, coppice woodland, and calcareous grassland). Specialist species are relatively sedentary and the larvae usually only utilise one or two species of foodplant (Asher et al., 2001). Habitat specialists include Silver-washed Fritillary (Argynnis paphia), Wood White (Leptidea sinapsis complex) and Brown Hairstreak (Thecla betulae).
Figure 4.1: Brimstone (Gonepteryx rhamni) on bramble (Rubus fructicosus) in Dysart, Co. Laois (Photo: Aoife Brennan).
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Figure 4.2: Small Tortoiseshell (Aglais urticae) left and Peacock (Inachis io) on bramble in Dysart, Co. Laois (Photo: Aoife Brennan).
Figure 4.3: Silver-washed Fritillary (Argynnis paphia) on bramble in Dysart, Co. Laois (Photo: Aoife Brennan).
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4.1.2 Habitat and Host Plant Affinities of Butterflies In general for butterflies, habitat quality is determined by the presence of nectar sources, specific host plants and a particular vegetation structure that will provide a suitable microclimate (WallisDeVries, 2001). However, the specific habitat requirements are usually specific for each species (WallisDeVries, 2001). Butterfly larvae, i.e. caterpillars, are host specific for the plants on which they feed (Feltwell, 1986). The availability of suitable habitats and foodplants define the range of an individual butterfly species and these are in turn influenced by topography, climate, underlying geology and soils, and land use (Asher et al., 2001). As well as the presence of particular larval foodplants, each resident butterfly species needs these plants to be growing in exactly the right conditions to enable successful completion of its life cycle (Asher et al., 2001). Caterpillars also require specific microhabitats that are related to both geological composition and plant community, thus adult butterflies, in selecting the most suitable microhabitat for oviposition, also show strong biotope selectivity. The female Brimstone (Gonepteryx rhami), for example, has the extraordinary ability to find even the most isolated bushes of both purging (Rhamnus catharticus) and alder buckthorn (Frangula alnus), on which to lay her eggs (Ferris and Humphrey, 1999). In addition, adult butterflies require food sources such as flowers for nectar or aphids for honeydew (Asher et al., 2001). Many butterflies also prefer sheltered positions in which they can establish territories or bask, or where conditions will be warm enough for egg and larval development. Butterflies use many different types of habitat in Britain and Ireland including: grassland; woodland and scrub; hedges and arable land; bogs; mountains and upland grasslands; moorland; lowland heathland; fens; and urban and post-industrial habitats. In the context of Irish lowland landscapes only the first four habitat categories are
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important. Grassland includes many important different habitats including limestone grassland, damp grassland and meadows, coastal grassland and dunes and acidic grassland and bracken/grass mixtures (Asher et al., 2001). All these grassland habitats are important for butterflies in Ireland which include species such as Dingy Skipper (Erynnis tages), Marsh Fritillary (Euphydryas aurinia), Dark Green Fritillary (Argynnis aglaja), Meadow Brown (Maniola jurtina), Wall Brown (Lasiommata megera), Small Heath (Coenonympha pamphilus), Green-veined White, Orange-Tip (Anthocharis cardamines), Grayling (Hipparchia semele), Ringlet (Aphantopus hyperantus), Common Blue (Polyommatus icarus) and Small Blue (Cupido minimus) (Asher et al., 2001). Approximately half of the British butterfly species are strongly or wholly associated with wild or traditionally farmed grassland (Erhardt and Thomas, 1991). However, most of the grassland in Ireland is improved grassland with smaller areas of unimproved grassland, although this type is still usually heavily grazed. Improved grassland is intensively managed for grazing or silage production or both and is usually planted with highly productive grasses such as Lolium perenne. They usually contain no larval foodplants and the only butterflies occurring there are mobile species, such as Small Tortoiseshell, in transit (Asher et al., 2001). Set-aside that has naturally regenerated is more beneficial to butterflies as it resembles seminatural grassland and contains more foodplants and flowering plants. Wider countryside species such as Meadow Brown, Green-veined White and Ringlet are common in this type of set-aside grassland. Very little research has been conducted on butterflies within intensively managed arable farmland and there are no published studies that would allow comparison of population trends in these areas with semi-natural habitats (Feber and Smith, 1995). Fields of arable crops are usually believed to be of limited value for
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butterflies as cultivation and herbicides have removed all larval foodplants (Asher et al., 2001). However, brassica crops can provide breeding habitats for Large and Small Whites (Pieris brassicae and P. rapae), and crops such as Oil-seed Rape (Brassica napus) may provide nectar sources for adult butterflies. The most important habitats for butterflies in the modern agricultural landscape are hedges and field margins as these provide shelter and larval foodplants such as Blackthorn (Prunus spinosa), buckthorns (Rhamnus cathartica and Frangula alnus), Holly (Ilex aquifolium), nettles (Urtica spp.) and grasses. Characteristic species of hedgerows and margins include Brimstone, Large White, Small White, Green-veined White, Orange-Tip, Brown Hairstreak, Holly Blue (Celastrina argiolus), Small Tortoiseshell, Peacock (Inachis io), Gatekeeper (Pyronia tithonus), Meadow Brown and Ringlet (Asher et al., 2001). Mature broad-leaved woodland can be home to characteristic butterflies such as Brimstone, Purple Hairstreak (Quercusia quercus), Silver-washed Fritillary and Speckled Wood. Butterfly diversity is improved in this type of woodland by the presence of sunny rides and clearings and foodplants such as oaks (Quercus spp.), buckthorns, Holly, Honeysuckle (Lonicera periclymenum) and violets (Viola spp.) (Asher et al., 2001). Rides and glades in forests are also important for butterflies because they provide warm, sheltered and sunny conditions along with varied vegetation structure and foodplants such as Common Dog-violet (Viola riviniana), Bird’s-foot-trefoil (Lotus corniculatus), Gorse (Ulex europaeus), vetches (Vicia spp.) and grasses. As a result rides and glades may be used by species such as Dingy Skipper (Erynnis tages), Wood White, Green Hairstreak (Callophrys rubi), Common Blue, Ringlet and Meadow Brown. Conifer plantations can contain many of the woodland species mentioned above if rides and glades are present or if they are planted on ancient woodland sites with a varied ground flora (Asher et al., 2001).
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Bogs do not have many characteristic species with Large Heath (Coenonympha tullia) being the only species found exclusively in boggy areas in Ireland where its larval foodplants are Bog Cotton (Eriophorum spp.) (Shackleton et al., 1999). 4.1.3 Butterflies as Indicators of Global Climate Change Global warming has occurred in the last century with mean global temperatures rising and further temperature increases of 2.1 to 4.6°C are predicted in the next 50-100 years, which may lead to global climate change (Parmesan et al., 1999). Temperatures in Europe have increased by approximately 0.8°C in the last century (Parmesan et al., 1999). In some areas, this warming is even more pronounced. For example, in central England where spring temperatures increased by approximately 1.5°C between 1976 and 1998, and summer temperatures rose by about 1°C (Roy and Sparks, 2000). Butterflies may be used as indicators of global climate change and are sometimes characterised as early indicators of environmental change, because their specialised life cycles makes them vulnerable and because they have the ability to respond quickly to changes in their environment (Van Strien et al., 1997). Butterfly activities are also closely controlled by the weather and many species’ ranges are restricted by climate (Roy and Sparks, 2000). Changes in abundance and distribution of butterflies can be detected over a relatively short time-scale due to their high dispersal ability and short life cycles with some species producing two or more generations per year (Roy and Sparks, 2000). As butterflies have a high public appeal and are easily identified, there is a wealth of information on their distribution and abundance and several countries have set up national monitoring schemes, such as in Britain and The Netherlands (Pollard and Yates, 1993; Van Swaay, 1995). However, 153
butterflies are also sensitive to weather conditions leading to large annual changes, which may hamper the early detection of long-term trends (Van Strien et al., 1997). The geographical distributions of butterflies have responded to land use and climate changes, although some species have become specialised to sub-optimal environmental conditions (Asher et al., 2001). As was noted earlier, many butterfly species are near the edge of their European range in Britain and Ireland, which means that they are vulnerable to even slight changes to their environment (Asher et al., 2001). The vulnerability of butterfly species at their range limits, combined with their short life spans and limited mobility, makes these species important as indicators of environmental changes (Asher et al., 2001). Climate change is expected to benefit butterflies as the warmer temperatures will lead to an increase in flight-dependant activities such as mate-location, egglaying, nectaring, predator-evasion and dispersal (Dennis and Shreeve, 1991). Some areas will experience drought that may have negative effects on some species. Dry summers in some parts are also likely to affect egg survival, host plant growth and habitat structure (Roy and Sparks, 2000). In Britain, increases in temperature due to global warming would be less favourable for butterflies in the south and east due to the increased risk of drought in these areas (Pollard and Yates, 1993). However, butterflies would probably respond more favourably in the north and west as these areas are wetter and species diversity and abundance would probably increase (Pollard and Yates, 1993). Increases in temperature will increase the abundance of foodplants and may also allow these plants to colonise different places. Increased temperatures in the future could result in some butterfly species being lost to Britain but others may arrive from continental Europe
154
(Pollard and Yates, 1993). Ireland may also be colonised by new species of butterfly from Britain and Europe. Global warming is driven by increased CO2 and other gases in the atmosphere. Higher levels of carbon dioxide are known to increase photosynthetic activity of plants and this may in turn affect plant-insect herbivore interactions (Roy and Sparks, 2000). Several recent studies have found that global warming has already affected butterflies in Europe. In the last 20 years the first appearances of most British butterflies has advanced and is strongly related to earlier peak appearance and longer flight period for multibrooded species (Roy and Sparks, 2000). Roy and Sparks (2000) predict that an increase of 1°C in temperatures could advance first and peak appearance of most butterfly species by 2-10 days, if other confounding factors are ignored. A study by Parmesan et al. (1999) provided evidence of large-scale poleward shifts in entire species’ ranges, as in a sample of 35 non-migratory European butterflies, 63% had ranges that had shifted to the north by 35 – 240km during the 20th century, while only 3% had shifted to the south. Most of the species that shifted their ranges to the north also expanded their ranges, as their southern boundary remained stable (Parmesan et al., 1999). It is unlikely that changing land use could account for these boundary changes (Parmesan et al., 1999). Climatic isotherms in Europe have shifted northwards in the last century by an average of 120km, which is the same order of magnitude as the range shifts in butterfly species, implying that these northward shifts represent responses to increased temperatures (Parmesan et al., 1999).
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However, the benefits of climate change may not be as great as first thought because habitat degradation may exhibit a stronger influence. The ability of individual species to colonise new areas where suitable habitat conditions will exist in the future will be extremely important in determining which species will increase their range and abundance as temperatures increase (Pollard and Yates, 1993). Habitat fragmentation will also play a crucial role in determining species distribution in the future as different species will be prevented from colonising new areas of suitable habitat or expanding their ranges by the highly fragmented nature of these habitats beyond their current range limits (Opdam and Wascher, 2004). Warren et al. (2001) studied the changes in distribution sizes and abundances of 46 species of non-migratory butterflies that approach their northern climatic range margins in Britain. Despite climate warming over the past 30 years, approximately 75% of these species declined in distribution, as any gain caused by warming temperatures was negated and surpassed by habitat loss. However, half of the species that were mobile and habitat generalists increased their distribution, while the other generalists and 89% of the specialists declined. Changes in abundance were very similar to distribution. Thus, most butterfly species cannot benefit from climate warming, as areas with suitable climate cannot be utilised due either to the lack of suitable habitats or isolation. In areas where the degree of habitat fragmentation still allows butterfly populations to persist, the shifting of species’ ranges due to climate change can still occur, although this process will be impeded (Opdam and Wascher, 2004). However, the expansion of species ranges will be prevented in landscapes where the habitat is so fragmented that it is below the critical level for metapopulation persistence (Opdam and Wascher, 2004). Changes in habitat and climate in the future will probably lead to
156
declines in specialist species, which will reduce species diversity and result in areas dominated by mobile and widespread habitat generalist butterfly species (Warren et al., 2001). 4.1.4 Aims This study was designed to assess butterfly diversity and abundance in lowland landscapes in Ireland. The aim of Study 1 was to assess the extent of difference in butterfly species diversity and abundance between different woodland and agricultural habitats. Study 2 aimed to assess the degree of variation between years in butterfly diversity and abundance in landscapes. The relative importance of small-scale habitat and vegetation features to the presence or absence of different butterfly species along transects was investigated in Study 3. Finally, the aim of Study 4 was to determine the degree of difference in butterfly diversity and abundance between transects placed along hedgerows or in the middle of grassland or tillage fields in the same farms. 4.2 METHODS 4.2.1 Butterfly Sampling The method of butterfly sampling was based on the line-transect count method of the British Butterfly Monitoring Scheme (Pollard and Yates, 1993) and modified for the BioAssess project by Dr. Chris van Swaay (Van Swaay, 2003). Butterfly transects are a way of measuring the number and variety of butterflies present at a site from year to year, and require a weekly recording, throughout the main period in which butterflies fly. The transects were divided into 50m sections with 250m transects walked for Study 1 and 1-km transects for Study 2. Each 1-km transect took 20-40 minutes to walk depending on field conditions, the season and the number of
157
butterflies, with the 250m transects taking between 5 and 10 minutes. For the 1-km transects, the number of transect sections in a particular habitat were more or less equivalent to the area of that habitat in the LUU.
Figure 4.4: Recording distances for butterfly transect sampling.
Transect walks were only made between 10:00 and 17:00 hours, in warm and at least bright weather, with no more than moderate winds. This resulted in no counts being made in some weeks. Transects were walked at a slow, steady pace counting all butterflies seen within a fixed distance of 2.5m at either side of the transect line and 5m ahead and above (Figure 4.4). The sites were visited at different times of the day on successive visits to randomise the counts. It was planned that transects be walked once a week from April 1st to the end of September in both years. However, in 2001, the restrictions imposed due to the Foot and Mouth Crisis meant that transect counts could only be carried out from the start of June to the end of September. In 2002, the extremely wet weather in the summer resulted in no counts taking place in many weeks and this severely affected the numbers of butterflies recorded in this season. 158
4.2.2 Study 1 In this study, the effect of habitat type on the composition of butterfly communities was examined. Descriptions of the habitat types and study sites are given in Chapter 2. Butterflies were sampled in 2002 from the start of April to the end of September in the four replicate sites assigned to each habitat type: broadleaf woodland, coniferous woodland, pasture, set-aside, and tillage. Each replicate site contained a 250m transect which was broken down into five 50m sections. 4.2.3 Study 2 In this study, the temporal change in butterfly communities and abundance were examined between two consecutive years. The study sites were six 1-km squares or land-use units (LUUs) which represented a land-use intensity gradient ranging from a site dominated by mature broadleaf forest to a site dominated by intensive arable agriculture. Each 1 km-square or LUU was sampled by walking a 1-km transect, which was divided into twenty 50m sections. All six LUUs were sampled in 2002 from the start of April to the end of September, but it was only possible to sample four LUUs in 2001 from the start of June to the end of September due to the restrictions imposed by the Foot and Mouth crisis. The six LUUs were located in Co. Laois and Co. Kildare (see Figure 2.1) and were: LUU1 (Old-growth forest): Abbeyleix (centred at S 415 825): This historically important woodland, dominated by Quercus robur, is located in the De Vesci Estate. While many of the trees were planted (the oldest documented tree is 700 years old), the estate is managed in order to allow natural regeneration of the oaks. (2001 and 2002).
159
LUU2 (Managed forest): Dooary (centred at S 498 896): This managed woodland site consists of plantation forestry of mixed age, dominated by Sitka spruce (Picea sitchensis). (2001 and 2002). LUU3 (Mixed-use landscape dominated by forest or woodland): Dysart (centred at S 530 972): This is a mixed-use site incorporating agricultural land, which is given over to intensive grassland production. The grassland is variously managed for intensive silage production (1-2 cuts), while cattle are rotated through the other land parcels. The adjacent woodland is characterised by Beech (Fagus sylvatica) and Pine (Pinus sylvestris). (2001 and 2002). LUU4 (Mixed-use landscape not dominated by a single land-use): Ballykilcavan (centred at S 601 965): This is a mixed forest and farmland site. The forest comprises both semi-natural deciduous woodland and conifer plantations. The fields adjacent to the forest were used for tillage and set-aside grassland. The primary crop grown in 2002 was wheat with oats and barley also present. The set-aside area was in the scheme more than 3 years and was grazed by cattle in winter. (2002 only). LUU5 (Mixed-use landscape dominated by pasture): Fallowbeg (centred at S 567 902): This site is dominated by agricultural grassland, which is used mainly to graze cattle. Some of the fields are used for silage and most of the fields haven’t been reseeded. One third of the site is tilled and the main crop is winter wheat. A drain with a marshy area is also present in the site. (2002 only). LUU6 (Mixed-use landscape dominated by arable crops): Coursetown (centred at S 650 948): Situated to the west of Athy in the traditional area where tillage farming dominates the landscape. By far the greatest proportion of land is planted with barley followed by sugar beet. A small percentage is given over to
160
growing winter wheat. Long-term non-rotational set-aside is also present on the farm. (2001 and 2002). In addition an extra 1-km transect was sampled in both years in the Baunreagh coniferous forest (centred at N 209 025) in Co. Laois. The Baunreagh estate is one of the largest forestry plantations in Ireland and is situated in the heart of the Slieve Bloom Mountain range. Sitka spruce (Picea sitchensis) is the dominant tree species, and with some trees planted between 1922 and 1925, it has no equal among plantation forestry in Ireland. 4.2.4 Study 3 The aim of this study, was to assess whether habitat and vegetation characteristics of the transect sections affected the butterfly species diversity and abundance of these sections. Therefore, habitat and vegetation variables were measured for each butterfly transect section following methods similar to WallisDeVries (2001). Fourteen different characteristics were recorded 5m either side of the transect line for each 50m section (thus each section was 500m2) including the host plants abundance for the six most abundant butterflies recorded in the LUUs in the 2001 season (Tables 4.1 and 4.2). All of the characteristics were recorded in July and August 2002, including the numbers of flowers in each section even though this parameter fluctuates throughout the butterfly sampling period.
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Table 4.1: Factors recorded for each butterfly transect including their value parameters.
1 2 3 4 5 6 7
Characteristic % Cover Nettles % Cover Cultivated Brassicas % Cover Wild Crucifers % Cover Speckled Wood food grasses % Cover Coarse grasses % Cover Fine grasses Flower Abundance (Numbers)
8 9 10 11 12 13 14
% Cover Short Vegetation % Cover Standing Dead Vegetation % Cover Trees % Cover Shrubs % Cover Herbs Sun Shelter
Values % cover of 500m2 section % cover of 500m2 section % cover of 500m2 section % cover of 500m2 section % cover of 500m2 section % cover of 500m2 section Low (< 50); Moderate (50-500); High (> 500) % cover of 500m2 section % cover of 500m2 section % cover of 500m2 section % cover of 500m2 section % cover of 500m2 section Shady; Some shade; Sunny None; Some; Sheltered
Table 4.2: The six most abundant butterfly species recorded in 2001 and their caterpillar foodplants. Butterfly Species 1
Foodplants
Speckled Wood (Parage
Various grasses, including False Brome
aegeria)
(Brachypodium sylvaticum), Cock’s-foot (Dactylis glomerata), Yorkshire-fog (Holcu lanatus) and Common Couch (Elytrigia repens).
2
Peacock (Inachis io)
Common Nettle (Urtica dioica).
3
Meadow Brown (Maniola
Wide range of grasses but those with finer leaves
jurtina)
preferred.
Ringlet (Aphantopus
Coarser grasses.
4
hyperantus) 5
6
Green-veined White
Wild crucifers mainly but sometimes cultivated
(Pieris napi)
ones also.
Small White (Pieris rapae)
Cultivated brassicas mainly with wild ones used to a lesser extent. 162
4.2.5 Study 4 The aim of this study was to determine whether placing a transect near a hedgerow or in the centre of an intensive sown pasture or tillage field would make a significant difference to the numbers and species richness of the butterflies recorded. Field interior and boundary transects of 250m in length were walked in the same sites: an improved grassland site at Dysart and a tillage site in Coursetown. The field interior transects for Dysart (P1-LUU3) and Coursetown (T1-LUU6) were the transects that were included in Study 1 and the field boundary transects were extra transects that were walked for this study. The grassland field interior transect at Dysart was sampled on 13 occasions from 10th April 2002 to 21st September 2002. This transect was located in the middle of the intensive pasture fields along a stone farm track and was distant from any hedgerows. The grassland field boundary transect at Dysart was walked along a hedgerow at the boundary of the same farm. This boundary transect was walked on 11 dates from 10th April 2002 to 21st September 2002. Both Dysart transects were walked on the same day on 10 occasions. The tillage field interior transect at Coursetown was sampled on 9 separate dates from 23rd April 2002 to 21st September 2002. This transect was also placed in the middle of a large field far removed from field boundary features, except that the crop type was spring barley in this instance. The tillage boundary transect at Coursetown was sampled along a hedgerow which bordered a busy main road and a field of sugar beet. Butterflies were sampled along this transect on eight dates from 23rd April 2002 to 21st September 2002. Both of these tillage transects were walked on the same day on 7 occasions.
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4.2.6 Data Processing 4.2.6.1 Study 1 The standard dataset for the habitat replicates was the total number of butterflies recorded for each of the twenty 250m transects from seven count dates. Generally, the transects were walked between 7 and 13 times, but in the case of the coniferous site at Baunreagh (C1-Baun) it was only possible to sample it five times. Overall seven dates were selected for each site so that each count date for the 20 sites was within a few days of each other (see Appendix VII for the table of count dates used for each replicate site). The standard habitat replicates dataset was used for all analyses except for the bar graphs of the distribution of individual species in the habitat types (Figures 4.7 – 4.11). For these graphs, the dates on which counts were completed were allocated to one of twelve broad date categories: early April; late April; early May; late May/early June; late June; early July; late July; early August; mid-August; late August; early September; and late September (see Appendix VII for details). Only one count date was permitted per category and if two counts from one site qualified for a category the count date with the greatest number of butterflies was used. These graphs used the average number of butterflies recorded in the four sites of each habitat type. 4.2.6.2 Study 2 The data used for Study 2 were the number of butterfly species and the total number of each species recorded in each 1-km transect of the six LUUs and Baunreagh over all counts in both 2001 and 2002.
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4.2.6.3 Study 3 This study amalgamated data from the sites sampled in Study 1 and Study 2 to examine the effects of vegetation and local habitat variables on butterflies in 2001 and 2002. All sections from the LUUs and the habitat replicate transects were included in the analysis of the 2002 season. There was a certain amount of overlap in the transect sections used for Study 1 and Study 2 in 2002, which explains why the total number of sections in the analysis was 206. From the 2001 dataset, ninety-nine sections were used in the analyses and these included all sections from the four LUUs and Baunreagh except for sections 2 to 9 in LUU2. These sections were excluded for 2001 because in that year the habitat was mature Sitka spruce forest but the trees were clearfelled by 2002 when the habitat and vegetation characteristics of the transects were recorded. For the analyses of the vegetation and local habitat variables, the total number of butterflies recorded in each section was used from thirteen matched count dates in 2001 (see Appendix VII for details of the 13 count dates in 2001) and the seven habitat replicate count dates of Study 1 in 2002. The habitat and vegetation characteristics datasets included the values for each parameter in each transect section from July or August 2002. The values for Flower Abundance (Numbers), Sun and Shelter were replaced with 0, 1 or 2 with 0 referring to low flower numbers, shady, and no shelter; 1 referring to moderate flower numbers, some shade, and some shelter; and 2 referring to high flower numbers, sunny, and sheltered. No cultivated brassicas were recorded in any of the transects so this parameter was not included in the analyses. In 2001, the Speckled Wood Grasses and the Coarse Grasses parameters were identical so these two parameters were included as one variable in the analyses.
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4.2.7 Statistical Analysis 4.2.7.1 Ordinations and Diversity Indices These were calculated, used and compared as described in Chapter 2 for the bird studies. 4.3 RESULTS 4.3.1 Study 1 No significant difference was found between the five habitat types in the total number of butterflies recorded in each site during 7 transect walks (F1,19 = 0.36, P < 0.8351). Coniferous habitat had the highest mean abundance of butterflies with a mean of 21.8 butterflies recorded per site, but the standard error was 11.3, illustrating the high variation in numbers of butterflies between the four studied coniferous sites (Table 4.3). Broadleaf had the lowest mean abundance and standard error of 9.8 and 4.1 respectively (Table 4.3). The other habitats had similar means and standard errors (Table 4.3).
Table 4.3: Table of means and standard errors of the total number of butterflies recorded in each site for the five different habitat types for the 7 replicate counts. Habitat Type
Mean
Standard Error
Broadleaf
9.8
4.1
Coniferous
21.8
11.3
Pasture
14.5
6.0
Set-aside
16.3
5.7
Tillage
15.5
6.7
Twelve species and 311 individual butterflies were recorded in the twenty sites of the five habitat types during the seven replicate counts in 2002. Two species, 166
Speckled Wood and Ringlet, comprised almost half of the recorded butterflies with 24% each (Figure 4.5). These two species along with Meadow Brown (18%) and Green-veined White (11%) made up over three-quarters of the recorded butterfly community (Figure 4.5). The other recorded species were Peacock (6%), Small White (5%), Small Tortoiseshell (4.5%), Silver-washed Fritillary (3.5%), Red Admiral (2%), Brimstone (0.6%), Large White (0.6%) and Orange Tip (0.3%) (Figure 4.5).
Red Admiral 2%
Brimestone 0.6%
Silver-washed Fritillary 3.5% Small Tortoiseshell 4.5%
Large White 0.6% Orange Tip 0.3% Speckled Wood 24%
Small White 5% Peacock 6%
Green-veined White 11%
Ringlet 24% Meadow Brown 18%
Figure 4.5: Species composition of the total number of butterflies recorded in all twenty habitat replicate sites during the seven transect walks in the 2002 season.
Thirty-nine butterflies were recorded in broadleaf forest sites, with Speckled Wood being the dominant species with 46% of the community, followed by Ringlet (18%) and Peacock (13%) (Figure 4.6). The four coniferous forest sites had 87 butterflies recorded in them, with 32 of these being Ringlets (37%), 21% Speckled Woods and 17% Meadow Browns (Figure 4.6). Meadow Brown (26%), Speckled Wood (24%), Green-veined White (14%), Peacock (9%) and Small Tortoiseshell (9%) were the main species of the 58 butterflies recorded in pasture (Figure 4.6). 167
Meadow Brown was the dominant species in set-aside with 37% of the 65 recorded butterflies (Figure 4.6). Other common species in set-aside were Ringlet (17%), Speckled Wood (12%) and Small White (11%) (Figure 4.6). Sixty-two butterflies were recorded in the four tillage sites with Ringlet (34%) being the most common, followed by Speckled Wood (29%) and Green-veined White (16%) (Figure 4.6).
100%
80%
Orange Tip Large White Brimestone Red Admiral Silver-w. Fritil. Small Tort. Small White Peacock Green-v. White Meadow Brown Ringlet Speckled Wood
60%
40%
20%
0%
Broadleaf
Coniferous
Pasture
Set-aside
Tillage
All Habitats
Figure 4.6: Species composition of the total number of butterflies recorded during the seven transect walks in the 2002 season for the five different habitat types and all habitats combined. 4.3.1.1 Speckled Wood The following bar graphs look at the distribution of individual species in the habitat types in twelve date categories from early April to late September (Figures 4.7 – 4.11). In total 82 Speckled Woods were recorded with the vast majority being recorded in August and early September (53 individuals) (Figure 4.7). Before the end of August, almost all of the individuals were recorded in the forest habitats with
168
Speckled Woods only being recorded in the farmland habitats from late August onwards (Figure 4.7). In late August, an average of 2 butterflies were recorded per pasture site, 1.5 each in coniferous and set-aside, 0.75 in tillage and none were recorded in broadleaf habitat (Figure 4.7). However, broadleaf habitat had the highest number of Speckled Wood with 23 recorded in the four sites in total. Coniferous forest, pasture and tillage all had relatively high numbers of Speckled Wood, but only eight individuals were recorded in set-aside in all sites during the season (Figure 4.7).
Broadleaf
Coniferous
Pasture
Set-aside
Tillage
2.5
Numbers
2
1.5
1
0.5
0 Early April Late April Early May
Late Late June Early July Late July Early Aug Mid Aug May/Early June
Late Aug Early Sept Late Sept
Figure 4.7: Mean number of Speckled Wood (Parage aegeria) per site in the five habitat types throughout the 2002 recording season. 4.3.1.2 Ringlet In the twenty sites, all of the observed 77 Ringlets occurred from early July to late August, with a peak in late July (Figure 4.8). In late July, an average of 7 Ringlets were observed per coniferous forest site, with 4.5 butterflies recorded per tillage site, 2.25 in set-aside, 0.75 and 0.5 were recorded in broadleaf and pasture habitat (Figure 4.8). Thirty-two Ringlets were recorded in coniferous forest sites during the season 169
(Figure 4.8). The four tillage sites had 21 observations of Ringlets while set-aside had 11, broadleaf forest 7 and pasture 6 (Figure 4.8).
Broadleaf
Coniferous
Pasture
Set-aside
Tillage
8
7
6
Numbers
5
4
3
2
1
0 Early April
Late April
Early May
Late May/Early June
Late June
Early July
Late July
Early Aug
Mid Aug
Late Aug
Early Sept
Late Sept
Figure 4.8: Mean number of Ringlet (Aphantopus hyperantus) per site in the five habitat types throughout the 2002 recording season. 4.3.1.3 Meadow Brown A total of 59 individual Meadow Brown butterflies were recorded from early July to late August with a peak in late July, like the Ringlet (Figure 4.9). In late July, an average of 3.25 individuals were recorded per set-aside site, with an average of 2.75 in coniferous and 2 in pasture sites (Figure 4.9). Twenty-four Meadow Browns were recorded in the four set-aside sites in 2002, with 17 recorded in pasture sites, 15 in coniferous forest and 3 in broadleaf, while no individuals were recorded in the tillage sites (Figure 4.9).
170
Broadleaf
Coniferous
Pasture
Set-aside
Tillage
4
3.5
3
Numbers
2.5
2
1.5
1
0.5
0 Early April
Late April
Early May
Late May/Early June
Late June
Early July
Late July
Early Aug
Mid Aug
Late Aug
Early Sept
Late Sept
Figure 4.9: Mean number of Meadow Brown (Maniola jurtina) per site in the five habitat types throughout the 2002 recording season. 4.3.1.4 Green-veined White From early April to early September, 36 Green-veined Whites were recorded, with a third of these in late July (Figure 4.10). In the peak period of late July, an average of 1.75 Green-veined Whites were recorded in tillage sites, with an average of 1 and 0.25 recorded in set-aside and pasture respectively (Figure 4.10). Nine Green-veined Whites were recorded in both coniferous forest and tillage, 7 in pasture and set-aside and only four in broadleaf forest (Figure 4.10).
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Broadleaf
Coniferous
Pasture
Set-aside
Tillage
2
1.75
1.5
Numbers
1.25
1
0.75
0.5
0.25
0 Early April Late April
Early May
Late Late June May/Early June
Early July
Late July
Early Aug
Mid Aug
Late Aug
Early Sept Late Sept
Figure 4.10: Mean number of Green-veined White (Pieris napi) per site in the five habitat types throughout the 2002 recording season. 4.3.1.5 Peacock Twenty-four Peacocks were recorded in total from early April to late September, with a quarter occurring in late August (Figure 4.11). In late August, an average of 0.75 butterflies were observed per pasture site and 0.25 per broadleaf, setaside and tillage site (Figure 4.11). Thirteen Peacocks were recorded in broadleaf forest sites during the recording season, while only four were seen in coniferous forest sites, 3 in pasture and 2 each in set-aside and tillage (Figure 4.11).
172
Broadleaf
Coniferous
Pasture
Set-aside
Tillage
1.25
Numbers
1
0.75
0.5
0.25
0 Early April Late April
Early May
Late Late June May/Early June
Early July
Late July
Early Aug
Mid Aug
Late Aug
Early Sept Late Sept
Figure 4.11: Mean number of Peacock (Inachis io) per site in the five habitat types throughout the 2002 recording season. 4.3.1.6 Species Diversity The Shannon-Weiner Index (H’), Simpson’s Index (D) and species richness were calculated for each site and then analysed with the Kruskal-Wallis Test to test the significance of differences between the five habitat types for butterflies recorded during the seven designated count dates in 2002. Butterfly species richness was not significantly different between the habitat types (P = 0.5343). The average species richness per site for each habitat type and the total number of species in all sites of each habitat type are shown in Table 4.4. The average species richness was lowest in broadleaf (3.25 species) and was highest in set-aside with 6 species (Table 4.4). Eight species were present between the four broadleaf sites, 8 also in coniferous, 9 in setaside and only 6 species were found in the tillage sites (Table 4.4). The four pasture sites contained the highest number of species with 10 recorded (Table 4.4).
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Table 4.4: The number of species in all sites for each habitat type, and the mean species richness per site (S), Shannon-Weiner Index (H’) and Simpson’s Index (D) for each habitat type, with standard errors (SE). Habitat
No.
No. of
S
S
H’
H’
D
D
Type
of
Spp. in
Mean
SE
Mean
SE
Mean
SE
Sites
All Sites
Broadleaf
4
8
3.25
1.25
0.92
0.36
3.36
1.49
Coniferous
4
8
4.5
1.44
1.09
0.37
3.66
0.9
Pasture
4
10
5.75
1.65
1.28
0.43
4.62
1.31
Set-aside
4
9
6
1.29
1.41
0.22
4.26
0.73
Tillage
4
6
4.25
0.85
1.03
0.11
2.53
0.27
There were also no significant differences between the habitat types for the Shannon-Weiner and Simpson’s Indices (P-values of 0.4688 and 0.6331 respectively). Pasture and set-aside habitats had the highest mean Shannon-Weiner Index (1.28 and 1.41 respectively) and Simpson’s Index values (4.62 and 4.26) (Table 4.4). Broadleaf forest and tillage had low values of both indices (Table 4.4). There were no significant differences between Jaccard Similarity Indices in each of the five habitat types (Kruskal Wallis Test, P = 0.109). Sites in coniferous forest, pasture, set-aside and tillage had mean Jaccard Similarity Index values of 0.42, 0.39, 0.49 and 0.5 respectively (Table 4.5). This implies that β (Beta) diversity is intermediate in these habitats. Sites in broadleaf forest had a low mean similarity score of 0.15 and, thus, high beta diversity, which means that the species composition of the individual sites of broadleaf is not very similar in terms of butterfly diversity (Table 4.5).
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Table 4.5: Table of means and standard errors from analysis of variance of the Jaccard Similarity Index for butterflies for the five different habitat types. Habitat Type
N
Mean
Standard Error
Broadleaf
6
0.148
0.074
Coniferous
6
0.418
0.109
Pasture
6
0.388
0.115
Set-aside
6
0.488
0.069
Tillage
6
0.5
0.123
Furthermore, none of the axes were significant when the distribution of butterfly species in relation to all five habitats was analysed with canonical correspondence analysis (CCA). The P-value of the 1st axis was 0.74 and of all canonical axes was 0.61. 4.3.2 Study 2 During the two years of sampling 14 species of butterfly were recorded (Table 4.3). All 14 species were recorded in the four LUUs and Baunreagh that were sampled in 2001, while only 12 species were recorded in the six LUUs and Baunreagh in 2002 (Table 4.6). In 2001, counts were only possible for four months from 31st May to 25th September due to delays caused by restrictions imposed to contain the spread of Foot and Mouth disease into this country. Despite this, between 14 and 18 counts per site were possible in 2001 (Table 4.6). Even though counts began on 7th April in 2002 and the last counts took place on 23rd September, only between 5 and 13 counts were possible per LUU due to unsuitable weather conditions (Table 4.6). In 2001, 709 butterflies were recorded in five 1-km transects over four months compared to only 357 individuals in 2002 in seven 1-km transects over 6 months (Table 4.6). However, it should be noted again that more counts took place in the shorter time span of the sampling in 2001 compared to 2002. 175
In the first season, the five commonest species were: Speckled Wood (189 individuals), Ringlet (174), Meadow Brown (108), Green-veined White (83), and Small White (56) (Table 4.6). In the second season in 2002, the top five species were Meadow Brown (98), Speckled Wood (69), Green-veined White (59), Ringlet (54) and Peacock (35) (Table 4.6). The highest numbers of butterflies were recorded in LUU2 in both 2001 and 2002 with 299 and 91 individuals recorded respectively (Table 4.6). In 2001, ninety-five Speckled Woods and eighty-four Ringlets were recorded in LUU2 (Table 4.6). In 2002, the highest number of individuals recorded in LUU2 was for Green-veined White (29) with 22 Speckled Woods also recorded (Table 4.6). The lowest numbers were recorded on the 1-km transect in Baunreagh in both years, with 49 butterflies recorded in 2001 and only 10 in 2002 (Table 4.6). In conclusion, it is obvious from this study that large fluctuations in butterfly numbers at the same sites were possible between successive years.
176
Table 4.6: Total numbers of butterfly species and individuals recorded in the 1-km transects of the six LUUs and Baunreagh in the 2001 and 2002 seasons. LUU1
No. of Counts Common Name Small Tortoiseshell
LUU2
LUU3
LUU4
LUU5
LUU6
Baunreagh
‘01
‘02
‘01
‘02
‘01
‘02
‘02
‘02
‘01
‘02
‘01
‘02
15
8
16
10
18
13
11
10
14
9
16
5
0
1
1
0
7
2
6
1
4
2
0
Totals ‘01
‘02
0
12
12
Species Name
Aglais urticae
Ringlet
Aphantopus hyperantus
55
18
84
18
13
4
10
1
1
0
21
3
174
54
Orange Tip
Anthocharis cardamines
0
0
2
0
0
1
0
2
0
0
0
0
2
3
Silver-washed Fritillary
Argynnis paphia
2
7
1
0
3
4
0
0
0
0
0
0
6
11
Brimstone
Gonepteryx rhami
2
1
0
0
0
2
1
0
0
0
0
0
2
4
Peacock
Inachis io
8
4
15
5
25
19
4
2
1
1
0
0
49
35
Wall Brown
Lasiommata megera
0
0
1
0
0
0
0
0
0
0
0
0
1
0
Wood White
Leptidea sinapsis complex
1
0
9
2
0
0
0
0
0
0
0
0
10
2
Meadow Brown
Maniola jurtina
6
11
50
14
18
2
49
13
32
7
2
2
108
98
Speckled Wood
Parage aegeria
26
19
95
22
66
17
2
8
2
0
0
1
189
69
Large White
Pieris brassicae
3
0
2
0
2
0
0
0
4
0
1
0
12
0
Green-veined White
Pieris napi
3
8
36
29
11
3
5
9
16
1
17
4
83
59
Small White
Pieris rapae
7
0
1
0
3
0
0
2
37
0
8
0
56
2
Red Admiral
Vanessa atalanta
1
0
2
1
2
3
2
2
0
0
0
0
5
8
114
69
299
91
150
57
79
40
97
11
49
10
709
357
Totals
177
4.3.3 Study 3 4.3.3.1 2002 Detrended correspondence analysis (DCA) showed that the length of the gradient was 3.667 for butterfly species recorded in all transect sections surveyed in 2002. Therefore canonical correspondence analysis (CCA) was used to analyse the distribution of butterfly species in relation to the local habitat and vegetation variables. The 1st canonical axis was very significant (P = 0.005) and divided the butterflies preferring sheltered sections with trees and shrubs on the positive side of the diagram (right-hand side) from those showing a preference for sunny sections with various types of non-woody vegetation on the negative side (left-hand side) (Figure 4.12). The 1st canonical axis accounted for 9.1% of the species inertia and 41.3% of the inertia between the species and the environment (Table 4.7). The second canonical axis divided the butterfly species preferring Speckled Wood grasses, sun, herbs, high flower number, wild crucifers and shelter in the positive half (top) of the diagram from those preferring coarse and fine grasses, standing dead and short vegetation, nettles, trees and shrubs in the negative half (bottom) of the diagram (Figure 4.12). All of the canonical axes were significant also (P-value = 0.005) and explained 19% of the species inertia and 86% of the species-environment relation (Table 4.7). Silver-washed Fritillary and Brimstone showed a preference for sheltered sections that had a high percentage of tree cover (Figure 4.12). Speckled Wood preferred sections with a high proportion of tree cover, while Orange Tip preferred more shrub cover. Meadow Brown was more plentiful in sections with a high cover of standing dead vegetation and fine grasses. The presence of Green-veined White, Small White and Ringlet were influenced mainly by high flower numbers and the
178
presence of wild crucifers in the transect section. Peacocks were most common in sections where nettles, short vegetation and shrubs were present. Large White exhibited a preference for sections with a high cover of coarse grasses and Speckled
+1.0
Wood grasses (see Table 4.1).
Wild Crucifers
Sun Flower No.s Herbs Ringl
Shelter
SmWhi
Sp. Wood Grasses
Sfrit
GVWhi LarWh
Brime
SmTor
Coarse Grasses
ReAdm MeBro
SpWoo
Nettles
Peaco
Trees
Standing Dead Veg. OrTip
Fine Grasses
Short Veg.
-1.0
Shrubs
-1.0
+1.0
Figure 4.12: CCA of butterfly distribution in all transect sections in relation to all habitat variables for the 7 replicate counts during the 2002 season. P-value of 1st canonical axis = 0.005. P-value of all canonical axes = 0.005. The fourteen butterfly species recorded during both field seasons and their individual five-letter code are listed in Appendix VIII.
179
Table 4.7: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the CCA of butterfly distribution in all transect sections in relation to all habitat variables for the 7 replicate counts during the 2002 season. Axes 1 2 3 4 Total inertia Eigenvalues : .349 .181 .118 .079 3.834 Species-environment correlations: .777 .636 .529 .464 Cumulative percentage variance of species data : 9.1 13.8 16.9 19.0 of species-environment relation: 41.3 62.7 76.7 86.0
4.3.3.2 2001 DCA showed that the length of the gradient was 3.731 for butterflies recorded in all transect sections surveyed in 2001, with the exception of sections 2 to 9 in LUU2. CCA was used to analyse the distribution of butterfly species in relation to all of the habitat and vegetation variables recorded in July and August 2002. As the transect descriptions were taken in 2002, they may be inaccurate for the 2001 butterfly species dataset. However, as the habitat in the sites with the exception of LUU2 remained very similar between 2001 and 2002, these transect descriptions are still believed to give useful information. The 1st canonical axis was very significant (P = 0.005) and divided the butterflies preferring sunny sections with wild crucifers and herbs on the right-hand side of the diagram from those showing a preference for sheltered sections with trees, shrubs, grasses, nettles, high flower numbers and standing dead and short vegetation on the left-hand side (Figure 4.13). The 1st canonical axis accounted for 13.8% of the species inertia and 35.8% of the inertia of the species-environment relation (Table 4.8). The second canonical axis divided the butterfly species preferring trees, shrubs, high flower number, wild crucifers and shelter in the positive half of the diagram from those preferring the other habitat variables in the negative half of the diagram (Figure 4.13). All of the canonical axes 180
were significant (P-value = 0.005) and explained 87.6% of the inertia between the species and the environment (Table 4.8). Green-veined White, Small White and Large White showed a preference for sections containing wild crucifers (Figure 4.13). Small Tortoiseshell preferred sections with a large percentage cover of herbs. Ringlet and Brimstone showed a preference for nettles in sections. Speckled Wood and Orange Tip preferred sections with a high proportion of tree and shrub cover. Meadow Brown and Wall Brown seemed to prefer sections with a high cover of short vegetation and fine grasses. Peacock, Red Admiral and Silver-washed Fritillary were most common in sections where standing dead vegetation and coarse and Speckled Wood grasses were present.
181
+1.0
Trees
Shelter
Shrubs OrTip SpWoo
Wild Crucifers
Flower No.s WoWhi Ringl Nettles PeacoBrime
Standing Dead Veg.
GVWhi
SmWhi
LarWh
ReAdm
SmTor
Sfrit MeBro
WallB
Fine Grasses Coarse/Sp. Wood Grasses
Herbs
Short Veg.
-1.0
Sun
-1.0
+1.0
Figure 4.13: CCA of butterfly distribution in all transect sections in relation to all habitat variables for the 13 matched counts undertaken in the 2001 season. P-value of 1st canonical axis = 0.005. P-value of all canonical axes = 0.005. The fourteen butterfly species recorded during both field seasons and their individual five-letter code are listed in Appendix VIII.
Table 4.8: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the CCA of butterfly distribution in all transect sections in relation to all habitat variables for the 13 matched counts undertaken in the 2001 season. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total inertia .361 .234 .167 .122 2.624 .823 .791 .741 .715 13.8 22.7 29.0 33.7 35.8 59.0 75.6 87.6
182
4.3.4 Study 4 This study compared butterfly species richness and abundance between transects in field interiors and along field boundaries in pasture and tillage. In the pasture field interior transect at Dysart, only one butterfly, a Small Tortoiseshell, was observed during all the counts but this was outside the 10 matched sampling dates. In the grassland field boundary transect, six butterflies were recorded during all counts and the 10 matched counts. The species recorded were 1 Small Tortoiseshell, 4 Speckled Woods and 1 Green-veined White. In the tillage field interior transect at Coursetown, a single Small Tortoiseshell and a single Peacock were recorded during both all and the 7 matched count dates. In the tillage hedgerow boundary transect at Coursetown, seventeen butterflies were recorded during all walks with 13 of these being observed during the 7 matched count dates. During all counts 5 Small Tortoiseshells, 3 Ringlets, 1 Peacock, 3 Meadow Browns, 1 Speckled Wood, 1 Large White, 1 Green-veined White and 2 Small Whites were recorded. No Meadow Browns and one less Ringlet were recorded during the 7 matched count dates. The higher numbers of butterflies in both the field boundary (or hedgerow) transects compared to the field interior transects are probably due mainly to the increased shelter and numbers of flowers that the hedgerow provides (Table 4.9). Both pasture and tillage hedgerow transects also had much larger area of nettles (Urtica dioica) than their respective field interior transects and coarse grasses were absent in the tillage interior transect but covered 19% of the tillage hedgerow transect area (Table 4.9). These plants are caterpillar foodplants for butterflies with Peacock, Red Admiral and Small Tortoiseshell using nettles and Ringlet and Speckled Wood
183
utilising coarse grasses. The presence of these foodplants may also further explain the higher butterfly numbers along the hedgerow transects. The differences in butterfly numbers between the tillage hedgerow transect and the tillage field interior transect may be due also to the different crop types. The sugar beet in the hedgerow transect had a far greater number and variety of wildflowers and wild plants than the spring barley in the interior. This increase in wild plants in the sugar beet may be due in part to the proximity of the hedgerow, which would be a source for these plants in the neighbouring crop. Sugar beet is sown and develops later than spring barley, which results in a large amount of bare soil during the early spring and summer that may be colonised by wild plants, especially as this particular farmer did not spray extra herbicides during the year to kill these ‘unsightly’ but relatively harmless plants.
Table 4.9: The mean habitat variable values for the five 50m sections in the two Dysart pasture transects and the two Coursetown tillage transects (The values for numbers of flowers was taken on 12th and 19th August 2002 for Coursetown and Dysart respectively).
Habitat Characteristic % Nettles % Wild Crucifers % Coarse Grasses % Fine Grasses Flower Numbers % Short Veg. %Standing Dead Veg. % Trees % Shrubs % Herbs Sun Shelter
Pasture (Dysart) Field Hedgerow Interior Transect Transect 1.2 15 0 0 32 30 10 10 None High 5 1 1 1.6 0 14 0 23 84 80 Sunny Mainly sunny None Mainly sheltered 184
Tillage (Coursetown) Field Hedgerow Interior Transect Transect 0 6 0.2 0 0 19 11 5 Low High 2.4 5 0 2 0 0 0 24 95 65 Sunny Sunny None
Some shelter
4.4 DISCUSSION 4.4.1 Study 1 I found no significant difference between broadleaf forest, coniferous forest, pasture, set-aside and tillage in terms of butterfly abundance, species richness and diversity as measured by the Shannon-Weiner Index, Simpson’s Index and the Jaccard Similarity Index (Tables 4.3, 4.4 and 4.5). Approximately 60% of the butterflies in broadleaf forest, coniferous forest and tillage were individuals of two species, Speckled Wood and Ringlet (Figure 4.6). These two species also constituted a large proportion of the recorded butterflies in pasture and set-aside but Meadow Brown comprised a larger percentage of these two grassland habitats than in the other three habitats (Figure 4.6). As the habitat of Meadow Brown is open grassland and its foodplants are various grass species, it was not surprising that this species was present in relatively large numbers in set-aside and pasture grasslands (Asher et al., 2001). In total, eleven Silver-washed Fritillaries were recorded with ten of these observed in forest, as this is a woodland specialist species (Figure 4.6). Brimstone was only recorded in forest habitat while Orange Tip was unique to pasture and Large White was only found in set-aside (Figure 4.6). Flight periods of butterfly species vary between species and depend a great deal on the life strategy of the species. Univoltine species have a single abundance peak each year while bivoltine species have two and other species such as Speckled Wood have complex life cycles (Pollard and Yates, 1993). It is also important to realise that times of flights and number of broods depend on weather, altitude and latitude and this means that the exact characteristics of species’ flight periods can vary from year to year and also from locality to locality (Pollard and Yates, 1993; Shackleton et al., 1999). Weather in both the current and previous year may have an 185
important effect on the timing of the appearance of butterfly species (Roy and Sparks, 2000). Speckled Wood generally has three generations a year but the flight periods of each often overlap so they can be difficult to separate precisely (Pollard and Yates, 1993; Asher et al., 2001). Speckled Wood was recorded in Study 1 from early April to late September with numbers peaking in late August and none recorded in early May (Figure 4.7). This corresponds well with the general flight periods for Ireland, with the first starting between late March and early April, and extends to early or late May (Shackleton et al., 1999). The second flight period is from the end of May to the end of June but may extend to early July (Shackleton et al., 1999). The final flight period is from early August to the end of September but may start in late July and extend until the middle of October (Shackleton et al., 1999). Ringlet and Meadow Brown are both single-brooded species and in Study 1 both were recorded from early July to late August with numbers peaking in late July (Figures 4.8 and 4.9). The peak for Ringlet corresponds with Asher et al. (2001) who state that numbers of Ringlet peak in the third week of July. The flight period of Ringlet is from the start of July to the middle of August, and from mid June to the third week in September for Meadow Brown (Shackleton et al., 1999). Green-veined White has two generations per year in Ireland with the first flying from mid April or the start of May to early June and the second brood appearing from the end of July to the end of August but may appear from early July to mid September (Shackleton et al., 1999). In Study 1, Green-veined White was recorded from early April to early September but was absent from late April to early May and again in early July (Figure 4.10). In this study the second brood appeared to
186
be larger than the first as the numbers of Green-veined White peaked in late July (Figure 4.10). Peacock is generally considered to have one generation with the adults emerging from late July onwards (Asher et al., 2001). The adults hibernate over winter and can be seen from February onwards to the following winter with the exception of a period from early June to late July (Shackleton et al., 1999; Asher et al., 2001). Numbers of Peacock generally peak in late April and August and this coincides with the peaks from Study 1, where Peacocks were only recorded from early April to early May and mid August to late September (Figure 4.11) (Asher et al., 2001). The flight periods of the five commonest species matched the general flight periods of these species in Ireland fairly closely. However, it is important to note that variations of the flight periods of the selected species in this study from the general pattern of flight periods in Ireland may be a result of counts being missed due to the poor weather (Shackleton et al., 1999). Speckled Wood was different from the other species, as it did not use the different habitats uniformly throughout the year. Almost all of the Speckled Wood butterflies were recorded in broadleaf or coniferous forest habitats before the end of August, but from late August onwards individuals were recorded in all habitats including the farmland habitats (Figure 4.7). Although according to the classification of Asher et al. (2001), Speckled Wood is a wider countryside species, this species prefers woodland and according to Robertson, Clarke and Warren (1995) it is one of the true woodland butterfly species that breed in Ireland, along with Wood White, Purple Hairstreak, Silver-washed Fritillary and Pearl-bordered Fritillary. Speckled Wood forms sizeable colonies in woodland or along wooded tracks but is found at
187
lower densities in other habitats such as hedgerows (Asher et al., 2001). Therefore, in this study in the early part of the season, during the first two generations, the overall abundance of Speckled Wood may have been relatively low so most of the individuals were utilising the preferred woodland habitat. However, by late August and September the third generation had appeared, swelling the numbers of butterflies, which resulted in more Speckled Woods being recorded in farmland later in the season. The fact that no significant differences were found between the five studied habitats in terms of butterfly species diversity may be mainly due to the low numbers recorded as a result of the poor weather during the 2002 recording season, rather than any real effects. However, another reason may be the lack of habitat specialist species recorded in the study and in common habitats in Ireland in general. Of the fourteen species recorded during the two field seasons in 2001 and 2002, only the Silverwashed Fritillary is classified as a habitat specialist as it breeds in broadleaf woodland, especially oak woodland or woods with sunny rides and glades, although it can use mixed broad-leaved and conifer plantations also (Asher et al., 2001). All of the other species recorded in Study 1 are regarded as wider countryside species (Asher et al., 2001; see Appendix VI). 4.4.2 Study 2 It is obvious from Study 2 that butterfly abundance at the same location can vary hugely between years, even in successive years. The numbers of butterflies recorded were approximately halved from 2001 to 2002 (Table 4.6). Year-to-year variation in butterfly numbers results from mortality, reproductive success and mobility of species, and is influenced by several factors including weather conditions, the number of predators and parasitoids and the availability of foodplants (Van Strien 188
et al., 1997). However, it is the broad pattern of variation in weather from year to year that largely determines the fluctuations of butterflies (Pollard and Yates, 1993). It is clear that the reason for the decrease in butterfly numbers in this study was mainly due to the weather conditions in the summer of 2002. If the weather is poor during a sampling season then the number of times the transects can be walked is reduced which means that it is more likely that lower numbers of butterflies would be recorded. Counts should not occur if temperatures and sunlight levels are too low or if the wind is too strong. However, butterfly diversity did not seem to be as badly affected by adverse weather as numbers of butterflies, with 14 species recorded in the relatively warm and dry summer of 2001 compared to 12 in the wetter and cooler 2002 season (Table 4.6). Previous studies have shown that weather conditions have a major influence on butterfly numbers, as the numbers of many species increase after warm, dry summers (Pollard, 1988; Pollard and Yates, 1993). Pollard (1988) also found a frequent association between wet conditions early in the previous year and increased numbers in the current year, but found no effects of winter temperature or rainfall on butterfly numbers. Weather can affect butterfly populations differently in different regions, for example, Van Strien et al. (1997) found that weather was more important in determining butterfly numbers in the upland and warm oceanic regions of the UK than in the lowland and continental regions. As many butterfly species have flexible life cycles, favourable conditions can lead to large increases in numbers, but likewise bad weather can cause high mortality (Pollard and Yates, 1993). Certain species can produce substantial second and even third generations in warm summers due to a more rapid development of larvae, which also reduces predation risk (Pollard and Yates, 1993). In cool summers only a partial
189
second generation may appear in some species, as some of the individuals may not become adult until the following spring (Van Strien et al., 1997). 4.4.3 Study 3 The occurrence of butterfly species in an area is strongly influenced by the local environment and habitat (Asher et al., 2001; WallisDeVries, 2001). Study 3 supports this point, as certain species were associated with specific vegetation and local habitat characteristics. In 2002, numbers of Green-veined White, Small White and Ringlet were greater in transect sections that had high flower numbers and the presence of wild crucifers, while Large White exhibited a preference for sections with a high cover of coarse grasses (Figure 4.12). In 2001, Green-veined White, Small White and Large White showed a preference for sections containing wild crucifers (Figure 4.13). The three white butterfly species were present in large numbers in sections with wild crucifers, as these are larval foodplants for these species. Silver-washed Fritillary and Brimstone showed a preference for sheltered habitat with a high proportion of tree cover in 2002 (Figure 4.12). In both years, Speckled Wood and Orange Tip preferred sections with a high proportion of tree and shrub cover (Figures 4.12 and 4.13). This is not surprising for Speckled Wood and Silver-washed Fritillary as their preferred habitat is woodland (Robertson, Clarke and Warren, 1995; Asher et al., 2001). In 2002, Meadow Brown showed a preference for sections with a high cover of standing dead vegetation and fine grasses, which can be explained by that fact that its preferred foodplants are grasses with fine leaves (Figure 4.12). In 2001, Ringlet, Meadow Brown, Wall Brown, Peacock, Red Admiral and Silver-washed Fritillary preferred sections with a high proportion of grasses, short and standing dead 190
vegetation (Figure 4.13). These transect sections would probably have contained the foodplants for each one of these species. Peacocks preferred sections with nettles, short vegetation and shrubs in 2002 (Figure 4.12). Thus, we can see that the presence of foodplants in a habitat is a very important determinant of the presence or absence of a butterfly species in a habitat. Each species uses different plant species as their foodplant, with some species like Silver-washed Fritillary only using a very narrow range of foodplants (i.e. it mainly uses Common Dog-violet but may use other violet species), while Speckled Wood and other species use a wide range of plant species, which in this example include False Brome, Cock’s-foot, Yorkshire-fog, Common Couch and various other grasses (Asher et al., 2001). The strategies of the larval foodplants of butterflies have been shown to affect butterfly biology (Dennis et al., 2004). Butterflies whose foodplants were labelled to have competitive or ruderal strategies were more likely to have rapid development, short early stages, multivoltinism, long flight periods, early seasonal emergence, higher mobility and other attributes that helped make them resistant to range contractions as a result of environmental change (Dennis et al., 2004). On the other hand butterfly species whose host plants were labelled as stress-tolerant strategists had reversed tendencies and were vulnerable to current environmental changes and were species of conservation concern (Dennis et al., 2004). The abundance of flowers in a habitat is an important factor for butterflies but a recent study shows certain flower species are preferred over others and that butterflies differ in their range of flower use with some being generalists and others specialists (Tudor et al., 2004). Generalist and specialist flower users are also generalists and specialists in larval foodplant range and habitat occupancy and are typically open habitat and woodland butterflies respectively (Dennis et al., 2004).
191
Green-veined White and Meadow Brown were shown to be flower generalists, while Silver-washed Fritillary and Ringlet specialised on bramble (Rubus fruticosus) and Brimstone on ground ivy (Glechoma hederecea), violet (Viola spp.), red campion (Silene dioica) and bluebell (Hyacinthoides non-scripta) (Dennis et al., 2004). Therefore in Study 3, it may have been useful to have recorded flower species as well as an estimate of flower abundance. 4.4.4 Study 4 Study 4 showed that more butterflies and species were recorded in transects placed along hedgerows than in open fields. Thus, an important consideration in the sampling of butterflies is that the path chosen for the transect will have a significant impact on the numbers and diversity of the butterflies recorded. Hedgerows and field margins usually provide better butterfly habitat than the interior of intensively managed arable or grassland fields due to increased shelter and higher densities of flowers and caterpillar foodplants (Table 4.9). Thus, whether transects are placed along hedgerows or not is an important consideration before a study commences. Similarly if transects are placed in forest interior then butterfly numbers will be lower than if the transects are along rides or forest tracks, as the interior is usually very shaded, which decreases plant and flower abundance in the understorey. Warren (1985) and Greatorex-Davies et al. (1992) showed that butterfly species richness and total numbers decreased with increasing shade in woodland rides. Species that were typical of open grassland habitats were only found in the least shaded rides, while other species occurred at their highest levels in either partially shaded or heavily shaded rides. The most shade-tolerant species was the Speckled Wood, which was recorded at peak abundance at about 60% shade when assessed using vertical
192
hemispherical photography (Warren, 1985). Thus, the level of shade in a habitat is a very important factor in determining the butterfly community of the habitat. 4.4.5 Conclusions The transect-count method has now become the standard method of recording butterflies in many countries. The main advantage of the transect-count method is that data on many species can be collected over a large area in a relatively short period of time and it does not require much labour (Pollard and Yates, 1993). The transectcount method may be more robust than capture-mark-recapture techniques if the main need is to estimate change in population size rather than absolute population size. This is because transect recording requires fewer assumptions about the behaviour of individual butterflies and about the structure of populations; and there is no handling of butterflies, with possible subsequent effects on their behaviour (Pollard and Yates, 1993). Disadvantages of the method include the need for weekly counts from April to September as many butterflies have short flight periods and also the need for good weather conditions for recording. However, as has been shown, the main problem associated with recording butterflies in Ireland is the poor weather. The strict conditions of the transect-count method may be a hindrance in this country due to our wet and cool climate. The method was developed in southeast England where the weather during the summer months is warmer and drier. The transect method has also been used successfully in other countries in continental Europe, such as The Netherlands, Spain and Switzerland (Van Swaay, 2003). These countries also have hotter and drier summers than Ireland. These reasons may explain why no other published study exists, to my knowledge, of butterfly abundance and diversity in Irish habitats as it is just too frustrating and unpredictable to try to quantitatively sample butterfly populations from April to 193
September in a given year or years. Other methods of sampling butterflies may be more appropriate for use in this country, such as capture-mark-recapture methods, methods using a Malaise trap or methods using an attractant (Owen, 1975). However, there are disadvantages to these methods also (Owen, 1975). In conclusion, no significant differences were found in terms of species diversity and abundance of butterflies between broadleaf forest, coniferous forest, pasture, set-aside and tillage habitats in lowland Ireland. However, this may not be a real effect but may be a result of the low numbers recorded due to the cool, wet weather conditions during the sampling season. Butterfly numbers can also fluctuate greatly from year to year depending on weather conditions. Different species showed preference for different aspects of habitat and vegetation along transects, with the presence of species specific foodplants often important. The numbers and species richness of butterflies increased in the same farmland site if transects were placed along hedgerows rather than in the middle of fields.
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Chapter 5: The Diversity of Birds and Butterflies in Relation to Landscape Structure. 5.1 INTRODUCTION 5.1.1 Landscape Ecology The landscape which organisms, including birds and butterflies, inhabit is of utmost importance in determining the biodiversity of these organisms. However, the term ‘landscape’ has no standard definition in ecology and several authors have proposed different definitions. Most definitions include an area of land containing a mosaic of patches or landscape elements (McGarigal et al., 2002). Forman and Godron (1986) defined landscape as “a heterogeneous land area composed of a cluster of interacting ecosystems that is repeated in similar form throughout”. Landscape could also be defined, from a wildlife perspective, as an area of land containing a mosaic of habitat patches, within which a ‘focal’ habitat patch is often contained (Dunning et al., 1992). Different organisms scale the environment in different ways and therefore the spatial scale of the landscape which needs to be considered varies between taxa (Wiens, 1976; McGarigal et al., 2002). Thus, there is no absolute size for a landscape as it depends on the organism being studied and what constitutes a mosaic of habitat or resource patches meaningful to that particular organism. For example, a buzzard may view the landscape as being tens of kilometres in diameter incorporating several forests within a matrix of farmland, while a woodlouse may view its landscape as being a small patch of forest containing dead wood over a scale of tens of metres. Forman and Godron (1986) set a minimum area on a landscape of a few kilometres in
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diameter, while recognising that most of the principles of landscape ecology apply to ecological mosaics at any level of scale. Landscape ecology, by definition, is the ecology of landscapes, with ecology generally being defined as the study of the interactions among organisms and their environment (Forman, 1995). One of the founding principles of landscape ecology is that environmental patterns strongly influence ecological processes (Turner, 1989). Landscape ecology involves studies of both the principles concerning structure, function and change (the three characteristics of landscapes), and their application in the formulation and solving of problems (Forman and Godron, 1986). The structure aspect of the landscape is the spatial relationships among the distinctive ecosystems present and deals with the distribution of energy, materials and species in relation to the sizes, shapes, numbers, types and configurations of the ecosystems. The function characteristic is the interactions among the spatial elements and is therefore, concerned with the flows of energy, materials and species among the component ecosystems. Finally, change is the alteration in the structure and function of the ecological mosaic over time. Every landscape is composed of spatial elements, which are the components that comprise a landscape. The patch-corridor-matrix model proposes that each landscape is made up of patches, corridors and a matrix (Forman, 1995). Landscapes are composed of a mosaic of patches (Urban et al., 1987). Forman (1995) defines a patch as ‘a relatively homogeneous non-linear area that differs from its surroundings’. As with most aspects of landscape ecology, scale is the most important factor when defining a patch, as a landscape does not contain a single patch mosaic, but contains a hierarchy of patch mosaics across a range of scales (McGarigal et al., 2002).
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Corridors differ from patches, as they are linear landscape elements (Forman, 1995). They are “narrow strips of land which differ from the matrix on either side. Corridors may be isolated strips, but are usually attached to a patch of somewhat similar vegetation” (Forman and Godron, 1986). Several distinct types of corridor exist and are defined on the basis of structure or function (McGarigal et al., 2002). Corridors may function as habitat, dispersal conduits or barriers (Forman, 1995). The matrix is the most extensive and most connected landscape element type, and therefore plays the dominant role in the functioning of the landscape (Forman and Godron, 1986). The matrix is the background ecosystem or land-use type in a mosaic, and is characterised by extensive cover, high connectivity and/or major controls over dynamics (Forman, 1995). For example, in a large contiguous area of mature forest embedded with numerous small clearfell patches, the mature forest constitutes the matrix element type because it is greatest in a real extent, is mostly connected, and exerts a dominant influence on the area’s flora and fauna and ecological processes (McGarigal et al., 2002). Man has disrupted the structural integrity of landscapes, through activities such as urban development and clearfelling of forests. These may impede or facilitate ecological flows (such as movement of organisms) across the landscape (Forman and Godron, 1986). Such disruptions in landscape patterns can have a detrimental effect on the landscape’s functional integrity by interfering with ecological processes that are critical for population persistence and the maintenance of biodiversity and ecosystem health (McGarigal et al., 2002). Many landscape ecologists have spent considerable effort developing methods to quantify landscape patterns, which are required in order to be able to study pattern-process relationships (e.g. Turner and Gardner, 1991; McGarigal and Marks, 1995). The result of this concerted effort has
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been the development of hundreds of indices of landscape patterns, which has also been aided by recent advances in computer processing and geographic information system (GIS) technologies. However, one drawback to the development of all these indices is that just because it is possible to map and measure certain patterns, does not necessarily mean that they are ecologically relevant to the phenomenon under investigation (Gustafson, 1998 cited in McGarigal et al., 2002). 5.1.2 Land-use Intensity Gradient In this study the effects of landscape on breeding and wintering bird and butterfly abundance, diversity and community composition were examined. This was done by studying the bird and butterfly communities along a land-use intensity gradient in lowland farmland/forest mosaics in central Ireland. This approach to understanding implications of landscape changes for biological communities across gradients of landscape disturbance has been used in several landscape ecological studies (e.g. Matson, 1990; McDonnell and Pickett, 1990; Blair, 1996 and 1999; O’Connell et al., 2000; Coppedge et al., 2001; Ribera et al., 2001). Gradients can provide insights into the generality of responses of communities to human-induced landscape changes (Chamberlain et al., 2003; Van Swaay, 2003). The land-use gradient in this study progressed from old-growth forest through coniferous plantations to mixed agriculture and forest sites, to pasture and intensive tillage sites. Thus, the intensity of land-use in the landscapes increased steadily as the gradient progressed from the mature broadleaf forest landscape to arable farmland landscape. 5.1.3 Remote Sensing Remote sensing was chosen as the method for examining the landscapes in this study. In its broadest sense, remote sensing means the acquisition of information
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about an entity without being in physical contact with it, and this includes photography, videography, satellite imagery and other imaging systems (Johnston, 1998). All remote sensing involves the detection of electromagnetic energy (Johnston, 1998; Lillesand and Kiefer, 2000). Several countries have launched satellites for image acquisition and these include the American Landsat satellites and the French SPOT satellites (Johnston, 1998; Lillesand and Kiefer, 2000). These satellites house scanners that build a two-dimensional image from scan lines using detectors that produce electrical signals proportional to the energy received from Earth surfaces (Johnston, 1998). Satellite imagery is very useful for studies of landscape ecology as a single image covers an extensive area and this greatly reduces the time required to process images compared to aerial photographs, for example. Satellite images are also already in digital form and there is frequent recurrence of coverage (Johnston, 1998). Analysis of satellite images can also provide quantitative information about ecological properties, such as the normalised difference vegetation index (NDVI), that cannot be easily extracted from aerial photography or field studies (Johnston, 1998). 5.1.4 Types of Landscape Indices As different components of the landscape are important for bird and butterfly diversity, different groups of indices have been developed to try to quantify the variation in the landscape. These indices are calculated from various landscape characteristic parameters including number, extent, size and perimeter of patches. The following descriptions of landscape indices are based heavily on McGarigal et al. (2002), who provide an excellent overview of these metrics. In landscape ecology, there are generally three levels at which landscape indices (or landscape metrics) are defined:
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•
Patch metrics are defined for individual patches and characterise the spatial character of and context of patches.
•
Class metrics are integrated over all patches of a given class (or habitat type). For example, the metrics may be calculated for all the forest patches in the landscape only. Therefore, if there were 5 different habitat types in the landscape the entire set of metrics would be calculated 5 times to produce class metrics for each of the habitat types or classes separately.
•
Landscape metrics are integrated over all patch types or classes over the full extent of the data (i.e. the entire landscape). Thus, the landscape as a whole is used to calculate one landscape metrics dataset (McGarigal et al., 2002).
Landscape metrics are, therefore, mathematical formulae that quantify specific spatial characteristics of patches, classes of patches or entire landscapes (McGarigal et al., 2002). Composition metrics and spatial configuration metrics are the two categories of metrics. Landscape composition refers to the relative amounts of each habitat type contained within the landscape (Dunning et al., 1992). Thus composition metrics measure the presence, absence or relative proportions of landscape components without reference to spatial character, placement or location of patches within the landscape (Dunning et al., 1992; McGarigal et al., 2002). Composition metrics are only applicable at the landscape level, as composition requires integration over all patch types. Examples of composition metrics are the proportional abundance of each class, landscape dominance, relative richness, landscape evenness and landscape diversity (Dunning et al., 1992). Spatial configuration deals with the physical layout of elements within the landscape (Dunning et al., 1992). The spatial configuration metrics take the
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arrangement, position, or orientation of patches in the class or landscape into account in their calculation. This group of metrics also includes metrics such as shape and core area which describe the spatial character of the patches. Configuration metrics are more numerous than composition metrics because of the difficulty in quantifying spatial configuration. Metrics of patch isolation or patch contagion are measures of the placement of patch types relative to other patches, patch types, or other features of interest. Many of the configuration metrics are calculated at the patch scale, so at the class and landscape levels these metrics simply quantify some aspect of the statistical distribution (e.g. mean, max, variance) of the corresponding patch metric (e.g. size, shape) (McGarigal et al., 2002). Some of the main measures of spatial configuration are: patch size distribution and density; patch shape complexity; core area; isolation/proximity; contrast; dispersion; contagion and interspersion; subdivision; and connectivity. 5.1.4.1 Area/Density/Edge Metrics This group of metrics focuses on the number and size of patches and the amount of edge created by these patches. One of the simplest and most important metrics is patch area or size, which has been shown in several studies to be strongly correlated with bird species richness and the occurrence and abundance of some species (e.g. Robbins et al., 1989). As most species require a minimum area in order to successfully complete their life cycles, the size and number of patches that comprise a habitat type or the landscape as a whole are of utmost importance. Habitat fragmentation influences many aspects of ecology, including individual behaviour of organisms, habitat use patterns, and intra- and inter-specific interactions, by reducing the area of habitat and by increasing the amount of habitat influenced by edge (McGarigal et al., 2002). If 201
habitat loss and fragmentation continues unchecked eventually there will not be enough habitat available of a large enough size or quality to support individuals and viable populations (Forman, 1995). The total amount of edge in a landscape is an important measure in ecology, as much theoretical and empirical literature on the subject of edges exist, particularly studies of wildlife-edge relationships (e.g. Strelke and Dickson, 1980; Morgan and Gates, 1982). The proportion of a habitat patch that is influenced by edge effects is dependent upon patch shape and orientation, and by adjacent land cover (McGarigal et al., 2002). If for example, a patch is large but has a convoluted shape, the patch could be entirely edge habitat. The effects of edge habitat on organisms is not universal as some species have adapted to this type of habitat and thrive in it, while interior species are usually adversely affected by edge habitat (Forman and Godron, 1986; Forman, 1995). This helps explain why forest interior species have declined in recent decades as habitat fragmentation has increased. These species may be sensitive to patch shape as if patch area is kept constant, increasing the shape complexity of the patch will also increase the edge-to-interior ratio. Thus, total amount of edge for a habitat type (or class) in a landscape is so important to the study of fragmentation, and many of the indices reflect this measure either directly or indirectly (McGarigal et al., 2002). Also, the degree of spatial heterogeneity in a landscape is directly related to the total amount of edge in a landscape (McGarigal et al., 2002). 5.1.4.2 Shape Metrics One of the main reasons why patch shape is so significant seems to be its interaction with patch size which can influence several ecological processes such as the dispersal and foraging of organisms, small mammal migration and woody plant colonization (Forman and Godron, 1986; Hardt and Forman, 1989; Forman, 1995; 202
McGarigal et al., 2002). However, patch shape is of primary importance in landscape ecology due to its relationship with the ‘edge effect’ (Forman and Godron, 1986; Forman, 1995; McGarigal et al., 2002). 5.1.4.3 Core Area Metrics Core area is defined by McGarigal et al. (2002) as ‘the area within a patch beyond some specified depth-of-edge influence (i.e. edge distance) or buffer width’. The main importance of core area is also related to the ‘edge effect.’ Patches are affected by edge as the habitat in the interior of the patch may vary considerably from that along the edges of the patch due to biotic and abiotic factors combining to alter environmental conditions (McGarigal et al., 2002). As stated previously the effect of edges is not universal, with the effects on different species and ecological processes varying, for example, some bird species are adversely affected by predation, competition, brood parasitism, and other factors along forest edges (Hansen and di Castri, 1992; McGarigal et al., 2002). Thus, some studies of forest interior specialists have found that core area was a far superior predictor of habitat quality than patch area (Temple, 1986 cited in McGarigal et al., 2002). Core area is affected by patch shape; therefore a certain patch may be large enough to support a given species but if it is complexly shaped it may not contain enough suitable core area (McGarigal et al., 2002). 5.1.4.4 Isolation/Proximity Indices Isolation is concerned with the spatial and temporal context of habitat patches and ignores their individual spatial characteristics (McGarigal et al., 2002). Patch isolation is an extremely important concept in ecology and is a vital factor of island biogeographic theory (MacArthur and Wilson, 1967) and metapopulation theory
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(Hanski and Gilpin, 1997; Hanski, 1999). The role of patch isolation is central to metapopulation theory which has been used in conservation efforts for endangered species (e.g. Lamberson et al., 1992; McKelvey et al., 1992). In fragmented habitats, isolation has been proposed as the main reason why these fragments often contain fewer bird species than contiguous habitats (Moore and Hooper, 1975; Forman et al., 1976; Helliwell, 1976; Dickman, 1987). There are several ways in which habitat patches can become functionally isolated. Firstly, movement in to and out of the patch may be impeded or prevented causing isolation, if the patch edge acts as a filter or barrier (Wiens et al., 1985). Secondly, the distance between similar habitat patches can cause isolation if the distance involved impedes or prevents individuals moving among these habitat patches, however it must be acknowledged that different species scale the environment in different ways, which influences their movement rates. For example, a 100m wide tillage field may form an impenetrable barrier for invertebrates but the movement of birds may be completely unaffected (McGarigal et al., 2002). Finally, the composition and structure of the matrix may determine how easy organisms can move through the landscape. The permeability of the matrix largely depends on which landscape model is used: an island biogeographic perspective or a landscape mosaic perspective (McGarigal et al., 2002). From an island biogeographic perspective, habitat patches are presumed to be surrounded by a matrix that is a hostile environment that contains no meaningful structure and prevents both survival and dispersal (MacArthur and Wilson, 1967). In this model, isolation is influenced mainly by the distance between favourable habitat patches (McGarigal et al., 2002). However, from a landscape mosaic perspective, the matrix that surrounds the focal habitat patches may not be a completely hostile environment and may contain other
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habitat patches that are either more or less similar to the focal habitat (McGarigal et al., 2002). Thus, in the landscape mosaic model, connectivity is assessed by the extent to which movement is facilitated or impeded through different habitat types across the landscape (McGarigal et al., 2002). The result of habitat patch isolation in this model is that fewer individual movements among habitat patches are made. Spatial isolation can be measured in several different ways, with the simplest measures being based on Euclidean distance between nearest neighbours (McGarigal et al., 2002) or the cumulative area of neighbouring habitat patches (weighted by nearest neighbour distance) within some ecological neighbourhood (Gustafson and Parker, 1992 cited in McGarigal et al., 2002). These measures adopt an island biogeographic perspective, as the role of the matrix is ignored and the context of a patch is defined by the proximity and area of neighbouring habitat patches (McGarigal et al., 2002). 5.1.4.5 Contagion/Interspersion Metrics The meanings of the terms contagion and interspersion are not intuitive in the context of landscape ecology and therefore need to be defined. Contagion refers to the tendency of patch types to be grouped together spatially in large aggregations, while interspersion refers to the intermixing of patches of different types and is based solely on patch adjacencies (McGarigal et al., 2002). Contagion also reflects the intermixing of patch types, but unlike interspersion it deals with the spatial distribution of patch types as well (McGarigal et al., 2002). Both contagion and interspersion are aspects of landscape texture that reflect the adjacency of patch types. Contagion and subdivision are similar concepts but there are subtle differences. Contagion measures the extent to which cells of similar class are aggregated and reflects the overall clumpiness of the landscape without specifically 205
referring to the patches (McGarigal et al., 2002). However, subdivision, in contrast, does refer explicitly to the degree to which patch types are subdivided into separate patches or fragments, but does not refer to the shape, relative location, or spatial arrangement of those patches (McGarigal et al., 2002). Therefore, contagion reflects the ‘compactness’ of patches, while subdivision also deals with the aggregation of patch types and it deals with the number and size of the actual patches as well. Habitat fragmentation is one of the processes crucial in shaping the pattern of the landscape and involves the disaggregation and subdivision of contiguous habitat into disaggregated and/or disjunct patches (McGarigal et al., 2002). As the habitat becomes more fragmented, habitat contagion decreases and habitat subdivision increases and, if this process continues, then ecological function will be impaired (Saunders et al., 1991). Thus fragmentation can cause populations to become subdivided and isolated which can cause a reduction in the ability of organisms to disperse and colonise new patches successfully. This can in turn lead to a decline in the persistence of individual populations and an increased likelihood of regional extinction for entire populations in the landscape (e.g. Lande, 1987; With and King, 1999). However, disturbances, such as disease and fire, are more likely to occur in a landscape where a habitat type is highly aggregated and/or contiguous than one that is highly disaggregated and/or subdivided (McGarigal et al., 2002). But some disturbance types, such as windthrow, may occur at higher rates in highly disaggregated and/or subdivided habitat types than in more aggregated and/or contiguous distributions.
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5.1.4.6 Connectivity Metrics Landscape connectivity is the degree to which the landscape facilitates or impedes movement among resource patches (Dunning et al., 1993). Habitat loss and fragmentation can lead to a large decrease in connectivity in a landscape, which may in turn affect patch occupancy and other aspects of metapopulation dynamics (McGarigal et al., 2002). Connectivity is important from the point of view of landscape structure but it is a difficult concept to define and quantify (Taylor et al., 1993; Forman, 1995). Two types of connectivity exist: “structural connectedness” of habitat types and the “functional connectedness” of the landscape (McGarigal et al., 2002). Structural connectedness refers to the physical continuity of a habitat type across the landscape, whereas functional connectedness between patches depends on the organism or the ecological process of interest (e.g. patches that are connected for bird dispersal might not be connected for newts). Measures of habitat extent, subdivision, and contagion can be used to assess structural connectedness. Functional connectedness, in contrast to structural connectedness, relates to the interaction of ecological flows with landscape pattern, and is thus difficult to measure but various indices can be derived based on the pairwise connections between patches (McGarigal et al., 2002). 5.1.4.7 Diversity Metrics Diversity indices have been used extensively for many decades in ecology, especially as measures of species diversity. Many diversity indices have been devised and, as with biodiversity, the most important diversity metrics in landscape ecology are influenced by two components: richness and evenness. In landscape ecology, patch type replaces species in the calculations of diversity indices. Richness, therefore, refers to the number of patch types present in the landscape, while evenness 207
refers to the distribution of area among the different patch types. Some indices such as Shannon’s diversity index are more sensitive to richness than evenness and therefore rare patch types influence the index disproportionately. On the other hand, Simpson’s diversity index and other indices place more weight on the common patch types, as they are relatively less sensitive to richness (McGarigal et al., 2002). These diversity indices have been used to measure landscape composition by landscape ecologists (e.g. Romme, 1982). 5.1.5 Aims It is important to place the diversity of birds and butterflies into the landscape context, and not just concentrate on their diversity within specific habitats, as has been the case in previous chapters (see Chapters 2, 3 and 4). It has been shown in the landscape ecological literature that the numbers and types of habitat patches are important for organisms, including birds and butterflies (e.g. Andrén, 1994; Opdam et al., 1994; Hanski and Gilpin, 1997). The aim of this part of the study is to assess the effects of different habitat types in a landscape on the diversity of birds and butterflies, through the study of a land-use gradient. Remote sensing using satellite imagery was the chosen method of assessing the structure of the landscapes in this study, in conjunction with landscape indices. It is also anticipated that this study will assess whether the spatial arrangement and complexity of habitat patches and the extent of individual habitat types are associated with particular aspects of bird and butterfly communities.
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5.2 METHODS 5.2.1 Study Sites The study sites were comprised of six 1-km squares or land-use units (LUUs) which represented a land-use intensity gradient ranging from a site dominated by mature broadleaf forest to a site dominated by intensive arable agriculture. The LUUs were sampled for breeding bird and butterfly diversity in 2002 and birds during winter 2002/2003. The six LUUs were located in Co. Laois and Co. Kildare and consisted of: LUU1 (Old-growth forest), LUU2 (Managed forest), LUU3 (Mixed-use landscape dominated by forest or woodland), LUU4 (Mixed-use landscape not dominated by a single land-use), LUU5 (Mixed-use landscape dominated by pasture) and LUU6 (Mixed-use landscape dominated by arable crops). For details of these LUUs see Study 2 of Chapter 4, section 4.2.3. 5.2.2 Breeding Bird Counts Point counts were again the method used to sample birds during the breeding season in the LUUs. Within each 1-km square, sampling of birds was carried out on a regular 200m grid of 16 sample points. Hence, 12
200 m
1
2
3
4
points were located 200m from the edge of the square
5
6
7
8
and 4 points were 400m from the edge of the square.
9
10
11
12
During each visit, point counts were undertaken at
13
14
15
16
each of the 16 sample points. Four visits to each sample point were conducted between the start of April
and the end of June. All 16 points were counted on the same morning. The route taken between the points was reversed on alternate visits so that particular points were not always counted at one time of the morning.
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5.2.3 Winter Bird Counts The methods for the winter bird survey were the same as in Chapter 2, except that one transect of 2 km length was walked for each land use unit (LUU). These transects were divided over the main habitats of each LUU. The LUUs were surveyed four times between November 2002 and December 2003. Each transect was divided into forty 50m sections, with the length of the sections being roughly proportional to the area of that habitat in the square. 5.2.4 Butterfly Sampling The butterfly sampling involved walking 1-km transects in each LUU. The exact methodology was the same as in Chapter 4 and is described in detail in section 4.2.1. 5.2.5 Remote Sensing Methodology German colleagues at the Department of Remote Sensing and Landscape Information System (FeLis) at the University of Freiburg guided me with the remote sensing aspect of this project and more details on the technical aspects are given in Ivits et al. (2003). The satellite images used in this study were IRS and Landsat TM images that were subsequently fused. The IRS image gave information from the panchromatic spectrum with a ground resolution of 5m. The Landsat TM images included multispectral information with 30m ground resolution. The IRS image used was taken on 7th May 2000 and the Landsat TM images were from 2nd July 2001. The first stage of the procedure involved data pre-processing and the position accuracy step. This process required knowledge of the local area. Ground control points were collected from digital maps of the LUUs and the surrounding landscape 210
and matched with the corresponding point on the satellite images. The satellite images were then orthorectified using the ground control points and the DEM (digital elevation model) for the area, which contains the area’s topographic information. Orthorectification is the process by which the satellite images are matched up with digital maps and the DEM to remove the geometric errors and to bring the image into a real world coordinate system. The images were then fused by the Adaptive Image Fusion Method (Steinocher, 1999). Image fusion involves combining IRS and Landsat images. The colour of the fused images results from the Landsat image, while the good geometric resolution is provided by the IRS image. The band combination of the fused image which was used was 4 3 2, where the Landsat-IRS channel 4 (near infrared 0.78 – 0.9µm) was displayed in the red, channel 3 (red 0.6 – 0.69µm) in the green, and channel 2 (green 0.52 – 0.6µm) in the blue colour gun of the screen. With this combination water appears black-blue on the fused image; vegetation with high levels of chlorophyll appears red and appears red-brown with less chlorophyll; and soil appears white-blue. The fused satellite images were then visually interpreted. Visual interpretation involved drawing polygons around each patch of different colour and/or texture in the fused image within the 1 km buffer around each 1 km square LUU. The interpretation scale was 1:15,000 and the delineation was done on screen using the software ArcView GIS 3.3 (ESRI, 2002). The minimum size of an area that was interpreted as a polygonal (patch) object on the fused images was 0.5 ha. The minimum width of gaps to be delineated was 30m. This means that objects with a minimum width of less than 30m (important for roads or hedges) or an area smaller than 0.5 ha were not mapped as a patch object.
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An interpretation key was produced, which included the description of the appearance of all different classes (land cover types) that were interpreted in the image. The description and definition of all occurring land cover types was done by examples and by words: colour, saturation, texture, size, shape and/or relative position to other objects. An interpretation key helps the interpreter to evaluate the information presented on satellite images in an organised and consistent manner (Lillesand and Kiefer, 2000). Field visits were necessary for the definition and description of a small number of the patch objects, which were interpreted in the images. All land use types were defined before the beginning of the interpretation. A classification scheme with two levels was drawn up and each polygon was assigned to both a level 1 class and a level 2 class. The classification schemes are presented in Table 5.1 along with the classes that were present in the six LUUs and their 1-km buffers.
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Table 5.1: Level 1 and Level 2 classification schemes. (* = class present in this study) Level 1 Artificial surface
Open spaces with little or no vegetation* Arable land* Grassland* Agro-Forestry area
Shrub land and Heath land*
Forest*
Wetland*
Water bodies*
Level 2 Cities Roads Human construction Open soil* Rocks Gravel* Rocks and open soils Arable land* Agricultural* Natural Grassland with scattered trees Arable land with scattered trees Shrubland Shrub land with scattered trees* Heath land Heath land with scattered trees Broadleaf closed* Broadleaf open Broadleaf very open* Coniferous closed* Coniferous open Coniferous very open* Coniferous clearcut* Coniferous storm Mixed closed* Mixed open* Mixed very open* Mixed storm Marshland Swamps Peat bogs* Mire Moor Lake* River
The second level forest classes had precise definitions with coniferous and broadleaf forests containing over 70% of their corresponding tree species. Mixed forest contained both coniferous and broadleaf species with both representing over
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30% of the stand area. A closed stand had over 60% crown cover, with 30-60% for an open stand and less than 30% for a very open stand. The normalised difference vegetation index (NDVI) was calculated from the Landsat images for the six LUUs. NDVI gives the amount of vigorous vegetation in the area and is calculated by dividing the difference of the near infrared and red channel with the sum of the near infrared and red channel. Mean and standard deviation of the NDVI values over the LUUs and the LUUs with 1-km buffer were calculated. Finally, a suite of landscape indices was calculated from the completed visually interpreted images using FRAGSTATS version 3.3 software on patch, class and landscape levels (McGarigal et al., 2002). Landscape level metrics use the entire landscape as a computational basis. In this study, two landscapes are defined with the first being the 1-km square or LUU (referred to as LUU in the Results section) and the other being the LUU and the 1-km buffer around it. Class level metrics correspond to the number of classes interpreted (or classified) within the landscape in question, i.e. inside the LUU or LUU and buffer. Thus, patch level metrics are based on the number of patches within one class. Metrics were calculated on all the three levels: landscape, class and patch, but in the further analysis only landscape level metrics and class level metrics were used. The metrics which proved to be important in the current study are described in detail below in Table 5.2 but all the metrics calculated are described in detail with the corresponding formulae in the FRAGSTATS metrics documentation (McGarigal et al., 2002). Note that while some of these metrics are calculated at the class or landscape scales, some are patch scale metrics that can be used to calculate class and landscape distribution statistics (Table 5.3).
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Table 5.2: The most important landscape metrics with definitions as per McGarigal et al. (2002). Metric
CA PLAND NP LPI TE ED
LSI
nLSI
GYRATE
AREA PARA SHAPE
FRAC
CIRCLE
Definition Area/ Density/ Edge Metrics Class Area equals total class area or the sum of the areas (m2) of all patches of the corresponding patch type, divided by 10,000 (to convert to hectares) Percentage of Landscape equals the sum of the areas (m2) of all patches of the corresponding patch type, divided by total landscape area (m2), multiplied by 100. Number of Patches equals the number of patches of the corresponding class (or patch type). Largest Patch Index equals the area (m2) of the largest patch of the corresponding patch type divided by total landscape area (m2), multiplied by 100. Total Edge equals the sum of the lengths (m) of all edge segments involving the corresponding patch type. Edge Density equals the sum of the lengths (m) of all edge segments involving the corresponding patch type, divided by the total landscape area (m2), multiplied by 10,000 (to convert to hectares). Landscape Shape Index equals the total length of edge (or perimeter) involving the corresponding class, divided by the minimum length of class edge (or perimeter) possible for a maximally aggregated class, which is achieved when the class is maximally clumped into a single, compact patch. LSI = 1 when the landscape consists of a single square or maximally compact patch of the corresponding type; LSI increases without limit as the patch type becomes more disaggregated. Normalized Landscape Shape Index equals the total length of edge (or perimeter) involving the corresponding class minus the minimum length of class edge (or perimeter) possible for a maximally aggregated class, which is achieved when the class is maximally clumped into a single, compact patch, divided by the maximum minus the minimum length of class edge. The difference between NLSI and LSI is that nLSI rescales LSI to the minimum and maximum values possible for any class area. Radius of Gyration equals the mean distance (m) between each cell in the patch and the patch centroid. GYRATE = 0 when the patch consists of a single cell and increases without limit as the patch increases in extent, until it reaches it’s maximum when the patch comprises the entire landscape. Area equals the area (m2) of the patch, divided by 10,000 (to convert to hectares). Shape Metrics Perimeter-Area Ratio equals the ratio of the patch perimeter (m) to area (m2). Shape equals patch perimeter divided by the minimum perimeter possible for a maximally compact patch of the corresponding patch area. SHAPE = 1 when the patch is square or almost square and increases without limit as patch shape becomes more irregular. The Fractal Dimension Index equals twice the logarithm of patch perimeter (m) divided by the logarithm of patch area (m2). FRAC approaches 1 for shapes with very simple perimeters such as squares, and approaches 2 for shapes with highly convoluted, planefilling perimeters. Related Circumscribing Circle equals 1 minus patch area (m2) divided by the area (m2) of the smallest circumscribing circle. CIRCLE = 0 for circular patches and approaches 1 for elongated, linear patches one cell wide.
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CONTIG
CORE
TCA CPLAND NDCA
DCAD
CAI
PROX
ENN
AI
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DIVISION
SPLIT
Contiguity Index equals the average contiguity value for the cells in a patch (i.e. sum of the cell values divided by the total number of pixels in the patch) minus 1, divided by the sum of the template values minus 1. CONTIG ranges from a value of 0 for a one-pixel patch to a limit of 1, for maximum patch contiguity or connectedness. Core Area Metrics Core Area equals the area (m2) within the patch that is further than the specified depthof-edge distance from the patch perimeter, divided by 10,000 (to convert to hectares). The depth-of-edge distances used for the core area metrics were 100m for 0-100m breeding season birds, winter birds and butterflies analyses and 50m for the 0-50m breeding season birds analyses. Total Core Area equals the sum of the core areas of each patch (m2) of the corresponding patch type, divided by 10,000 (to convert to hectares). Core Area Percentage of Landscape equals the sum of the core areas of each patch (m2) of the corresponding patch type, divided by total landscape area (m2), multiplied by 100. Number of Disjunct Core Areas is the sum of the number of disjunct core areas contained within each patch in the landscape; that is, the number of disjunct core areas contained within the landscape. Disjunct Core Area Density equals the sum of number of disjunct core areas contained within each patch of the corresponding patch type, divided by total landscape area (m2), multiplied by 10,000 and 100 (to convert to 100 hectares). Core Area Index equals the patch core area (m2) divided by total patch area (m2), multiplied by 100. CAI equals the percentage of a patch that is core area. Isolation/ Proximity Metrics Proximity Index equals the sum of patch area (m2) divided by the nearest edge-to-edge distance squared (m2) between the patch and the focal patch of all patches of the corresponding patch type whose edges are within a specified distance (m) of the focal patch. In this case, the proximity index was calculated in a 500m searching radius. Euclidean Nearest Neighbour Distance equals the distance (m) to the nearest neighbouring patch of the same type, based on shortest edge-to-edge distance. Contagion/ Interspersion Metrics Aggregation Index equals the number of like adjacencies involving the corresponding class, divided by the maximum possible number of like adjacencies involving the corresponding class, which is achieved when the class is maximally clumped into a single, compact patch; multiplied by 100 (to convert to a percentage). Clumpiness Index equals the proportional deviation of the proportion of like adjacencies involving the corresponding class from that expected under a spatially random distribution. CLUMPY = – 1 when the focal patch type is maximally disaggregated; CLUMPY = 0 when the focal patch type is distributed randomly, and approaches 1 when the patch type is maximally aggregated. Landscape Division Index equals 1 minus the sum of patch area (m2) divided by total landscape area (m2), quantity squared, summed across all patches of the corresponding patch type. DIVISION = 0 when the landscape consists of single patch and approaches 1 as the proportion of the landscape comprised of the focal patch type decreases and as those patches decrease in size. Splitting Index equals the total landscape area (m2) squared divided by the sum of patch area (m2) squared, summed across all patches of the corresponding patch type. SPLIT = 1 when the landscape consists of single patch and increases as the focal patch type decreases in area and becomes split into smaller patches.
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IJI
MESH
COHESION
Interspersion and Juxtaposition Index equals minus the sum of the length (m) of each unique edge type involving the corresponding patch type divided by the total length (m) of edge (m) involving the same type, multiplied by the logarithm of the same quantity, summed over each unique edge type; divided by the logarithm of the number of patch types minus 1; multiplied by 100 (to convert to a percentage). IJI approaches 0 when the corresponding patch type is adjacent to only one other patch type and the number of patch types increases. IJI = 100 when the corresponding patch type is equally adjacent to all other patch types (i.e. maximally interspersed and juxtaposed to other patch types). Effective Mesh Size equals the sum of patch area squared, summed across all patches of the corresponding patch type, divided by the total landscape area (m2), divided by 10,000 (to convert to hectares). The value of MESH is lowest when the corresponding patch type consists of a single one pixel patch, and is at its maximum when the landscape consists of a single patch. Connectivity Metrics Patch Cohesion Index equals 1 minus the sum of patch perimeter divided by the sum of patch perimeter times the square root of patch area for all patches in the landscape, divided by 1 minus 1 over the square root of the total number of cells in the landscape, multiplied by 100 to convert to a percentage. (Just to note that the connectivity index was defined in a 25m distance.)
One way to quantify the configuration of patches at the class and landscape scales is to summarise the aggregate distribution of the patch metrics for all patches in the class or landscape respectively (McGarigal et al., 2002). The FRAGSTATS program computes six different class and landscape distribution statistics that are shown below in Table 5.3 (McGarigal et al., 2002). Note that these statistics are computed in slightly different ways at the class and landscape scales.
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Table 5.3: The class and landscape distribution statistics, with definitions as per McGarigal et al. (2002). Statistic MN (Mean)
AM (Area-Weighted Mean)
MD (Median)
RA (Range)
SD (Standard Deviation)
CV (Coefficient of Variation)
Definition The sum, across all patches in the class or landscape, of the corresponding patch metric values, divided by the total number of patches in the class or landscape. The sum, across all patches in the class or landscape, of the corresponding patch metric value multiplied by the proportional abundance of the patch (i.e. patch area (m2) divided by the sum of patch areas). The value of the corresponding patch metric for the patch representing the midpoint of the rank order distribution of patch metric values based on all patches in the corresponding class or the landscape as a whole. The value of the corresponding patch metric for the largest observed value minus the smallest observed value (i.e. the difference between the maximum and minimum observed values) for all patches in the class or landscape. The square root of the sum of the squared deviations of each patch metric value from the mean metric value computed for all patches in the class or landscape, divided by the total number of patches in the corresponding class or the whole landscape. The standard deviation divided by the mean, multiplied by 100 to convert to a percentage, for the corresponding patch metric.
5.2.6 Data Processing 5.2.6.1 Breeding Bird Datasets The values used for each LUU were the sums over the 16 sampling points of the maximum number of birds of each species recorded at each sampling point during an individual visit. The datasets used for all the analyses were processed in the same way. The analyses of the breeding season birds used both the 0-50m and the 0-100m datasets but only the results the analyses of the 0-100m dataset are presented here as this dataset yielded more significant results than the 0-50m dataset.
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5.2.6.2 Winter Bird Datasets The winter season datasets were calculated slightly differently to the breeding season datasets due to differences in methodology between the surveys. The total numbers of each species seen during each of the 4 visits were calculated per LUU. Then the maximum number of each species recorded during a single visit was calculated per LUU. These final datasets were used for all subsequent analyses. As in the Chapter 2 analysis of winter birds, birds recorded within 50m of the transect line was the dataset that was used in all the analyses. 5.2.6.3 Butterfly Datasets The dataset was the total number of butterflies recorded during the season in 20 sections of each LUU. This dataset was used for all analyses. 5.2.6.4 Remote Sensing Datasets Eight basic remote sensing datasets were available for use in this study: Level 1 and Level 2 Landscape metrics for the LUUs only (185 metrics); Level 1 and Level 2 Class metrics for the LUUs only (183 metrics); Level 1 and Level 2 Landscape metrics for the Total Area (LUUs with 1-km buffers) (185 metrics); and Level 1 and Level 2 Class metrics for the Total Area (LUUs with 1-km buffers) (183 metrics). However, only analyses with the LUU datasets are included in this chapter as analysis using the LUU and buffer data gave the same general results to those presented. Also, in the BioAssess Bird Report of Chamberlain et al. (2003) there were more significant associations with bird diversity indices and landscape metrics with the LUU datasets than with the LUU and buffer datasets. Principal Component Analysis (PCA) and correlations were performed on the entire landscape or class metric datasets. The variable TA (Total Area) was removed 219
from all landscape level datasets for the Redundancy Analysis (RDA) because the total areas were either 1 km2 for each LUU or 9 km2 when the buffers were included. For the class level, only datasets from those LUUs in which the class (habitat type) was present were included in the analysis. RDA was performed with only the core area depth-of-edge distance of 100m variables included (i.e. excluding the other four core area metric distances 10m, 25m, 50m and 250m) with all metrics that contained any blank values for an LUU deleted. However, for analyses with all birds recorded within 50m of the sampling points during the breeding season, the core area distance of 50m metrics were used instead but the results of these analyses are not presented. The metrics for the core area distance of 50m and 100m were chosen for the RDA analyses as principle components analysis (PCA) and correlations showed that all of the core area metrics were highly correlated among the five different distances, so the 50m and 100m core area distances were chosen as being representative of all the core area metrics. In addition, several of the analyses were conducted with the five different core area distances metrics separately. The forward selection summary tables produced from these analyses generally showed that there was little difference in the results between the different distances. The LUU landscape metrics produced the same results except that sometimes at the 100m and 250m distances more metrics were significant than at the shorter distances. However, results from preliminary analyses with the different core area distances show that all of the distances produce very similar results and that any of them could have been chosen.
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5.2.7 Statistical Analysis 5.2.7.1 Ordinations Much of the analyses were carried out using principal component analysis to summarise relationships between landscape metrics and redundancy analysis with forward selection to identify the landscape metrics which were most closely related to bird and butterfly assemblage structure. 5.2.7.2 Diversity Indices Species richness, Shannon-Weiner Index (H’) and Simpson’s Index (D) were calculated for all the species datasets in each of the six LUUs, using Species Diversity and Richness version 2.1 (Henderson and Seaby, 1998). 5.2.7.3 Correlations Pearson correlation coefficients and P-values were computed for the diversity indices of each species dataset in relation to the variables of the remote sensing metrics datasets. The correlations were performed using SAS version 8.2 (SAS, 2001). Rank correlation measures were also calculated and they produced identical results therefore only the former are reported here. 5.3 RESULTS 5.3.1 Remote Sensing Images of the Land-Use Units (LUUs) The Landsat, IRS and fused images for LUU1 are shown in Figures 5.1 – 5.3. The visually interpreted images with Level 1 and Level 2 classifications for the six LUUs are shown in Figures 5.4 – 5.15.
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Figure 5.2: IRS image of LUU1 with the 16 bird sampling points marked.
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Key: LUU1 Points LUU1 1km-Square LUU1 1km Buffer Level 1 Classification Grassland Forest No Vegetation Water bodies Wetland Shrubland Arable land #
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Figure 5.4: Level 1 classification of LUU1 with the 16 bird sampling points, the 1km LUU square and the 1-km buffer marked.
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Key: LUU1 Points LUU1 1 km-Square LUU1 1km Buffer Level 2 Classification Mixed closed Coniferous closed Broadleaf closed Agricultural Open soil Peat bogs Rubble/gravel Arable land Broadleaf very open Conifer clearcut Lake Shrubland with trees Mixed open Mixed very open #
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Figure 5.6: Level 1 classification of LUU2 with the 16 bird sampling points, the 1km LUU square and the 1-km buffer marked.
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Figure 5.7: Level 2 classification of LUU2 with the 16 bird sampling points, the 1km LUU square and the 1-km buffer marked.
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Figure 5.8: Level 1 classification of LUU3 with the 16 bird sampling points, the 1km LUU square and the 1-km buffer marked.
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Figure 5.9: Level 2 classification of LUU3 with the 16 bird sampling points, the 1km LUU square and the 1-km buffer marked.
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Figure 5.10: Level 1 classification of LUU4 with the 16 bird sampling points, the 1km LUU square and the 1-km buffer marked.
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Figure 5.11: Level 2 classification of LUU4 with the 16 bird sampling points, the 1km LUU square and the 1-km buffer marked.
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Figure 5.12: Level 1 classification of LUU5 with the 16 bird sampling points, the 1km LUU square and the 1-km buffer marked.
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Figure 5.13: Level 2 classification of LUU5 with the 16 bird sampling points, the 1km LUU square and the 1-km buffer marked.
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Figure 5.14: Level 1 classification of LUU6 with the 16 bird sampling points, the 1km LUU square and the 1-km buffer marked.
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Figure 5.15: Level 2 classification of LUU6 with the 16 bird sampling points, the 1km LUU square and the 1-km buffer marked. 5.3.2 Distribution of Land-Use Classes across the LUUs For Level 1 classification five land-use classes were identified within the six Land-Use Units (LUUs): forest, grassland, arable land, open spaces with little or no vegetation and water bodies (Figure 5.16). The forest (43%), grassland (33%) and arable land (23%) classes accounted for 99% of the land area of the six LUUs combined, with grassland being the only class present in each LUU. Ten classes were present in the second level interpretation: agricultural grassland, arable land, closed broadleaf forest, very open broadleaf forest, closed coniferous forest, clearcut coniferous forest, closed mixed forest, open soil, lake and gravel (Figure 5.17). Grassland and arable land were exactly the same as in the Level 1 classification, whereas forest was subdivided into several distinct classes. Closed broadleaf forest (15%), closed coniferous forest (20%) and closed mixed forest (7%)
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were the most extensive forest classes in the six LUUs as a whole. None of the other classes covered more than 1% of the LUUs’ combined area. LUU1 held the highest number of different habitats with four of the Level 1 classes and eight of the Level 2 classes. This LUU was dominated by forest (87% of the LUU land cover) of which the majority was closed broadleaf forest (52% of the area of the LUU) (Figures 5.16 and 5.17). LUU2 comprised of only two classes for both Level 1 and Level 2 classifications with closed coniferous forest dominating (89%) and grassland also present (11%) (Figures 5.16 and 5.17). LUU3 consisted of forest (45.5%), grassland (51%) and arable land (3.5%) (Figure 5.16). The forest class in LUU3 was split evenly between closed broadleaf forest and closed mixed forest (Figure 5.17). LUU4 held a variety of habitats with four 1st level classes: arable land (46%); forest (38%); grassland (13%); and open spaces with little or no vegetation (3%) (Figure 5.16). The forest area in LUU4 was all closed forest and consisted of all 3 tree species groups: broadleaf, coniferous and mixed (Figure 5.17). Grassland covered practically all the land area of LUU5 with the arable land and gravel classes accounting for the remaining one per cent (Figures 5.16 and 5.17). LUU6 was dominated by arable land (86%) with grassland occupying only 12% of the land area (Figures 5.16 and 5.17). Closed mixed forest and open soil covered the remaining two per cent of the area of LUU6 (Figure 5.17).
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100%
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Figure 5.16: Distribution of land classes for Level 1 classification in the six LUUs.
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Figure 5.17: Distribution of land classes for Level 2 classification in the six LUUs.
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5.3.3 Normalised Difference Vegetation Index (NDVI) NDVI measures the amount of vigorous vegetation in an area. In NDVI images, areas with sparse vegetation have low values and with dense vegetation have high values. Coniferous forest normally has lower NDVI values than deciduous forest, and similarly, rich, extensively used meadows have higher values than poorer, intensively managed ones. Of the eight countries studied in the BioAssess project, France and Ireland had the highest NDVI values while Finland, Hungary, Spain, Switzerland and UK had lower values with the lowest recorded in Portugal (Ivits et al, 2003). In this study in Ireland, all the six LUUs had high mean NDVI values with LUU1 having marginally the highest value at 220 and LUU6 having the lowest mean at 192 (Figure 5.18). However, these values are only means and some of the LUUs had large standard deviations, such as LUU6 (Figure 5.18).
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NDVI Value
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LUU 2
LUU 3
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Figure 5.18: NDVI mean and standard deviation values of the six LUUs.
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LUU 6
5.3.4 Species Diversity Indices for the LUUs There was very little variation between the LUUs in the values of the three diversity indices for the breeding season birds recorded within 100m of the sampling points (Table 5.4). Twenty-four winter bird species were recorded within 50m of the transect line in LUU4 and LUU5, while only 13 were observed in LUU2 (Table 5.4). Butterfly species richness was highest in LUU3, which had 10 recorded species, and lowest in LUU6 with only 4 different species recorded during the transect walks (Table 5.4). Differences in the rankings of the LUUs in relation to diversity occurred according to the index used. For example, Simpson’s Index for butterflies was highest in LUU1, which only had 8 species, while LUU3 was only ranked third even though 10 species were recorded there (Table 5.4).
Table 5.4: Species richness, Shannon-Weiner Index (H’) and Simpson’s Index (D) for breeding season birds, winter season birds and butterflies in the six Land-Use Units (LUUs). Dataset
Index
LUU1
LUU2
LUU3
LUU4
LUU5
LUU6
21
24
26
27
28
25
H’
2.62
2.59
2.69
2.84
2.87
2.89
D
11.50
10.53
11.07
14.02
13.77
15.83
Winter Birds
Sp.Rich.
20
13
22
24
24
19
0-50m
H’
2.49
2.09
2.18
2.66
2.63
2.08
D
9.01
6.50
4.84
10.85
10.77
5.84
Sp.Rich.
8
7
10
8
9
4
H’
1.77
1.61
1.83
1.32
1.81
1.03
D
5.41
4.59
4.87
2.46
5.34
2.50
Breeding Birds Sp.Rich. 0-100m
Butterflies
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5.3.5 Level 1 LUU Landscape Metrics 5.3.5.1 Principal Component Analysis (PCA) Principal Component Analysis (PCA) was performed with 183 Level 1 LUU landscape metrics (or variables) (Figures 5.19 and 5.20). The first axis accounted for virtually all of the variation (90.1% from Table 5.5), with LUU4 being very different than the rest of the LUUs (Figure 5.19). The second axis separated the other five LUUs (Figure 5.19). The main difference between LUU4 and the other LUUs was that this LUU had more patches with more complicated shapes than the other LUUs. At Level 1, LUU4 contained 12 different patches, compared to 8 in LUU1, five each in both LUU5 and LUU6, four in LUU3 and three in LUU2. This interpretation is also supported by Figure 5.20, which shows the majority of the metrics separating LUU4, such as the Contiguity Index Distribution, Shape Index Distribution and Perimeter-
+1.0
Area Ratio Distribution, were shape metrics.
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Figure 5.19: PCA of Level 1 LUU landscape metrics (183 variables) illustrating the spread of LUUs. 234
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DCORE_MD DCORE_MD DCORE_MN DCORE_MN CAI_CV
TCA TCA CAI_AM TCA GYRATE_S TCA DCORE_MD CAI_SD CAI_AM CAI_AM CAI_RA DCORE_MN CAI_AM CAI_SD PLADJAI CORE_AM CAI_RA CORE_MN CORE_SD CORE_AM DCORE_MNCAI_MN COHESION CORE_MN DCORE_AM CORE_RA DCORE_SD CORE_SD CORE_AM CORE_SD CORE_MN DCORE_AM CORE_AM CORE_SD AREA_AM MESH CONTAG AREA_SD DCORE_AM DCORE_AM CORE_AM CORE_RA GYRATE_R CORE_MNCORE_SD DCORE_AM DCORE_MN DCORE_MD CORE_RA TCA CORE_MN GYRATE_A AREA_MN CAI_SD CAI_MN CAI_AM CORE_RACAI_RA CORE_RA DCORE_RA AREA_RA CAI_MN
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CONNECT PROX_SD PROX_MN PROX_RA CAI_MD CORE_MD CORE_MD CAI_MD CORE_MD DCORE_MD CORE_MD AREA_MD
CAI_CV PARA_MN FRAC_CV CONTIG_R PARA_CV PROX_AM PARA_RA PARA_SD CONTIG_S CONTIG_C PROX_MD
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CIRCLE_S ENN_SD ENN_RA FRAC_MN FRAC_MD AREA_CVCIRCLE_A FRAC_AM CORE_CV CIRCLE_C PROX_CV CORE_CV ENN_AM CONTIG_M CORE_CV CAI_CV DCORE_CV CORE_CV CAI_MD
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NP PD SIDI DCAD NDCA DIVISION NDCA DCAD DCAD NDCA SHDI ED
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Figure 5.20: PCA of Level 1 LUU landscape metrics (183 variables).
Table 5.5: Eigenvalues and cumulative percentage variance of the species data of the canonical axes of the PCA of Level 1 LUU landscape metrics (183 variables). Axes
1
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Total variance
Eigenvalues : .901 .074 .014 .008 Cumulative percentage variance of species data : 90.1 97.5 98.9 99.8
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1.000
5.3.5.2 Breeding Season Birds 0-100m 5.3.5.2.1 Correlation with Diversity Indices Shape Index Distribution Mean (SHAPE_MN) and Fractal Index Distribution Mean (FRAC_MN) were the only metrics significantly correlated with any of the three diversity indices calculated. They were positively correlated with Simpson’s Index (D) (Table 5.6). Both of these metrics are related to the shape complexity of patches in a landscape therefore, bird species diversity of the LUUs, as expressed by Simpson’s index, increased with increasing complexity of patch shape in the landscape.
Table 5.6: Pearson Correlation Coefficients (r) and number of observations/LUUs (n) for the significant correlations between Level 1 LUU Landscape metrics and species diversity indices for breeding season birds 0-100m (S = Species Richness; H’ = Shannon-Weiner Index; D = Simpson’s Index; ns = not significant; * = P-value ≤ 0.05; ** = P-value ≤ 0.01; *** = P-value ≤ 0.001). Metric
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0.838*
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ns
ns
0.890*
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5.3.5.2.2 Redundancy Analysis The lengths of all the gradients of the species datasets analysed in this chapter were small and therefore redundancy analysis (RDA) was used in all cases to analyse the distribution of the species recorded in relation to the landscape or class metrics. In each analysis, the first RDA used forward selection on a reduced dataset containing between 87 and 98 metrics, which included only one set of core area variables (c.f. section 5.2.6.4: Data Processing – Remote Sensing Datasets for details). A second
236
RDA was then performed using only the most significant variables as chosen by forward selection. RDA with forward selection selected NDCA (Number of Disjunct Core Areas) as the only significant variable of the Level 1 LUU Landscape metrics for all birds recorded within 100m of the sampling points (P-value = 0.035) (Table 5.7). The second RDA included NDCA, GYRATE_SD (Radius of Gyration Distribution Standard Deviation) and SHAPE_AM (Shape Index Distribution Area-Weighted Mean) and the bird species dataset (Figure 5.21 and Table 5.7). Various species were closely associated with the each of the four landscape metrics (Figure 5.21). GYRATE_SD, which increased in landscapes that had a wide variation in the size and extent of patches, was closely associated with swallow, dunnock and rook. GYRATE_SD also had some association with hooded crow, sand martin, stonechat, linnet, pied wagtail and starling. House sparrow, skylark, collared dove, jackdaw and yellowhammer were associated strongly with NDCA and SHAPE_AM suggesting that these species need landscapes with a large number of core areas and patches with high shape complexity.
Table 5.7: Forward selection summary results (Lambda A, P-value and F-value) for the Conditional Effects of variables resulting from automatic selection of the RDA of all birds recorded within 100m of the sampling points and the Level 1 LUU Landscape metrics. (Lambda A = the additional variance each variable explains at the time it was included) Variable
LambdaA
P
F
NDCA*
0.47
0.035
3.48
GYRATE_SD
0.22
0.155
2.11
SHAPE_AM
0.19
0.11
3.06
COHESION
0.07
0.42
1.41
NP
0.05
1
0
237
+1.0
PW HC
SC
SM
LI
SG
GYRATE_SD LT CH SL
R.
RO MG
WW
D.
GR
MP BC
WR CC
J. GT
BF CK GC
BZ WH
CT
BT
B.
ST
SD TC
WK
RB
HS S.
FF
M.
NDCA
CD PH
JD
WP Y.
-1.0
SHAPE_AM
-1.0
+1.0
Figure 5.21: RDA of all birds recorded within 100m of the sampling points and the NDCA, GYRATE_SD and SHAPE_AM metrics for Level 1 LUU Landscape metrics. P-value of the 1st canonical axis = 0.025. P-value of all canonical axes = 0.005.
Table 5.8: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of all birds recorded within 100m of the sampling points and the NDCA, GYRATE_SD and SHAPE_AM metrics for Level 1 LUU Landscape metrics. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total variance .634 .155 .087 .073 1.000 .994 .989 .894 .000 63.4 78.9 87.6 94.9 72.4 90.1 100.0 .0
238
5.3.5.3 Winter Season Birds 0-50m 5.3.5.3.1 Correlation with Diversity Indices The species diversity indices of the birds recorded within 50m of the transect line in the 2002/2003 winter season were also correlated with the 184 Level 1 LUU landscape metrics (Table 5.9). GYRATE_MN (Radius of Gyration Distribution Mean) was significantly negatively correlated with Shannon-Weiner Index (H’) and Simpson’s Index (D) (Table 5.9). This implies that winter bird diversity decreased in landscapes containing large and extensive patches. In contrast, GYRATE_CV (Coefficient of Variation of the Radius of Gyration Distribution) was significantly positively correlated with all three diversity indices (Table 5.9). CIRCLE_MD (Related Circumscribing Circle Distribution Median) was significantly positively correlated with all three diversity indices, which suggested that diversity is greater in landscapes with more non-circular long patches (Table 5.9). PARA_MD (PerimeterArea Ratio Distribution Median) was significantly positively correlated with Shannon-Weiner Index (H’) and Simpson’s Index (D) (Table 5.9). Thus, winter bird diversity increased when landscapes contained patches with more complex shapes. ENN_MN (Euclidean Nearest Neighbour Distance Distribution Mean) and ENN_MD (Euclidean Nearest Neighbour Distance Distribution Median) were significantly negatively correlated with species richness (Table 5.9). This implies that diversity was higher when patches of the same habitat type are close together in a landscape. On the other hand, ENN_RA (Euclidean Nearest Neighbour Distance Distribution Range) and ENN_CV (Coefficient of Variation of the Euclidean Nearest Neighbour Distance Distribution) were significantly positively correlated with Shannon-Weiner Index (H’), while ENN_CV was also significantly positively correlated with Simpson’s Index (D) (Table 5.9). This implies that diversity was higher when there was a large 239
variation and range of distances between patches of the same habitat type in a landscape.
Table 5.9: Pearson Correlation Coefficients (r) and number of observations/LUUs (n) for the significant correlations between Level 1 LUU Landscape metrics and species diversity indices for winter birds (S = Species Richness; H’ = Shannon-Weiner Index; D = Simpson’s Index; ns = not significant; * = P-value ≤ 0.05; ** = P-value ≤ 0.01; *** = P-value ≤ 0.001). Metric
S
H’
D
n
GYRATE_MN
ns
-0.881*
-0.819*
6
GYRATE_CV
0.823*
0.886*
0.848*
6
PARA_MD
ns
0.960**
0.923**
6
CIRCLE_MD
0.888*
0.946**
0.839*
6
ENN_MN
-0.872*
ns
ns
6
ENN_MD
-0.904*
ns
ns
6
ENN_RA
ns
0.823*
ns
6
ENN_CV
ns
0.959**
0.941**
6
5.3.5.3.2 Redundancy Analysis None of the Level 1 LUU Landscape metrics were found to be significant for winter birds when RDA with forward selection was performed, nor was there a significant relationship when the four most important variables were included together in an RDA. 5.3.5.4 Butterflies 5.3.5.4.1 Correlation with Diversity Indices The Mean Shape Index (SHAPE_MN) was significantly negatively correlated with species richness and Shannon-Weiner Index (H’) indices for the butterflies recorded in the 2002 season and similarly, Area-Weighted Mean Shape Index 240
(SHAPE_AM) was correlated with Shannon-Weiner Index (H’) and Simpson’s Index (D) (Table 5.10). This suggests that butterfly diversity was lower in landscapes with high patch shape complexity.
Table 5.10: Pearson Correlation Coefficients (r) and number of observations/LUUs (n) for the significant correlations between Level 1 LUU Landscape metrics and species diversity indices for butterflies (S = Species Richness; H’ = Shannon-Weiner Index; D = Simpson’s Index; ns = not significant; * = P-value ≤ 0.05; ** = P-value ≤ 0.01; *** = P-value ≤ 0.001). Metric
S
H’
D
n
SHAPE_MN
-0.853*
-0.907*
ns
6
SHAPE_AM
ns
-0.817*
-0.845*
6
5.3.5.4.2 Redundancy Analysis Forward selection of the Level 1 LUU Landscape metrics selected several significant variables: FRAC_RA, ENN_MN and CIRCLE_CV (Table 5.11). When only these three environmental variables were included in the RDA (Figure 5.22) it was seen that Meadow Brown was very closely associated with FRAC_RA (Fractal Index Distribution Range) and was also associated with CIRCLE_CV (Related Circumscribing Circle Distribution Coefficient of Variation) (Figure 5.22). This suggests that this butterfly species preferred a variety of patch shapes and a large range of patch shape complexity in the landscape. ENN_MN (Euclidean Nearest Neighbour Distance Distribution Mean) was associated with Wood White, Greenveined White, Ringlet and Speckled Wood. This suggests that these butterfly species preferred landscapes that had more isolated patches.
241
Table 5.11: Forward selection summary results (Lambda A, P-value and F-value) for the Conditional Effects of variables resulting from automatic selection of the RDA of butterflies and the Level 1 LUU Landscape metrics. LambdaA
P
F
FRAC_RA**
0.51
0.005
4.18
ENN_MN*
0.28
0.05
4.1
CIRCLE_CV*
0.13
0.05
3.51
AREA_MN
0.06
0.24
2.35
NP
0.02
1
0
+1.0
Variable
ENN_MN SpWoo
Ringl GVWhi
WoWhi
CIRCLE_CV
FRAC_RA
Sfrit
MeBro
Peaco
Brime ReAdm SmWhi
SmTor
-1.0
OrTip
-1.0
+1.0
242
Figure 5.22: RDA of butterflies and the FRAC_RA, ENN_MN and CIRCLE_CV metrics for Level 1 LUU Landscape metrics. P-value of 1st axis = 0.025. P-value of all canonical axes = 0.005.
Table 5.12: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of butterflies and the FRAC_RA, ENN_MN and CIRCLE_CV metrics for Level 1 LUU Landscape metrics. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total variance .518 .317 .090 .053 1.000 .991 .993 .966 .000 51.8 83.5 92.5 97.8 56.0 90.3 100.0 .0
5.3.6 Level 1 LUU Class Metrics 5.3.6.1 Breeding Season Birds 0-100m 5.3.6.1.1 Correlation with Diversity Indices There was a relationship between the complexity of patch shape in the Arable Land class and bird diversity (Table 5.13). There were significant positive correlations between diversity indices and the metrics LSI (Landscape Shape Index), SHAPE_AM and FRAC_AM (Table 5.13). Bird diversity decreased with increasing area of forest in the landscape (Table 5.13). Negative correlations were found with CA (Class Area), PLAND (Percentage of Landscape), LPI (Largest Patch Index), AREA_AM, GYRATE_AM, MESH (Effective Mesh Size) and AI (Aggregation Index) all of which related to the amount and extent of the forest patches in the landscape or to how forest patches were aggregated as indicated by the aggregation metric (Table 5.13). In addition, diversity was higher in landscapes with more non-circular patches of forest and divided patches of forest. Significant positive correlations were seen
243
with CIRCLE_MN, CIRCLE_AM and DIVISION (Landscape Division Index) (Table 5.13). DIVISION is inversely correlated with MESH and means, in this instance, that diversity was higher in landscapes in which the Forest class was highly divided. Landscapes with large and extensive patches of grassland that were closely connected to each other had high diversity. There were positive significant correlations between the diversity indices and GYRATE_AM and COHESION (Patch Cohesion Index), and negative correlations with FRAC_MD, CIRCLE_MN and SPLIT (Splitting Index) (Table 5.13). This suggested that these landscapes also contained patches of grassland that had low shape complexity and were not highly fragmented. ENN_AM was also significantly negatively correlated with Simpson’s Index which means that diversity was higher in landscapes when patches of grassland were close together. The Open Spaces with Little or No Vegetation class was only present in LUU4, LUU5 and LUU6 but still showed highly significant negative correlations with three of the fractal index distribution statistics (range, standard deviation and coefficient of variation) and species richness (Table 5.13). This implies that bird diversity was high in landscapes in which the variation in the shape complexity of open spaces patches was low. CIRCLE_MN and CIRCLE_MD were significantly positively correlated with Simpson’s Index for the Open Spaces class, which implies that open spaces patches were non-circular in landscapes with high species diversity (Table 5.13). Only LUU 1 contained the Water Bodies class, so there was no significant correlation with this class for any of the species datasets.
244
Table 5.13: Pearson Correlation Coefficients (r) and number of observations/LUUs (n) for the significant correlations between Level 1 LUU Class metrics and species diversity indices for breeding season birds 0-100m (S = Species Richness; H’ = Shannon-Weiner Index; D = Simpson’s Index; ns = not significant; * = P-value ≤ 0.05; ** = P-value ≤ 0.01; *** = P-value ≤ 0.001). Class Arable Land (n = 5)
Metric S H’ LSI ns 0.949* SHAPE_AM ns 0.903* FRAC_AM ns 0.900* CA ns -0.942* Forest (n = 5) PLAND ns -0.938* LPI ns -0.938* AREA_AM ns -0.941* GYRATE_AM ns ns CIRCLE_AM ns 0.980** CIRCLE_MN ns 0.980** DIVISION ns 0.910* MESH ns -0.913* AI ns ns GYRATE_AM 0.836* ns Grassland (n = 6 except for ENN_AM FRAC_MD -0.843* ns a for which n = 3) CIRCLE_MN ns -0.823* ENN_AM ns ns COHESION 0.894* ns SPLIT -0.914* ns FRAC_RA -1.0** ns Open Spaces (n = 3) FRAC_SD -1.0** ns FRAC_CV -1.0** ns CIRCLE_MN ns ns CIRCLE_MD ns ns a = (all LUUs had Grassland class but only LUU1, LUU2 and LUU4 had values for metric)
D ns 0.990** ns -0.880* ns ns -0.879* -0.907* 0.891* 0.891* ns ns -0.906* ns ns ns -0.998* ns ns ns ns ns 0.997* 0.997* the ENN_AM
5.3.6.1.2 RDA of the Arable Land Class The Arable Land class was present in only five LUUs, as LUU2 did not contain this class. Forward selection with RDA found none of the Arable Land class
245
Level 1 LUU metrics to be significant and subsequent RDAs were also not significant. 5.3.6.1.3 RDA of the Forest Class Percentage of landscape (PLAND) was found to be significant when the Level 1 LUU Class metrics were analysed using RDA with forward selection (Table 5.14). The second RDA used PLAND, FRAC_MN (Mean Fractal Dimension Index) and NP (Number of Patches) (Figure 5.23 and Table 5.14). The forest species (wren, coal tit, robin, chiffchaff and goldcrest) were obviously associated with PLAND while dunnock was strongly associated with FRAC_MN, which suggested that this species preferred landscapes with complex forest patch shapes (Figure 5.23). Pied wagtail and magpie were closely associated with NP, which implies that these two species preferred habitats with a high number of forest patches.
Table 5.14: Forward selection summary results (Lambda A, P-value and F-value) for the Conditional Effects of variables resulting from automatic selection of the RDA of all birds recorded within 100m of the sampling points and the Level 1 LUU Class metrics for the Forest class. Variable
LambdaA
P
F
PLAND*
0.75
0.01
9.18
FRAC_MN
0.15
0.12
2.98
NP
0.07
0.395
1.92
CA
0.03
1
0
246
+1.0
PH BT
FRAC_MN D. TC B.
WK LT
SD
WP ST
RB FF Y. M.
GT
BC RO
JD GR
WR
SL BZ
CD S.
PLAND CT HS R. CC
SG GC WW MG
PW
CH CK BF HC
NP
WH J.
-1.0
MP
-1.0
+1.0
Figure 5.23: RDA of all birds recorded within 100m of the sampling points and the PLAND, FRAC_MN and NP metrics for Level 1 LUU Class Forest metrics. P-value of 1st canonical axis = 0.025. P-value of all canonical axes = 0.02.
Table 5.15: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of all birds recorded within 100m of the sampling points and the PLAND, FRAC_MN and NP metrics for Level 1 LUU Class Forest metrics. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total variance .756 .148 .063 .034 1.000 1.000 .999 .998 .000 75.6 90.3 96.6 100.0 78.2 93.5 100.0 .
247
5.3.6.1.4 RDA of the Grassland Class Grassland was the only habitat type present in all of the LUUs. Forward selection found none of the Level 1 Class metrics variables to be significant (Table 5.16). However, the first canonical axis of an RDA that included NDCA (Number of Disjunct Core Areas), FRAC_RA (Fractal Dimension Index Range) and DIVISION (Landscape Division Index) was significant (P-value = 0.03) and accounted for 63.3% of the species variance (Figure 5.24 and Tables 5.16 and 5.17). FRAC_RA was associated mainly with pheasant, fieldfare and reed bunting. This implies that these species showed a preference for landscapes that had a variety of grassland patches with different levels of shape complexity. Rook and greenfinch were associated with NDCA, which implies that these species preferred landscapes with a high number of separate grassland core areas. No species were very closely associated with DIVISION (Figure 5.24).
Table 5.16: Forward selection summary results (Lambda A, P-value and F-value) for the Conditional Effects of variables resulting from automatic selection of the RDA of all birds recorded within 100m of the sampling points and the Level 1 LUU Class metrics for the Grassland class. Variable
LambdaA
P
F
NDCA
0.47
0.065
3.55
FRAC_RA
0.23
0.075
2.23
DIVISION
0.15
0.21
1.99
AREA_CV
0.1
0.365
1.84
CA
0.05
1
0
248
+1.0
HC LI
SC SM
SG
LT PW SL D.
R. CH
RO
MG
WW
GR
BC
NDCA
MP
WR
BT GT
CC
B. ST
BF CK
GC
TC
SD J.
WH
CT
BZ WK
M.
WP
PH
FF RB
HS CD S.
FRAC_RA
-1.0
Y. JD
DIVISION
-1.0
+1.0
Figure 5.24: RDA of all birds recorded within 100m of the sampling points and the NDCA, FRAC_RA and DIVISION metrics for Level 1 LUU Class Grassland metrics. P-value of 1st canonical axis = 0.03. P-value of all canonical axes = 0.01.
Table 5.17: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of all birds recorded within 100m of the sampling points and the NDCA, FRAC_RA and DIVISION metrics for Level 1 LUU Class Grassland metrics. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total variance .633 .159 .056 .099 1.000 .994 .995 .795 .000 63.3 79.1 84.8 94.6 74.6 93.4 100.0 .0
249
Open Spaces with Little or No Vegetation and Water Bodies classes were present in too few LUUs to yield significant results from redundancy analysis for any of the species datasets. 5.3.6.2 Winter Season Birds 0-50m 5.3.6.2.1 Correlation with Diversity Indices The Arable Land class for winter bird diversity indices had no significant correlations with any Level 1 LUU class metrics. More bird species were found in winter in landscapes that contained forest patches with high shape complexity because species richness was positively correlated with LSI and SHAPE_AM (Table 5.18). For grassland, ENN_MD was significantly negatively correlated with species richness and Shannon-Weiner Index (Table 5.18). This means that diversity was higher in landscapes where patches of grassland were close together. ENN_CV was positively correlated with Simpson’s Index which implies that diversity was higher in landscapes when there was large variation in the distances between patches of grassland. IJI (Interspersion and Juxtaposition Index) was significantly positively correlated with Simpson’s Index for the Grassland class (Table 5.18). However, values of IJI could only be calculated for LUU1, LUU3, LUU4, LUU5 and LUU6 (Table 5.18). Thus diversity was higher in winter in LUUs when patches of grassland were intermixed with other patch types. The Open Spaces with Little or No Vegetation class showed significant negative correlations for diversity indices and range, standard deviation and coefficient of variation of the Shape Index and positive correlations with range, standard deviation and coefficient of variation of CIRCLE (Table 5.18). This implies
250
that bird diversity was high in landscapes in which open spaces patches had a high variation between circular and non-circular shapes but with low variation in their shape complexity.
Table 5.18: Pearson Correlation Coefficients (r) and number of observations/LUUs (n) for the significant correlations between Level 1 LUU Class metrics and species diversity indices for winter birds (S = Species Richness; H’ = Shannon-Weiner Index; D = Simpson’s Index; ns = not significant; * = P-value ≤ 0.05; ** = P-value ≤ 0.01; *** = P-value ≤ 0.001). Class Forest (n = 5) Grassland (n = 3 for ENN metrics and n = 5 for IJI)a Open spaces (n = 3)
Metric S H’ D LSI 0.942* ns ns SHAPE_AM 0.921* ns ns ENN_MD -0.999* -1.0** ns ENN_CV ns ns 0.997* IJI ns ns 0.895* SHAPE_RA -1.0** ns -1.0** SHAPE_SD -1.0** -0.999* -1.0** SHAPE_CV ns -0.998* ns CIRCLE_RA ns 0.999* 0.997* CIRCLE_SD ns 0.999* 0.997* CIRCLE_CV 0.999* 0.999* 1.0** a = (all LUUs had Grassland class but only LUU1, LUU2 and LUU4 had values for ENN_MD and ENN_CV, and only LUU1, LUU3, LUU4, LUU5 and LUU6 had values for IJI)
5.3.6.2.2 RDA of the Arable Land Class Forward selection did not find any of the Level 1 LUU Class metrics to be significant. 5.3.6.2.3 RDA of the Forest Class None of the Level 1 LUU Class metrics for the Forest class were found to be significant after forward selection (Table 5.19). A further RDA used NP and ED to try to explain the distribution of species (Figure 5.25). Number of patches (NP) was strongly associated with starling, redpoll, redwing and rook, which implies that these species preferred landscapes with a high number of forest patches (Figure 5.25). Edge 251
density (ED) was associated with dunnock, great tit, long-tailed tit and blackbird. These species preferred landscapes that contained a high amount of forest edge. Table 5.19: Forward selection summary results (Lambda A, P-value and F-value) for the Conditional Effects of variables resulting from automatic selection of the RDA of winter birds and the Level 1 LUU Class metrics for the Forest class. LambdaA
P
F
NP
0.49
0.14
2.91
ED
0.28
0.18
2.45
NLSI
0.14
0.315
1.68
CA
0.09
1
0
+1.0
Variable
S. LI
HS MP
SN FF CH PW MG
CR GO SK
GC Y.
LR
NP SG RE
M. WR
WP
RO
J. R.
PH
JD
BF BZ B.
BT
-1.0
TC
ST LT D. GT CT
ED
-1.0
+1.0
252
Figure 5.25: RDA of winter birds and the NP and ED metrics for Level 1 LUU Class Forest metrics. P-value of 1st canonical axis = 0.02. P-value of all canonical axes = 0.025.
Table 5.20: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of winter birds and NP and ED metrics for Level 1 LUU Class Forest metrics. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total variance .507 .266 .144 .084 1.000 .993 .946 .000 .000 50.7 77.2 91.6 100.0 65.6 100.0 .0 .0
5.3.6.2.4 RDA of the Grassland Class Neither RDA with forward selection nor RDA including the four most important metrics were significant in relation to the Grassland class of the Level 1 LUU Class metrics. 5.3.6.3 Butterflies 5.3.6.3.1 Correlation with Diversity Indices For arable land, diversity indices were negatively correlated with 27 metrics, which showed that butterfly diversity was low in landscapes with large areas of arable land irrespective of their size or shape (Table 5.21). The core area metrics were significant for all the distances analysed, 10m, 25m, 50m, 100m and 250m. However, DIVISION was positively correlated with species richness and Shannon-Weiner Index (Table 5.21). As landscape division index is also inversely correlated with MESH, this suggests that diversity was higher in landscapes in which the Arable Land class was highly divided.
253
Butterfly species richness increased in landscapes where patches of forest were intermixed with other patch types as IJI was significantly positively correlated with species richness for the forest class (Table 5.21).
Table 5.21: Pearson Correlation Coefficients (r) and number of observations/LUUs (n) for the significant correlations between Level 1 LUU Class metrics and species diversity indices for butterflies (S = Species Richness; H’ = Shannon-Weiner Index; D = Simpson’s Index; ns = not significant; * = P-value ≤ 0.05; ** = P-value ≤ 0.01; *** = P-value ≤ 0.001). Class Arabl e Land (n = 5)
Forest (n = 4 for IJI)a Grassl and (n = 6) Open Spaces (n = 3)
Metric CA
S -0.904*
H’ -0.990**
D -0.908*
PLAND LPI TE ED AREA_MN AREA_AM AREA_MD GYRATE_MN GYRATE_AM GYRATE_MD SHAPE_AM IJI DIVISION MESH Same values for TCA, CPLAND, NDCA, DCAD, CORE_MN, CORE_AM, CORE_MD, DCORE_MN, DCORE_AM, DCORE_MD, CAI_MN, CAI_AM and CAI_MD for all core area distances. IJI
-0.898* -0.898* ns ns -0.943* -0.904* -0.924* -0.928* ns -0.888* ns -0.906* 0.947* -0.948* -0.932*
-0.993*** -0.992*** -0.955* -0.945* ns -0.990** ns ns -0.991*** ns -0.888* ns 0.942* -0.938* ns
-0.916* -0.916* -0.987** -0.986** ns -0.908* ns ns -0.956* ns ns ns ns ns ns
0.966*
ns
ns
SHAPE_MD
-0.817*
ns
ns
SHAPE_AM
ns
-0.998*
ns
FRAC_MN FRAC_MD
ns ns
-0.997* -0.997*
ns ns
254
CIRCLE_MN -1.0** ns CIRCLE_AM -0.999* ns CIRCLE_MD -1.0** ns PROX_MN ns -0.999* PROX_MD ns -0.999* a = (Five LUUs had Forest class but only LUU1, LUU3, LUU4 and LUU6 had values metric)
ns ns ns ns ns for the IJI
For the grassland class, species richness was significantly negatively correlated with SHAPE_MD, which implies that more butterfly species were found in landscapes in which grassland patches had simple shapes (Table 5.21). The Open Spaces with Little or No Vegetation class showed significant negative correlations for diversity indices and several of the FRAC, CIRCLE and PROX distribution statistics (Table 5.21). This implies that butterfly diversity was high in landscapes in which open spaces patches were more circular in shape with low shape complexity and were distant from other open spaces patches. 5.3.6.3.2 RDA of the Arable Land Class FRAC_SD was found to be significant (P = 0.025) when the Arable Land class of the Level 1 LUU Class metrics was analysed using RDA with forward selection (Table 5.22). A subsequent RDA was run with FRAC_SD and SHAPE_MD (Figure 5.26, Table 5.22 and Table 5.23). Small White had an association with SHAPE_MD, which implies that this species preferred landscapes with complex arable land patch shapes (Figure 5.26). FRAC_SD was associated with Small Tortoiseshell and Meadow Brown. These species preferred landscapes that had a high variation of arable land patch shape complexity.
Table 5.22: Forward selection summary results (Lambda A, P-value and F-value) for the Conditional Effects of variables resulting from automatic selection of the RDA of butterflies and the Level 1 LUU Class metrics for the Arable Land class. 255
LambdaA
P
F
FRAC_SD*
0.65
0.025
5.65
SHAPE_MD
0.2
0.055
2.64
IJI
0.05
0.69
0.54
CA
0.1
1
0
+1.0
Variable
SHAPE_MD
SmWhi
OrTip SmTor MeBro
FRAC_SD
GVWhi ReAdm
Sfrit
Peaco Ringl
-1.0
SpWoo Brime
-1.0
+1.0
Figure 5.26: RDA of butterflies and the FRAC_SD and SHAPE_MD metrics for Level 1 LUU Class Arable Land metrics. P-value of 1st canonical axis = 0.08. P-value of all canonical axes = 0.04.
Table 5.23: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of butterflies and the FRAC_SD and SHAPE_MD metrics for Level 1 LUU Class Arable Land metrics. Axes
1
2 256
3
4 Total variance
Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
.664 .186 .124 .026 .985 .978 .000 .000
1.000
66.4 85.0 97.4 100.0 78.1 100.0 .0 .0
5.3.6.3.3 RDA of the Forest Class RDA with forward selection found CAI_MN to be significant from the Forest class Level 1 LUU metrics (Table 5.24). CAI_MN and CIRCLE_MN were used in the second RDA (Figure 5.27, Table 5.24 and Table 5.25). Mean core area index (CAI_MN) was closely associated with Ringlet and also had some association with Wood White and Green-veined White (Figure 5.27). This implies that these species preferred landscapes in which the amount of forest core area in patches was high.
Table 5.24: Forward selection summary results (Lambda A, P-value and F-value) for the Conditional Effects of variables resulting from automatic selection of the RDA of butterflies and the Level 1 LUU Class metrics for the Forest class. Variable
LambdaA
P
F
CAI_MN*
0.49
0.035
5.82
CIRCLE_MN
0.34
0.2
1.58
CA
0.1
0.315
1.54
TCA
0.07
1
0
257
+1.0
CIRCLE_MN
SmTor OrTip ReAdm
Peaco Brime
MeBro Sfrit
WoWhi SpWoo
-1.0
GVWhi
CAI_MN
Ringl
-1.0
+1.0
Figure 5.27: RDA of butterflies and the CAI_MN and CIRCLE_MN metrics for Level 1 LUU Class Forest metrics. P-value of 1st canonical axis = 0.02. P-value of all canonical axes = 0.02.
Table 5.25: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of butterflies and the CAI_MN and CIRCLE_MN metrics for Level 1 LUU Class Forest metrics. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total variance .536 .296 .102 .065 1.000 .990 .967 .000 .000 53.6 83.2 93.5 100.0 64.4 100.0 .0 .0
258
5.3.6.3.4 RDA of the Grassland Class GYRATE_RA and SHAPE_AM were significant (P = 0.005 and 0.04 respectively) after the Grassland class of the Level 1 LUU Class metrics was analysed by forward selection and these two metrics were used in a subsequent RDA (Figure 5.28, Table 5.26 and Table 5.27). SHAPE_AM and GYRATE_RA were associated with Meadow Brown, which imply that this butterfly species was more abundant when grassland patches had complex shapes and had a wide range of sizes (Figure 5.28). Small Tortoiseshell was also associated with SHAPE_AM implying that this species also preferred landscapes that had complexly shaped grassland patches.
Table 5.26: Forward selection summary results (Lambda A, P-value and F-value) for the Conditional Effects of variables resulting from automatic selection of the RDA of butterflies and the Level 1 LUU Class metrics for the Grassland class. Variable
LambdaA
P
F
GYRATE_RA**
0.32
0.005
5.25
SHAPE_AM*
0.49
0.04
3.86
TE
0.09
0.305
1.71
AREA_SD
0.07
0.21
2.83
CA
0.03
1
0
259
+1.0
Ringl GVWhi SpWoo
WoWhi
GYRATE_RA
MeBro Sfrit
SHAPE_AM Peaco
Brime ReAdm SmTor
SmWhi
-1.0
OrTip
-1.0
+1.0
Figure 5.28: RDA of butterflies and the GYRATE_RA and SHAPE_AM metrics for Level 1 LUU Class Grassland metrics. P-value of 1st canonical axis = 0.07. P-value of all canonical axes = 0.005.
Table 5.27: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of butterflies and the GYRATE_RA and SHAPE_AM metrics for Level 1 LUU Class Grassland metrics. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total variance .495 .320 .109 .051 1.000 .971 .997 .000 .000 49.5 81.5 92.4 97.5 60.7 100.0 .0 .0
260
5.3.7 Level 2 LUU Landscape Metrics 5.3.7.1 Principal Component Analysis (PCA) Principal Component Analysis (PCA) was performed with 185 Level 2 LUU Landscape metrics (Figures 5.29 and 5.30). The first PCA axis accounted for a very large proportion of the variation (81.4%), with the first and second axes accounting for 99.1% of the variation (Table 5.29). The separation of the LUUs was quite different in this PCA to that based on the Level 1 metrics (c.f Figure 5.19) as the LUUs were more evenly spread through the ordination space. However, LUU4 was still the most unique because it had many patches with more complicated shapes than the others. Many of the PARA, SHAPE and FRAC metrics were associated with LUU4. At Level 2, LUU1 contained the highest number of patches with 22, while LUU4 contained 17 different patches, compared to 13 in LUU3, five each in both
+1.0
LUU5 and LUU6, and three in LUU2. LUU4
LUU5
LUU2
LUU6
-1.0
LUU3
LUU1
-1.0
+1.0
Figure 5.29: PCA of Level 2 LUU landscape metrics (183 variables) illustrating the spread of LUUs. 261
+1.0
FRAC_CV
PARA_MN CONTIG_C PARA_SD
CIRCLE_C
PARA_RA CONTIG_R CAI_CV
PARA_CV
GYRATE_C
CAI_SD
CAI_CV
DCORE_MD DCORE_MD CAI_RA DCORE_MN DCORE_MN COHESION CORE_AM TCA TCACORE_RA CORE_RA DCORE_AM DCORE_AM CAI_RA TCA CAI_AM CORE_AM DCORE_AM TCA CORE_AM GYRATE_A TA CAI_AM CORE_RA CAI_SD CORE_AM CAI_AM MESH AREA_AM DCORE_AM DCORE_RA CORE_RA CAI_SDDCORE_MN AREA_RA CORE_SD CORE_SD CORE_SD GYRATE_R AREA_SD AI DCORE_SD PLADJ LPI CAI_SD DCORE_MD CONTAG CORE_MN CORE_MN CORE_AM CORE_MN CORE_MN CAI_MN DCORE_MN AREA_MN DCORE_MN DCORE_AM DCORE_MD CORE_RA TCA CORE_SD CAI_AM CORE_MN CAI_SD CAI_RA CAI_MN CAI_MN
PARA_MD CAI_RA
CAI_RA
CIRCLE_R FRAC_RA
CIRCLE_S
FRAC_SD
DCAD NDCA
CONTIG_A
CIRCLE_M
DCORE_RA CORE_MD DCORE_RA CONTIG_M
NDCA DCAD ENN_AM
FRAC_MN FRAC_AM FRAC_MD
CAI_MD DCORE_MD ENN_MD CAI_MN CORE_CV ENN_MN CAI_CV DCORE_SD DCORE_SD
SHAPE_RA CONNECT
CAI_CV
CORE_MD NDCA DCAD GYRATE_M
SHAPE_CV SHAPE_SD
CONTIG_S
GYRATE_S
CORE_MD AREA_MD
DCORE_CV DCORE_CV AREA_CV ENN_CV SHAPE_AM MSIDI MSIEI DCORE_CV CONTIG_MCIRCLE_MSHAPE_MN CIRCLE_A CORE_CV NDCA DCAD SHDI CORE_CV DCORE_RA SHEI CORE_CV NDCA DCAD SIDI LSI DCORE_SD SIEI SPLIT CAI_MD PARA_AM PROX_MD ED TE CORE_CV DCORE_CV DIVISION CAI_CV NP PD RPR PR PRD CAI_MD ENN_SD
SHAPE_MD
CAI_MN
ENN_RA PROX_CV
GYRATE_M
PROX_MN
-1.0
PROX_AM PROX_RA PROX_SD
-1.0
+1.0
Figure 5.30: PCA of Level 2 LUU landscape metrics (183 variables).
Table 5.28: Eigenvalues and cumulative percentage variance of the species data of the canonical axes of the PCA of Level 2 LUU landscape metrics (183 variables). Axes Eigenvalues : Cumulative percentage variance of species data :
2 1 .814 .177
3 4 Total variance .005 .003 1.000
81.4 99.1
99.7 99.9
5.3.7.2 Breeding Season Birds 0-100m 5.3.7.2.1 Correlation with Diversity Indices AREA_MD was significantly negatively correlated with Shannon-Weiner Index (H’) when diversity indices for breeding season birds 0-100m were correlated 262
with the Level 2 LUU landscape metrics (Table 5.29). This suggests that diversity was greater in landscapes in which the habitat patches were small.
Table 5.29: Pearson Correlation Coefficients (r) and number of observations/LUUs (n) for the significant correlations between Level 2 LUU Landscape metrics and species diversity indices for breeding season birds 0-100m (S = Species Richness; H’ = Shannon-Weiner Index; D = Simpson’s Index; ns = not significant; * = P-value ≤ 0.05; ** = P-value ≤ 0.01; *** = P-value ≤ 0.001). Metric
S
H’
D
n
AREA_MD
ns
-0.888*
ns
6
5.3.7.2.2 Redundancy Analysis PARA_MD was selected as the only significant variable from the Level 2 LUU Landscape metrics (Table 5.31). The second RDA was performed using only PARA_MD and AREA_MD (Figure 5.31 and Table 5.31). PARA_MD was closely associated with long-tailed tit, hooded crow, pied wagtail, linnet, sand martin and stonechat (Figure 5.31). This indicates that these species were associated with landscapes that had patches with complex shapes. Cuckoo, meadow pipit, whitethroat and goldcrest were associated with AREA_MD. This suggests that these species preferred landscapes that had large patches.
Table 5.30: Forward selection summary results (Lambda A, P-value and F-value) for the Conditional Effects of variables resulting from automatic selection of the RDA of all birds recorded within 100m of the sampling points and the Level 2 LUU Landscape. Variable PARA_MD* AREA_MD CAI_RA LPI TA
LambdaA 0.29 0.43 0.15 0.09 0.04
P 0.03 0.065 0.125 0.335 1
263
F 3.04 2.98 2.27 2.23 0
+1.0
PARA_MD LT SM SC LI BT
BC
HC PW
SG
D. RO
GT SL R.
WR
PH
B. TC
CH ST
WK SD
RB FF
CC WP CT
GR
BZ
MG
M.
WW
J. Y. JD
GC
CD HS S.
MP
AREA_MD
CK BF
-1.0
WH
-1.0
+1.0
Figure 5.31: RDA of all birds recorded within 100m of the sampling points and the AREA_MD and PARA_MD metrics for Level 2 LUU Landscape metrics. P-value of 1st axis = 0.025. P-value of all canonical axes = 0.02.
Table 5.31: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of all birds recorded within 100m of the sampling points and the AREA_MD and PARA_MD metrics for Level 2 LUU Landscape metrics. Axes 1 2 3 4 Total variance Eigenvalues : .594 .121 .156 .089 1.000 Species-environment correlations: .966 .945 .000 .000 Cumulative percentage variance of species data : 59.4 71.5 87.2 96.0 of species-environment relation: 83.1 100.0 .0 .0
264
5.3.7.3 Winter Season Birds 0-50m 5.3.7.3.1 Correlation with Diversity Indices Winter bird diversity was greater in landscapes that contained patches with a wide variety of sizes with complex shapes. Diversity was lower if the landscape had large and extensive patches and patches of the same class were isolated. AREA_CV, GYRATE_CV and PARA_MD were significantly positively correlated with species richness, while GYRATE_MN, ENN_MN and ENN_MD were negatively correlated (Table 5.32). PARA_MD was also significantly positively correlated with ShannonWeiner Index (H’) (Table 5.32).
Table 5.32: Pearson Correlation Coefficients (r) and number of observations/LUUs (n) for the significant correlations between Level 2 LUU Landscape metrics and species diversity indices for winter birds (S = Species Richness; H’ = ShannonWeiner Index; D = Simpson’s Index; ns = not significant; * = P-value ≤ 0.05; ** = Pvalue ≤ 0.01; *** = P-value ≤ 0.001). Metric
S
H’
D
n
AREA_CV
0.819*
ns
ns
6
GYRATE_MN
-0.888*
ns
ns
6
GYRATE_CV
0.833*
ns
ns
6
PARA_MD
0.850*
0.829*
ns
6
ENN_MN
-0.896*
ns
ns
6
ENN_MD
-0.895*
ns
ns
6
5.3.7.3.2 Redundancy Analysis No variables were found to be significant when RDA with forward selection was performed nor was the first axis of an RDA using the four most important metrics significant.
265
5.3.7.4 Butterflies 5.3.7.4.1 Correlation with Diversity Indices None of the Level 2 LUU landscape metrics were significantly correlated with any of the three diversity indices for butterflies. 5.3.7.4.2 Redundancy Analysis RDA with forward selection found CIRCLE_MN to be highly significant (Pvalue = 0.005) from the Level 2 LUU Landscape metrics (Table 5.33). Another RDA analysed CIRCLE_MN, FRAC_MN, GYRATE_AM, GYRATE_SD and the butterfly dataset (Figure 5.32 and Table 5.34). GYRATE_AM was associated with Brimstone (Figure 5.32). This suggested that this species preferred landscapes with large and extensive patches. Orange Tip and Small White showed an association with GYRATE_SD, which implies that these species preferred landscapes with a wide variety of patch sizes. Peacock was associated with CIRCLE_MN, implying that this butterfly species preferred landscapes with non-circular patches.
Table 5.33: Forward selection summary results (Lambda A, P-value and F-value) for the Conditional Effects of variables resulting from automatic selection of the RDA of butterflies and the Level 2 LUU landscape metrics. Variable
LambdaA
P
F
CIRCLE_MN**
0.52
0.005
4.34
FRAC_MN
0.27
0.055
3.83
GYRATE_AM
0.14
0.09
3.94
GYRATE_SD
0.06
0.11
8.59
TA
0.01
1
0
266
+1.0
Ringl
GVWhi WoWhi SpWoo
MeBro Sfrit
Peaco
CIRCLE_MN
Brime
GYRATE_AM
-1.0
OrTip
ReAdm SmWhi
GYRATE_SD
SmTor
FRAC_MN
-1.0
+1.0
Figure 5.32: RDA of butterflies and the CIRCLE_MN, FRAC_MN, GYRATE_AM and GYRATE_SD metrics for Level 2 LUU Landscape metrics. P-value of 1st axis = 0.015. P-value of all canonical axes = 0.005.
Table 5.34: Eigenvalues, species-environment correlations and cumulative percentage variances of the canonical axes of the RDA of butterflies and the CIRCLE_MN, FRAC_MN, GYRATE_AM and GYRATE_SD metrics for Level 2 LUU Landscape metrics. Axes Eigenvalues : Species-environment correlations: Cumulative percentage variance of species data : of species-environment relation:
1 2 3 4 Total variance .527 .322 .097 .046 1.000 1.000 1.000 1.000 .988 52.7 85.0 94.7 99.3 53.1 85.6 95.4 100.0
267
5.3.8 Level 2 LUU Class Metrics 5.3.8.1 Breeding Season Birds 0-100m 5.3.8.1.1 Correlation with Diversity Indices Level 2 LUU class metrics were correlated with the three diversity indices for birds recorded in the 0-100m distance band during the breeding season (Table 5.35). In the cases of the Arable Land and the Agricultural Grassland classes the correlations were exactly the same as those for Level 1 Class metrics shown in Table 5.13 above. Thus, bird species diversity was higher in landscapes in which patches of arable land had a high degree of shape complexity. High bird diversity also existed in landscapes that had large and extensive patches of grassland that were closely connected to each other. Several metrics were also significantly correlated with bird diversity in the Broadleaf Closed Forest class at the second level interpretation (Table 5.35). These metrics showed that bird diversity decreased with increasing area and edge length of closed broadleaf forest. Bird diversity was also low when patches of broadleaf closed forest were close to each other in the landscape. Negative correlations were found with PLAND (Percentage of Landscape), LPI (Largest Patch Index), ED (Edge Density), AREA_AM, GYRATE_SD, PROX_MN, PROX_AM, COHESION (Patch Cohesion Index) and MESH (Effective Mesh Size) (Table 5.35). Significant positive correlations were seen with FRAC_MD and DIVISION (Landscape Division Index) (Table 5.35). This implies that diversity was higher in landscapes in which the Broadleaf Closed class was highly divided. In the case of closed coniferous forest, diversity indices had positive significant correlations: PARA_RA, PARA_SD, CONTIG_SD and CONTIG_CV
268
(Table 5.35). Thus, high bird diversity was found in landscapes with large variation and range in closed coniferous forest patch shape complexity.
Table 5.35: Pearson Correlation Coefficients (r) and number of observations/LUUs (n) for the significant correlations between Level 2 LUU Class metrics and species diversity indices for breeding season birds 0-100m (S = Species Richness; H’ = Shannon-Weiner Index; D = Simpson’s Index; ns = not significant; * = P-value ≤ 0.05; ** = P-value ≤ 0.01; *** = P-value ≤ 0.001). Class Arable Land (n = 5)
Metric S H’ D LSI 0.949* SHAPE_AM 0.903* 0.990** FRAC_AM 0.900* PLAND -0.999* Broadleaf Closed (n = 3) LPI -0.999* ED -0.999* AREA_AM -0.999* GYRATE_SD -0.999* FRAC_MD 1.0** PROX_MN -0.998* PROX_AM -0.999* COHESION -1.0*** DIVISION 0.999* MESH -0.999* PARA_RA 1.0** Coniferous Closed (n = 3) PARA_SD 0.998* CONTIG_SD 0.998* CONTIG_CV 0.999* GYRATE_AM 0.836* Grassland Agricultural (n = 6 except for ENN_AM FRAC_MD -0.843* a for which n = 3) CIRCLE_MN -0.823* ENN_AM -0.998* COHESION 0.894* SPLIT -0.914* GYRATE_CV -0.956* Mixed Forest Closed (n = 4 except for ENN metrics PROX_CV -0.966* for which n = 3) b ENN_RA -1.0** ENN_SD -0.999* a = (all 6 LUUs had Grassland Agricultural class but only LUU1, LUU2 and LUU4 had values for ENN_AM) b = (LUU1, LUU3, LUU4 and LUU6 had Mixed Forest Closed class but only LUU1, LUU3 and LUU4 had values for ENN_RA and ENN_SD)
269
In the case of closed mixed forest there were significant negative correlations between bird diversity and GYRATE_CV, PROX_CV, ENN_RA and ENN_SD (Table 5.35). These metrics imply that diversity was greater in landscapes in which there was a wide variation in the sizes of, and the distances between, the closed mixed forest patches present. The Gravel, Broadleaf Very Open, Coniferous Clearcut and Lake classes were present in only one LUU each and Open Soil was present in two LUUs. Thus, these classes yielded no significant correlations for any of the species datasets. 5.3.8.1.2 RDA of the Arable Land Class The Arable Land class for the second level of interpretation was identical to Level 1 but as some metrics for this class were based on the overall landscape, which varied between Level 1 and Level 2, some slight differences in results occurred between the two levels. As with Level 1, forward selection with RDA found none of the Level 2 LUU Class metrics to be significant and the second RDA also yielded no significant results. 5.3.8.1.3 RDA of Other Classes The Agricultural Grassland class in Level 2 LUU was also exactly the same as the Level 1 LUU Grassland class. However, in this case the results of the RDAs of the Agricultural Grassland class and breeding season birds, winter birds and butterflies were identical to the Level 1 Grassland results as given in Tables 5.16, 5.17, 5.26 and 5.27 and Figures 5.24 and 5.28. The Broadleaf Closed Forest, Coniferous Closed Forest, Mixed Closed Forest, Broadleaf Very Open, Coniferous Clearcut, Open Soil, Gravel and Lake classes were present in too few LUUs to yield significant results from redundancy analysis for any of the species datasets.
270
5.3.8.2 Winter Season Birds 0-50m 5.3.8.2.1 Correlation with Diversity Indices The diversity indices for winter birds were correlated with Level 2 LUU class metrics and no significant correlations were found for the Arable Land class (Table 5.36). The correlations for the Agricultural Grassland class for winter birds were very similar to those for the Grassland class for Level 1 LUU Class metrics shown in Table 5.18 above. The only difference was that IJI was significantly positively correlated with the Shannon-Weiner Index, as well as Simpson’s Index (Tables 5.18 and 5.36). This suggests that bird diversity was higher in winter in landscapes when patches of agricultural grassland were intermixed with other patch types. The Broadleaf Closed Forest class yielded significant negative correlations with AREA_RA, AREA_SD, SHAPE_RA, SHAPE_CV, FRAC_RA, FRAC_SD, FRAC_CV and the winter bird diversity measures (Table 5.36). SHAPE_MD and SPLIT were positively correlated with closed broadleaf forest (Table 5.36). Landscapes with fragmented areas of closed broadleaf forest in which the patches were complexly shaped held a high diversity of birds in winter. Low winter bird diversity occurred in landscapes in which there was large variation in the area and shape complexity of the closed broadleaf forest patches. PARA_CV was significantly positively correlated with species richness and Simpson’s Index in the closed coniferous forest class, implying that in landscapes in which there was large variation in the shape complexity of the closed coniferous forest patches, winter bird diversity was high (Table 5.36). Closed mixed forest was positively significantly correlated with species richness and AREA_MN, GYRATE_AM, SHAPE_MN, SHAPE_AM, SHAPE_MD, 271
SHAPE_RA, SHAPE_SD, SHAPE_CV, FRAC_MN, FRAC_AM, FRAC_MD and CIRCLE_AM (Table 5.36). Thus, species richness was greater in landscapes that had large, extensive, complexly shaped, non-circular patches of closed mixed forest with a wide range of patch shapes.
Table 5.36: Pearson Correlation Coefficients (r) and number of observations/LUUs (n) for the significant correlations between Level 2 LUU Class metrics and species diversity indices for winter birds (* = P-value ≤ 0.05; ** = P-value ≤ 0.01; *** = Pvalue ≤ 0.001). Class Broadleaf Closed (n = 3)
Coniferous Closed (n = 3) Grassland Agricultural (n = 3 for ENN metrics and n = 5 for IJI)a Mixed Forest Closed (n = 4)
Metric AREA_RA AREA_SD SHAPE_MD SHAPE_RA SHAPE_CV FRAC_RA FRAC_SD FRAC_CV SPLIT PARA_CV
S -0.998* -1.0** ns ns ns ns ns ns 0.999* 0.999*
H’ ns ns 0.999* -1.0** -0.997* -0.997* -1.0** -1.0*** ns ns
D ns ns 0.999* -0.998* ns -1.0** -0.998* -0.998* ns 0.999*
ENN_MD -0.999* -1.0** ns ENN_CV ns ns 0.997* IJI ns 0.946* 0.985** AREA_MN 0.986* ns ns GYRATE_AM 0.974* ns ns SHAPE_MN 0.955* ns ns SHAPE_AM 0.988* ns ns SHAPE_MD 0.982* ns ns SHAPE_RA 0.950* ns ns SHAPE_SD 1.0*** ns ns SHAPE_CV 0.966* ns ns FRAC_MN 0.992** ns ns FRAC_AM 0.991** ns ns FRAC_MD 0.988* ns ns CIRCLE_AM 0.961* ns ns a = (all LUUs had Grassland class but only LUU1, LUU2 and LUU4 had values for ENN_MD and ENN_CV, and only LUU1, LUU3, LUU4, LUU5 and LUU6 had values for IJI)
272
5.3.8.2.2 RDA of the Arable Land Class Forward selection did not find any of the Level 2 LUU Class metrics for Arable Land to be significant. Another RDA was run with SHAPE_AM, IJI and DCAD, but the first canonical axis was not significant (P = 0.165)
5.3.8.3 Butterflies 5.3.8.3.1 Correlation with Diversity Indices The correlations with the Arable Land and the Agricultural Grassland classes were the same as those for the Arable Land and Grassland classes for Level 1 LUU Class metrics shown in Table 5.21 above. These metrics all imply that butterfly diversity decreased with increasing area and core area of arable land in the LUU, and with increasing length of arable land edge in the landscape. Diversity was also lower when arable land patches had high shape complexity and were not highly divided, and where patches of arable land were intermixed with other patch types. Grassland was significantly negatively correlated with species richness and SHAPE_MD, which implies that more butterfly species were found in landscapes in which agricultural grassland patches had simple shapes (Table 5.37). Closed broadleaf forest had negative significant correlations between diversity indices and GYRATE_MN, GYRATE_MD, FRAC_MN, CONTIG_MN and CONTIG_MD (Table 5.37). Positive correlations were found between closed broadleaf forest and GYRATE_AM, GYRATE_SD, PARA_MD and CIRCLE_CV (Table 5.37). This implies that butterfly diversity, as measured by Simpson’s Index, decreased with increasing closed broadleaf patch size, extent and shape complexity,
273
but numbers of butterfly species (species richness) increased with increasing size and extent of patches and perimeter-to-area ratios. Table 5.37: Pearson Correlation Coefficients (r) and number of observations/LUUs (n) for the significant correlations between Level 2 LUU Class metrics and species diversity indices for butter1flies (* = P-value ≤ 0.05; ** = P-value ≤ 0.01; *** = Pvalue ≤ 0.001). Class Arable Land (n =5)
Broadleaf Closed (n = 3)
Metric CA PLAND LPI TE ED AREA_MN AREA_AM AREA_MD GYRATE_AM GYRATE_MD SHAPE_AM IJI DIVISION MESH Same values for TCA, CPLAND, NDCA, DCAD, CORE_MN, CORE_AM, CORE_MD, DCORE_MN, DCORE_AM, DCORE_MD, CAI_MN, CAI_AM and CAI_MD for all core area distances. GYRATE_MN
S -0.904* -0.898* -0.898* ns ns -0.943* -0.904* -0.924* ns -0.888* ns -0.898* 0.947* -0.948* -0.932*
H’ -0.990** -0.993*** -0.992*** -0.955* -0.945* ns -0.990** ns -0.991*** ns -0.888* ns 0.942* -0.938* ns
D -0.908* -0.916* -0.916* -0.987** -0.986** ns -0.908* ns -0.956* ns ns ns ns ns ns
-0.999*
ns
ns
GYRATE_AM GYRATE_MD GYRATE_SD FRAC_MN PARA_MD CIRCLE_CV CONTIG_MN CONTIG_MD LPI
ns -1.0** ns ns 0.999* ns -0.999* -0.999* -0.998*
ns ns 0.998* ns ns 0.998* ns ns ns
0.998* ns ns -0.998* ns ns ns ns ns
AREA_MN
-1.0**
ns
ns
Coniferous Closed (n = 3)
Continued on next page.
274
Coniferous Closed (n = 3)
Agricultural Grassland (n = 6) Mixed Forest Closed (n = 4)
AREA_AM AREA_MD GYRATE_MN GYRATE_MD GYRATE_SD GYRATE_CV SHAPE_SD
-0.999* -1.0** -0.999* -0.999* 0.998* 0.999* 0.999*
ns ns ns ns ns ns ns
ns ns ns ns ns ns ns
FRAC_RA FRAC_SD COHESION DIVISION MESH Same values for TCA, CPLAND, NDCA, DCAD, CORE_MN, CORE_AM, CORE_MD, DCORE_MN, DCORE_AM, DCORE_MD, CAI_MN, CAI_AM and CAI_MD for all core area distances. SHAPE_MD
0.998* 0.998* -0.999* 1.0** -1.0** -1.0***
ns ns ns ns ns ns
ns ns ns ns ns ns
-0.817*
ns
ns
LSI
0.992**
ns
ns
AREA_SD AREA_CV GYRATE_RA GYRATE_CV CIRCLE_SD CIRCLE_CV CLUMPY
0.954* ns 0.982* 0.969* 1.0*** 0.997** -0.965*
ns 0.995** ns ns ns ns ns
ns ns ns ns ns ns ns
Species richness was significantly negatively correlated for closed coniferous forest with 21 metrics that all imply that butterfly diversity decreased with increasing area and core area of closed coniferous forest in the landscape (Table 5.37). The core area metrics were significant for all the distances analysed, 10m, 25m, 50m, 100m and 250m. Species richness was also significantly positively correlated with GYRATE_SD, GYRATE_CV, SHAPE_SD, FRAC_RA, FRAC_SD and DIVISION, which implies that diversity was higher when closed coniferous patches were not 275
closely connected to each other and had a large variation in shape complexity and size, and when closed coniferous forest was highly divided in landscapes (Table 5.37). In the case of mixed closed forest, diversity was significantly positively correlated with LSI, AREA_MD, AREA_CV, GYRATE_RA, GYRATE_CV, CIRCLE_SD and CIRCLE_CV and negatively correlated with CLUMPY (Table 5.37). This indicates that butterfly diversity was greater in landscapes in which the shape complexity of closed mixed forest patches was high and there was a large variation in the size and circular shapes of patches. Butterfly diversity was low when patches of closed mixed forest were clumped tightly together in the landscape. 5.3.8.3.2 RDA of the Arable Land Class FRAC_SD was also found to be the only significant variable after forward selection for the Arable Land class of Level 2 as well as Level 1 (Tables 5.22 and 5.38). A subsequent RDA was run with FRAC_SD and SHAPE_MD, which produced exactly the same RDA results as shown in the Level 1 Arable Land results in Figure 5.26 and Table 5.23, except that the lower ranked variables in the forward selection tables were slightly different (Tables 5.22 and 5.38).
Table 5.38: Forward selection summary results (Lambda A, P-value and F-value) for the Conditional Effects of variables resulting from automatic selection of the RDA of butterflies and the Level 2 LUU Class metrics for the Arable Land class. Variable
LambdaA
P
F
FRAC_SD*
0.65
0.025
5.65
SHAPE_MD
0.2
0.055
2.64
IJI
0.04
0.785
0.33
FRAC_RA
0.11
1
0
276
5.4 DISCUSSION 5.4.1 Land-use Gradient The land-use gradient used in this study ranged from a landscape dominated by mature broadleaf forest to an intensively managed arable landscape. LUU1 and LUU2 were completely dominated by forest with this habitat covering over 85% of these landscapes (Figure 5.16). However, woodland in LUU1 was comprised mainly of mature broadleaf forest with some closed coniferous and mixed forest also (Figure 5.17). In contrast, the forests in LUU2 were all closed coniferous forest (Figure 5.17). LUU3 and LUU4 were mixed use landscapes that contained varying proportions of grassland, forest and arable land (Figures 5.16 and 5.17). Grassland covered almost all of the area of LUU5 and arable land was the major land-use in LUU6, covering 86% of the 1-km square (Figures 5.16 and 5.17). The NDVI values show that all of the LUUs had high levels of vigorous vegetation growth, so this measure was not very useful in distinguishing reasons for differences in species diversity between the LUUs (Figure 5.18). There was very little difference between the LUUs in terms of bird diversity within 100m radius during the breeding season but LUU4 and LUU5 had slightly higher numbers of species (Table 5.4). Winter bird diversity was highest in LUU4 and LUU5 and lowest in LUU2 (Table 5.4). Butterfly diversity varied between the LUUs depending on which index was used to measure diversity, but 10 species were recorded in LUU3 and 9 in LUU5 with only 4 in LUU6 (Table 5.4). Thus the results showed our expectation to be incorrect as neither species diversity of birds nor butterflies decreased consistently across the gradient. This study was also part of a larger project called BioAssess in which similar data from eight European countries, in six of the major biogeographical zones, were compared and similar results were 277
found with no general response of bird and butterfly diversity to the gradient across the countries (Chamberlain et al., 2003; Van Swaay, 2003). Chamberlain et al. (2003) found that bird diversity and species richness were generally higher in the more open habitats (LUU4 to LUU6) across the eight countries and these LUUs also made the greatest overall contribution to diversity. The managed forest landscape of LUU2 had consistently the lowest avian diversity. Van Swaay (2003) found no consistent pattern in butterfly species richness across the gradient, but the highest numbers of butterfly species were often found in the old growth forest landscape (LUU1) and mixed-use landscapes of LUU3, LUU4 and LUU5. In the various countries, the lowest numbers of butterfly species were in either LUU2 or LUU6. Both Chamberlain et al. (2003) and Van Swaay (2003) support the conclusion of this study that the LUU gradient was not a good predictor of bird and butterfly diversity. Other studies have used a similar gradient technique to study the responses of birds and butterflies to varying intensities of disturbance (Blair, 1999; O’Connell et al., 2000). For example, Blair (1999) assessed bird and butterfly diversity along a gradient of urban land-use ranging from relatively undisturbed to highly developed and found that species richness and Shannon diversity of both taxa peaked at intermediate levels of development, and the oak-woodland species gradually disappeared at the more developed sites. Thus, this disturbance gradient similarly did not provide a good indicator of bird and butterfly diversity (Blair, 1999). The studies reviewed above, including this study, all provide similar results as the highest diversity was found at intermediate levels of disturbance along the gradient (Blair, 1999; Chamberlain et al., 2003; Van Swaay, 2003). However, in this study, LUU3 and LUU4 also had high habitat heterogeneity so it was not surprising that these landscapes had high species richness but LUU5 was almost entirely covered
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by a single habitat, i.e. grassland, and therefore it was not expected that this landscape would yield such high numbers of bird and butterfly species (Figures 5.16 and 5.17). 5.4.2 Birds and Landscape Structure Associations Many studies have been published that focus on birds in single habitats, particularly forests, and investigate the effects of wood size and fragmentation on the diversity of birds and woodland specialists (e.g. Forman et al., 1976; Helliwell, 1976; Robbins et al., 1989; Burke and Nol, 2000; Boulinier et al., 2001; Roslin, 2002; Sallabanks, 2002; Schotman, 2002; Castelletta et al., 2005). However, even though features such as the sizes and distributions of habitat patches were examined, they generally looked at single habitat types in isolation and the importance of the surrounding habitats in the landscape to the bird communities inhabiting the focal habitat was often ignored. For example, Lee et al. (2002) found that within patch characteristics, patch size and landscape forest cover were all factors influencing variation in the species studied, but their relative importance varied among the species. Forest cover at the landscape scale was also found by Trzcinski et al. (1999) to have a positive effect on the distribution of forest breeding birds. This effect was greater than that of forest fragmentation, which did not always have a negative impact. However, several studies have examined landscapes that contain several distinct habitats and thus addressed the limitations of the studies mentioned above. For example, McGarigal and McComb (1995) and Cushman and McGarigal (2003) investigated the relationships between landscape structure and breeding bird abundance and diversity in forest dominated landscapes in the Oregon Coast Range, USA. The landscapes examined represented a range of structure based on the proportion and the spatial configuration of mature forest. Landscape structure 279
generally explained less than 50% of the variation in each species’ abundance among the landscapes, but abundances were greater in the more heterogeneous landscapes. Only one species, wren (Troglodytes troglodytes), showed evidence of association with the least fragmented landscapes, which is unusual as in Britain and Ireland this species is a generalist species and not a woodland specialist (McGarigal and McComb, 1995). As in the study by Trzcinski et al. (1999), species richness and density were more strongly influenced by the area of mature forest than by fragmentation, but were lower in landscapes dominated by mature forest than more heterogeneous landscapes with a mixture of forest ages (Cushman and McGarigal, 2003). In fragmented landscapes, the most dominant species decreased in abundance, while species that were only moderately abundant increased in relative abundance (Cushman and McGarigal, 2003). However, as with the findings of Trzcinski et al. (1999) the effects did not increase as the habitat area decreased. Other studies have explicitly examined the influence of land-use in the surrounding landscape on bird species richness, composition and turnover in small woods and found patch-level attributes of woods, especially area, were most important (Bennett et al., 2004). Species richness of woodland migrant and woodlanddependent species were also dependent on the context of each wood at a local or regional scale, such as the extent of hedges and cover of woodland in the local vicinity (Bennett et al., 2004). Fuller et al. (1997) showed that the habitat composition in English agricultural landscapes had a major influence on breeding bird communities. The models, mainly influenced by woodland density, field size and altitude, were relatively successful in explaining variations in densities of wren, robin, willow warbler, blue tit, great tit and chaffinch but were unsuccessful in explaining total bird density, song thrush density
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and whitethroat density. Structural attributes of landscapes, especially density of hedgerow and woodland, were frequent predictors of species densities, but variables relating to farming system were not, with the exception of skylark and yellowhammer which were positively associated with extent of cereal crops (Fuller et al., 1997). Saab (1999) showed that, in cottonwood riparian forests, landscape patterns were the primary influence on the distribution and occurrence of most bird species, whereas local vegetation characteristics and forest patch characteristics were of secondary importance. Flather and Sauer (1996) found that Neotropical migrants in the eastern United States were more abundant in landscapes with a greater proportion of forest and wetland habitats, fewer edge habitats, larger forest patches, and with well dispersed forest habitats. Permanent resident bird species did not show much response to landscape structure, while temperate migrants were associated with habitat diversity and edge attributes rather than with the amount, size, and dispersion of forest habitats. Virkkala et al. (2004) examined the effects of landscape composition on farmland and red-listed birds in boreal agricultural-forest mosaic landscapes and found that cover of semi-natural grasslands and built-up areas (mainly farmyards) were more important in explaining the variation in farmland bird density than that of arable land, and these habitats also had the most positive effects on the density of redlisted species. Leitão et al. (2002) assessed the influence of landscape metrics on bird populations of arable farmland in southern Portugal and found that landscape diversity was the most important variable contributing to total species richness and abundance, and ground nester richness, while the occurrence of “montado” woodlands and scattered trees were associated with the presence of tree nesting birds.
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In this study I have examined the effects of landscape structure and diversity on bird and butterfly diversity and found significant relationships. During the breeding season bird species diversity was high in landscapes: (i) where habitat patches were small and complexly shaped (Tables 5.6 and 5.29); (ii) that contained arable land patches with high shape complexity (Tables 5.13 and 5.35); (iii) where the area of forest was low and where patches of forest were small and more fragmented (Table 5.13); (iv) where the variation in the shape complexity of closed coniferous forest patches was high or where the variation and range in the sizes of, and the distances between, the closed mixed forest patches was high (Table 5.35); (v) containing large and extensive grassland patches with low shape complexity (Tables 5.13 and 5.35); (vi) where grassland patches were closely connected and not highly fragmented (Tables 5.13 and 5.35). Diversity was lower where the area and edge length of closed broadleaf forest in the landscape was high, but increased if patches of this forest type were far apart from each other in the landscape or if the area of broadleaf closed forest was highly divided (Table 5.35). In other words, landscapes with high diversity had small amounts of forest that was fragmented, and/or with arable patches which were complex in shape and/or with large unfragmented areas of grassland. These features could also be related to individual bird species. For example, swallow, dunnock, rook and starling, and to a lesser extent, hooded crow, sand martin, stonechat, linnet and pied wagtail were closely associated with landscapes with a wide variation in the size and extent of patches (Figure 5.21). In other words, these species were more abundant in landscapes where heterogeneity of the spatial configuration of patches was high, regardless of the habitat type. House sparrow, skylark, collared dove, jackdaw and yellowhammer were associated with landscapes that contain a
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large number of core areas and patches with high shape complexity (Figure 5.21). A large percentage of forest in the landscape was associated with forest species like wren, coal tit, robin, chiffchaff and goldcrest (Figure 5.23). Landscapes with complex forest patch shapes, as measured by the mean fractal dimension, were strongly associated with dunnock (Figure 5.23). Krummel et al. (1987) found that the fractal dimension of forest patch shapes showed a different dimension for small compared to large forest patches and that this is probably related to differences in the scale of anthropogenic versus ecological processes that affect the underlying forest pattern. Magpie and pied wagtail were associated with landscapes with a high number of forest patches, and pied wagtail also preferred landscapes that had patches with complex shapes (Figures 5.23 and 5.31). Landscapes that had a variety of grassland patches with different levels of shape complexity were associated with pheasant, fieldfare and reed bunting, which are all species that utilise farmland, especially grassland habitats (Figure 5.24). Other common farmland species, rook and greenfinch, preferred landscapes with a high number of separate grassland core areas (Figure 5.24). Landscapes that had patches with complex shapes were preferred by long-tailed tit, hooded crow, pied wagtail, linnet, sand martin and stonechat (Figure 5.31). Cuckoo, meadow pipit, goldcrest and whitethroat were associated with landscapes that had large patches (Figure 5.31). In winter, bird species diversity was high under similar circumstances to those seen for breeding birds. The circumstances where diversity was high include (i) landscapes where the size and extent of habitat patches were low, but where patches were non-circular and complexly shaped (Tables 5.9 and 5.32); (ii) when patches of the same habitat type were close together in a landscape (Tables 5.9 and 5.32); (iii) landscapes containing forest patches or closed broadleaf forest patches with complex
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shapes (Table 5.18); (iv) landscapes that had a high degree of fragmentation of areas of closed broadleaf forest and had low variation in the area and shape complexity of patches of this habitat in the landscape (Table 5.36); (v) where there was variation in the shape complexity of closed coniferous forest patches in the landscape (Table 5.36); (vi) landscapes where patches of closed mixed forest were large and extensive, with shapes that were complex and non-circular (Table 5.36); (vii) when patches of grassland were close together in the landscape and were intermixed with other patch types in the landscape (Tables 5.18 and 5.36); (viii) landscapes where variation in the circularity of open spaces patches was high but variation in the shape complexity of these patches was low (Table 5.36). In winter, landscapes with a high number of forest patches were strongly associated with starling, redpoll, redwing and rook, while dunnock, great tit, longtailed tit and blackbird preferred landscapes that contained a high amount of forest edge (Figure 5.25). Thus, area of forest, particularly mature closed broadleaf forest, in a landscape had a negative effect on bird diversity in the breeding season, while farmland, especially large patches of grassland, had a positive effect. In winter, bird species diversity was positively influenced largely by the presence of grassland patches close together in the landscape, and by the presence of large patches of closed mixed forest. Chamberlain et al. (2003) found that breeding season bird species diversity and species richness were significantly associated with landscape structure in the eight study countries as a whole. In general, all aspects of diversity were greater in complex, heterogeneous landscapes. They found that bird species diversity increased with the extent of arable cover in the landscape and conversely decreased with forest cover, whereas abundance showed the opposite trend. Forest type was also important
284
as the greater the cover of coniferous forest in the landscape the lower the levels of species diversity and richness. On the other hand, species diversity, species richness and abundance increased with increasing broad-leaved forest cover. 5.4.3 Butterflies and Landscape Structure Associations In contrast to breeding and winter bird diversity, butterfly species diversity decreased with high shape complexity of patches in the landscape (Tables 5.6, 5.9, 5.10 and 5.32). Butterfly diversity was low in landscapes with large areas of arable land irrespective of their size or shape (Tables 5.21 and 5.37). Species diversity was high when the area of arable land class was highly divided in the landscape (Tables 5.21 and 5.37). It increased when patches of forest were intermixed with other patch types in the landscape (Table 5.21). An unusual result was that butterfly diversity as measured by Simpson’s Index increased as closed broadleaf patch size and extent and shape complexity decreased in the landscape, but species richness increased with increasing size and extent of patches of closed broadleaf forest and perimeter-to-area ratios in the landscape (Table 5.37). This may be a consequence of the low numbers of butterfly species recorded in the LUUs and the differences in the rankings of the LUUs, as regards butterfly diversity, with the various diversity indices (Table 5.4). Butterfly diversity increased as the area of closed coniferous forest in the landscape decreased and when closed coniferous forest became highly divided in the landscape (Table 5.37). Diversity was high when there was high variation in shape complexity and size of closed coniferous patches in the landscape (Table 5.37). Butterfly species diversity was also high when patches of closed mixed forest were complexly shaped and when the patches of this forest type were spread far apart and were not clumped tightly together in the landscape (Table 5.37). Landscapes that contain grassland patches with simple shapes had high species diversity (Tables 5.21 and 5.37). 285
Diversity increased with increasing circularity and decreasing shape complexity of patches of open space with little or no vegetation and increasing distance between these patches in the landscape (Table 5.21). Meadow Brown was associated with landscapes with a variety of patch shapes and a large range of patch shape complexity; landscapes that had a high variation of arable land patch shape complexity; and also landscapes that had grassland patches with complex shapes and a wide range of sizes (Figures 5.22, 5.26 and 5.28). Such landscapes obviously provided the preferred habitat of Meadow Brown, which is open grassland that includes field margins (Asher et al., 2001). Small Tortoiseshell preferred landscapes that had a high variation of arable land patch shape complexity, and landscapes that had complexly shaped grassland patches (Figures 5.26 and 5.28). Landscapes with a wide variety of patch sizes were associated with Orange Tip and Small White (Figure 5.32). Small White also preferred landscapes with complex arable land patch shapes (Figure 5.26). Ringlet, Wood White and Green-veined White are commonly found in woodland clearings and rides and in this study were associated with landscapes that had more isolated patches, and landscapes in which the amount of forest core area in patches was high (Figures 5.22 and 5.27) (Asher et al., 2001). Speckled Wood also preferred landscapes that had more isolated patches (Figure 5.22). Landscapes with large and extensive patches were associated with Brimstone (Figure 5.32). Peacock preferred landscapes with non-circular patches (Figure 5.32). Thus butterfly diversity was high in landscapes where: habitat patches in general and particularly grassland patches had simple shapes; the areas of arable land and coniferous forest were small; patches of forest were intermingled with other patch types; and patches of mixed forest were far apart and had complex shapes.
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Van Swaay (2003) found that in the LUUs of the eight European countries studied butterfly species richness was high when patches had several larger, well connected and distributed core areas with preferably a non-circular shape. The managed forest landscapes of LUU2 and arable land dominated landscapes of LUU6 had low numbers of butterflies as these landscapes were often characterised by small patches of suitable butterfly habitat divided by large patches of unsuitable habitat. Like birds, habitat fragmentation has also been the focus of many studies for butterflies (e.g. Thomas et al., 1992; Baz and Garcia-Boyero, 1995). Baz and GarciaBoyero (1995) studied the effects of forest fragmentation on butterfly communities in central Spain and found that butterfly diversity was significantly correlated with both woodland area and isolation. Rounded rather than long thin fragments were found to increase butterfly diversity, along with increased patchiness of forest fragments (Baz and Garcia-Boyero, 1995). Ries and Debinski (2001) studied the behaviour of a habitat specialist butterfly species and a habitat generalist at habitat edges in the highly fragmented prairies of Central Iowa and found that the specialist responded strongly to all edges, even those with low structural contrast, while the generalist only responded strongly to tree-line edges. The authors suggest that in highly fragmented landscapes, butterflies that show little or no response to edges may have high emigration rates because the probability of encountering an edge in small habitat patches is so high (Ries and Debinski, 2001). Krauss et al. (2003) studied habitat specialist and generalist butterfly species in calcareous grassland in relation to habitat area and habitat isolation and found that large habitat fragments were of special importance for the conservation of the specialist butterflies, which were also the most endangered, and that habitat isolation was less important.
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Wettstein and Schmid (1999) found that butterfly species richness was negatively influenced by the degree of habitat fragmentation in montane wetlands, with both the size of habitat and the area of wetland habitats in the surrounding landscape increasing the number of specialist wetland butterflies. Many of the studies of fragmented landscapes have taken a metapopulation point of view (e.g. Baguette and Schtickzelle, 2003). A metapopulation is a collection of small populations that are prone to frequent extinctions but are also frequently recolonised by individuals dispersing from adjacent populations (Litvaitis and Villafuerte, 1996; Pannell, 1997). Hill et al. (1996) employed metapopulation theory when investigating the effects of habitat fragmentation on the Silver-spotted Skipper (Hesperia comma) in the chalk hills on the North Downs, Surrey, England. Lei and Hanski (1997) studied the Glanville Fritillary (Melitaea cinxia), which had a classical metapopulation structure on the Åland islands in southwest Finland. However, very few studies of butterflies have assessed the effects of landscapes with a mosaic of habitats on diversity. 5.4.5 Limitations of Landscape Metrics It is important to note that in some circumstances metrics can give misleading information, so they must be looked at in context of the patch arrangement in the landscape from which the metrics are derived. For example, the Euclidean nearestneighbour distance (ENN) was high for LUU2 because in the Level 1 interpretation there were only three patches in the LUU. A large forest patch covered most of the LUU and separated the two small grassland patches at opposite ends of the LUU (Figure 5.6). This led to an extreme value of 665.9m for the mean ENN in LUU2, while LUU1 was also high with a mean ENN of 364.5m and the other LUUs had
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values of 140m or less. This resulted in forest bird and butterfly species, which preferred LUU2 also appearing to prefer landscapes containing more isolated patches. 5.4.6 Conclusions In it important that future ecological studies take into account the landscape perspective. Habitats cannot be broken down into perfectly independent units. All habitats are patches in a larger landscape mosaic where organisms, and various biotic and abiotic processes interact with many different patches and habitat types within this mosaic. Thus it is fundamentally important that ecologists study organisms at larger spatial scales as well as the more traditional finer grained studies. It is clear that the matrix of habitats that surround patches of suitable or optimum habitat of an organism is also of great importance to the organism in question as it determines its ability to move between optimal habitats and also determines if the organism can utilise the matrix habitats. Several recent papers have shown that habitat fragments are not real islands and the surrounding matrix has an important effect on bird diversity and abundances in fragments (Norton et al., 2000; Brotons et al., 2002, 2003). Thus, future work on landscape ecology in terrestrial landscapes should abandon the island biogeographic model (MacArthur and Wilson, 1967) for landscapes in favour of the landscape mosaic perspective (McGarigal et al., 2002). In conclusion, in this study breeding bird diversity decreased when forest area increased in the landscape but was positively influenced by farmland. This underlines the point made in Chapter 2 that in Ireland there are very few woodland specialist bird species but many more farmland, especially arable land, specialists. Therefore as forest area increased and farmland area decreased in the landscape all of the generalist species still occurred but the farmland specialists were lost, so diversity decreased. This phenomenon did not occur in winter as birds are more mobile in this season and 289
move around the landscape more in search of food, which helps to explain why both grassland and mature mixed forest were important winter habitats. Butterfly diversity in the landscape was positively influenced by heterogeneity of habitat patches with grassland and forest patches having positive effects. However, as expected, large areas of arable land and closed coniferous forest had negative effects on butterfly diversity due to these habitats generally being hostile environments for butterflies (Asher et al., 2001). Thus, the effects of different habitat types on bird and butterfly diversity have been assessed. Also it has been shown that the structure of the landscape, in terms of patch shape, distance to nearest neighbour and many other measures, was important to bird and butterfly diversity. For example, both breeding and winter bird diversity increased with increasing complexity of patch shape in the landscape, whereas butterfly diversity decreased. This study has also shown that the spatial arrangement and complexity of habitat patches and the extent of individual habitat types were associated with particular aspects of bird and butterfly communities. For example, house sparrow, skylark, collared dove, jackdaw and yellowhammer were associated with landscapes with a large number of core areas and patches with high shape complexity in the breeding season. However, it is important that the results of this study are only interpreted within the scope and limitations of the study (McGarigal and McComb, 1995). Care must be taken if these results are extrapolated to other spatial scales, whether finer or coarser, as interactions and distributions of species at other scales may be very different to those presented here (McGarigal and McComb, 1995; McGarigal et al., 2002).
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Chapter 6: General Discussion and Conclusions. This study used the principles of landscape ecology to examine the factors determining the diversity of birds and butterflies in lowland Irish landscapes by: (i) describing and comparing the species assemblages in the main habitats in the region; (ii) examining how management of one of these habitats, set-aside, affects species diversity; and (iii) examining the structure of the landscapes and how it affects diversity. The region is dominated by agriculture with some scattered patches of woodland. Therefore, bird and butterfly diversity in the region was studied in broadleaf woodland, coniferous woodland, pasture, non-rotational set-aside and tillage habitats. The species assemblages in these habitats were found to differ from one another and, while the farmland bird assemblages were more similar in composition to those found in studies of British sites than to the sites used in previous Irish studies (O’Connor and Shrubb, 1986; Lysaght, 1989; Moles and Breen, 1995; Holt, 1996; Flynn, 2002), the assemblages in woodland were similar to those reported in both Ireland and Britain in terms of the most abundant species recorded (Fuller, 1982; Whelan, 1995) and the real difference between the two countries was in the lack of woodland specialist species such as woodpeckers, nuthatch, tawny owl and willow tit. However, it is clear that despite the impoverished nature of the bird fauna in Ireland compared to Britain and continental Europe (Lack, 1976; Reed, 1981; Gibbons et al., 1993), especially the lack of woodland specialists (Wilson, 1977), there are differences between the bird communities of different agricultural and woodland habitats in lowland Ireland. 291
The commonest farmland species during the breeding season were skylark, wren, blackbird, meadow pipit, robin, dunnock, yellowhammer, blue tit, chaffinch and woodpigeon. Skylark had not been identified as being the commonest species in any other study of Irish farmland but a woodland species, the goldcrest, was often very common. This probably reflects the abundance of hedgerows in many Irish landscapes but may also reflect the fact that transects along hedgerows or territory mapping were used in many of the other studies of agricultural landscapes (Lysaght, 1989; Moles and Breen, 1995; Holt, 1996; Flynn, 2002). Point count methods, like the one used in this study, may under represent small and relatively quiet species, such as goldcrest, if points are far from hedgerows (Bibby et al., 2000). However, the low numbers of skylarks in previous studies in Ireland is probably due to a lack of suitable habitat in the sites studied as transects and territory mapping methods would easily pick up such a conspicuous bird as skylark which sings loudly during the breeding season. In winter, redwing, blackbird, meadow pipit, robin, starling, rook, fieldfare, chaffinch, house sparrow and wren accounted for the vast majority of the farmland birds recorded. Goldcrest, wren, coal tit, robin, chaffinch, blue tit and blackbird were the commonest woodland species in the breeding season, while coal tit, goldcrest, blackbird, robin, wren, blue tit, log-tailed tit, chaffinch, song thrush and pheasant were the commonest in winter. In both breeding and winter seasons, farmland habitats contributed far more unique species to the overall species richness than forest habitats, which highlights the lack of woodland specialist bird species and relative abundance of farmland specialists in Ireland.
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However, in the breeding season, the highest number of species was found in individual patches of broadleaf forest but the beta (β) diversity was low in the woodland habitats and high in the farmland habitats. Thus meaning that higher numbers of species were found across the several patches of farmland studied. The numbers and species diversity of summer migrants seen were very low compared with Britain, and this appears to be the case for Ireland in general, which may lend support to the theory that summer migrant species are out competed by resident species that get a head start (Lack, 1976; O’Connor, 1986; Hutchinson, 1989; Nairn and Farrelly, 1991; Fuller and Crick, 1992). However, low numbers and species richness of summer migrants may also be influenced by distance from source populations. Within patches in the landscape, smaller scale habitat features, specifically vegetation structure, are also important for birds during the breeding season (MacArthur and MacArthur, 1961; Bibby et al., 2000). Some species obviously require short vegetation and others need tall trees. In winter, the woodland habitats had similar bird communities and the assemblages in pasture and tillage were also very similar. However, the winter bird community of set-aside were very different to the other four habitats mainly due to the presence of skylark, meadow pipit and linnet. Agricultural habitats are therefore important in the maintenance of gamma diversity within these landscapes and the management of such systems could critically affect the bird assemblages which occur there. I therefore decided to investigate the effects of set-aside management on assemblage structure, as the bird communities of non-rotational set-aside were different in composition to the other habitats, even to those of pasture and tillage.
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Set-aside is a measure of the Common Agricultural Policy (CAP) that removes arable land from production (Henderson et al., 2000a). Land assigned to the set-aside scheme can be managed in several different ways and this study looked at four of these set-aside types in detail. The main set-aside management types studied were rotational set-aside regenerated from stubble, non-rotational set-aside (land in the scheme 3 years or older), first year pasture set-aside, and long-term set-aside that is grazed by animals in winter. Regardless of management regime, bird species richness and diversity in the breeding season were greater in set-aside than in neighbouring grassland or arable fields. As has been observed in many previous studies in different parts of Europe, most bird species including skylark, meadow pipit, linnet, pheasant and snipe showed a preference for set-aside over non-set-aside sites (c.f. Sears, 1992; Berg and Part, 1994; Watson and Rae, 1997; Henderson and Evans, 2000; Henderson et al., 2000a, b). However, set-aside management was also found to be important as each of the four main set-aside management types contained characteristic bird species assemblages. Non-rotational set-aside seems to be the most beneficial type of management for farmland birds. Skylark, meadow pipit, pheasant and snipe all occurred most frequently with this type of management. Species richness was greater compared to the other set-aside types. This result is in contrast to those reported in several studies from the UK where many bird species, including skylark, were shown to prefer rotational set-aside to non-rotational (Henderson et al., 2000a, b and 2001; Watson and Rae, 1997). A possible explanation of this difference is that much of the cereal production continues to be spring sown in Ireland, whereas in England, where most of the previous studies occurred autumn sown cereals dominate, so in Ireland a
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substantial amount of winter stubbles are available to birds even in the absence of stubbles associated with rotational set-aside. Therefore in Ireland, species such as meadow pipit and skylark may prefer to remain on the more permanent non-rotational set-aside and feed in neighbouring stubble fields, rather than seeking out new areas of rotational set-aside each breeding season. There were no significant differences in butterfly abundance, species richness and diversity among the habitat types. In total only 14 species were recorded in this study, with Speckled Wood, Meadow Brown, Ringlet, Green-veined White and Peacock being the commonest species recorded. The flight periods of these five species matched the general flight periods of these species in Ireland fairly closely despite poor weather during much the recording season, which is known to affect the timing of flight periods (Shackleton et al., 1999). It is likely that the reason for not finding significant differences in butterfly diversity between the five studied habitats were the low numbers of butterflies recorded as a result of the poor weather. However, the lack of habitat specialists in Ireland could also be a factor as only one habitat specialist butterfly species, Silver-washed Fritillary, was recorded in the study (Asher et al., 2001). Despite this, certain species were associated with specific vegetation and local habitat characteristics, with the presence of foodplants in a habitat being a very important determinant of the presence or absence of a butterfly species (New, 1997). For example, Green-veined White, Small White and Large White showed a preference for transect sections containing wild crucifers, which are larval foodplants for these species. Also, Meadow Brown showed a preference for sections with a high cover of its preferred foodplants, fine-leaved grasses.
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This suggests that local habitat conditions appear to be more important than larger scale habitat type in determining butterfly species diversity. It also suggests that care should be taken in the design of a sampling programme because the location of foodplants may be more important than overall habitat structure. For example, I showed this to be true in farmland because higher species richness and abundance were recorded in transects placed along hedgerows than in open fields. This is perhaps to be expected as hedgerows and field margins are preferred by butterflies because they usually provide more shelter and higher numbers of flowers and larval foodplants than the interior of intensively managed arable or grassland fields. Pollard and Yates (1993) also show that butterflies exhibit large changes in localised distribution as the abundance of individual butterfly species can vary greatly even over small distances of hundreds of metres along transects. The numbers of butterflies recorded in the same sites were substantially lower in 2002 compared to 2001, which showed that butterfly abundance at the same location could vary hugely between years. Weather is the main factor in explaining the fluctuations of butterflies as the summer of 2001 was warm and relatively dry compared to the cool and wet 2002 season. The overall structure of the landscapes themselves was shown to be extremely important in determining bird and butterfly diversity. In the breeding season, high bird diversity was found in landscapes with small amounts of fragmented forest, and/or with arable patches that were complex in shape, and/or with large unfragmented areas of grassland. Therefore, area of forest, especially mature closed broadleaf forest, in a landscape had a negative effect on bird diversity, while farmland, especially large patches of grassland, had a positive effect. This result shows that because Ireland lacks many woodland bird specialists, increasing the area of forest in a landscape will
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not increase species diversity substantially, but the opposite holds for farmland as Ireland has more farmland specialists. In winter, bird species diversity was positively influenced largely by the presence of grassland patches close together in the landscape, and by the presence of large patches of closed mixed forest. Butterfly diversity in the landscape was positively influenced by heterogeneity of habitat patches with grassland and forest patches having positive effects. Butterfly diversity was high in landscapes where the area of closed coniferous forest was small and fragmented. Large areas of arable land also had negative effects on butterfly diversity. Both coniferous forest and intensively managed arable land are generally regarded as poor habitats for butterflies. The spatial arrangement and complexity of habitat patches were also shown to be important aspects of landscape for individual bird and butterfly species. For example, Meadow Brown abundance was high in landscapes with a wide range of patch shape complexity, particularly of arable land, and also landscapes that had grassland patches with complex shapes and a wide range of sizes. In similar land-use gradients in eight European countries, Chamberlain et al. (2003) found that breeding bird species diversity was greater in complex, heterogeneous landscapes. Species diversity increased with increasing area of arable land in the landscape and decreased with increasing area of forest, however, increasing cover of coniferous forest lowered species diversity while, species diversity increased with increasing broad-leaved forest cover in the landscape (Chamberlain et al., 2003). Van Swaay (2003) found that in the same eight European countries butterfly species richness was high when patches had several larger, well connected and distributed core areas with preferably a non-circular shape.
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Thus, the importance of the landscape approach to ecology was emphasised as many structural elements and arrangements in the landscape affect bird and butterfly diversity in the landscape mosaics that constitute lowland Ireland. In this study and many others habitat heterogeneity was shown to be an important factor in increasing species diversity of birds and butterflies. A recent review of studies on the habitat heterogeneity-animal species diversity relationship looked at 85 studies and found that the majority of studies found a positive correlation between habitat heterogeneity/diversity and animal species diversity, including bird and butterfly diversity in several studies (Tews et al., 2004). Thus, habitat heterogeneity is one of the key determinants of biodiversity, as an increase in the variation of types, size, shape and spatial arrangement of habitat patches in a landscape will increase the amount of suitable habitat available for different species in that landscape. However, most of the species that occur in these heterogeneous landscapes are generalist species and large core areas of habitats are required to increase diversity of habitat specialists. Many studies have shown that patch shape has an important effect on both bird and butterfly diversity in landscapes. However, patch shape is important only for relatively small areas, as there is a size beyond which shape has no real consequence (Saunders et al., 1990). In small patches, shape determines the edge-tointerior ratio, or the perimeter-to-core ratio, which is important as many species can only use habitat that is not affected by edge effects (Saunders et al., 1990). Many studies have shown the importance of patch shape in ecology (e.g. Krummel et al., 1987) and some of these are described below. Gustafson et al. (1994) assessed habitat quality for wild turkeys (Meleagris gallopavo) using indices of habitat fragmentation and comparing the selected good
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quality sites with those subjectively chosen by experienced wild turkey biologists. A proximity index that distinguished isolated forest patches from those that were part of a cluster of forest patches was found to be a valuable quantitative measure of habitat spatial pattern that provided information about the quality of wild turkey habitat that correlated well with the expert knowledge. Helzer and Jelinski (1999) found that the perimeter-area ratio, which reflects both the area and shape of a patch, was the strongest predictor of both individual species presence and overall species richness of grassland breeding birds in wet meadow grassland patches. The authors conclude that, in the habitat studied, species richness was maximised when patches were large and shaped so that interior core areas were abundant. Pogue and Schnell (2001) examined the effects of agriculture on habitat complexity in a prairie-forest ecotone in the Southern Great Plains of North America and found that fractal dimension (a measure of shape complexity of habitat patches) was significantly lower for the agriculture area than the managed-native area, which was mainly explained by the fact that habitat edges were straightened and less complex for many of the land-cover types in the cropland-dominated area. Martínez-Morales (2005) studied the effects of cloud forest fragmentation on bird diversity in eastern Mexico and found that fragment shape was the main characteristic positively influencing species richness of the bird community. Fragment size was the main characteristic influencing forest interior and generalist species, while the extent of forest edge, expressed as fragment shape, was positively related to forest border species.
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Thus, as specialist bird and butterfly species are few and far between in lowland Ireland, habitat heterogeneity is a more important factor in increasing diversity of these taxa than the presence of large core areas of certain habitats. Several studies have shown that many species of birds and butterflies have suffered huge losses in numbers and/or range contractions in Europe in recent decades (e.g. Fuller et al., 1995; Krebs et al., 1999; Asher et al., 2001). In Britain, Thomas et al. (2004) compared population and regional extinctions of birds and butterflies at the national scale and found that birds and butterflies disappeared on average from 2% and 13% of their previously occupied 10-km squares respectively and also that 54% of native birds and 71% of butterflies declined over a 20 year period. Farmland birds have suffered more severe declines that bird species in other habitats due to agricultural intensification. Population declines and range contractions of farmland birds in Europe were greater in countries with more intensive agriculture such as established EU countries than in former communist countries where farming is less intensive (Donald et al., 2001). In Britain, the distributions of most farmland birds contracted with the majority also showing population decreases (Fuller et al., 1995). In Ireland, several farmland bird species including grey partridge, stock dove, turtle dove, skylark, house sparrow, greenfinch, goldfinch, linnet, twite, redpoll, yellowhammer and reed bunting have also suffered large range contractions between 1970 and 1990 (Taylor and O’Halloran, 1999; 2002). However, it is not clear if populations of certain species such as skylark have decreased to the same extent in Ireland as in the UK but it is believed that populations of the commonest farmland birds have not been as badly hit in Ireland (Siriwardena et al., 1998; Coombes et al., 2002).
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It has been proposed that increasing habitat heterogeneity in the farmland landscape at various spatial scales will halt and even reverse the declines in farmland bird species experienced in many European countries (Benton et al., 2003). In Ireland and Britain, certain types of agriculture are confined to specific regions of the country resulting in large areas dominated almost entirely by either grassland or arable land (Aalen et al., 1997; Chamberlain and Fuller, 2001). The presence of arable habitat in landscapes dominated by grassland has been shown to have positive effects on several species of farmland birds including grey partridge, skylark, tree sparrow, corn bunting, reed bunting, yellowhammer and whitethroat (Robinson et al., 2001). Thus, species diversity will be higher in landscapes with mosaics of patches of different types of arable land and grassland. This may also result in range expansions of many species, as many areas will now contain the required habitat for these species. For example, increasing the heterogeneity of the farmed landscape of the west of Ireland would reintroduce cereal crops to many parts of this mainly grassland region which would allow the ranges and populations of several seed-eating species to expand. The yellowhammer is one such species and is currently only present in very low numbers in the west of the country but large populations of the species reside in the tillage areas of the east and southeast of Ireland (Coombes et al., 2002). However, for butterflies the situation is different. In Britain, most of the habitat specialist butterfly species have declined over the past 20-30 years with species such as High Brown Fritillary (Argynnis adippe), Wood White, Marsh Fritillary and Large Heath showing the largest declines (Asher et al., 2001). In Ireland, the situation with habitat specialists is unclear, due to lower levels of recording, but certain species such as Wood White, Brown Hairstreak, and Pearlbordered Fritillary have not suffered such serious range contractions in Ireland as in
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Britain. In contrast, most wider countryside or habitat generalist species showed an increase in range in Britain and Ireland. Species that have declined have done so as a result of habitat loss including the loss of semi-natural grasslands, bog, hedgerows and woodland. In addition, changes in land management have been detrimental for many species, as they require very precise habitat conditions. Therefore, increasing heterogeneity of farmland habitats in predominantly agricultural landscapes would probably have little influence on butterfly diversity, as these precise habitat conditions would be unlikely to be met. Very few butterfly species are associated with modern intensive agriculture so mixing up patches of arable and grassland habitat in the landscape would still not provide the specific habitat types and foodplants required by habitat specialist butterflies. Thus, increasing habitat heterogeneity in farmland landscapes would result in a positive response in terms of diversity and abundance by birds but would not affect butterflies a great deal. This shows that birds and butterflies can respond differently to habitat heterogeneity as butterflies are usually affected more by small scale local habitat features than birds. In conclusion, bird diversity in Irish lowland landscapes was mainly influenced by large scale habitat features such as the types of habitat available in the landscape. Different woodland habitats had similar bird assemblages but farmland habitat assemblages varied more in terms of species richness. Smaller scale habitat features such as vegetation structure were important but to a lesser degree. Management of these habitats also plays an important role in determining bird species diversity. In the example of set-aside even relatively subtle changes in management can affect what species will be present. In contrast, butterfly diversity was mainly determined by small scale habitat and vegetation features especially the presence of
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larval foodplants. More butterflies and species were present along hedgerows compared to field interiors in the same farms, which showed the importance of local habitat features and foodplants to butterflies. Habitat type at the scale of forest or farmland habitat type was not found to be of the same importance as no significant differences in butterfly diversity occurred between habitats. Poor weather can cause butterfly abundance to be lowered and this might explain the absence of differences between habitat types. The structure of the landscape was important for both birds and butterflies with the area of habitats and the size, shape and spatial arrangement of the habitat patches shown to be important for diversity. Thus, birds and butterflies differ in their perception of habitat quality due to how they scale their surroundings, with birds, as considerably larger organisms, influenced in general by larger scale habitat features than butterflies. It is obvious that heterogeneity at various spatial scales, from small-scale variation in vegetation structure to large biogeographical zones, determines the species diversity of an area. These spatial scales cannot be looked at in isolation and all scales must be considered to get an accurate reflection of the processes determining species richness and diversity of a given area.
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APPENDICES Appendix I: Table A1: List of the bird species included in the text with their common and scientific names and the British Trust for Ornithology (BTO) species codes (sequence follows Fuller (1982)). Common Name Red-throated Diver Black-throated Diver Little Grebe Slavonian Grebe Black-necked Grebe Mallard Goldeneye Sparrowhawk Buzzard Kestrel Red Grouse Capercaillie Red-legged Partridge Grey Partridge Quail Pheasant Corncrake Moorhen Stone-curlew Little Ringed Plover Golden Plover Lapwing Snipe Woodcock Curlew Stock Dove Woodpigeon Collared Dove Turtle Dove Cuckoo Barn Owl Tawny Owl Green Woodpecker Great Spotted Woodpecker Lesser Spotted Woodpecker Woodlark Skylark Sand Martin Swallow Meadow Pipit Yellow Wagtail
Scientific Name Gavia stellata Gavia arctica Tachybaptus ruficollis Podiceps auritus Podiceps nigricollis Anas platyrhynchos Bucephala clangula Accipiter nisus Buteo buteo Falco tinnunculus Lagopus lagopus Tetrao urogallus Alectoris rufa Perdix perdix Coturnix coturnix Phasianus colchicus Crex crex Gallinula chloropus Burhinus oedicnemus Charadrius dubius Pluvialis apricaria Vanellus vanellus Gallinago gallinago Scolopax rusticola Numenius arquata Columba oenas Columba palumbus Streptopelia decaocto Streptopelia turtur Cuculus canorus Tyto alba Strix aluco Picus viridis Dendrocopos major Dendrocopus minor Lullula arborea Alauda arvensis Riparia riparia Hirundo rustica Anthus pratensis Motacilla flava
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BTO Species Code RH BV LG SZ BN MA GN SH BZ K. RG CP RL P. Q. PH CE MH TN LP GP L. SN WK CU SD WP CD TD CK BO TO G. GS LS WL S. SM SL MP YW
Pied Wagtail Dipper Wren Dunnock Robin Whinchat Stonechat Blackbird Fieldfare Song Thrush Redwing Mistle Thrush Sedge Warbler Dartford Warbler Lesser Whitethroat Whitethroat Blackcap Chiffchaff Willow Warbler Goldcrest Spotted Flycatcher Long-tailed Tit Marsh Tit Willow Tit Crested Tit Coal Tit Blue Tit Great Tit Nuthatch Treecreeper Jay Magpie Jackdaw Rook Hooded Crow Starling House Sparrow Tree Sparrow Chaffinch Greenfinch Goldfinch Siskin Linnet Redpoll Bullfinch Yellowhammer Cirl Bunting Reed Bunting Corn Bunting
Motacilla alba (yarrelli) Cinclus cinclus Troglodytes troglodytes Prunella modularis Erithacus rubecula Saxicola rubetra Saxicola torquata Turdus merula Turdus pilaris Turdus philomelos Turdus iliacus Turdus viscivorus Acrocephalus schoenobaenus Sylvia undata Sylvia curruca Sylvia communis Sylvia atricapilla Phylloscopus collybita Phylloscopus trochilus Regulus regulus Muscicapa striata Aegithalos caudatus Parus palustris Parus montanus Parus cristatus Parus ater Parus caeruleus Parus major Sitta europaea Certhia familiaris Garrulus glandarius Pica pica Corvus monedula Corvus frugilegus Corvus corone Sturnus vulgaris Passer domesticus Passer montanus Fringilla coelebs Carduelis chloris Carduelis carduelis Carduelis spinus Carduelis cannabina Carduelis flammea Pyrrhula pyrrhula Emberiza citrinella Emberiza cirlus Emberiza schoeniclus Miliaria calandra
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PW DI WR D. R. WC SC B. FF ST RE M. SW DW LW WH BC CC WW GC SF LT MT WT CI CT BT GT NH TC J. MG JD RO HC SG HS TS CH GR GO SK LI LR BF Y. CL RB CB
Appendix II:
Figure A2: An example of the recording sheet used for bird point counts in this project.
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Appendix III: History of the Common Agricultural Policy (CAP) The European Union (EU)’s Common Agricultural Policy (CAP) appears to have driven agricultural intensification, which in turn has led to bird population declines (Donald et al., 2001; 2002). Therefore to fully understand bird life on agricultural land in the EU, we should look at the CAP and consider its origins and mechanisms. Following the harshness of the Second World War, many European countries believed that there was a pressing need for food security within the region and also a need to reduce poverty amongst farmers (Donald et al., 2002). The Treaty of Rome established the European Economic Community (EEC) in 1957. The original six member states were comprised of Germany, France, Italy, Belgium, Luxembourg and the Netherlands. The EEC evolved over the decades to become the European Community (EC) and finally the EU (European Union). The EU increased the number of member states from the original six to 15 in 1995. On 1st May 2004 ten new member states, including Poland, the Czech Republic and Latvia, joined the EU to bring the total number of states to 25. The main objectives for agriculture in the EEC contained in Article 39 of the Treaty of Rome are to: •
increase agricultural productivity thus to ensure a fair standard of living for agricultural producers,
•
stabilise markets,
•
assure availability of supplies,
•
ensure reasonable prices to consumers (Donald et al., 2002).
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In 1962, the EEC introduced the CAP as the mechanism to achieve these aims. From the outset, politicians believed that increasing agricultural productivity would increase the wealth of farmers, and thus set out on the road of intensification rather than restructuring agriculture and allowing for more extensive farming (Robson, 1997). The CAP protected producers in member states in two ways. Firstly, fixed prices were guaranteed for agricultural produce, which established a price threshold (intervention prices) below which the EU became the buyer, and removed the produce from the market and stored it (Donald et al., 2002). This practice kept prices high and stable. Secondly, import taxes and export subsidies were used to protect the domestic market and to allow surpluses to be traded competitively on the world market. Farmers also benefited from capital grants to facilitate increased mechanisation and access to the most modern agricultural technology. These measures resulted in major intensification of agriculture because higher yields would guarantee higher incomes (Donald et al., 2002). However, the distribution of wealth from the CAP was unequal with the farmers in the most productive areas, who used the most intensive methods of production gaining the most economically. Thus, ‘the top 20% of producers receive 80% of CAP funds’ (Donald et al., 2002). This can be seen in Ireland in the differences in the wealth of farmers in the agriculturally productive east and south compared to those farming the poorer lands in the west of the country. Therefore, the majority of farmers are still economically vulnerable despite production-related subsidies (Donald et al., 2002). The CAP subsidies have also led to friction with other suppliers on the world market who were not so reliant on subsidy. Robson (1997) shows that despite around €100 billion being paid by European taxpayers and consumers to farmers each year, the agricultural sector has suffered an
327
annual decline in income of 2.5 – 2.8% since 1975. In addition, over 50% of the jobs in agriculture have been lost in the EU in the last 35 years. Another problem associated with the CAP was that it encouraged farmers to increase productivity by becoming more specialised. This resulted in certain regions becoming almost solely devoted to one type of agriculture and a loss of mixed farming. The decrease in farmland habitat heterogeneity is believed to be major factor in the decreasing farmland biodiversity of many areas in the EU (Benton et al., 2003). Bignal (1998) suggested that the CAP led to intensification by directly encouraging it, by rewarding the most intensive producers and by making extensification more costly. Advances in crop breeding resulted in new cereal varieties that were responsive to increased use of nitrogen fertilisers (Fischbeck, 1991 cited in Sotherton, 1998). This resulted in 5 to 9 times the 1950s’ levels of nitrogen being applied to cereals in the UK (Sotherton, 1998). The number of different herbicides, insecticides and fungicides also increased along with the number of applications per year (Sotherton, 1998). The CAP also encouraged the removal of hedgerows, woodland and other non-agricultural features from farmland. All of these factors resulted in the CAP having a negative influence on wildlife and biodiversity on farmland. The main changes to tillage practice in the UK since the 1970s have been the dramatic increases in the use of pesticides and inorganic fertilisers, the switch from spring- to autumn-sowing of cereals, and the simplification of crop rotations with the loss of grass leys (Brickle et al., 2000). The switch from spring-sown to autumn-sown cereals does not seem to have been as pronounced in Ireland. Figures from the 2002 barley harvest, show that 65.1% of the total area sown with barley in England was winter barley compared with 13.2% in the Republic of Ireland and 13.8% in Northern Ireland (Table A3).
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Table A3: The amount of land under wheat and barley in England, the Republic of Ireland and Northern Ireland for the 2002 harvest (Sources: the Department of Agriculture and Food in the Republic of Ireland and the Department for Environment, Food and Rural Affairs (DEFRA) in the UK). 2002 Harvest Figures England Republic of Northern Ireland Ireland 1,876,000 102,700 7,000 Total Wheat Area (ha) No figure available No figure available N/A
80,000
77.9
No figure available No figure available N/A
722,000
176,000
29,000
Winter Barley Area (ha)
470,000
23,200
4,000
Spring Barley Area (ha)
252,000
152,800
25,000
% of Total Barley Area that is Winter-sown
65.1
13.2
13.8
Winter Wheat Area (ha) Spring Wheat Area (ha) % of Total Wheat Area that is Winter-sown Total Barley Area (ha)
22,700
This change in the timing of sowing reduced the amount of stubbles, which are very important feeding sites for granivorous birds, available each winter. Also the heights and densities of the cereal crops were greater in spring which reduced their suitability for field-nesting species like skylark and lapwing. Spring tilling and drilling were reduced which also led to a decrease in food availability for birds at a critical time of year. Another major agricultural change has been the reduction in rotations and mixed farming in the countryside. This has resulted in the loss of farmland habitat heterogeneity at both the individual farm and the landscape scales. The increased use and number of applications of pesticides has led to decreased food sources for insectivorous and granivorous bird species.
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Grassland management has also intensified with high rates of inorganic fertiliser usage, frequent reseeding and high stocking rates. The resultant sward is dense and unsuitable for some ground-nesting birds and is also lacking in some important bird-food invertebrate groups. Grassland-nesting birds have had nests, young and adults increasingly destroyed through earlier and more frequent mowing brought about by the change from hay to silage production. By the early 1980s, productivity had increased to such an extent that surpluses of many agricultural products had already been created (Donald et al., 2002). This storage of this excess production in great warehouses across the EU was expensive. In 1992 the McSharry reforms of the CAP resulted in cuts in support prices in order to comply with international agreements on trade of agricultural products (Robson, 1997). In order to compensate farmers for these price cuts, more of the CAP budget was spent on direct payments. Other reforms were designed to reduce production. These included milk quotas and compulsory set-aside for arable crops. Payments were also introduced to encourage environmentally beneficial forms of farming and support for environmentally damaging investments has been reduced (Anon., 2003a). Rural development has received more attention and farmers are encouraged to look to markets and diversified forms of income to reduce their dependence on subsidy (Anon., 2003a). The annual CAP budget was approximately €40 billion in 2000, which was broken down between €10.8 billion for market price support, €25.5 billion on direct payments and €4.2 billion on rural development and agri-environment schemes (Anon., 2003a).
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The Berlin European Council finalised the agreement on Agenda 2000 in March 1999. Agenda 2000 continued the CAP reforms of 1992 by continuing the shift from price support to direct payments (Anon., 2003b). Agenda 2000 also attempts to deal with the economic imbalances of the CAP and tries to help the farming sector deal with further trade liberalisation (Anon., 2003a). Environmental and rural economy measures are to play a larger role in the CAP of the future by replacing the emphasis on production support. On 22 January 2003, the Commission published the detailed proposals for the Mid-term Review of Agenda 2000 in the form of draft legislation. These proposals were aimed at addressing some of the detrimental aspects of the CAP and making production fairer and the CAP more environmentally sound. The four main elements of the proposals are: •
The decoupling of direct payments from production
•
The modulation and degression of direct payments over the period 2006 to 2012
•
Enhanced rural development measures
•
Sectoral proposals (Anon., 2003b). However, by July 2003 these proposals had been substantially diluted with the
final agreement much weaker than the original proposal (Croton, 2003). Decoupling of payments from production means that there would no longer be a direct link between producing more and the level of payment received. Individual farmers would get one payment as long as they continued to farm, but they would not be obliged to reach production targets. The EU gave each member state the option of whether to decouple all payments or to keep some of the payments linked to production (Croton, 2003). The Irish Minister for Agriculture and Food, Joe Walsh, announced on 20th October 2003 that all sheep, beef and arable payments would be fully decoupled in Ireland. This measure will be important for Ireland’s environment,
331
as it will eliminate incentives that have resulted in overgrazing by large numbers of stock on upland areas with the subsequent declines in bird species such as red grouse and meadow pipit (Croton, 2003). Also, incentives to invest in high chemical fertiliser loads will be reduced, which will aid restoration of water quality and will make it less attractive to bring semi-natural habitats into intensive agricultural production (Croton, 2003). The final agreement only redirects 5% of the total CAP budget to rural development, instead of the 20% originally envisaged (Croton, 2003). It is suggested that supports should be re-coupled to support farming systems that deliver more than just production. Croton (2003) recommends that payments should be made through the Rural Development pillar of the CAP, and through “National Envelopes,” to farmers that farm in a sustainable manner, deliver biodiversity, landscape and other environmental benefits and provide the basis for sustainable development of rural communities. The reforms of the CAP and the recent enlargement of the EU by the eastern European countries and the Mediterranean countries will lead to serious changes in agriculture across the continent. Hopefully, the experience gained over the previous half century will enable the agricultural environment to be improved or, at least, not to be damaged further and will also prevent any more losses to farmland biodiversity. Set-aside One of the developments of the CAP has been the introduction of set-aside land into the arable landscape. Set-aside involves removing land from production and was introduced initially to reduce agricultural surpluses. The first set-aside scheme, initiated in 1988, provided for the voluntary withdrawing of at least one-fifth of the
332
farmer’s arable land out of production for five years. The land set-aside could be left fallow; used for extensive (rather than intensive) grazing; planted with trees; or used for non-agricultural purposes (Commission Regulation (EEC) No. 1272, 1988). However, uptake of the scheme was very low and it was not until the McSharry reforms of 1992 that set-aside became widespread. Under these reforms subsidy payments to farmers under the Arable Area Payments initiative was conditional on 15% of arable land being set-aside (Buckingham et al., 1999). Although the primary aim of set-aside was still to reduce production, the 1992 reforms placed more emphasis on the environmental contribution of set-aside. At this time there were two different types of set-aside, rotational and non-rotational set-aside. Rotational setaside involved changing the area of farmland left fallow each year. Non-rotational setaside is land that is taken out of production on a long-term basis. The Rotational Set-aside scheme (RSA) in 1992/93 required that vegetation be cut before 1 July or be cultivated between 1 May and 1 June (Sotherton, 1998). These regulations resulted in much destruction of wildlife, especially ground-nesting birds during the nesting season, and were widely criticised. The 1993/94 changes to the Setaside Scheme provided some potential for benefits to farmland biodiversity by (1) allowing some Member States (e.g. France, UK and Ireland) to manage rotational setaside with non-residual herbicides and by (2) introducing a Non-rotational Set-aside Scheme (NRSA) which enabled farmers to (a) plant specifically designed seed mixtures to create valuable cover known in the UK as Wild Bird Cover and to (b) plant such mixtures in relatively small strips and plots distributed around the farm (Sotherton, 1998). Managing non-rotational set-aside as Wild Bird Cover requires that the cover should be an unharvestable mixture of at least two crop groups other than legumes
333
(Sotherton, 1998). Two distinct types of valuable habitat can be created in this way: winter cover and brood-rearing cover. Apart from the changes mentioned above, the rules on management of setaside have changed almost yearly since its inception. However, a green cover must be established on the set-aside land in order to prevent leaching of nutrients (Buckingham et al., 1999). Natural regeneration and sowing can both be used to achieve this. Natural regeneration is more beneficial as the stubble field from the previous harvest is left over winter and volunteer plants are allowed to emerge untouched in the spring. Cereal stubbles that are left undisturbed over winter can provide important feeding grounds for partridges, pheasants and other seed-eating birds provided that herbicide use in previous crops and efficient harvesting have not left the land so ‘clean’ that grain and weed seeds are not present (Sotherton, 1998). Also, this green cover had to be cut or destroyed by using certain herbicides in spring or summer to reduce the build-up of weeds (Firbank et al., 2003). Set-aside Regulations In 2003 the rules for set-aside in Ireland stated that ‘producers claiming Area Aid on more than 15 hectares are obliged to set-aside a minimum of 10% of the area claimed plus the set-aside area’ (O’Sullivan, 2002). In addition to the compulsory setaside, all farmers can voluntarily set-aside up to 40% of the total area. The land used for set-aside must also cover an area of at least 0.3 hectares in one block and have a width of at least 20 metres. Sotherton (1998) states that putting the entire set-aside allocation into two or three fields at one end of the farm will only benefit a few individuals or pairs as many species of farmland wildlife (including gamebirds) have discreet home ranges. However, if small blocks were scattered across the farm then
334
the benefits to game and other wildlife could be increased considerably. Smaller areas are considered if they run alongside watercourses or lakes and are to be managed with environmental objectives. The set-aside period is from 15th January to the following 14th January. ‘During the ‘core’ set-aside period from 15th January to 31st August it is forbidden to use land in set-aside for any type of agricultural production or for any other lucrative use’ (O’Sullivan, 2002). However, it is permissible to grow certain types of non-food crops. A green cover must be established before 15th January and should be retained until 15th April. In cases where a late harvested root crop has been sown, it is allowed to delay the establishment of the green cover. From 16th April to 31st August, ploughing of set-aside land is only permitted from 16th April to establish a green cover and from 16th July to prepare land for sowing crops for harvesting not earlier than 15th May (O’Sullivan, 2002). Cutting of the green cover to a height of 10cm or less must be conducted at least once between 16th July and 15th August. The cuttings must be left undisturbed on the land. The green cover may be cut during the period 16th April to 15th July if it is necessary to control weeds or to maintain an acceptable visual appearance. However, the green cover must not be cut below 20cm in order to prevent destruction of nests and young mammals. Watson and Rae (1997) claim that the importance of mowing at a minimum of 20cm is that it allows ‘noxious’ weeds to be eliminated, while at the same time allowing short species to support insects and set seed, and providing cover for nests and fledglings. If a green cover has been established the application of fertiliser or lime or the control of weeds is permitted after 16th April. Shallow cultivation and the use of non-residual herbicides such as glyphosate and paraquat/diquat can be used to control weeds (O’Donoghue, 2003).
335
From 1st September to 14th January farmers are allowed to use set-aside land to feed their own animals either by grazing or as hay or silage. The Council Regulation (EC) No 1782/2003 on 29th September 2003 establishes the new rules for set-aside under the CAP reform. The current (10%) setaside requirement will be maintained, but set-aside can be rotational or non-food crops may be grown. Member States shall also be authorised to pay national aid up to 50% of the costs associated with establishing multiannual crops intended for bio-mass production on set-aside land. Farmers may be able to receive set-aside payments on additional land (up to at least 10% of the arable area) voluntarily set-aside. Article 107(9) of this Regulation states that ‘Set aside areas shall not be less than 0.1 ha in size and 10 metres wide. For duly justified environmental reasons, Member States may accept areas at least 5 metres wide and 0.05 ha in size’.
Note: References for Appendix III are included in the general bibliography of the thesis.
336
Appendix IV: Table A4: Details of set-aside sites including location, number of years in set-aside, type of crop in paired field, number of sampling points and other additional notes. Set-aside Category
Location of Site
A: Rotational
Sheffield, Co. Laois
A: Rotational A: Rotational
Kyledellig, Co. Laois Ratheniska, Co. Laois Srowland, Co. Kildare Coursetown, Co. Kildare
1 1
Bray, Co. Kildare Killalooghan, Co. Laois Knocknambraher, Co. Laois Bennetsbridge, Co. Kildare Raheenaniska, Co. Laois Knocknambraher, Co. Laois
B: Non-rotational B: Non-rotational
B: Non-rotational B: Non-rotational B: Non-rotational B: Non-rotational
C: 1st Yr. Pasture C: 1st Yr. Pasture
Years in Set-aside 1
Crop
No. of Set-aside Pts. 3
No. of Tillage /Grass Pts. 3
None Spring barley
4 3
0 4
5
Winter wheat
4
4
4
3
4
5 5
Spring barley for 1 pt & sugar beet for 3 pts. Winter barley Spring barley
4 3
4 4
6
Spring barley
4
4
9
Peas
3
4
1
Spring barley
3
4
1
Sugar beet
3
4
Grass for silage
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Other notes
Sprayed with round-up previous autumn, not much grass at start of May. Beside river.
Ditch running along road in set-aside. 1st year seeded set-aside beside longterm set-aside Set-aside in 9th year but burnt off (pesticides) in Sept 2002 & volunteers emerging in April 2003
15 yrs in grass but this is 1st yr as setaside
Set-aside Category
Location of Site
Years in Set-aside 1
C: 1st Yr. Pasture
Kyledellig, Co. Laois
C: 1st Yr. Pasture
Avoley, Co. Laois
1
D: Long-term (Grazed) D: Long-term (Grazed) D: Long-term (Grazed) D: Long-term (Grazed) E: Other 1st Yr. Setaside Type E: Other 1st Yr. Setaside Type
Ballymanus, Co. Laois Borris, Co. Laois
4 or 5
F: Wildflower
Crop
No. of Set-aside Pts. 4
No. of Tillage /Grass Pts. 4
Spring barley
3
4
None
4
0
10
Spring barley
4
4
1st year setaside, grazed previous year, never ploughed, drained but still marsh plants Field never reseeded, lots of rushes, wet. Setaside was grazed previous winter but other years weren’t. Fertiliser added in August 2002
Timogue, Co. Laois
3
Spring barley
4
4
River runs along tillage & setaside
Stradbally, Co. Laois
3
Spring barley
4
4
Money, Co. Laois
1
Sugar beet
3
4
Ballymanus. Co. Laois
1
Winter barley
4
4
Killyganard, Co. Laois
1
Spring barley
3
4
Grass with cattle
338
Other notes
Sugar beet harvested and field left to regenerate Was winter wheat previous year, cultivated in autumn 2002 & volunteer wheat in spring/summer 2003 Wild flower setaside - 1 ha
Appendix V: Table A5: The 36 field boundary species, which were included in the field boundary species only RDA analysis in Figure 3.13 in Chapter 3. Common Name Scientific Name Blackbird Turdus merula Blackcap Sylvia atricapilla Blue Tit Parus caeruleus Bullfinch Pyrrhula pyrrhula Buzzard Buteo buteo Chaffinch Fringilla coelebs Chiffchaff Phylloscopus collybita Coal Tit Parus ater Collared Dove Streptopelia decaocto Dunnock Prunella modularis Goldcrest Regulus regulus Goldfinch Carduelis carduelis Great Tit Parus major Greenfinch Carduelis chloris Hooded Crow Corvus corone House Sparrow Passer domesticus Jackdaw Corvus monedula Jay Garrulus glandarius Linnet Carduelis cannabina Long-tailed Tit Aegithalos caudatus Magpie Pica pica Mistle Thrush Turdus viscivorus Reed Bunting Emberiza schoeniclus Robin Erithacus rubecula Rook Corvus frugilegus Sedge Warbler Acrocephalus schoenobaenus Song Thrush Turdus philomelos Starling Sturnus vulgaris Stonechat Saxicola torquata Tree Sparrow Passer montanus Treecreeper Certhia familiaris Whitethroat Sylvia communis Willow Warbler Phylloscopus trochilus Woodpigeon Columba palumbus Wren Troglodytes troglodytes Yellowhammer Emberiza citrinella
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Appendix VI: Table A6: The 28 resident butterfly species in Ireland and their classification as wider countryside species or habitat specialist species. * = species that could be considered habitat specialists in Ireland (Asher et al., 2001). Common Name
Species Name
Classification
Dingy Skipper
Erynnis tages
Habitat specialist
Wood White
Leptidea sinapsis complex
Habitat specialist
Brimstone
Gonepteryx rhami
Wider countryside species
Large White
Pieris brassicae
Wider countryside species
Small White
Pieris rapae
Wider countryside species
Green-veined White
Pieris napi
Wider countryside species
Orange Tip
Anthocharis cardamines
Wider countryside species
Green Hairstreak
Callophrys rubi
Habitat specialist
Brown Hairstreak
Thecla betulae
Habitat specialist
Purple Hairstreak
Quercusia quercus
Wider countryside species*
Small Copper
Lycaena phlaeas
Wider countryside species
Small Blue
Cupido minimus
Habitat specialist
Common Blue
Polyommatus icarus
Wider countryside species
Holly Blue
Celastrina argiolus
Wider countryside species*
Small Tortoiseshell
Aglais urticae
Wider countryside species
Peacock
Inachis io
Wider countryside species
Pearl-bordered Fritillary
Clossiana euphrosyne
Habitat specialist
Dark Green Fritillary
Argynnis aglaja
Habitat specialist
Silver-washed Fritillary
Argynnis paphia
Habitat specialist
Marsh Fritillary
Euphydryas aurinia
Habitat specialist
Speckled Wood
Parage aegeria
Wider countryside species
Wall Brown
Lasiommata megera
Wider countryside species
Grayling
Hipparchia semele
Habitat specialist
Gatekeeper
Pyronia tithonus
Wider countryside species
Meadow Brown
Maniola jurtina
Wider countryside species
Ringlet
Aphantopus hyperantus
Wider countryside species
Small Heath
Coenonympha pamphilus
Wider countryside species
Large Heath
Coenonympha tullia
Habitat specialist
340
Appendix VII: Table A7a: The seven count dates in 2002 for each site used for the habitat replicate analyses (Study 1) and the transect section habitat and vegetation variables analyses (Study 3). Site Code Count 1 Count 2 Count 3 Count 4 Count 5 Count 6 Count 7 B1- LUU1 7-Apr-02 17-Jul-02 5-Aug-02 16-Aug-02 20-Aug-02 29-Aug-02 23-Sep-02 B2- LUU3 24-Apr-02 14-Jul-02 4-Aug-02 16-Aug-02 22-Aug-02 26-Aug-02 21-Sep-02 B3- LUU4 23-Apr-02 14-Jul-02 25-Jul-02 15-Aug-02 21-Aug-02 26-Aug-02 21-Sep-02 B4- Ough 24-Apr-02 14-Jul-02 26-Jul-02 5-Aug-02 22-Aug-02 26-Aug-02 23-Sep-02 C1- LUU1 7-Apr-02 17-Jul-02 5-Aug-02 16-Aug-02 20-Aug-02 29-Aug-02 23-Sep-02 C2- LUU2 7-Apr-02 17-Jul-02 4-Aug-02 16-Aug-02 20-Aug-02 29-Aug-02 23-Sep-02 C3- Baun 21-Jul-02 5-Aug-02 16-Aug-02 20-Aug-02 3-Sep-02 C4- LUU4 23-Apr-02 14-Jul-02 25-Jul-02 15-Aug-02 21-Aug-02 26-Aug-02 21-Sep-02 P1- LUU3 24-Apr-02 14-Jul-02 4-Aug-02 16-Aug-02 22-Aug-02 26-Aug-02 21-Sep-02 P2- LUU5 24-Apr-02 17-Jul-02 4-Aug-02 16-Aug-02 20-Aug-02 29-Aug-02 23-Sep-02 P3- Bal2 23-Apr-02 14-Jul-02 25-Jul-02 15-Aug-02 21-Aug-02 2-Sep-02 21-Sep-02 P4- Bela 31-May-02 14-Jul-02 26-Jul-02 15-Aug-02 22-Aug-02 29-Aug-02 21-Sep-02 S1- LUU6 23-Apr-02 13-Jul-02 26-Jul-02 15-Aug-02 21-Aug-02 26-Aug-02 21-Sep-02 S2- Benn 23-Apr-02 13-Jul-02 26-Jul-02 15-Aug-02 21-Aug-02 26-Aug-02 21-Sep-02 S3- Bray 23-Apr-02 13-Jul-02 26-Jul-02 15-Aug-02 21-Aug-02 26-Aug-02 21-Sep-02 S4- Srow 23-Apr-02 13-Jul-02 26-Jul-02 15-Aug-02 21-Aug-02 26-Aug-02 21-Sep-02 T1- LUU6 23-Apr-02 13-Jul-02 26-Jul-02 15-Aug-02 21-Aug-02 26-Aug-02 21-Sep-02 T2- Benn 23-Apr-02 13-Jul-02 26-Jul-02 15-Aug-02 21-Aug-02 26-Aug-02 21-Sep-02 T3- Bray 23-Apr-02 13-Jul-02 26-Jul-02 15-Aug-02 21-Aug-02 26-Aug-02 21-Sep-02 T4- Srow 23-Apr-02 13-Jul-02 26-Jul-02 15-Aug-02 21-Aug-02 26-Aug-02 21-Sep-02
341
Table A7b: The 2002 count dates for each site that were used for the bar graphs of the distribution of individual species in the habitat types that were allocated to date categories (Study 1). Site Code Early Late Early Late May Late Early Late Early Mid Late Early Late April April May /Early June July July Aug Aug Aug Sept Sept June B1- LUU1 7-Apr 17-Jul 5-Aug 20-Aug 29-Aug 3-Sep 23-Sep B2- LUU3 B3- LUU4 B4- Ough C1- LUU1 C2- LUU2 C3- Baun C4- LUU4 P1- LUU3 P2- LUU5 P3- Bal2 P4- Bela S1- LUU6 & T1- LUU6 S2- Benn & T2- Benn S3- Bray & T3- Bray S4- Srow & T4- Srow
10-Apr 9-Apr
24-Apr 23-Apr 24-Apr
9-May 9-May 9-May
7-Apr 7-Apr 9-Apr 10-Apr
23-Apr 24-Apr 24-Apr 23-Apr 23-Apr 23-Apr 23-Apr 23-Apr
9-May 9-May 5-May 9-May
1-Jun
21-Jun 17-Jun
27-May
17-Jun
1-Jun
21-Jun
31-May
21-Jun 19-Jun
5-Jun
342
14-Jul 14-Jul 14-Jul
14-Jul 14-Jul 14-Jul 14-Jul 13-Jul 13-Jul 13-Jul 13-Jul
25-Jul 26-Jul 17-Jul 17-Jul 21-Jul 25-Jul 17-Jul 25-Jul 26-Jul 26-Jul 26-Jul 26-Jul 26-Jul
4-Aug 5-Aug 5-Aug 5-Aug 4-Aug 5-Aug 5-Aug 4-Aug 4-Aug 5-Aug 5-Aug
19-Aug 15-Aug 15-Aug 20-Aug 20-Aug 20-Aug 15-Aug 19-Aug 16-Aug 15-Aug 15-Aug 15-Aug 15-Aug 15-Aug 15-Aug
26-Aug 21-Aug 26-Aug 29-Aug 29-Aug 21-Aug 26-Aug 29-Aug 21-Aug 29-Aug 26-Aug 21-Aug 21-Aug 21-Aug
2-Sep 2-Sep 2-Sep 3-Sep 3-Sep 3-Sep 2-Sep 2-Sep 3-Sep 2-Sep 3-Sep 2-Sep 2-Sep 2-Sep
21-Sep 21-Sep 23-Sep 23-Sep 23-Sep 21-Sep 21-Sep 23-Sep 21-Sep 21-Sep 21-Sep 21-Sep 21-Sep 21-Sep
Table A7c: The thirteen count dates in 2001 for each site used for the transect section habitat and vegetation variables analyses (Study 3). LUU1 LUU2 LUU3 LUU6 Baunreagh Site 13-Jun-01 13-Jun-01 12-Jun-01 12-Jun-01 15-Jun-01 Count 1 21-Jun-01 21-Jun-01 21-Jun-01 23-Jun-01 20-Jun-01 Count 2 29-Jun-01 29-Jun-01 29-Jun-01 29-Jun-01 30-Jun-01 Count 3 2-Jul-01 2-Jul-01 2-Jul-01 2-Jul-01 2-Jul-01 Count 4 9-Jul-01 9-Jul-01 9-Jul-01 9-Jul-01 9-Jul-01 Count 5 16-Jul-01 16-Jul-01 19-Jul-01 19-Jul-01 16-Jul-01 Count 6 25-Jul-01 23-Jul-01 25-Jul-01 26-Jul-01 25-Jul-01 Count 7 30-Jul-01 30-Jul-01 30-Jul-01 30-Jul-01 30-Jul-01 Count 8 16-Aug-01 15-Aug-01 13-Aug-01 17-Aug-01 15-Aug-01 Count 9 20-Aug-01 20-Aug-01 20-Aug-01 20-Aug-01 20-Aug-01 Count 10 27-Aug-01 27-Aug-01 28-Aug-01 27-Aug-01 28-Aug-01 Count 11 10-Sep-01 10-Sep-01 10-Sep-01 10-Sep-01 10-Sep-01 Count 12 19-Sep-01 19-Sep-01 18-Sep-01 18-Sep-01 19-Sep-01 Count 13
Appendix VIII: Table A8: The 14 butterfly species recorded during the 2001 and 2002 recording seasons and their five-letter code for canonical correspondence analysis (CCA). Common Name Five-Letter Code Brimstone
Brime
Green-veined White
GVWhi
Large White
LarWh
Meadow Brown
MeBro
Orange Tip
OrTip
Peacock
Peaco
Red Admiral
ReAdm
Ringlet
Ringl
Silver-washed Fritillary
Sfrit
Small Tortoiseshell
SmTor
Small White
SmWhi
Speckled Wood
SpWoo
Wall Brown
WallB
Wood White
WoWhi
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