Fo

FFa

b

c

ARRAA

KALFCFS

1

csecDKlcpdh

0h

Agriculture, Ecosystems and Environment 176 (2013) 17– 23

Contents lists available at SciVerse ScienceDirect

Agriculture, Ecosystems and Environment

jo ur nal ho me page: www.elsev ier .com/ locate /agee

orecasting the potential impacts of CAP-associated land use changesn farmland birds at the national level

ranc ois Chirona,∗, Karine Princéa,b, Maria Luisa Paracchini c, Claudia Bulgheronic,rédéric Jigueta

Muséum National d’Histoire Naturelle, UMR 7204 MNHN-CNRS-UPMC, 55 Rue Buffon, 75005 Paris, FranceUniversity of Wisconsin-Madison, Department of Forest and Wildlife Ecology, 1630 Linden Drive, Madison, WI 53706, United StatesJoint Research Center of the European Commission, Institute for Environment and Sustainability, TP 262, Via E. Fermi 2749, 21027 Ispra (VA), Italy

a r t i c l e i n f o

rticle history:eceived 8 December 2012eceived in revised form 14 May 2013ccepted 15 May 2013vailable online 17 June 2013

eywords:gricultureand use changesranceAP scenariosBIcale effect

a b s t r a c t

The European Farmland Bird Indicator (FBI) has been adopted as a Structural and Sustainable Develop-ment Indicator by the EU. It identifies farmland bird trends and uses them as a proxy for wider farmlandbiodiversity health. This study analyzed the potential impacts of future Common Agricultural Policy (CAP)land uses on the abundances of the 20 farmland bird species included in the French FBI. Four agricul-tural policy scenarios were studied using the Common Agricultural Policy Regionalized Impact analysis(CAPRI) agricultural model. These four scenarios describe the most likely changes in crop areas and includeregional bird population data from the French Breeding Bird Survey. A habitat association model was usedto predict the potential effects that changes to five crop categories, as well as the total arable area, wouldhave on species indices and the FBI. Our study demonstrates that the relative abundances of specialistfarmland bird species depend on both crop cover type and the total crop area. Model predictions show ageneral decline in the abundance of farmland birds between 2007 and 2020. However, the loss of farmlandbirds is predicted to be less pronounced in the ‘CAP Greening’ scenario, although the predicted FBI valueshave relatively large errors. Moreover, whatever the forecasted CAP, such uniform agricultural changes

do not affect bird populations or the FBI equally across all regions. The FBI’s geographical variability inresponse to applied agricultural changes clearly indicates that a nationwide policy will not yield equalresults but will instead depend on where in the country the agricultural changes occur. To optimize theeffectiveness of the CAP on biodiversity at the national and continental levels, policies should be tested atsmaller spatial levels, such as regions or farmlands, and then, the policies that represent the best optionsfor biodiversity at these sublevels should be combined to create a national plan.

. Introduction

In Europe, agriculture is the most important form of land use,overing almost half of the total land area (FAO, 2009). The conver-ion of land from natural complex systems to simplified agriculturalcosystems and the increasing exploitation of resources are majorauses of the high rate of farmland biodiversity loss (Fuller, 2000;onald et al., 2001; Green et al., 2005; Tscharntke et al., 2005;leijn et al., 2009). Agricultural policy plays an important role in

andscape management. The European Union (EU) Common Agri-ultural Policy (CAP) has often been cited as encouraging this

rocess of agriculture intensification through production subsi-ies (Donald et al., 2001; Stoate et al., 2001). Recently, the publicas become more aware of the environmental issues caused by

∗ Corresponding author. Tel.: +33 1 40 79 58 53; fax: +33 1 40 79 38 35.E-mail address: fchiron@mnhn.fr (F. Chiron).

167-8809/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.agee.2013.05.018

© 2013 Elsevier B.V. All rights reserved.

modern agriculture, encouraging decision makers to promote farm-ing practices that are considered less environmentally damaging(EU regulations 1782/2003 – 1788/2003, 10/21/2003). In 2012, theEU agreed on a mid-term (by the year 2020) headline target tohalt the loss of biodiversity and the degradation of ecosystem ser-vices (EC, 2011; EP Resolution 20/04/2012). Thus, the ongoing CAPreform, which should be finalized by the end of 2013 and takeplace in the programming period of 2014–2020, is a unique oppor-tunity to promote environmentally friendly policies, enabling theEU to reach its mid-term target for biodiversity (Plieninger et al.,2012).

Proposing biodiversity-friendly land use policies on a largescale (e.g., country and EU levels) requires forecasting future landuse patterns and their impact as accurately as possible. Many

processes drive land use changes in rural areas, such as agricul-tural land abandonment (Rounsevell et al., 2005, 2006), bio-energycrop introduction and expansion, agriculture modernization, graz-ing abandonment and crop specialization. Thus, to achieve a

1 tems a

btvtp2Vd

afbc(2be(mE2tulltrasetmcamfiaiccF

uwFlcnadTlt

optasaiatetp

8 F. Chiron et al. / Agriculture, Ecosys

iodiversity target through CAP reform, it is fundamental to assesshe impacts that various policy options would have on biodi-ersity. To achieve this goal, scenarios have become a popularool for exploring plausible or extreme land use futures; theyrovide images of how alternative futures may unfold (Ewert et al.,005; Rounsevell et al., 2005, 2006; Rounsevell and Metzger, 2010;erburg et al., 2006). Scenarios also offer a way to think about theifferent possible changes (Tress and Tress, 2003).

The Farmland Bird Index (FBI) is an EU structural and sustain-ble development indicator (Gregory et al., 2005) that identifiesarmland bird trends and uses them as a proxy for the wideriodiversity health of farmlands. Today, the FBI contributes to indi-ator sets used for national policies in many European countriesGregory et al., 2005), as well as at the EU level (CEC, 2006; EC,006; EEA, 2012). However, the FBI has rarely been used to forecastird populations in future agricultural land use projections (Butlert al., 2010; Scholefield et al., 2011). Recently, Scholefield et al.2011) developed a method to forecast the FBI using CAPRI (Com-

on Agricultural Policy Regionalized Impact analysis), which is anU-wide quantitative agricultural sector modeling system (Britz,005). Scholefield et al. (2011) note some limitations, includinghe predictive capacity of the regression models and parameterncertainty, which are linked to the scale at which the land is ana-

yzed (national). In this study, we attempted to solve some of theseimitations by integrating the individual species’ responses intohe predicted land use changes, studying their variability acrossegions. In particular, we examined the extent to which the speciesnd the FBI responses vary with past land use and with land-cape changes, according to the region (Newson et al., 2009; Riselyt al., 2009). We verified how a change in crop cover composi-ion or area could affect bird species. Crop composition would

ostly affect the specialist species that depend on a particularrop type, while it is assumed that changes to the global arablerea would affect all crop covers and species equally. Future CAPsay alternatively promote, at the farm level, crop type diversi-

cation (as opposed to homogenization) and/or the introductionnd maintenance of landscape elements (as opposed to landscapentensification). Thus, it is important to disentangle the relativeontributions of crop cover composition from those of land areahanges in the alterations that occur to species diversity and theBI.

This study assessed the potential impacts of future CAP landse policies on the 20 farmland bird species of the French FBI, asell as their effect on bird variability across many French regions.

our agricultural policy scenarios were used, representing the mostikely changes to crop cover. Those possible policies include (1) theurrent CAP proposal (or so-called ‘CAP Greening’), (2) a CAP sce-ario to abolish market support and direct payments in Pillar I,nd (3) a CAP scenario to increase the production of crops that areedicated to biomass energy, according to the Biofuels Directive.hese scenarios were compared to (4) a continuation of the base-ine current policy. Cropped land use projections were provided byhe CAPRI agricultural model (Britz, 2005).

Unlike the previous study, which directly modeled the impactf land use on the FBI (Scholefield et al., 2011), this study used therojected species index from predicted abundances to recalculatehe FBI. The relative effects of the crop cover composition and therable land area were disentangled. To understand which farmlandpecies are at risk under each of the four scenarios, a trait-basedpproach was adopted. This approach quantified the detrimentalmpact of land use changes on farmland bird species across France,s determined by their specialization to individual farmland habi-

ats. Finally, because bird responses to land use changes can also bexpected to vary spatially (Newson et al., 2009; Risely et al., 2009),his study assessed the extent to which national species and FBIredictions mask regional differences.

nd Environment 176 (2013) 17– 23

2. Materials and methods

Data from the French Breeding Bird Survey (FBBS), a standard-ized monitoring scheme in which skilled volunteer ornithologistsidentify breeding birds by song or visual contact (Jiguet et al., 2012),was used. In this scheme, each observer provided the name ofa municipality. For the survey, a 2 km × 2 km plot was randomlyselected from the area within a 10 km radius from the municipal-ity’s gravity center. Random selection ensures that the surveyedhabitats closely match the actual distribution of available habitatsin France. Each plot was monitored twice in the spring, before andafter May 8, with 4–6 weeks between the two events. In each plot,the observer carried out 10 evenly distributed point counts thatwere separated by at least 300 m; at each point, a 5 min surveyrecorded every individual bird that was heard or seen. The countswere repeated yearly by the same observer at the same points,occurring on the same date and at approximately the same timeof day (±15 min).

2.1. Selection of the regions and species used in the study

To correlate bird data with agricultural variables, the plots usedin further calculations had at least five points located within farm-land and were surveyed at least once between 2002 and 2007. Ateach point, the habitat was noted by the observers using a stan-dardized code (Julliard et al., 2006). We further selected the Frenchregions with at least 10 separate plots examined each year to be ableto determine species populations at a regional level. Ultimately,data from 15 regions and a total of 584 plots were analyzed (Fig.A.1). Of these plots, 138 were monitored for one, 93 for two, 117 forthree and 236 for four or more years. Among the birds monitoredby the scheme, we focused on the 20 species classified as farmlandspecialists by the French FBI (Jiguet et al., 2012). For each monitoredplot, the local species relative abundance was calculated as follows.First, for each species each year, the maximum number of individ-uals detected during either the first or the second point count wasdetermined. This maximum was used as the year’s species relativeabundance at each point, and then all points in a plot were summedto obtain its yearly local relative abundance. The species local rel-ative abundances at the regional level were obtained by summingthe yearly regional relative abundance of the plots in each region.The numbers of point counts made per region were incorporatedinto the analyses to account for differences in the regional samplingrate.

2.2. Species vulnerability

To assess the efficiency of various policies that aim to increasegrassland and fallow areas or promote the development of arablelands, an index of species’ grassland specialization was used (Princéet al., in press). This index follows the methodology of Julliardet al. (2006) and provides levels for a species’ index of specializa-tion to either arable or grassland habitats that allows species to beranked from arable- to grassland-specialized (Table A.1). Finally,species trends calculated by Jiguet et al. (2012) over the period of2001–2011 were used to evaluate the benefits that land use changescould have by 2020, specifically on species that are currently indecline in France.

2.3. CAPRI and land use data

The CAPRI model is a multi-purpose tool used to assess the

future impacts of European agriculture policies. It is based primar-ily on interlinked economic models. For the purposes of this study,the regional crop area data from 2002 to 2007 (containing 28 crops)were used to coincide with the available bird survey data. From

tems and Environment 176 (2013) 17– 23 19

itbdrwRcurtowC

2

iian(d(ubFlr2r

oti‘ealoaaod

2

tte2tuasf

S

a,

per

cen

tage

, an

d

pro

por

tion

al

chan

ges

un

der

fou

r

scen

ario

s

in

2020

(200

7

bein

g

the

refe

ren

ce

year

)

for

all F

ren

ch

regi

ons,

crop

area

(CA

)

bein

g

the

par

t

of

the

Uti

lize

d

Agr

icu

ltu

ral A

rea

per

crop

cate

gory

uch

that

the

sum

doe

s

not

mat

ch

the

tota

l cro

pp

ed

area

).

ud

ied

Des

crip

tion

of

crop

s

stu

die

d

Ref

eren

ce

2007

(are

a,

1000

ha)

Ref

eren

ce

2007

Scen

ario

s

(CA

chan

ge

in

2020

)

(CA

, %)

[min

;

max

])

Bas

elin

e

CA

P

Gre

enin

g

No

pil

lar

I

Bio

fuel

s

ow

lan

d

Exte

nsi

ve

and

inte

nsi

ve

graz

ing

pro

du

ctio

ns,

set-

asid

e

and

fall

ow

lan

ds

11,0

88

38

[4;6

0]

−12

−7

−13

−16

Soft

and

du

rum

wh

eat,

barl

ey

and

oats

7109

24

[9;4

7]

0

−2

−10

1Fo

dd

er

and

grai

n

mai

ze

2863

10

[4;1

6]

4

1

−5

5R

ape

and

sun

flow

er

2120

7

[2;1

5]

7

4

−21

1s

Suga

r

beet

and

pot

ato

and

oth

er

ind

ust

rial

crop

s

585

2

[0;1

3]

−16

−16

−14

−16

al

Are

a

(all

All

crop

typ

es

29,2

34

100

0

0

−7

−2

F. Chiron et al. / Agriculture, Ecosys

nitial CAPRI data on the 28 crop areas, we aggregated crop coverypes into functional groups and calculated the areas representedy cereals, other annual products, maize production, oil seed pro-uction and grassland and fallow lands. The proportion that wasepresented by the studied crop groups were pooled togetherithin each studied region and at the country level (called theegional Utilized Agricultural Area, RUAA, and the Utilized Agri-ultural Area, UAA, respectively). The RUAA is the ratio of the areased for farming to the total area of the region, and the UAA is theatio of the area used for farming to the total area of the country (inhis study, the net area of the 15 studied regions). Finally, the ratiof the land area used for each crop to the total area used for farmingas calculated for each studied region (crop group regional area,RA) and for the country (crop group area, CA).

.4. Evaluated scenarios

Four scenarios were created to forecast the indicator responsen 2020, i.e., (1) the CAP reform proposal for 2014–2020, whichncludes an increase of the semi-natural vegetation proportion ingricultural areas (hereafter called ‘CAP Greening’); (2) a CAP sce-ario to remove farm payments while maintaining market support‘No Pillar I’); (3) a CAP scenario to increase the production of cropsedicated to biomass energy, according to the Biofuels Directive‘Biofuel’); (4) a baseline scenario (‘Baseline’) that describes the sit-ation in 2020 under the assumption that the current policy lawook, including already approved future changes, is maintained.or each scenario, the relative changes in UAA and CA were calcu-ated between the years 2020, the projection year, and 2007, theeference year. The equations used for those calculations are (UAA020 − UAA 2007)/UAA 2007 and (CA 2020 − CA 2007)/CA 2007,espectively.

Table 1 shows the predicted UAA and CA changes under eachf the four scenarios in 2020. The ‘CAP Greening’ scenario aimso preserve permanent grasslands, diversify crop production andntroduce Ecological Focus Areas, which include fallow lands. TheNo Pillar I’ strategy provokes the land abandonment phenom-na, causing a reduction in the UAA (−7%, Table 1) as a wholend of crop areas dedicated to cereals, maize, oil seeds and grass-ands in particular. The main consequence of the ‘Biofuel’ scenarion land use is an increase in the sunflower area, and of maizend barley to a lesser extent. Under the baseline scenario, barleynd maize areas will increase moderately, while the proportionsf areas with permanent herbaceous vegetation and rape willecrease.

.5. Statistics and analyses

A calibration model was developed to determine the impact ofhe RUAA and CRA on regional relative species abundances. A habi-at association model was used to determine independently theffects that land use factors have the on relative abundance of the0 species, using the data gathered from all 15 tested regions. Tohis purpose, generalized linear mixed models (GlmmPQL) weresed to model the variations in species relative abundances whileccounting for the fact that the observed data were counts with akewed distribution. The equation for the calibration model is asollows:

pecies abundance∼ + ˇ1 × Grass and set-aside CRA

+ ˇ2 × Cereals CRA + ˇ3 × Maize CRA + ˇ4 × Oil seeds CRA

+ ˇ5 × other annual crops CRA + ˇ6 × RUAA

+ ˇx × Control variables + random(year) (1) Tab

le

1M

ain

crop

cove

r

are

(cro

ps

are

a

subs

et

s

Cro

p

cate

gori

es

st

Gra

ssla

nd

and

fall

Cer

eals

Mai

ze

Oil

seed

s

Oth

er

ann

ual

crop

Uti

lize

d

Agr

icu

ltu

rFr

ench

regi

ons)

20 F. Chiron et al. / Agriculture, Ecosystems and Environment 176 (2013) 17– 23

Table 2Correlation coefficients between crop areas.

Pearson’s correlation coefficient Regional utilizedarable area

Cereals Oilseeds Grassland andset-aside

Maize Other annualcrops

Cereals 0.36* 1Oilseeds 0.14 0.59* 1Grassland and set-aside −0.47* −0.80* −0.59* 1Maize 0.17 −0.34* −0.28 −0.12 1

tasoiotipaa

fwrtteulwdFeablPPmspv(lwffgsmt

ospa

aauo

t

Other annual crops 0.32* 0.71*

* Significant and −/+, direction.

In Eq. (1), the latitude, longitude, number of count points, andhe interaction between latitude and longitude are control vari-bles. As a basis, the model assumed a Poisson distribution ofpecies regional relative abundance. Because there were signs ofver-dispersion in the original count data of 13 of the species exam-ned, a quasi-Poisson distribution was used to model the abundancef those species. One random term identifying the year (from 2002o 2007) was included in the model because we are not interestedn a temporal trend in the abundance; rather, we consider the sam-les obtained in different years as replicates of a measure of localbundance. The CRA and RUAA were defined as fixed terms, as wells the number of count points per region.

Before modeling the species abundance, the data of the dif-erent variables were transformed to normalize their distribution,hen necessary. The crop area proportions for soft wheat, barley,

ape, fodder and grain maize, and other annual crops were log-ransformed, while those for cereals and oil seeds were square rootransformed. Data transformation enabled us to achieve a moreven distribution of species abundance along the predictor val-es and to avoid problems of model convergence. The latitude and

ongitude were standardized according to the equation (x − m)/s,here x is the measurement, m is the mean and s is the standardeviation, to enable a comparison of the magnitude of their effects.or each species separately, the fixed effects were first estimated byxamining the corresponding Wald statistics, which are distributedsymptotically as �2. The means of the factors that were found toe significant were then derived. Finally, the correlations between

and use factors and crop type proportions were examined usingearson’s correlation tests (Table 2). Predictions from the Glmm-QL models were derived first to assess the predictive power of theodel and then to examine the impact of each of the four case study

cenarios. The predictive power of the models was determined bylotting the predicted relative abundance of each species (the x-ariable) against the observed relative abundance of each speciesthe y-variable) per region per year, and then fitting a regressionine with an intercept of zero and a slope of 1 (i.e., y = x), from

hich an R2 value was calculated. We did this for each of the 20armland species. For this purpose, the model calibration was per-ormed using test data representing only half of the regional dataathered to explain the relative abundances of the other half ofampled regions. Specifically, 39 of the yearly regional measure-ents were randomly selected from the 78 total measurements in

he set, which covered the 15 initial regions from 2002 to 2007.After assessing the validity of the model, the regional predictions

f species abundances in 2007, as well as under the four proposedcenarios in 2020, were derived using full datasets. These regionalredictions were summed to estimate the species abundance forll of France.

In a derived version of the four scenarios, we predicted speciesbundances assuming no change in the UAAs between 2007nd 2020. Comparing the FBIs forecasted in the scenarios with

nchanged UAAs enabled us to better understand the net effectsf crop composition and its changes on farmland birds.

The index for each species t, denoted by It, was calculated ashe ratio of the 2020 and 2007 predicted abundances. The FBI was

−0.04 −0.51* −0.23* 1

then calculated as the geometric mean of the species indices, asdescribed by Gregory et al. (2005). If the number of indices is T,with T = 20 species, then the geometric mean can be expressed bythe following equation:

I = exp

(1T

×∑

t

ln It

)(2)

The geometric mean is a nonlinear function of the componentindices, in which each species is weighted equally when combinedin the FBI. Per Gregory et al. (2005), the variance approximation forI can be expressed by the following equation:

Var(I) ≈(

I

T

)2∑t

(Var(It)

I2t

)(3)

Var(It) is calculated as the square of the standard deviation of It,which is yielded by the habitat association model. Var(I) (Eq. (3))was calculated as the variance of the ratio of FBI2020 and FBI2007using the Delta method (Kendall and Stuart, 1998) at both theregional and national levels.

We concluded this study by determining the correlationbetween the FBI2020 values under all four scenarios and the speciesspecialization indices to grassland habitats, or species trends(Jiguet, 2010), which were calculated from 2001 to 2009 usingPearson’s correlation test and BBS data from France. Analyses weremade using R software, version 2.13.1 (R Development Core Team,2011), the ‘MASS’ package for mixed models (function ‘glmmPQL’,Venables and Ripley, 2002) and the ‘BradleyTerry2’ package forpredicting species abundance and associated standard deviations(including the functions ‘predict.glmmPQL’ and ‘predict’, Venablesand Ripley, 2002; Turner and Firth, 2012).

3. Results

The relative abundances of most farmland bird species (16 of the 20 testedspecies) were correlated with the area covered by one or several crop types (Table 3).Globally, the relative abundance of all farmland bird species was positively cor-related with cereals, as represented by wheat, barley and oats, and negativelycorrelated with grain and fodder maize covers (Table 3). The correlation betweenthe crop covers of oil seeds, grass and other annual crop productions and individualspecies’ relative abundances were mixed, either positive or negative depending onthe bird species. Finally, we found a positive correlation between the proportionof arable area and the relative abundance of all bird species, which was expected(Table 3).

3.1. FBI forecasts

The FBI observed in the French regions in 2007 was explained relatively wellby the regression model; 70% of the total variance of the FBI was described. Thecorrelations between the predicted and observed species abundances (for 2007)were also significant for all species (results not shown). The model predictions sug-gested an overall decline in the FBI between 2007 and 2020, but at a low rate andwith some important differences between scenarios, species and regions (Table 4).The predicted FBI declined rapidly under the ‘No pillar I’ scenario, which yielded

FBI2020 = 0.82 ± 0.06 (mean ± SE, standard error) when FBI2007 = 1. However, onlysmall decreases were observed under the ‘CAP Greening’, ‘Baseline’ and ‘Biofuel’scenarios (FBI2020 = 0.98 ± 0.07, 0.97 ± 0.06 and 0.95 ± 0.07, respectively, Fig. 1). Thedifferences between the ‘No Pillar I’ scenario and the others were primarily due tothe arable land abandonment that was predicted by this scenario; a UAA change of

F. Chiron et al. / Agriculture, Ecosystems and Environment 176 (2013) 17– 23 21

Tab

le

3St

atis

tics

rep

rese

nti

ng

slop

es

of

rela

tion

ship

s

betw

een

spec

ies

rela

tive

abu

nd

ance

, cro

p

area

s

and

con

trol

vari

able

s

if

p-va

lue

<

0.05

(ns

=

non

-sig

nifi

can

t).

Exp

lan

ator

yva

riab

leC

irl

bun

tin

gC

omm

onbu

zzar

dC

omm

onke

stre

lC

omm

onqu

ail

Com

mon

ston

e-ch

at

Cor

nbu

nti

ng

Gre

ater

wh

ite-

thro

at

Gra

yp

artr

idge

Hoo

poe

Lin

net

Mea

dow

pip

itN

orth

ern

lap

win

gR

ed-

back

edsh

rike

Red

-le

gged

par

trid

ge

Roo

k

Skyl

ark

Wh

in-

chat

Woo

dla

rkY

ello

ww

agta

ilY

ello

w-

ham

mer

Tota

lab

un

dan

ce

Cro

p

grou

p

area

Cer

eals

2.33

ns

ns

0.91

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

3.06

1.15

2.33

0.32

6O

ilse

eds

ns

0.43

ns

ns

ns

0.58

−0.5

0

0.94

−1.0

7

ns

ns

ns

ns

ns

ns

ns

ns

−1.1

1 n

s

ns

ns

Gra

ssla

nd

and

fall

own

s

ns

ns

0.07

ns

0.06

ns

ns

−0.0

5

ns

ns

−0.1

1

ns

−0.0

8

ns

ns

ns

ns

ns

ns

0.00

4

Mai

ze

ns

ns

0.48

ns

−0.8

0

ns

ns

−1.6

0

−1.2

3

ns

ns

ns

−1.1

5

−0.6

8

ns

ns

ns

ns

ns

ns

−0.3

22O

ther

ann

ual

crop

s−1

.10

ns

ns

0.31

−0.2

40.

62

−0.2

00.

82

−1.8

0

ns

ns

ns

ns

ns

−1.1

4

ns

ns

−1.6

5

0.30

−1.1

0

ns

Uti

lize

d

arab

lear

ea8.

74

3.24

ns

ns

ns

ns

4.91

6.40

17.2

4

3.72

ns

12.7

6

7.87

10.6

9

10.1

1

3.56

ns

12.3

4

ns

8.74

1.38

2

Con

trol

vari

able

Nu

mbe

r

ofsa

mp

lin

g

poi

nts

0.01

0.00

0.01

0.01

0.00

0.01

0.01

0.00

0.01

0.01

0.01

0.01

0.00

0.00

0.01

0.01

0.00

0.00

0.01

0.01

0.00

5

Lati

tud

e

−2.3

2n

s

−0.4

2

−0.9

4

−0.4

7

−0.9

6

−0.5

2

1.06

−1.7

6

ns

1.20

ns

−0.7

7

−2.0

2

ns

ns

ns

−1.7

9

ns

−2.3

2

ns

Lon

gitu

de

ns

0.47

ns

ns

0.57

ns

0.67

ns

1.62

ns

ns

ns

1.99

ns

2.38

0.56

ns

0.99

ns

ns

0.25

8

Lati

tud

e:lo

ngi

tud

e−1.8

1

ns

ns

0.52

ns

ns

ns

ns

ns

ns

ns

−1.8

6

ns

-1.8

9

−1.1

5 n

s

ns

ns

ns

−1.8

1

0.15

8Fig. 1. Predicted Farmland Bird Index in 2020 under four scenarios for France. The

‘Baseline’ scenario represents the reference case, with 2007 as the reference year(FBI = 1). Values below dotted line indicate a decrease in the FBI between 2007 and2020. Bars are standard errors.

−7% was observed, compared to changes of −2%, 0% and 0% in the ‘Biofuel’, ‘Base-line’ and ‘CAP Greening’ scenarios, respectively (Table 1). When an assumption of nochange to the UAA was made while the changes to the crop cover composition weremaintained, the differences between the FBIs of different scenarios were smaller(the 2020 predicted FBIs were 0.96, 0.97, 0.98 and 0.99 for the ‘No Pillar I’, ‘Biofuel’,‘Baseline’ and ‘CAP Greening’ scenarios, respectively). These results also showed thatfor a specific scenario, the model predicted an increase in the relative FBI value (>1)in some regions and a decline (<1) in others (Table 4).

Contrary to our expectation of the ‘CAP Greening’ scenario, the predicted speciesindices were correlated with neither the species grasslands specialization index northe species trend. The ‘Baseline’ scenario, however, decreased the relative abun-dance of grassland species while increasing the abundances of arable and mixedfarming bird species (r = −0.63, t = −3.44, p-value < 0.01). Finally, in the ‘No Pillar I’scenario, the few species that decreased the most between 2001 and 2009 werepredicted to increase between 2007 and 2020 (r = −0.70, t = −4.11, p-value < 0.001).

4. Discussion

According to data collected by the FBBS, the FBI declined by 27%in France between 1989 and 2011 (Jiguet et al., 2012). However,the decline has been less pronounced since 2001 (a change of −2%,Jiguet et al., 2012). Based on the species’ responses to crop vari-ables, as well as on the CAPRI forecasts of agricultural land use inthe year 2020, our models have predicted that this decline in FBIwill continue in the future, but at a low rate. Given that 1.00 is the2007 FBI reference value, the values estimated for 2020 were 0.98and 0.97 for the ‘CAP Greening’ and the ‘Baseline’ scenarios, respec-tively. The ‘CAP Greening’ scenario shows that the CAP’s impact onthe loss of farmland birds can be mitigated and made less dam-aging than the changes of the past decades (Butler et al., 2010).Compared to the ‘Baseline’ (i.e., the business as usual scenario),the land use changes under the ‘CAP Greening’ scenario would bemore beneficial to the grassland bird species than to the mixed orarable land bird species. Extending the cover of energy crops, asdescribed in the ‘Biofuel’ scenario, appears to detrimentally affectthe FBI (FBI2020 = 0.95) more than the ‘Baseline’ or ‘CAP Greening’scenarios would. However, the relatively high standard deviationsin the FBI caused by the habitat association model make it diffi-cult to reliably distinguish between the forecasted values in the‘Baseline’, ‘CAP Greening’ and ‘Biofuel’ scenarios.

There is strong evidence that abolishing direct payments to agri-culture, as described by the ‘No Pillar I’ scenario, would inevitably

lead to the largest loss of farmland bird biodiversity (FBI = 0.82),linked to land abandonment, which automatically reduces avail-able habitats for most farmland species and limits their populationdistribution and abundance (Fuller et al., 1995; Robinson et al.,

22 F. Chiron et al. / Agriculture, Ecosystems and Environment 176 (2013) 17– 23

Table 4Predicted Farmland Bird Index in 2020 under four scenarios at regional and national levels, with underscored predictions indicating best scenario(s). The ‘Baseline’ scenariorepresents the reference case, with 2007 as the reference year (FBI = 1). SE = standard deviation.

Region FBI2020

‘Baseline’ SE ‘CAP Greening’ SE ‘No Pillar I’ SE ‘Biofuel’ SE

Auvergne 0.94 0.09 0.95 0.09 0.80 0.07 0.90 0.09Basse-Normandie 0.87 0.08 0.86 0.08 0.75 0.06 0.92 0.08Bourgogne 0.96 0.06 0.95 0.06 0.86 0.06 0.93 0.06Bretagne 1.11 0.20 1.12 0.20 0.92 0.17 1.13 0.21Center 0.91 0.05 0.91 0.05 0.83 0.05 0.89 0.05Champagne-Ardennes 1.02 0.05 1.04 0.05 0.93 0.05 0.97 0.05Franche-Comté 0.83 0.13 0.84 0.13 0.71 0.12 0.85 0.15Ile-de-France 1.09 0.11 1.09 0.11 0.98 0.09 1.08 0.10Lorraine 1.02 0.13 0.99 0.14 0.85 0.12 0.94 0.14Midi-Pyrénées 0.96 0.09 0.89 0.08 0.67 0.06 0.87 0.09Nord-Pas-de-Calais 1.03 0.09 1.03 0.09 0.82 0.07 1.04 0.11Pays-de-la-Loire 1.01 0.05 0.98 0.05 0.79 0.05 0.97 0.05Picardie 1.04 0.07 1.05 0.06 0.99 0.06 0.83 0.06Poitou-Charentes 1.13 0.07 1.05 0.06 0.85 0.04 0.99 0.05Rhône-Alpes 0.82 0.10 0.81 0.10 0.67 0.09 0.83 0.12Minimum value 0.82 0.10 0.81 0.10 0.67 0.06 0.83 0.06

2tsS0dr

naatdcmo2arpat2

scecoctAti

4

dvpt

Maximum value 1.13 0.07 1.12

Median value 1.01 0.05 0.98

FBI (France) 0.97 0.1 0.98

001; Wretenberg et al., 2006). If we fix the RUAA values of 2020o 2007 levels, the overall results show that the ‘CAP Greening’cenario performs well, scoring slightly better than the baseline.pecifically, if the trend is normalized by RUAA, the FBI scores are.99 for ‘CAP Greening’ and 0.98 for ‘Baseline’. However, the smallifference between those scenarios is not significant because ofelatively high standard errors.

Compared to the ‘Baseline’ and the ‘Biofuel’ scenarios, the neteutral effect of ‘Greening’ the CAP may be attributed to the rel-tive conservation of grassland and fallow lands, which positivelyffected farmland birds, and the relative stability of maize produc-ion, which negatively affected them. Farmland bird species haveifferent ecological requirements, so they locate near particularrop types according to the vegetation cover, structure, height, fieldanagement, fallow lands, adjacent habitats, presence or absence

f hedgerows, and other landscape parameters (Devictor and Jiguet,007; Bas et al., 2009; Chiron et al., 2010). These variables directlynd indirectly determine nesting availability and food resources foreproduction and survival (Bas et al., 2009). Our study corroboratesrevious research that occurred at finer spatial scales (e.g., at farmnd field scales), revealing the negative effects of maize crops andhe positive effects of grassland and fallow land areas (e.g., Laiolo,005; Joannon et al., 2008).

In general, the differences predicted in the FBIs of the testedcenarios were due to potential effects of land use compositionalhanges and should be interpreted with caution. The relatively largerrors in the forecasted FBIs do not enable us to make definitive con-lusions on the net effects that crop compositional changes haven farmland birds and the FBI at the national level. However, it islear that farmland area and, more hypothetically, crop composi-ion are important variables that drive bird abundance and the FBI.dditionally, given the differences in the responses of bird species

o crop composition, reliable FBI forecasts must compute speciesndices from predicted abundances and then recalculate the FBI.

.1. Variability of the FBI

The net increase of crop cover due to agricultural changes

epends on the initial coverage level of the different crops, whicharies greatly between regions. In France, cereal productions arerimarily present in the North and Northeast of France, maize inhe West and East, and grasslands in the center and South (Agreste,

0.20 0.99 0.06 1.13 0.210.05 0.83 0.05 0.93 0.060.1 0.82 0.1 0.95 0.1

2010). Additionally, species occurrence is not homogeneous acrossFrance, varying among regions according to species preferencesfor particular habitats and climatic zones. For instance, the cirlbunting (Emberiza cirlus), the hoopoe (Upupa epops) and the red-legged partridge (Alectoris rufa) occur in the South of France, theyellowhammer (Emberiza citrinella) and the gray partridge (Perdixperdix) in the North. Thus, a national policy altering crop coverchanges will not affect the regions’ FBIs equally because of differentbird geographical and ecological distributions.

The results in this study suggest that to optimize the effects thatagricultural policies have on biodiversity at the national and EU lev-els, policy impacts should be tested at smaller scales, and policiesrepresenting the best ecological options at the lower levels shouldbe combined to create national goals. Such a bottom-up approachmay increase the efficiency of the CAP, allowing for a rapid reversalof the farmland bird decline. This study also suggests that a uni-form policy will not solve farmland species conservation problemsover the entire range of species. Conservation planning of farmlandlandscapes must account for scale dependency and species flexibil-ity to construct an effective and efficient policy (Whittingham et al.,2007).

4.2. Study limitations

The model used in this paper relied on simple habitat associa-tions and did not account for population dynamics, spatial temporalinteractions or species interactions. The predictions arising fromthis model assume that abundance is determined by habitat avail-ability within the breeding season and that population densitieswill vary in relation to crop area and composition. Thus, we did notconsider emigration or immigration events from other areas or theeffects of winter land use, competitors or predators; the only factorin consideration was direct habitat association during the breed-ing season. Our projections were also limited to a mid-term goalof 2020. Forecasting the FBI beyond 2020 may allow to compareagricultural policy influences on farmland birds while consideringmajor environmental pressures such as climate change.

In conclusion, this study demonstrated that forecasting the FBI

at the national and regional levels is feasible. The spatial and speciesvariabilities observed in the simple scenarios presented here indi-cate that the FBI models must integrate the unique responses ofindividual species to agricultural changes as well as the species’

tems a

rbrsis‘lmbtp

A

idSfb

A

t

R

A

B

B

B

B

C

C

D

D

E

E

E

E

E

F

birds show regional not national patterns of habitat association. Ecol. Lett. 10,

F. Chiron et al. / Agriculture, Ecosys

egional variability. Overall, changes in crop patterns, as predictedy the CAPRI modeling system, are likely to influence species’elative abundance and the associated FBI. Among the analyzedcenarios, the ‘Baseline’ and ‘CAP Greening’ options resulted in sim-lar FBI values. However, the ‘Baseline’ scenario better preservedpecies that were specialized to arable crop landscapes, while theCAP Greening’ scenario better supported species linked to grass-and habitats. Furthermore, we suggest that the current top-down

odeling approaches should be accompanied by region-specificottom-up modeling of the land use and abundance relationshipso strengthen the value of quantitative scenario studies and supportolicy discussions (Busch, 2006; Plieninger et al., 2012).

cknowledgments

We gratefully thank the hundreds of volunteers who took partn the national breeding bird survey in France (the ‘STOC Points’écoute’ program). We also thank two anonymous reviewers, S.oundarya, J. Erin and B. Cara for providing helpful comments andor editing a previous version of this paper. This study was fundedy the Joint Research Center of the European Commission.

ppendix A. Supplementary data

Supplementary data associated with this article can be found, inhe online version, at http://dx.doi.org/10.1016/j.agee.2013.05.018.

eferences

greste, 2010. Statistique agricole annuelle – résultats 2008 définitifs et 2009 semi-définitifs. Chiffres et Données – Série Agriculture 212, 89 pp.

as, I., Renard, M., Jiguet, F., 2009. Nesting strategy predicts farmland bird responseto agricultural intensity. Agric. Ecosyst. Environ. 134, 143–147.

ritz, W., 2005. CAPRI Modelling System Documentation. http://www.agp.uni-bonn.de/agpo/rsrch/capri/capri-documentation.pdf

usch, G., 2006. Future European agricultural landscapes – what can we learn fromexisting quantitative land use scenario studies? Agric. Ecosyst. Environ. 114,121–140.

utler, S.J., Boccaccio, L., Gregory, R.D., Vorisek, P., Norris, K., 2010. Quantifyingthe impact of land-use change to European farmland bird populations. Agric.Ecosyst. Environ. 137, 348–357.

hiron, F., Filippi-Codaccioni, O., Jiguet, F., Devictor, V., 2010. Effects of non croppedlandscape diversity on spatial dynamics of farmland birds in intensive farmingsystems. Biol Conserv. 43, 2609–2616.

ommission of the European Communities, 2006. Communication from theCommission to the Council and the European Parliament. Development ofagri-environmental indicators for monitoring the integration of environmentalconcerns into the common agricultural policy. COM(2006) 508 final.

evictor, V., Jiguet, F., 2007. Community richness and stability in agricultural land-scapes: the importance of surrounding habitats. Agric. Ecosyst. Environ. 120,179–184.

onald, P.F., Green, R.E., Heath, M.F., 2001. Agricultural intensification andthe collapse of Europe’s farmland bird populations. Proc. R. Soc. B 268,25–29.

uropean Commission, 2006. Handbook on Common Monitoring and Evalua-tion Framework – Guidance Document, Available from: http://ec.europa.eu/agriculture/rurdev/eval/guidance/document en.pdf (accessed 09.10.12).

uropean Commission, 2011. Our life insurance, our natural capital: an EU biodi-versity strategy to 2020. Brussels, 2011-05-03. COM(2011) 244.

uropean Environment Agency, 2012. Streamlining European biodiversity indica-tors 2020: building a future on lessons learnt from the SEBI 2010 process. EEATechnical Report No. 11/2012.

uropean Parliament Resolution of 20 April 2012 on our life insurance, our naturalcapital: an EU biodiversity strategy to 2020 (2011/2307(INI)). Official Journal ofthe European Union C 50 E, 21.2.2012.

wert, F., Rounsevell, M.D.A., Reginster, I., Metzger, M.J., Leemans, R., 2005. Futurescenarios of European agricultural land use I. Estimating changes in crop pro-ductivity. Agric. Ecosyst. Environ. 107, 101–116.

AO, 2009. The Agricultural Statistics of the Food and Agriculture Organisation ofthe United Nations. http://faostat.fao.org

nd Environment 176 (2013) 17– 23 23

Fuller, R.J., 2000. Relationships between recent changes in lowland British agricul-ture and farmland bird populations: an overview. Ecology and Conservation ofLowland Farmland Birds. British Ornithologists’ Union, Tring, pp. 5–16.

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

Green, R.E., Cornell, S.J., Scharlemann, J.P.W., Balmford, A., 2005. Farming and thefate of wild nature. Science 307, 550–555.

Gregory, R.D., van Strien, A., Vorisek, P., Meyling, A.W.G., Noble, D.G., Foppen, R.P.B.,Gibbons, D.W., 2005. Developing indicators for European birds. Philos. Trans. R.Soc. Lond. B 360, 269–288.

Jiguet, F., 2010. Les résultats nationaux du programme STOC de 1989 à 2009.www2.mnhn.fr/vigie-nature

Jiguet, F., Devictor, V., Julliard, R., Couvet, D., 2012. French citizens monitoring ordi-nary birds provide tools for conservation and ecological sciences. Acta Oecol. 44,58–66.

Joannon, A., Bro, E., Thenail, C., Baudry, J., 2008. Crop patterns and habitat prefer-ences of the grey partridge farmland bird. Agron. Sustain. Dev. 28, 379–387.

Julliard, R., Clavel, J., Devictor, V., Jiguet, F., Couvet, D., 2006. Spatial segregation ofspecialists and generalists in bird communities. Ecol. Lett. 9, 1237–1244.

Kendall, M., Stuart, A., 1998. The Advanced Theory of Statistics, vol. 1., 6th ed. Stuart& Ord, London, pp. 351.

Kleijn, D., Kohler, F., Baldi, A., Batary, P., Concepcion, E.D., Clough, Y., Diaz, M., Gabriel,D., Holzschuh, A., Knop, E., Kovacs, A., Marshall, E.J.P., Tscharntke, T., Verhulst, J.,2009. On the relationship between farmland biodiversity and land-use intensityin Europe. Proc. R. Soc. B 276, 903–909.

Laiolo, P., 2005. Spatial and seasonal patterns of bird communities in Italian agroe-cosystems. Conserv. Biol. 19, 1547–1556.

Newson, S.E., Ockendon, N., Joys, A., Noble, D.G., Baillie, S.R., 2009. Comparison ofhabitat-specific trends in the abundance of breeding birds in the UK. Bird Study56, 233–243.

Plieninger, T., Schleyer, C., Schaich, H., Ohnesorge, B., Gerdes, H., Hernandez-Morcillo, M., Bieling, C., 2012. Mainstreaming ecosystem services throughreformed European agricultural policies. Conserv. Lett. 5, 281–288.

Princé, K., Lorrillière, R., Barbet-Massin, M., Jiguet, F., 2013. Predicting the fate ofFrench bird communities under agriculture and climate change scenarios. Envi-ron. Sci. Policy, in press

R Development Core Team, 2011. R: A Language and Environment forStatistical Computing. R Foundation for Statistical Computing, Viennahttp://www.Rproject.org (accessed September 2012).

Risely, K., Noble, D.G., Baillie, S.R., 2009. The Breeding Bird Survey 2008. BTOResearch Report 537. British Trust for Ornithology, Thetford.

Robinson, R.A., Wilson, J.D., Crick, H.Q.P., 2001. The importance of arable habitat forfarmland birds in grassland landscapes. J. Appl. Ecol. 38, 1059–1069.

Rounsevell, M.D.A., Ewert, F., Reginster, I., Leemans, R., Carter, T.R., 2005. Futurescenarios of European agricultural land use II. Projecting changes in croplandand grassland. Agric. Ecosyst. Environ. 107, 117–135.

Rounsevell, M.D.A., Metzger, M.J., 2010. Developing qualitative scenario storylinesfor environmental change assessment. Wiley Interdiscip. Rev. Climate Change1, 606–619.

Rounsevell, M.D.A., Reginster, I., Araujo, M.B., Carter, T.R., Dendoncker, N., Ewert, F.,House, J.I., Kankaanpaa, S., Leemans, R., Metzger, M.J., Schmit, C., Smith, P., Tuck,G., 2006. A coherent set of future land use change scenarios for Europe. Agric.Ecosyst. Environ. 114, 57–68.

Scholefield, P., Firbank, L., Butler, S., Norris, K., Jones, L.M., Petit, S., 2011. Modellingthe European Farmland Bird Indicator in response to forecast land-use changein Europe. Ecol. Ind. 11, 46–51.

Stoate, C., Boatman, N.D., Borralho, R.J., Carvalho, C.R., de Snoo, G.R., Eden, P., 2001.Ecological impacts of arable intensification in Europe. J. Environ. Manage. 63,

337–365.Tress, B., Tress, G., 2003. Scenario visualisation for participatory landscape planning

– a study from Denmark. Landscape Urban Plann. 64, 161–178.Tscharntke, T., Klein, A.M., Kruess, A., Steffan-Dewenter, I., Thies, C., 2005. Landscape

perspectives on agricultural intensification and biodiversity – ecosystem servicemanagement. Ecol. Lett. 8, 857–874.

Turner, H., Firth, D., 2012. Bradley–Terry models in R: the BradleyTerry2 package.J. Statist. Softw. 48, 1–21.

Venables, W.N., Ripley, B.D., 2002. Modern Applied Statistics with S, 4th ed. Springer,New York, ISBN 0-387-95457-0.

Verburg, P.H., Veldkamp, A., Rounsevell, M.D.A., 2006. Scenario-based studies offuture land use in Europe. Agric. Ecosyst. Environ. 114, 1–6.

Whittingham, M.J., Krebs, J.R., Swetnam, R.D., Vickery, J.A., Wilson, J.D., Freckleton,R.P., 2007. Should conservation strategies consider spatial generality? Farmland

25–35.Wretenberg, J., Lindstrom, A., Svensson, S., Thierfelder, T., Part, T., 2006. Population

trends of farmland birds in Sweden and England: similar trends but differentpatterns of agricultural intensification. J. Appl. Ecol. 43, 1110–1120.