Constance Blary

Christian Kerbiriou

Isabelle Le Viol

Kévin Barré
email: kevin.barre@edu.mnhn.fr

Assessing the importance of field margins for bat species and communities in

Keywords: acoustic monitoring, bat community, farmland biodiversity, field borders, habitat specialisation, landscape composition

come

Introduction

The global land area is dominated by agricultural landscapes (37%, World Bank Open Data; https://data.worldbank.org/), and the general trend towards the conversion of natural habitats into agricultural land is recognized as one of the main drivers of biodiversity decline (Maxewell et al., 2016). In addition to land conversion, landscape simplification appears to be a second process affecting biodiversity in agricultural landscapes. It consists in (i) a reduction of crop diversity within a landscape, (ii) a decrease in diversity and amount of semi-natural habitats (e.g. ponds, small woodland, groves of trees, hedgerows, fallows), (iii) a progressive fragmentation of the remaining natural vegetation, and (iv) a removal of key structural elements (e.g. hedgerows; [START_REF] Benton | Farmland biodiversity: is habitat heterogeneity the key?[END_REF][START_REF] Gaston | Habitat conversion and global avian biodiversity loss[END_REF][START_REF] Sirami | Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions[END_REF]. These changes lead to a reduction of crop varietal diversity and an intensification of farming practices (i.e. through tillage, fertilizer, pesticides; [START_REF] Robinson | Post-war changes in arable farming and biodiversity in Great Britain[END_REF]. Indeed, such changes are well known to deeply affect biodiversity by reducing the abundance and diversity of species (e.g. [START_REF] Peeters | Wild and Sown Grasses: Profiles of a Temperate Species Selection Ecology, Biodiversity and Use. Food and Agriculture Organization of the United Nations[END_REF][START_REF] Flynn | Loss of functional diversity under land use intensification across multiple taxa[END_REF][START_REF] Flohre | Agricultural intensification and biodiversity partitioning in European landscapes comparing plants, carabids, and birds[END_REF], their interactions (e.g. Tscharntke et al., 2005;Tamburini et al., 2016) and homogenizing communities towards those best adapted to poorly diversified and open landscapes (e.g. [START_REF] Filippi-Codaccioni | Effects of organic and soil conservation management on specialist bird species[END_REF][START_REF] Ekroos | Homogenization of lepidopteran communities in intensively cultivated agricultural landscapes[END_REF]. Thus, the more landscapes are intensively farmed, the more remaining patches of semi-natural habitats and connections between them by linear landscape elements (e.g. hedgerows, grass strips, wetlands) are of high importance for the persistence of many taxa [START_REF] Michel | Role of habitat and landscape in structuring small mammal assemblages in hedgerow networks of contrasted farming landscapes in Brittany, France[END_REF][START_REF] Diekötter | Effects of landscape connectivity on the spatial distribution of insect diversity in agricultural mosaic landscapes[END_REF][START_REF] Schippers | Sacrificing patches for linear habitat elements enhances metapopulation performance of woodland birds in fragmented landscapes[END_REF][START_REF] Gil-Tena | Woodland bird response to landscape connectivity in an agriculture-dominated landscape: a functional community approach[END_REF][START_REF] Haenke | Landscape configuration of crops and hedgerows drives local syrphid fly abundance[END_REF][START_REF] Correa Ayram | Habitat connectivity in biodiversity conservation: A review of recent studies and applications[END_REF].

Especially, linear landscape elements influence species dispersal across the farming matrix [START_REF] Joyce | Influences of hedgerow intersections and gaps on the movement of carabid beetles[END_REF][START_REF] Runge | Conserving mobile species[END_REF], in particular for mobile species such as bats [START_REF] Lookingbill | Influence of wetland networks on bat activity in mixed-use landscapes[END_REF]Boughey et al., 2011;[START_REF] Frey-Ehrenbold | Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices[END_REF][START_REF] Pinaud | Modelling landscape connectivity for greater horseshoe bat using an empirical quantification of resistance[END_REF]. Among studies dealing with the importance of linear habitats for bats, hedgerows have been identified as a key element because they provide prey insects and they also facilitate bat commuting across the farming matrix (Verboom & Huitema, 1997;[START_REF] Pinaud | Modelling landscape connectivity for greater horseshoe bat using an empirical quantification of resistance[END_REF][START_REF] Froidevaux | Managing hedgerows for nocturnal wildlife: Do bats and their insect prey benefit from targeted agrienvironment schemes?[END_REF].

Surprisingly, the importance of other treeless linear landscape elements such as herbaceous field margins for bats has received no attention. However, herbaceous field margins surrounding crops are linear habitats widely present in agricultural landscapes, and have been identified as a key habitat for numerous taxa of both flora and fauna, from arthropods to birds [START_REF] Pfiffner | Overwintering of arthropods in soils of arable fields and adjacent seminatural habitats[END_REF][START_REF] Marshall | Field margins in northern Europe: their functions and interactions with agriculture[END_REF][START_REF] Arx (von) | Land-use intensity and border structures as determinants of vegetation diversity in an agricultural area[END_REF]Vickery et al., 2009). Indeed, spontaneous or sown herbaceous field margins host an important proportion of farmland plant diversity [START_REF] Wuczyński | Applying red lists to the evaluation of agricultural habitat: regular occurrence of threatened birds, vascular plants, and bryophytes in field margins of Poland[END_REF], and many species of conservation concern had disappeared or seriously decreased in the field core but still persist in herbaceous field margins [START_REF] Fried | Arable weed decline in Northern France: Crop edges as refugia for weed conservation?[END_REF]. Field margins also host a significant farmland insect diversity [START_REF] Frouz | Spatial distribution of different life stages of one Dipteran community along hedgerow and field margin[END_REF][START_REF] Meek | The effect of arable field margin composition on invertebrate biodiversity[END_REF][START_REF] Olson | Management of field margins to maximize multiple ecological services[END_REF][START_REF] Smith | Can arable field margins be managed to enhance their biodiversity, conservation and functional value for soil macrofauna?[END_REF][START_REF] Smith | Can arable field margins be managed to enhance their biodiversity, conservation and functional value for soil macrofauna?[END_REF][START_REF] Merckx | Effect of field margins on moths depends on species mobility: field-based evidence for landscape-scale conservation[END_REF][START_REF] Kuussaari | Pollinator insects benefit from rotational fallows[END_REF]. Through the diversity of plants and invertebrates they host, field margins provide a wide range of food resources for higher taxa in the food chain like birds in both summer and winter periods (Vickery et al., 2002;[START_REF] Wuczyński | Applying red lists to the evaluation of agricultural habitat: regular occurrence of threatened birds, vascular plants, and bryophytes in field margins of Poland[END_REF][START_REF] Šálek | Bringing diversity back to agriculture: Smaller fields and non-crop elements enhance biodiversity in intensively managed arable farmlands[END_REF]. In addition, field margins are also known for playing a role as a refuge, a habitat and a corridor for small mammals [START_REF] Hof | Food or fear? Predation risk mediates edge refuging in an insectivorous mammal[END_REF][START_REF] Redon (de) | Road network in an agrarian landscape: Potential habitat, corridor or barrier for small mammals?[END_REF] and can even help to improve crop yields by providing natural pest control thanks to sheltered predators [START_REF] Marshall | Field margins in northern Europe: their functions and interactions with agriculture[END_REF][START_REF] Pywell | Wildlife-friendly farming increases crop yield: evidence for ecological intensification[END_REF]. Nevertheless, the interest of herbaceous field margins for bats has not been assessed yet.

The aim of the study was to measure the role of herbaceous field margins for bats compared to other landscape variables in agricultural landscapes. Insectivorous bats forage arthropod orders (e.g. Diptera, moths; Vaughan, 1997) whose abundance is known to be enhanced with the presence of herbaceous field margins surrounding crops [START_REF] Frouz | Spatial distribution of different life stages of one Dipteran community along hedgerow and field margin[END_REF][START_REF] Meek | The effect of arable field margin composition on invertebrate biodiversity[END_REF][START_REF] Olson | Management of field margins to maximize multiple ecological services[END_REF][START_REF] Merckx | Effect of field margins on moths depends on species mobility: field-based evidence for landscape-scale conservation[END_REF][START_REF] Kuussaari | Pollinator insects benefit from rotational fallows[END_REF]. We therefore assume that herbaceous field margins are an interesting source of food for bats, as it is already documented for birds (Vickery et al., 2009). Thus, considering potential roles of herbaceous field margins for concentrating prey resources, we hypothesized that bat species activity or bat community diversity would be (i) higher close to herbaceous field margins compared to (crop) field cores and (ii) positively correlated with the density of herbaceous field margins.

As bats have varied ecological requirements, they use landscape structures in different ways depending on their foraging and commuting strategies [START_REF] Korine | Guild structure, foraging space use, and distribution in a community of insectivorous bats in the Negev Desert[END_REF]. In particular, narrow-space foragers fly very close to vegetation structures and can be distinguished from open-space foragers that tend to commute and forage further away in open space. Thus, we also hypothesize that the positive effect of herbaceous field margins would be stronger for narrow-space foragers, known to fly closer to vegetation and to mainly follow linear landscape habitats, as Myotis or Plecotus species do [START_REF] Denzinger | Bat guilds, a concept to classify the highly diverse foraging and echolocation behaviors of microchiropteran bats[END_REF].

We finally expected that other landscape variables, such as hedgerows, forests or water bodies, should have at least equivalent or even stronger effects on bat activity compared to herbaceous field margins, as these are widely recognized as strong drivers of the composition and activity of the entire bat community (e.g. Heim et al., 2017).

In order to assess the importance of herbaceous field margins for bats, we used passive acoustic sampling of bats in an intensive agricultural landscape by simultaneously recording species activity and community diversity at different distances to herbaceous field margins, and along a gradient of relative density of herbaceous field margins. We also compared respective effects of herbaceous field margins with other landscape variables.

Material and methods

Study area

The study was conducted in France, in the Île-de-France region (Yvelines, Essonne and Seineet-Marne departments; Fig. 1a) in an intensively used agricultural landscape. https://www.data.gouv.fr/en/datasets/corine-land-cover-occupation-des-sols-en-france). The agricultural areas were dominated by arable land (90%) for intensive cropping (73 % of rapeseed, 7% of wheat, 3% of barley, based on manual mapping during the experiment in a 100 m radius around sampling sites). Crops were harvested before the study between the end of June and the beginning of July.

We defined field margins as an herbaceous linear structure with only spontaneous vegetation, without fertilizers or pesticides immediately applied, without trees or bushes, 2 ± 1 m wide, and that were surrounding fields.

Sampling design

The aim of the study was to investigate the potential of field margins to support bat activity compared to other landscape variables in agricultural landscapes. As a consequence, we chose the location of sampling sites in a way that limited correlations between field margins and other landscape variables to avoid statistical issues (see Statistical analysis section), and to maximise the gradient of each (Table S1; see also Fig. S1 for more information about gradients of landscape variables). The weather was highly stable and favourable to bats throughout the sampling period (see Table S2). We recorded bat activity on 112 sampling sites along a gradient of distances from 0 to 447 m from field margins (median: 60 m, median absolute deviation: 57 m) and along a gradient of relative density of field margins ranging from 0 to 64 m/ha (computed in different sizes of buffers around sampling sites, from 250 m to 4,000 m, see Landscape variable and Statistical analysis sections for more details about buffer sizes) (Fig. S1a). Each night, we sampled simultaneously three to 11 sites at different densities of and distances from field margins (see Figs. 1b & 1c for examples of sampling nights and Table S3 for more details about the field margin gradient each night), in order to limit potential correlations with temporal variables (i.e. weather, moon). Each site was sampled one time and echolocation calls were recorded on sites separated by at least 230 m from each other to avoid simultaneous recordings (see acoustic sampling section). Recorders were associated with a single microphone throughout the study and were homogeneously distributed between distances to the field margin and relative densities of field margins, in order to limit potential correlations between recorders and field margins variables (Table S4).

Acoustic sampling

Sampling was carried out over 17 nights from the 8th of July to the 2nd of August 2015, during the seasonal peak in bat activity. Recordings were performed during the entire night, from 30 min before sunset to 30 min after sunrise. Standardized acoustic bat survey was carried out using Song Meter SM2Bat+ and omnidirectional microphones SMX-US (Wildlife Acoustics Inc., Concord, MA, USA) put horizontally at one meter above the ground on a pole and pointed perpendicularly to the field margin. Recording was triggered automatically by ultrasounds between eight and 192 kHz, using a trigger level set to 6 dB Signal Noise Ratio and set to continue recording until two seconds after last trigger event, and a 384 kHz sampling rate, as widely used in many previous studies (e.g. [START_REF] Azam | Disentangling the relative effect of light pollution, impervious surfaces and intensive agriculture on bat activity with a national-scale monitoring program[END_REF]Millon et al., 2017;[START_REF] Barré | Tillage and herbicide reduction mitigate the gap between conventional and organic farming effects on foraging activity of insectivorous bats[END_REF][START_REF] Barré | Accounting for automated identification errors in acoustic surveys[END_REF].

As it is currently impossible to distinguish individual bats from their echolocation calls, it is not possible to count bat abundance. Instead, we used the number of bat passes recorded during a night as a measure of bat activity (however this metric is correlated to the abundance, see [START_REF] Mimet | Contribution of private gardens to habitat availability, connectivity and conservation of the common pipistrelle in Paris[END_REF]. We defined a bat pass as one or more echolocation call within a 5second interval, which is a commonly accepted standard in Europe (Stahlschmidt and Brühl;2012, Millon et al.;2015, Kerbiriou et al. 2018;;2019;[START_REF] Put | Bats respond negatively to increases in the amount and homogenization of agricultural land cover[END_REF].

We used the software TADARIDA [START_REF] Bas | Tadarida : A Toolbox for Animal Detection on Acoustic Recordings[END_REF] to automatically detect sound events and assign bat passes (11,324 bat passes for a total of ~450,000 calls) to the most accurate taxonomic level associated to a confidence score calculated by the software (i.e. the probability that a bat pass has been assigned to the right species). Two species groups were firstly constructed in order to limit identification errors: (i) a Myotis group (including Myotis nattereri, Myotis myotis, Myotis mystacinus and Myotis daubentonii) and (ii) a Plecotus group (including Plecotus austriacus and Plecotus auritus). In addition to these species-groups, we considered four species: (i) P. pipistrellus that are edge space foragers, (ii) Nyctalus leisleri and (iii) Nyctalus noctula that are open space foragers, and (iv) Eptesicus serotinus, that are edge space foragers [START_REF] Robinson | Home range and habitat use by the serotine bat, Eptesicus serotinus, in England[END_REF].

Given that automated identification can generate high error rates at species level, we followed the [START_REF] Barré | Accounting for automated identification errors in acoustic surveys[END_REF] approach proposing a cautious method to ensure results robustness against automated identification errors. The method allows to model the error rate for each species or group, according to confidence scores of automated identifications provided by the software TADARIDA [START_REF] Bas | Tadarida : A Toolbox for Animal Detection on Acoustic Recordings[END_REF]. This method allows to perform statistical analyses based on two levels of maximum error rate tolerance: first on a 0.5 maximum error rate tolerance and then by confirming results on a safer threshold of 0.1 maximum error rate tolerance. Indeed, each threshold of error rate tolerated in data involve different caveats which potentially induce biases in acoustic data (i.e. false positives and false negatives). The 0.5 maximum error rate tolerance keeps high number of bat passes for analysis including false positives, while a more restrictive 0.1 threshold limits false positives, but at the cost of discarding more true positives, which thus induce a loss of statistical power. As a consequence, checking the consistence in results whatever the threshold considered ensures robust conclusions (see [START_REF] Barré | Accounting for automated identification errors in acoustic surveys[END_REF] for more details).

Computing community metrics

In addition to bat species activity (i.e. a relative abundance metric), we used community metrics: the species richness and the community specialization index (CSI). Both metrics

were calculated from species level activity.

The species richness was computed as the total number of species recorded during one sampled night for each site.

The CSI follows a community trait-based approach already used for bats [START_REF] Regnery | Tree microhabitats as indicators of bird and bat communities in Mediterranean forests[END_REF]Kerbiriou et al. 2018;[START_REF] Lacoeuilhe | The relative effects of local and landscape characteristics of hedgerows on bats[END_REF]. It is calculated as the arithmetic mean of the 'Species Specialization Index' (SSI) weighted by the standardized number of bat species passes (Equation 1), as follows:

CSI j = (Eq. 1)
where n is the total number of species recorded, aij is the number of bat passes of the species in a given site j, and SSIi is the species specialization index.

The SSI is computed as the coefficient of variation of the number of bat passes across 20 habitat classes over France (ratio of the standard deviation to the mean, expressed as a percentage; [START_REF] Julliard | Spatial segregation of specialists and generalists in bird communities[END_REF]. A high SSI corresponds to a species specialized to a specific habitat, and in turn a high CSI means that the community tends to be composed of species specialized to a specific habitat. For the calculation we used data based on 3,596 sites from the French Bat Monitoring Program (http://www.vigienature.fr/fr/chauves-souris), for more details on SSI calculation see Supplementary material S1.

Landscape variables

In order to assess the effect of field margin distance and density on bat activity in comparison with other landscape variables, we focused on the importance of six landscape variables known to affect bat activity: hedgerows [START_REF] Lacoeuilhe | Effects of hedgerows on bats and bush crickets at different spatial scales[END_REF][START_REF] Froidevaux | Factors driving population recovery of the greater horseshoe bat (Rhinolophus ferrumequinum) in the UK: implications for conservation[END_REF], forests (Boughey et al., 2011;Heim et al., 2017), water bodies [START_REF] Sirami | Artificial wetlands and surrounding habitats provide important foraging habitat for bats in agricultural landscapes in the Western Cape, South Africa[END_REF]Heim et al., 2017;[START_REF] Conno (de | Testing the performance of bats as indicators of riverine ecosystem quality[END_REF], main roads [START_REF] Berthinussen | The effect of a major road on bat activity and diversity[END_REF][START_REF] Fensome | Roads and bats: A meta-analysis and review of the evidence on vehicle collisions and barrier effects[END_REF], urban areas [START_REF] Dixon | Relationship between land cover and insectivorous bat activity in an urban landscape[END_REF][START_REF] Azam | Disentangling the relative effect of light pollution, impervious surfaces and intensive agriculture on bat activity with a national-scale monitoring program[END_REF], grassland [START_REF] Lentini | Bats in a Farming Landscape Benefit from Linear Remnants and Unimproved Pastures[END_REF][START_REF] Roeleke | Habitat use of bats in relation to wind turbines revealed by GPS tracking[END_REF][START_REF] Froidevaux | Factors driving population recovery of the greater horseshoe bat (Rhinolophus ferrumequinum) in the UK: implications for conservation[END_REF]. These variables are reported as key habitats for bats by facilitating foraging or commuting between foraging sites and roosts [START_REF] Charbonnier | Numerical and functional responses of forest bats to a major insect pest in pine plantations[END_REF][START_REF] Pinaud | Modelling landscape connectivity for greater horseshoe bat using an empirical quantification of resistance[END_REF]. We distinguish main roads from field margins, which can also be on the edge of dirt pathways or between two crops.

We computed environmental variables using (i) the Euclidean distance in meters from the sampling site to the nearest hedgerow, forest, main road, urban area and water body, (ii) the relative density of a linear element in meters per hectare for hedgerows and main roads, and

(iii) the percentage of land covered by forests, urban areas and grasslands in different buffer sizes (see below). Variables on forests, grasslands, urban areas and water bodies were calculated from the CES OSO land cover map 2016 [START_REF] Derksen | Geometry Aware Evaluation of Handcrafted Superpixel-Based Features and Convolutional Neural Networks for Land Cover Mapping Using Satellite Imagery[END_REF] [START_REF] Li | The adequacy of different landscape metrics for various landscape patterns[END_REF].

As the study area was an intensive agricultural landscape, crops covered the biggest part of land around each sampling site. Thus, in our study case, we interpreted an increase of landcover Shannon diversity index (e.g. by switching from the landscape in Fig. 1b to the landscape in Fig. 1c) as an increased of semi-natural habitat amount and diversity in the landscape. Furthermore, we added two other supplementary covariates to account for the agricultural context: the number of different crops and the percentage of rapeseed crops.

These two variables were manually mapped during the experiment in a 100 m radius around each site at the sampling date. Concerning the rapeseed percentage, we choose to include it as this was the highly dominant crop type around sampling sites (73.30 ± 35.97 %) known to concentrate much more diptera that are widely consumed by bats (Vaughan, 1997) than other types of crops [START_REF] Holland | Farmland bird invertebrate food provision in arable crops[END_REF].

Given that bat response to landscape variables at different scales (e.g. Boughey et al., 2011;[START_REF] Kalda | Multi-scale ecology of insectivorous bats in agricultural landscapes[END_REF][START_REF] Lacoeuilhe | Effects of hedgerows on bats and bush crickets at different spatial scales[END_REF][START_REF] Put | Bats respond negatively to increases in the amount and homogenization of agricultural land cover[END_REF], we computed the land-cover Shannon diversity index, density and proportion variables at 250, 500, 750, 1000, 1500, 2000 and 4000 m radii around each sampling site. The 250 m radius was the smallest one maintaining enough variability to perform analysis, while the 4000 m one covered the foraging area of the most common bat species Pipistrellus pipistrellus [START_REF] Nicholls | Habitat selection as a mechanism of resource partitioning in two cryptic bat species Pipistrellus pipistrellus and Pipistrellus pygmaeus[END_REF]. Thus, the land-cover Shannon diversity index and each density and proportion variables were calculated on seven buffer sizes for each sampling site (Table S1), in order to use the best scale for each variable in statistical analysis (see statistical analysis section).

According to [START_REF] Zuckerberg | A Review of Overlapping Landscapes: Pseudoreplication or a Red Herring in Landscape Ecology?[END_REF] the risk of pseudo-replication related to this method due to potential overlapping landscapes between sites does not constitute a violation of independence.

Statistical analysis

We performed generalized linear mixed models (GLMM, R package lme4) to test for potential differences in bat activity among a continuous gradient of distances to field margins and relative density of field margins. We also tested for landscape composition (see Sampling design and Landscape variables sections for a description of gradients). We built full models using bat activity (i.e. the number of bat passes) as response variable for each group or species, associated to a negative binomial error distribution in order to avoid overdispersion (the ratio of deviance to the number of degrees of freedom was between 0.82 and 1.20 for full models). As the distance to field margins and the relative density of field margins were correlated (r spearman = -0.6) we built for each species two full models, one (i) including the distance to field margins as fixed effect and another (ii) including the relative density of field margins as fixed effect. For both, we included standardized (i.e. by subtracting the mean and dividing by the standard deviation) landscape covariates (i.e. water bodies, hedgerows, main roads, grasslands, forests, urban areas, rapeseeds, number of different crops and land-cover Shannon diversity index variables). For each of these variables and each group or species we previously selected the radius having the smaller corrected Akaike Information Criterion (AICc) using univariate GLMMs, in order to include only one scale of each variable in full models. We also checked the potential need for adding quadratic effect by visual inspection of generalized additive models, i.e. in case of non-linear relationship (GAM, R package mgcv;

see Tables 1 and2 for variables which required quadratic effects). According to the sampling design (i.e. simultaneous recordings of bat activity at 3 to 11 sites on the same night), we included the date in models as a random effect to account for inter-night variations. For each landscape covariate we never included distance and proportion/density variations of the same variable in the same models as they were highly correlated (i.e. an r > 0.7 for forests, main roads, urban areas and hedgerows; see Table S5). We also checked for spearman correlations between covariates (Table S5), and we never included simultaneously in the model two variables with an r > 0.7. In addition, no variable was correlated with the sampling date (Table S5). Then, we checked for multicollinearity problems by calculating variance inflation factors (VIF) using the check_collinearity function (R package performance) on each full model. All variables showed a VIF value < 2, meaning there was no evidence of multicollinearity [START_REF] Chatterjee | Regression analysis by example[END_REF]. These steps allowed to build two full models (i.e.

one including the distance to field margins and another including their relative density) per group or species.

Based on each full model, we generated a set of candidate models containing all possible variable combinations ranked by corrected Akaike Information Criterion (AICc) using the dredge function. It should be noted that while we used the dredge function, we did not follow a "data dredging" approach, because we initially used variables known to drive bat activity from literature while selecting their more relevant scale of computing. Variables and their scale of computing were thus not randomly selected. So, we used the dredge function in our workflow in order to (i) never simultaneously include together correlated covariates mentioned above in a same model, (ii) avoid overparameterization due to a limited dataset For each set of candidate models, we did multi-model inference averaging on a delta AICc < 2 using the model.avg function to obtain an averaged regression coefficient, associated standard error and p-value for each fixed effect (R package MuMln; [START_REF] Barton | MuMIn: Multi-Model Inference[END_REF]. From multi-model inference averaging we also extracted the relative importance value for each variable defined as the sum of model weights over all candidate models including the variable [START_REF] Barton | MuMIn: Multi-Model Inference[END_REF]. All averaged candidate models always had much lower AICc than null models (i.e. a difference of 8 to 52 points between candidate models AICc and null models AICc; Table S6). We compared effect size of field margins with other variables when significant using estimates from models. We only compared comparable variables, i.e. the relative density of field margins with other density/proportion variables having comparable gradients (Fig. S1).

Finally, in order to check if results were the same when considering constant radius size for variables calculation instead of best ones based on AIC, we re-ran analysis for species significantly affected by field margins by fixing calculation of each landscape covariates at the same buffer size as the relative density of field margin. Although we carried out the study in a way that limited biases (i.e. under stable and favourable weather conditions (Table S2) which were accounted for by including date as a random effect, and by randomizing bat recorder identifier throughout the field margin gradient (Table S4)), we re-ran the modelling procedure by including recorder identifier as a random effect and the maximum wind speed and minimum temperature as fixed effects to ensure these do not change results. All analyses were performed using a significant threshold of 5% in R statistical software v.3.6.2.

Results

Results presented below are based on the 0.5 maximum error rate tolerance threshold and are consistent when applying a 0.1 threshold (Tables S7 &S8).

Bat monitoring

We recorded 11,445 bat passes in the 112 study sites. The most abundant species was P.

pipistrellus, representing 81% of the total activity and occurring in 92% of sites. Other groups or species were less abundant, each of them having an activity representing less than 6% of the total bat activity. N. leisleri and the Myotis group were detected in 45% of sites and E. serotinus, N. noctula and Plecotus group in 43%, 35% and 27% of sites, respectively (Table 1). Across all sites, the mean species richness was 4.8 (standard deviation: 2.6).

Effect of distance to field margins

The activity of the Myotis group decreased with the distance to field margins (Table 2; Fig. 2).

Models detected a 50% loss of the Myotis group activity at 117 m compared to 0 m from field margins, while this distance was 303 m when we looked at the Myotis group response to hedgerows (Table 2; Fig. S2). The community metrics and the activity of species other than Myotis sp. were not affected by field margins (Tables 1 &2). Except for the Myotis group, the distance to field margins exhibited low relative importance values, and was thus rarely selected in candidate models with higher Akaike weights (Tables 1 &2).

Effect of relative density of field margins

We found a significant positive effect of the relative density of field margins on the activity of the Myotis group while no effect was found for the other species and community metrics (Tables 1 &2; Fig. 2). Except for Myotis group, the relative density of field margins variable exhibited low relative importance values and was thus little selected in candidate models (Tables 1 &2).

The effect size of the relative density of field margins for the Myotis group was higher than other density or proportion of other landscape variables (i.e. the relative density of main roads, the proportion of urban areas and the rapeseed percentage) (Table1; Fig3).

When all proportion and density covariates were fixed to the same buffer size as the best one selected for the relative density of field margins, results (i.e. model estimates) were the same as those for which variables were calculated in the most appropriate buffer size (Table S9).

Effect of other environmental covariates

Concerning the influence of the other landscape covariates on open-space forager activity, N.

noctula activity decreased with the distance to water bodies and the density of hedgerows, while we found the activity of this species increased with the proportion of forest land cover and the land-cover Shannon diversity index (Table 2; Fig. 2). N. leisleri and E. serotinus activity decreased with the distance to forests and the rapeseed percentage, while E. serotinus activity increased with the land-cover Shannon diversity index (Table 2; Fig. 2).

Concerning the influence of these landscape variables on the activity of edge-space foragers, P. pipistrellus activity decreased with the distance to hedgerow, the distance to forest and the rapeseed percentage while we found the activity of this species increased with the proportion of grassland land cover (Table 2; Fig. 2).

We found narrow-space foragers activity to be also affected by these landscape variables,

given that the Myotis group activity decreased with the distance to forests, the distance to hedgerows and the rapeseed percentage (Table 2; Fig. 2). We also found that the Plecotus group activity decreased with the proportion of urban land cover and increased with the proportion of grassland land cover (Table 2). Finally, we found that the Myotis group activity increased with the land-cover Shannon diversity index and the relative density of main roads (Table 2; Fig. 2).

Concerning community metrics, we found that the Community Specialization Index increased with the proportion of forest land cover, the distance to hedgerow and the land-cover Shannon diversity index, and decreased with the proportion of grassland cover (Table 3; Fig. 4). We also found that the species richness increased with the proportion of forest land cover, the land-cover Shannon diversity index and is maximised by a balanced percentage of rapeseed (~50%), and decreased with the distance to water bodies and hedgerows (Table 3).

Regarding the overall bat community, some landscape covariates such as those related to forests, hedgerows or land-cover Shannon diversity index almost systematically exhibited high relative importance values, and were thus often selected in candidate models unlike field margin variables (Tables 1 &2). In addition, these variables also almost systematically drove bat activity, species richness and community specialization index: forests significantly affected four out of six species and both community metrics, hedgerows and land-cover Shannon diversity index significantly affected three out of six species and both community metrics (Tables 1 &2).

Finally, including recorder identifier as a random effect and the maximum wind speed and minimum temperature as fixed effects did not change results (Table S10).

Discussion

Among six species-group's activity levels and two community metrics, we only found an effect of field margins on the activity of the Myotis group (i.e. narrow-space foragers).

Specifically, the Myotis group activity decreased with distance to field margins (i.e. towards field crop cores), and increased with relative density of field margins. These responses had a comparable or even a higher effect size than other landscape variables such as the distance to forest, the relative density of main roads, the proportion of urban areas or the rapeseed percentage. However, results also showed that unlike field margins, other landscape covariates such as forests, hedgerows or land-cover Shannon diversity index almost systematically drove bat activity, species richness and community specialization index. Our results thus highlight that herbaceous field margins can have a positive effect on the activity of narrow-space bat foragers, but do not replace other landscape variables that almost systematically drive bat activity, community richness and specialization of bat community to specific habitat.

Even though results about the relative density of field margins were highly consistent when we checked it at the lower maximum error rate tolerance in data, the effect of the distance to field margins switched from a significant to a close to the significant p-value. We thus have to be cautious with these results as the change in p-value could either be due to an environmental bias in our acoustic data or to a too great loss of occurrence when applying the maximum error rate tolerance of 0.1 [START_REF] Barré | Accounting for automated identification errors in acoustic surveys[END_REF] which discarded one third of passes in this study. Thus, the effect of the distance to field margins on the activity of the Myotis group should be confirmed in further studies with greater data set.

Mechanism hypotheses and limitations

The effect of herbaceous field margins on bats was so far unknown. We only found an effect for the Myotis group, which mostly commute and forage by flying close to semi-natural habitats [START_REF] Denzinger | Bat guilds, a concept to classify the highly diverse foraging and echolocation behaviors of microchiropteran bats[END_REF][START_REF] Roemer | Bat sonar and wing morphology predict species vertical niche[END_REF]. To explain this response, we could hypothesize that herbaceous field margins can increase the abundance of prey such as moths [START_REF] Merckx | Effect of field margins on moths depends on species mobility: field-based evidence for landscape-scale conservation[END_REF][START_REF] Kuussaari | Pollinator insects benefit from rotational fallows[END_REF] or Coleoptera [START_REF] Smith | Can arable field margins be managed to enhance their biodiversity, conservation and functional value for soil macrofauna?[END_REF], which are consumed by Myotis species (Vaughan, 1997;[START_REF] Ware | Guano morphology has the potential to inform conservation strategies in British bats[END_REF]. However, such prey are also widely consumed by other bat species, especially Plecotus group. Further studies are thus needed to understand why we did not detect any effect on this genus.

A second hypothesis to explain these field margins effects could be that the presence of field margins mostly induces a physical break between field crops and other landscape elements.

Indeed, when a field margin is located at the edge of a crop there is potentially a difference in vegetation height between these two elements, which generates a relief that could facilitate orientation by echolocation for bats that would in turn move along it. This is especially true for narrow-space foragers, which fly closer to vegetated structures [START_REF] Schnitzler | Echolocation by Insect-Eating Bats: We define four distinct functional groups of bats and find differences in signal structure that correlate with the typical echolocation tasks faced by each group[END_REF][START_REF] Denzinger | Bat guilds, a concept to classify the highly diverse foraging and echolocation behaviors of microchiropteran bats[END_REF] and preferentially use linear structures to move from one habitat patch to another [START_REF] Frey-Ehrenbold | Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices[END_REF][START_REF] Pinaud | Modelling landscape connectivity for greater horseshoe bat using an empirical quantification of resistance[END_REF], and rarely fly over crop field cores [START_REF] Wickramasinghe | Bat activity and species richness on organic and conventional farms: impact of agricultural intensification[END_REF].

For the Plecotus group, which consists of narrow-space foragers, we cannot exclude that the absence of response to field margins could be linked to a lack of data (0.5 passes per site in average). Thus, further studies should investigate the response to field margins in a study area where this group is more abundant. Concerning results about edge-space foragers such as P.

pipistrellus, the main taxa they consumed, Diptera [START_REF] Beck | Fecal analyses of European bat species[END_REF], is reported to be enhanced by field margin areas at larval state, but not at flying adult state eaten by bats [START_REF] Frouz | Spatial distribution of different life stages of one Dipteran community along hedgerow and field margin[END_REF]. Concerning edge to open-space foragers, we did not detect any effect while we could expect a positive one on E. serotinus. Further studies are needed to explore the links between their diet and agricultural habitats. They are more specialized on big prey from different Coleoptera families (e.g. Scarabaeidae, Carabidea, Cerambycidea;Vaughan, 1997) whose abundance is potentially lower at field margins than wooded habitats or crops (e.g. French & Eliott, 1999;[START_REF] Spector | Rapid Turnover and Edge Effects in Dung Beetle Assemblages (Scarabaeidae) at a Bolivian Neotropical Forest-Savanna Ecotone[END_REF]. In addition, one study found field margins to be less attractive for smaller Coleoptera than field crops [START_REF] Olson | Management of field margins to maximize multiple ecological services[END_REF].

Concerning open-space foragers, N. leisleri and N. noctula, the absence of field margin effect may be due to their call detection distance by recorders. These species fly high and fast, and their echolocation calls can easily carry to more than 100 m (while, in comparison, it is only 15 m for Myotis group; Denzinger & Schnitzer 2013;[START_REF] Barataud | Acoustic ecology of European bats; species identification, study of their habitats and foraging behaviour. Biotope Editions, Mèze[END_REF]. Thus, a bat pass from Nyctalus species detected by the recorder can therefore correspond to a bat close to the recorder (and in turn close to the field margin), or to a bat 100m further away (i.e. far from the field margin). It is difficult to distinguish at this scale the effects of the different local habitats.

Another non-exclusive hypothesis is that the flight height niche of open-space foragers may explain the absence of response to field margins for this guild. Indeed, to forage at field margins bats are likely forced to fly close to the ground, while open-space foragers forage at high altitude and likely do not exploit insects at field margins [START_REF] Roemer | Bat flight height monitored from wind masts predicts mortality risk at wind farms[END_REF].

Overall, our study does not provide information on how bats deal with field margins in term of behavioural reactions, which could help in further studies to better understand the significant responses and no responses. Indeed, the number of bat passes does not necessarily provide a full picture of responses since bat activity metric does not allow to distinguish foraging from commuting [START_REF] Charbonnier | Numerical and functional responses of forest bats to a major insect pest in pine plantations[END_REF].

Finally, other landscape variables we used as covariates in our models affected bats similarly to what is known in the literature. Indeed, the activity of species or groups and the species richness were found to be positively correlated with the proportion of grasslands [START_REF] Froidevaux | Factors driving population recovery of the greater horseshoe bat (Rhinolophus ferrumequinum) in the UK: implications for conservation[END_REF], the relative density of hedgerows [START_REF] Lacoeuilhe | Effects of hedgerows on bats and bush crickets at different spatial scales[END_REF] and the land-cover Shannon diversity index (Monk-Whipp, 2018); and negatively correlated with the proportion of urban areas [START_REF] Azam | Disentangling the relative effect of light pollution, impervious surfaces and intensive agriculture on bat activity with a national-scale monitoring program[END_REF] and the distance to water bodies (Heim et al., 2017), to forests (Heim et al., 2017) and to hedgerows [START_REF] Lacoeuilhe | Effects of hedgerows on bats and bush crickets at different spatial scales[END_REF]. Such congruence reinforces our finding about the similar size effect of the relative density of field margins compared to the relative density of main roads, the proportion of urban areas and the rapeseed percentage. At the bat community level, some landscape covariates (i.e. variables related to forests, hedgerows and the land-cover Shannon diversity index) almost systematically affected species activity and community metrics, unlike field margin variables which only affected Myotis group. In comparison, hedgerows even drove the Myotis group activity at a much greater distances than field margins (i.e. a difference of 186 m; Fig. S2). We also found that when the land-cover Shannon index or the proportion of forest land cover increase, or when the proportion of grassland decreases, the community tends to be composed of species specialized in a specific habitat. We cannot compare these results with other studies since, to our knowledge, none have tested such relationships. Given that we assume that an increase in the land-cover Shannon index would mainly induce an increase of the proportion of forest land cover, we hypothesize that in turn it would enhance the activity of narrow-space foragers (Plecotus and Myotis groups) having higher Species Specialization Indices (see Supplementary material S1). Concerning grasslands, the results could be explained by their positive effect on the dominant species activity (P. pipistrellus) which have a low species specialization index (see Supplementary material S1). Finally, it should be noted that found positive effects of main roads are likely the reflect of road margin characteristics (e.g. the presence of vegetation or physical break with adjacent habitat), which could enhance foraging or commuting of bats along it though greater food resource or easier echolocation.

Perspectives and implications

Our study demonstrates that when studying bat responses to landscape variables and hence to composition of intensive agricultural landscapes, incorporating information about herbaceous field margins in addition to other key landscape variables could help to improve the understanding of, at least, narrow-space forager activity. Moreover, although field margins constitute one of the easier ways to improve landscape attractiveness for Myotis sp., thanks to their low monetary cost (e.g. 5 to 25 times less expensive than hedgerows, BASF SE, 2018; PNRHL, 2014) and constraints (e.g. little shadow over crops, little management required and little growth delay), they have a lowerquite marginalinterest for the whole bat community compared to other habitats such as hedgerows, forests or water bodies. Although field margins can be useful at least for narrow-space foragers, they cannot be considered as an equivalent alternative to these other key landscape variables, which, unlike field margins, almost systematically drove bat activity and community.

(

  112 sites) by forcing each candidate model not to exceed ten degrees of freedom. Then, if spatial autocorrelation in residuals of each best model (R package spatial, Moran's I test) was detected, we took it into account by adding a spatial correction variable to the full model (autocov_dist function, R package spatial).

Figure 1 .

 1 Figure 1. Land cover map of the study area showing sampled sites (A) and examples of two sampling nights: one in a low diversified landscape (land-cover Shannon diversity index of 0.07; B) and one in a more diversified landscape (land-cover Shannon diversity index of 1.08; C).

Figure 2 .

 2 Figure 2. Predicted relationships between the number of bat passes per night and the distance to field margins (B), the relative density of field margins in a 250 m buffer size (E) and others significant environmental covariates with a p-value<0.01 (A, C-D, F-I) for E. serotinus (A), Myotis group (B-E), N. leisleri (F), N. Noctula (G) and P. pipistrellus (H-I); and associated 95% confidence intervals from best models of the multi-model inference averaging. Green plots show results on field margins.

Figure 4 .

 4 Figure 4. Predicted relationships between the Community Specialization Index (CSI; A-C) or the species richness (D-E) and most significant environmental covariates (p-value<0.01), and associated 95% confidence intervals from best models of the multi-model inference averaging.

  In addition, with the aim to take into account landscape diversity, we computed a land-cover Shannon diversity index known to affect bat activity in agricultural contexts[START_REF] Monck-Whipp | Farmland heterogeneity benefits bats in agricultural landscapes[END_REF], calculated from water bodies, urban, forests, grasslands, arable lands, orchard/vineyard and moorlands cover (i.e. all land cover habitats available from CES OSO map). Depending on the context, Shannon's index applied to landscape metrics can be used as a fragmentation metric(Shiliang et al., 2014) or as a diversity metric

	and main road
	variables from the French National Institute of Geography (www.ign.fr; BD CARTO map)
	using R and QGIS 3.10 software. Hedgerows and field margins data were provided by the

French Institute of Development and Urbanism of the Ile-de-France region (www.iau-idf.fr; ECOLINE map), and were validated by checking of aerial images and completed manually if needed.

Table 1 .

 1 Number of bat passes per species/groups and the corresponding percentage of total passes and occurrences (i.e. percentage of sites with at least one bat pass of the considered species) according to the maximum error risk tolerance applied for data selection (maximum error risk tolerance of 0.5 and 0.1).

	Species	Number of passes recorded	% of total passes	Occurrence (%)
		0.5	0.1	0.5	0.1	0.5	0.1
	Pipistrellus pipistrellus	8,039	7,215	81	86	92	91
	Nyctalus leisleri	531	433	5	5	45	39
	Myotis sp.	305	205	3	2	45	34
	Eptesicus Serotinus	397	247	4	3	43	33
	Nyctalus noctula	166	136	2	2	35	31
	Plecotus sp.	56	23	< 1	< 1	27	13

Table 2 .

 2 Results of the relationship between the number of bat passes and the distance to field margins or the relative density of field margins and other landscape covariates, using a multimodel inference averaging of candidate models in a delta AICc < 2. For each group or species estimates from scaled variables, associated standard errors (SE), significance and relative importance value are shown. Distances are in meters, relative density in meters per hectare and habitat land cover in percentage. Variables not presented were not selected in the multimodel inference averaging. Significant effects are shown in bold.

		Distance to field margin model	Relative density of field margin model
	Variables			Relative			Relative
		Estimate ± SE p-value	importance	Estimate ± SE	p-value	importance
				value			value
	Eptesicus serotinus
	Intercept	-0.345 ± 0.590 0.559		-0.340 ± 0.590	0.564
	Distance to field margin	0.186 ± 0.175	0.289	0.08
	Distance to water bodies	-0.137 ± 0.169 0.417	0.06	-0.137 ± 0.169	0.417	0.07
	Distance to hedgerow	0.197 ± 0.180	0.274	0.63	0.196 ± 0.181	0.280	0.60
	Distance to main road	-0.069 ± 0.146 0.635	0.30	-0.069 ± 0.146	0.635	0.33
	Distance to forest	-1.665 ± 0.522 0.001	1.00	-1.682 ± 0.524	0.001	1.00
	Distance to forest ^2	1.123 ± 0.404	0.005	1.00	1.137 ± 0.406	0.005	1.00
	Distance to urban area	0.304 ± 0.180	0.092	0.86	0.298 ± 0.180	0.099	0.85
	Relative density of hedgerow -750 m	-0.174 ± 0.202 0.390	0.37	-0.174 ± 0.202	0.390	0.40
	Relative density of main roads -4,000 m	0.148 ± 0.170	0.382	0.70	0.146 ± 0.171	0.392	0.67
	Urban area land cover -500 m	0.247 ± 0.191	0.195	0.14	0.247 ± 0.191	0.195	0.15
	Grassland land cover -4,000 m	0.136 ± 0.167	0.414	0.06	0.136 ± 0.167	0.414	0.07
	Land-cover Shannon diversity index -250 m	0.687 ± 0.825	0.405	0.68	0.641 ± 0.839	0.445	0.65
	Land-cover Shannon diversity index^2 -250 m -1.394 ± 0.651 0.032	0.55	-1.386 ± 0.656	0.035	0.51
	Myotis group
	Intercept	-0.062 ± 0.265 0.814		-0.150 ± 0.196	0.445
	Distance to field margin	-0.626 ± 0.226 0.006	1.00
	Distance to hedgerow	-0.534 ± 0.255 0.036	1.00	-1.139 ± 0.685	0.097	1.00
	Distance to hedgerow^2				1.137 ± 0.666	0.088	0.57
	Distance to forest	-0.543 ± 0.318 0.087	0.73	-0.857 ± 0.273	0.002	1.00
	Distance to urban area				0.192 ± 0.191	0.315	0.23
	Relative density of field margins -250 m				0.656 ± 0.191	< 0.001	1.00
	Relative density of main roads -1, 000 m	0.476 ± 0.179	0.008	1.00	0.394 ± 0.153	0.010	1.00
	Urban area land cover -250 m				-0.144 ± 0.139	0.298	0.23
	Land-cover Shannon diversity index -500 m	1.830 ± 0.626	0.003	1.00
	Land-cover Shannon diversity index^2 -500 m

-1.531 ± 0.566 0.007 1.00

	Rapeseed percentage	-0.382 ± 0.250 0.127	0.53	-0.346 ± 0.167	0.038	0.91
	Number of different crops	-0.389 ± 0.267 0.145	0.35
	Nyctalus leisleri
	Intercept	-0.013 ± 0.583 0.982		-0.017 ± 0.581	0.977
	Distance to field margin	0.229 ± 0.151	0.129	0.43
	Distance to water bodies	-0.120 ± 0.158 0.448	0.08	-0.102 ± 0.158	0.520	0.07
	Distance to forest	-1.087 ± 0.389 0.005	1.00	-1.101 ± 0.386	0.004	1.00
	Distance to forest^2	0.970 ± 0.301	0.001	1.00	0.976 ± 0.300	0.001	1.00
	Relative density of hedgerow -2,000 m	-0.133 ± 0.158 0.400	0.12	-0.093 ± 0.159	0.560	0.07
	Relative density of main roads -750 m	0.067 ± 0.126	0.595	0.04	0.067 ± 0.126	0.595	0.07
	Grassland land cover -4,000 m	0.159 ± 0.165	0.337	0.10	0.154 ± 0.167	0.359	0.09
	Land-cover Shannon diversity index -250 m	0.236 ± 0.161	0.143	0.40	0.210 ± 0.161	0.190	0.31
	Rapeseed percentage	-0.337 ± 0.161 0.037	0.82	-0.334 ± 0.167	0.045	0.78
	Number of different crops	0.251 ± 0.162	0.121	0.32	0.257 ± 0.163	0.114	0.40
	Nyctalus noctula
	Intercept	-0.556 ± 0.459 0.226		-0.483 ± 0.425	0.256
	Distance to field margin	0.441 ± 0.240	0.066	0.70

Acknowledgments

This work was supported by DIM ASTREA grants from Region Ile-de-France and by the Fondation pour la Recherche sur la Biodiversité. We thank IN2P3 Computing Centre for providing facilities to process and archive in the long-term all the recordings of this study, and Didier Bas for help in this process. We are also grateful for Léa Mariton help with proofreading, and we also especially thank the farmers who agreed to participate in the study.

Data accessibility

Data used for analyses will be available on dedicated platform.

Author's contribution K.B. conceived the ideas, designed the methodology and collected the data. C.B., K.B., C.K. and I.L. analysed and interpreted results; all authors led the writing of the manuscript. All authors contributed to the drafts and gave their final approval for publication.

Conflict of Interest

We declare we have no competing interest. Stahlschmidt, P., Brühl, C.A., 2012