Modelling land-use sustainability using farmland birds as indicators

Ecological Indicators 10 (2010) 15–23

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Modelling land-use sustainability using farmland birds as indicators Thomas K. Gottschalk a,*, Ralf Dittrich a,1, Tim Dieko¨tter a,1, Patrick Sheridan b, Volkmar Wolters a,1, Klemens Ekschmitt a,1 a b

Justus Liebig University, Department of Animal Ecology, Heinrich-Buff-Ring 26-32, D-35392 Giessen, Germany Justus Liebig University, Institute of Agricultural and Food Systems Management, Senckenbergstr. 3, D-35390 Giessen, Germany

A R T I C L E I N F O

A B S T R A C T

Article history: Received 17 September 2008 Received in revised form 10 May 2009 Accepted 15 May 2009

Biodiversity on farmland is declining due to agricultural intensification and occurs across many taxa such as plants, insects or birds. Here, we modelled population sizes of five farmland birds in central Germany as the German Sustainability Indicator for Species Diversity (SISD) is based on this taxon. We explored options for sustainable farmland management by generating land-use scenarios at the regional scale. For individual bird species, high SISD scores could be reached by changing environmental variables, such as landscape or crop diversity, percent cover of spring cereals or hedge density. However, contrasting species responses to these variables prevented from reaching high scores for all species simultaneously. We were able to improve the total SISD score from 0.77 at present to 0.94 by increasing landscape and crop diversity or to 0.87 by increasing hedge density and reducing spring cereals, respectively. An economic evaluation of the return losses associated with these changes revealed that annual costs of approx. 5.5 s/ha farmland would suffice for this latter increase by optimizing hedges and spring cereals towards high SISD. We conclude that balancing three levels of trade-offs, i.e. contrasting requirements of species, diverging responses in different landscapes, and alternative economic options, is difficult to achieve without systematic modelling. Yet, by accounting for differences in landscape structure and species distributions at a regional spatial scale and by focusing on clearly defined measures such as hedge density or the cover of spring cereals rather than composite indices like landscape or crop diversity it seems possible to develop realisable, affordable and sustainable management strategies. ß 2009 Elsevier Ltd. All rights reserved.

Keywords: German sustainability indicator for species diversity Agri-environmental scheme Sustainable management Landscape planning Biodiversity conservation Cost efficiency

1. Introduction For centuries, agricultural practices have created habitat for countless numbers of species across numerous taxa (Bignal and McCracken, 1996). In recent decades, however, many previously common species have become scarce or have disappeared from agricultural landscape all across Europe (Donald et al., 2006; Stoate et al., 2001; Krebs et al., 1999). Declines in abundance and species richness have been reported for many taxa, including flowering plants (Liira et al., 2008), arthropods (Schweiger et al., 2005), birds (Krebs et al., 1999) and mammals (Smith et al., 2005). Generally, land-use change has been identified as one of the major factors driving biodiversity decline (Wretenberg et al., 2007; Robinson and

* Corresponding author. Tel.: +49 641 99 35711; fax: +49 641 99 35709. E-mail addresses: [email protected] (T.K. Gottschalk), [email protected] (R. Dittrich), [email protected] (T. Dieko¨tter), [email protected] (P. Sheridan), [email protected] (V. Wolters), [email protected] (K. Ekschmitt). 1 Tel.: +49 641 99 35701; fax: +49 641 99 35709. 1470-160X/$ – see front matter ß 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2009.05.008

Sutherland, 2002; Jones et al., 2001). Changes in land use may relate to environmental stress, such as pesticide or fertilizer application, or to spatiotemporal patterns, such as crop diversity, the proportion of intensely used area and the share of semi-natural habitat in the landscape, all of which have been shown to directly or indirectly affect biodiversity (e.g. Billeter et al., 2008; Firbank et al., 2008; Schweiger et al., 2005). Within the scope of the European Biodiversity Action Plan for Agriculture, different agri-environment schemes (AES) aim at counteracting the negative effects of intensive agriculture on biodiversity (Commission of the European Communities, 2001). Yet, these AES have not always been successful in preserving or enhancing species diversity (Wrbka et al., 2008; Whittingham, 2007; Kleijn and Sutherland, 2003; Kleijn et al., 2001). Failures of AES in supporting biodiversity may not be surprising considering the divergence of responses to landscape parameters observed across species, habitats and regions (Whittingham et al., 2007; Schweiger et al., 2005; Steffan-Dewenter et al., 2002; Cody, 1985). Diverging responses of different species make it inherently difficult to optimise land management in a complex landscape towards many taxa simultaneously. Despite the obvious importance of

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T.K. Gottschalk et al. / Ecological Indicators 10 (2010) 15–23

considering these species-specific responses, the multitude of species inhabiting most ecosystems and the resulting impossibility of addressing every species individually inevitably require the use of indicators in planning and evaluating management and conservation measures. Birds constitute the German Sustainability Indicator for Species Diversity (SISD), which is among the 21 key indicators of the German National Sustainability Strategy. Like the UK wild bird indicator (Department for Environment, 2008) or the Pan-European wild bird indicator (Gregory et al., 2005), the SISD builds on monitoring population sizes of selected indicator bird species, with trends in the population size of these species being assumed to reflect changes in the overall quality of landscapes (cf. Billeter et al., 2008). Across Europe, the trends of common farmland birds are negative, with an average decline of population sizes of 44% from 1980 to 2005 (PECBMS, 2007). Despite the national goal to enhance biodiversity (Bundesregierung, 2002), population sizes of the 10 indicator bird species for farmland did not increase in Germany over the last 10 years and presently lack far behind the national target for the year 2015 (Statistisches Bundesamt, 2008). The difficulty in optimising land management in landscapes towards biodiversity has been addressed for various taxa including farmland birds (Butler et al., 2007; Westphal et al., 2007; Holzka¨mper and Seppelt, 2007; Holzka¨mper et al., 2006; Swetnam et al., 2005). These studies generated very valuable insights into the effects of land-use change on birds. Yet, spatially explicit and fine-grained studies were often restricted to a small number of ecologically contrasting bird species (Holzka¨mper et al., 2006; Swetnam et al., 2005), whereas studies encompassing high numbers of species were often of a large spatial grain (Westphal et al., 2007) or spatially implicit (Butler et al., 2007) and very seldom the economic costs of conservation management actions were assessed (Holzka¨mper and Seppelt, 2007). In this paper, we illustrate the difficulty to balance the contrasting requirements of ecologically diverging indicator bird species in structurally different subspaces of the landscape in an intensive farmland area in Central Germany. We explored the potential to optimize the SISD across three major land-use types by hypothetically modifying landscape and crop diversity, hedge density and cover of spring cereals and by taking into account the agroeconomic costs associated with such modifications of the landscape.

from 500 to 1300 mm, respectively (Deutscher Wetterdienst, 2005). 2.2. Bird surveys The 10 nationally defined SISD farmland bird species were recorded on 304 sampling locations (arable land: N = 119, grassland: N = 94, orchards: N = 12, deciduous forest: N = 42, coniferous forest: N = 19, mixed forest: N = 18) using 5-min Point Counts (Buckland et al., 2001; Bibby et al., 2000). At each location, birds were surveyed five times between April and June in 2006 and 2007, respectively. Bird species abundances were calculated for each sampling location using the program Distance 5.0 (Thomas et al., 2006). Abundance calculation was restricted to birds recorded within a maximum distance of 200 m from the survey locations. Out of the 10 SISD farmland bird species, five species could not be used for modelling purposes as they either occurred with less than 50 breeding pairs (Bar-tailed Godwit Limosa lapponica, Woodlark Lullula arborea, Whinchat Saxicola rubetra and Corn Bunting Emberiza calandra) or bred in loose colonies (Lapwing Vanellus vanellus), which did not allow for reliably estimating the population size of this species. Five SISD species, namely Red Kite, Little Owl, Skylark, Redbacked Shrike and Yellowhammer provided sufficient records for further analysis (Table 1). Correcting for observer bias resulted in a varying number of sampling locations used for predicting their abundances within the total study area. Because the rare Red Kite, Red-backed Shrike and Little Owl could not be sufficiently surveyed by Point Counts, nest records from Hausmann et al. (2004) together with additional breeding site recordings for the Red Kite from 2004 and nest records for the Little Owl from 2004 and 2006 were utilized for estimating the species’ population sizes. Thus, for modelling Red Kite, Red-backed Shrike and Little Owl we used a ‘‘presence/available design’’ (Boyce et al., 2002). Presence records were complemented by an equal number of ‘‘pseudoabsences’’ (Engler et al., 2004; Poirazidis et al., 2004; Osborne et al., 2001) that were generated by stratified random selection within the study area; to control against spatial autocorrelation, the distance between presence points and pseudo-absence points was restricted not to fall below 500 m. 2.3. Landscape analysis

2. Material and methods 2.1. Study area The study area encompassed the catchment of the Nidda river in Hesse, Central Germany. It covered 1420 km2 with an elevation increasing from 106 m a.s.l. in the south to 765 m a.s.l. in the northeast. While the central lower part of the Wetterau region is characterized by intensive arable land use, the higher western and eastern highlands of the Taunus and the Vogelsberg encompass larger areas of forests and meadows. In total, the study area was composed of 53% farmland, 32% forest, 11% urban area and 4% other land-use types. Depending on altitude, annual average temperature and mean annual rainfall ranged from 5 to 10 8C and

Modelling the spatial distribution of the bird species was based on (i) a land-use map derived from high resolution colour infrared (CIR) aerial photographs (0.5 m  0.5 m) from 2005 and (ii) topographical parameters obtained from a digital elevation model (25 m  25 m; HLBG, 2005). The land-use map was constructed from an unsupervised and a supervised classification. To achieve a maximum separation of habitat classes, the supervised classification was conducted with a process chain of proprietary algorithms by (EFTAS Fernerkundung Technologietransfer GmbH, 2007). In addition to the spectral characteristics from the CIR pictures, texture analysis was used to separate e.g. forests from crops, and shape analysis was used to properly distinguish e.g. between roads and house roofs. Information on the European Union integrated

Table 1 The five bird species for which species distributions were modelled. The table lists the common and scientific names of the modelled bird species, whether abundances or nest records were obtained, the number of locations sampled, and the breeding densities as means and 95% confidence limits recorded in occupied habitats. Common name

Scientific name

Data type

Sampled locations

Breeding density

Skylark Yellowhammer Little Owl Red-backed Shrike Red Kite

Alauda arvensis Emberiza citrinella Athene noctua Lanius collurio Milvus milvus

Abundance Abundance Nest records Nest records Nest records

266 268 222 114 60

3.9 2.1 2.5 1.5 0.1

(0.5–9.0) pairs/10 ha (0.6–5.3) pairs/10 ha (1.0–5.0) pairs/km2 (1.0–3.0) pairs/m2 (0.06–0.3) pairs/km2

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administrative and control system (IACS) was incorporated to classify the habitat map. User accuracy of map pixels correctly categorized reached 85%. The CIR-based land-use map was classified on a polygon basis and was then converted into a 10 m resolution raster map for the purpose of modelling. The original land-use map contained 24 land-use classes, which were merged into 11 land-use classes (coniferous, deciduous and mixed forests, farmland, grassland, fallow, garden, orchards, water, urban and others) relevant for modelling the SISD bird species. Species-habitat relationships were established exploiting both, local environmental characteristics of sampling locations, and characteristics of the landscape matrix within a distance of 1000 m surrounding each sampling location. Local variables included (1) geographic co-ordinates to account for spatial trends, as well as (2) altitude above sea level, slope, and Topographic Wetness Index (TWI), derived from the digital elevation model, and (3) the local land use (11 classes) registered in the land-use map. TWI = ln(As/ tan b) is a compound terrain attribute that estimates the potential distribution of soil moisture calculated from the specific catchment area of each grid cell (As) and the surface slope of the grid cell (b) (Beven and Kirkby, 1979). Matrix parameters were estimated within a radius of 1000 m using the Moving Window software Slicer (Gottschalk et al., 2008) and encompassed (4) percent cover of each of the 11 land-use classes and additionally of winter cereals, spring cereals, rape and root crops, (5) landscape diversity (Shannon index) and crop diversity (Shannon index) as well as (6) landscape fragmentation (Interspersion and Juxtaposition Index (IJI) (McGarigal and Marks, 1995). In addition, we included (7) the densities of orchards, fallows, grassy field boundaries/waysides/ roadsides, hedges/smaller wooded areas and fields. For model building only those variables were used that inter-correlated by less than R2 = 0.7 (Fielding and Haworth, 1995). All GIS work was conducted using ArcInfo GIS 9.2 (Environmental Science Research Institute Inc., Redlands, CA, USA). 2.4. Resource-selection functions Resource-selection functions (Manly et al., 2002) were obtained using generalized linear models (GLM) with Gaussian error

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distribution and logarithmic link function for abundance data, and with binomial error distribution and logit link function (i.e. logistic regression) for presence–absence data, on nest records (Table 1). The corrected Akaike Information Criterion (AICc) was applied for model selection (Burnham and Anderson, 2002). We used the GEPARD tool (Gottschalk et al., 2009) to integrate GLM analysis based on the statistic package R (R Development Core Team, 2007) into the working environment of ArcGIS 9.2. The overall fit of models was assessed from the explained variance (R2). Additionally, prediction accuracy was quantified by means of 1st order jack-knife permutation (Sokal and Rohlf, 1995). To enable the calculation of SISD scores for the Little Owl, Red Kite and Red-backed Shrike from presence/absence model predictions, the breeding population size N of each species was calculated by multiplying the sum of all estimated breeding probabilities per pixel pi times the mean breeding density d (Table 1). Thus, each of the three species was estimated as: n X pi ½ind km2  N ¼d i

An overestimation of available habitat was avoided by postprocessing the prediction map using a threshold of breeding probability = 0.5, thereby taking the prevalence of the species’ site selection as the threshold (Liu et al., 2005). To estimate the relative effects of individual predictor variables, the data were z-transformed and beta values were calculated for each predictor variable. 2.5. Landscape scenarios Following Hoffmann and Kiesel (2007), we classified the study area according to its typical landscape character, and we delineated four land-use types (Fig. 1): (A) farmland characterized by intensively managed arable farmland, (B) farmland characterized by grassland (>20% cover), (C) farmland characterized by hedges and/or orchards (>5% percent cover), and (D) other landscapes, such as built environment, forest and open water. The latter was not used in this investigation. We investigated the birds’ responses to a range of hypothetical land-use modifications. These landscape scenarios aimed at

Fig. 1. Research area and its subdivision into major land-use types. The study area covers the complete watershed of the Nidda river catchment in central Germany.

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Fig. 2. Changes in hedge density in an example area: (a) decrease of hedge density (minimum stage), (b) present status, (c) increase of hedge density (maximum stage). White: arable farmland, light grey: grassland, middle grey: hedgerows, dark grey: water, black: urban.

evaluating possible measures to be taken in order to reach the national SISD target. Scenarios were created by varying a set of landscape parameters that exhibited high beta values in the resource-selection functions and thus strongly influenced the modelled bird populations. Landscape diversity, crop diversity, hedge density and cover of spring cereals were systematically and independently modified in the scenarios. Landscape diversity and crop diversity were directly manipulated in the corresponding

matrix layer maps. Density of hedges was increased by setting single pixels of hedges randomly within a belt of twenty meters along field margins and along roads in farmland (Fig. 2). Density of hedges was decreased by randomly replacing single pixels of hedges by the dominant land use in this area determined by the classification of the major land-use types. The cover of spring cereals was modified by randomly selecting a defined set of spring cereal fields and replacing them by other crops, and vice versa.

Table 2 Resource-selection functions. Relative effects of local and matrix variables on the five analyzed bird species. Effects are quantified as beta values obtained from the analysis of z-transformed data in order to make effect sizes comparable between predictors measured in different units. The importance of each variable for the ensemble of bird species is indicated by the number of birds affected (Count) and the sum of absolute beta values (Abs. Sum). Relative effects (beta values) Predictor variables

Little Owl

Intercept Local variables Geographic x-coordinate Geographic y-coordinate Altitude above sea level Slope Curvature Soil humidity (TWI) Land use: orchard Land use: urban Land use: deciduous forest Land use: grassland Land use: coniferous forest Land use: mixed forest Land use: others Land use: fallow Land use: farmland Land use: garden Land use: water

2.01

Matrix variables (1000 m radius) Landscape diversity (Shannon) Diversity of crops (Shannon) Landscape fragmentation (IJI) Density of fields Density of hedges Density of fallows, margins and unpaved roads Density of orchards Percent cover of deciduous forest Percent cover of spring cereals Percent cover of root crops Percent cover of grassland Percent cover of fallow Percent cover of winter cereals Percent cover of rape Percent cover of farmland Percent cover of garden Percent cover of mixed forest Percent cover of coniferous forest Total model Model R2 Prediction accuracy

Red Kite 0.003

Importance Red-backed Shrike 1.05

Skylark

Count

Abs. sum

0.021

2 1 1 1 1

0.45 0.30 0.36 0.77 0.30

0.26

0.088

0.29 0.11 0.30 0.32 0.06

0.052 0.038 0.055 0.049 0.102

4 2 4 4 3 3 4 1

4.38 3.54 1.87 1.56 1.44 1.37 1.26 1.09

0.02

0.019

3 2

0.20 1.25

0.05

0.024

1 3 1

0.35 0.33 0.23

2 3 1 1 1

0.98 0.60 0.33 0.30 0.25

0.30

0.43 0.30

Yellowhammer 0.078

0.36 0.77 0.31 2.24 1.40 1.22 0.65

1.79 2.13 0.30 0.76 1.08 1.00 0.64

0.45 1.09

0.16 0.42

0.83

0.35 0.26 0.23 0.51 0.26

0.47 0.30 0.34

0.044

0.30 0.25

0.50 0.40

0.55 0.38

0.24 0.43

0.56 0.13

0.31 0.08

T.K. Gottschalk et al. / Ecological Indicators 10 (2010) 15–23 Table 3 Status of the Sustainability Indicator for Species Diversity (SISD). Shown is the target and present population sizes of the five analyzed bird species and the resulting scores. Population estimates were not conducted for the land-use types: built environment, forest and open water. The total SISD score is calculated as the arithmetic mean of the score values. Species

Present population

Target population

Present score

Target score

Skylark Yellowhammer Little Owl Red-backed Shrike Red Kite

25 400 7 690 460 167 28.5

34 000 8 500 540 283 37.1

0.746 0.905 0.852 0.588 0.769

1.00 1.00 1.00 1.00 1.00

0.772

1.00

Total

Thereafter, all matrix layer maps that were conditional on these changes, such as landscape diversity and crop diversity were recalculated, and the models were re-run on a consistent set of data belonging to each new scenario. Landscape scenarios were evaluated by expressing the scenario’s estimated SISD value as percentage of the national SISD target for 2015. The total SISD was calculated as the arithmetic mean across all species, i.e. as the average of the species’ achieved percentage of their population size targets for 2015. However, the contributions of individual species to the SISD value were not allowed to exceed 100%, and therefore over-achievements in some species could not compensate for low population sizes of other species.

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Table 4 Univariate optimization. Modifications of single variables required to achieve the target population size of individual bird species. For example, an increase of landscape diversity by +0.25 from presently 1.26, to 1.51, is predicted to augment the Red Kite population to its target size. Empty cells denote no response of the species to the variable, according to our models. n.a. (not attainable) denotes negative correlations that do not attain the target population, even if the variable is reduced to the minimum possible value. Required change Landscape diversity (Shannon) Present state Little Owl Red Kite Red-backed Shrike Skylark Yellowhammer

1.264

Crop diversity (Shannon) 1.256 +0.10

Hedge density (% cover)

Spring cereals (% cover)

2.02

2.71 2.00

+2.33 n.a. +1.16

n.a. +6.00

+0.25 +0.40 n.a. +0.45

and after the intervention. Inducing farmers to adopt any of the measures requires compensation of not only expenses but also profit forgone. Thus, in the case of hedges, effects (a)–(c) need compensation, in the case of spring cereals only effect (c). Establishment costs of the hedges were neglected as these costs are relatively low compared to the long-term costs of yield loss. 3. Results 3.1. Landscape classification

2.6. Economic costs of landscape scenarios The economic effects of the landscape scenarios analyzed include (a) higher production costs caused by smaller field sizes, (b) lower biomass yields per unit area caused by relatively longer field edges of smaller fields, and (c) opportunity costs, i.e. utility forgone. Effects were estimated using biomass yield functions and production systems defined in the simulation model ProLand (Weinmann et al., 2006; Kuhlmann et al., 2002). Effects were averaged over the entire watershed of the Nidda river catchment, and were based on current crop rotations. The biomass yield along field edges is about 20% lower than in the field’s center (Rothmund, 2006). As production costs increase linearly with yield, some of the revenue losses are offset by lower production costs. Utility forgone makes up the most substantial part of the measures’ costs. It is calculated as the difference between the utility of the production system per unit area before

The research area was separated into four more or less homogenous sub-units of the dominant land-use type, and presumably with different effects of land-use change on SISD scores. These land-use types occupied 25.8, 16.6, 19.3 and 38.3% of the research area respectively, and they formed a strongly interspersed mosaic with large amounts of border between adjacent patches of differing character (Fig. 1). The mosaic with patch dimension in the order of one to few kilometres gave rise to considerable bilateral effects across adjacent land-use types in our modelling approach, where we calculated matrix effects within a radius of 1000 m. 3.2. Species models The resource-selection functions differed strongly between species and often showed opposite effects of the same environ-

Fig. 3. Effects on the SISD scores in the total landscape: (a) Simulated effects of landscape diversity and crop diversity. Both, higher diversity of land use and higher diversity of crops contributed to the simulated total SISD score in the landscape, up to saturation at approx 0.94. (b) Simulated effects of hedge density and spring cereals cover. The total SISD score resulted highest (0.87) if spring cereals were reduced and if hedge density was augmented by to approx. 24.5%.

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mental variable on different species (Table 2). Across species, local variables accounted for approx. 80% of effects, the strongest influence being exerted by orchards and by urban areas, both with positive or negative impact on bird populations, depending on the bird species. Matrix variables representing the surrounding landscape within 1000 m radius accounted for approx. 20% of effects. Here, deciduous forest and spring cereals showed the strongest influence overall, as indicated by high values of the absolute sum in Table 2. Prediction accuracy, as calculated by jackknife permutation, ranged between 0.08 (Yellowhammer) and 0.43 (Red-backed Shrike) with an average of 0.29 for all five species. The models explained between 24.3% (Red-backed Shrike) and 55.5% (Skylark) of total variance in the respective bird population. To assess the present population sizes of bird species in the study area, the resource-selection functions were used to extrapolate bird populations from the sampling plots to the entire research area by means of GIS-based modelling. The resulting population sizes represented 59–91% of the target sizes, with a total SISD score (arithmetic mean across species) of 77% (Table 3). From a plethora of theoretical alternatives, we selected two pairs of environmental variables in order to analyze the potential for improvement of bird populations through a targeted landscape management. (1) Landscape diversity and crop diversity were chosen as examples for complex indexes, where similar index values can be realized by very different configurations in the landscape. Landscape diversity and crop diversity generally affected bird populations positively, with the exception of the Skylark, which responded negatively to higher landscape diversity and was unaffected by crop diversity. (2) Hedge density and cover of spring cereals were selected as the second pair of variables, because they are amenable to direct management, and because conversion costs can easily be specified. Hedges and spring cereals had species-specific positive or negative effects on four of the bird species, whilst the Red Kite did not respond to these variables in our models. 3.3. Univariate analysis A preliminary univariate analysis of whether modifications of single environmental variables could bring individual bird species to their target population size showed that for most bird species two solutions were theoretically possible (Table 4). However, this analysis clearly illustrated that modification of a single environmental variable would not improve all bird species simultaneously and would thus not enable optimization of the total SISD score. 3.4. Landscape diversity and crop diversity Combined augmentation of landscape diversity and crop diversity substantially improved the total SISD score from 0.77 at the present state of the research area to saturation at approx 0.94 in a simulation where both, landscape diversity and crop diversity (Shannon indices) were augmented from approx. 1.25 in the present study area to approx. 1.5 in the scenario (Fig. 3a). Conversely, a reduction of landscape diversity or crop diversity below the present state was predicted to potentially degrade the total SISD score to less than 0.5. 3.5. Hedges and spring cereals Land-use scenarios with combined modification of hedge density and cover of spring cereals revealed that more hedges generally augmented the total SISD score, the more so, the less spring cereals were implemented in the scenario (Fig. 3b). The total SISD score saturated at 0.87, if hedges were augmented from presently 2% to approx. 4.9% cover in the scenario, and if spring

Fig. 4. Effects on the SISD scores due to an increase in percent cover of spring cereals and hedge density in the three landscape types: (a) farmland characterized by intensively managed arable farmland, (b) farmland characterized by grassland and (c) farmland characterized by hedges and/or orchards.

cereals were simultaneously reduced from presently 2.7% cover to nearly zero. Conversely, reduction of hedges combined with high proportions of spring cereals in the study area was predicted to reduce the total SISD score to below 0.5. Increasing hedge density beyond 5% cover tended to re-decrease the SISD score.

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4. Discussion and conclusions

Fig. 5. Analysis of cost efficiency: estimated annual costs due to increased hedge density and/or increased cultivation of spring cereals. The relationship between costs and SISD score is non-smooth and restricted because the contributions of individual bird species to the SISD score were not allowed to exceed 100%. point a: present situation; points b–d: possible improvement of SISD score, or income, or both; point e: maximum achievable improvement (costs 413 000 s p.a.); point f: worst case with the same cost as the optimum.

3.6. Detailed analysis A separate analysis of the different land-use types revealed that modifications of hedge density and spring cereals exerted different effects on SISD scores in each of the land-use types (Fig. 4). Whilst higher proportions of spring cereals had slightly positive effects on total SISD score in intensively managed arable farmland (Fig. 4a), almost no effect was visible in farmland characterized by grassland (Fig. 4b), and strong negative effects were predicted in farmland characterized by hedges and orchards (Fig. 4c). Augmented hedge density, in turn, had positive effects on total SISD score in all landuse types, the strongest effect being predicted in farmland with hedges and orchards. Despite these differences, the maximum total SISD score was predicted for a combination of low spring cereal cover and high hedge density in all land-use types. 3.7. Associated costs Economic costs associated with the modifications of hedge density and percent cover of spring cereals were estimated as losses/gains of income due to losses/gains of cultivated area in the case of hedges, and due to losses/gains of profit compared to alternative crop in the case of spring cereals. Fig. 5 illustrates that according to our simulations, a moderate cost of 413 000 s for the 756 km2 farmland area, i.e. roughly 5.5 s/ha, would suffice to compensate for losses of agricultural production and to increase the total SISD score from presently 0.77 (Fig. 5, point a) to 0.87 (Fig. 5, point e), with an optimal combination of augmenting hedges and reducing spring cereals. The figure also holds the caveat that with the same investment, but with an unfavourable combination of hedge density and spring cereals, the total SISD score could be deteriorated almost down to 0.6 (Fig. 5, point f). Finally, the figure suggests that the total SISD score could be augmented at no cost, or that alternatively, profit from agriculture could be improved with no loss of SISD score, compared to the present state of the study area (Fig. 5, points b–d).

In this study, we illustrated that agri-environment schemes (AES) may well be optimized towards supporting single species, but that the diverging or even opposing responses among different species or differently structured landscapes may render it inherently difficult to envisage optimal management schemes for supporting many taxa simultaneously in a complex landscape. Our modelling results predicted that good – though not perfect – solutions may exist for supporting several taxa, even at very moderate agroeconomic costs. Our results highlight the prominent importance of environmental heterogeneity for the sustainment of viable populations of characteristic bird species in agro-ecosystems. Congruent with many other studies on bird species diversity in agricultural landscapes (e.g. Billeter et al., 2008; Firbank et al., 2008; Benton et al., 2003) an increase in landscape diversity and/or crop diversity resulted in a significant improvement of bird species performance, here expressed as the total SISD score. Yet, even though the modelled increase of land-use and crop diversity of 40% resulted in an increase of the total SISD score of 22% to a value of 0.94, the predefined target value of 1.0 could not be reached. This failure in reaching the aimed for SISD target value may be attributed to contrasting species-specific habitat requirements that prevent optimization for all species simultaneously. Whereas Red Kite and Yellowhammer could be shown to prefer areas of high landscape diversity, the Skylark preferred low landscape diversity. Similarly did crop diversity positively affect Little Owl and Red-backed Shrike but no such effect could be observed for the Skylark and Yellowhammer in our data. This is congruent with the findings of Fuller et al. (1997) who did not find ‘‘mixed farming’’ as a predictor for skylark and yellowhammer abundances and of Chamberlain et al. (1999) who reported a decreasing skylark density with increasing habitat diversity on farmland plots. These contrasting species reactions to agricultural changes accord well with recent models that explicitly incorporate the effects of these changes on components of diet, foraging habitat and nesting habitat and the specific reliance of farmland bird species thereon (Butler et al., 2007). Red Kites are known to benefit from landscapes with small woodlands for breeding and roosting and open areas for hunting (Carter, 2007), while Yellowhammers favour a mixture of bushes, hedgerows and forests (Bauer et al., 2006). Skylarks, in contrast, prefer extensively used open farmland and actively avoid areas with dense trees and hedges, and forests (Donald, 2004). Modelling land-use sustainability in our study was based on five selected indicator bird species, i.e. Red Kite, Little Owl, Skylark, Red-backed Shrike and Yellowhammer. However, the German Sustainability Indicator for Species Diversity lists five more farmland bird species, and including these in our modelling work would have considerably complicated reaching the SISD target value of 1.0. For example, four of the species not considered here (Bar-tailed Godwit, Whinchat, Corn Bunting and Lapwing), may be expected to benefit from an increase in extensively used, wet and species-rich lowland grassland (Winspear and Davies, 2005). Considering their habitat requirements, such a change in grassland management may be expected to go along without affecting the remaining species’ SISD farmland bird species. A significant increase in hedge density in favour of Red-backed Shrike and Yellowhammer in contrast, could well be expected to negatively affect the populations of Bar-tailed Godwit and Lapwing and therefore avert overall benefits for all farmland bird species simultaneously. Thus, conservation measures will only be successful in delivering substantial biodiversity gain without setting priorities for regional management (Whittingham et al., 2007).

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Despite their strong effects in our models, composite indices like landscape diversity and crop diversity appear rather unsuitable as direct target variables for landscape planning and nature conservation. Increases in both, land-use or crop diversity, may be achieved by altering the spatial configuration of a multitude of landscape components in an infinite number of combinations, and a targeted decision between equally valid alternatives appears difficult in practice. Landscape planning and nature conservation will preferentially address measures that are more clearly defined. We regard hedge density and the cover of spring cereals more accessible to management by landscape planners and nature conservationists than landscape diversity and crop diversity. Alterations of hedge density and of spring cereal cover proved suitable to enhance bird diversity in our models, but our results also highlighted that even such well defined measures require a careful definition of conservation priorities. We could show that the Skylark’s population target was reached when spring cereals covered 8.7% of the study area (an increase of 6% from the current situation), whereas for the Little Owl a decrease of spring cereals to zero percent cover (a decrease of 2% from the current situation) was necessary to reach its population target. These findings accord well with the known preferences of the Little Owl for grassland over arable farmland as habitat for hunting (Ge´not and van Nieuwehuyse, 2002) and the higher breeding densities of Skylarks documented in spring than in winter cereals (Chamberlain et al., 1999). They underline once more the dilemma of simultaneously meeting the habitat requirements of a multitude of species when implementing environmental measures to enhance biodiversity. In order to meet the habitat requirements of a multitude of species, implementing AES often requires the management of not only one but several landscape elements with known benefits for biodiversity. Thus, in addition to the cover of spring cereals one might also want to manage hedges, which are known to benefit the Yellowhammer and especially the Red-backed Shrike (Titeux et al., 2007; Swetnam et al., 2005; Bradbury et al., 2000; Fuller et al., 1997). Looking solely at a management of hedgerows, the population target for the Yellowhammer or the Red-backed Shrike could be achieved by increasing the density of hedges by 1.2% or 2.3%, respectively. The highest total SISD score for all selected indicator bird species together was attained by increasing hedgerow density by 3%. Yet, managing hedgerow density and spring cereal cover in combination, the highest total SISD score was attained by completely eliminating spring cereals from the landscape while simultaneously increasing hedgerow density by 1.5%. Implementing AES measures by increasing or decreasing spring cereal cover or hedge density, however, does not only demand a careful weighing of contrasting habitat requirements among species but also the consideration of structural and compositional differences within or among landscapes and regions in which AES are to be applied (Whittingham et al., 2007). We could show that augmenting the density of hedges had a positive effect in areas dominated by arable land, grassland or hedgerows, though to a varying extent, whereas an increase in spring cereals had opposing effects on SIDS scores in areas composed of mainly arable land as compared to those with high shares of hedgerows. Thus, measures to counteract a loss in biodiversity need to be landscape specific in order to be successful, also, because the causes for species declines most likely vary among land-use types and therefore require different counter actions (Wretenberg et al., 2006, 2007). In addition to balancing opposing needs of different species and diverging responses in different regions, our analysis illustrates that optimization of cost efficiency introduces a third level of balancing trade-offs. In our example, the costs for augmenting hedge density and the costs for growing spring cereals instead of growing more profitable crop needed to be weighed against each

other. Unexpectedly, the highest total SISD score was achieved at a very moderate annual cost of 5.5 Euro per ha farmland in our scenarios. The most effective solution as to how agricultural landscapes should be modified in order to maximise biodiversity benefits, however, may strongly depend on the underlying assumptions of economic costs (Westphal et al., 2007). We do not claim that our predictions of SISD are precise, and we are aware that economic costs are subject to change. However, we conclude from our investigation that this kind of multi-level optimization is almost impossible without the aid of quantitative models. Therefore, we look forward to increased application of predictive modelling for the purpose of balancing the multiple trade-offs inherent in multi-species conservation tasks. Specifically, we expect including optimization algorithms (e.g. genetic algorithm: Westphal et al., 2007; Holzka¨mper et al., 2006; Seppelt and Voinov, 2002) that enable a systematic search amongst locally and regionally differentiated management measures within a landscape to reveal previously unanticipated solutions for creating landscapes of high sustainability. Acknowledgements This study was funded by the Deutsche Forschungsgemeinschaft (DFG) in the framework of the Collaborative Research Centre SFB 299. We are grateful to all colleagues working in this project for continuous discussion and support. We thank Wilfried Hausmann, Ralf Eichelmann, Gu¨nther Herbert, Udo Seum, Werner Peter, Rainer Holler and Karl-Heinz Clever from the HGON (Hessische Gesellschaft fu¨r Ornithologie und Naturschutz e.V.) for providing nest records of Red Kite, Little Owl and Red-backed Shrike. We thank two anonymous reviewers for constructive comments on an early draft of this manuscript. The study complies with the current laws of Germany. References Bauer, H.G., Bezzel, E., Fiedler, W., 2006. Das Kompendium der Vo¨gel Mitteleuropas, 2nd ed. Aula, Wiebelsheim, Germany, 1767 pp. (in German). Benton, T.G., Vickery, J.A., Wilson, J.D., 2003. Farmland biodiversity: is habitat heterogeneity the key? Trends Ecol. Evol. 18, 182–188. Beven, K.J., Kirkby, M.J., 1979. A physically based, variable contributing area model of basin hydrology. Hydrol. Sci. Bull. 24, 43–69. Bibby, C.J., Burgess, N.D., Hill, D.A., Mustoe, S., 2000. Bird Census Techniques, 2nd ed. Academic Press, London, England, 302 pp. Bignal, E.M., McCracken, D.I., 1996. Low-intensity farming systems in the conservation of the countryside. J. Appl. Ecol. 33, 413–424. Billeter, R., Liira, J., Bailey, D., Bugter, R., Arens, P., Augenstein, I., Aviron, S., Baudry, J., Bukacek, R., Burel, F., Cerny, M., De Blust, G., De Cock, R., Diekoetter, T., Dietz, H., Dirksen, J., Dormann, C., Durka, W., Frenzel, M., Hamersky, R., Hendrickx, F., Herzog, F., Klotz, S., Koolstra, B., Lausch, A., Le Coeur, D., Maelfait, J.P., Opdam, P., Roubalova, M., Schermann, A., Schermann, N., Schmidt, T., Schweiger, O., Smulders, M.J.M., Speelmans, M., Simova, P., Verboom, J., van Wingerden, W., Zobel, M., Edwards, P.J., 2008. Indicators for biodiversity in agricultural landscapes: a pan-European study. J. Appl. Ecol. 45, 141–150. Boyce, M.S., Vernier, P.R., Nielsen, S.E., Schmiegelow, F.K.A., 2002. Evaluating resource selection functions. Ecol. Mod. 157, 281–300. Bradbury, R.B., Kyrkos, A., Morris, A.J., Clark, S.C., Perkins, A.J., Wilson, J.D., 2000. Habitat associations and breeding success of yellowhammers on lowland farmland. J. Appl. Ecol. 37, 789–805. Buckland, S.T., Anderson, D.R., Burnham, K.P., Laake, J.-L., Bochers, D.L., Thomas, L., 2001. Introduction to Distance Sampling. Estimating Abundance of Biological Populations. Oxford University Press, New York, USA, 432 pp. Bundesregierung, 2002. ‘‘Perspektiven fu¨r Deutschland’’ Unsere Strategie fu¨r eine nachhaltige Entwicklung. Berlin, Germany, 343 pp. (in German). Burnham, K.P., Anderson, D.R., 2002. Model Selection and Multimodel Inference: A Practical Information–Theoretic Approach, 2nd ed. Springer, New York, USA, 488 pp. Butler, S.J., Vickery, J.A., Norris, K., 2007. Farmland biodiversity and the footprint of agriculture. Science 315, 381–384. Carter, I., 2007. The Red Kite, 2nd ed. Arlequin Press, Shrewsbury, England, 245 pp. Chamberlain, D.E., Wilson, A.M., Browne, S.J., Vickery, J.A., 1999. Effects of habitat type and management on the abundance of skylarks in the breeding season. J. Appl. Ecol. 36, 856–870. Cody, M.L., 1985. Habitat Selection in Birds. Academic Press, London, England.

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