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Agriculture, Ecosystems and Environment 84 (2001) 191–206

Changes in agricultural land-use and breeding performance of зерноїдних some granivorous farmland passerines in Britain Gavin M. Siriwardena a,b,∗ , Stephen R. Baillie a , Humphrey Q.P. Crick a , Jeremy D. Wilson b,1 b

a British Trust for Ornithology, The Nunnery, Thetford, Norfolk IP24 2PU, UK Ecology and Behaviour Group, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK

Received 25 March 1999; received in revised form 1 November 1999; accepted 13 July 2000

Abstract Analysis of the variation in demographic rates with respect to environmental heterogeneity in space and time represents a valuable technique with which the causes and mechanisms behind population changes can be elucidated. Here, extensive spatially referenced data on agricultural land-use at the 10 km square scale for England, Wales and Scotland were analysed in relation to similar data on breeding performance for six granivorous passerines which have declined in recent years. Five principal component axes described the variation in agricultural land-use from 1969–1988 adequately and explained the variation in breeding performance (measured as daily nest failure rates, chick:egg ratio, clutch size and brood size) to varying degrees across species. The overall influence of agricultural land-use tended to be species-specific, with principal component axes describing gradients between pastoral and arable agriculture and between intensively arable and more extensive agriculture being particularly important, but having different effects across species. The clearest general pattern suggested that more intensive agriculture tends to be associated with poorer breeding performance. Although influences of agricultural land-use on breeding performance are unlikely to have driven the major, long-term declines of any of the species except linnet, the results are consistent with those of other work suggesting that less intensive farming provides better habitat for farmland birds. The results both suggest directions for the management of farmland which could aid population recoveries via improvements in breeding performance and provide hypotheses for further intensive field studies of the influences of farming practices on bird populations. © 2001 Elsevier Science B.V. All rights reserved. Keywords: Farmland birds; Agriculture; Breeding success; Population declines; Land-use in Britain

1. Introduction Changes in agricultural practice have been implicated as the cause of severe declines in farmland bird populations over recent decades (O’Connor and ∗ Corresponding author. Tel.: +44-1842-750050; fax: +44-1842-750030. E-mail address: gavin.siriwardena@bto.org (G.M. Siriwardena). 1 Present address: Royal Society for the Protection of Birds, The Lodge, Sandy, Bedfordshire SG19 2DL, UK.

Shrubb, 1986; Fuller et al., 1995; Baillie et al., 1997; Campbell et al., 1997; Siriwardena et al., 1998a). The widespread intensification of agriculture in Britain (and elsewhere in Europe) has proceeded at a steady pace since the second world war, driven by a desire for national self-sufficiency and backed by government subsidies for increased outputs. The lethal effects of organochlorine pesticides on birds now led to a well-known environmental crisis in the 1950s and 1960s (Carson, 1963). More recently, more subtle

0167-8809/01/$ – see front matter © 2001 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 8 8 0 9 ( 0 0 ) 0 0 2 1 0 - 3


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effects of intensification on bird populations have been postulated. Changes in cropping patterns, the simplification of crop rotations, the decline of mixed farming and indirect effects of the more specific pesticides now being used may have been detrimental (Fuller et al., 1995; Baillie et al., 1997; Campbell et al., 1997). While there is evidence that such changes underlie the declines of grey partridge Perdix perdix L., corncrake Crex crex L., lapwing Vanellus vanellus L. and skylark Alauda arvensis L. (Potts, 1986; Shrubb and Lack, 1991; Green et al., 1997; Wilson et al., 1997), it is lacking for many of the species that have declined. Historical data on demography may allow historical national-scale changes in key demographic parameters to be related directly to changes in abundance and in the environment (Thomson et al., 1997; Peach et al., 1999; Siriwardena et al., 1999). The production of fledged offspring per nesting attempt is one of several key demographic parameters which determine bird abundance. The British Trust for Ornithology’s (BTO’s) nest record database provides opportunities to investigate historical variation in breeding performance and to assess the importance of possible causes of the variation (Crick and Baillie, 1996). In this study, spatially referenced nest record data are used to investigate the relationships between breeding performance and agricultural land-use for six predominantly granivorous passerine species: skylark, tree sparrow Passer montanus L., linnet Carduelis cannabina L., bullfinch Pyrrhula pyrrhula L., reed bunting Emberiza schoeniclus L. and yellowhammer E. citrinella L. In common with several other seed-eating species, all these have undergone significant declines in abundance on farmland since 1975 (Siriwardena et al., 1998a), but these species are notable in having large, available nest record data sets. Significant proportions of the populations of all six species (the majority for each species except bullfinch: ca. 48%) are found on farmland (Gregory and Baillie, 1998; Gregory, 2000), although they vary in their dependence on crops per se. The June agricultural census conducted annually by the Ministry for Agriculture, Fisheries and Food (MAFF) and the Scottish Office Agriculture and Fisheries Department (SOAFD) provides a spatiallyreferenced record of changes in agricultural land-use in Britain over time. These data, summarised at the level of 10 km squares of the National Grid, are examined here in relation to the breeding performance

of the above species between 1967 and 1994. The Agricultural Census was not designed to monitor agricultural characteristics important for farmland birds and the data set does not include records of several aspects of agricultural practice whose inclusion would have improved the analyses. Examples include levels of agrochemical application (Campbell et al., 1997) and the lengths of hedgerow present (Green et al., 1994). Grassland could not be identified as grazed pasture, hay meadows or silage fields and no information was available on the areas of barley sown in spring and autumn before 1979. These limitations mean that the importance of the variables concerned could not be assessed in the present study, but they do not invalidate the analyses involving the agricultural variables that could be included. Specifically, this study asks whether agricultural land-use in general, in terms of crop type, pasture type and stocking densities, has been related to breeding performance, and investigates the nature of these relationships. Although the analyses presented are historical and non-experimental, and thus cannot provide proof of cause-and-effect for the declines of the six seed-eaters investigated, any correlative patterns found can nevertheless suggest hypotheses as to the causes, both facilitating future, more focused investigations and providing supporting or refuting evidence regarding existing hypotheses. 2. Methodology 2.1. Agricultural Census data Data from the MAFF and SOAFD June censuses of 1969, 1972, 1976, 1979, 1981, 1985, 1988 and 1993 were obtained from the University of Edinburgh’s Data Library as summaries at the 10 km square level for England, Wales and Scotland. Data were obtained on the areas under a range of agricultural land-uses and on numbers of livestock. Some categories differed between countries and some category definitions changed from year to year. Thus, a complete set of variables which covered all land-uses and which were consistent across years could not be derived from the data. In addition, only a subset of the England and Wales census results was available for 1993 because of recent legislation regarding the confidentiality of


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Table 1 Definitions of variables describing livestock numbers, crop areas and farmland heterogeneity that were available in June agricultural census data for all 10 km squares across England, Wales and Scotland and for all census years from 1969 to 1988a Variable

Definition

COWS SHEEP WHEAT BARLEY OATS RYE HOPS MAIZE BEET STOKNEMA STOKRCK RAPE PEAS OLDGRASS

Total number of cows Total number of sheep Area under wheat Triticum spp. L. (ha) Area under barley Hordeum spp. L. (winter and spring) (ha) Area under oats Avena sativa L. (ha) Area under rye Secale cereale L. (ha) Area under hops Humulus lupulus L. (ha) Area under maize Zea mays L. (ha) Area under sugar beet Beta vulgaris L. (ha) Area under turnips Brassica rapa L. and mangolds Beta vulgaris L. for stockfeed (ha) Area under rape Brassica napus L. and cabbage/kale B. oleracea L. for stockfeed (ha) Area under rape Brassica napus L. (almost all for oilseed) (ha) Area under peas Pisum sativum L. (ha) Area of older grassland (ha): permanent grass and grass sown ≥5 years before (England/Wales and Scotland 1976 onwards), or ≥7 years before (Scotland 1969, 1972) Area of young grassland (ha): grass sown <5 (England/Wales and Scotland 1976 onwards) or <7 (Scotland 1969, 1972) years before Area left fallow (ha) Area under carrots Daucus carota L. (ha) Area under cauliflowers, broccoli and kale Brassica oleracea L. for human consumption (ha) Area under cabbages, savoys, etc. Brassica oleracea L. for human consumption (ha) Area under potatoes Solanum tuberosum L. (seed, early and late crops) (ha) Area under lettuce Lactuca sativa L. (ha) Area under sprouts Brassica oleracea L. (ha) Proportion of the total area of agricultural land under arable crops (as opposed to grassland) The degree to which the agriculture in a 10 km square is a mixture of grassland and arable land. Calculated as ((grass × arable)/(grass + arable)2 )/0.25. Equal to 1 if there is 50% arable and 50% grass and 0 if either covers all agricultural land in the square. An index of farmland heterogeneity Shannon diversity index (Begon et al., 1986) calculated using the areas of wheat, barley, oats, rye, maize, hops, beet, rape, turnips/mangolds for stockfeed, brassicas for stockfeed, potatoes, fallow land, young grassland, older grassland and total under vegetables for human consumption (each treated as a separate ‘species’). An index of farmland heterogeneity

YNGGRASS FALLOW CARROTS CAULIS CABBAGE POTATO LETTUCE SPROUTS PROPARA MIXCOEF

SHANNON

a Because the absolute area censused within each 10 km square varied according to the area of water and non-agricultural land in the square, the area variables were converted into proportions of the total agricultural area (calculated as the total of all the area variables shown plus the miscellaneous crop and vegetable variables in the data set; the total area censused was not directly available in the data for all years). Livestock numbers were also divided by this total to give numbers per hectare of farmland.

data: agricultural land-use in 1993 could not therefore be characterised in full. The variables for which a consistent or near-consistent definition could be assigned through all the census years available (excepting 1993) are listed in Table 1. Three additional indices of the diversity and the arable/pastoral heterogeneity of farmland were derived from the area variables (Table 1). A further variable, the number of pigs, was available in the census data, but was omitted from the models because no information was available on whether the numbers represented indoor animals or those in open fields: results with respect

to number of pigs would therefore be difficult to interpret. Together, the area, livestock and heterogeneity variables provide an overview of agricultural land-use, analysis of which can reveal the important factors for breeding performance analytically. 2.2. Preparatory analyses of Agricultural Census data The various agricultural census variables were intercorrelated to varying degrees, such that including them all in analyses of overall agricultural land-use


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effects on breeding performance simultaneously would present substantial statistical problems in terms of variable selection. In addition, the use of large numbers of statistical tests or of large numbers of candidate predictor variables in the models described later would carry a significant concomitant risk of the generation of spurious results. A principal components analysis (PCA) based on the correlation matrix between the variables was conducted using the PRINCOMP procedure of SAS (SAS Institute, Inc., 1990) to reduce the number of variables in the census data (transformed as described above) before the effects of agricultural land-use on breeding performance were investigated. Using PCA created an objective, parsimonious combination of the agricultural land-use variables into uncorrelated PC axes. Combining variables by any other means would have been both subjective and unlikely to remove the problem of inter-correlation. The data from all 10 km squares in all agricultural census years were entered into the PCA simultaneously, so it incorporated spatial and temporal variation; although using multiple observations from the same 10 km squares violates the assumption of independence in PCA, this will not have caused bias because the same set of squares made up the data sets for each year. All the area variables listed in Table 1 were included in the PCA, except RYE, HOPS, SPROUTS, CARROTS and LETTUCE, which were excluded because they were too rare to be likely to be important for bird populations (mean proportion of agricultural land in a 10 km square for each <0.1%). The PC axes derived from this analysis that, together, explained an adequate proportion (>60%) of the variance in the land-use data set were used as independent variables in analyses of breeding performance data as described later. 2.3. Nest record data Since 1939, the BTOâ&#x20AC;&#x2122;s Nest Record Scheme has accumulated a database of a range of breeding performance parameters with national coverage. Volunteer nest recorders visit nests repeatedly and complete standardised nest record cards (NRCs), recording nest contents, location, habitat and evidence of success or failure (Crick et al., 1994; Crick and Baillie, 1996). Depending on the number and timing (relative to nest progress) of the visits recorded on each NRC, some or all of a number of important components of

breeding success can be estimated. Data on clutch and brood sizes, chick:egg ratio and daily nest failure rates during the egg and nestling periods were extracted from nest record data for 1967â&#x20AC;&#x201C;1994 for each species. The habitat data recorded on NRCs (Baillie, 1988; Crick, 1992; Crick et al., 1994) were used to exclude records that did not come from agricultural habitats (Siriwardena et al., 2000). All computerised NRCs meeting the selection criteria were used in the analyses. All usable NRCs received by the BTO for bullfinch, skylark and yellowhammer have been computerised; resources have not permitted the computerisation of all cards received for the other species, but the samples of linnet and reed bunting NRCs selected for computerisation were chosen at random. For tree sparrow, annual samples of NRCs were selected to maximise the number of observers contributing (an equal number of NRCs was drawn randomly from those submitted by each observer). Nest record data from the years for which agricultural census data were available were used for this study, together with data from years adjacent to these years. Each NRC was assigned the agricultural data for the 10 km square in which it occurred and for the agricultural census year closest to the year from which it came. The sample sizes of NRCs associated with each set of agricultural census data for each species and the years from which they were drawn are shown in Table 2. Clutch size was defined as the maximum number of eggs found in a nest and brood size as the maximum number of young (omitting zeroes). Clutch size data were rejected if egg laying could have continued after the last visit of the recorder. Clutch and brood size data were analysed using ordinal logistic regression (McCullagh, 1980; Thomson et al., 1998) in the LOGISTIC procedure of SAS (SAS Institute, Inc., 1990). Chick:egg ratio was defined as the proportion of eggs hatching in nests where the whole nest did not fail, i.e. brood size/clutch size. The measure of brood size is likely to overestimate the brood size at fledging, but will approach it if mortality early in nestling life (when chicks are most vulnerable) is the most significant form of partial brood loss. The chick:egg ratio measure used here will therefore incorporate these early losses, as well as hatching success (the proportion of the eggs in the clutch which hatch successfully). Chick:egg ratio was modelled using a generalised linear model (GLM), also using the LOGISTIC procedure, with


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Table 2 Sample sizes of nest record cards (NRCs) for each species for each set of annual Agricultural Census data Agricultural Census year

Years for NRCsa

1969 1972 1976 1979 1981 1985 1988 1993

1967–1970 1971–1973 1975–1977 1978–1979 1980–1982 1984–1986 1987–1989 1992–1994

Nest record card sample sizes Bullfinch

Linnet

Reed Bunting

Skylark

Tree Sparrow

Yellowhammer

209 197 200 67 74 82 52 52

498 366 325 198 262 462 284 352

173 176 158 87 96 118 170 101

229 211 214 160 172 183 128 160

121 92 119 97 117 244 322 340

464 362 360 208 210 273 279 244

a The NRCs associated with each census year were drawn both from that year and from up to three other adjacent years, as indicated. Initially, NRCs from the Agricultural Census year itself were chosen, followed by the two surrounding years (if an adjacent year had already been chosen, a further earlier or later year was used). Where a year might then be chosen twice, it was replaced by a more distant year. NRCs from 1980 could equally have been assigned the agricultural data from either 1979 or 1981; arbitrarily, we chose 1981.

brood size/clutch size as a binomial response variable, a logit link function and a binomial error distribution. Daily nest failure rates before and after hatching were estimated using a formulation of the Mayfield (1961, 1975) method as a GLM in which success (0) or failure (1) was modelled as a proportion of the number of days over which a nest was ‘exposed to failure’, and using a logit link and binomial errors (Etheridge et al., 1997; Aebischer, 1999). Numbers of exposure days during the egg and nestling periods were calculated as the mid-point between the maximum and minimum possible given the timing of nest visits. These analyses were conducted using the LOGISTIC procedure of SAS. 2.4. Linking agricultural and nest record data For each species, each breeding performance parameter was analysed with respect to PC axes obtained as described above. The NRC data were matched to Agricultural Census data by census year and 10 km square. Within generalised linear models set up as appropriate for each nest record variable (see above), a stepwise protocol was used to select parsimonious combinations of the PC axis variables which were significant predictors of the variation in breeding performance. A PC axis was included if the score χ 2 statistic comparing models with and without the term was significant at P = 0.05; similarly, a term was subsequently deleted if the Wald χ 2 statistic comparing models with and without the term was non-significant at P = 0.05

(SAS Institute, Inc., 1990). The models selected thus included the predictors which explained as much of the variation in the relevant breeding performance parameter as possible without including more predictors than necessary. The PC axes were considered for each model only as linear terms; this may have limited the explanatory power of the models, but it greatly simplified the interpretation of the results. Much of the variation in the agricultural census data set is confounded with latitude, longitude and altitude; certain agricultural variables such as SHEEP and BEET are particularly greatly affected. Further analyses were therefore conducted to check whether the effects of the selected agricultural PC axis variables could still be detected after the easting, northing and altitude of the relevant 10 km grid square for each NRC were controlled for. These geographical variables were added to each selected model as linear predictors, with a squared term also being added for altitude to allow for curvilinear responses (Green et al., 1994), and the significances of the appropriate PC axis variables were reassessed. (A squared term caused no difficulty for interpretation here because only a control for the effect of altitude was needed, not interpretation of the effects themselves.) Easting and northing variables were derived from 10 km square grid references and altitude was calculated as the mid-point between the maximum and minimum given for each 10 km square in the Institute of Terrestrial Ecology’s Land Characteristic data bank (Ball et al., 1983).


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Just as a spatially heterogeneous agricultural variable could act as an alias for other spatially variable influences in the analyses, PC axes which have undergone strong temporal trends in abundance nationally could be selected as surrogates for causally unrelated trends in breeding performance. Therefore, check analyses were also run which included year, as a linear term, in the models selected by the initial stepwise process to show where the effects identified might be equally well explained by such a temporal trend. Again, the significances of the selected PC axis variables were reassessed after the control had been added.

3. Results 3.1. Analyses of Agricultural Census data The first five principal component axes each explained more than 6% of the total variance in the agricultural land-use data set and together explained a total of 66% (Table 3). These five variables could therefore be used as a more parsimonious alternative to including all 25 raw agricultural variables in analyses of breeding performance which accounted for most of the variation in the data. Each of the axes represented combinations of agricultural variables which could readily be interpreted in terms of broader patterns of land-use. Axis PC1 shows a general gradient from pastoral to arable land-use through variables which are typical of either farming regime while PC2 may show a gradient between pastoral and ‘traditional’ mixed farming regimes (Table 3). In comparison, PC3 is dominated by livestock densities and PC4 shows

a gradient from rape to other brassicas or potato and probably reflects the characteristics of arable break crops. Finally, axis PC5 reflects a gradient from beet and peas, two crops found almost exclusively in the most intensively arable areas of Britain, to the more diverse agriculture incorporating more fallow land that characterises less intensive management. The changes between Agricultural Census years in the mean score per 10 km square for each of the first five PC axes are illustrated in Fig. 1. The trends for all of England, Wales and Scotland are clearly only relevant to the interpretation of our results insofar as the trends are representative of the areas in which the bulk of the populations of the species concerned occur. The latter are shown on maps presented by Gibbons et al. (1993). If Britain is divided into three regions (north, south-east and south-west), derived from a north-south division around northing 50 of the National Grid (the approximate latitude of the northern tip of the Isle of Man) and an east-west division of the southern region around easting 40 (the approximate longitude of Aberdeen, Manchester and Bournemouth), the populations of all the species considered here except skylark and bullfinch are concentrated in the south-east. The latter two species also reach high densities in the south-east, but bullfinches are equally concentrated in the south-west and skylarks equally in the north. Sub-dividing the agricultural data by the same regions showed temporal trends in similar directions across all regions (although the magnitude of the changes varied according to the influence of each land-use type in each region) for PCs 1, 2 and 4. Regional biases should not, therefore, be a problem with these variables and Fig. 1 shows their national mean

Table 3 Results of the principal component analysis of agricultural land-use dataa PC axis

Variance explained (%)b

Important variablesc

1 2 3 4 5

27 12 11 9 7

PROPARA (0.43), WHEAT (0.37), BARLEY (0.35), OLDGRASS (−0.31) STOKNEMA (0.47), OATS (0.40), YNGGRASS (0.40), OLDGRASS (−0.37), MIXCOEF (0.33) SHEEP (0.57), COWS (0.55) CABBAGE (0.51), CAULIS (0.53), RAPE (−0.32), POTATO (0.31) FALLOW (0.47), SHANNON (0.47), MIXCOEF (0.33), PEAS (−0.32), BEET (−0.30)

a

Consisting of twenty variables describing crop types pasture age, livestock densities and farm heterogeneity (Table 1) The five principal components described accounted for a total of 66% of the total variance in the data set, which incorporates both spatial and temporal variation (see text for details). c A variable was considered important for a PC axis if the absolute value of its eigenvector exceeded 0.3; the eigenvectors are shown in parentheses. b


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Fig. 1. Overall trends in the principal component axis variables derived from Agricultural Census data (see text and Table 3 for details) for 1969, 1972, 1976, 1979, 1981, 1985 and 1988 and subsequently used in models of the dependence of breeding performance on agricultural land-use. Data from 1993 were omitted because they were incomplete. The data illustrated are mean PC scores calculated across all 27700 km squares in Britain for which we had data, weighted by the total area of agriculture in each square. The three regions of Britain had different trends for each of PCs 3 and 5, so regional trends are shown instead of national ones for these variables: squares show trends in the north, triangles trends in the south-east and diamonds trends in the south-west. See text for definitions of the three regions. (a) First principal component; (b) second principal component; (c) third principal component; (d) fourth principal component; (e) fifth principal component.


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Table 4 Results of stepwise generalised linear modelling analyses investigating the effects of overall agricultural land-use (in terms of five principal component axes derived from the 20 agricultural land-use variables are given in Table 1) on breeding performancea Species

Bullfinch Linnet Reed Bunting Skylark Tree Sparrow Yellowhammer

Predictors selected for each breeding performance parameter (P-value) Egg period failure rate

Nestling period failure rate

Chick:Egg ratio

Clutch size

Brood size

None PC1b (0.003)+ PC2b (0.048)− None None PC1 (0.001)− PC5c (0.005)− PC1b (0.002)−

PC3b,c

(0.005)+ PC4b (0.002)−

None PC1b (0.018)+

PC5b,c

(0.011)− PC4b,c (0.039)−

None None

PC5b,c (<0.001)+ None PC3b,c (0.009)+

None PC1c (0.034)− PC4b,c (0.018)+

PC2b,c (0.014)− PC1b (0.043)− PC1b (<0.001)−

PC1b (0.017)− None None

PC5b,c (0.014)−

PC3b (0.026)+

None

PC4b (0.016)+

a Intercepts were included in all models; the predictor variables (principal component axes) shown explained enough of the variation to meet the required Wald (deletion) or score (inclusion) test P-value of 0.05 after the addition of any further variables. P-values from Wald chi-squared tests for deletion from the final models are shown in brackets after each variable name. A plus after the variable name indicates a positive relationship, a minus a negative one. Note that a positive relationship with a failure rate represents a trend for lower breeding performance, whereas a positive relationship with any of the other variables represents the converse trend. b Still significant after the effects of latitude, longitude and altitude had been controlled for. c Still significant after a control for a linear time-trend was included.

trends. Regional variations were found for PCs 3 and 5, however. The trend for PC3 in the south-west and south-east increased with time, but values in the north were more stable (Fig. 1). Conversely, the values of PC5 increased in the north while declines occurred in the south-east and the values for the south-west were stable (Fig. 1). The patterns in both PC3 and PC5 could therefore have implications for the results with respect to skylark breeding performance, while the pattern in PC5 could affect interpretation for bullfinch. 3.2. Tests of the effects of overall agricultural land-use on breeding performance The results of the stepwise model selection analyses are summarised in Table 4. Each of the PC axis variables input was selected in several of the final models, suggesting that all the variations in agricultural practice indicated by the variables were at least correlated with some factors determining breeding performance across species. Both positive and negative effects on breeding performance were found for each PC axis. At least one component of breeding performance was significantly related to one or more PC axis variable for each species, showing that they were, potentially, all affected by agriculture (Table 4). However, different sets of relationships were found for each species.

Most (i.e. 18 out of 21) of the significant effects of the PC axes identified were still significant after latitude, longitude and altitude were controlled for, showing that they are unlikely only to have acted as aliases for other factors which have varied with geographical location. The effects which were not significant after controls were added were therefore confounded with other geographical variation. Around half (10 out of 21) of the effects remained significant after the control for temporal trends was introduced, showing that they were not potentially confounded with simple changes over time in other environmental variables.

4. Discussion 4.1. Pastoral versus arable farming Substantial, statistically significant proportions of the spatial and temporal variation in the breeding performance of the species investigated can be explained by variations in agricultural practices. The gradients in agricultural land-use represented by the principal component axes were each associated with gradients in breeding performance for two or more species. In particular, the increasing trend in PC1, which describes differences between pastoral and arable farming


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(Table 3), was selected as having a significant effect on breeding performance for all species except bullfinch. More arable farming was associated with lower breeding performance for reed bunting and skylark and with higher breeding performance for yellowhammer, while there were mixed effects for linnet and tree sparrow (Table 4). For each of the latter two species, the effect involving a failure rate (indicating negative and positive associations, respectively), is likely to be the more important for their demography. These results suggest that the effects of the broad nature of the agricultural landscape on breeding performance cannot be generalised across species. Arable and pastoral farming differ in many respects and it would not be surprising if the mechanisms by which the contrast affected breeding performance were species-specific. 4.2. Intensive and extensive agriculture The breeding performance of four of the six species investigated was significantly related to PC5, the axis representing the gradient between intensive arable and more extensive, mixed agriculture (Tables 3 and 4). More diverse (or mixed) farmland and the use of fallows (in traditional crop rotation systems) are features of extensive farmland management, which is generally believed to offer higher quality habitat for breeding birds (Fuller et al., 1995). Intensive management tends to reduce feeding and nesting opportunities for many species through diminished weed floras (reducing seed and invertebrate availability), more quickly growing crops and swards, reduced crop diversity and increased pesticide inputs, among other mechanisms (Potts, 1986; Schläpfer, 1988; Fuller et al., 1995; Baillie et al., 1997; Campbell et al., 1997). Despite these benefits potentially associated with high values of PC5, this axis variable was positively associated with breeding performance for only yellowhammer and tree sparrow, and negatively associated with it for bullfinch and reed bunting. The effects of less intensive agriculture were investigated further by testing the variation in each of SHANNON, MIXCOEF and FALLOW (Table 1) against the breeding performance of each species using univariate tests (but incorporating the same control protocols as were applied to our multivariate tests: Appendix A). Habitat diversity, as reflected by one or both of regime heterogeneity (MIXCOEF) and crop diversity (SHANNON), was

199

positively related to breeding performance for all the species except reed bunting. There were also negative relationships between breeding performance and one of these variables for bullfinch, linnet and skylark, but positive relationships were more common. The presence of more fallow land was associated with better breeding performance for linnet, reed bunting and tree sparrow and with worse performance for only one species, skylark. Taken together, therefore, the results tend to support the idea that more extensive agriculture often promotes better breeding performance, but the occasional contradictory patterns suggest that the effects can be complex, and that the positive influences of extensive management might sometimes be countered by other factors. The latter might include density-dependent constraints caused by crowding in high-quality habitat, including, for example, the facilitation of nest predation by corvids. 4.3. Arable break crops and oilseed rape The decline in the fourth PC axis variable (Fig. 1) reflects changes in arable break crops in favour of oilseed rape, the latter crop being a common feature of modern, intensive agriculture (Table 3). Negative influences of such a change are perhaps more likely to be due to the ways in which the crops are managed than to differences between the plants themselves. For example, rape grown for oilseed will be harvested later than cabbage and cauliflower grown for leaves and florets, respectively, and will be subject to different chemical applications (such as desiccants and fertilisers), leading to differences in risks of nest destruction and in food availability. Further differences will characterise the management of potato crops. Axis PC4 was positively associated with breeding performance for tree sparrow and yellowhammer, suggesting that the switch to rape could have been detrimental, while both positive and negative relationships were found for linnet (Table 4). The latter pattern is particularly interesting because, following their earlier decline, linnets have increased in abundance since 1986, probably because rape seeds have replaced those of increasingly rare arable weeds in the diet of nestlings (Wilson et al., 1996; Moorcroft et al., 1997). The results suggest that, through PC4, larger areas of oilseed rape have been associated with larger linnet clutch sizes but also with higher nest failure rates in the nestling


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period (Tables 3 and 4), the latter contradicting the effect that rape would be predicted to have. Further, the latter effect is likely to be the stronger of the two (the middle 95% of the variation in PC4 would lead to a range of clutch size values of only 0.13 under the model selected), but was not robust to a control for a linear time trend (Table 4), suggesting that PC4 could simply have acted as an alias for a temporal decline in linnet breeding performance. The importance of rape for linnet breeding performance was investigated explicitly using univariate tests (Appendix A), and these identified only one significant relationship: a negative effect of RAPE on breeding performance through the egg period daily nest failure rate. This result was robust to controls for geography and a linear time-trend. This indicates that the results with respect to PC4 do not primarily reflect the influence of rape, but also that this crop appears to have an effect on breeding performance inconsistent with the results of intensive work on linnets (Wilson et al., 1996; Moorcroft et al., 1997; Eybert and Constant, 1998). Moreover, changes in the egg period failure rate represent the most likely demographic cause yet identified for the decline of the linnet (Siriwardena et al., 1999, 2000) and areas under oilseed rape increased concurrently, making the latter a potential mechanism for the decline. A control for the area of wheat crops (which are much more common) showed that this pattern is not explained by the high correlation (0.70) between RAPE and WHEAT. Alternatively, greater availability of rape may increase breeding performance locally (at the scale typical of intensive field studies), but the improvement might not be detectable nationally if 10 km squares where RAPE has increased most tend otherwise to represent poor nesting habitat (perhaps intensively arable farmland). Whatever the precise mechanism, it is in rape-rich habitats (and those where values of PC1 are high and of PC2 are low: Table 4) that higher egg period failure rates appear to have driven the decline of the British linnet population. At the population level, this effect may be countered to some extent by benefits of rape for chick survival (Moorcroft and Wilson, 2000). 4.4. Livestock Axis PC3, representing the stocking densities of cows and sheep (Table 3), was selected in models for three species, bullfinch, tree sparrow and yellow-

hammer (Table 4). The effects on the breeding performance of the former two species were the stronger and were negative (Table 4). High stock densities can reduce the quality of farmland habitats for breeding birds through overgrazing and trampling (O’Connor and Shrubb, 1986; Andrews and Rebane, 1994): it is conceivable that overgrazing has devalued farmland as bullfinch and tree sparrow breeding habitat, but trampling would not affect them. There was a notable lack of significant negative associations between axis PC3 and breeding performance for species such as skylark and linnet (Table 4), which are more dependent on open field habitats for nesting and/or feeding. This suggests that livestock densities per se, as opposed to other, correlated landscape features, have not been important among the species and demographic variables considered. 4.5. Mixed farming and permanent pasture The temporal decline in axis PC2 (Fig. 1) reflects reductions in the heterogeneity of predominantly pastoral farmland as rotational grass leys and fodder crops have been converted to permanent pasture (Table 3). More mixed land-use is likely to multiply the feeding and nesting opportunities available to farmland birds and to promote both high densities and high diversity. However, a positive influence on breeding performance was found for only one species, reed bunting, and there was one negative effect, for linnet (Table 4). The lack of a general, positive effect of mixed farming may indicate that its benefits to birds appear at the population or community levels (in terms of abundance or diversity), rather than at the level of a pair’s breeding success. Indeed, density-dependent effects at high density or the presence of more predatory species at high diversity could actually reduce breeding performance somewhat in ‘good’ areas. 4.6. Unmeasured factors and potential problems As described in Section 1, this study could not test the importance of all the features of agricultural practice that are likely to have influenced the breeding performance of seed-eating birds on farmland because the necessary data were not available. One such potentially important variable is the area of cereals under spring-sown crops (Fuller et al., 1995;


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Wilson et al., 1997). Spring-sown crops represent a qualitatively different habitat to autumn-sown ones, throughout the breeding season, due to differences in vegetation height and density. The habitats available prior to the start of nesting also differ markedly between spring- and autumn-sowing patterns and could have critical effects on the attainment of an adequate physiological condition to allow successful breeding. Spring-sowing might therefore be expected to be associated with better breeding performance for many species, even if they do not nest within the crops themselves. Some data on sowing times have been collected (for barley) under the agricultural census, but only since 1979 (England and Wales) or 1985 (Scotland), so these data could not be included in the multivariate analyses. Univariate tests of the influence of the proportion of barley that was spring-sown, over the years for which data were available, on the breeding performance of each species revealed positive effects for bullfinch, tree sparrow and yellowhammer (see Appendix A). However, negative effects that will have had opposite impacts were also found for bullfinch and tree sparrow. Detailed field studies may be necessary to identify the net effects of spring- and winter-sown crops on breeding performance and the mechanisms by which the effects act. One important known effect of spring-sowing on skylarks, in cereal monocultures, is that it allows more breeding attempts to be made in a season because cereal crops do not then become prohibitively tall for nesting until late summer (Wilson et al., 1997; Chamberlain and Crick, 1999). This illustrates a limitation of this study’s analyses of breeding performance: annual breeding success cannot be measured for multi-brooded species from nest records because there is no information on the number of breeding attempts a pair makes. Similarly, nest records provide no data on situations where nesting was not possible: for example, field data on skylarks have shown that birds holding territories in crops such as rape and legumes may not nest at all because crop growth is too fast (Wilson et al., 1997). Other species are also known consistently to avoid autumn-sown crops as nesting habitat (Shrubb and Lack, 1991). It is unknown whether birds in such a situation can subsequently nest successfully elsewhere. A further potential general problem with the analyses is that the results only necessarily apply at

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the particular spatial scale investigated, i.e., that of 10 km squares. The breeding home ranges of the species considered, especially for the more territorial species such as skylark, will be much smaller, restricting the ability to detect relationships with agriculture at the territory scale. The importance of the difference in scale for the sensitivity of the analyses will increase as the difference between the heterogeneity in land-use at the 10 km square and territory scales increases. This difference is likely to be larger where a mixture of pasture and arable farming is found or in other situations where qualitatively very different habitats are found in close proximity. 4.7. Implications for trends in abundance The potential influences on trends in abundance of the significant relationships in Table 4 can be inferred from the national trends in the various agricultural variables (Fig. 1), insofar as the agricultural trends pertain in the geographical regions where the populations are found. The implications of the relationships are summarised in Table 5: for each species, there are some which are consistent in direction and others which are inconsistent with their having contributed to species’ declines (note, however, that the analyses are only correlative and do not provide evidence of causality). The combination of these various influences (together with others not measured here) could have led to a net increase or to no overall change in breeding performance. The influence of each relationship will depend on its strength and on the sensitivity of overall breeding performance to variation in the parameter affected. The results suggest that a range of relationships between features of agricultural land-use and breeding performance have existed across species. In combination with the different temporal trends shown by the agricultural variables (Fig. 1), the relationships indicate many different influences on abundance (Table 5). Previous work on granivorous farmland bird demography has suggested that only for linnet, bullfinch and reed bunting, of the species considered here, have undergone any change in breeding performance that was consistent with it having driven the species’ population trend, and only for linnet is such a change likely to have been the major demographic mechanism


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Table 5 Implications for trends in abundance of the relationships found between agricultural variables and breeding performance parameters for each species, as implied by the slopes of the relationships (Table 4) and of the temporal trends in the agricultural variables (Fig. 1)a Species

Bullfinch

Linnet

Reed bunting

Skylark Tree sparrow

Yellowhammer

Relationships between breeding performance and agricultural variablesb Leading to declines

Leading to increases

Increasing PC3:NFR Declining MIXCOEF:CER Declining SHANNON:CER Declining PROPSBAR:CER Increasing PC1:EFR Declining PC4:NFR Increasing RAPE:EFR Declining SHANNON:CS, BS Declining MIXCOEF:CS, BS Declining FALLOWc :BS Increasing PC1:BS Declining PC2:CS Declining FALLOWc :CER, BS Increasing PC1:CS Declining SHANNON:EFR, BS Increasing PC1:CS Increasing PC3:NFR Declining PC4:CER Declining PC5:EFR Declining PROPSBAR:EFR Declining MIXCOEF:NFR, CS, BS Declining SHANNON:NFR, CS, BS Declining FALLOWc :CS Declining PC4:BS Declining PC5:NFR Declining MIXCOEF:CS Declining PROPSBAR:BS

Declining PC5:CS Declining MIXCOEF:NFR Declining PROPSBAR:BS Increasing PC1:CER Declining PC2:EFR Declining PC4:CS Declining SHANNON:EFR

Declining PC5:NFR

Declining MIXCOEF:NFR Declining FALLOWc :EFR Increasing PC1:EFR Declining PROPSBAR:CS

Increasing PC1:EFR Increasing PC3:CER

a

‘Declining’ or ‘increasing’ beside the variable names in the table refers to the prevailing trend between 1969 and 1988 undergone by the variable concerned (Fig. 1). Relationships with MIXCOEF, SHANNON, FALLOW, PROPSBAR and RAPE refer to univariate tests described in Sections 4 and appendix A. Full details of these tests and plots of the temporal variation in the agricultural variables are available from the authors. b PC axis 5 is classified as ‘declining’ because its temporal trend was downward in south-east and south-west Britain, the key regions for the species considered. Positive relationships with increasing variables and negative ones with declining variables will tend to lead to population increases; the opposite relationships will tend to lead to declines. Abbreviations for breeding performance parameters are as follows: egg period failure rate EFR, nestling period failure rate NFR, chick:egg ratio CER, clutch size CS and brood size BS. c Agricultural trend occurred only in the south-east, so relationships with abundance only apply to part of the core area of the species’ range, limiting their potential influence (see Section 3.1).

(Kyrkos, 1997; Peach et al., 1999; Siriwardena et al., 1998b, 1999, 2000). Breeding performance has shown temporal trends opposite to those in abundance for skylark, tree sparrow and yellowhammer, and is unlikely to underlie the principal declines of bullfinch and reed bunting (Peach et al., 1999; Siriwardena et al., 2000). Changes in breeding performance are likely generally to have played roles secondary to changes in annual survival in driving changes in abun-

dance (Siriwardena et al., 1998b, 2000), although they could provide mechanisms by which recoveries after population declines are retarded.

5. Conclusions This study has found a variety of statistical relationships which suggest that agricultural land-use has


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affected breeding performance of granivorous farmland birds (Tables 4 and 5), but also that few simple generalisations cannot be made across different species. Each of the five principal component axis variables derived from Agricultural Census data described variation in land-use with a temporal component that could have contributed to the decline of at least one of the six bird species investigated (Table 5). Further such patterns were found from univariate tests of additional, specific hypotheses. The clearest general pattern indicates that more intensive agriculture tends to be accompanied by poorer breeding performance: this is consistent with many other studies noting detrimental effects of agricultural intensification on birds (reviews in Oâ&#x20AC;&#x2122;Connor and Shrubb, 1986; Baillie et al., 1997). However, many other relationships were found which could have counteracted those potentially contributing to population declines (Table 5). The balance between the different types of agricultural influence may explain why the net variations in breeding performance have not been consistent with their forming the mechanism behind the declines of most of the species considered (Siriwardena et al., 2000). Nevertheless, it important for conservation that a decline could be reversed by altering a different demographic rate to that which changed to cause it. Changing patterns of agricultural land-use in ways which would produce higher values of the key agricultural variables for a given species might therefore aid population recovery via improvements in breeding performance, even if it had been decreased survival, for example, that originally caused the decline. The relationships found may also help to predict the likely consequences of future changes in agriculture, such as might be caused by revised crop subsidy structures.

Acknowledgements We would like to thank all the volunteers who have submitted nest record cards over the years. We are also grateful to the BTO staff who have processed the data, especially Caroline Dudley, David Glue and Peter Beaven. Peter Beaven also assisted us with the spatial referencing of nest record data. Rhys Green gave valuable advice on the analysis of nest failure rates and Dan Chamberlain helped through discussions of

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the analysis of agricultural census data. Alison Bayley and others at the University of Edinburghâ&#x20AC;&#x2122;s Data Library supplied the Agricultural Census data and Tony Morris helped us to interpret the information. The comments of M.R. Carter and three anonymous reviewers contributed to the manuscript. The project under which this work was conducted is funded by the UK Ministry of Agriculture, Fisheries and Food as contract BD0906; MAFF also funded the purchase of grid-referenced Agricultural Census data from the University of Edinburgh. The Nest Record Scheme is funded by a partnership of the British Trust for Ornithology and the Joint Nature Conservation Committee (on behalf of English Nature, Scottish Natural Heritage and Countryside Council for Wales and also on behalf of the Environment and Heritage Service in Northern Ireland).

Appendix A. Univariate tests of specific agricultural hypotheses Four of the variables described in Table 1 were chosen to test specific hypotheses about the dependence of breeding performance on agriculture. FALLOW, MIXCOEF and SHANNON represented less intensive farming through the presence of larger areas of fallow land (perhaps signalling more traditional crop rotations), more heterogeneous (mixed) farming and more diverse land-use, respectively. RAPE represented a potentially important food source for linnets (Moorcroft et al., 1997; Eybert and Constant, 1998). Spring-sown barley may provide enhanced feeding and nesting opportunities in comparison to wintersown crops: the proportion of the area of barley grown that involved a spring-sown crop (PROPSBAR) was calculated for all years and squares in which the necessary data were available. The two area variables (FALLOW and RAPE) were converted into proportions of the total area of agricultural land, as in our main analyses. The importance of each of these single variables for breeding performance was investigated by entering each, as a linear term, into generalised linear models as appropriate for each breeding performance parameter. Checking analyses controlling for geographical and temporal variation were conducted as they had been with the PC axis models. The results are shown in Table 6.


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!Changes in agricultural land-use and breeding performance of some granivorous farmland passerines i