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

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

Changes in agricultural land-use and breeding performance ofsome granivorous farmland passerines in Britain

Gavin M. Siriwardenaa,b,∗, Stephen R. Bailliea,Humphrey Q.P. Cricka, Jeremy D. Wilsonb,1

a British Trust for Ornithology, The Nunnery, Thetford, Norfolk IP24 2PU, UKb 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 representsa valuable technique with which the causes and mechanisms behind population changes can be elucidated. Here, extensivespatially referenced data on agricultural land-use at the 10 km square scale for England, Wales and Scotland were analysedin relation to similar data on breeding performance for six granivorous passerines which have declined in recent years. Fiveprincipal component axes described the variation in agricultural land-use from 1969–1988 adequately and explained thevariation in breeding performance (measured as daily nest failure rates, chick:egg ratio, clutch size and brood size) to varyingdegrees across species. The overall influence of agricultural land-use tended to be species-specific, with principal componentaxes describing gradients between pastoral and arable agriculture and between intensively arable and more extensive agriculturebeing particularly important, but having different effects across species. The clearest general pattern suggested that moreintensive agriculture tends to be associated with poorer breeding performance. Although influences of agricultural land-useon breeding performance are unlikely to have driven the major, long-term declines of any of the species except linnet, theresults 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 improvementsin breeding performance and provide hypotheses for further intensive field studies of the influences of farming practices onbird 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 impli-cated as the cause of severe declines in farmlandbird populations over recent decades (O’Connor and

∗ Corresponding author. Tel.:+44-1842-750050;fax: +44-1842-750030.E-mail address:[email protected] (G.M. Siriwardena).

1 Present address: Royal Society for the Protection of Birds, TheLodge, Sandy, Bedfordshire SG19 2DL, UK.

Shrubb, 1986; Fuller et al., 1995; Baillie et al., 1997;Campbell et al., 1997; Siriwardena et al., 1998a). Thewidespread intensification of agriculture in Britain(and elsewhere in Europe) has proceeded at a steadypace since the second world war, driven by a desirefor national self-sufficiency and backed by govern-ment subsidies for increased outputs. The lethal ef-fects of organochlorine pesticides on birds now led toa well-known environmental crisis in the 1950s and1960s (Carson, 1963). More recently, more subtle

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192 G.M. Siriwardena et al. / Agriculture, Ecosystems and Environment 84 (2001) 191–206

effects of intensification on bird populations have beenpostulated. Changes in cropping patterns, the simplifi-cation of crop rotations, the decline of mixed farmingand indirect effects of the more specific pesticidesnow being used may have been detrimental (Fulleret al., 1995; Baillie et al., 1997; Campbell et al., 1997).While there is evidence that such changes underliethe declines of grey partridgePerdix perdixL., corn-crakeCrex crexL., lapwing Vanellus vanellusL. andskylark Alauda arvensis L. (Potts, 1986; Shrubb andLack, 1991; Green et al., 1997; Wilson et al., 1997), itis lacking for many of the species that have declined.

Historical data on demography may allow historicalnational-scale changes in key demographic parame-ters to be related directly to changes in abundanceand in the environment (Thomson et al., 1997; Peachet al., 1999; Siriwardena et al., 1999). The produc-tion of fledged offspring per nesting attempt is one ofseveral key demographic parameters which determinebird abundance. The British Trust for Ornithology’s(BTO’s) nest record database provides opportunitiesto investigate historical variation in breeding perfor-mance and to assess the importance of possible causesof the variation (Crick and Baillie, 1996). In thisstudy, spatially referenced nest record data are used toinvestigate the relationships between breeding perfor-mance and agricultural land-use for six predominantlygranivorous passerine species: skylark, tree sparrowPasser montanusL., linnet Carduelis cannabinaL.,bullfinch Pyrrhula pyrrhulaL., reed buntingEmber-iza schoeniclusL. and yellowhammerE. citrinellaL. In common with several other seed-eating species,all these have undergone significant declines in abun-dance on farmland since 1975 (Siriwardena et al.,1998a), but these species are notable in having large,available nest record data sets. Significant proportionsof the populations of all six species (the majority foreach species except bullfinch: ca. 48%) are found onfarmland (Gregory and Baillie, 1998; Gregory, 2000),although they vary in their dependence on crops per se.

The June agricultural census conducted annuallyby the Ministry for Agriculture, Fisheries and Food(MAFF) and the Scottish Office Agriculture andFisheries Department (SOAFD) provides a spatially-referenced record of changes in agricultural land-usein Britain over time. These data, summarised at thelevel of 10 km squares of the National Grid, are exa-mined here in relation to the breeding performance

of the above species between 1967 and 1994. TheAgricultural Census was not designed to monitoragricultural characteristics important for farmlandbirds and the data set does not include records of sev-eral aspects of agricultural practice whose inclusionwould have improved the analyses. Examples includelevels of agrochemical application (Campbell et al.,1997) and the lengths of hedgerow present (Greenet al., 1994). Grassland could not be identified asgrazed pasture, hay meadows or silage fields and noinformation was available on the areas of barley sownin spring and autumn before 1979. These limitationsmean that the importance of the variables concernedcould not be assessed in the present study, but they donot invalidate the analyses involving the agriculturalvariables thatcouldbe included.

Specifically, this study asks whether agriculturalland-use in general, in terms of crop type, pasturetype and stocking densities, has been related to breed-ing performance, and investigates the nature of theserelationships. Although the analyses presented arehistorical and non-experimental, and thus cannot pro-vide proof of cause-and-effect for the declines of thesix seed-eaters investigated, any correlative patternsfound can nevertheless suggest hypotheses as to thecauses, both facilitating future, more focused investi-gations and providing supporting or refuting evidenceregarding existing hypotheses.

2. Methodology

2.1. Agricultural Census data

Data from the MAFF and SOAFD June censuses of1969, 1972, 1976, 1979, 1981, 1985, 1988 and 1993were obtained from the University of Edinburgh’sData Library as summaries at the 10 km square levelfor England, Wales and Scotland. Data were obtainedon the areas under a range of agricultural land-usesand on numbers of livestock. Some categories dif-fered between countries and some category definitionschanged from year to year. Thus, a complete set ofvariables which covered all land-uses and which wereconsistent across years could not be derived from thedata. In addition, only a subset of the England andWales census results was available for 1993 becauseof recent legislation regarding the confidentiality of

G.M. Siriwardena et al. / Agriculture, Ecosystems and Environment 84 (2001) 191–206 193

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

Variable Definition

COWS Total number of cowsSHEEP Total number of sheepWHEAT Area under wheatTriticum spp. L. (ha)BARLEY Area under barleyHordeumspp. L. (winter and spring) (ha)OATS Area under oatsAvena sativaL. (ha)RYE Area under ryeSecale cerealeL. (ha)HOPS Area under hopsHumulus lupulusL. (ha)MAIZE Area under maizeZea maysL. (ha)BEET Area under sugar beetBeta vulgarisL. (ha)STOKNEMA Area under turnipsBrassica rapaL. and mangoldsBeta vulgarisL. for stockfeed (ha)STOKRCK Area under rapeBrassica napusL. and cabbage/kaleB. oleraceaL. for stockfeed (ha)RAPE Area under rapeBrassica napusL. (almost all for oilseed) (ha)PEAS Area under peasPisum sativumL. (ha)OLDGRASS 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)YNGGRASS Area of young grassland (ha): grass sown<5 (England/Wales and Scotland 1976 onwards) or

<7 (Scotland 1969, 1972) years beforeFALLOW Area left fallow (ha)CARROTS Area under carrotsDaucus carotaL. (ha)CAULIS Area under cauliflowers, broccoli and kaleBrassica oleraceaL. for human consumption (ha)CABBAGE Area under cabbages, savoys, etc.Brassica oleraceaL. for human consumption (ha)POTATO Area under potatoesSolanum tuberosumL. (seed, early and late crops) (ha)LETTUCE Area under lettuceLactuca sativaL. (ha)SPROUTS Area under sproutsBrassica oleraceaL. (ha)PROPARA Proportion of the total area of agricultural land under arable crops (as opposed to grassland)MIXCOEF 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 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, fallowland, young grassland, older grassland and total under vegetables for human consumption (eachtreated as a separate ‘species’). An index of farmland heterogeneity

a Because the absolute area censused within each 10 km square varied according to the area of water and non-agricultural land in thesquare, the area variables were converted into proportions of the total agricultural area (calculated as the total of all the area variablesshown plus the miscellaneous crop and vegetable variables in the data set; the total area censused was not directly available in the datafor 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 there-fore be characterised in full. The variables for whicha consistent or near-consistent definition could beassigned through all the census years available (ex-cepting 1993) are listed in Table 1. Three additionalindices of the diversity and the arable/pastoral het-erogeneity of farmland were derived from the areavariables (Table 1). A further variable, the numberof pigs, was available in the census data, but wasomitted from the models because no information wasavailable on whether the numbers represented indooranimals or those in open fields: results with respect

to number of pigs would therefore be difficult tointerpret. Together, the area, livestock and hetero-geneity variables provide an overview of agriculturalland-use, analysis of which can reveal the importantfactors for breeding performance analytically.

2.2. Preparatory analyses of Agricultural Censusdata

The various agricultural census variables were inter-correlated to varying degrees, such that includingthem all in analyses of overall agricultural land-use


effects on breeding performance simultaneouslywould present substantial statistical problems in termsof variable selection. In addition, the use of large num-bers of statistical tests or of large numbers of candidatepredictor variables in the models described later wouldcarry a significant concomitant risk of the generationof spurious results. A principal components analysis(PCA) based on the correlation matrix between thevariables was conducted using the PRINCOMP proce-dure of SAS (SAS Institute, Inc., 1990) to reduce thenumber of variables in the census data (transformedas described above) before the effects of agriculturalland-use on breeding performance were investigated.Using PCA created an objective, parsimonious com-bination of the agricultural land-use variables intouncorrelated PC axes. Combining variables by anyother means would have been both subjective and un-likely to remove the problem of inter-correlation. Thedata from all 10 km squares in all agricultural censusyears were entered into the PCA simultaneously, so itincorporated spatial and temporal variation; althoughusing multiple observations from the same 10 kmsquares violates the assumption of independence inPCA, this will not have caused bias because the sameset of squares made up the data sets for each year. Allthe area variables listed in Table 1 were included inthe PCA, except RYE, HOPS, SPROUTS, CARROTSand LETTUCE, which were excluded because theywere too rare to be likely to be important for birdpopulations (mean proportion of agricultural land in a10 km square for each<0.1%). The PC axes derivedfrom this analysis that, together, explained an adequateproportion (>60%) of the variance in the land-use dataset were used as independent variables in analyses ofbreeding performance data as described later.

2.3. Nest record data

Since 1939, the BTO’s Nest Record Scheme hasaccumulated a database of a range of breeding perfor-mance parameters with national coverage. Volunteernest recorders visit nests repeatedly and completestandardised nest record cards (NRCs), recording nestcontents, location, habitat and evidence of success orfailure (Crick et al., 1994; Crick and Baillie, 1996).Depending on the number and timing (relative tonest 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 clutchand brood sizes, chick:egg ratio and daily nest failurerates during the egg and nestling periods were ex-tracted from nest record data for 1967–1994 for eachspecies. The habitat data recorded on NRCs (Baillie,1988; Crick, 1992; Crick et al., 1994) were used toexclude records that did not come from agriculturalhabitats (Siriwardena et al., 2000). All computerisedNRCs meeting the selection criteria were used in theanalyses. All usable NRCs received by the BTO forbullfinch, skylark and yellowhammer have been com-puterised; resources have not permitted the computer-isation of all cards received for the other species, butthe samples of linnet and reed bunting NRCs selectedfor computerisation were chosen at random. For treesparrow, annual samples of NRCs were selected tomaximise the number of observers contributing (anequal number of NRCs was drawn randomly fromthose submitted by each observer). Nest record datafrom the years for which agricultural census data wereavailable were used for this study, together with datafrom years adjacent to these years. Each NRC wasassigned the agricultural data for the 10 km square inwhich it occurred and for the agricultural census yearclosest to the year from which it came. The samplesizes of NRCs associated with each set of agriculturalcensus data for each species and the years from whichthey were drawn are shown in Table 2.

Clutch size was defined as the maximum number ofeggs found in a nest and brood size as the maximumnumber of young (omitting zeroes). Clutch size datawere rejected if egg laying could have continued af-ter the last visit of the recorder. Clutch and brood sizedata were analysed using ordinal logistic regression(McCullagh, 1980; Thomson et al., 1998) in the LO-GISTIC procedure of SAS (SAS Institute, Inc., 1990).Chick:egg ratio was defined as the proportion of eggshatching in nests where the whole nest did not fail,i.e. brood size/clutch size. The measure of brood sizeis likely to overestimate the brood size at fledging, butwill approach it if mortality early in nestling life (whenchicks are most vulnerable) is the most significantform of partial brood loss. The chick:egg ratio measureused here will therefore incorporate these early losses,as well as hatching success (the proportion of the eggsin the clutch which hatch successfully). Chick:egg ra-tio was modelled using a generalised linear model(GLM), also using the LOGISTIC procedure, with


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

AgriculturalCensus year

Years for NRCsa Nest record card sample sizes

Bullfinch Linnet Reed Bunting Skylark Tree Sparrow Yellowhammer

1969 1967–1970 209 498 173 229 121 4641972 1971–1973 197 366 176 211 92 3621976 1975–1977 200 325 158 214 119 3601979 1978–1979 67 198 87 160 97 2081981 1980–1982 74 262 96 172 117 2101985 1984–1986 82 462 118 183 244 2731988 1987–1989 52 284 170 128 322 2791993 1992–1994 52 352 101 160 340 244

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 hadalready been chosen, a further earlier or later year was used). Where a year might then be chosen twice, it was replaced by a more distantyear. 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 hatchingwere 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 thenumber of days over which a nest was ‘exposed tofailure’, and using a logit link and binomial errors(Etheridge et al., 1997; Aebischer, 1999). Numbersof exposure days during the egg and nestling peri-ods were calculated as the mid-point between themaximum and minimum possible given the timing ofnest visits. These analyses were conducted using theLOGISTIC procedure of SAS.

2.4. Linking agricultural and nest record data

For each species, each breeding performance para-meter was analysed with respect to PC axes obtained asdescribed above. The NRC data were matched to Agri-cultural Census data by census year and 10 km square.Within generalised linear models set up as appropriatefor each nest record variable (see above), a stepwiseprotocol was used to select parsimonious combina-tions of the PC axis variables which were significantpredictors of the variation in breeding performance.A PC axis was included if the scoreχ2 statistic com-paring models with and without the term was signifi-cant atP = 0.05; similarly, a term was subsequentlydeleted if the Waldχ2 statistic comparing models withand without the term was non-significant atP = 0.05

(SAS Institute, Inc., 1990). The models selected thusincluded the predictors which explained as much ofthe variation in the relevant breeding performance pa-rameter as possible without including more predic-tors than necessary. The PC axes were considered foreach model only as linear terms; this may have limi-ted the explanatory power of the models, but it greatlysimplified the interpretation of the results.

Much of the variation in the agricultural censusdata set is confounded with latitude, longitude andaltitude; certain agricultural variables such as SHEEPand BEET are particularly greatly affected. Furtheranalyses were therefore conducted to check whetherthe effects of the selected agricultural PC axis vari-ables could still be detected after the easting, northingand altitude of the relevant 10 km grid square foreach NRC were controlled for. These geographicalvariables were added to each selected model as lin-ear predictors, with a squared term also being addedfor altitude to allow for curvilinear responses (Greenet al., 1994), and the significances of the appropriatePC axis variables were reassessed. (A squared termcaused no difficulty for interpretation here becauseonly a control for the effect of altitude was needed, notinterpretation of the effects themselves.) Easting andnorthing variables were derived from 10 km squaregrid references and altitude was calculated as themid-point between the maximum and minimum givenfor each 10 km square in the Institute of TerrestrialEcology’s Land Characteristic data bank (Ball et al.,1983).


Just as a spatially heterogeneous agricultural vari-able could act as an alias for other spatially variableinfluences in the analyses, PC axes which have under-gone strong temporal trends in abundance nationallycould be selected as surrogates for causally unrelatedtrends in breeding performance. Therefore, checkanalyses were also run which included year, as a lin-ear term, in the models selected by the initial stepwiseprocess to show where the effects identified mightbe equally well explained by such a temporal trend.Again, the significances of the selected PC axis vari-ables were reassessed after the control had been added.

3. Results

3.1. Analyses of Agricultural Census data

The first five principal component axes each ex-plained more than 6% of the total variance in theagricultural land-use data set and together explaineda total of 66% (Table 3). These five variables couldtherefore be used as a more parsimonious alternative toincluding all 25 raw agricultural variables in analysesof breeding performance which accounted for most ofthe variation in the data. Each of the axes representedcombinations of agricultural variables which couldreadily be interpreted in terms of broader patterns ofland-use. Axis PC1 shows a general gradient frompastoral to arable land-use through variables whichare typical of either farming regime while PC2 mayshow a gradient between pastoral and ‘traditional’mixed farming regimes (Table 3). In comparison, PC3is dominated by livestock densities and PC4 shows

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

PC axis Variance explained (%)b Important variablesc

1 27 PROPARA (0.43), WHEAT (0.37), BARLEY (0.35), OLDGRASS (−0.31)2 12 STOKNEMA (0.47), OATS (0.40), YNGGRASS (0.40), OLDGRASS (−0.37), MIXCOEF (0.33)3 11 SHEEP (0.57), COWS (0.55)4 9 CABBAGE (0.51), CAULIS (0.53), RAPE (−0.32), POTATO (0.31)5 7 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)b 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.

a gradient from rape to other brassicas or potato andprobably reflects the characteristics of arable breakcrops. Finally, axis PC5 reflects a gradient from beetand peas, two crops found almost exclusively in themost intensively arable areas of Britain, to the morediverse agriculture incorporating more fallow landthat characterises less intensive management.

The changes between Agricultural Census years inthe mean score per 10 km square for each of the firstfive PC axes are illustrated in Fig. 1. The trends for allof England, Wales and Scotland are clearly only rele-vant to the interpretation of our results insofar as thetrends are representative of the areas in which the bulkof the populations of the species concerned occur. Thelatter 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-southdivision around northing 50 of the National Grid (theapproximate latitude of the northern tip of the Isleof Man) and an east-west division of the southernregion around easting 40 (the approximate longi-tude of Aberdeen, Manchester and Bournemouth),the populations of all the species considered hereexcept skylark and bullfinch are concentrated in thesouth-east. The latter two species also reach highdensities in the south-east, but bullfinches are equallyconcentrated in the south-west and skylarks equallyin the north. Sub-dividing the agricultural data by thesame regions showed temporal trends in similar di-rections across all regions (although the magnitude ofthe changes varied according to the influence of eachland-use type in each region) for PCs 1, 2 and 4. Re-gional biases should not, therefore, be a problem withthese variables and Fig. 1 shows their national mean


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 onagricultural land-use. Data from 1993 were omitted because they were incomplete. The data illustrated are mean PC scores calculatedacross all 27700 km squares in Britain for which we had data, weighted by the total area of agriculture in each square. The three regionsof Britain had different trends for each of PCs 3 and 5, so regional trends are shown instead of national ones for these variables: squaresshow trends in the north, triangles trends in the south-east and diamonds trends in the south-west. See text for definitions of the threeregions. (a) First principal component; (b) second principal component; (c) third principal component; (d) fourth principal component; (e)fifth principal component.


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

Species Predictors selected for each breeding performance parameter (P-value)

Egg period failure rate Nestling period failure rate Chick:Egg ratio Clutch size Brood size

Bullfinch None PC3b,c (0.005)+ None PC5b,c (0.011)− NoneLinnet PC1b (0.003)+ PC4b (0.002)− PC1b (0.018)+ PC4b,c (0.039)− None

PC2b (0.048)−Reed Bunting None PC5b,c (<0.001)+ None PC2b,c (0.014)− PC1b (0.017)−Skylark None None PC1c (0.034)− PC1b (0.043)− NoneTree Sparrow PC1 (0.001)− PC3b,c (0.009)+ PC4b,c (0.018)+ PC1b (<0.001)− None

PC5c (0.005)−Yellowhammer PC1b (0.002)− 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 variationto meet the required Wald (deletion) or score (inclusion) testP-value of 0.05 after the addition of any further variables.P-values fromWald chi-squared tests for deletion from the final models are shown in brackets after each variable name. A plus after the variable nameindicates a positive relationship, a minus a negative one. Note that a positive relationship with a failure rate represents a trend for lowerbreeding 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 variationswerefound for PCs 3 and5, however. The trend for PC3 in the south-west andsouth-east increased with time, but values in the northwere more stable (Fig. 1). Conversely, the values ofPC5 increased in the north while declines occurred inthe south-east and the values for the south-west werestable (Fig. 1). The patterns in both PC3 and PC5 couldtherefore have implications for the results with respectto skylark breeding performance, while the pattern inPC5 could affect interpretation for bullfinch.

3.2. Tests of the effects of overall agriculturalland-use on breeding performance

The results of the stepwise model selection analysesare summarised in Table 4. Each of the PC axis vari-ables input was selected in several of the final models,suggesting that all the variations in agricultural prac-tice indicated by the variables were at least correlatedwith some factors determining breeding performanceacross species. Both positive and negative effects onbreeding performance were found for each PC axis.At least one component of breeding performance wassignificantly related to one or more PC axis variablefor each species, showing that they were, potentially,all affected by agriculture (Table 4). However, differ-ent sets of relationships were found for each species.

Most (i.e. 18 out of 21) of the significant effects ofthe PC axes identified were still significant after lati-tude, longitude and altitude were controlled for, show-ing that they are unlikely only to have acted as aliasesfor other factors which have varied with geographicallocation. The effects which were not significant aftercontrols were added were therefore confounded withother geographical variation. Around half (10 out of21) of the effects remained significant after the controlfor temporal trends was introduced, showing that theywere not potentially confounded with simple changesover time in other environmental variables.

4. Discussion

4.1. Pastoral versus arable farming

Substantial, statistically significant proportions ofthe spatial and temporal variation in the breeding per-formance of the species investigated can be explainedby variations in agricultural practices. The gradientsin agricultural land-use represented by the principalcomponent axes were each associated with gradients inbreeding performance for two or more species. In par-ticular, the increasing trend in PC1, which describesdifferences between pastoral and arable farming


(Table 3), was selected as having a significant effect onbreeding performance for all species except bullfinch.More arable farming was associated with lower breed-ing performance for reed bunting and skylark andwith higher breeding performance for yellowhammer,while there were mixed effects for linnet and treesparrow (Table 4). For each of the latter two species,the effect involving a failure rate (indicating negativeand positive associations, respectively), is likely tobe the more important for their demography. Theseresults suggest that the effects of the broad nature ofthe agricultural landscape on breeding performancecannot be generalised across species. Arable and pas-toral farming differ in many respects and it would notbe surprising if the mechanisms by which the contrastaffected breeding performance were species-specific.

4.2. Intensive and extensive agriculture

The breeding performance of four of the six speciesinvestigated was significantly related to PC5, the axisrepresenting the gradient between intensive arable andmore extensive, mixed agriculture (Tables 3 and 4).More diverse (or mixed) farmland and the use of fal-lows (in traditional crop rotation systems) are featuresof extensive farmland management, which is gener-ally believed to offer higher quality habitat for breed-ing birds (Fuller et al., 1995). Intensive managementtends to reduce feeding and nesting opportunities formany species through diminished weed floras (reduc-ing seed and invertebrate availability), more quicklygrowing crops and swards, reduced crop diversity andincreased pesticide inputs, among other mechanisms(Potts, 1986; Schläpfer, 1988; Fuller et al., 1995;Baillie et al., 1997; Campbell et al., 1997). Despitethese benefits potentially associated with high valuesof PC5, this axis variable was positively associatedwith breeding performance for only yellowhammerand tree sparrow, and negatively associated with it forbullfinch and reed bunting. The effects of less inten-sive agriculture were investigated further by testingthe variation in each of SHANNON, MIXCOEF andFALLOW (Table 1) against the breeding performanceof each species using univariate tests (but incorpo-rating the same control protocols as were applied toour multivariate tests: Appendix A). Habitat diversity,as reflected by one or both of regime heterogeneity(MIXCOEF) and crop diversity (SHANNON), was

positively related to breeding performance for all thespecies except reed bunting. There were also negativerelationships between breeding performance and oneof these variables for bullfinch, linnet and skylark, butpositive relationships were more common. The pres-ence of more fallow land was associated with betterbreeding performance for linnet, reed bunting and treesparrow and with worse performance for only onespecies, skylark. Taken together, therefore, the resultstend to support the idea that more extensive agricul-ture often promotes better breeding performance, butthe occasional contradictory patterns suggest that theeffects can be complex, and that the positive influ-ences of extensive management might sometimes becountered by other factors. The latter might includedensity-dependent constraints caused by crowdingin high-quality habitat, including, for example, thefacilitation 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 ofoilseed rape, the latter crop being a common featureof modern, intensive agriculture (Table 3). Negativeinfluences of such a change are perhaps more likelyto be due to the ways in which the crops are managedthan to differences between the plants themselves. Forexample, rape grown for oilseed will be harvested laterthan cabbage and cauliflower grown for leaves andflorets, respectively, and will be subject to differentchemical applications (such as desiccants and fertilis-ers), leading to differences in risks of nest destructionand in food availability. Further differences will char-acterise the management of potato crops. Axis PC4was positively associated with breeding performancefor tree sparrow and yellowhammer, suggesting thatthe switch to rape could have been detrimental, whileboth positive and negative relationships were found forlinnet (Table 4). The latter pattern is particularly inter-esting because, following their earlier decline, linnetshave increased in abundance since 1986, probably be-cause rape seeds have replaced those of increasinglyrare arable weeds in the diet of nestlings (Wilsonet al., 1996; Moorcroft et al., 1997). The results sug-gest that, through PC4, larger areas of oilseed rapehave been associated with larger linnet clutch sizesbut also with higher nest failure rates in the nestling


period (Tables 3 and 4), the latter contradicting theeffect 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 leadto a range of clutch size values of only 0.13 under themodel selected), but was not robust to a control for alinear time trend (Table 4), suggesting that PC4 couldsimply have acted as an alias for a temporal decline inlinnet breeding performance. The importance of rapefor linnet breeding performance was investigated ex-plicitly using univariate tests (Appendix A), and theseidentified only one significant relationship: a negativeeffect of RAPE on breeding performance through theegg perioddaily nest failure rate. This result was ro-bust to controls for geography and a linear time-trend.This indicates that the results with respect to PC4 donot primarily reflect the influence of rape, but alsothat this crop appears to have an effect on breedingperformance inconsistent with the results of intensivework on linnets (Wilson et al., 1996; Moorcroft et al.,1997; Eybert and Constant, 1998). Moreover, changesin the egg period failure rate represent the most likelydemographic cause yet identified for the decline of thelinnet (Siriwardena et al., 1999, 2000) and areas underoilseed rape increased concurrently, making the lattera potential mechanism for the decline. A control forthe area of wheat crops (which are much more com-mon) showed that this pattern is not explained by thehigh correlation (0.70) between RAPE and WHEAT.Alternatively, greater availability of rape may increasebreeding performance locally (at the scale typical ofintensive field studies), but the improvement might notbe detectable nationally if 10 km squares where RAPEhas increased most tend otherwise to represent poornesting habitat (perhaps intensively arable farmland).Whatever the precise mechanism, it is in rape-richhabitats (and those where values of PC1 are high andof PC2 are low: Table 4) that higher egg period failurerates appear to have driven the decline of the Britishlinnet population. At the population level, this effectmay be countered to some extent by benefits of rapefor chick survival (Moorcroft and Wilson, 2000).

4.4. Livestock

Axis PC3, representing the stocking densities ofcows and sheep (Table 3), was selected in modelsfor three species, bullfinch, tree sparrow and yellow-

hammer (Table 4). The effects on the breeding per-formance of the former two species were the strongerand were negative (Table 4). High stock densities canreduce the quality of farmland habitats for breedingbirds through overgrazing and trampling (O’Connorand Shrubb, 1986; Andrews and Rebane, 1994): it isconceivable that overgrazing has devalued farmland asbullfinch and tree sparrow breeding habitat, but tram-pling would not affect them. There was a notable lackof significant negative associations between axis PC3and breeding performance for species such as sky-lark and linnet (Table 4), which are more dependenton open field habitats for nesting and/or feeding. Thissuggests that livestock densitiesper se, as opposed toother, correlated landscape features, have not been im-portant among the species and demographic variablesconsidered.

4.5. Mixed farming and permanent pasture

The temporal decline in axis PC2 (Fig. 1) reflectsreductions in the heterogeneity of predominantlypastoral farmland as rotational grass leys and fod-der crops have been converted to permanent pasture(Table 3). More mixed land-use is likely to multiplythe feeding and nesting opportunities available tofarmland birds and to promote both high densities andhigh diversity. However, a positive influence on breed-ing performance was found for only one species, reedbunting, and there was one negative effect, for linnet(Table 4). The lack of a general, positive effect ofmixed farming may indicate that its benefits to birdsappear at the population or community levels (in termsof abundance or diversity), rather than at the level ofa pair’s breeding success. Indeed, density-dependenteffects at high density or the presence of more preda-tory species at high diversity could actually reducebreeding performance somewhat in ‘good’ areas.

4.6. Unmeasured factors and potential problems

As described in Section 1, this study could nottest the importance of all the features of agriculturalpractice that are likely to have influenced the breed-ing performance of seed-eating birds on farmlandbecause the necessary data were not available. Onesuch potentially important variable is the area ofcereals under spring-sown crops (Fuller et al., 1995;


Wilson et al., 1997). Spring-sown crops represent aqualitatively different habitat to autumn-sown ones,throughout the breeding season, due to differences invegetation height and density. The habitats availableprior to the start of nesting also differ markedly be-tween spring- and autumn-sowing patterns and couldhave critical effects on the attainment of an adequatephysiological condition to allow successful breeding.Spring-sowing might therefore be expected to be as-sociated with better breeding performance for manyspecies, even if they do not nest within the cropsthemselves. Some data on sowing times have beencollected (for barley) under the agricultural census,but only since 1979 (England and Wales) or 1985(Scotland), so these data could not be included in themultivariate analyses. Univariate tests of the influenceof the proportion of barley that was spring-sown, overthe years for which data were available, on the breed-ing performance of each species revealed positiveeffects for bullfinch, tree sparrow and yellowhammer(see Appendix A). However, negative effects thatwill have had opposite impacts were also found forbullfinch and tree sparrow. Detailed field studies maybe necessary to identify the net effects of spring- andwinter-sown crops on breeding performance and themechanisms by which the effects act.

One important known effect of spring-sowing onskylarks, in cereal monocultures, is that it allowsmore breeding attempts to be made in a season be-cause cereal crops do not then become prohibitivelytall for nesting until late summer (Wilson et al., 1997;Chamberlain and Crick, 1999). This illustrates alimitation of this study’s analyses of breeding perfor-mance: annual breeding success cannot be measuredfor multi-brooded species from nest records becausethere is no information on the number of breedingattempts a pair makes. Similarly, nest records pro-vide no data on situations where nesting was notpossible: for example, field data on skylarks haveshown that birds holding territories in crops such asrape and legumes may not nest at all because cropgrowth is too fast (Wilson et al., 1997). Other speciesare also known consistently to avoid autumn-sowncrops as nesting habitat (Shrubb and Lack, 1991).It is unknown whether birds in such a situation cansubsequently nest successfully elsewhere.

A further potential general problem with the anal-yses is that the results only necessarily apply at

the particular spatial scale investigated, i.e., that of10 km squares. The breeding home ranges of thespecies considered, especially for the more territo-rial species such as skylark, will be much smaller,restricting the ability to detect relationships withagriculture at the territory scale. The importanceof the difference in scale for the sensitivity of theanalyses will increase as the difference between theheterogeneity in land-use at the 10 km square andterritory scales increases. This difference is likelyto be larger where a mixture of pasture and arablefarming is found or in other situations where qual-itatively very different habitats are found in closeproximity.

4.7. Implications for trends in abundance

The potential influences on trends in abundance ofthe significant relationships in Table 4 can be inferredfrom the national trends in the various agriculturalvariables (Fig. 1), insofar as the agricultural trendspertain in the geographical regions where the popula-tions are found. The implications of the relationshipsare summarised in Table 5: for each species, thereare some which are consistent in direction and otherswhich are inconsistent with their having contributedto species’ declines (note, however, that the analysesare only correlative and do not provide evidence ofcausality). The combination of these various influ-ences (together with others not measured here) couldhave led to a net increase or to no overall change inbreeding performance. The influence of each relation-ship will depend on its strength and on the sensitivityof overall breeding performance to variation in theparameter affected.

The results suggest that a range of relationshipsbetween features of agricultural land-use and breedingperformance have existed across species. In combina-tion with the different temporal trends shown by theagricultural variables (Fig. 1), the relationships indi-cate many different influences on abundance (Table 5).Previous work on granivorous farmland bird demog-raphy has suggested that only for linnet, bullfinch andreed bunting, of the species considered here, have un-dergoneanychange in breeding performance that wasconsistent with it having driven the species’ popula-tion trend, and only for linnet is such a change likelyto have been the major demographic mechanism


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

Species Relationships between breeding performance and agricultural variablesb

Leading to declines Leading to increases

Bullfinch Increasing PC3:NFR Declining PC5:CSDeclining MIXCOEF:CER Declining MIXCOEF:NFRDeclining SHANNON:CER Declining PROPSBAR:BSDeclining PROPSBAR:CER

Linnet Increasing PC1:EFR Increasing PC1:CERDeclining PC4:NFR Declining PC2:EFRIncreasing RAPE:EFR Declining PC4:CSDeclining SHANNON:CS, BS Declining SHANNON:EFRDeclining MIXCOEF:CS, BSDeclining FALLOWc:BS

Reed bunting Increasing PC1:BS Declining PC5:NFRDeclining PC2:CSDeclining FALLOWc:CER, BS

Skylark Increasing PC1:CS Declining MIXCOEF:NFRDeclining SHANNON:EFR, BS Declining FALLOWc:EFR

Tree sparrow Increasing PC1:CS Increasing PC1:EFRIncreasing PC3:NFR Declining PROPSBAR:CSDeclining PC4:CERDeclining PC5:EFRDeclining PROPSBAR:EFRDeclining MIXCOEF:NFR, CS, BSDeclining SHANNON:NFR, CS, BSDeclining FALLOWc:CS

Yellowhammer Declining PC4:BS Increasing PC1:EFRDeclining PC5:NFR Increasing PC3:CERDeclining MIXCOEF:CSDeclining PROPSBAR:BS

a ‘Declining’ or ‘increasing’ beside the variable names in the table refers to the prevailing trend between 1969 and 1988 undergone bythe variable concerned (Fig. 1). Relationships with MIXCOEF, SHANNON, FALLOW, PROPSBAR and RAPE refer to univariate testsdescribed in Sections 4 and appendix A. Full details of these tests and plots of the temporal variation in the agricultural variables areavailable 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 regionsfor the species considered. Positive relationships with increasing variables and negative ones with declining variables will tend to lead topopulation increases; the opposite relationships will tend to lead to declines. Abbreviations for breeding performance parameters are asfollows: 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 showntemporal trends opposite to those in abundance forskylark, tree sparrow and yellowhammer, and is un-likely to underlie the principal declines of bullfinchand reed bunting (Peach et al., 1999; Siriwardenaet al., 2000). Changes in breeding performance arelikely generally to have played roles secondary tochanges in annual survival in driving changes in abun-

dance (Siriwardena et al., 1998b, 2000), althoughthey could provide mechanisms by which recoveriesafter population declines are retarded.

5. Conclusions

This study has found a variety of statistical rela-tionships which suggest that agricultural land-use has


affected breeding performance of granivorous farm-land birds (Tables 4 and 5), but also that few sim-ple generalisations cannot be made across differentspecies. Each of the five principal component axisvariables derived from Agricultural Census data de-scribed variation in land-use with a temporal compo-nent that could have contributed to the decline of atleast one of the six bird species investigated (Table 5).Further such patterns were found from univariate testsof additional, specific hypotheses. The clearest generalpattern indicates that more intensive agriculture tendsto be accompanied by poorer breeding performance:this is consistent with many other studies noting detri-mental effects of agricultural intensification on birds(reviews in O’Connor and Shrubb, 1986; Baillie etal., 1997). However, many other relationships werefound which could have counteracted those poten-tially contributing to population declines (Table 5).The balance between the different types of agricul-tural influence may explain why the net variations inbreeding performance have not been consistent withtheir forming the mechanism behind the declines ofmost of the species considered (Siriwardena et al.,2000). Nevertheless, it important for conservationthat a decline could be reversed by altering a differentdemographic rate to that which changed to cause it.Changing patterns of agricultural land-use in wayswhich would produce higher values of the key agri-cultural variables for a given species might thereforeaid population recovery via improvements in breed-ing performance, even if it had been decreased sur-vival, for example, that originally caused the decline.The relationships found may also help to predict thelikely consequences of future changes in agriculture,such as might be caused by revised crop subsidystructures.

Acknowledgements

We would like to thank all the volunteers who havesubmitted nest record cards over the years. We are alsograteful to the BTO staff who have processed the data,especially Caroline Dudley, David Glue and PeterBeaven. Peter Beaven also assisted us with the spa-tial referencing of nest record data. Rhys Green gavevaluable advice on the analysis of nest failure ratesand Dan Chamberlain helped through discussions of

the analysis of agricultural census data. Alison Bay-ley and others at the University of Edinburgh’s DataLibrary supplied the Agricultural Census data andTony Morris helped us to interpret the information.The comments of M.R. Carter and three anonymousreviewers contributed to the manuscript. The projectunder which this work was conducted is funded bythe UK Ministry of Agriculture, Fisheries and Foodas contract BD0906; MAFF also funded the purchaseof grid-referenced Agricultural Census data from theUniversity of Edinburgh. The Nest Record Scheme isfunded by a partnership of the British Trust for Or-nithology and the Joint Nature Conservation Com-mittee (on behalf of English Nature, Scottish NaturalHeritage and Countryside Council for Wales and alsoon behalf of the Environment and Heritage Service inNorthern Ireland).

Appendix A. Univariate tests of specificagricultural hypotheses

Four of the variables described in Table 1 were cho-sen to test specific hypotheses about the dependenceof breeding performance on agriculture. FALLOW,MIXCOEF and SHANNON represented less inten-sive farming through the presence of larger areas offallow land (perhaps signalling more traditional croprotations), more heterogeneous (mixed) farming andmore diverse land-use, respectively. RAPE repre-sented a potentially important food source for linnets(Moorcroft et al., 1997; Eybert and Constant, 1998).Spring-sown barley may provide enhanced feedingand nesting opportunities in comparison to winter-sown crops: the proportion of the area of barley grownthat involved a spring-sown crop (PROPSBAR) wascalculated for all years and squares in which the nec-essary data were available. The two area variables(FALLOW and RAPE) were converted into propor-tions of the total area of agricultural land, as in ourmain analyses. The importance of each of these singlevariables for breeding performance was investigatedby entering each, as a linear term, into generalisedlinear models as appropriate for each breeding perfor-mance parameter. Checking analyses controlling forgeographical and temporal variation were conductedas they had been with the PC axis models. The resultsare shown in Table 6.

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References

Aebischer, N.J., 1999. Multi-way comparisons and generalisedlinear models of nest success: extensions of the Mayfieldmethod. Bird Study 46, S22–S31.

Andrews, J., Rebane, M., 1994. Farming and Wildlife. A PracticalManagement Handbook. RSPB, Sandy, Beds.

Ball, D.F., Radford, G.L., Williams, M.W., 1983. A landcharacteristic data bank for Great Britain. Bangor OccasionalPaper, Institute of Terrestrial Ecology, Bangor, Gwynedd.

Baillie, S.R. (Ed.), 1988. Final Report of the Nest RecordScheme Technical Review Group. British Trust for Ornithology,Thetford, Norfolk.

Baillie, S.R., Gregory, R.D., Siriwardena, G.M., 1997. Farmlandbird declines: patterns, processes and prospects. In: Kirkwood,R.C. (Ed.), BCPC Proceedings No. 69: Weeds 1997, BCPC,Farnham, UK.

Begon, M., Harper, J.L., Townsend, C.R., 1986. Ecology.Individuals, Populations and Communities. Blackwell, Oxford.

Campbell, L.H., Avery, M.I., Donald, P.F., Evans, A.D., Green,R.E., Wilson, J.D., 1997. A Review of the Indirect Effects ofPesticides. JNCC report no. 227, Peterborough, UK.

Carson, R., 1963. The Silent Spring. Hamish Hamilton, London.Chamberlain, D.E., Crick, H.Q.P., 1999. Population declines

and reproductive performance of skylarksAlauda arvensisin different regions and habitats of Great Britain. Ibis 141,38–51.

Crick, H.Q.P., 1992. A bird-habitat coding system for use inBritain and Ireland incorporating aspects of land managementand human activity. Bird Study 39, 1–12.

Crick, H.Q.P., Baillie, S.R., 1996. A review of the BTO’s NestRecord Scheme. Its value to the Joint Nature ConservationCommittee and Country Agencies, and its methodology. BTOResearch Report no. 156, British Trust for Ornithology,Thetford, Norfolk.

Crick, H.Q.P., Dudley, C., Glue, D.E., 1994. Nest Record SchemeHandbook. BTO, Thetford, Norfolk, UK.

Etheridge, B., Summers, R.W., Green, R.E., 1997. The effectsof illegal killing and destruction of nests by humans on thepopulation dynamics of the hen harrierCircus cyaneusinScotland. J. Appl. Ecol. 34, 1081–1105.

Eybert, M.C., Constant, P., 1998. Diet of nestling linnets (AcanthiscannabinaL.). J. Ornithol. 139, 277–286.

Fuller, R.J., Gregory, R.D., Gibbons, D.W., Marchant, J.H., Wilson,J.D., Baillie, S.R., Carter, N., 1995. Population declines andrange contractions among lowland farmland birds in Britain.Cons. Biol. 9, 1425–1441.

Gibbons, D.W., Reid, J.B., Chapman, R.A., 1993. The New Atlasof Breeding Birds in Britain and Ireland: 1988–1991. Poyser,London.

Green, R.E., Osborne, P.E., Sears, E.J., 1994. The distributionof passerine birds in hedgerows during the breeding seasonin relation to characteristics of the hedgerow and adjacentfarmland. J. Appl. Ecol. 31, 677–692.

Green, R.E., Rocamora, G., Schäffer, N., 1997. Populations,ecology and threats to the corncrakeCrex crex in Europe.Vögelwelt 118, 117–134.

Gregory, R.D., 2000. Broad-scale habitat use of sparrows, finchesand buntings in Britain. Vögelwelt.

Gregory, R.D., Baillie, S.R., 1998. Large-scale habitat use ofdeclining British birds: implications for conservation. J. Appl.Ecol. 35, 785–799.

Kyrkos, A., 1997. Behavioural and demographic responses ofyellowhammers to variation in agricultural practices. D. Phil.Thesis, University of Oxford, Oxford.

Mayfield, H., 1961. Nesting success calculated from exposure.Wilson Bull. 73, 255–261.

Mayfield, H., 1975. Suggestions for calculating nest success.Wilson Bull. 87, 456–466.

McCullagh, P., 1980. Regression models for ordinal data. J. R.Stat. Soc. B 42, 109–142.

Moorcroft, D., Bradbury, R., Wilson, J.D., 1997. The diet ofnestling linnetsCarduelis cannabinabefore and after agricul-tural intensification. In: Kirkwood, R.C. (Ed.), BCPC Procee-dings No. 69: Weeds 1997, BCPC, Farnham, UK.

Moorcroft, D., Wilson, J.D., 2000. The ecology of linnetsCardueliscannabinaon lowland farmland. In: Aebischer, N.J., Evans,A.D. (Eds.), Proceedings of the 1999 BOU Spring Conference:Lowland Farmland Birds. British Ornithologists’ Union, Tring,UK.

O’Connor, R.J., Shrubb, M., 1986. Farming and Birds. CambridgeUniversity Press, Cambridge.

Peach, W.J., Siriwardena, G.M., Gregory, R.D., 1999. Long-termchanges in the abundance and demography of British reedbuntings Emberiza schoeniclus. J. Appl. Ecol. 36, 798–811.

Potts, G.R., 1986. The Partridge. Pesticides, Predation andConservation. Collins, London.

SAS Institute, Inc. 1990. SAS/STAT User’s Guide, Version 6,Fourth Edition, Vol. 2. GLM-VARCOMP. SAS Institute, Inc.,Cary, NC, USA.

Schläpfer, A., 1988. Populationsökologie der FeldlercheAlaudaarvensisin der intensiv genutzen Agrarlandschaft. Orn. Beob.85, 309–372.

Shrubb, M., Lack, P.C., 1991. The numbers and distribution ofbreeding lapwingsV. vanellusnesting in England and Wales in1987. Bird Study 38, 20–37.

Siriwardena, G.M., Baillie, S.R., Buckland, S.T., Fewster, R.M.,Marchant, J.H., Wilson, J.D., 1998a. Trends in the abundance offarmland birds: a quantitative comparison of smoothed commonbirds census indices. J. Appl. Ecol. 35, 24–43.

Siriwardena, G.M., Baillie, S.R., Crick, H.Q.P., Wilson, J.D., 2000.The importance of variation in the breeding performance ofseed-eating birds for their population trends on farmland. J.Appl. Ecol. 37, 122–138.

Siriwardena, G.M., Baillie, S.R., Wilson, J.D., 1998b. Variationin the survival rates of some British farmland passerines withrespect to their population trends on farmland. Bird Study 45,276–292.

Siriwardena, G.M., Baillie, S.R., Wilson, J.D., 1999. Temporalvariation in the annual survival rates of six granivorous birdswith contrasting population trends. Ibis 141, 621–636.

Thomson, D.L., Baillie, S.R., Peach, W.J., 1997. The demographyand age-specific annual survival of British song thrushesTurdus


philomelosduring periods of population stability and decline.J. Anim. Ecol. 66, 414–424.

Thomson, D.L., Furness, R.W., Monaghan, P., 1998. The analysisof ordinal response data in the behavioural sciences. Anim.Behav. 56, 1041–1043.

Wilson, J.D., Arroyo, B.E., Clark, S.C., 1996. The diet of birdspecies of lowland farmland: a literature review. Department of

the Environment, English Nature, University of Oxford, Oxford,UK.

Wilson, J.D., Evans, J., Browne, S.J., King, J.R., 1997. Territorydistribution and breeding success of skylarksAlauda arvensison organic and intensive farmland in Southern England. J. Appl.Ecol. 34, 1462–1478.

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

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