Predicting spatial and temporal habitat use of rodents in a highly intensive agricultural area

Agriculture, Ecosystems and Environment 189 (2014) 145–153

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Predicting spatial and temporal habitat use of rodents in a highly intensive agricultural area Christina Fischer a,b,∗ , Boris Schröder c,d a

Restoration Ecology, Department of Ecology and Ecosystem Management, Technische Universität München, Emil-Ramann-Str. 6, 85354 Freising, Germany Leibniz-Centre for Agricultural Landscape Research (ZALF) e.V., Eberswalder Str. 84, 15374 Müncheberg, Germany Environmental Systems Analysis, Institute of Geoecology, Technical University of Braunschweig, Langer Kamp 19c, 38106 Braunschweig, Germany d Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 Berlin, Germany b c

a r t i c l e

i n f o

Article history: Received 10 August 2013 Received in revised form 25 February 2014 Accepted 6 March 2014 Available online 13 April 2014 Keywords: Agricultural intensification Apodemus agrarius Kettle holes Myodes glareolus Small mammals

a b s t r a c t Landscape modifications in combination with a highly intensive agriculture are known to have negative impacts on farmland biodiversity. Small rodents play a crucial role in agricultural ecosystems because they provide important ecosystem functions. They are important links in food webs, but also pest species to various kinds of crops. Here, we want to find trade-offs between small rodent conservation and rodent pest control in agriculture. We predicted abundance, species richness and community composition of small rodents in relation to landscape scale effects (measured as % of arable land along a gradient of landscape complexity), local scale effects (agricultural fields vs. semi-natural vs. natural habitats, as well as vegetation cover) and temporal variations within a growing season (before and after the crop harvest) simultaneously. Results show that increasing vegetation cover increased rodent abundance and species richness and influenced community composition after the crop harvest. Local scale effects influenced rodent abundance with lowest abundance in grasslands compared to semi-natural and natural habitats, whereas landscape scale effects influenced species richness which increased with increasing % of arable land. Apodemus agrarius (striped field mouse) was most abundant in field margins and increased with increasing vegetation cover after the crop harvest. Myodes glareolus (bank vole) was most abundant in kettle holes, but as isolation of these habitats increased, abundance decreased. A compromise between nature conservation and crop protection would be an agricultural landscape with crop field interspersed by natural and semi-natural habitats, which are arranged within rodent dispersal distances, providing a high vegetation cover. Grasslands can act as sink habitats and may reduce rodent spillover into agricultural fields facilitating pest control. Special attention has to be paid to the protection of natural habitats such as kettle holes, because they can act as source habitat facilitating rodent conservation. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Landscape modification in terms of e.g. habitat loss, degradation, isolation, and simplified crop rotations are major threats to species richness in human-modified landscapes (Fischer and Lindenmayer, 2007). Landscapes, especially agricultural areas, are fragmented by replacing semi-natural habitats, such as forest fragments and hedges, with agricultural fields (Benton et al., 2003; Firbank et al., 2008). This reduces native vegetation and habitat connectivity and increases edge effects through an increasing

∗ Corresponding author at: Department of Ecology and Ecosystem Management, Technische Universität München, Emil-Ramann-Str. 6, 85354 Freising, Germany. Tel.: +49 8161 71 2503; fax: +49 8161 71 4143. E-mail address: christina.fi[email protected] (C. Fischer). http://dx.doi.org/10.1016/j.agee.2014.03.039 0167-8809/© 2014 Elsevier B.V. All rights reserved.

patch-matrix contrast (Fischer and Lindenmayer, 2007). Furthermore, an intensification of crop management, such as high input farming related to high levels of pesticide and fertiliser applications, has also negative impacts on farmland biodiversity (Geiger et al., 2010; Stoate et al., 2001). The effects of these processes have been studied on many kinds of organisms (Robinson and Sutherland, 2002; Stoate et al., 2001), but further research is needed on a landscape scale, studying biodiversity in modified landscapes as well as in remnants of (semi-) natural habitat patches (Fischer and Lindenmayer, 2007). Trade-offs have to be found between biodiversity conservation in combination with related ecosystem functions and a sustainable food production in order to meet increased global food demand (Barraquand and Martinet, 2011; Tscharntke et al., 2012). Small rodents (<60 g) populate various habitats and are common throughout agricultural landscapes (Heroldová et al., 2007).

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However, it is predicted that global warming may reduce small mammal species richness in the future (Blois et al., 2010). There are only a few long term studies on small rodent occurrence in relation to agricultural intensification. In addition to regular population fluctuations, Butet and Leroux (2001) found declining vole populations in grasslands and crop fields over a 25-year period with increasing area of crop production. The importance of small rodents can be determined by their various ecological functions, which can have positive or negative impacts on agricultural areas. Positive effects are rodents’ contribution to soil aeration (Laundre and Reynolds, 1993) and their function as consumers of weeds and insects (Gliwicz and Taylor, 2002). As they may also maintain ectomycorrhizal fungi (Schickmann et al., 2012), their presence may increase plant community productivity and diversity, and they are important links in food webs. They represent the main food resources (prey) for mammalian and avian predators (Arlettaz et al., 2010; Aschwanden et al., 2005; Salek et al., 2010) enhancing the persistence and survival of species of higher trophic levels (Butet and Leroux, 2001). Rodents detrimentally effect human interests by their negative impacts on agricultural crops throughout the world (Brown et al., 2007; Heroldová and Tkadlec, 2011), their ability to disperse weed seeds in agricultural fields (Kiviniemi and Telenius, 1998) and their function as reservoir hosts for various diseases (Vorou et al., 2007). Until now, most of the studies on the impacts of agricultural intensification on small rodent occurrence mainly focused on abundances and species richness in either agricultural used fields (Fischer et al., 2011; Heroldová et al., 2004) or in semi-natural habitats interspersed in the agricultural matrix (Michel et al., 2006; Renwick and Lambin, 2011), but not in modified landscapes and habitat patches simultaneously (but see Aschwanden et al., 2007; Panzacchi et al., 2010). Landscape scale effects on small mammals shown by Silva et al. (2005) indicate that species richness decreases with increasing amount of cultivated areas, whereas Michel et al. ˜ et al. (2003) found only limited effects. (2006) and Millán de la Pena On a local scale in temperate Europe, small rodent abundance and species richness are higher in forests, field margins or in ecological compensation areas, such as wild-flower strips, compared to agricultural fields, meadows and grassland (Arlettaz et al., 2010; Aschwanden et al., 2007). Local habitat preferences also depend on species-specific habitat specialization (Panzacchi et al., 2010). Open-land species such as Microtus arvalis PALLAS (common vole) mainly occur in agricultural fields. Forest species such as Myodes glareolus SCHREBER (bank vole) frequently occur in forests and woody habitats like hedges, whereas forest-field species such as Apodemus sylvaticus L. (long-tailed field mouse) are habitat generalists and can occur in almost all habitats within the agricultural ˜ et al., 2003). landscape (Heroldová et al., 2007; Millán de la Pena Furthermore, local micro-habitat conditions, such as increasing vegetation height and density, provide refuges from predators and increase small mammal abundance and species richness (Jacob, 2008; Silva and Prince, 2008; but see Aschwanden et al., 2007). To find trade-offs between small rodent conservation, promoting biodiversity as whole and sustainable food production decreasing rodent abundances, it is necessary to study landscape and local scale effects, as well as temporal variations simultaneously in more detail. We conducted landscape wide rodent sampling, where we studied small rodent abundance, species richness and community composition in the most frequent habitats (agricultural fields, semi-natural and natural habitats, such as extensive agricultural production or uncultivated habitats) of a highly intensive agricultural area selected along a gradient of landscape complexity, measured as % of arable land in a radius of 500 m around trapping transects. Furthermore, we considered local micro-habitat conditions such as vegetation cover and temporal variations within a

growing season comparing rodent occurrence before and after the crop harvest. We hypothesize that: 1. Increasing landscape complexity increases rodent abundance and species richness and influences community composition by providing a wide variety of different habitats for species specialized to open habitats as well as forest specialists. 2. Natural and semi-natural habitats support highest rodent abundance and species richness, where management intensity is lowest and structural heterogeneity is highest. 3. High vegetation cover, especially after the crop harvest, increases small rodent abundance and species richness. 2. Materials and methods 2.1. Study area The study area was located in the Uckermark region in NorthEast Germany (state of Brandenburg) in the catchment area of the River Quillow (centred at 53◦ 21 N, 13◦ 39 E; Fig. A1). The landscape is characterized by highly intensive agriculture with 62% of the study region being arable land planted mainly with cereals, maize and oilseed rape. Furthermore, 13% of the study region is covered by forests, 11% by cultivated grassland, 4% by urban fabric and 10% by other landscape units, such as industrial units, artificial non-agricultural vegetated areas, scrubs and herbaceous vegetation, wetlands, and water bodies. The young moraine landscape is characterized by hummocks and kettle holes, which are small standing waters and ponds (Pätzig et al., 2012). The study area had a maximal extension of 28 km from East to West and 15 km from North to South around the study centre (surface covered: 290 km2 ). 2.2. Environmental variables To analyse effects of landscape, local (micro-) habitat conditions and temporal variation within a growing season on rodent abundance, we recorded 15 environmental variables (Table 1) by selecting 60 study sites from six different habitats (10 sites per habitat type) representing a range of utilized agricultural areas and (semi-) natural habitats along a continuous gradient of landscape complexity sampled before and after the crop harvest. Distance between sites ranged between 0.48 km and 23.15 km. Since cereal and oilseed rape fields were replanted after the crop harvest by farmers in August, sample size was reduced during the second trapping session to 36 out of 60 previously used study sites, representing the parts of the landscape with minimum, mean and maximum complexity for each habitat (6 sites per habitat). 2.2.1. Landscape variables Study sites were selected along a continuous gradient of landscape complexity by measuring % of arable land (range: 0–98%) in a radius of 500 m (area: 78.54 ha) around study sites, because it was shown to be the most important factor for small mammal abundance, species richness and diversity (Fischer et al., 2011; Silva et al., 2005) using official digital topographical maps (ATKIS DTK 25) and the Geographical Information System ArcGIS 10.0 (1999-2010 ESRI Inc.). To study functional landscape heterogeneity, we calculated regional perimeter–area ratio by P/AR =

m  i=1

m 

Pi /

i=1

Ai where

P is the perimeter, A the patch area; i the patch number, and m is the number of patches in the landscape sector (following e.g. Gabriel et al., 2005). Furthermore, Shannon habitat diversity was m calculated by H = − i=1 Pi log pi where m is the number of habitat types in the landscape sector and pi is the proportion of each habitat type from all available habitat types within the landscape

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Table 1 Environmental variables characterizing study sites on a landscape (n = 60), local (micro-) habitat (n = 96) and temporal scale (n = 2). Variables highlighted in grey were included in the global models predicting small rodent occurrence. Variable

Mean ± SE

Minimum

(A) Landscape variables (in a radius of 500 m around study sites) Arable land 66.94 ± 3.45 0.00 9.37 ± 2.32 0.00 Forest 14.56 ± 2.22 0.00 Grassland 1.59 ± 0.24 0.00 Inland wetlands 0.10 ± 0.01 0.04 Perimeter-area ratio Shannon habitat diversity 0.77 ± 0.05 0.09 Transitional woodland-scrub 1.77 ± 0.27 0.00 Urban fabric 3.07 ± 0.66 0.00 Water bodies 1.36 ± 0.51 0.00 Water courses 0.05 ± 0.03 0.00 (B) Local (micro-) habitat variables Habitat – – Vegetation cover (5 cm) Vegetation cover (50 cm) Vegetation height (C) Temporal variables Trapping session

Maximum

Definition

98.29 84.23 64.50 8.81 0.31 1.86 10.30 31.49 21.47 1.22

Relative cover of arable fields, in % Relative cover of broad-leaved, coniferous and mixed forest, in % Relative cover of pastures and natural grassland, in % Relative cover of peat bogs and kettle holes, in % Edge density averaged over all patches, in m/m2 Shannon - Wiener index Relative cover of hedges, in % Relative cover of settlements, commercial and public units, in % Relative cover of lakes, in % Relative cover of rivers and streams, in %

–

53.09 ± 2.90 26.94 ± 2.71 45.32 ± 4.80

0.00 0.00 0.00

93.33 95.00 401.67

Cereal fields, oilseed rape fields, grassland, forest, field margins, kettle holes, factor with six levels Plant cover in a height of 5 cm above ground, estimated within 1 m2 , in % Plant cover in a height of 50 cm above ground, estimated within 1 m2 , in % Mean plant height within 1 m2 , in cm

–

–

–

Before and after the crop harvest, factor with two levels

sector, which could be arable land, forest, grassland, inland wetlands, transitional woodland-scrub, urban fabric, water bodies and water courses (Fahrig et al., 2011). 2.2.2. Local (micro-) habitat variables Rodents were sampled in six different habitats, which frequently occurred in the study region. We chose three agricultural habitats: conventionally managed cereal fields (mainly winter wheat or rye), oilseed rape fields, grasslands and three (semi-) natural habitats: mixed deciduous forests, field margins besides corn or sugar beet fields covered by shrubs and/or trees and the terrestrial buffer strip of kettle holes (see Pätzig et al., 2012). Grasslands were managed as meadows, which were mown frequently but not used for livestock grazing during the study period. The selected habitats were chosen to cover a range from highly intensive used areas such as agricultural fields, to undisturbed natural habitats such as kettle holes, which are under legal protection in Germany. At each study site, we characterized vegetation structure by estimating mean vegetation cover (in %) in heights of 5 cm and 50 cm. Additionally mean vegetation height was measured. For measurements three 1 m × 1 m plots were selected along a 100 m transect at 0 m, 50 m and 100 m, where rodent trapping took place. From a total of three 1 m × 1 m plots mean vegetation cover and height were calculated per transect. Vegetation sampling took place in June and August 2011 simultaneously to rodent trapping. 2.3. Temporal variables Rodents were trapped twice, in June (01.06.2011–18.06.2011) before the crop harvest and in August (17.08.2011–28.08.2011) after the crop harvest. The trapping session before the crop harvest took place during the time of cereal flowering and fruit development of oilseed rape, while in August all crops in the study region were already harvested and only corn and sugar beet remained grown. 2.4. Rodent trapping Rodent occurrence was measured by using a capture-markrecapture approach. Therefore, 20 Ugglan multiple capture live traps (240 mm × 60 mm × 90 mm; Grahnab, Gnosjö, Sweden; details in Lambin and MacKinnon, 1997) were placed in each habitat along a transect of 100 m spaced every 5 m apart. In the case of cereal and oilseed rape fields, forests and grasslands traps were

placed at least 10 m away from the habitat edges. In the case of field margins and kettle holes, traps were placed directly into the habitats ca. 1 m away from the bordering fields. Trapping was carried out on two successive trap-nights per trapping session, with two additional pre-baiting days before each trapping session. In the evening before sunset, traps were baited with rolled oats and checked in the morning after sunrise. During daytime, traps were inactivated because of increasing temperatures and higher risks of mortality. Individuals trapped for the first time were marked by fur clipping at the left thigh to identify recaptures. Captured rodents were identified to species, sexed, weighed and released at the place of capture. Rodent abundance was calculated as the total number of individuals per habitat and trapping session (Michel et al., 2006). Abundances calculation may be critical when calculating abundances of rare species as traps can be already occupied by common species. However, as % of occupied traps for each trap-night was just 18.5 ± 2.3%, potential biases can be neglected. 2.5. Statistical analyses First correlation analysis (Spearman’s rank correlation according to non-normality of most variables) were performed to test for multicollinearity between continuous landscape (Appendix: Table A1) or local variables (Appendix: Table A2; Dormann et al., 2013). Correlated variables, as well as variables which covered only very short gradients, representing maximally one-third of the potential gradient length (% of inland wetlands, urban fabric and water courses; cf. Table 1) were excluded from further analysis (see variables used in the modelling procedures, highlighted in grey in Table 1). For all analyses, R version 2.15.0 (R Development Core Team, 2012) was used. To predict total rodent’s abundance and species richness and the abundance of the two most common species A. agrarius PALLAS (striped field mouse) and M. glareolus in relation to landscape, local (micro-) habitat and temporal variables we used linear mixedeffects models (lme function; Pinheiro and Bates, 2000) with a maximized log-likelihood implemented in the R package nlme (Pinheiro et al., 2012). The global models contained landscape variables (% of arable land, perimeter–area ratio), local (micro-) habitat variables (habitat: cereals field, oilseed rape field, grassland, forest, field margin and kettle hole; vegetation cover to a height of 5 cm), temporal variables within a growing season (trapping sessions: before and after the crop harvest) and two-way interactions between habitat and trapping session and the other explanatory

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Fig. 1. Boxplots of small rodent (a) abundance, (b) species richness, (c) abundance of A. agrarius, and (d) abundance of M. glareolus sampled in the six different habitat types (C: cereal field, R: oilseed rape field, G: grassland, F: forest, FM: field margin, KH: kettle hole). Overlapping notches suggest non-significant differences between medians. Significance was assessed from an ANOVA table of the best model. For better visualization raw data are presented.

variables (% of arable land, perimeter–area ratio, vegetation cover), respectively. Study sites were included as random effects to model the independence of errors with respect to temporal autocorrelations (two trapping sessions within the same study sites; Pinheiro and Bates, 2000). To achieve a normal error distribution and/or to avoid heteroscedasticity, response variables were log-transformed and different variance functions implemented in the nlme library were used for modelling the variance structure of the withingroup errors using covariates, whenever necessary. Fitted models with different variance functions were compared by choosing the lowest Akaike’s Information Criterion (AIC; Pinheiro and Bates, 2000). Model selection was done by an information-theoretic approach using multi-model inference from the R package MuMIn (Barton, 2012). Therefore, a full submodel set (312 different models) from the global model was fitted to the data. We calculated AICc values (Akaike Information Criterion corrected for small sample size), AICc values in relation to the model with the minimum AICc, and Akaike weights, which provide a relative weight of evidence for each model and likelihood-ratio based R2 values. The relative variable importance, weighted averages of parameter estimates,

Fig. 2. Small rodent (a) abundance, (b) species richness, (c) abundance of A. agrarius, and (d) abundance of M. glareolus in relation to % of vegetation cover. Regression lines from the first trapping session before the crop harvest (TS1) and the second trapping session after the crop harvest (TS2) represent predictions (with other explanatory variables held at mean values) from averaged linear mixed effects models. For better visualization raw data are presented.

standard errors and confidence intervals were calculated by averaging models with AICc ≤ 3 which provide substantial empirical evidence for the model (Burnham and Anderson, 2002). Contrasts between different habitats were investigated by re-ordering factor levels. Confidence intervals of parameter estimates, which did not include zero, were considered to influence response variables significantly (Grueber et al., 2011). Model performance for each averaged model was assessed using a 10-fold cross validation. Therefore, the dataset was split into 10 parts. Nine parts were used as the training data set to fit averaged models and each remaining part was used as the test dataset to calculate model predictions once. The mean crossvalidated R2 value (cross-validated squared Pearson’s correlation coefficient between observed and predicted values) and the mean squared error with standard deviation, which was calculated by n  

MSE = 1/n

y − yˆ

2

where n is the sample size, y the observed

i=1

value, and y ˆ is the predicted value from the model represented the averaged model performance.

Parameter

Abundancec Estimate ± SE

(Intercept) %A P/AR H(R)a H(G)a H(F)a H(FM)a H(KH)a VC TS2b %A:TS2b P/AR :TS2b VC:TS2b %A:H(R)a %A:H(G)a %A:H(F)a %A:H(FM)a %A:H(KH)a a

0.68 ± 0.47 0.01 ± 0.01 −0.70 ± 1.20 −0.19 ± 0.31 −1.02 ± 0.34** −0.25 ± 0.32 0.24 ± 0.32 −0.14 ± 0.35 0.01 ± 0.01 0.40 ± 0.48 – – 0.01 ± 0.01* – – – –

H(C) was the reference category. TS1 was the reference category. c Log transformed. (.) p < 0.10. * p < 0.05. ** p < 0.01. *** p < 0.001. b

A. agrariusc

Species richness Confidence interval (–−0.248, 1.615) (−0.005, 0.015) (−4.702, 3.301) (−0.813, 0.435) (−1.703,−0.330) (−0.894, 0.399) (−0.404, 0.889) (−0.841, 0.555) (−0.008, 0.021) (−0.570, 1.369) – – (0.000, 0.023) – – – – –

Estimate ± SE 0.01 ± 0.32 0.12 ± 0.00** 2.56 ± 2.21 − − − − − 0.00 ± 0.01 0.31 ± 0.36 –0.01 ± 0.01 –5.29 ± 3.12 0.02 ± 0.01* – – – – –

Confidence interval (−0.518, 0.762) (0.004, 0.019) (−1.853, 6.963) − − − − − (−0.011, 0.010) (−0.420, 1.035) (−0.019, 0.002) (−11.635, 1.058) (0.004, 0.032) – – – – –

Estimate ± SE 0.77 ± 0.25 0.00 ± 0.00 0.12 ± 0.56 −0.26 ± 0.27 −0.52 ± 0.27(.) −0.73 ± 0.23** 0.12 ± 0.33 −0.31 ± 0.32 0.00 ± 0.00 −0.17 ± 0.12 – −1.70 ± 0.70* 0.01 ± 0.00*** – – – – –

M. glareolusc Confidence interval (0.274, 1.259) (−0.002, 0.001) (−1.016, 1.229) (−0.809, 0.291) (−1.059, 0.014) (−1.190, −0.262) (−0.545, 0.790) (−0.938, 0.327) (−0.005, 0.002) (−0.408, 0.070) – (−3.121, −0.274) (0.010, 0.019) – – – – –

Estimate ± SE 0.55 ± 0.70 0.00 ± 0.01 −3.57 ± 1.57* 1.21 ± 1.18 −0.40 ± 0.73 −0.41 ± 0.73 0.04 ± 0.90 2.78 ± 0.91** 0.00 ± 0.00 0.77 ± 0.28** 0.00 ± 0.01 −3.12 ± 1.87 – −0.01 ± 0.02 0.01 ± 0.01 0.02 ± 0.01* 0.01 ± 0.01 −0.03 ± 0.01**

Confidence interval (−0.864, 1.956) (−0.019, 0.014) (−6.715, − 0.434) (−1.162, 3.577) (−1.870, 1.065) (−1.881, 1.069) (−1.776, 1.851) (0.956, 4.605) (−0.002, 0.011) (0.210, 1.331) (−0.014, 0.009) (−6.916, 0.674) – (−0.044, 0.016) (−0.012, 0.028) (0.002, 0.043) (−0.013, 0.032) (−0.055, −0.010)

C. Fischer, B. Schröder / Agriculture, Ecosystems and Environment 189 (2014) 145–153

Table 2 Results of multi-model averaging showing landscape, local and temporal effects on small rodent abundance, species richness and abundance of A. agrarius and M. glareolus. Parameter estimates with standard error (SE), confidence intervals and levels of significance are given. Bold values indicate predictor variables, which affect response variables significantly. %A: percentage of arable land, P/AR : perimeter–area ratio, H: habitat (C: cereal field, R: oilseed rape field, F: forest, G: grassland, FM: field margin, KH: kettle hole), VC: vegetation cover, TS: trapping session (1: before the crop harvest, 2: after the crop harvest), “:” indicates two-way interaction.

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Fig. 3. M. glareolus abundance in (a) C: cereal field, (b) R: oilseed rape field, (c) G: grassland, (d) F: forest, (e) FM: field margin, and (f) KH: kettle hole in relation to % of arable land. Regression lines represent predictions (with other explanatory variables held at mean values) from averaged linear mixed effects models. For better visualization raw data are presented.

To assess effects of environmental variables on community composition non-metric multidimensional scaling (NMDS) implemented in the R package vegan (Oksanen et al., 2012) was performed for each trapping session separately. NMDS is a robust unconstrained ordination method to plot community dissimilarities nonlinearly onto the ordination space (Oksanen, 2011). For NMDS a site-species-matrix from rodent abundances, removing study sites where no individuals were trapped, as well as stress values were calculated using Bray-Curtis dissimilarity. Abundances were Hellinger-transformed giving low weights to rare species (Legendre and Gallagher, 2001). Landscape and local (micro-) habitat variables (Table 1) were fitted onto the ordination diagram and the significance of the variables was assessed using 1000 permutations. 3. Results In total 455 small rodents of seven species were trapped in 3840 trap-nights. Four mice species: A. agrarius, A. flavicollis MELCHIOR (yellow-necked field mouse), A. sylvaticus, Micromys minutus PALLAS (Eurasian harvest mouse), and three vole species: Microtus agrestis L. (field vole), M. arvalis, M. glareolus were recorded and used for analysis. Overall, 4.70 ± 0.65 individuals and 1.30 ± 0.13 species were recorded per study site (n = 96). Before the crop harvest we found 2.72 ± 1.03 (n = 60) and after the crop harvest 8.11 ± 2.56 (n = 36) individuals (mean ± SE; for total abundance, species richness and abundance of the different species in the six different habitats see Appendix: Table A3). In general, agricultural fields harboured low rodent densities with a mean of 15 and 29 rodents/ha (calculated for a 20 m buffer around transects) in cereal fields and 14 and 9 rodents/ha in oilseed rape fields before and after the crop harvest, respectively. 3.1. Relative importance of landscape, local and temporal effects For the total rodent abundance and rodent species richness, five models were found to be part of the confidence set based on evidence ratio, respectively. They explained 31–39% of the variance for abundance and 39–42% for species richness (Appendix: Table A4). Model-averaged predictions (abundance: MSE = 0.93 ± 0.61; R2 = 0.48 ± 0.37; species richness: MSE = 1.43 ± 0.88; R2 = 0.35 ± 0.31) showed that rodent abundance was lowest in grassland compared to the other habitats (Fig. 1a), whereas species richness did not differ between habitats (Fig. 1b). Rodent abundances and species richness increased with increasing vegetation cover after the

crop harvest (Fig. 2a and b). Landscape variables had only a little impact on rodent abundance, whereas species richness increased with increasing % of arable land (Table 2). For the abundance of A. agrarius four models were found to be part of the confidence set based on evidence ratio, explaining 70–72% of the variance (Appendix: Table A4). Model-averaged predictions (MSE = 0.64 ± 0.60; R2 = 0.46 ± 0.13) showed that A. agrarius abundance was lower in forests compared to cereal and oilseed rape fields and field margins and also in grassland compared to field margins (Fig. 1c). Abundance of A. agrarius increased with increasing vegetation cover and decreased with increasing perimeter–area ratio after the crop harvest, respectively (Fig. 2c; Table 2). For the abundance of M. glareolus, five models were found to be part of the confidence set based on evidence ratio, explaining 55–56% of the variance (Appendix: Table A4). Model-averaged predictions (MSE = 1.08 ± 0.54; R2 = 0.24 ± 0.32) showed that M. glareolus abundance was higher in kettle holes compared with cereal fields, field margins, forests and grassland (Fig. 1d) and abundance was also higher after the crop harvest compared with the trapping session before the crop harvest (Table 2). The abundance of M. glareolus decreased with increasing % of arable land in kettle holes compared with cereal fields, grassland and forests and increased in forests compared to cereal and oilseed rape fields and kettle holes (see Fig. 3 for graphical analysis of interaction term between % of arable land and habitat, only shown for M. glareouls as for the other response variables this interaction was excluded from the averaged models), whereas there was no impact of vegetation cover (Fig. 2d).

3.2. Small mammal community composition For the first trapping session before the crop harvest, NMDS fitting environmental variables into the two-dimensional species space (two dimensions, stress = 0.06, best solution after 20 tries) revealed that habitat type explained rodent community composition significantly (r2 = 0.26, p = 0.03). Landscape variables and vegetation cover had no impact on community composition. The rodent community in cereal and oilseed rape fields was dominated by A. agrarius, A. sylvaticus and M. arvalis, whereas A. flavicollis, M. agrestis and M. glareolus mainly occurred in natural and semi-natural habitats, namely kettle holes and forests (Fig. 4a). For the second trapping session after the crop harvest, NMDS (two dimensions, stress = 0.05, two convergent solutions found after two tries) revealed that habitat type (r2 = 0.37, p = 0.02) as well as vegetation cover (r2 = 0.23, p = 0.03) explained community composition significantly. Landscape variables had no impact on community composition. The rodent community in kettle holes and field margins was dominated by A. agrarius, A. flavicollis and M. agrestis, which also needed high vegetation cover. In forests, the rodent community was dominated by M. glareolus, whereas A. sylvaticus mainly occurred in oilseed rape fields (Fig. 4b).

C. Fischer, B. Schröder / Agriculture, Ecosystems and Environment 189 (2014) 145–153

Fig. 4. Graphical interpretation of small rodent community dissimilarities (A. agrarius = A.a; A. flavicollis = A.f; A. sylvaticus = A.s; M. minutus = M.m; M. agrestis = M.ag; M. arvalis = M.ar; M. glareolus = M.g) by plotting site scores (light grey points) with NMDS for the (a) first trapping session before the crop harvest (TS1) and (b) second trapping session after the crop harvest (TS2). Environmental parameters shaded in dark grey influenced community composition significantly, whereas parameters shaded in light grey did not influence community composition. %A: percentage of arable land, P/AR : perimeter–area ratio, H: habitat (C: cereal field, R: oilseed rape field, G: grassland, F: forest, FM: field margin, KH: kettle hole), VC: vegetation cover.

4. Discussion Small rodents were surveyed before and after the crop harvest in 60 study sites from six different habitats selected along a gradient of landscape complexity. This approach allowed us to disentangle the effects of landscape complexity, local (micro-) habitat conditions and temporal variation within a growing season on rodent occurrence. Previous studies analysed either landscape or local (micro-) habitat effects separately (Arlettaz et al., 2010; Aschwanden et al., 2007; Michel et al., 2006; Renwick and Lambin, 2011). Local habitat parameters, especially in relation to temporal variations within a growing season, were more important for rodent abundance and community composition than landscape scale effects, whereas species richness and abundance of individual rodent species were also affected by landscape scale effects. Rodent abundance, but not species richness was significantly lower in grasslands compared with the other habitats. This supports the findings of Arlettaz et al. (2010) who also found the lowest small mammal densities in meadows compared with wild-flower areas, canal banks, wood edges and winter wheat fields (but see Panzacchi et al., 2010). As grasslands and meadows are mown one to four times per year, management decreases vegetation cover frequently and therefore, negatively affects small mammal abundances (Garratt et al., 2012). In contrast, the ground cover of forests, field margins and kettle holes remains constant during the whole growing season and vegetation cover of agricultural fields is also well developed in June after cereal and oilseed rape flowering, increasing small rodent densities (Jacob, 2008; Panzacchi et al., 2010). This suggests that vegetation

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cover is a key factor determining rodent abundance, species richness, community composition, and also abundances of the habitat ´ ´ generalist A. agrarius (Jacob, 2008; Panzacchi et al., 2010; Vukicevi cRadic´ et al., 2006). Due to high vegetation cover the accessibility and detectability of prey in (semi-) natural habitats, such as forests, field margins and kettle holes, is lower for raptors compared to agricultural fields (Arlettaz et al., 2010). Therefore, abundances of A. agrarius and M. glareolus are higher in these (semi-) natural habi´ ´ tats (Amori et al., 2008b; Vukicevi c-Radi c´ et al., 2006) compared with highly nutrient rich cereal and oilseed rape fields. Landscape scale effects influence species richness, but not rodent abundance and community composition (see e.g. Gomes et al., 2011; but see Michel et al., 2006). In our study species richness increased from one to two species with increasing % of arable land around study sites, independently of habitats sampled. Various rodent species are specialized to certain habitats. Open-land species such as M. arvalis and M. minutus mainly occur in agricultural areas or field margins (Amori et al., 2008a; Aplin et al., 2008). Generalists such as A. agrarius and A. sylvaticus frequently occur in field margins and habitat edges as well as in agricultural fields (Kaneko et al., 2008; Schlitter et al., 2008). Therefore, it is likely that fragments of semi-natural habitats, such as field margins and small natural habitat patches, for example kettle holes, lead to an accumulation of generalists and open habitat specialists, resulting in higher species richness in areas with a high % of arable land. Myodes glareolus, a forest species, mainly occurred in woody habitats such as kettle holes, but also in field margins and forests according to their species-specific habitat preferences (Amori et al., 2008b). However, as isolation of woodlots increased M. glareolus abundances decreased, showing that the connectivity of suitable habitat plays an important role for M. glareolus occurrence (van Apeldoorn et al., 1992). In our study, M. glareolus abundances increased in forests, when surrounded by >80% of arable land, whereas in areas with <80% of arable land also small woody habitat patches, such as kettle holes, were populated. This seems to be related to maximum dispersal distances of 500 m (Dickman and Doncaster, 1989). Vegetation cover was less important for M. glareolus occurrence, as preferred woody habitats are permanent throughout the year and are not removed after the crop harvest.

5. Conclusions Rodent occurrence in agricultural landscape should be reasonably examined with respect to two traditionally opposed management strategies: first from a nature conservation point of view because rodents are important links in food webs (Aschwanden et al., 2005; Salek et al., 2010) and second from an economic point of view, because rodents are often considered as harmful pests in agricultural areas throughout the world (Brown et al., 2007; Heroldová and Tkadlec, 2011). From the conservation point of view, we showed that (semi-) natural habitats are most important for single species occurrence and community composition. To our knowledge this is the first study analysing rodents in kettle holes, which is an important landscape element in young moraine landscapes of North America and North Europe (Pätzig et al., 2012). Kettle holes are small, more or less isolated habitats with highly diverse and dense vegetation from macrophytes interspersed in the agricultural matrix (Pätzig et al., 2012). However, besides high plant diversity, kettle holes also provide refuges for rodents. Furthermore, microhabitat conditions, such as a high vegetation cover, which offer shelter, especially after the crop harvest, are important factors enhancing rodent occurrence (c.f. Garratt et al., 2012; Panzacchi et al., 2010). From a pest control point of view, we found that overall rodent abundance in agricultural fields was generally very

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low. As the economic injury level for voles in cereal fields is specified to 5–10 active burrow entrances/0.01 ha in spring and 20–30 in autumn (Hoffmann and Schmutterer, 1999) which equals 900–1800 voles/ha in spring and 3600–5400 voles/ha in autumn (calculated for 1.8 voles/active burrow entrance; Liro, 1974), it is likely that crop damage was rather low. Furthermore, high predation in habitat edges (Salek et al., 2010) as well as in areas with low vegetation cover such as grasslands reduces abundances of potential rodent pest species such as Microtus spp. and Apodemus spp. (Arlettaz et al., 2010; Garratt et al., 2012). Therefore, from a crop protection point of view highly productive agricultural landscapes are needed with cereal fields and grasslands. Interspersed (semi-) natural habitats enhance predator abundances potentially regulating rodent population densities (Paz et al., 2013). These results tie directly into the ecologically-based rodent management (EBRM) concept by Singleton et al. (1999), which proposes management strategies on the basis of the ecological requirements of pest species. However, small rodent populations, as well as their resources and predators, need to be monitored over full population cycles to reliably predict spatial and temporal habitat use. A compromise between nature conservation and crop protection could be an agricultural landscape with crop fields interspersed by natural and semi-natural habitats, which are arranged within rodent dispersal distances. Semi-natural habitats should provide high vegetation cover, whereas grasslands between annual crop fields act as sink habitats due to low vegetation cover and may reduce rodent spillover. Furthermore, special attention has to be paid to the protection of natural habitats such as kettle holes in agricultural areas, because they can act as source habitats facilitating rodent conservation. Acknowledgements We thank the AgroScapeLabs project (www.scapelabs.org) providing a platform for the present research. G. Verch head of the ZALF research station Dedelow provided valuable information on the study area. We also thank C. Hönicke for help with the field work, P. Vorpahl for statistical support, the Biodiversity Exploratories for providing traps, E. Walker for language correction, and the land owners for allowing us to work on their land. All procedures were obtained for the research in accordance with the Federal Nature Conservation Act (§ 45 Abs. 7 Nr. 3) approved by the local nature conservation authority (reference number LUGV 7RO4610/34 + 5#86908/2011). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.agee.2014.03.039. References Amori, G., Hutterer, R., Kryˇstufek, B., Yigit, N., Mitsain, G., Palomo, L.J., 2008a. Microtus arvalis. IUCN 2012. IUCN Red List of Threatened Species. Version 2012.2 (accessed 17.12.12). Amori, G., Hutterer, R., Kryˇstufek, B., Yigit, N., Mitsain, G., Palomo, L.J., Henttonen, H., Vohralík, V., Zagorodnyuk, I., Juˇskaitis, R., Meinig, H., Bertolino, S., 2008b. Myodes glareolus. IUCN 2012. IUCN Red List of Threatened Species. Version 2012.2 (accessed 17.12.12). Aplin, K., Lunde, D., Batsaikhan, N., Kryˇstufek, B., Meinig, H., Henttonen, H., 2008. Micromys minutus. IUCN 2012. IUCN Red List of Threatened Species. Version 2012.2 (accessed 17.12.12). Arlettaz, R., Krahenbuhl, M., Almasi, B., Roulin, A., Schaub, M., 2010. Wildflower areas within revitalized agricultural matrices boost small mammal populations but not breeding Barn Owls. J. Ornithol. 151, 553–564. Aschwanden, J., Birrer, S., Jenni, L., 2005. Are ecological compensation areas attractive hunting sites for common kestrels (Falco tinnunculus) and long-eared owls (Asio otus)? J. Ornithol. 146, 279–286. Aschwanden, J., Holzgang, O., Jenni, L., 2007. Importance of ecological compensation areas for small mammals in intensively farmed areas. Wildlife Biol. 13, 150–158.

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