Agriculture, Ecosystems and Environment 129 (2009) 37–42

Predicting the effects of agricultural change on farmland bird populations inPoland

Fiona J. Sanderson a,*, Agnieszka Kloch b,1, Konrad Sachanowicz b,2,3, Paul F. Donald a

a Royal Society for the Protection of Birds, The Lodge, Sandy, Bedfordshire SG19 2DL, United Kingdomb Ogolnopolskie Towarzystwo Ochrony Ptakow, ul. Odrowaza 24, 05-270 Marki k. Warszawy, Poland

A R T I C L E I N F O

Article history:

Received 12 September 2007

Received in revised form 22 June 2008

Accepted 1 July 2008

Available online 13 August 2008

Keywords:

Agricultural intensification

European Union

Farmland birds

Species of European conservation concern

(SPECs)

New member states

A B S T R A C T

Measures of bird species richness, and the abundance or distribution of 20 farmland species, 12 of them

species of European conservation concern (SPECs), were modelled as a function of a number of habitat

variables in six regions of Poland using information-theoretic methods. The strongest positive predictor

of species richness of all species, of SPECs and of farmland specialists was the length of woody edge

habitat. There was a curvilinear relationship between cereal cover and species richness measures, with

total species richness reaching a peak at about 30% cereal cover. Species richness was therefore highest in

areas of mixed farming with a high proportion of woody edge habitat. The length of woody edge habitat

was also the strongest predictor of the abundance of individual species, although the direction of this

relationship varied. This modelling approach generated predictions about changes in bird species

richness and abundance in response to agricultural change across Poland. Mixed farming with a high

proportion of grassland and woody edge habitat is likely to maintain many of Poland’s important

farmland bird populations, but species-specific prescriptions will be needed for those species which

avoid woody edge habitat.

� 2008 Elsevier B.V. All rights reserved.

Contents lists available at ScienceDirect

Agriculture, Ecosystems and Environment

journal homepage: www.e lsev ier .com/ locate /agee

1. Introduction

During the last 30 years, farmland bird populations throughoutthe European Union (EU) have suffered severe and continuingdeclines (Tucker and Heath, 1994; BirdLife International, 2004;Donald et al., 2006). Temporal and spatial relationships betweenagricultural intensification and farmland bird declines (Chamber-lain et al., 2000; Donald et al., 2001, 2006) or betweenextensification and increase in abundance or species richness(Peach et al., 2001; van Buskirk and Willi, 2004) suggest that this isas a result of agricultural intensification, which has occurred at ahigher rate in western European countries (primarily EU) thanelsewhere over the past 30 years (Donald et al., 2002).

Rapid agricultural intensification in the EU has been driven bythe Common Agricultural Policy (CAP), which encouragesincreased productivity by guaranteeing fixed prices for produce,

* Corresponding author. Tel.: +44 1767 680551.

E-mail address: Fiona.Sanderson@rspb.org.uk (F.J. Sanderson).1 Present address: Institute of Environmental Sciences, Jagiellonian University,

ul. Gronostajowa 7, 30-387 Krakow, Poland.2 Present address: Department of Animal Ecology, Nicolaus Copernicus

University, Gagarina 9, 87-100 Torun, Poland.3 Present address: Museum and Institute of Zoology, Polish Academy of Sciences,

Wilcza 64, 00-679, Warszawa, Poland.

0167-8809/$ – see front matter � 2008 Elsevier B.V. All rights reserved.

doi:10.1016/j.agee.2008.07.001

supporting farm specialisation, and offering direct grants andpayments (Donald et al., 2002). The 12 new EU countries (10 ofwhich are former communist states) that acceded to the EU on 1May 2004 and 1 January 2007 are less intensively farmed than theEU15 (first 15 EU members) (Donald et al., 2002). They continue tosupport large populations of farmland birds of conservationconcern (BirdLife International, 2004). Poland holds over 31% ofthe total utilised agricultural land of the 12 new member states(EEA, 2004) and supports 26% of the total population of birds thatare dependent on farmland (BirdLife International, 2004). Agri-cultural management in Poland is therefore likely to have aprofound impact on farmland bird populations of the new EUcountries and therefore in the EU as a whole.

All EU Member States are currently required to implement agri-environment measures to protect and restore farmland environ-mental quality (Anon., 2004). As the EU committed at theGothenburg Summit to halting biodiversity loss by 2010, it isessential that such agri-environment schemes address the needs offarmland birds. It is therefore necessary to quantify changes infarmland birdspecies richnessandabundancethatare likely toresultfrom agricultural change in the new EU countries, whose differingecological conditions and lower level of agricultural intensity makeextrapolation from similar research in western Europe unreliable.

In this paper, information-theoretic methods were used toexamine the relationship between various measures of agricultural

F.J. Sanderson et al. / Agriculture, Ecosystems and Environment 129 (2009) 37–4238

management and farmland bird communities across a widegradient of agricultural intensity, allowing assessment of theimportance of different habitat features in maintaining farmlandbird species richness and abundance and prediction of thepotential impact of agricultural change. Various measures ofspecies richness in six regions across Poland were modelled as afunction of habitat type and heterogeneity in order to generatepredictions about likely impacts of future agricultural change onbird populations. The relationship between these measures ofagricultural management and the abundance or presence of 20farmland species, 12 of which are Species of European Conserva-tion Concern (SPECs), was also examined. This allows theidentification of habitat features related to the presence andabundance of individual declining farmland species, and providesdata to help set priorities for agri-environment prescriptionsdesigned to protect these species in Poland and in other new EUcountries.

2. Methods

Field data were collected in 2002 and 2003. In total, 180 1-kmsquares were surveyed in six regions representing the broadspectrum of agricultural management, heterogeneity and croptype found in Poland (GUS, 2003), 90 in each year. These wereWestern Wielkopolska (WK; 168150E, 528350N, covered in 2002),Pomorze Zachodnie (PZ; 148400E, 538200N, 2003), PomorzeGdanskie (PM; 188400E, 548050N, 2002), Kurpie (MZ; 21800E,538300N, 2003), Połnocne Podlasie (PD; 238150E, 53800N, 2002)and Rzeszowszczyzna (RZ; 228250E, 508180N, 2003). A 50 km �50 km grid was overlaid over the centre of each region and 30 1-kmsquares randomly selected for survey. Squares containing less than75% farmland were removed and replacements randomly selected.A spatial scale of 1 km2 was considered an appropriate compro-mise between the needs to reduce edge effects and to increasereplication, and is the scale used by a number of European birdmonitoring schemes for the same reasons.

Each square was surveyed twice for all bird species byexperienced bird surveyors. The first visit took place betweenApril 10th and May 10th starting between 6:00 and 7:00 a.m.(Central European Summer Time), the second between May 20thand June 20th, starting between 5:00 and 6:00 a.m. Each visit

Table 1Species whose abundance or presence analysed in relation to habitat (SPECs in bold).

Species Scientific name Number of sq

observed in

White stork Ciconia ciconia 84

Grey partridge Perdix perdix 52

Common quail Coturnix coturnix 95

Northern lapwing Vanellus vanellus 103

Common wood-pigeon Columba palumbus 142

Eurasian skylark Alauda arvensis 180

Barn swallow Hirundo rustica 104

Meadow pipit Anthus pratensis 112

Yellow wagtail Motacilla flava 161

Whinchat Saxicola rubetra 140

Fieldfare Turdus pilaris 101

Common whitethroat Sylvia communis 161

Red-backed shrike Lanius collurio 108

Common starling Sturnus vulgaris 152

Eurasian tree sparrow Passer montanus 73

European goldfinch Carduelis carduelis 132

Eurasian linnet Carduelis cannabina 132

Yellowhammer Emberiza citrinella 163

Ortolan bunting Emberiza hortulana 62

Corn bunting Miliaria calandra 110

a Derived from the midpoint of population estimates in BirdLife International (2004b By the equivalent of 50% in 10 years (Chylarecki et al., 2006).

lasted from 2 to 3 h. All birds seen or heard were marked on a1:10,000 scale map. Birds were not counted in bad weatherconditions such as heavy rain, fog or strong wind. The observerssurveyed the whole square evenly, excluding woodland and built-up areas, marking the route on a map in order to replicate thesurvey during the second visit. During the second visit, fieldwor-kers carried out a habitat survey, plotting the areas covered bydifferent habitat types on similar 1:10,000 maps. Six different landcover types were distinguished: cereal; other arable (maize,oilseed rape, potatoes and beets); recently abandoned, set-aside orunused farmland; grassland; wooded areas; and ‘‘other’’ (build-ings, roads, etc.). The presence or absence of water bodies withinthe surveyed area was also noted. Habitat maps were digitised andthe length of woody edge habitat and area of each crop typecalculated.

2.1. Statistical analysis

All statistical modelling was carried out in SAS version 9.1 (SAS,2003). Measures of bird species richness and abundance ofindividual species were modelled using generalized linear modelswith a Poisson distribution. Response variables were total birdspecies richness over the two visits of the following groups: allspecies; farmland specialists (species which are largely dependenton farmland in Europe); SPECs (BirdLife International, 2004);farmland specialist open ground nesters and farmland specialistsnesting in or close to trees and shrubs (Snow and Perrins, 1998).Species were classified as farmland specialists or not using thehabitat categories of Tucker and Evans (1997) as described inDonald et al. (2006). The abundance of all farmland specialistspecies occurring in more than 80 squares and the distribution (interms of presence/absence) of any other farmland specialistsoccurring in 50–80 squares was also individually modelled; otherfarmland specialists were not recorded with sufficient frequency toconstruct models. Species that have shown a decline from 2002 to2004 in Poland that would be equivalent to 50% in 10 years if itcontinued (Chylarecki et al., 2006; Table 1) and which show astrong association with farmland in Northern Europe (Tucker andEvans, 1997), were modelled where sample size was sufficientlylarge as indicated above. The abundance of 16 species wasmodelled, of which eight are SPECs (Table 1). Sturnus vulgaris

uares Estimated EU27

population in Poland (%)a

Declined in Poland

from 2002 to 2004b

38

32

10 Yes

11 Yes

5

21

15

4

17

17

8

17

6

9

5 Yes

5 Yes

4 Yes

18

28

3

).

F.J. Sanderson et al. / Agriculture, Ecosystems and Environment 129 (2009) 37–42 39

abundance was not modelled as the data were highly over-dispersed; instead, its distribution (presence/absence) was mod-elled. The distribution of three other species, all of which are SPECs,was modelled using a binomial distribution and logit link function:Perdix perdix; Passer montanus; Emberiza hortulana. The responsevariable for all models was offset by ln(farmland area) to accountfor small differences between squares in the area surveyed.

Explanatory variables were: region (REGN); the areas of cereal(TOTC; mean 46.21%, range 0–100%), grassland (GRAS; mean23.35%, range 0–98.6%), other arable land (ARAB; mean 13.9%,range 0–94.9%) and abandoned or unused land (ABAN; mean6.9%, range 0–89%); presence of ponds and wet ditches (WATR);Shannon–Wiener diversity of land use types listed above (HHAB;mean 0.76, range 0–1.22); number of habitat patches (fields andwoods) within the 1 km2 (PATC; mean 26.77, range 3–168); totallength of woody edge habitat (km; WOED; mean 1.21 km km�2,range 0–7.98). Non-cereal crops were grouped as ‘‘other arable’’as individual crop types were frequently absent from squares andtherefore there were too many zero values to allow individualcrops to be used as explanatory variables. Length of woody edgehabitat describes the edge length of small woods, often midfieldwoods, and the length of treelines and hedgerows, characteristicfeatures of Polish farmland. We did not use area of woodland as apredictor variable as we did not record birds in the woodlandinterior and as many of the woody habitat features were linear,area was less relevant than length; however, in practice, length ofwoody edge habitat and wood area were closely correlated(r = 0.88). As >75% of each square was farmland, woody edgehabitat was almost always part of the farmland itself and itspresence therefore related to farm management. Both the linearand quadratic terms (SQTO; SQAR; SQGR; SQAB) for area of cerealcover, other arable crops, grassland and abandoned farmlandrespectively were fitted in order to examine polynomial effects onspecies richness and abundance, as the effects of land use onthese response variables have been shown to be non-linear in thepast (e.g. Berg, 2002). The presence of water bodies, a highproportion of woody edge and crop heterogeneity/number offields were selected as explanatory variables as they indicate amore extensively farmed and heterogeneous habitat (Pain andPienkowski, 1997; Robinson and Sutherland, 2002; Newton,2004).

Modelling was carried out using an information-theoreticapproach (Burnham and Anderson, 2002) by running all possiblemodels (n = 144) within the following criteria: (1) Areas of crops,grassland and abandoned land were fitted in separate models asthese proportions sum to total farmland area and are thereforenon-independent. Although this approach precludes the possibi-lity of examining synergistic effects between different crop types,the lack of independence between these variables and the highintercorrelations (maximum r = 0.72) rendered models fittingseveral crop cover variables unreliable (Graham, 2003). Termsfitted together in other models were not strongly intercorrelated(maximum r = 0.53). (2) Quadratic terms were only fitted in

Table 2Model-averaged parameter estimates of explanatory variates for measures of species r

Response variable Explan

WOED

Total species richness 0.0778

Farmland specialist species richness 0.0231

Species richness of SPECs 0.0460

Species richness of tree or margin nesting farmland specialists 0.0520

Only variables selected as important are shown (see Section 2 for details), with the ex

variables were selected as important in explaining variation in the species richness of

models that already contained the linear term. (3) Region wasincluded in every model as an explanatory factor in order to controlfor systematic differences between regions in unmeasured factorssuch as climate or soil type. Models were assessed using thecorrected Akaike’s Information Criterion (AICc) and assignedAkaike weights, the weights ðwmÞ of all models summing to one(Burnham and Anderson, 2002). The 95% confidence set of modelsfor each response variable was identified as the smallest set ofmodels in which

Pwm�0:95. Explanatory variable weights or

selection probabilities were also generated (Burnham andAnderson, 2002).

Model-averaged parameter estimates and confidence intervalsof two standard errors (S.E.) (Burnham and Anderson, 2002) weregenerated for explanatory variables using the 95% confidence set ofmodels. Explanatory variables were only considered important inexplaining variation if the confidence intervals of the parameterestimate do not encompass 0, giving 95% confidence that the actualeffect size differs from 0. Model-averaged parameter estimateswere used to generate predictions about the impact of habitatchange in Poland by varying the habitat variable of interest andholding all other explanatory variables with high selectionprobabilities (that is, higher than the selection probability wouldbe if all variables were equally selected) at their mean value.

3. Results

Of the 123 species recorded and retained in the analyses, 41species were SPECs and 28 were classified as farmland specialists.Individual species whose abundance or distribution (presence/absence) were analysed are shown in Table 1.

The length of woody edge habitat was the most consistentpositive predictor of species richness, explaining variation in totalspecies richness, farmland specialist species richness and speciesrichness of SPECs (Table 2). Woody edge length was also positivelyassociated with species richness of tree and shrub nestingfarmland specialists, but not ground nesting birds. Total speciesrichness was predicted from model parameters to decrease fromabout 62 species at 8 km of woody edge km�2 (the maximumrecorded in the field) to 45 species at 4 km of woody edge km�2 and33 species at 0 km of woody edge. SPEC species richness waspredicted to decrease from about 12 species at 4 km woody edgekm�2 to about 10 species at 0 km woody edge km�2. Speciesrichness of farmland specialists and tree and margin nestingfarmland specialists were both predicted to increase by about onespecies with each 2 km increase in woody edge length, indicatingthat the increase in farmland specialist species richness withincreased woody edge length was accounted for by an increase intree/margin nesters only, and that this habitat, unsurprisingly, didnot benefit ground-nesting birds. A curvilinear relationshipbetween area of cereal and total species richness and the richnessof SPECs was apparent, although for SPECs only the quadratic termhad confidence limits smaller than the parameter estimate.Predicted total species richness increased from 25 at 100% cereal

ichness (95% confidence intervals in parentheses).

atory variable

TOTC SQTO

(0.0192) 0.0061 (0.0052) �0.0001 (0.00002)

(0.0226)

(0.0334) 0.0011 (0.0032) �0.00003 (0.000016)

(0.0388)

ception of linear variables where the relevant quadratic variable was selected. No

open ground nesting farmland specialists.

F.J. Sanderson et al. / Agriculture, Ecosystems and Environment 129 (2009) 37–4240

cover (the maximum recorded) to a peak of 40 at 30% cover (Table 2and Fig. 1), suggesting that species richness was higher in areaswith mixed farming rather than in monocultures.

Two extreme counts were removed from the analysis of Ciconia

ciconia abundance as they were considerably higher than any othercount and represented flocks rather than breeding pairs. The singlemost important variable in explaining variation in individualspecies’ abundance was again length of woody habitat edge(Table 3). The effect size was different from 0 (95% confidencelimits did not encompass 0) for eight species. For four species (C.

ciconia, Columba palumbus, Lanius collurio and Emberiza citrinella)the effect was positive; for four it was negative (Coturnix coturnix,Vanellus vanellus, Alauda arvensis and Motacilla flava). There waslittle effect of the other non-crop percentage habitat variables,with crop diversity (HHAB) positively related to the abundance of

Fig. 1. Relationship between crop cover and abundance of various species (a–g) and tot

farmland surveyed (outlier removed from the Vanellus vanellus data set to allow the d

two species (Saxicola rubetra and L. collurio) and negatively to one(C. palumbus) and number of habitat patches negatively related tothe abundance of Anthus pratensis only.

Cereal cover was curvilinearly related to the abundance of fivespecies (Fig. 1). Grass cover was related to the abundance of threespecies, linearly and positively to C. ciconia and A. pratensis

abundance and curvilinearly to V. vanellus abundance; andabandoned farmland was curvilinearly related to Carduelis

cannabina abundance. There was, however, considerable variationin the exact shape of quadratic relationships between crop coverand bird abundance, and therefore the percentage cover at whichabundance of each species is predicted to reach a peak varied(Fig. 1), with S. rubetra abundance predicted to be at its maximumat 0% cereal cover, Carduelis carduelis, C. palumbus and E. citrinella

abundance peaking at 40% cover, and A. arvensis abundance

al species richness (h): (^) represents original data points standardised for area of

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F.J. Sanderson et al. / Agriculture, Ecosystems and Environment 129 (2009) 37–42 41

peaking at 100% cereal cover. V. vanellus abundance peaked atapproximately 60% grassland cover and C. cannabina abundance at20% unused farmland.

Habitat diversity was positively associated with the presence ofS. vulgaris and area of unused farmland was positively related to E.

hortulana presence (Table 3).

4. Discussion

The results indicate that retaining mixed farming systems withabundant woody edge habitats, such as tree lines and hedgerows,is likely to retain both high overall avian species richness andrichness of target groups such as SPECs and farmland specialists onPolish farmland. These predictions, like all predictions presentedhere, rely on the assumption that the observed relationshipsbetween farmland habitat and bird species richness and abun-dance are functional rather than simply correlative. It should benoted that for the individual species investigated, the availabilityof woody edge habitat had equally positive and negative effects.This is likely to relate to the fact that some Polish farmlandcontains large numbers of small woods, tree lines and hedgerowsand hence a very high proportion of woody edge habitat (with amaximum in this study of 7.98 km km�2), and a highly fragmentedfarmland habitat such as this is likely to be inimical to ground-nesting species which prefer large open spaces (Chamberlain andGregory, 1999; Milsom et al., 2000). The abundance of four of thefive species studied here that declined in Poland from 2002 to 2004(Chylarecki et al., 2006) was negatively related to woody edge(Tables 1 and 3). Management will therefore need to be targeted atthese species where important populations occur.

Because predicted species richness reached a maximum atabout 30% cereal coverage, the richest bird community is likely tobe supported by some crop heterogeneity in the farmed landscape,which could be provided by mixed farming systems. The mostconsistent predictor of individual species abundance was cerealcover, which was consistently curvilinear with the majority ofspecies peaking in abundance at around 40% cereal cover. Thissuggests that mixed farming would be beneficial to a number ofspecies, although it should be noted that the exact shape of therelationship is likely to be scale-dependent (Robinson et al., 2001)and should be investigated in Poland at various spatial scales inorder to clarify this. Crop diversity was associated with abundanceof S. rubetra and L. collurio and with S. vulgaris presence, againindicating that mixed farming is likely to be beneficial for thesespecies.

Where grassland cover affected abundance of individualspecies, it had a positive effect, except in the case of V. vanellus

where the effect was curvilinear and abundance peaked at about60% grassland cover. An increase in overall bird abundance withincreasing proportion of grassland has previously been demon-strated in western Poland (Kosinski and Tryjanowski, 2000),contrasting with some Western European studies which havefound lower overall farmland bird abundance in predominantlypastoral than in arable landscapes (Atkinson et al., 2002), althoughit is not known whether this difference may relate to theproportion of grassland in the landscape. Although it wasimpractical to ascertain in this study, much of the grassland inthe plots studied here is likely to be semi-natural, as is 48% ofpermanent pasture in Poland (EEA, 2004). This is a particularlyimportant habitat for birds and other taxonomic groups (Bignaland McCracken, 1996; Virkkala et al., 2004) and has declinedmarkedly in much of western Europe, with less than 3% ofagricultural grassland remaining unimproved in the UK (Vickeryet al., 1999). Semi-natural grasslands are under considerable threatin the new EU Member States from both intensification and

F.J. Sanderson et al. / Agriculture, Ecosystems and Environment 129 (2009) 37–4242

abandonment (EEA, 2004), and maintenance of their importantbird populations is therefore only likely to be possible throughappropriate targeting of agri-environment schemes.

The presence of E. hortulana was positively related to the coverof unused farmland and C. cannabina abundance peaked at c. 20%cover. In this study, unused farmland comprised only a smallproportion (mean = 6.9%) of the area. The presence of a smallamount of short-term set-aside may therefore be beneficial forsome farmland birds in Poland (Orłowski, 2005).

The most important of the habitat variables examined werelength of woody edge habitat and mixed farming. However, as wellas protecting farmland bird species richness, prescriptions alsoneed to be targeted at individual species whose habitat require-ments may not be met by more general prescriptions. Agri-environment prescriptions therefore need to be tailored regionallyto the requirements of the most important species in that region.For example, in this sample, Pomorze Gdanskie supported thehighest densities of V. vanellus, an edge-avoiding species, whereasspecies associated with wood edge such as C. ciconia and L. collurio

occurred at low densities. As V. vanellus is one of several speciesthat appear to be declining in Poland (Chylarecki et al., 2006), agri-environment measures should also be targeted towards the needsof such species where they still occur at high density.

Acknowledgements

We would like to thank the fieldworkers Jerzy Grzybek, MarekJobda, Łukasz Meina, Damian Nowak, Daniel Piec, Rafał Rzep-kowski, Adrian Surmacki, Piotr Tryjanowski, Cezary Wojcik, RafałWyszynski, Piotr Zduniak and Przemysław Zurawlew. We are alsograteful to Agnieszka Wower, Paweł Sidło, Mateusz Ciechanowski,Tomasz Cofta, Piotr Tryjanowski, Bogumiła Blaszkowska, MaciejGromadzki, Irina Herzon, Lars Lachmann, and Kevin Standring foradditional assistance. We are grateful to Stijn Bierman forstatistical advice and Richard Bradbury for statistical discussionsand helpful comments on an earlier version of the manuscript, andtwo anonymous referees for helpful comments on an earlierversion of the manuscript.

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