Sep 15, 2017 - University of Southampton for providing the thesis by Nick Smith. And finally, this .... vascular plants and scrub (Hackman 2012). ... There is a more open area across the road to the east, just south of Daneway House, but it is grazed by ..... The red dots in Figure 3 represent the locations of these refuges.
Determining The Habitat Variables That Influence Reptile Distribution by
Mike Caiden
Presented as final requirement for the degree of Master of Conservation Ecology
Oxford Brookes University Faculty of Health and Life Sciences
September 2017
CONTENTS LIST OF FIGURES AND TABLES
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ACKNOWLEDGEMENTS
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ABSTRACT
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ABBREVIATIONS USED IN THE TEXT
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INTRODUCTION
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Background to the project: Reptiles and habitat
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The study site – Daneway Banks
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Environmental variables
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METHODOLOGY
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Surveying reptiles
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Legal and ethical issues
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Measuring variables
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Aspect
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Slope
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Temperature under refuge
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Scrub extent
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Vegetation height
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Statistical analysis
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RESULTS
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Maps of variables and their distributions
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Aspect
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Slope
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Temperature underneath refuges
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Scrub extent
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Vegetation height
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Slow-worm results
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Slow-worm distribution
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Slow-worm response to variables
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Common lizard results
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Common lizard distribution
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Common lizard response to variables
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DISCUSSION
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Distribution of slow-worms and common lizards
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The effect of environmental variables on slow-worms and common lizards
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Aspect
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Slope
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Temperature underneath refugia
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Scrub extent
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Vegetation height
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Refuge material preference
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CONCLUSION
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REFERENCES
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LIST OF FIGURES AND TABLES Figure 1: Reptile distribution in Gloucestershire
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Figure 2: Daneway Banks with surrounding land
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Figure 3: Map of Daneway Banks
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Figure 4: Drop disc dimensions
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Figure 5: Map of aspect and slope
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Figure 6: Aspect data distribution
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Figure 7: Slope data distribution
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Figure 8: Map of temperature recorded under refuges
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Figure 9: Temperature data distribution
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Figure 10: Map of scrub extent
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Figure 11: Scrub extent data distribution
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Figure 12: Map of vegetation height
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Figure 13: Vegetation height data distribution
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Figure 14: Number of surveys where refuge was occupied by at least one slow-worm
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Figure 15: Plot of aspect against slow-worm presence likelihood
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Figure 16: Plot of slope against slow-worm presence likelihood
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Figure 17: Plot of temperature below refuges against slow-worm presence likelihood
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Figure 18: Plot of scrub extent against slow-worm presence likelihood
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Figure 19: Plot of vegetation height against slow-worm presence likelihood
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Figure 20: Total number of slow-worms found under each refuge material
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Figure 21: Number of surveys where refuge was occupied by at least one common lizard
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Figure 22: Plot of aspect against common lizard presence likelihood
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Figure 23: Plot of slope against common lizard presence likelihood
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Figure 24: Plot of temperature against common lizard presence likelihood
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Figure 25: Plot of scrub extent against common lizard presence likelihood
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Figure 26: Plot of vegetation height against common lizard presence likelihood
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Figure 27: Total number of common lizards found under each refuge material
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Table 1: Slow-worm models with lowest AIC
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Table 2: Common lizard models with lowest AIC
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ACKNOWLEDGEMENTS
I would like to thank several people, without whom this project could not have been completed. Firstly, Dr Kathy Meakin and Alan Sumnall of the Gloucestershire Wildlife Trust, who offered advice, equipment, and knowledge of the site, as well as direction for this study. Dr Andrew Lack and Professor Tim Shreeve for advice and providing the tin refuges used in this study. Dr Bruce Riddoch of Oxford Brookes University and Dr Helen Mayfield, who both provided advice on the statistical analysis involved. Dr Angela Julian of ARG UK and Record Pool for providing data on the distribution of reptiles in Gloucestershire. Dr Andrew Fletcher of the University of Queensland, who took the aerial photos used in the measurement of the scrub at Daneway Banks. Sue Brown of Oxford Brookes University Library and Anne-Marie McCann, of the Hartley Library at the University of Southampton for providing the thesis by Nick Smith. And finally, this project most certainly would not have been completed without Kitty McKenny and Anne and Paul Caiden, who provided fantastic emotional, moral, and financial support.
My heartfelt thanks to everyone involved.
This thesis is dedicated to the memories of Geela Caiden, without whom this degree would not have been possible, and to Matt Nussbaum, a gentle soul.
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DECLARATION OF INDIVIDUAL AUTHORSHIP
The author confirms that this research project contains no unacknowledged work or ideas from any publication or work by any other author.
……………………………………………………………………………………….. (signature)
……………………………………… (date)
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ABSTRACT
Reptiles are one of the fastest declining groups of species in Europe. The main cause of their decline is reduction in suitable habitat through loss, degradation, and fragmentation. A greater understanding of the habitat requirements of reptiles can allow more effective site management to be implemented, in order to ensure this decline in habitat quality does not continue. This study was carried out to evaluate the effect that the scrub extent and other habitat variables have on the distribution of reptiles at a site in Gloucestershire, in order to gain a better understanding of the factors that affect reptile distribution. Daneway Banks is a 16 hectare nature reserve in Gloucestershire. A total of 68 refuges, 34 tin and 34 felt, were placed in suitable locations across the reserve in order to attract reptiles. The site was then surveyed 12 times in order to find the distributions of the resident reptile populations. Two species were counted: slow-worm Anguis fragilis and common lizard Zootoca vivipara. A total of 236 slow-worms were found, and although their distribution was patchy, they were found in most areas of the site. A total of 29 common lizards were also found. However, the common lizards were almost entirely confined to the south-west of the site, suggesting the existence of a barrier to their dispersal. Slow-worms were found to prefer tin refuges, while common lizards preferred felt refuges. Both of these preferences were statistically significant. Five variables were also selected for analysis: aspect, angle of slope, temperature below refuge, scrub extent and vegetation height. These variables were measured for each refuge, providing sufficient data for a detailed analysis of the relationship between the presence of reptiles and the values of these variables. In order to analyse the results statistically, generalised additive mixed models (GAMMs) were used. GAMMs allow non-normal, non-linear, repeated measures data to be assessed. Temperature under refuge was found to be an important factor in the presence or absence of both slowworms and common lizards. Slope was found to be an important factor for slow-worms, but not common lizards, while aspect was almost but not quite significant for slow-worms. Scrub extent and vegetation height had no influence at all on the presence of either species. Suggestions are made for adjustments to the survey methodology, to allow a greater chance of detecting stronger relationships between reptile presence and each of the variables. Recommendations are made for continuation of these reptile surveys, in order to collect long-term data and to monitor how reptiles respond to changes in site management.
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ABBREVIATIONS USED IN THE TEXT AIC – Akaike Information Criterion GAMM – Generalised Additive Mixed Model GIS – Geographic Information System GPS – Global Positioning System GWT – Gloucestershire Wildlife Trust SSSI – Site of Special Scientific Interest
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DETERMINING THE HABITAT VARIABLES THAT INFLUENCE REPTILE DISTRIBUTION INTRODUCTION
Background to the project: Reptiles and habitat Reptiles and amphibians across Europe have suffered more substantial declines than any other group of vertebrates. This is directly linked to habitat loss due to human development (Beebee et al 2009). Over 70% of European reptiles are thought to be threatened by habitat loss, a much greater figure than by any other single factor (Beebee et al 2009). It is important to understand how such declines can be slowed or reversed. Conservation efforts for reptiles should therefore be focussed on improving conservation management and restoring habitat, measures that have been shown to work in the past (Beebee et al 2009). The four widespread species of British reptile – slow-worm Anguis fragilis, common (viviparous) lizard Zootoca vivipara, adder Vipera berus, and grass snake Natrix natrix – are all thought to be declining in Britain (JNCC 2010a, 2010b, 2010c, 2010d), although all are under-recorded (Natural England 2011). They are also considered to be sensitive indicator species (Gleed-Owen et al 2005), so their status can be used to evaluate the quality of the wider landscape. The needs of slow-worms in particular have been subject to few studies, and it is likely that less is known about the slow-worm than any other British reptile (Beebee and Griffiths 2000). This is despite the fact that the slow-worm is probably the most numerous and widespread reptile in Great Britain (Platenberg and Griffiths 1999, Edgar et al 2010). In Great Britain, there are three main reasons for the decline of the four widespread species, all linked with habitat: habitat loss, degradation, and fragmentation (Edgar et al 2010). Habitat loss mainly occurs when a site undergoes development, for example the construction of housing, or the creation of agricultural land. The pressure from development on slow-worms and common lizards is worse in areas where their populations are already declining (JNCC 2010a, 2010b). Habitat degradation occurs when a site becomes neglected, allowing habitat succession to take place, so that scrub develops into woodland. This makes the habitat much less suitable for reptiles as it reduces the size of crucial basking areas (Beebee and Corbett 2003). Habitat fragmentation occurs when areas of suitable habitat become disconnected due to changes in land type – for example when a site is divided by a new road, or movement corridors become overgrown and unsuitable. Reptiles have limited dispersal abilities, so when a site’s habitat quality declines, it is difficult for the resident population to relocate, and this problem is made worse when the habitat becomes fragmented (Brady and Phillips 2012). This means the population becomes vulnerable to extinction, either through a disaster such as a fire, or through inbreeding which leads to declining genetic quality and increased susceptibility to disease (Edgar et al 2010). The reduction in suitable habitat for reptiles means that the areas that remain will become ever more important to their continued survival. A greater understanding of the habitat requirements of reptiles can enable a more effective management regime to be implemented, in order to maximise the site’s benefits to reptiles (Bullock et al 2003, Edgar et al 2010). Improving both habitat quality and connectivity – particularly in the face of climate change and the species range shifts it will bring – is therefore essential to the long-term survival of reptiles (Beebee 2013). For reptiles, the physical and thermal properties of a habitat are much more important that the floristic composition (Edgar et al 2010). Reptiles are ectothermic, which means they are unable to generate body
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heat internally. Instead they rely on their external surroundings to provide the heat needed to raise their body temperature high enough to allow physical activity, such as movement and digestion (Beebee and Griffiths 2000). Once the reptile has raised its body temperature to its selected level, this physical activity can occur at its maximum rate and allow the reptile to operate most efficiently (Gaywood and Spellerberg 1996). Beebee et al (2009) suggest that the ectothermic nature of reptiles is the reason they are so threatened – their physiology limits the extent of suitable habitat to areas that can provide their need for sunny basking sites. Most reptiles raise their body temperature by basking in direct sunlight, but the slow-worm prefers to maintain its body temperature through conduction, by staying under cover in contact with warm surfaces (Beebee and Griffiths 2000). Slow-worms do not move quickly so this is likely to be a strategy that enables them to avoid being easily taken by predators; a danger the faster-moving common lizard, which basks in the open, is less vulnerable to (Meek 2005). Reptiles require basking areas that warm up in the sun, yet are sheltered from the wind. They also need places nearby to quickly hide from predators. In practice, this means the best locations are south-facing slopes that contain a variety of grass sward heights, so that reptiles can select an exact temperature in which to bask, along with patches of scrub to escape into if needed (Beebee 2013). As well as offering protection from predators, scrub provides a windbreak and also allows reptiles to cool down rapidly if necessary (Edgar et al 2010). As habitat quality is so important to reptiles, an effective habitat management regime must be implemented to give them the best possible advantage (Edgar et al 2010). Habitat management for the widespread species should provide suitable areas for basking, feeding, breeding and hibernation, all within a landscape that is not fragmented (JNCC 2010a). Surveying reptiles is a crucial way to understand how a site is used by the resident reptile population (Platenberg 1999, Bullock et al 2003). A programme of surveys can reveal which species are present and their distribution across the site. This makes it easier to identify the site’s key features, allowing site managers to enhance them and avoid inadvertently damaging them in future operations (Baker et al 2004). If these surveys continue into the long term, they can also provide insight into the impact that any change in habitat management has on reptile distribution, breeding success, and population size (Edgar et al 2010). The findings from these surveys will allow the site manager to adjust the management plan if necessary, ensuring the management is as effective as possible to maximise the benefit to reptiles (Foster 1996). This study surveyed reptiles at a site in Gloucestershire, and evaluates the influence that a series of habitat variables has on their distribution.
The study site – Daneway Banks Daneway Banks is a 16 hectare nature reserve near Stroud in southern Gloucestershire. It was designated as a Site of Special Scientific Interest (SSSI) in 1983 due to its limestone grassland (CG3 and CG5), vascular plants and scrub (Hackman 2012). It has been managed by the Gloucestershire Wildlife Trust (GWT) since 1969 (Gloucestershire Wildlife Trust 2016a) and was finally purchased by the Trust and the Royal Entomological Society in 2016 (Gloucestershire Wildlife Trust 2016b). It is located on a south to south-east facing slope and the habitat predominately consists of calcareous lowland grassland with some areas of woodland (Natural England no date).
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Figure 1 shows the location of Daneway Banks, along with the known distribution of the four widespread reptile species in Gloucestershire. This is data from the last five years that has been submitted by experts and members of the public to Record Pool, a citizen science website that collects reptile and amphibian data (Record Pool 2017). It shows large gaps around the county, and that little data has been recorded from the area around Daneway Banks. These are probably gaps in knowledge rather than reptile distribution (Gleed-Owen et al 2005). Most records come from the Stroud area to the west of Daneway Banks, and the Forest of Dean, at the far west of the county.
Figure 1: Reptile distribution in Gloucestershire (figures in brackets refer to number of records for that species)
Although this is not recorded in Figure 1, Daneway Banks is home to three species of reptile: slow-worm, common lizard, and adder (pers. obs.). All three species are considered Species of Principal Importance in England in section 41 of the Natural Environment and Rural Communities Act 2006.
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Despite the site’s rural location, it is actually fairly isolated, with barriers to dispersal on all sides (Figure 2).
Figure 2: Daneway Banks with surrounding land
To the north and north-west, the land is managed for intensive agriculture and consists solely of ploughed fields. There is dense woodland to the west and south, while the south and east are bordered by roads. There is a more open area across the road to the east, just south of Daneway House, but it is grazed by sheep and so has a short sward. It also faces south-west so will take longer to warm up than Daneway Banks, which mostly faces south-east. Even though the roads have low levels of traffic, there is no highquality habitat in the immediate vicinity for the reptiles to move to. All of these areas will act as barriers to dispersal for reptiles (Beebee and Griffiths 2000). The poor dispersal ability of reptiles means that it is very important that the habitat at Daneway Banks remains suitable for them, or else they could quickly become extinct. The reserve is well known for its breeding population of large blue butterflies Maculinea arion, which became extinct from the county in the 1960s (Gloucestershire Wildlife Trust 2016a), and extinct in the UK in 1979 (Butterfly Conservation no date), before being reintroduced to Daneway Banks in 2002 (Gloucestershire Wildlife Trust 2016a). Large blues require patches of scrub for shelter, but too much scrub can inhibit growth of their foodplant, wild thyme Thymus polytrichus. Scrub should therefore be limited to around 10-20% of the area of the site (Butterfly Conservation no date). However, these values might not be optimal for reptiles, and this is an example of the conflict that can arise between different groups of species. Site managers can benefit both groups by studying their requirements and implementing their findings in a careful way (Beebee and Grayson 2003). Preliminary analysis of Daneway Banks using ESRI ArcGIS (ESRI 2015) geographic information system (GIS) software shows the total area of the site to be approximately 16.4 hectares. The current area of scrub across the entire site is approximately 2.2 hectares, or 13.4% (see Methodology). The grassland accounts for approximately 12.8 hectares, so the total proportion of grassland that is scrub is 17.2%. While this is between the 10-20% limit recommended by Butterfly Conservation (no date), it does not
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take into account the variation of scrub extent across the site. Dividing the site into a series of 80 m x 80 m squares (see Methodology) shows the variation in scrub extent – 18 of the 34 squares contained less than 10% scrub while six contained more than 20%. This report will analyse the relationship between scrub extent and reptile presence to try to find the optimal level for reptiles. Two of the reasons for Daneway Banks’ designation as a SSSI are its unimproved calcareous grassland and its scrub (Hackman 2012). The site is mainly managed for the grassland, with the scrub considered to be an important element of this grassland. Since GWT’s purchase of Daneway Banks and the employment of a new reserve manager in 2016, the scrub is managed on rotation to create a varied structure – ensuring the scrub is not too dominant and not too sparse (A. Sumnall pers. comm.). This study has been carried out to discover the distribution of reptiles at Daneway Banks, to evaluate the effect that the scrub extent and other environmental factors have on this distribution, and the implications this may have on future site management. The methodology and results will be passed onto GWT so that they can implement the findings and continue monitoring the reptile populations. Brady and Phillips (2012) recommend that site analysis of this kind should be performed on a much wider scale than it is currently, in order to cover as many sites and habitat types as possible. This would allow the creation of a scoring system to assess the likelihood of a particular site containing reptiles, or its future suitability as a receptor site for receiving translocated reptiles. Their work built on a paper by Oldham et al (2000) that created a similar scoring system for assessing the suitability of ponds as breeding locations for great crested newts, which became widely implemented by ecologists and site managers.
Environmental variables A range of environmental variables affect the distribution of reptiles on a site (Edgar et al 2010). Brady and Phillips (2012) listed the site-level environmental variables they considered to be important indicators of habitat quality. The variables that influence site distribution, and can be reliably and easily quantified as continuous data, have been selected for inclusion in this study. Collecting continuous data avoids the problem of subjectivity which can arise if this study is repeated in future (Brady and Phillips 2012). Using refuges to survey reptiles also helps to achieve a degree of standardisation (Cheung and Gent 1996). After this study, GWT will continue to monitor reptiles at Daneway Banks to examine the impact that different management techniques have on the pattern of reptile distribution on site, and the replicability of the current study will allow them to re-examine the same variables (K. Meakin pers. comm.). Five variables were thus chosen as having the potential to explain the distribution of reptiles at Daneway Banks:
Aspect of refuge Slope steepness where refuge is located Temperature underneath refuge Scrub extent around each refuge Vegetation height around each refuge
The first two factors: the aspect (direction) and steepness of slope, if any, of the landscape are linked. In theory, south-facing slopes are particularly attractive to reptiles, as they rely on the warmth of the sun to raise their body temperature (Beebee & Griffiths 2000). South-facing slopes that are steeper can capture more of this sunlight compared to flatter ones. Conversely, north-facing slopes are more shielded from sunlight, which will limit their suitability for basking reptiles (Brady and Phillips 2012). These factors
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were measured to assess what effect slope aspect and steepness had on reptile numbers at Daneway Banks in reality. Air temperature is thought to be mostly unrelated to the temperatures experienced by reptiles on the ground (Gaywood and Spellerberg 1996) and so the temperature underneath each refuge is a more direct measurement. As reptiles are ectotherms, they must move around in order to find a suitable temperature in which to bask, and will avoid areas that are too cool or too hot (Gent 1996). The actual body temperature of each reptile was not measured. Measuring body temperature of reptiles in the wild is a difficult task (Platenberg 1999), and it was felt that measuring the temperature of reptiles themselves would be outside the scope of this study, which was to investigate the effect of environmental variables on reptile distribution. The temperature under refuge cannot be considered to be a proxy for the body temperature of the reptiles found there. This is because there is no way to tell how long the reptile has been there: it may have just arrived and be relatively cool or it may have been there for a long time and have almost raised its body temperature to its desired level (Smith 1990). However, inferences about the preferences of reptiles can be made from the temperatures at which they choose to bask in, as reptiles will not usually be found under refuges that are too warm or too cold for them (Gent 1996). Platenberg (1999) found no link between the temperature under refuge and the likelihood of slow-worms being present. However, she used a single refuge in the middle of her survey site as a proxy for all refuges, which could not represent the conditions under every other refuge. By measuring under every refuge on every survey, the current study has the potential to record more precise data about reptiles’ response to temperature. Scrub is an important habitat feature for reptiles, as it offers shelter from predators and from the wind (Edgar et al 2010). Reptiles will rarely stray far from the protection offered by scrub (Brady and Phillips 2012). However, excessive scrub is detrimental to the suitability of a site, as it can shade out the ground, lowering the temperature and eventually reducing the complexity of the ground vegetation (Edgar et al 2010). Once the scrub extent in an area has been measured, comparisons can be made across the site to see what impact, if any, it has on the number of reptiles found in each area. The vegetation height immediately adjacent to each refuge was measured, in order to determine the influence that vegetation height had on reptile numbers under each refuge. The structure of vegetation is thought to be more important than the specific flora in the vegetation (Edgar et al 2010), as it influences feeding, basking, and sheltering opportunities (Brady and Phillips 2012). Therefore it is important to gain an understanding of how specific heights of vegetation influence the number of reptiles found in the immediate vicinity. This is particularly important at Daneway Banks, due to the presence of large blues. These butterflies have a unique life cycle and very specific habitat requirements. They are entirely dependent on the red ant Myrmica sabuleti, which is tricked into caring for their caterpillars. These caterpillars attract foraging red ants with a sweet secretion, and are then carried by the ants into their nest. The caterpillars proceed to eat the ant grubs before emerging as adults (EU Wildlife and Sustainable Farming Project 2009). These ants have a very specific temperature requirement which is provided by the short sward (Centre for Ecology & Hydrology no date). The best sward height to encourage the ants, as well as wild thyme, the preferred foodplant of the large blue, is 2-5 cm (Butterfly Conservation no date). Historically, this sward height was maintained by rabbits Oryctollagus cuniculus, but as myxomatosis devastated rabbit populations in the 1950s, the grazing pressure from rabbits was reduced. The red ants’ habitat became increasingly rare which lead to the extinction of large blues in the UK (Thomas 1995). In order to achieve this short sward, Daneway Banks is grazed by Welsh mountain ponies and Norfolk horn sheep. Grazing animals have been present at Daneway Banks since at least 1969, when GWT began
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management of the site, but grazing intensity has increased since the introduction of the large blues in 2002. These grazing animals are kept in grazing compartments by a network of fences to ensure each area is grazed sufficiently. They are removed before the emergence of the large blues in June, which avoids the problem of overgrazing which creates too short a sward for the ants (A. Sumnall pers. comm). This also allows the wild thyme time to flower (Butterfly Conservation no date). Reptiles, however, have different sward preferences to red ants. Common lizards in particular prefer a wider variety of vegetation structure (Bullock et al 2003). If the vegetation sward is too short, it offers little food and no cover from predators such as common buzzards Buteo buteo and carrion crows Corvus corone (Beebee and Griffiths 2000, Edgar et al 2010), both of which are commonly seen on site (pers. obs.). Conversely, if the sward is too tall, it blocks sunlight from reaching the ground, and becomes rank and impenetrable (Beebee and Corbett 2003). Slow-worms spend less time basking in the open than common lizards so are more tolerant of uniform vegetation height, but the vegetation must be dense (Bullock et al 2003). This means that there must be compromise between the extent of areas managed for red ants and areas managed for reptiles.
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METHODOLOGY Surveying reptiles Reptiles are a difficult group of species to survey in Britain as they are small, secretive, and cryptic (Inns 2009). This means that reptiles are frequently under-recorded and so their local status is often poorly understood (Foster 1996). Direct observation of reptiles can be time-consuming and unreliable, as it is dependent on the weather conditions at the time (Griffiths and Inns 2003, Beebee 2013). This technique is unsuitable for surveying slow-worms, which tend to bask under the cover of refuges or among vegetation (Beebee 2013). A more effective way to survey the lizard species found at Daneway Banks is to use artificial refuges (Beebee 2013). These are flat materials that are laid on the ground, which warm up during the day so that they have a higher temperature than their surroundings. As reptiles are ectothermic species (Beebee & Griffiths 2000), and rely on external sources to generate their body heat, they are attracted to these warmer refuges, and lie underneath to warm up. As well as a warmer environment, the refuges also offer protection from predators (Beebee 2013). Surveying with the use of refuges allows a degree of standardisation between surveys, particularly when conducted by surveyors with different levels of experience, as this technique relies less on the field-craft of the observer (Cheung and Gent 1996). This is an important benefit, especially if GWT continue monitoring Daneway Banks with a series of volunteers rather than one or two experienced reptile surveyors. Standardising the surveying method is an important way to allow the results of multiple surveys to be directly comparable with each other (Reading 1997), especially if the same locations are used for the refuges between years. In order to ensure the refuges were evenly distributed, the site was divided into a series of 80 metre x 80 metre squares (Figure 3). This is the same size that is used by GWT to survey for limestone grassland (CG5) indicator species, which is used to gauge the condition of this SSSI feature. Assessing the state of reptile populations using the same scale as the vegetation will aid in GWT’s assessments of the site in future (K. Meakin pers. comm.).
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Figure 3: Map of Daneway Banks
Refuges are typically made of either corrugated metal tin or roofing felt, although other materials can also be used (Sewell et al 2013). Each material has different thermal properties. Tins tend to heat up quickly and have a higher peak temperature compared to roofing felt. However, they will also cool down faster than felt once the sun is no longer heating them directly (Riddell 1996). These different thermal properties will appeal to different species (Edgar et al 2010). In her study in Kent, Riddell (1996) found that slow-worms and common lizards preferred roofing felt as tin heated up too quickly for them, although Natural England (2011) says that both materials should appeal equally to slow-worms, and also to common lizards. To attract as many reptiles as possible, both tin and felt refuges were used at Daneway Banks, as recommended by Sewell et al (2013), with one of each type placed in each square.
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Recommendations for refuge size vary from 0.5 metres x 0.5 metres (e.g. Griffiths and Inns 2003, Sewell et al 2013) to “at least a metre or so square” (Beebee 2013). A size of 0.5 metres x 0.5 metres was chosen for surveying Daneway Banks for several reasons. Firstly, if each refuge was one metre square, this would immediately quadruple the costs of the refuges. They would become unwieldy to carry across the site, which is an important consideration when the furthest refuges were located approximately 750 metres from the nearest car park (Gent 1996). Finally, such large refuges have a visual impact on visiting members of the public. This means that members of the public are more likely to tamper with them (Edgar et al 2010). Smaller refuges are easier to place in less obvious locations. The refuges were placed close to patches of cover rather than in the middle of open areas, as these locations are more likely to attract reptiles (Beebee 2013), and where possible they were placed in southfacing locations (Sewell et al 2013). Where possible, pairs of refuges were placed well away from pairs in adjacent squares. The smallest distance between nearest pairs was 34 metres and the largest distance was 76 metres. This distance means that reptiles will not be affected by the presence of refuges in nearby squares. Refuges were placed in 34 grid squares, which resulted in a total of 68 refuges being placed across 16.4 hectares. Areas of woodland are unsuitable for reptiles (Edgar et al 2010), and removing these from the calculation leaves the grassland: an area of 12.8 hectares. This resulted in 5.31 refuges per hectare of suitable habitat. This is above the recommendation of 5 refuges per hectare by Inns (1996), and is within the range of 5-10 refuges per hectare recommended by Froglife (1999). The locations of these refuges were logged using a handheld GPS receiver (model GPSmap 60CSx, Garmin Ltd., Kansas, USA). This allowed their locations to be plotted with GIS software and spatial analysis performed with them. The red dots in Figure 3 represent the locations of these refuges. As the two types of refuge were placed near to each other, comparison of the preferences by different species is made possible. These preferences can be compared to the findings of Riddell (1996) and Natural England (2011). All 68 refuges were visited on every survey, and the number and species of any reptiles underneath was recorded on a survey sheet. On the first survey, the refuges in square 1 were visited first, and then square 2, all the way up to square 34. This visitation sequence was then reversed each time to reduce the potential bias that would result from surveying each refuge at the same time of day on every survey. Refuges were placed at Daneway Banks 18 days before the first survey, which allowed the reptiles time to find and utilise them. This time is sufficient according to Natural England (2011), which considers “at least two weeks in good conditions” to be a good guideline, but Edgar et al (2010) consider “several weeks” to be appropriate, while Beebee (2013) suggests that “at least a couple of months” is needed. For the current project, logistical problems with acquiring refuge materials meant that the refuges could not be placed any earlier. This also meant that surveys began on the 30th of May and ran until the 1st of July. Most authors state that the most productive months for reptile survey in Britain are April and May, with September also a good month (e.g. Froglife 1999, Griffiths and Inns 2003, Sewell et al 2013). This current study was mostly conducted in June, which tends not to be as productive due to the higher temperatures typically achieved. This means that reptiles need to bask less, and so will remain underneath refuges for shorter periods of time (Griffiths and Inns 2003). Nevertheless, if surveys are conducted in cooler weather conditions, June can also be a productive month (Natural England 2011). During the most of the surveys, Daneway Banks was found to warm up very quickly, particularly later in the month. Therefore surveying was conducted on overcast days, where cloud cover was between 6/8 and 8/8, to avoid surveying when the site was too hot.
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Reptile surveys should either be conducted in the morning or late afternoon, when temperatures are more suitable to observe basking reptiles (Griffiths and Inns 2003). The south-easterly aspect and general topography of Daneway Banks meant that sunlight stopped reaching the site relatively early in the day and so all surveys were conducted in the morning – mostly between approximately 9:00 a.m. and 11:00 a.m. The site was surveyed 12 times. This is more than the four surveys recommended by Beebee (2013), and the five surveys recommended by Griffiths and Inns (2003) to detect presence. Sewell et al (2013) recommended that seven surveys were sufficient to determine presence on a site where the refuges had not been placed for a long period of time prior to surveying. It is therefore likely that the site was surveyed an adequate number of times in order to detect reptile presence across the site.
Legal and ethical issues The study had no negative impact on the reptiles themselves. Using refuges to survey can even benefit reptiles by providing them with places to hide from predators (Brady and Phillips 2012). To minimise undue disturbance and stress, the reptiles were never handled and each refuge was only lifted to count the animals underneath and to take a temperature measurement. The refuge was then carefully lowered back to its original position, ensuring that no animals were trapped or crushed by its weight. This process probably took less than ten seconds each time. Prior to placement, any sharp edges on the metal refuges were filed smooth, to avoid injury to reptiles or any other animals. Volunteer GWT wardens who were patrolling the site were asked to observe the refuges and ensure they were not disturbed by members of the public. Each refuge had a label attached which asked people not to disturb it, and no evidence was ever seen that they had been disturbed. Discussions with GWT staff prior to surveying suggested that the rare reptile species (i.e. sand lizard Lacerta agilis and smooth snake Coronella austriaca) were not expected to be present at Daneway Banks (K. Meakin and A. Sumnall pers. comm.). These species are protected under Schedule 5 of the Wildlife and Countryside Act 1981 and a licence is required to survey a site if these species are expected to be present. As this was not the case, no protected species licences were necessary. Finally, all refuges were removed at the end of the survey period, as recommended by Griffiths and Inns (2003). This ensured they will not continue to attract reptiles, and thus become vulnerable to collection or persecution by members of the public, when the site is not being monitored by the surveyor or the volunteer wardens.
Measuring variables Aspect For each refuge, a compass was used to measure the aspect of the line of greatest slope. The aspect was measured to the nearest 10° as it was difficult to be more precise than this. If a refuge’s slope was found to be zero, i.e. flat, then the refuge was considered to have no aspect. Slope The slope of each refuge was measured using an iPad2 tablet (Apple Inc., California, USA) which contains a gyroscope for measuring angle of tilt. Research into the sensitivity of such devices has shown they can provide data that is comparable to dedicated clinical hardware that is used to quantify the postural stability of patients (Alberts et al 2015). Therefore it was considered to be suitable for the much simpler
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task of measuring the angle of a slope. The iPad2 was placed on each refuge and aligned with the line of greatest slope. A reading was then taken from the Angle Pro mobile app (5fuf5 2015), which was sensitive to the nearest degree. Temperature under refuge The temperature underneath each refuge was measured using an infrared thermometer (model Lasergrip 774, Etekcity Corporation, California, USA). If a reptile was present, this temperature was measured from a point directly next to the location of the reptile, and if multiple reptiles were present, this point was the mid-way point between them all. If no reptile was present, the temperature was taken from the point under the centre of the refuge. Occasionally, common lizards were recorded basking on top of refuges, rather than underneath. When this happened, the temperature was recorded from the point the common lizard had been sitting. The thermometer’s stated accuracy was ±2°C and its resolution was 0.1°C. The temperature was measured on every survey, unlike the other variables, which were assumed to remain the same between each survey. Scrub extent Aerial photography was used to calculate the extent and distribution of scrub at Daneway Banks. The photographs were taken by Dr Andrew Fletcher of the University of Queensland, using a drone, in the summer of 2015. These photographs were merged using Adobe Photoshop CS3 (Adobe Systems 2007) and fitted to a map using ArcGIS. ArcGIS was then used to trace around any scrub that was visible on the aerial photos, creating a vector of the distribution of scrub. This was then verified on the ground to be fairly representative of the scrub at Daneway Banks. The total area of scrub was 2.2 hectares. As each refuge in a pair was located within a few metres of the other, there would be considerable overlap in the amount of scrub around each. To avoid this problem, a point equidistant to the two refuges in each square was used as the basis for calculating the scrub extent. A 20 metre radius circle was then plotted around each of these points, and the area of scrub that lay within each circle was calculated using ArcGIS. This means that scrub extent was calculated for each square, rather than for each refuge. The lizard species have home ranges in the hundreds of square metres rather than thousands (Gent 1996). Measuring scrub extent using a circle of 20 metre radius gives a circle with an area of 1,257 m2, which should cover the home range of both lizard species found at Daneway Banks, if it is assumed that the refuge is in the centre of their range. Vegetation height To calculate the height of the vegetation in the immediate vicinity of each refuge, a drop disc was used. This is a disc of 30 cm diameter and 200g in weight, with a slot in the middle (Figure 4). The disc is dropped from a height of 1 metre along a ruler and the vegetation height was measured to the nearest 5 mm. This measurement was taken four times per refuge – one on each side, at a distance of 15 cm, and an average was calculated. The drop disc technique is objective, consistent, and simple to use (Stewart et al 2001). Due to the relatively short timeframe of the surveys (33 days), it was assumed that the vegetation did not change its height over this period. Vegetation height was measured in the second week of surveying. Another advantage of using this technique is that it does not measure the very top of the vegetation but rather the height of the “bulk” of it. This is a more relevant metric for reptiles, which would be too heavy to bask at the very top of the vegetation, which mostly consisted of species of grass.
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Figure 4: Drop disc dimensions
Statistical analysis The statistical technique that was used to analyse the data that was collected was the Generalised Additive Mixed Model (GAMM). The GAMM is an extension of linear modelling that is used when certain criteria are met (Zuur et al 2009). In a simple analysis, when attempting to predict the outcome of a dependent variable based on an independent variable, the default option is linear regression, which assumes that the residuals of the data being regressed can be modelled by the normal distribution (Beckerman et al 2017). This is a continuous distribution that is symmetrical and unbounded: it can include fractional and negative values (Thomas et al 2017). However, the normal distribution does not adequately describe ecological data that is based on counts: one could not count half a reptile or negative reptiles, only 0, 1, or 2 reptiles, and so on. A better way to model count data is to use the Poisson distribution (Beckerman et al 2017). This is a discrete distribution that is bounded at zero, and has a variance that is equal to the mean (Thomas et al 2017). The term “generalised” in GAMM means that it is able to accept data distributions other than the normal distribution (Zuur et al 2009). Generalised models are better at capturing and explaining the natural variation found in biological and ecological processes (Buckley 2015), which reduces bias and increases explanatory power of the model (Warton and Hui 2011). However, a common problem with count-based data is overdispersion (Zuur et al 2009). Essentially, overdispersion occurs when the variance of the dependent variable is larger than the mean: as a value increases, the variance around it does too. This is a problem as it violates the assumption of the Poisson distribution that the mean of the dependent variable is equal to the variance (Thomas et al 2017). The data from this study were found to be quite heavily overdispersed. One solution to this problem is to use the binary distribution instead of the Poisson distribution, by converting the count data to binary (i.e. presence/absence) data. This is because overdispersion cannot occur in data with a binary response (Crawley 2013). This type of transformation can be particularly useful in surveys such as the current one, where counts were typically low, with many zeroes (absences) recorded. This is because there is much uncertainty associated with these low counts, as it is unknown how many animals were actually present but not recorded. However, when found, it is accurate to say at
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least one reptile was present, and modelling the data in this way will lead to a better model of population density (Qian 2010). Therefore, rather than using the raw count of reptiles, the data was converted to binary data. GAMMs utilise additive modelling, which allows them to be used when the dependent variable does not have a linear relationship with the independent variable (Zuur et al 2007). To do this, additive models fit a non-parametric smoothing curve through the data, rather than trying to fit a straight line as a linear model would do (Zuur et al 2009). This is a benefit as it is expected that certain environmental variables, such as temperature, will show a non-linear response: it is likely that more reptiles will be found at an air temperature of 20°C than 10°C or 30°C, for example (Cheung and Gent 1996). Variables that have a linear response can also be included in GAMMs (Thomas et al 2017), allowing all variables to be assessed using the same statistical technique. The curve of each GAMM was determined through the process of cross-validation. Cross-validation determines the optimal smoothing amount and reduces the risk of over- or under-fitting a model to the available data (Zuur et al 2009). Another advantage of GAMMs is their ability to account for repeated measures. In order to adequately monitor reptiles, which are difficult to study, multiple surveys are needed. This gives the surveyor greater confidence that they are detecting more reptiles and therefore more accurately recording their distribution (Sewell et al 2013). Statistically, however, these repeated measures create a problem. Most statistical techniques work on the assumption that the errors of a variable are independent of each other: that is one data point has not been affected by another (Crawley 2013). With repeated measures, however, this is not the case. The results from each survey cannot be considered to be independent of the others, as the same refuges are observed each time. Reptiles are not randomly distributed across the site: by definition, refuges in good reptile habitat are more likely to contain more reptiles than those in poor habitat. Therefore a refuge that has many reptiles on one survey is more likely to have many reptiles on the next survey. If the repeated measures are not accounted for, the degrees of freedom of errors will be many times larger than they should be. This will lead to false conclusions of statistically significant relationships, i.e. type 1 errors (Crawley 2013). This issue is called temporal pseudoreplication, and can be accounted for by using a mixed-effect model, such as a GAMM (Crawley 2013). By treating refuge as a random effect, the many characteristics, some unobserved, that influence each count can be incorporated into the model. Including refuge as a random effect models the correlational structure that is created by the repeated measures, which then avoids the problem of type 1 errors (Everitt and Hothorn 2011). The fact that two refuges were placed in close proximity in each square of the site also needed to be accounted for, as this can introduce a bias known as spatial pseudoreplication. This is because the environmental conditions for each pair are likely to be more similar to each other than the refuges in other pairs (Crawley 2013). The spatial pseudoreplication was accounted for by treating each refuge as being nested within its square, and the GAMM is then able to incorporate these nested random effects. The dependent variables (i.e. environmental variables: aspect, slope, temperature, scrub extent and vegetation height) were then treated as fixed effects. GAMMs were fitted with the statistical software R (R Core Team 2017), using the gamm4 package (Wood and Scheipl 2017). The variable graphs were produced using R and the ggplot2 package (Wickham 2009).
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RESULTS In this section, the geographical trends of the five measured variables are shown in a series of maps, along with the data trends for each variable. In the next section, the distribution of the slow-worms and common lizards are shown, along with the relationship that was found between presence/absence and each of the environmental variables. Only one adder was found on these surveys, so this species will not be considered further. Maps of variables and their distributions Aspect As aspect and slope are intrinsically linked (Brady and Phillips 2012), it is more helpful to show their geographical trends together on one map (Figure 5). This map shows that the majority of refuges faced between east (90°) and south (180°). This pattern broadly followed the general topography of the site. The arrows indicate the aspect of each refuge and the size of each arrow represents the steepness of slope. The steepness in degrees is also shown in a label next to each arrow.
Figure 5: Map of aspect and slope
This tendency for refuges to face broadly easterly to southerly is shown in the histogram in Figure 6 by the large peak between 90° and 180°, which accounted for over 70% of the refuges. The main exception to this is shown in Figure 5 by an area to the west of the site, from grid squares 29 to 33, which faced from south-west to north-west. Five refuges were level and so had no aspect at all.
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Figure 6: Aspect data distribution
Slope The steepest slopes all lay at the north-east of the site, in squares 15, 17, 20 and 21. There was also a fairly flat section that ran along the length of the north-west perimeter of the site (Figure 5). The rest of the site lay between these two extremes. The histogram of the slope values (Figure 7) shows that most of the refuges were placed in relatively level locations, with over 70% of the refuges being on slopes less than 12°. After this point, steeper slopes become increasingly rare.
Figure 7: Slope data distribution
Temperature underneath refuges The map in Figure 8 shows the mean temperature recorded underneath each refuge. This temperature appeared to be fairly uniform across the site, with no major variation recorded. The only refuges that
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showed a noticeably lower temperature were the pair in square 28. They were located in a partly wooded area that was never found to be warm despite gaps in the tree canopy.
Figure 8: Map of temperature recorded under refuges
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The histogram of these temperature values (Figure 9) shows that the temperature appeared to closely follow a normal distribution, with the most common temperature being 22°C. There was a minor secondary peak at the 30°C point which then proceeded to drop off as the temperature increased. The lowest temperature recorded was 12.4°C and the highest temperature was 38.0°C: a range of 25.6°C.
Figure 9: Temperature data distribution
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Scrub extent The extent of scrub found within a 20 metre radius of each of the refuges was variable. The greatest amount was found in squares 3 and 8 in the south, squares 11, 12, and 31 to the west, and 16, 18, 23, 24, 26 and 27 in the north (Figure 10). The other squares had substantially less scrub than this.
Figure 10: Map of scrub extent
The histogram of scrub extent shows a positive skew (Figure 11), and the area around most of the refuges contained less than 20% scrub.
Figure 11: Scrub extent data distribution
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Vegetation height The refuges surrounded by the tallest vegetation were located in squares 3, 7, 22, 27, 30, and 31 but there was no clear distribution pattern to these squares (Figure 12).
Figure 12: Map of vegetation height
The histogram of vegetation height (Figure 13) appears to be approximately normally distributed, aside from a smaller secondary peak around the 16 cm point. Most of the vegetation was between 3 cm and 13 cm in height
. Figure 13: Vegetation height data distribution
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Slow-worm results Slow-worm distribution The distribution of refuges that were occupied by slow-worms is shown below in Figure 14. Refuges that were occupied on more surveys are displayed larger and redder than those occupied on fewer occasions. Only one refuge was occupied on each of the 12 surveys, while 27 refuges were never found to be occupied. There was a nearly unbroken line of occupied refuges in squares 12-18 but the distribution of the other occupied refuges was patchy – some refuges that were occupied many times were next to refuges that were never occupied. Broadly speaking, the refuges that were most occupied were located in the centre to the north-east of the site, and along the very south edge. Even in these areas, there were several large gaps. There was a clear line of empty refuges in squares 28-33, which together made up over a third of all empty refuges. However, there was no clear pattern to any of the other absences.
Figure 14: Number of surveys (n=12) where refuge was occupied by at least one slow-worm
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Slow-worm response to variables A series of statistical models, containing every combination of different variables, was constructed using R. In order to select the most efficient one, the models were compared using the Akaike Information Criterion (AIC), a value that is produced with each model. The AIC assesses how well a given model fits the data relative to the other models, but cannot by itself state how well that model explains the data (Richards 2015). When comparing AICs of each model, the model with the lowest AIC is considered to have the “best” fit of all the models. However, if the difference in AIC (∆AIC) between a particular model and the model with the lowest AIC is less than 2, both of the models are considered to have essentially equivalent ability to explain the data (Thomas et al 2017). In order to avoid selecting overly complex models, a model that is within the ∆AIC threshold of 2 but includes more variables and has a greater AIC should be removed. While using AIC in this way is conservative, it is a justifiable method for assessing the relative support that each model has (Richards 2015). However, Whittingham et al (2006) argue that there is rarely one single best model. By singling one out for further analysis, while ignoring other models that can explain the data nearly as well, can suggest a degree of confidence in the model that cannot be backed up by the data. Instead, several models should be considered together if they have a similar ability to explain the data. By doing this, a more honest appraisal of the uncertainty in the data can be given (Whittingham et al 2006). One approach to doing this is to assign greater importance to a variable if it appears in all of the best models, and less importance to a variable that occurs in fewer models (Richards 2015). P-values that are produced with each model cannot be considered to be exact, as they have been estimated as a result of the cross-validation. P-values near to the significance threshold (e.g. p=0.02 to p=0.05) should therefore be treated with caution (Zuur et al 2009). Each variable’s p-value is shown in the tables below, produced by the model with the lowest AIC that contained that variable. The results of the five best slow-worm models are shown below in Table 1: Table 1: Slow-worm models with lowest AIC
Variables included in model Model number
Aspect
1 2
×
Slope
Temperature under refuge
× ×
3
Scrub extent
d.f.
AIC
∆AIC
×
7
712.103
–
×
9
712.574
0.471
×
5
713.807
1.704
×
7
714.257
2.154
11
715.853
3.750
4
×
5
×
×
×
×
0.094
0.015
0.003
0.374
Variable
Vegetation height
0.840
p-value
The model with the lowest AIC had slope and temperature as independent variables, suggesting that these variables were the best for predicting the presence of slow-worms at Daneway Banks. Two other models had a ∆AIC of less than two: one included aspect along with slope and temperature, and one just
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included temperature. Using Richards’ (2015) method to avoid selecting overly complex models, the model that included aspect, slope, and temperature should not be considered as its AIC was larger and it included an extra variable. The model just containing temperature had a larger AIC but included fewer variables, so should be retained as an alternative option. Clearly, temperature under refuge was a very important variable, as it occurred in all of the five best models. Slope occurred in both of the top two models, while aspect occurred in the second and fourth best models. This suggests that slope was more important than aspect for predicting slow-worm presence. Scrub extent did not occur until the fifth best model, while vegetation height did not occur in any of the top five models. This suggests that these latter two variables had little to no ability to predict slow-worm presence. Variable plots The following plots show the relationships between reptile presence and the measured environmental variables. There are two aspects to each plot. The points represent each surveyed refuge. The x-axis is the recorded value for that particular environmental variable and the y-axis shows whether a reptile was present (1) or absent (0) for that refuge. An amount of random noise, or “jitter”, has been applied to each point to prevent overlapped points from becoming obscured (Buckley 2015). The trendline represents the predicted probability of reptile presence with any given value for the environmental variable, along with confidence intervals showing the standard error of each predicted value. The shape of this line was calculated using predicted values from a GAMM with binary errors and a logit link function. Each variable was predicted with a separate GAMM, which only used that variable as input.
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Aspect The plot of occupied refuges against aspect (Figure 15) shows that the occupied refuges mostly faced between 100° and 200°, i.e. from approximately east-southeast to approximately south-southwest. No slow-worms were found under any refuge that faced more westerly than south-west, despite several refuges facing further west than this. There were also some records from a refuge with an aspect of 10°. Aspect was a variable in the second-best model for slow-worms (Table 1). Examining this model showed that aspect was not significant, although it was close (p=0.094). Plotting the data showed a trendline that was a smooth downward curve from an aspect of 10° through to 300°. However, there was a large gap between the refuges at 10° until the refuges at 100° where there were no records, so not much can be read into the shape of this trendline.
Figure 15: Plot of aspect against slow-worm presence likelihood
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Slope The plot of occupied refuges against slope (Figure 16) shows the slow-worms were found across all values of slope steepness. However, the absences became less likely as steepness increased, so the overall effect is that the probability of slow-worm presence increased as slope steepness did. Slope was found to be a variable in the best model for predicting slow-worm presence (Table 1), and examining the GAMM showed a significant result for this relationship (p=0.015). The trendline shows this relationship clearly: the steeper the slope, the greater the likelihood of slow-worm presence. There is a slight curve upwards which suggests that this relationship was not linear – steeper slopes were disproportionally more attractive to slow-worms.
Figure 16: Plot of slope against slow-worm presence likelihood
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Temperature underneath refuges The plot of occupied refuges against temperature beneath those refuges is shown in Figure 17. This plot shows that despite some considerable overlap, slow-worms tended to be found in the middle of the general temperature range, mostly between 18°C and 28°C. Only a small proportion of sightings were made outside of this range, and the warmest temperature a slow-worm was found under was 34.5°C. This finding agrees with Beebee and Griffiths (2000) who found that slow-worms will not tolerate environmental temperatures greater than 35°C. While the majority of occupied and unoccupied temperatures were similar, the range of occupied refuge temperature was narrower at both extremes, showing that slow-worms would not tolerate extreme temperatures, and would not be found below refuges that were too warm or too cool. The temperature underneath refuge was also found to be a variable in the best model for predicting slowworm presence (Table 1), and in fact was found in all of the five best models. Examining the GAMM showed a significant result for this relationship (p=0.003). Plotting the data shows that this relationship was non-linear: the trendline is a smooth, yet fairly flat curve, with its peak at approximately 24°C. The curve was not quite symmetrical – it declined at a slightly lower rate on the warmer side of the peak than the cooler side. Due to the large number of absence records, this peak remained low, only reaching a maximum likelihood of 0.15, with the upper confidence interval reaching 0.20. Despite this, the relationship was strong enough to be significant. This significance, coupled with the fact that temperature was found in all of the top five models, is strong evidence for the importance of temperature as a factor for predicting slow-worm presence.
Figure 17: Plot of temperature below refuges against slow-worm presence likelihood
Scrub extent The plot of slow-worm presence against scrub extent (Figure 18) shows that the occupied refuges tended to have less scrub around them: the majority of sightings (over 75%) were in areas with less than 15% scrub. The trendline of scrub extent to slow-worm presence shows this slight negative relationship, where slow-worms were more likely to be found in areas with less scrub in them. However, scrub extent
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was not a variable in any of the top four models, and only appeared in the fifth-best model. Examining this model showed that scrub extent did not have a significant relationship with slow-worm presence (p=0.374).
Figure 18: Plot of scrub extent against slow-worm presence likelihood
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Vegetation height Figure 19 shows the relationship between mean vegetation height around refuges and the likelihood of slow-worm presence under those refuges. The occupied refuges were evenly distributed along the range of vegetation heights and so no inference can be made from this. The trendline showed a slight negative relationship, but the slope of this line was very gentle. Vegetation height did not appear as a variable for predicting slow-worm presence until the seventh-best model and examining this model showed that the p-value was strongly non-significant (p=0.840).
Figure 19: Plot of vegetation height against slow-worm presence likelihood
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Refuge material preference In order to test the refuge material preference of slow-worms, the total that was found under each refuge material was compared using the Wilcoxon signed-rank test. A total of 138 slow-worms were found underneath tins, while 98 were found underneath felts (Figure 20). This preference for tin refuges was found to be significant (V=10976, p=0.037).
Figure 20: Total number of slow-worms found under each refuge material
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Common lizard results Common lizard distribution The vast majority of common lizards were seen clustered to the south-west of Daneway Banks (Figure 21). None at all were seen in the north-west or very north of the site. Many fewer refuges were occupied by common lizards compared to slow-worms, and the ones that were occupied had common lizards on fewer occasions. No refuge was occupied more than 5 times out of 12, and only 12 of the 68 refuges were occupied by common lizards at least once, compared to 41 occupied at least once by slow-worms. A total of 29 common lizards were recorded, compared to 236 slow-worms: eight times fewer.
Figure 21: Number of surveys (n=12) where refuge was occupied by at least one common lizard
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Common lizard response to variables The results of the five best common lizard models are shown below in Table 2: Table 2: Common lizard models with lowest AIC
Variables included in model Model number
Aspect
Slope
Temperature under refuge
1
×
2
×
3
×
p-value
×
×
5 0.577
Vegetation height
×
4
Variable
Scrub extent
×
×
0.990
0.005
×
0.808
d.f.
AIC
∆AIC
5
196.224
–
7
197.984
1.760
7
199.641
3.417
7
200.142
3.918
7
200.224
4.000
0.252
Again, temperature under refuge was a very important variable, as it occurred in all of the top five best models. However, the other variables had much less support than was seen in the slow-worm models, as none of them occurred in more than one model. Although the model with temperature and vegetation height had a ∆AIC of less than 2, it includes one more variable than the “best” model and so is more complicated with a greater AIC and so should not be considered as one of the best models (Richards 2015). Therefore temperature is the only variable that can be considered to be important when predicting the distribution of common lizards with this data.
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Variable plots Aspect The grouping of common lizard aspect preference was closer than the slow-worm preference – all but three were seen between east and south (Figure 22). However, aspect only appeared in the third-best model for common lizard presence, and examining this model found that the preference for aspect was not significant (p=0.577).
Figure 22: Plot of aspect against common lizard presence likelihood
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Slope The common lizards differed from the slow-worms as they were all found on slopes that were inclined at less than 20°, with none found under refuges any steeper than this. There were no clear trends to the values of slope steepness that the common lizards occupied (Figure 23). Slope did not appear as a variable until the fifth-best model, and examining this model found that the preference was strongly nonsignificant (p=0.990).
Figure 23: Plot of slope against common lizard presence likelihood
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Temperature underneath refuges On average, the temperature of refuges that had common lizards was greater than the temperature of those refuges without common lizards (Figure 24). However, the range of temperatures in which they were seen was very wide, ranging from around 16°C to around 40°C. This meant that there was no clear pattern in the temperatures at which the common lizards were seen at. This, coupled with the relatively low number of common lizards found, mean the trendline barely rose above a likelihood of 0, and only started to increase after 35°C. Despite this, temperature was a variable that was found in each of the top five best models for predicting common lizard preference. The model that was the best fit, according to its AIC value, included temperature as the sole independent variable. Examining this model found that temperature was a significant factor in predicting common lizard presence (p=0.005).
Figure 24: Plot of temperature against common lizard presence likelihood
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Scrub extent Like the slow-worms, most common lizards were found in areas with less than 15% of scrub (Figure 25), although they were found in a wide range of scrub extent values. The trendline never rose very far above a common lizard presence likelihood of 0.0. Scrub extent did not appear as a variable until the fourth-best model, and examining this model found that the relationship of common lizards to scrub extent was not significant (p=0.808).
Figure 25: Plot of scrub extent against common lizard presence likelihood
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Vegetation height Common lizards were found at refuges surrounded by a wide range of vegetation heights (Figure 26), suggesting that vegetation height did not influence their choice of refuge. The trendline never rose very far above a common lizard presence likelihood of 0.0. Vegetation height occurred in the second-best model for predicting common lizard presence, although according to Richards’ (2015) method for not selecting overly-complex models, this model should not be included as although it had a ∆AIC value less than 2, its AIC was larger than the “best” model and it included one extra variable. Examining this model found that vegetation height was not a significant factor in predicting common lizard presence (p=0.252).
Figure 26: Plot of vegetation height against common lizard presence likelihood
36
Refuge material preference In order to test the refuge material preference of common lizards, the total that was found under each refuge material was compared using the Wilcoxon signed-rank test. A total of 5 common lizards were found on or below tins, while 24 were found on or below felts: nearly five times as many (Figure 27). This preference for felt refuges was found to be significant (V=65, p
