Relationships between vegetation structure and ...

Bird Study

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Relationships between vegetation structure and breeding bird densities in fallow cereal steppes in Castro Verde, Portugal F. Moreira To cite this article: F. Moreira (1999) Relationships between vegetation structure and breeding bird densities in fallow cereal steppes in Castro Verde, Portugal, Bird Study, 46:3, 309-318, DOI: 10.1080/00063659909461144 To link to this article: https://doi.org/10.1080/00063659909461144

Published online: 29 Mar 2010.

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Bird Study (1999) 46, 309–318

Relationships between vegetation structure and breeding bird densities in fallow cereal steppes in Castro Verde, Portugal FRANCISCO MOREIRA Centro de Ecologia Aplicada Prof. Baeta Neves, Instituto Superior de Agronomia, Tapada da Ajuda, P-1399 Lisboa Codex, Portugal

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Fallow fields represent a large proportion of cereal steppes in southern Portugal. A study of the bird populations using fallows in the Castro Verde region during the breeding season was made during spring 1996, with the objective of characterizing the bird community and describing relationships between bird density and vegetation structure, for selected species. For each of 50 transects, birds were counted once and variables related to vegetation structure were measured. Multivariate analyses were used to explore the relationships between bird density and habitat characteristics. The bird community of fallows was characterized by the numerical abundance of two species – Calandra Lark Melanocorypha calandra (8.5 birds/km) and Little Bustard Tetrax tetrax (4.5 birds/km) – which represented 60% of the total number of birds detected. Of the 28 bird species observed, only four others reached average relative densities greater than 1 bird/km: Great Bustard Otis tarda, Cattle Egret Bubulcus ibis, Corn Bunting Miliaria calandra and Short-toed Lark Calandrella brachydactyla. The variables influencing bird density were related mainly to vegetation height, cover by bare ground and presence of shrubs. The results suggest that agricultural management practices which promote the simultaneous presence of fallows with different habitat characteristics will increase species diversity at the local level. The models built were also used to predict changes in bird populations as a consequence of changes in grazing intensity and land abandonment.

T

he cereal steppes of the Iberian Peninsula hold significant percentages of the European populations of several threatened bird species, such as the Great Bustard Otis tarda, Little Bustard Tetrax tetrax, Lesser Kestrel Falco naumanii, Montagu’s Harrier Circus pygargus and Sandgrouse Pterocles spp.1–3 In fact, most populations of these species have declined steeply in the last 20 years2 due mainly to changes in agricultural policies, either agricultural intensification (especially through irrigation) or agricultural abandonment, sometimes with afforestation of the least productive land.4 Email: [email protected]

© 1999 British Trust for Ornithology

In Portugal, cereal steppes originated mostly from the clearance of the natural evergreen oak Quercus spp. forests and are characterized by their flat topography and absence of woodland (except for some isolated small patches of trees). The Castro Verde region is the main area of cereal steppes in Portugal. It has national and international importance for several populations of cereal steppe birds, e.g. Great Bustard (500 birds), Little Bustard (300 pairs), Blackbellied Sandgrouse Pterocles orientalis and Calandra Lark Melanocorypha calandra,5 although for most species accurate population estimates, as well as population trends, are not available. The cultivation of cereals in this area follows traditional land use characterized by cereal


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cultivation in two consecutive years (generally, wheat Triticum spp. and barley Hordeum spp.). After cultivation the fields have no agricultural management for a variable period of generally two to three years (in some cases six or seven years), due to the low soil productivity. The land is then ploughed to re-initiate the rotation cycle. This creates a landscape mosaic where fallow land (‘fallows’) usually occupies a large proportion (50% or more) of the land. Fallows are usually used as pastures for sheep. There are no available data on variations in the amount of fallows in Castro Verde region in recent decades, but evidence suggests that the current trend is for a loss of this habitat, mainly through afforestation and agricultural abandonment (leading to extensive cover by shrubs such as Cistus ladanifer). Unlike other steppe regions in Portugal, there is no current intention for large scale irrigation projects in the area. Preliminary studies6,7 have suggested that the Castro Verde fallows are important for several species of birds during the breeding season. The present paper has two specific objectives: (i) to characterize the bird populations using fallows during the breeding season in detail; and (ii) to explore the relationships between vegetation structure and bird densities. METHODS Study site The fieldwork was carried out within the Castro Verde plains, in an area of about 120 km2 (37°43′N, 7°57′W), located in the Meso-mediterranean bioclimatic stage.8 The percentage of fallows was around 60% of total land area. Altitude ranges from 110 m to 290 m above sea level. Annual average temperature and rainfall are, respectively, 15.5 to 17.0°C and 500 mm, with most rainfall from November to March and virtually no rain during June to August.9 Originally, the region was covered by Holm Oak Quercus rotundifolia forests,8 but progressive clearance for cereal cultivation, particularly at the beginning of the 20th century,10 created a very open landscape with no significant forested areas. Average farm size in the region (around 160 ha) is the largest in the country.

© 1999 British Trust for Ornithology, Bird Study,

Fallows in the study area have a diverse floristic composition. Common plant families found are Gramineae, Compositae (e.g. Matricaria spp., Chamaemaelum spp. and Leontodon spp.) and Leguminosae (e.g. Trifolium spp. and Lotus spp.). Bird censuses Birds were counted using line transects.11 A total of 50 transects was defined by systematic sampling of every 1-km2 UTM (Universal Transverse Mercator) grid in the study area. In each square kilometre, based on maps (scale 1:25 000), aerial photography (scale 1:15 000) and previous landscape knowledge, the following sequence was used to define transect location and length: (i) choose largest fallow in the quadrat (a quadrat was not sampled when the largest fallow was less than 400 m long and 200 m wide); (ii) the exact location of the transect was chosen so as to maximize transect length within the quadrat, with transect limits marked in the field prior to the bird counts; (iii) two transects in adjacent quadrats were at least 250 m apart at their closest point. The purpose of this sampling design was to obtain a representative sample of the fallows, minimizing edge effects (thus excluding ecotone species) and duplication of bird counts. Average transect length (± sd) was 635.5 ± 121 m (range = 420–900 m), and a total of 31 775 m was sampled. Bird counts were made between 20 March and 24 May 1996, from sunrise to 10:30 hours. Mean travel speed (± sd) was 2.70 ± 0.5 km/h (n = 43). Each transect was counted once with the exception of the bird counts made in March (n = 7), which were repeated in April or May, as the cold weather in early spring might have delayed the beginning of the breeding season for some species. Of the two counts, the one which yielded the highest estimate of density was used in the analyses. Bird observations were recorded using the Järvinen and Väisänen12 method, separating observations within and beyond a main belt of 25 m on both sides of the observer for passerines, and 50 m for the other species. Birds detected outside the fallow in which the transect was located were not registered. Whenever possible, birds seen were classified according to sex.

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Bird densities in fallows of cereal steppes


Vegetation structure In all transects except one, five equally distanced sites (inter-site distance depended on transect length, but was generally larger than 100 m) were defined. In each site, within two to three days of the bird counts, several habitat variables were measured, based on procedures described by Hays et al.13 and Noon:14 1 At each site, a 50 × 50 cm quadrat with a marked side was thrown in an arbitrary direction. The presence or absence of shrubs (mostly Cistus ladanifer) in a circle of 25 m around the quadrat was noted. 2 The marked side of the quadrat was used to place a vegetation profile board in order to estimate vegetation density at various height intervals. This was made by using a 30 × 40 cm board with three defined height intervals: 0–10 cm, 10–20 cm and 20–30 cm. Each interval had 24 equally spaced dots (four rows with six dots each) with a diameter of 1 cm. The board was observed at a distance of 2 m, from a crouching position, and the number of dots at least 50% obscured by vegetation was noted for each height interval. 3 The marked side of the quadrat was used to orientate a 5 m tape stretched on the ground, parallel to the quadrat, along which segments with bare ground and stones were measured to the nearest centimetre. 4 In the same orientation, vegetation height was measured to the nearest centimetre with a 40-cm ruler, at ten points located at 1 m intervals. Height was defined as the highest point of vegetation projection on the ruler, for plants within 3 cm of the ruler. When no vegetation was present at a given point, the procedure was repeated at the following point until ten measurements were obtained. In a few cases (2.7%) when vegetation was higher than 40 cm, a value of 45 cm was used for calculation purposes. The maximum vegetation height recorded on other occasions was 55 cm. Statistical analyses Using the data for all transects pooled, the linear model of Järvinen and Väisänen12 for correcting detectability was used to estimate bird density, expressed as birds/10 ha or males/10 ha. Instead of estimating relative density using the main belt data (in which it is

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assumed that all birds were detected) alone, this method uses observations outside the main belt (the so-called supplementary belt) after correcting for detectability by using either a linear, exponential or normal model which usually yields similar results. This allows an increase in sample size and the inclusion of rarer species in the study.12 These authors also showed that the standard deviation of the density estimates could be calculated from the average density, the number of transects and a species-specific coefficient.15 Thus, an estimate of the variability of bird density was obtained by applying their equation for the survey belt (ref. 15, Equation 3). For the multiple regression models (see below), an index of bird abundance for each transect was obtained by dividing the total number of birds detected (sum of detections within and beyond the belt) by transect length. Although birds within the belt could be considered likely to respond to the habitat variables measured more strongly than birds outside the belt, to omit the latter would have yielded inadequate samples. All results were expressed as birds/km. Eight habitat variables were defined (Table 1) which, based on previous research,4,16,17 had been shown to influence the selection of breeding and/or feeding habitat by the different bird species. They were related to vegetation height and cover, extent of bare ground and shrub density. As several of them were highly inter-correlated, Principal Components Analysis18,19 based on a correlation matrix was used to summarize the information of the original variables into a few independent variables. For the more common bird species, multiple and logistic regression analyses20,21 were used to explore the relationships between these new variables (independent variables) and bird density (dependent variable) measured as birds/km. Linear multiple regression analysis was used for the species which occurred in more than 40% of the transects (using the total data set). Initially, two analyses were made for each species, with the dependent variable without transformation and transformed to logarithms (log10x + 1), to stabilize the variance and allow for relationships being non-linear. To check whether any patterns identified within the data


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Table 1. Habitat variables used in this study and descriptive statistics of data obtained in 49 transects. Variable code

Description

1. VEG_HEIGHT 2. SHRUB

Average vegetation height (in cm) (n = 50) Presence/absence of shrubs in the transect (n = 5) (considered presence if ocurred in any of the 5 sites) Average percentage cover (0–100%) by vegetation in the 0–10 cm height class, measured as the number of dots obscured by vegetation (n = 5) Average percentage cover (0–100%) by vegetation in the 10–20 cm height class, measured as the number of dots obscured by vegetation (n = 5) Average percentage cover (0–100%) by vegetation in the 20–30 cm height class, measured as the number of dots obscured by vegetation (n = 5) Average percentage cover (0–100%) by bare ground or rocks in a 5-m transect (n = 5) Standard deviation of variable 6 Standard deviation of variable 1

3. %COV_0–10

4. %COV_10–20


5. %COV_20–30

6. %BARE_GROUND 7. SD_BARE_GROUND 8. SD_VEG_HEIGHT

Mean

Median

Range

13.2 0.1

11.8 0.

3.3–33.6 0–1

47.3

44.1

1.7–100.0

14.7

1.7

0–95.0

4.6

0.

0–70.8

12.1

8.2

0.3–47.7

7.8 6.4

6.7 5.8

0.5–23.9 2.1–12.6

For further details, see Methods. n = number of measurements per transect.

were robust, both backward, forward, stepwise and manual variable selection procedures were used. After the selection of variables had been made, plots of the standardized residuals × the predicted values and independent variables were made to look for systematic variations that might suggest that transformations in the independent variables should be considered. The analyses were then re-run with some independent variables transformed to logarithms or squared (as some residuals suggested quadratic relationships). The model which explained the most variation in the bird species density was chosen. For the remaining species, owing to the large number of absences in transects, logistic regression21,22 was used to calculate the probability of detecting a given species in a transect. Variables entering the models were selected by forward stepwise selection based on the likelihood-ratio test.22 The goodness-of-fit of the models was assessed by comparing the predicted with the observed values in a classification table (using a cut-off probability value of 0.5 to predict presence/absence) and determining the percentage of correctly classified locations. Interaction terms were allowed in both linear regression and logistic models.


All statistical analyses were made using the SPSS computer program. 22 RESULTS Bird density Twenty-eight bird species were observed during the present work (see Appendix). Only two species occurred in more than half of the transects (Little Bustard and Calandra Lark) and these represented around 60% of the total number of birds. Over 90% of the total number of birds detected belonged to seven species. Several species (e.g. Cattle Egret Bubulcus ibis, Black Kite Milvus migrans, Buzzard Buteo buteo, White Stork Ciconia ciconia) used fallows only as a feeding ground, while others were marginal species, occurring in higher densities on adjacent habitats such as woodland, cereal fields or scrub (e.g. Woodlark Lululla arborea, Woodchat Shrike Lanius senator, Great Grey Shrike Lanius excubitor, Red-Legged Partridge Alectoris rufa, Quail Coturnix coturnix). Table 2 shows the density estimates for the more frequent species. Only six species reached densities higher than 1 bird/km: Calandra Lark, Little Bustard, Corn Bunting Miliaria

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Table 2. Estimates of densities of the ten most frequent species in the studied fallows (n = 50 transects). Species


Melanocorypha calandra Tetrax tetrax (total) Tetrax tetrax (males) Miliaria calandra Otis tarda (total) Otis tarda (males) Bubulcus ibis Calandrella brachydactyla Galerida theklae Burhinus oedicnemus Upupa epops Cisticola juncidis aEstimates

Birds/10 haa ± sdb

Birds/km, mean ± sd

Birds/km, median (range)

10.6 ± 1.33 3.0 ± 0.38 2.1 ± 0.31 2.0 ± 0.47 1.2 ± 0.23 0.9 ± 0.20 1.1 ± 0.19 1.1 ± 0.25 0.4 ± 0.17 0.4 ± 0.10 0.3 ± 0.13 0.2 ± 0.09

8.5 ± 6.48 6.5 ± 5.42 4.5 ± 3.07 1.6 ± 2.02 1.7 ± 4.35 1.3 ± 4.25 2.3 ± 7.86 1.1 ± 1.49 0.3 ± 0.88 0.6 ± 1.16 0.2 ± 0.65 0.3 ± 0.75

7.9 (0–23.9) 5.5 (0–24.3) 4.4 (0–18.6) 0 (0–8.0) 0 (0–20.8) 0 (0–20.8) 0 (0–42.8) 0 (0–6.0) 0 (0–4.7) 0 (0–6.1) 0 (0–3.6) 0 (0–3.6)

based on the method of Järvinen & Väisänen.12 bEstimates based on the method of Järvinen &

Väisänen.15

calandra, Great Bustard, Cattle Egret and Shorttoed Lark Calandrella brachydactyla. It should be noted that two of these species (Great Bustard and Cattle Egret) usually occurred in loose flocks (in lekking areas for the former, and feeding flocks for the latter), thus violating the assumption of independence of observations needed to estimate densities by line transects.11 Thus, their density estimates should be interpreted with caution. Correlates of bird density The eight original variables were reduced to four independent principal components which retained 94% of the variance of the former (Table 3). The first component was related to vegetation height and density. The second

reflected the amount of bare ground in the transects. The third component expressed the presence of shrubs in transects. The fourth component was more difficult to interpret, as there was no clear association with a single (or group of related) variable. None of the remaining components had correlations with the original variables greater than 0.30. The percentage of variance of the dependent variable explained by the linear regression models (Table 4) was low, ranging from 9 to 32%. Calandra Lark and male Little Bustard densities seemed to be influenced by vegetation height, with the former occurring in higher densities in areas with short vegetation and the latter having a quadratic relationship, with higher density at intermediate vegetation height. Female Little Bustard, Corn Bunting

Table 3. Principal component loadings for the eight original variables. Component number New variable name Percentage variance explained VEG_HEIGHT SHRUB %COV_0–10 %COV_10–20 %COV_20–30 %BARE_GROUND SD_BARE_GROUND SD_VEG_HEIGHT

1 Height 52.6

2 Soil 20.9

3 Shrub 13.6

4 Other 6.8

0.94 0.22 0.93 0.93 0.77 –0.45 –0.31 0.81

0.19 –0.12 –0.02 0.14 0.17 0.83 0.88 0.30

0.03 0.91 0.17 –0.19 –0.37 0.07 0.10 0.18

–0.14 0.32 –0.11 0.18 0.45 –0.04 0.13 –0.39

Loadings larger than 0.70 are underlined. For habitat variable definition, see Table 1.


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Table 4. Results of the multiple and logistic regression analyses. Species

Model

Significance of model

Multiple regression models Tetrax tetrax (males)

log (y + 1) = –0.11 Height2 + 0.17 Height + 0.78

Tetrax tetrax (females)

log (y + 1) = –0.11 Soil + 0.32

Melanocorypha calandra

log (y + 1) = –0.18 Height + 0.83

Calandrella brachydactyla

y = 0.38 Soil2 + 1.67 log (Soil + 2) + 0.32

Miliaria calandra

y = –3.23 log (Soil + 2) + 2.35

Logistic regression models Burhinus oedicnemus

g(x) = 0.79 (Shrubs + 2.5) – 2.76

Bubulcus ibis

g(x) = 0.41 ((Height + 2.5) × (Shrubs + 2.5)) – 4.54

Galerida theklae

g(x) = 1.37 (Shrubs + 2.5) – 6.08

Cisticola juncidis

g(x) = 0.14 (Height + 2.5)2 – 3.23

F = 10.8*** r2 = 32.0% F = 4.8* r2 = 9.3% F = 12.1*** r2 = 20.5% F = 8.1*** r2 = 26.1% F = 6.4* r2 = 12.0%

χ2 = 6.2* 73.5% χ2 = 16.1*** 89.9% χ2 = 11.8*** 89.8% χ2 = 4.99* 89.9%

For linear regression models, y = birds/km. For logistic models, expressed by the equation P(x) = 1/1 + eg(x), g(x) is indicated, as well as the model chi-square and the percentage of correct classifications (presences and absences). *P < 0.05; **P < 0.01; ***P < 0.001.

and Short-toed Lark densities seemed to be influenced by the amount of bare ground in the fallows, with the first two species avoiding higher percentages of bare ground and the latter preferring it. The logistic regression models obtained (Table 4) were much more effective in predicting absence than presence. The probabilities of finding Stone Curlews Burhinus oedicnemus and Thekla Larks Galerida theklae were higher in the transects where shrubs occurred. It should be noted that shrub cover in the studied transects was always low (maximum of about 10%). Fan-tailed Warbler Cisticola juncidis was more frequent in fallows with tall vegetation whereas Cattle Egrets seemed to be more frequent in transects with tall vegetation and shrubs (interaction term selected). For Great Bustard and Hoopoe Upupa epops, no variables entered the models. DISCUSSION Bird density This study shows the simple bird community


structure of Castro Verde fallows, with two species being clearly dominant: Calandra Lark and Little Bustard. When compared with preliminary studies,6,7,23 the much larger number of species detected in the present study is probably a consequence of a much larger sample size, which allowed the detection of rarer and marginal species (i.e. occurring in higher densities in adjacent habitats). Other studies of birds of cereal steppes16,24,25 also showed a simple community structure. The density estimates for most species are within the range described in the previous studies in the region. The main differences compared to other studies elsewhere in the Iberian Peninsula are the absence of breeding Skylarks Alauda arvensis, as in Southern Spain,25,26 and the high densities of Little Bustard, the highest recorded in Europe so far.27 The conservation value of the bird populations of the Castro Verde region is high. Based on the conservation status categories of Tucker and Heath,2 species using the studied fallows included two SPEC 1 (Great Bustard and Lesser Kestrel), seven SPEC 2 and nine SPEC 3. For the two most abundant species in the studied

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fallows, based on the average densities (Table 2), the estimated population sizes for the study area (estimating fallow global area) were 2400 Little Bustards (at least 12% of the Portuguese population), much higher than the estimate of Grimmet and Jones5 for the whole region, and 8500 Calandra Larks (at least 8.5% of the Portuguese population). It should be noted that these species also occur in cereal fields and ploughed land, although in lower densities (0.6 to 0.8 birds/km and 3.6 birds/km, respectively, for Little Bustard and Calandra Lark).6 Thus these population sizes are probably underestimated. Correlates of bird density The three main axes of the PCA, summarizing the relationships between habitat variables, were related to vegetation height, extent of bare ground and presence of shrubs. The relatively low r2 values obtained for the linear regression models can probably be explained by two types of factors. First, by the number of zero values for some bird species (caused by low density and detectability). Secondly, other factors potentially influencing bird density were not measured: (i) floristic composition of fallows, which can influence the bird community composition, either directly or indirectly (influencing insect abundance);28 (ii) grazing intensity, which, along with altering vegetation structure causes trampling of eggs and disturbance;29 (iii) inadequacy of the procedure to sample vegetation heterogeneity within a given fallow field (for example, the presence of a stream might produce a strip of high and dense vegetation where several birds were recorded, but none of the spots measuring habitat variables was located in this strip); and (iv) distance of sampled fallows to adjacent habitats. Thus, the models should be considered to be explanatory, to understand the main patterns of habitat selection by the species, and not used for predictive purposes. Four species responded to the first PCA axis, which expressed the height and density of the vegetation. Of these, two – Cattle Egret and Fan-tailed Warbler – showed a higher frequency of occurrence with increasing vegetation height, whereas Calandra Lark density decreased. Male Little Bustards had a quadratic relationship with greatest densities

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at intermediate vegetation height levels. Three species were influenced by the amount of bare ground in the transects expressed by the second axis, with Short-toed Lark showing a positive response to an increase in bare ground, and Corn Bunting and female Little Bustards having a negative response. Three species – Stone Curlew, Cattle Egret and Thekla Lark – were also influenced by the presence of shrubs, showing increased frequencies when shrubs were present. The densities of most species varied with changes in habitat features according to what is known about their habitat selection from other studies.2,4,30–36 Furthermore, preliminary data on the bird communities of other agricultural habitats in the region support the observed results.6 For example, Thekla Lark was six times more abundant in scrubland than in fallows, Short-toed Lark was ten times more abundant on ploughed land (having a high percentage of bare ground), and Fan-tailed Warbler was ten times more abundant in cereal fields (having taller vegetation). The absence of variable selection by the regression models for the Great Bustard is probably a consequence of the complex number of factors (micro-habitat, landscape features, human disturbance, etc.) determining their occurrence during the breeding season.37 For the Stone Curlew, a detailed study on the habitat selection of the species in southern Portugal38 also showed that areas with scrub constitute an important breeding habitat. The pattern of habitat selection for the Little Bustard seemed to differ between sexes. Females occurred in higher densities in fallows with less bare ground cover. This could be explained by the fact that for nesting birds bare ground might be more important than vegetation height. Males preferred fallows with intermediate vegetation height (15–20 cm). This agrees with the results of Martinez17 who found that the most frequent average vegetation height class within male territories was 10–20 cm, with higher vegetation being avoided. Conservation and management implications This study shows the importance of fallows for the conservation of several bird species with threatened status. Other studies in Europe have also shown the ecological importance of


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fallows and the similar set-aside fields both for threatened and game species.39,40 Irrespective of their use of other habitats of cereal steppes (e.g. cereal fields, ploughed land, stubble) during the breeding season, it was shown that vegetation height, presence of shrubs and extent of bare ground influenced differentially the densities of the species in the Castro Verde region. Thus, the promotion of management practices leading to fallows with different characteristics will increase species diversity at the local level. From a management perspective, two major controllable factors influence vegetation height and density in fallows – fallow age and grazing intensity29 – and further research in the region is needed to understand how these influence the resulting habitat. This can yield useful management guidance to be included in the Zonal Programme of Castro Verde (created within the scope of the agri-environmental regulation), which provides subsidies to farmers using agricultural practices compatible with nature conservation. In spite of their low predictive power, the models built can be used to provide an indication of what will happen to a bird population as a consequence of a given management practice. Thus, for example, assuming that an increase in stock density would lead to a decrease in vegetation height and an increase in bare ground,29 this would potentially benefit the Short-toed and Calandra Larks and be detrimental to the Little Bustard and Fan-tailed Warbler. Nevertheless, excessive sheep density would also lead to trampling of eggs and nests of the lark species. Land abandonment, on the other hand, would lead to a progressive increase in shrub cover. This would be equivalent to habitat loss for most steppe species. Even species that select fallows with some shrubs do not occur in the shrublands of the region, where the bird community is dominated by Sylviid warblers.41 Of course, these predictions are valid only for the breeding season, and a study of wintering bird populations is necessary before assessing the impact of a given management practice on species biodiversity. ACKNOWLEDGEMENTS Thanks are due to Pedro Beja, Rui Borralho,


Åke Berg, Chris Stoate, Ian Henderson and an anonymous referee for comments on versions of the manuscript. Special thanks are also due to Rui Borralho for his help with logistic regression, and Rui Alves for tips on management practices. This work was done within the scope of the second phase of the project Conservation of Steppe Birds in Castro Verde Region (BA-3200/95/510), carried out by the Portuguese League for Nature Conservation (LPN) and partially funded by the EU LIFE programme and Fundação Luso-Americana para o Desen-volvimento (FLAD). REFERENCES 1. Tucker, G.M. (1991) The status of lowland dry grassland birds in Europe. In The Conservation of Lowland Dry Grassland Birds in Europe (eds P.D. Goriup, L.A. Batten & J.A. Norton), pp 15–36. JNCC, Peterborough. 2. Tucker, G.M. & Heath, M.F. (1994) Birds in Europe: their conservation status. BirdLife International, Cambridge. 3. Heath, M.F. & Tucker, G.M. (1995) Ornithological value and pastoral farming systems. In Farming on the Edge: the nature of traditional farmland in Europe (eds D.I. McCraken, E.M. Bignal & S.E. Wenlock), pp. 54–59. JNCC, Peterborough. 4. Suárez, F., Naveso, M.A. & de Juana, E. (1997) Farming in the drylands of Spain: birds of the pseudosteppes. In Farming and Birds in Europe. The Common Agricultural Policy and its implications for bird conservation (eds D. Pain & M.W. Pienkowski), pp. 297–330. Academic Press, San Diego. 5. Grimmet, R.F.A. & Jones, T.A. (1989) Important Bird Areas in Europe. ICBP, Cambridge. 6. Leitão, D. & Moreira, F. (1996) Estrutura e composição das comunidades de aves nidificantes na região de Castro Verde. Ciência e Natureza, 2, 103–107. 7. Moreira, F. & Leitão, D. (1996) A comunidade de aves nidificantes nos pousios da região de Castro Verde. Ciência e Natureza, 2, 109–113. 8. Rivas-Martinez, S., Lousa, M., Diaz, T.E., Férnandez-González & Costa, J.C. (1990) La vegetación del sur de Portugal (Sado, Alentejo y Algarve). Itinera Geobotanica, 3, 5–126. 9. CNA (1982–87) Atlas do Ambiente. Comissão Nacional do Ambiente, Lisboa. 10. Ribeiro, O. (1991) Portugal, o Mediterrâneo e o Atlântico. Sá da Costa, Lisboa. 11. Bibby, C., Burgess, N.D. & Hill, D.A. (1992) Bird Census Techniques. Academic Press, London. 12. Järvinen, O. & Väisänen, R.A. (1975) Estimating relative densities of breeding birds by the line transect method. Oikos, 26, 316–322.

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Bird densities in fallows of cereal steppes 13. Hays, R.L., Summers, C. & Seitz, W. (1981) Estimating Wildlife Habitat Variables. USDI Fish and Wildlife Service, FWS/OBS-81/47, Washington. 14. Noon, B.R. (1981) Techniques for sampling avian habitat. In The Use of Multivariate Statistics in Studies of Wildlife Habitat (ed. D.E. Capen), pp. 42–52. US Fish and Wildlife Service, USDA Forest Service, General Technical Report RM-87, Vermont. 15. Järvinen, O. & Väisänen, R.A. (1983) Confidence limits for estimates of population density in line transects. Ornis Scand., 14, 129–134. 16. Telleria, J.L., Santos, T., Alvarez, G. & Sáez Royela (1988) Avifauna de los campos de cereales del interior de España. In Aves de los medios urbanos y agrícolas en las mesetas españolas (ed. F. Berniz), pp. 173–319. SEO, Madrid. 17. Martinez, C. (1994) Habitat selection by the little bustard Tetrax tetrax in cultivated areas of central Spain. Biol. Conserv., 67, 125–128. 18. Neff, N.A. & Marcus, L.F. (1980) A Survey of Multivariate Methods for Systematics. American Museum of Natural History, New York. 19. Chatterjee, S. & Price, B. (1991) Regression Analysis by Example. John Wiley & Sons, New York. 20. Draper, N.R & Smith, H. (1981) Applied Regression Analysis. John Wiley & Sons, New York. 21. Hosmer Jr., D.W. & Lemeshow, S. (1989) Applied Logistic Regression. John Wiley & Sons, New York. 22. Norusis, M.J. (1992) SPSS for Windows. SPSS Inc., Chicago. 23. Moreira, F. & Leitão, D. (1996) A preliminary study of the breeding bird community of fallows of cereal steppes in southern Portugal. Bird Conserv. Int., 6, 255–259. 24. Diaz, M., Naveso, M.A. & Rebollo, E. (1993) Respuestas de las comunidades nidificantes de aves a la intensificación agrícola en cultivos cerealistas de la Maseta Norte (Valladolid - Palencia, España). Aegypius, 11, 1–6. 25. Martinez, C. & de Juana, E. (1996) Breeding bird communities of cereal crops in Spain: habitat requirements. In Conservacion de las aves esteparias y su habitat (eds J. Fernandez Gutiérres & J. SanzZuasti), pp. 99–206. Junta de Castilla y Leon, Valladolid. 26. Valverde, A. (1958) Aves esteparias de la Peninsu-

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(MS received 29 May 1998; revised MS accepted 23 October 1998)


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F. Moreira

APPENDIX Bird species observed in the studied fallows.


Species Tetrax tetrax Melanocorypha calandra Calandrella brachydactyla Miliaria calandra Otis tarda Burhinus oedicnemus Bubulcus ibis Upupa epops Galerida theklae Cisticola juncidis Anthus campestris Lanius excubitor Ciconia ciconia Oenanthe hispanica Glareola pratincola Falco naumanni Circus pygargus Sturnus unicolor Saxicola torquata Pterocles orientalis Milvus migrans Lululla arborea Lanius senator Coturnix coturnix Corvus corax Coracias garrulus Buteo buteo Alectoris rufa

Frequency of occurrence

%

Number of birds

%

48 44 23 22 16 15 9 6 6 6 5 4 3 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1

96 88 46 44 32 30 18 12 12 12 10 8 6 4 4 4 4 2 2 2 2 2 2 2 2 2 2 2

207 269 36 48 60 20 67 7 8 7 5 5 6 3 6 2 2 1 1 2 1 2 2 1 1 1 1 4

26.7 34.7 4.6 6.2 7.7 2.6 8.6 0.9 1.0 0.9 0.6 0.6 0.8 0.4 0.8 0.3 0.3 0.1 0.1 0.3 0.1 0.3 0.3 0.1 0.1 0.1 0.1 0.5

For each species, the frequency of occurrence and the total number of birds observed in the line transects (n = 50) are indicated. The species are ordered by decreasing frequency of occurrence.


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