Isis Poinas
email: isispoinas@gmail.com

Guillaume Fried

Laura Henckel

INRAE Christine N Meynard

Agricultural drivers of field margin plant communities are scale-dependent

Keywords:

In recent decades, agricultural intensification has led to a strong decline in biodiversity. Field margins act as shelters and dispersal corridors for biodiversity in highly disturbed landscapes, and are critical to the maintenance of ecosystem services. However, they are also impacted by agricultural practices in neighboring fields. Agricultural impacts are often studied at field to landscape scales, and rarely across biogeographic regions. One of the challenges in large-scale studies is the lack of standardized monitoring schemes including both biodiversity and accurate estimation of agricultural practices. Here, we take advantage of a national monitoring scheme in 462 sites in France, to assess the effects of agricultural practices on field margin flora at different extents and resolutions. We used spatial simultaneous autoregressive and generalized dissimilarity models to assess the response of plant richness and composition to climatic, soil and landscape conditions, and to agricultural (fertilization, herbicides) and margin management drivers. Analyses were repeated at the site-level, 40 and 75 km resolutions, and at regional and national extents. We found that the impact of agricultural practices on species richness was most important at the site-level, whereas climate and crop diversity became more important at the 75 km resolution. Compositional variations responded differently, with climate being more important at the site-level, and fertilization and crop diversity at the coarsest resolution. There was a strong variation in the variance explained by models among regions, but climate effects were weaker within biogeographic units compared to the national level, and different agricultural practices stood out as influential in different regions, suggesting that the regional context is fundamental in determining plant community structure. To efficiently conserve biodiversity, we therefore recommend the implementation of agricultural measures adapted to each region.

Introduction

Since the second half of the 20th century, agricultural intensification has resulted in steady declines in biodiversity across a wide range of taxa and habitats [START_REF] Emmerson | How Agricultural Intensification Affects Biodiversity and Ecosystem Services[END_REF]. The mechanization of agriculture and the resulting aggregation of cropped fields has contributed to the loss of semi-natural areas, such as herbaceous field margins, defined here as uncultivated vegetated strips located between the cropland and the adjacent habitat. Despite their modest area in agroecosystems, field margins play an important ecological role as sources of food, habitat and dispersal corridors for endangered species [START_REF] Storkey | The impact of agricultural intensification and land-use change on the European arable flora[END_REF], pollinators [START_REF] Zamorano | Field margin floral enhancements increase pollinator diversity at the field edge but show no consistent spillover into the crop field: A meta-analysis[END_REF] and pest predators [START_REF] Woodcock | Spill-over of pest control and pollination services into arable crops[END_REF]. Locally, they also buffer pesticide and fertilizer drift into adjacent terrestrial and aquatic habitats [START_REF] Haddaway | The multifunctional roles of vegetated strips around and within agricultural fields[END_REF]. Their importance has been increasingly recognized in recent European agricultural policies, for example through the conditioning of a green payment provided that margin strips are preserved [START_REF] Matthews | Greening agricultural payments in the EU's Common Agricultural Policy[END_REF]. Field margins also represent ecotones, with a mix of weed and ruderal species, which are well adapted to agricultural disturbances, along with some other species that are more typical of grasslands and forests [START_REF] Aavik | Agrotolerant and high nature-value species-Plant biodiversity indicator groups in agroecosystems[END_REF]. All these characteristics make field margins a valuable environment to study the unintended impacts of agriculture on biodiversity.

At the field scale, agricultural practices play a key role in shaping the species distribution and diversity of plants in field margins [START_REF] Marshall | Field margins in northern Europe: Their functions and interactions with agriculture[END_REF]. Chemical inputs such as pesticides and fertilizers often drift to adjacent habitats creating unintended disturbances in field margin communities. Practices such as sowing date, fertilization level, herbicide use and management of the margin flora using mowing, herbicides or grazing, vary by crop and production type, selecting species that are adapted to these disturbances [START_REF] Fried | Assessing non-intended effects of farming practices on field margin vegetation with a functional approach[END_REF][START_REF] Bassa | Effects of farming management and landscape heterogeneity on plant species composition of Mediterranean field boundaries[END_REF]. Agricultural practices also impact dispersal dynamics [START_REF] Tscharntke | Landscape perspectives on agricultural intensification and biodiversity-Ecosystem service management[END_REF]. At the landscape scale, the proportion and diversity of non-crop habitats [START_REF] Rader | Organic farming and heterogeneous landscapes positively affect different measures of plant diversity[END_REF] and crop diversity [START_REF] Sirami | Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions[END_REF] have positive effects on field margin biodiversity. At the regional and national scale, cropping systems are defined by the sequence of crop successions along with the cultural practices associated with each crop [START_REF] Leenhardt | Describing and locating cropping systems on a regional scale. A review[END_REF], which are themselves correlated to climate and topography for example. In France, this has been the basis to define stable uniform agricultural units called "agricultural regions" [START_REF] Richard-Schott | Le Recensement Général de l'Agriculture de 1955, une référence pour les géographes? Géocarrefour[END_REF]. Long-term practices over these homogeneous regions could potentially influence the available species pool at larger spatial and temporal scales. It is therefore possible that a given agricultural practice has different local effects on biodiversity depending on the regional species pool [START_REF] Cornell | What Are Species Pools and When Are They Important? Annual Review of Ecology[END_REF]. Considering local and regional effects at different scales is therefore important to have a better understanding of agricultural practices that could favor biodiversity.

In ecology, spatial scale is usually defined by two attributes: resolution (or grain) and extent, both of them having a strong influence on the processes unveiled. Spatial grain is the size of the unit of analysis. It is often assumed that coarser resolutions allow ignoring local stochasticity to focus on the main patterns structuring communities [START_REF] Chase | Spatial scale resolves the niche versus neutral theory debate[END_REF]. For example, landscape variables are expected to have the greatest influence when the grain size is closest to the average area of the different habitats. However, using excessively coarse resolutions may lead to uninformative patterns [START_REF] Viana | Spatial scale modulates the inference of metacommunity assembly processes[END_REF]. Spatial extent, on the other hand, is the total area covered by the sampling. It determines the range of variability in some environmental gradients, as well as the potential of inclusion of dispersal processes [START_REF] Viana | Spatial scale modulates the inference of metacommunity assembly processes[END_REF]. Naturally, local filters such as farming practices are easier to detect at small extents and within biogeographic regions, where different study sites are more likely to represent homogeneous conditions for large-scale gradients such as climate [START_REF] Viana | Assembly mechanisms determining high species turnover in aquatic communities over regional and continental scales[END_REF].

Few studies have examined the integration of several spatial scales in agroecosystems. Some have suggested that agricultural effects could prevail at fine spatial grains. For instance, [START_REF] Guerrero | Non-linear responses of functional diversity and redundancy to agricultural intensification at the field scale in Mediterranean arable plant communities[END_REF] found that weed species richness in Spain decreased linearly with field-level intensification, but remained constant at the landscape level. However, [START_REF] Berquer | Landscape Is the Main Driver of Weed Assemblages in Field Margins but Is Outperformed by Crop Competition in Field Cores[END_REF], focusing at a resolution under 1 km, found the opposite pattern, with field margin communities showing a greater influence of landscape than agricultural practices. [START_REF] Concepción | Interactive effects of landscape context constrain the effectiveness of local agrienvironmental management: Landscape constrains the effectiveness of local management[END_REF] revealed that extensive farming has a weaker positive impact on weed diversity at the field center and inner field edge when the landscape is less diversified within a 500 m radius. This suggests that landscape-scale simplification or intensification may result in a depletion of the regional species pool, which then becomes primarily composed of species adapted to agricultural disturbances. Regarding spatial extent, although some studies have addressed national [START_REF] Rader | Organic farming and heterogeneous landscapes positively affect different measures of plant diversity[END_REF], continental [START_REF] Billeter | Indicators for Biodiversity in Agricultural Landscapes: A Pan-European Study[END_REF] or regional scales [START_REF] Nascimbene | Organic Farming Benefits Local Plant Diversity in Vineyard Farms Located in Intensive Agricultural Landscapes[END_REF], they have included a small sample size or have lacked standard survey protocols across plots, often using proxies rather than direct measures of the targeted agricultural practices. Therefore, issues of scale in agroecosystems remain largely under-represented in the literature.

Here we aimed at studying the effects of spatial resolution and extent, for a better understanding of the effects of agricultural practices on plant diversity and composition in field margins. We relied on a standardized national monitoring effort in 462 agricultural field margins covering France between 2013 and 2019, and including monitoring of plant communities as well as agricultural practices. Previous analyses of this dataset at a site-level showed that vegetation composition was primarily structured by landscape and soil, and secondarily by agricultural intensification [START_REF] Andrade | A real-world implementation of a nationwide, long-term monitoring program to assess the impact of agrochemicals and agricultural practices on biodiversity[END_REF][START_REF] Fried | Assessing non-intended effects of farming practices on field margin vegetation with a functional approach[END_REF]. Composition was driven by margin management and fertilization, while species richness responded to herbicide use. However, these previous analyses did not explore the role of scale in their results, or whether the relative influence of agricultural practices on margin plant communities were consistent across regions. Building on these first findings, here we investigated the potential influence of spatial extent and resolution of analysis on the drivers of field margin plant communities. Because agricultural practices are site-specific whereas landscape, soil and climatic conditions tend to have a greater range of values within larger grid cells, we hypothesized that the effects of the former would dominate at finer resolutions, while the latter would stand out at coarser resolutions. Regarding spatial extent, we expected soil and climate variables to dominate plant community structure at the national extent and become less important within biogeographic regions, providing a clearer picture of the influence of agricultural practices at the regional extent.

Materials and methods

Vegetation surveys

We used vegetation data from a national monitoring effort, the 500-ENI network, which is funded by the French Ministry of Agriculture (see details in [START_REF] Andrade | A real-world implementation of a nationwide, long-term monitoring program to assess the impact of agrochemicals and agricultural practices on biodiversity[END_REF]. Note that raw data access requires a request to the Ministry and is conditional on confidentiality of some information, such as site coordinates; however, all datasets used in the following analyses are available in a repository (see the data accessibility statement). Agricultural field margins covering continental France, were surveyed yearly since 2013, representing a total of 543 unique sites between 2013 and 2019 (including some site turnover between years, see Appendix A, Fig. SA.1). Here we selected a subset of sites that had at least five years of botanical data between 2013 and 2019, resulting in a total of 462 sites for the rest of the analysis. These survey sites were located in field margins representing four main crop types: annual crops (with winter wheat or maize as the main crop production, but including other crops in the rotation), market gardening crops (mainly lettuce) and vineyards. The proportion of sites under organic farming was roughly 20%, but agricultural practices covered a wide range of pesticide application, fertilizers and soil management. Botanical surveys were performed at peak flowering (between the end of April and the beginning of August, depending on the region). At the national scale, this represented 3079 observations (year x site) and 689 plant species. The transect line within each site was located in the middle of the field margin, equidistant from the cropland and the adjacent habitat. Along this transect, plant species were identified in ten 1 m 2 sub-plots (Appendix A, Fig. SA.2). Presence-absence was recorded for each species and observation. Here we used frequency of occurrence averaged across years (0 = was never detected in that site; 1 = was registered for all surveyed years in that site), as an index of relative abundance in the species-by-site matrices.

Explanatory variables

We gathered two sets of explanatory variables: the first set came directly from the 500-ENI network and reflects agricultural practices assessed directly on the sites (see [START_REF] Andrade | A real-world implementation of a nationwide, long-term monitoring program to assess the impact of agrochemicals and agricultural practices on biodiversity[END_REF]; the second set was compiled from external open access databases. These include soil, climate and landscape data (see below).

Agricultural practices were reported yearly from interviews of farmers into an online database according to a national standardized acquisition protocol. This data related to fertilization, pesticide use, tillage and boundary management (Appendix A, Fig. SA.3). Soil characteristics in the 0-5 cm layer were acquired at a resolution of 250 m from SoilGrids [START_REF] Hengl | SoilGrids1km-Global Soil Information Based on Automated Mapping[END_REF]. Climatic data by year and season were obtained from the Chelsa database for the period 1979-2013, at a resolution of 1 km [START_REF] Karger | Climatologies at high resolution for the earth's land surface areas[END_REF]. By using this dataset, we assumed that climate did not change dramatically between this period and our sampling period, or that any change that did occur conserved a similar spatial structure of climatic variability across sites. We also extracted landscape descriptors from OSO land cover maps at a 10 m resolution for 2016 [START_REF] Inglada | Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series[END_REF]. We considered percent cover of eight landscape classes (Appendix A, Fig. SA.3) within buffers of 1 km radius, and used the Shannon diversity index (SHDI) [START_REF] Turner | Landscape Metrics[END_REF] to characterize compositional landscape heterogeneity. Shannon diversity was also estimated for crop landscape diversity within a buffer of 1 km, considering 17 types of crops (Appendix A, Fig. SA.3) retrieved from the external databases OSO [START_REF] Inglada | Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series[END_REF], TOPO (IGN, 2019) and the Graphical Parcel Register (IGN & ASP, 2019).

Our dataset contained missing values for agricultural drivers that were occasionally left empty by the local observers. We used a multivariate imputation method based on a random forest algorithm (package mice of R v.4.0.0, [START_REF] Buuren | mice: Multivariate Imputation by Chained Equations in R[END_REF] to complete these data (see Appendix B for details).

For compositional analyses, all explanatory variables were standardized to have equal mean and standard deviation. We limited the number of explanatory variables to seven (Table 1) so that we could include the same ones at different resolutions (see section on data analysis below). We aimed at representing the essential variables in different categories (climate, soil, landscape and practices) ensuring that they were not highly correlated (Pearson correlation coefficient < 0.6). We retained mean annual temperature and soil pH, since they are known to be tightly related to floristic composition and richness in agroecosystems [START_REF] Fried | Environmental and management factors determining weed species composition and diversity in France[END_REF]. Landscape and crop diversity were selected as indicators of landscape heterogeneity [START_REF] Rader | Organic farming and heterogeneous landscapes positively affect different measures of plant diversity[END_REF][START_REF] Sirami | Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions[END_REF]. Finally, we included the herbicide treatment frequency index, the nitrogen dose applied in fertilizers (within adjacent agricultural fields) and the number of margin management events (Table 1). These variables have consistently been reported to have a significant effect on field margin communities [START_REF] Aavik | Agrotolerant and high nature-value species-Plant biodiversity indicator groups in agroecosystems[END_REF][START_REF] Bassa | Effects of farming management and landscape heterogeneity on plant species composition of Mediterranean field boundaries[END_REF][START_REF] Fried | Assessing non-intended effects of farming practices on field margin vegetation with a functional approach[END_REF].

Data analysis

We used two statistical methods: spatial simultaneous autoregressive models (SAR) with species richness as response variable, and generalized dissimilarity models (GDM) to study species composition while dealing with nonlinear relationships between environmental distance and species turnover [START_REF] Ferrier | Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment[END_REF]. These two types of analyses were applied at the national extent using different resolutions, and then at regional and national extents using the site-level resolution.

SAR models were implemented with the package spdep (function errorsarlm) using a correlated spatial error term, which is a function of distance between sites up to a threshold distance, which corresponds to the distance at which autocorrelation becomes non-significant (Appendix C) [START_REF] Cressie | Statistics for spatial data[END_REF]. We can thus model linear relationships that take into account spatial autocorrelation in the data, i.e. the tendency of nearby points to have more similar values than expected by chance. Hereafter, the term "spatial structuring" will be used interchangeably with "spatial autocorrelation", which can result from an effect of the environment or from an internal process linked to community assembly, such as dispersal limitations [START_REF] Borcard | Numerical ecology with r[END_REF]. Species richness was square root transformed to comply with normality. We assessed the residual autocorrelation of each model with Moran's I test [START_REF] Thioulouse | Analysing Spatial Structures[END_REF]. We also computed partial regressions using the Nagelkerke pseudo-R 2 (partial determination coefficient) to quantify the relative importance of each predictor [START_REF] Lichstein | Spatial autocorrelation and autoregressive models in ecology[END_REF]; Appendix C).

The use of GDMs (R package gdm, function gdm) to explain variability in species composition is appropriate when species turnover responds non-linearly to environmental factors [START_REF] Ferrier | Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment[END_REF]. The Bray-Curtis dissimilarity among all pairs of sites was modeled as a function of environmental distance (computed for each factor) and geographical distance, which accounts for spatial structure. The environmental table was permuted 500 times to assess variable significance, as recommended for analyses with distance matrices [START_REF] Ferrier | Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment[END_REF]. We then evaluated the importance of each variable in the model with the same procedure as above (partial regressions based on the explained deviance; [START_REF] Maestri | Parasite beta diversity, host beta diversity and environment: Application of two approaches to reveal patterns of flea species turnover in Mongolia[END_REF].

Table 1

Description of explanatory factors used in analyses, with their abbreviations and scale of computation. The variables computed by observation and then averaged by sites are in bold. The different analyses were reiterated at different spatial scales. The first approach was to increase the study resolution by aggregating data into grid cells. We present here results from three resolutions: site-level, 40 km, and 75 km, (Fig. 1A, but see Appendix D & E for other resolutions). To mitigate the influence of outliers at aggregated scales, we excluded grid cells that contained all sites within less than 5 km from each other in the same grid cell. We also removed grid cells with only one site, as well as cells along land-sea borders with more than 50% of their surface in the water. We also shifted grid placement in three different iterations, to make sure results were robust to the grid placement (Appendix D). Overall, this led to some variation in the number of sites and grid cells across analyses, which were investigated by resampling (Table 2, Appendix D). Whenever possible, explanatory data were directly extracted at the scale of interest (climate, soil and landscape). All other factors were averaged by grid cell from site-level data, including the frequency of occurrence by site. Species richness in each grid cell was obtained by aggregating site-level species lists to recalculate richness at the aggregated scale. In all spatial analyses involving aggregated datasets, we used the coordinates of the grid cell centroids.

Lastly, we carried out the same analysis using site-level data at two different extents: national and regional. For this purpose, sites were grouped within ten biogeographical regions standing for the wide pedoclimatic and floristic gradients in France (Fig. 1B, based on the VégétalLocal map; Office français de la biodiversité, 2021). Only six regions had a sufficient number of sampling sites to be considered in our analyses (>40 sites): Northern Parisian Basin, Southern Parisian Basin, Armorican Massif, Mediterranean Zone, North-Eastern France and South-Western France.

Results

Effects of spatial resolution

At the site-level resolution, the predictors explained 27.8% of the variance in species richness. Herbicides and nitrogen dose were the main drivers and explained respectively 1.5 (p = 0.002) and 1.3% (p = 0.004) of the total variance in species richness, both having a negative effect on richness (Table 3A). There was a positive correlation of margin management with species richness that was mainly apparent at the site resolution (p = 0.018). With respect to plant community composition, the GDM explained 29.5% of deviance (Table 3B), the main drivers being mean annual temperature (6.9%, p < 0.001) and soil pH (1.2%, p < 0.001).

With increasing spatial resolution, the importance of climate and landscape (i.e. crop diversity) for species richness increased, while agricultural effects remained constant and small (Fig. 2A-C). Temperature had a positive correlation with richness (p = 0.004), while soil pH had no significant effect. Crop diversity was the predominant landscape factor at all aggregated scales, showing a consistent positive correlation with richness, but only significant at the coarsest resolution (75 km, p = 0.017, Table 3A). On the contrary, landscape diversity did not seem to have any effect (Table 3A). Both herbicides and nitrogen dose reduced the number of species at the site-level, but these effects were not significant at coarser resolutions (Table 3A). The proportion of richness that can be explained by spatial autocorrelation was higher at the siteresolution (11.4%, p < 0.001) compared to coarser resolutions where

Table 2

Sample size for each resolution. Grid positions in the aggregated resolutions were shifted to obtain three different positions of the grid system. Means and standard deviations below reflect variations across these grids within each resolution.

Table 3

Significance of each predictor (within rows) for each spatial resolution (within columns; in km). For species richness in A), the direction of the relationship is indicated by +/-. The number of +/-/* indicates the number of times the predictor was significant over the three grid shifts. We only report significant relationships with pvalues in brackets. it was non-significant (Fig. 2A-C).

With regard to composition, the R 2 of the models increased at coarser spatial grains (Appendix E, Fig. SE.1). Climate and landscape (mainly through crop diversity) together contributed to overall differences in species composition among sites, much more strongly than farming practices and soil at all scales (Fig. 2D-F). Climate had less effect on composition at coarser resolutions while the effect of landscape increased. Fertilization was more strongly related to species composition at the coarsest resolution (75 km, 4.3%, p = 0.010) than at the finest, for which it was not significant (Fig. 2D-F, Table 3B). Herbicides did not have any effect on species composition. Also, spatial autocorrelation was relatively important at all aggregated scales (Fig. 2E-F).

Effects of spatial extent

We compared the outcomes between national and regional extents across different regions. At the regional extents, the influence of environmental filters on species richness and composition varied greatly among regions (Fig. 3). For example, the high R 2 of the model for the Mediterranean zone (approximately 38.8%) contrasted with the lower one for the Armorican Massif (15.1%). Some landscape and agricultural effects emerged at a regional extent as more significant than at the national extent. For example, landscape diversity was negatively correlated with richness (6.5%, p = 0.016) in the northern Parisian Basin, a relationship that was not visible at the national extent with all regions analyzed together (Table 4A). Agricultural effects on richness were often more predominant at the regional extent (Fig. 3A). This scale allowed for a clearer identification of the effects of nitrogen fertilization, herbicides, and margin management. The direction of the relationships between richness and predictors remained consistent with analyses at the national level, except for southwestern France, which depicted a negative relationship of species richness with temperature, (p = 0.003, Table 4A).

With respect to composition, climatic conditions appeared to have higher explanatory values at a national rather than regional extent (Fig. 3B). As with richness, new and contrasting effects of landscape and practices were highlighted within regions, such as the effect of landscape diversity on composition in the Mediterranean zone (2.2%, p = 0.020), and the effects of the number of margin management events in the Mediterranean zone (5.6%, p < 0.001) and Armorican Massif (6.9%, p < 0.001) (Fig. 3B, Table 4B).

Discussion

Our results reveal the importance of scale, both resolution and extent, in understanding the non-intended effects of agricultural practices on field margin vegetation. In line with our expectations, the effects of agricultural practices on field margin flora were more prominent within biogeographic regions than when analyzed at the national extent. Herbicides and fertilization were important to explain species richness at the site level, whereas fertilization stood out in explaining species composition at coarse resolutions. Conversely, climate and landscape variables always dominated when data were aggregated, and when analyzed at the national extent. Climate also explained a large percentage of variation in species composition, but not in species richness, at the site level.

Influence of spatial resolution

Interestingly, richness and composition did not respond to the same

Table 4

Significance of each predictor (within rows) for each biogeographic region and for national extent (within columns, see Fig. 1 for abbreviations of region names). For species richness in A), the direction of the relationship is indicated by +/-. We only report significant relationships with p-values in brackets. environmental variables. Richness was mostly impacted by local agricultural intensification, such as high herbicide use and nitrogen fertilization, while composition was influenced by large-scale landscape and agricultural effects, such as landscape crop diversity and high regional fertilization. This is consistent with some studies reporting that the effects of local agricultural practices on field margin plant communities are at least as important as landscape effects [START_REF] Bassa | Effects of farming management and landscape heterogeneity on plant species composition of Mediterranean field boundaries[END_REF][START_REF] Gabriel | Beta diversity at different spatial scales: Plant communities in organic and conventional agriculture[END_REF]; but see [START_REF] Martin | Effects of farmland heterogeneity on biodiversity are similar to-Or even larger than-The effects of farming practices[END_REF]. Negative impacts of herbicides and nitrogen on field margin richness have already been reported [START_REF] Aavik | Agrotolerant and high nature-value species-Plant biodiversity indicator groups in agroecosystems[END_REF][START_REF] Fried | Assessing non-intended effects of farming practices on field margin vegetation with a functional approach[END_REF], with nitrogen fertilization affecting both richness and composition, and herbicides only impacting richness [START_REF] Fried | Assessing non-intended effects of farming practices on field margin vegetation with a functional approach[END_REF]. Our results revealed a large-scale effect of nitrogen use on composition, which contrasts with previous studies showing its effect on richness [START_REF] Billeter | Indicators for Biodiversity in Agricultural Landscapes: A Pan-European Study[END_REF][START_REF] Kleijn | On the relationship between farmland biodiversity and land-use intensity in Europe[END_REF]. [START_REF] Storkey | The impact of agricultural intensification and land-use change on the European arable flora[END_REF] demonstrated that large-scale eutrophication is a major explanatory factor of the threat status of arable species in Europe and even more than herbicide use, although the two factors are difficult to disentangle.

Regarding landscape variables, crop diversity was the most important predictor of richness and composition at large scales. While the response of plant species richness to Shannon crop diversity is inconsistent among studies [START_REF] Martin | Effects of farmland heterogeneity on biodiversity are similar to-Or even larger than-The effects of farming practices[END_REF][START_REF] Sirami | Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions[END_REF], we found here a consistently positive effect regardless of the scale. This can be viewed as landscape complementation for species that are specialists of a single crop [START_REF] Storkey | The impact of agricultural intensification and land-use change on the European arable flora[END_REF]. Another explanation would be that crop diversity is negatively related to field size [START_REF] Martin | Effects of farmland heterogeneity on biodiversity are similar to-Or even larger than-The effects of farming practices[END_REF], and this latter might have an additional effect on richness [START_REF] Fahrig | Farmlands with smaller crop fields have higher within-field biodiversity[END_REF]. Furthermore, crop diversity may reflect the varied environmental conditions within a grid cell, and it is possible that species richness exhibits a stronger response to this environmental diversity. In contrast, habitat diversity did not show any effect on richness, but only on composition at the site level. Although a diverse landscape does not necessarily support more species, it may favor functionally more diverse communities, supporting the idea that species richness alone cannot capture all aspects of biodiversity [START_REF] Aavik | What is the role of local landscape structure in the vegetation composition of field boundaries?[END_REF][START_REF] Fried | Assessing non-intended effects of farming practices on field margin vegetation with a functional approach[END_REF]. The effect of crop diversity on composition increased with resolution, while this was not the case for landscape diversity, likely because landscapes became more homogeneous in terms of habitat diversity, while they still remained different in terms of crops (Appendix F,

Fig. SF.1).

Influence of spatial extent

More interestingly, our results pointed to strong regional specificities, underlining complex interactions between local and regional scales. For example, the influence of landscape diversity on richness was very high in the northern Parisian Basin, a region mainly composed of large cereal open-fields (Appendix F, Fig. SF.2-3). The direction of this effect was unexpected, since landscape diversity decreased the number of species in field margins. Given the high level of local intensification in this region, the species from natural habitats are likely too vulnerable to establish locally. Because diverse landscapes have fewer croplands and more natural habitats, species adapted to agricultural disturbances could be less prevalent in the landscape species pool, ultimately leading to a loss of species in highly disturbed sites. This apparently counterintuitive finding could thus be explained by interactions between the regional pool and local conditions [START_REF] Cornell | What Are Species Pools and When Are They Important? Annual Review of Ecology[END_REF][START_REF] Myers | Seed arrival, ecological filters, and plant species richness: A meta-analysis[END_REF]. Likewise, the detrimental effect of nitrogen fertilization on richness was only significant in the Mediterranean Zone. In this region, soils had low nitrogen content (Appendix F, Fig. SF.3), and the species pool is mostly composed of stress-tolerant species adapted to nutrient-poor soil conditions. Due to its regional composition, Mediterranean flora would thus be more sensitive to soil enrichment. This supports the results of [START_REF] Kleijn | On the relationship between farmland biodiversity and land-use intensity in Europe[END_REF] who concluded that the impact of nitrogen is greater in less intensive regions. Clarifying regional specificities should thus be a strong priority for future agroecological studies, as it paves the way for proposing concrete agricultural management practices that minimize impacts on biodiversity while considering regional characteristics.

Implications for management in agricultural context

Our results have practical implications for crop management. Crop diversity stands out with a major positive impact on plant diversity in field margins. The importance of crop diversity has just begun to be recognized in public policies [START_REF] Galán-Martín | Multi-stage linear programming model for optimizing cropping plan decisions under the new Common Agricultural Policy[END_REF], and our results suggest that this effect will extend to a large scale. Promoting a wide range of crops at the landscape level could therefore slow the decline of some highly specialized arable plant species, particularly in landscapes with large semi-natural cover [START_REF] Sirami | Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions[END_REF]. Fertilization significantly altered floristic assemblages and is also more impactful at a large scale, which is of major interest for national agricultural policies.

Beyond large-scale agricultural impacts, the effects of local agricultural practices are also regionally dependent. The region in which regulatory or management measures are implemented can determine their cost-benefit [START_REF] Kleijn | On the relationship between farmland biodiversity and land-use intensity in Europe[END_REF]. For instance, a drastic reduction of fertilizers in highly intensive regions mainly composed of nitrophilous species, such as the Parisian Basin here, will have little impact on plant communities of field margins if this action is not coupled with landscape restoration. In contrast, implementing stricter fertilization control in the Mediterranean zone, where plants are adapted to poor soils, would have a much higher impact on the native flora of this region. Regions that still have a diverse pool of species (such as the Mediterranean region) are thus more susceptible to respond to less costly mitigation measures and are of higher conservation concern [START_REF] Stevens | Nitrogen deposition threatens species richness of grasslands across Europe[END_REF]. We therefore encourage agricultural stakeholders and scientists involved in monitoring programs to further evaluate the changes in field margin flora and its protection measures across different spatial scales, and implement regulatory measures that incorporate landscape-level management planning adapted to each biogeographic and agricultural region. like to thank everyone that has collected data in the field, the farmers who provided information on their practices, and everyone involved in the coordination of the 500-ENI data network. We would also like to thank the three anonymous reviewers who contributed to improving previous versions of this article.

Fig. 1 .

 1 Fig. 1. Distribution map of the field margins surveyed in France distributed in a grid of 40 km and colored according to their biogeographic region. Only cells selected for analyses are presented. A = « Alps », BPN = « Northern Parisian Basin », BPS = « Southern Parisian Basin », BRSJ = « Rhone-Saone-Jura Basin », C = « Corsica », MA = « Armorican Massif », MC = « Massif Central », ZM = « Mediterranean Zone », ZNE = « North-Eastern France » and ZSO = « South-Western France ». Note that the regions C, A, MC and BRSJ have been removed from analyses because of their small number of sites.

Fig. 2 .

 2 Fig. 2. Percentage of explained variance of species richness and composition by each factor for each spatial resolution. Factors significant in less that one grid are in gray. (A, D) 0 km; (B, E) 40 km; (C, F) 75 km. Standard deviations are computed from the three resamplings of grids. Percentages of explained variance are reported in Appendix E, Table SE.1.

Fig. 3 .

 3 Fig. 3. Percentage of explained variance at the site-scale resolution for (A) richness and (B) composition for each biogeographic region and national extent. Nonsignificant factors are in gray. Regions are ordered by area from left to right. Percentages of explained variance are reported in Appendix E, Table SE.2.

Acknowledgements

The 500-ENI network is supported by the French Ministry of Agriculture under the Ecophyto framework with funding from the French Biodiversity Agency (Office Français de la Biodiversité, OFB). We would

Funding

This work was supported by an INRAE-ANSES thesis fellowship to Isis Poinas and by the Ecophyto II+ project GTP 500 ENI (OFB-21-1642), as well as from the BIODIVIN project funded by the Office Français de la Biodiversité (OFB) and the Fédération Régionale des Chasseurs d'Occitanie (FRC Occitanie).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data accessibility statement

Data available via the Data INRAE Repository, at https://doi.org/10.

57745/NMIPCY

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.baae.2023.08.003.