Rémi Duflot
email: duflot.remi@gmail.com

Hervé Daniel

Stéphanie Aviron

Audrey Alignier

Véronique Beaujouan

Francoise Burel

Arnaud Cochard

Aude Ernoult

Guillaume Pain

Joséphine A Pithon

Duflot Rémi

Adjacent woodlands rather than habitat connectivity influence grassland plant, carabid and bird assemblages in farmland landscapes

Keywords: Multi-taxon biodiversity, habitat network, random forests, multi-model inference, dispersal, hedgerow, human-dominated landscapes

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Introduction

In human-dominated, highly fragmented landscapes, site protection alone is unlikely to achieve the objective of stopping or reversing biodiversity declines [START_REF] Bennett | Habitat fragmentation and landscape change[END_REF]. Rather enhancing habitat connectivity or linkages, such as ecological corridors and networks, will be needed to ensure the viability of plant and animal populations [START_REF] Crooks | Connectivity Conservation[END_REF]. This connectivity depends on the amount (area) and spatial configuration of suitable habitat as well as on matrix permeability [START_REF] Bennett | Linkages in th Landscape: The Role of Corridors and Connectivity in Wildlife Conservation[END_REF]. Nature conservation policy has accordingly evolved towards the implementation of habitat networks (ecological networks or greenveining), which are increasingly integrated into international, national or regional planning; particularly in Europe [START_REF] Boitani | Ecological networks as conceptual frameworks or operational tools in conservation[END_REF][START_REF] Jongman | The Pan European Ecological Network: PEEN[END_REF]. For example, since 2009 in France, it has become a legal requirement that habitat networks be established at national, regional and local levels [START_REF] Vanpeene-Bruhier | Schémas régionaux de cohérence écologique : les questionnements, les méthodes d'identification utilisées, les lacunes[END_REF]. Most territory may potentially be concerned by this policy's implementation, including areas subject to more or less intense human activity. The need to develop nature conservation outside protected areas has been accompanied by the recognition that most conservation efforts have tended to focus on rare species and habitats, sometimes to the detriment of common biodiversity [START_REF] Hothorn | Common European birds are declining rapidly while less abundant species' numbers are rising[END_REF]. However, such biodiversity dominates ecosystem composition and makes an important contribution to ecosystem functioning [START_REF] Gaston | Commonness, population depletion and conservation biology[END_REF][START_REF] Gaston | Biodiversity and extinction: the importance of being common[END_REF].

Agriculture is clearly one of the major human activities to be concerned by these shifts in policy.

Farming landscapes occupy large areas of land (approximatively 75% in Europe, [START_REF] Robinson | Post-war changes in arable farming and biodiversity in Great Britain[END_REF][START_REF] Robinson | Post-war changes in arable farming and biodiversity in Great Britain[END_REF]up to 40% globally, Foley et al. 2005) and are associated with a valuable and partly typical flora and fauna [START_REF] Benton | Farmland biodiversity: is habitat heterogeneity the key?[END_REF], which has declined severely over the past few decades [START_REF] Chamberlain | Changes in the abundance of farmland birds in relation to the timing of agricultural intensification in England and Wales[END_REF][START_REF] Donald | Agricultural intensification and the collapse of Europe's farmland bird populations[END_REF][START_REF] Liira | Plant functional group composition and large-scale species richness in European agricultural landscapes[END_REF]. There is some consensus on the importance of landscape complexity, or the amount of semi-natural habitat, in determining levels of biodiversity in farmland landscapes [START_REF] Benton | Farmland biodiversity: is habitat heterogeneity the key?[END_REF][START_REF] Tscharntke | Landscape moderation of biodiversity patterns and processes -eight hypotheses[END_REF]. In this context, attempts are being made to preserve and enhance semi-natural habitats of ecological value, like hedgerows and permanent grasslands. These habitats have generally diminished [START_REF] Meeus | The transformation of agricultural landscapes in Western-Europe[END_REF] and are the focus of some considerable attention in the context of nature conservation in farming areas. For instance, in France permanent grasslands declared by farmers represented 6.7 million ha of agricultural land at national scale in 2006. By 2010 6.3% of them had been lost, replaced by temporary grasslands or scrubland [START_REF] Faïq | Les prairies permanentes : Evolution des surfaces en France -Analyse à travers le Registre Parcellaire Graphique[END_REF]. Enhanced connectivity of these semi-natural habitats is expected to counteract the negative effects of such reductions in biodiversity (e.g. [START_REF] Hendrickx | How landscape structure, land-use intensity and habitat diversity affect components of total arthropod diversity in agricultural landscapes[END_REF].

Studies of connectivity effects in farmland contexts have tended to focus on wooded habitat types (e.g. [START_REF] Gil-Tena | Woodland bird response to landscape connectivity in an agriculture-dominated landscape: a functional community approach[END_REF][START_REF] Neumann | The compositional and configurational heterogeneity of matrix habitats shape woodland carabid communities in woodedagricultural landscapes[END_REF]. The effectiveness of linear woodland habitats, like hedgerows, as corridors facilitating forest specialist dispersal has been demonstrated for a diversity of taxonomic groups: birds [START_REF] Davies | Are hedgerows effective corridors between fragments of woodland habitat? An evidence-based approach[END_REF], arthropods (e.g. carabid beetles, [START_REF] Petit | Diffusion of forest carabid species in hedgerow network landscapes[END_REF][START_REF] Millan-Pena | Landscape context and carabid beetles (Coleoptera : Carabidae) communities of hedgerows in western France[END_REF], plants [START_REF] Jamoneau | Drivers of plant species assemblages in forest patches among contrasted dynamic agricultural landscapes[END_REF]) and small mammals [START_REF] Gelling | Are hedgerows the route to increased farmland small mammal density? Use of hedgerows in British pastoral habitats[END_REF]). Meanwhile the possible effects of increasing permanent grassland connectivity for farmland biodiversity have received relatively less attention. Most studies of grassland connectivity have concentrated on rarer grasslands of high conservation value, such as wet grasslands (e.g. [START_REF] Lafage | Disentangling the influence of local and landscape factors on alpha and beta diversities: opposite response of plants and grounddwelling arthropods in wet meadows[END_REF], calcareous grasslands (e.g. [START_REF] Brückmann | Butterfly and plant specialists suffer from reduced connectivity in fragmented landscapes[END_REF][START_REF] Rösch | Landscape composition, connectivity and fragment size drive effects of grassland fragmentation on insect communities[END_REF], heathland (e.g. [START_REF] Piessens | The role of fragment area and isolation in the conservation of heathland species[END_REF], species-rich grasslands at higher altitudes (e.g. [START_REF] Marini | Vascular plant and Orthoptera diversity in relation to grassland management and landscape composition in the European Alps[END_REF] or indigenous grasslands fragmented by commercial forestry (e.g. Samways & Pryke, 2016). These studies, involving a diversity of taxonomic groups, have shown that species of open, grassland habitats may respond positively to increasing grassland connectivity. Common permanent grasslands of agricultural mosaics, which are mesophilic, managed grasslands that have not been ploughed for several years, have been understudied in landscape connectivity research. With their lack of vertical heterogeneity and dependence on highly variable forms of management, such grasslands are difficult to describe [START_REF] Allen | An international terminology for grazing lands and grazing animals[END_REF] and until the recent focus on common biodiversity may have been expected to harbour few species of conservation interest. Only a few studies in Europe have begun to explore how connectivity of more managed grasslands may influence arthropods and plant assemblages [START_REF] Öckinger | Landscape matrix modifies richness of plants and insects in grassland fragments[END_REF][START_REF] Wamser | Trait-specific effects of habitat isolation on carabid species richness and community composition in managed grasslands[END_REF][START_REF] Villemey | Mosaic of grasslands and woodlands is more effective than habitat connectivity to conserve butterflies in French farmland[END_REF]. These studies found contrasting effects of grassland connectivity on species richness. Thus, it is important to further explore to what extent connectivity of common permanent grassland contributes to maintaining biodiversity in farmland mosaics.

The effects of landscape context depend on the varying scales of perception of taxa with differing dispersal abilities [START_REF] Jackson | What size is a biologically relevant landscape?[END_REF] and within-taxa differences in dispersal ability may potentially obscure the effects of connectivity at assemblage level (e.g. [START_REF] Wamser | Trait-specific effects of habitat isolation on carabid species richness and community composition in managed grasslands[END_REF]). In addition, species with different habitat affinities may display opposing responses to landscape structure [START_REF] Mauremooto | Permeability of Hedgerows to Predatory Carabid Beetles[END_REF][START_REF] Thomas | Isolating the components of activity-density for the carabid beetle Pterostichus melanarius in farmland[END_REF][START_REF] Filippi-Codaccioni | Toward more concern for specialisation and less for species diversity in conserving farmland biodiversity[END_REF]. Therefore, using two spatial scales, we aimed to assess how variation in grassland connectivity (amount and spatial configuration) influenced the richness and abundance of various taxonomic groups with potentially different scales of landscape perception: plants, carabids and birds observed in common, mesophilic, permanent grasslands. We hypothesized that grassland connectivity would increase richness and / or abundance of these assemblages. We also looked at the richness and abundance of species groups based on habitat affinity and dispersal ability, and compared the influence of grasslands with that of wooded habitats and crops in the surrounding landscape. We further hypothesized that grassland specialist or open habitat species would benefit most from grassland connectivity or amount of crops. Wooded habitats (particularly hedgerows) may act as barriers for these species, while they may help forest species to disperse and therefore increase richness and abundance of this group in grasslands. We expected a stronger influence of landscape structure on species with low dispersal ability.

Method

Study areas and permanent grassland selection

The study was conducted in north-western France, where farmland landscapes contain annual crops (mostly winter cereals, but also spring maize and oilseed rape), along with temporary and permanent grasslands (not ploughed for at least 5 years), separated by woodland and hedgerow networks (which will be referred to as wooded habitats). We selected four study areas located close to Nantes, Angers, La-Roche-sur-Yon and Rennes which shared the same climate (temperate oceanic), geo-morphological conditions (acidic substrate: schists, granites, sandstones) and a similar agricultural history of mixed dairy farming systems (Appendix 1A). In these areas, we selected permanent grasslands to maximize variation in the proportion of permanent grassland in the surrounding landscape. These production grasslands were originally established by farmers, using a mixture of sown species of grasses and sometimes clover, but most plant species were native. In western Europe and elsewhere, such grasslands have a long history of agricultural management, and their biodiversity has long been determined by the dynamics of regular mowing and grazing. To minimise other sources of variation, we excluded wet grassland and grasslands that were close to major roads or urban infrastructure. All were grazed, as this factor is known to have a strong influence on grassland plant assemblages [START_REF] Gaujour | Factors and processes affecting plant biodiversity in permanent grasslands. A review[END_REF].

Sampling and biodiversity measures

Biodiversity was sampled in between 21 and 55 permanent grasslands, depending on taxonomic group. The proportion of grassland in the landscape surrounding sampled grasslands (200m-radius buffer) ranged from 12 to 85% for plant, from 10 to 58% for carabids and 28 to 85% for birds. Plants were sampled in 55 permanent grasslands (4 Nantes, 18 Angers, 14 La Roche-sur-Yon, 19 Rennes).

Each grassland was visited once between 2011 and 2015, in the late spring or summer (May -July).

All vascular species were listed within 3 quadrats of 2 x 2m per grassland, placed at least 2 m from the field edge, and these were subsequently pooled for calculation of species richness measures.

Carabid beetles were sampled in 40 permanent grasslands (40 Rennes), using pitfall traps. Two pitfall traps per grassland were located 1m from each other and 10m from grassland edge. Carabids were sampled during six sampling periods, two per month in May, June and September 2011, to coincide with the seasons during which carabid beetles emerge (spring and late summer). Traps were collected every two weeks, after being open for seven consecutive days. Eliminating a few lost traps (destroyed by cattle), we measured activity-density as the number of individuals per trap per week and we checked that the number of valid traps did not influence species richness. The six sampling periods were then pooled to calculate species richness and activity-density measures for each grassland.

Birds were sampled in 21 permanent grasslands (10 Angers, 11 La Roche-sur-Yon) and their associated field margin vegetation, using standard territory mapping (Bibby et al. 2000). The grasslands were visited six times per breeding season in 2014 and 2015 (between mid-March and mid-June). All bird surveys were carried out by a single observer between 1 and 4 hours after sunrise, on days without continuous rain or wind. Species defending one or more territories in at least one of the two years were considered to be breeding birds. The two years were pooled for calculation of species richness measures while abundance values corresponded to the maximum number of territories a species defended over the two years.

Plants, carabids and birds were classified according to their habitat affinity (generalists, grassland or open farmland specialists, woodland specialists) or to their dispersal ability (dispersal mode for plants, wing system for carabids and a morphological dispersal ability predictor for birds; see below). Species richness and abundance (for carabids and birds) were calculated for each habitat affinity or dispersal ability group. Plant ecological preferences were adapted from Baseflor database [START_REF] Julve | Répertoire synonymique des groupements végétaux de France[END_REF] and plant dispersal mode extracted from Biolflor [START_REF] Kuhn | BiolFlor -a new plant-trait database as a tool for plant invasion ecology[END_REF]) and Baseflor [START_REF] Julve | Répertoire synonymique des groupements végétaux de France[END_REF] databases (Appendix 1B). Carabid habitat affinity data were adapted from [START_REF] Neumann | The compositional and configurational heterogeneity of matrix habitats shape woodland carabid communities in woodedagricultural landscapes[END_REF] and [START_REF] Roger | Clé de détermination des carabidés : Paysages agricoles de la Zone Atelier d'Armorique[END_REF]. Carabid species dispersal ability was estimated by the wing system [START_REF] Kotze | Species decline -but why? Explanations of carabid beetle (Coleoptera, Carabidae) declines in Europe[END_REF][START_REF] Purtauf | Carabid communities in the spatio-temporal mosaic of a rural landscape[END_REF]) using information from [START_REF] Barbaro | Linking bird, carabid beetle and butterfly life-history traits to habitat fragmentation in mosaic landscapes[END_REF], [START_REF] Ribera | Effect of land disturbance and stress on species traits of ground beetle assemblages[END_REF]BETSI database (2012, Appendix 1C). Bird habitat affinity groups were based on analysis of national bird monitoring data [START_REF] Jiguet | Les résultats nationaux du programme STOC de 1989 à[END_REF]) except for 14 missing species, for which we used regional atlas descriptions of habitat use [START_REF] Marchadour | Oiseaux nicheurs des Pays de la Loire. Coordination régionale LPO Pays de la Loire[END_REF]. Bird dispersal ability was estimated by using the quotient of Kipp's distance (distance between the tip of the first primary feather to the tip of the wing) and bill depth (measured at the proximate edge of the nostrils), which has been shown to be a good surrogate for natal dispersal distance in European passerines [START_REF] Dawideit | Ecomorphological predictors of natal dispersal distances in birds[END_REF]. Biometric data were provided by the Senckenberg Biodiversity and Climate Research Centre in Frankfurt, Germany (for details, see [START_REF] Laube | Towards a more mechanistic understanding of traits and range sizes[END_REF]. Based on the obtained dispersal ability predictor we classified birds into 4 dispersal categories (low, medium, high and long distance) using Jenks natural breaks (Appendix 1D). Some bird ecological groups containing very few species, at low levels of frequency or abundance, were excluded from analysis (urban species and long-distance dispersers).

Land-cover maps and landscape descriptors

We produced land-cover maps of the four study areas using existing land cover databases coupled with automatic classification of satellite imagery, and corrected by photo-interpretation and groundtruthing. Land-cover classification was as follows: built-up area (roads and buildings), water bodies, crop fields, permanent grasslands, and wooded habitats (woodland and hedgerows). We paid particular attention to the accurate estimation of permanent grassland distribution, using photo-interpretation and the Land Parcel Identification System (i.e. Registre Parcellaire Graphique), where farmers subsidised by the Common Agricultural Policy (CAP) declare field use. These maps were produced using QGIS (QGIS Development Team, 2015).

Landscape descriptors were calculated from these maps within 200m and 500m radius, circular buffers centred on each sampled grassland, in order to describe two components of the connectivity of permanent grassland, i.e. grassland amount and grassland configuration. These circular buffers were centred on field centroids for plants and birds, or on exact trap locations for carabids (recorded with a GPS). The choice of two spatial scales was to account for expected differing grains of landscape perception between plants, carabids and birds [START_REF] Jackson | What size is a biologically relevant landscape?[END_REF]. Amount of permanent grassland was described as the proportion of surface area covered by these habitats in buffers.

Configuration of permanent grassland was assessed via three contrasting landscape descriptors, chosen to investigate the possible influence of different forms of spatial arrangement: largest grassland patch area, length of permanent grassland edges and a grassland connectivity index (derived from Hanski 1999, see [START_REF] Steffan-Dewenter | Importance of Habitat Area and Landscape Context for Species Richness of Bees and Wasps in Fragmented Orchard Meadows[END_REF]:

𝐶 𝑘 = ∑ 𝑒 -𝐷 𝑖𝑘 𝐴 𝑖 𝑛 𝑖
where k is the focal sampled permanent grassland, n the number of other (non-sampled) permanent grasslands in the 200 m or 500 m scale landscape, D ik is the distance between the sampled grasslands k and the neighbouring permanent grassland i and A i the area of the neighbouring permanent grassland i. This measure of connectivity increases when many, large, permanent grasslands are located near the sampled grassland. The proportion of other main habitat types was also calculated for the two buffer sizes: wooded habitat (woodlands + hedgerows), and crops (including temporary grasslands which are part of crop rotations). Total length of wooded habitat edges was also measured as it reflects fragmentation of open habitats and adjacencies between open and wooded habitats. In the studied landscapes, wooded habitat edges mostly corresponded to the presence of hedgerows. Landscape descriptors were calculated at the two spatial scales using Chloe 2012 [START_REF] Boussard | Chloe2012 : a software for landscape pattern analysis[END_REF].

The extent of variation in each landscape descriptor, obtained at each scale and for each study taxon, is presented in Appendices 1E and 1F.

Statistical analyses

To test the representativeness of our sampled communities compared to potential richness, we calculated non-parametric species richness estimators of Chao2 across all grasslands [START_REF] Chao | Estimating the population size for capture-recapture data with unequal catchability[END_REF].

These estimated richness values were compared to the observed total number of species, for each taxon. We tested the effect of landscape descriptors on biodiversity measures using two successive steps (e.g. see [START_REF] Puech | Organic vs. conventional farming dichotomy: Does it make sense for natural enemies?[END_REF], Aviron et al. 2016): (i) preselection of landscape descriptors with random forest procedure, (ii) multi-model inference (MMI) and averaging of multiple regression models. Pearson's correlations between landscape descriptors and across scales are presented in Appendices 1G-I.

All seven landscape descriptors at both scales (14 variables) were included in a random forest analysis [START_REF] Breiman | Random Forests[END_REF][START_REF] Strobl | An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests[END_REF]), a recursive partitioning method recommended to deal with "small n large p problems" (i.e. few replicates and many environmental variables), complex interactions and correlated environmental variables [START_REF] Strobl | Conditional variable importance for random forests[END_REF]. For each biodiversity measure, 10,000 trees were grown and landscape descriptor importance was evaluated as the difference in model accuracy before and after 10 permutations of values of the considered descriptor, averaged over all trees.

Conditional importance that adjusts for correlations between environmental variables was used. The absolute importance value of the lowest negative-scoring landscape descriptor was used as a threshold to determine relevant and informative variables to retain for regression models (for full details, see [START_REF] Strobl | Conditional variable importance for random forests[END_REF][START_REF] Strobl | An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests[END_REF]. Landscape descriptors selected for each biodiversity measure may be found in Appendices 1J-L. To assess the influence of landscape descriptors on the global assemblage, the conditional importance values of each variable were averaged across the biodiversity measures for each taxon.

The selected landscape descriptors were then included in multiple regression models, which was analysed using multi-model inference (MMI) and model averaging. MMI analysis deals with model selection uncertainty [START_REF] Burnham | Model selection and Multi-Model Inference. A practical information-theoretic Approach[END_REF][START_REF] Arnold | Uninformative parameters and model selection using Akaike's information criterion[END_REF] and is robust against correlation among descriptors [START_REF] Smith | Confronting collinearity: comparing methods for disentangling the effects of habitat loss and fragmentation[END_REF](Smith et al. , 2011)). All landscape descriptors were mean-centred and divided by the standard deviation to make the coefficients comparable [START_REF] Smith | Confronting collinearity: comparing methods for disentangling the effects of habitat loss and fragmentation[END_REF](Smith et al. , 2011)). Following the MMI procedure, we created linear models for each possible combination of landscape descriptors and ranked them based on the corrected Akaike information criterion (AICc). Then, we computed standardised average regression coefficients weighted by the Akaike weights across supported best models (ΔAICc < 4) and tested their significance using unconditional 95 % confidence intervals [START_REF] Burnham | Model selection and Multi-Model Inference. A practical information-theoretic Approach[END_REF][START_REF] Smith | Confronting collinearity: comparing methods for disentangling the effects of habitat loss and fragmentation[END_REF]Smith et al. , 2011)). Averaged model coefficients and their 95 % confidence intervals are presented in Appendices 1M-P. We also checked that the detected effects were consistent across all individual models included in model averaging (appendix 2). We more specifically looked at the correct estimation of relative effects of correlated landscape descriptors (grassland-related vs. wooded habitat descriptors, composition vs. configuration descriptors, and between scales).

Residuals of averaged models were tested for normality (Shapiro-Wilcoxon test and quantile-quantile plots) and spatial auto-correlation (Moran correlogram). When residuals were not normally distributed a new average model was built using the adequate distribution using a generalized linear model, either

Poisson distribution for non-overdispersed data or negative binomial distribution for overdispersed data [START_REF] Crawley | The R Book[END_REF][START_REF] Bouche | Application of detecting and taking overdispersion into account in Poisson regression model[END_REF]. No model showed spatial auto-correlation. Models included study area (plants and birds) or locality (carabids) as random factors (mixed models). All statistical tests were performed using R software 3.3.1 (R Core Team 2016) using the 'vegan' package for Chao2 estimation [START_REF] Oksanen | Vegan: community ecology package[END_REF], the 'party' package for random forest analyses [START_REF] Hothorn | Common European birds are declining rapidly while less abundant species' numbers are rising[END_REF], the 'MuMin' package for MMI analyses [START_REF] Barton | MuMIn: Multi-Model Inference[END_REF], and the 'lme4' package for generalized linear mixed-effects models [START_REF] Bates | Fitting Linear Mixed-Effects Models Using lme4[END_REF], the 'qcc' package for over-dispersion testing [START_REF] Scrucca | qcc: an R package for quality control charting and statistical process control[END_REF]), and the 'ncf' package for spatial-autocorrelation test [START_REF] Bjornstad | ncf: Spatial Nonparametric Covariance Functions[END_REF].

Results

In total, we sampled 108 plant species (Chao2 = 125.0 ± 8.2), 76 carabid beetle species (Chao2 = 84.3 ± 5.5, 1922 individuals) and 63 bird species (Chao2 = 73.4 ± 7.3), 41 of which were breeding (Chao2 = 54.7 ± 10.7, 672 breeding territories). Observed species richness was therefore close to expected for all taxonomic groups, indicating that the sampling intensity was adequate. Measures of richness and abundance at the assemblage level varied between sampled permanent grasslands (Fig. 1), but could not be explained by permanent grassland connectivity. Proportion and spatial configuration descriptors of permanent grassland were sometimes selected by the random forest analysis, at both spatial scales, but they had limited explanatory power for carabid and bird assemblages (Fig. 2). The only exception was largest grassland patch area at the 200m scale for carabids, but this descriptor was selected only twice (Fig. 2). For plants, grassland-related descriptors were of higher importance than for the animal groups and were often selected, particularly the configuration descriptors at the 500m scale (Fig. 2). However, they never significantly affected total species richness or activity-density / abundance of plants, carabids or birds (Appendices 1M-P). Nor did they have any significant influence on species richness or abundance measures of the different ecological groups, based on habitat affinity or dispersal ability. The only exception was a nearly significant negative effect of largest grassland patch area on total richness of forest bird specialists, at the 200m scale (Appendix 1O). So, despite considerable variation in the amount and configuration of permanent grasslands across sampled grasslands (e.g. 10-80 % of permanent grassland for plants at the 200 m scale, Appendix 1E), these factors had little or no influence on the different taxonomic groups. Instead, the grassland assemblages of the three studied taxa were significantly influenced by wooded habitats. Random forest results showed that landscape descriptors related to wooded habitats were amongst those that explained the most variation in biodiversity measures, for the three taxa (Fig. 2).

Significant relationships between landscape descriptors and biodiversity measures are shown graphically in Figures 345and relationships that fell just short of the 95% confidence level are also displayed, but without a trend line. The latter relationships are henceforth referred to as nearly significant (near. sig.). Wooded habitats had a particularly strong influence at the 200m scale for birds and, to a lesser extent, carabid assemblages. Total plant species richness and richness of grassland plant specialists increased with increasing length of wooded habitat edges at the 200m scale (Fig. 3a/ ). Birds with limited dispersal were not affected by landscape descriptors, while medium and high dispersal groups responded significantly (Appendix 1P) but this seems to have been more related to habitat affinity of the forest specialist or generalist species dominating these dispersal groups, than to dispersal ability per se, and so these results are not shown graphically.

Discussion

The substantial variation in biodiversity between sampled permanent grasslands was not explained by the connectivity of permanent grassland, either amount or spatial configuration, in the landscape, at least at the two spatial scales considered (200 and 500 m). It should be kept in mind that overall diversity in such human-modified landscapes is low (see [START_REF] Irmler | Assignment and evaluation of ground beetle (Coleoptera: Carabidae) assemblages to sites on different scales in a grassland landscape[END_REF], but our sampling protocol enabled us to detect a high proportion of the expected species pool for each taxonomic group.

Many species were not considered to be grassland specialists, particularly in the animal groups, so the lack of response of these assemblages as a whole is perhaps not surprising. However, contrary to our hypothesis, even grassland specialist groups were unaffected. The lack of response of grassland assemblages to grassland amount was also noted by [START_REF] Soderstrom | Plants, insects and birds in semi-natural pastures in relation to local habitat and landscape factors[END_REF], who found no effect on either species richness or composition of plants, ground beetles and other insects (butterflies, bumble bees, dung beetles), and birds . However, in their study the proportion of grassland varied only from 0 to 17%, while in this study it varied from at least 10 to 58% at the 200m scale and 4 to 32% at the 500m scale, and despite this, there was still little response from the different groups. In our study, grassland connectivity index (sensu Hanski 1999) also varied (0-7) without significantly influencing the grassland assemblages. Similarly, Öckinger et al. (2012) found grassland isolation (inverse of Hanski index) had no effect on either total species richness of plants and insects (butterflies, bees or hoverflies), or on richness of grassland specialist groups. One could argue that if we had used a wider range of connectivity indices we might have found different effects. However [START_REF] Villemey | Mosaic of grasslands and woodlands is more effective than habitat connectivity to conserve butterflies in French farmland[END_REF] explored a much wider range of connectivity measures including Hanski index but also nearest Euclidean distance, graph-based measures with cost (least-cost path) or resistance (circuit theoretic) distances, only to come to the same conclusion for the butterfly assemblages they studied. The only group to respond to any measure of grassland connectivity was the total species richness of forest birds, which included 6 species not breeding in our study area, while the richness of breeding forest species alone did not. This suggests that certain species with a greater affinity for forest habitats may avoid very open areas, even when engaged in more temporary activities than breeding, such as dispersal or foraging. Configuration of grassland at 500m was sometimes selected by random forest procedures for plant assemblages. However, as for the other taxa, the effects of these descriptors were not significant.

Grassland specialists dominated plant assemblages, forming on average 83% of total species richness.

Both grassland and total species richness were positively influenced by wooded habitats in the landscape as also shown by [START_REF] Soderstrom | Plants, insects and birds in semi-natural pastures in relation to local habitat and landscape factors[END_REF] and [START_REF] Ernoult | Potential landscape drivers of biodiversity components in a flood plain: Past or present patterns?[END_REF]. One hypothesis is that landscapes with more wooded habitats tend to be less disturbed and more species rich, and that wooded edges, particularly hedgerows, have well-developed herbaceous strata which may act as sources. Plant dispersal groups varied in their response to wooded habitats, independently from habitat affinity groups. Wind-dispersed species were unaffected by landscape context, in agreement with [START_REF] Piessens | The role of fragment area and isolation in the conservation of heathland species[END_REF]. However, we might have expected that wooded edges would inhibit flow of wind-dispersed seeds by forming physical barriers or reducing wind speed [START_REF] Gaujour | Factors and processes affecting plant biodiversity in permanent grasslands. A review[END_REF].

Wooded habitat edges did promote animal-dispersed species, probably because seed-dispersing animals (birds, mammals, insects) tend themselves to follow woodland edges and hedgerows as they move.

In the case of carabids, the lack of grassland connectivity effects might be explained by the limited number of true grassland specialists [START_REF] Roger | Clé de détermination des carabidés : Paysages agricoles de la Zone Atelier d'Armorique[END_REF][START_REF] Neumann | The compositional and configurational heterogeneity of matrix habitats shape woodland carabid communities in woodedagricultural landscapes[END_REF]. Although grassland assemblages are clearly different from those observed in crop fields or wooded habitats (Duflot et al. 2015) some species considered to be crop or forest specialists are known to utilize permanent grasslands. Richness of open habitat carabid species tend to increase in landscapes with a greater proportion of crops, where a greater amount and diversity of complementary resources may be available [START_REF] Duflot | Landscape level processes driving carabid crop assemblage in dynamic farmlands[END_REF]. Richness and activity-density of forest specialist species were higher in landscapes with more wooded habitat or edges (presence of hedgerows), which is a general observation for carabid assemblages found in farmland habitats (e.g. [START_REF] Millan-Pena | Landscape context and carabid beetles (Coleoptera : Carabidae) communities of hedgerows in western France[END_REF][START_REF] Aviron | Carabid assemblages in agricultural landscapes: impacts of habitat features, landscape context at different spatial scales and farming intensity[END_REF][START_REF] Duflot | Landscape heterogeneity as an ecological filter of species traits[END_REF]. This is probably why total activity-density tended to increase with the proportion of wooded habitat in the landscape. Meanwhile, activity-density of generalist carabid species increased with increasing length of hedgerows, known to be overwintering sites for these species [START_REF] Sotherton | The distribution and abundance of predatory coleoptera overwintering in field boundaries[END_REF][START_REF] Thorbek | Reduced numbers of generalist arthropod predators after crop management[END_REF]). These results concur with studies in other contexts showing that carabid assemblages are strongly influenced by habitats adjacent to the focal habitat [START_REF] Schneider | Landscape size affects the relative importance of habitat amount, habitat fragmentation, and matrix quality on forest birds[END_REF][START_REF] Yekwayo | Surrounding vegetation matters for arthropods of small,natural patches of indigenous forest[END_REF].

The majority of sampled birds were contacted in hedgerows delimiting each grassland or in trees or shrubs within, with relatively few observations of foraging in grasslands and < 1 breeding territory per hectare within the grassland habitat itself (data not shown). Therefore, it is not surprising that landscape descriptors of wooded habitat amount and configuration best explained distributions of observed assemblages and more specifically of generalist and forest birds. Species more typical of open farmland habitats including grasslands are known to be negatively impacted by wooded or shrubby habitats in the landscape [START_REF] Besnard | Measuring difference in edge avoidance in grassland birds: the Corncrake is less sensitive to hedgerow proximity than passerines[END_REF]) and we too found that wooded habitat edges reduced farmland bird richness and abundance of breeding farmland birds. Hedgerow avoidance behaviour of ground-nesting farmland birds is common and is partly due to increased predation risks associated with hedgerows and woodland edges [START_REF] Besnard | Measuring difference in edge avoidance in grassland birds: the Corncrake is less sensitive to hedgerow proximity than passerines[END_REF]. The low dispersal ability group, which was composed of species with a diversity of habitat affinities, was not significantly affected by landscape context, although this group was expected to be the most sensitive to habitat connectivity.

This study focused on breeding birds and we cannot rule out effects of landscape structure on this low dispersal group outside the breeding season and at wider geographical scales.

Given the composition of the sampled animal assemblages, it proved difficult to construct dispersal ability groups that would be independent from species habitat affinity and that would be comparable with other taxa. Although effects on plant dispersal groups were clearer, the positive, nearly significant, influence of wooded habitats on plant species dispersed by barochory was also difficult to explain. As [START_REF] Piessens | The role of fragment area and isolation in the conservation of heathland species[END_REF] has highlighted, some plant species may disperse in a variety of ways making it difficult to define dispersal groups and to link dispersal processes with landscape structure.

Contrary to expectations, all three taxa responded mainly to landscape variables at the same 200 m scale. As the scale of landscape perception is expected to be between 4 to 9 times the average dispersal distance [START_REF] Jackson | What size is a biologically relevant landscape?[END_REF], the response of plant and carabids at the finer scale is less surprising than that of birds. It should be noted that most of the significant bird-landscape relationships related to breeding birds only, which are sedentary during the sampling season. Further investigation of a wider range of spatial scales of influence may yield different results. For both animal taxa, landscape configuration had opposite effects on open habitat and farmland species compared to forest and generalist species, and at different spatial scales. Farmland or open specialists seemed to have a wider scale of perception (500 m) than forest specialists and generalists (200 m), which suggests that response to landscape context was more dependent on habitat affinity than taxonomic group. However, in the case of plants, ruderal species, more adapted to human disturbance and that included crop weeds, did not respond to increasing proportion of crops and/or decreased wooded habitats at the 500m scale. Interestingly, for both animal groups richness of forest species of tended to increase with connectivity of wooded habitats (i.e. hedgerows), while their abundance or activity-density increased with resource availability (i.e. amount of wooded habitat).

Conclusion

We found no evidence that increasing connectivity of common, mesophilic, permanent grassland would have positive or indeed negative effects on plant and animal assemblages of such grassland habitats. Hence conservation planning to enhance the surface area and linkages between such grasslands within agricultural landscapes alone is unlikely to produce biodiversity increases, at least as far as our three study taxa are concerned. Instead, studied assemblages responded mostly to wooded habitats surrounding sampled grasslands, including hedgerows. Lengths of wooded habitat edges were often more important than the amount of wooded habitat itself, although collinearity between these landscape descriptors made it difficult to disentangle their independent effects. For grassland plant assemblages as a whole, or for the generalist or forest specialist components of the grassland animal assemblages, more woodland habitat in the landscape matrix is generally positive. Preserving or recreating landscapes composed of permanent grasslands interspersed with woodlands and hedgerows may be good policy, especially in regions where such forms of landscape organisation have been historically present and match with established and adapted biodiversity. Therefore, schemes aiming to reintroduce landscape complexity to farmland areas through hedge and tree planting should enhance biodiversity. However, the presence of open habitat carabids as well as farmland specialist birds depended on sufficiently large expanses of open land, free of wooded habitats. So, though increasing the area of common, permanent, mesophilic grassland did not influence these groups, maintaining sufficient areas of open land is crucial and potentially contradictory with the objectives of policies aiming to restore semi-natural, wooded habitats in farmland. These results illustrate the importance of balancing landscape conservation planning in agricultural contexts, and of using multi-taxon approaches, to meet the needs of contrasting ecological groups.

Distribution of carabid beetle species among the different biological groups within the sampled permanent grasslands. Species occurrence is the number of species observations across all sampled permanent grasslands (maximum occurrence = 40). Apterous: windless, dimorphic: undeveloped wings, macropterous: winged. Activity-density is expressed as number of individuals / valid trap / week. NA: species for which data were not available.

Appendix 1D

Distribution of bird species among the different biological groups within the sampled permanent grasslands, for total species pool and nesters. Species occurrence is the number of species observations across all sampled permanent grasslands (maximum occurrence = 21). Dispersal ability groups were obtained by splitting a dispersal ability predictor into categories using natural breaks (Jenks). NA: species for which data were not available. * unstudied biological groups.

Appendix 2C

Corrected Akaike information criterion (AICc), AICc difference with best model (ΔAICc), and Akaike weights (wi) of all supported models (ΔAICc < 4) that were included in model averaging for the analysis of bird biodiversity measures. Models are ordered in terms of ΔAICc. In bold are the biodiversity measures with significant effects detected. % PG: proportion of permanent grassland (%), PG connect: permanent grassland connectivity index, PG LP: largest permanent grassland patch (ha), PG edges: permanent grassland edges (km), % WH: proportion of wooded habitat (%), WH edges: wooded habitat edges (km), % crops: proportion of crop (%)

Figure 1 .

 1 Figure 1. Boxplot of plant, carabid and bird biodiversity measures per sampled permanent grassland, all species included.

Figure 2 .

 2 Figure 2. Conditional importance scores of landscape descriptors provided by random forest analyses, averaged across biodiversity measures for each studied taxon. Error bars are the standard errors. n = 7 for plants, 16 for carabids and 21 for birds. In brackets are the number of times the considered landscape descriptor was selected by the random forest procedure. Detailed results on landscape descriptors selected for each biodiversity measure may be found in Appendices 1J-K. % PG: proportion of permanent grassland (%), PG connect: permanent grassland connectivity index, PG LP: largest permanent grassland patch (ha), PG edges: permanent grassland edges (km), % WH: proportion of wooded habitat (%), WH edges: wooded habitat edges (km), % crops: proportion of crop (%). "200m" and "500m" indicate the scale at which the considered landscape descriptor was measured.

  b, Appendix 1M). These two biodiversity measures responded similarly as most plant species were grassland specialists (Appendix 1B). The length of wooded habitat edges significantly positively influenced animal-dispersed plant species richness at the 200 m scale and gravity-dispersed plant species richness (near.sig.), at the 500 m scale (Fig.3c/d, Appendix 1M). Ruderal and wind-dispersed plant species were unaffected by landscape descriptors.

Figure 3 .

 3 Figure 3. Graphical representation of significant and nearly significant results for plants biodiversity measures. Barochorous: gravity-dispersed, zoochorous: animal-dispersed. Scale of effects is indicated on each graph corner as the radius of buffers surrounding sampled permanent grasslands. Plain lines are given for illustrative purposes (only for significant results). Coefficients from average models are used for drawing linear and exponential lines (Appendix 1M). Linear lines are used for biodiversity measures analysed with Gaussian distribution.

Figure 4 .

 4 Figure 4. Graphical representation of significant and nearly significant results for carabid beetles biodiversity measures. Full dots are used for species richness, while empty dot and dashed lines are used for activity-density. Activity-density is expressed as number of individuals / valid trap / week. Scale of effects is indicated on each graph corner as the radius of buffers surrounding sampled permanent grasslands. Lines are given for illustrative purposes (only for significant results). Coefficients from average models are used for drawing linear and exponential lines (Appendix 1N). Exponential lines are used for Poisson and Negative Binomial distributions (log link functions).

  For birds, richness of breeding forest specialists increased with increasing length of wooded habitat edges at the 200m scale while their abundance was positively influenced by proportion of wooded habitats also at the 200 m scale (Fig.5b/c, Appendix 1O). As mentioned above, total richness of forest specialist bird species was the only biodiversity measure to be influenced by a grassland landscape descriptor, i.e a nearly significant negative influence of large grassland patch area (Fig5a, Appendix 1O). Similarly, total richness and abundance of breeding generalist birds increased with increasing length of wooded habitat edges at the 200m scale (Fig. 5d/e, Appendix 1O). Conversely, richness and abundance of breeding farmland bird specialists decreased with increasing length of wooded habitat edges, this time at the 500m scale (Fig. 5f/g, Appendix 1O

Figure 5 .

 5 Figure 5. Graphical representation of significant and nearly significant results for bird habitat affinity groups. Scale of effects is indicated on each graph corner as the radius of buffers surrounding sampled permanent grasslands. Lines are given for illustrative purposes (only for significant results). Coefficients from average models are used for drawing linear (Appendix 1O), all models followed Gaussian distribution.

  Range of values of studied landscape descriptors, in a 200m-radius buffer surrounding sampled permanent grasslands, for each taxon. Each dot represents a landscape. Proportions are expressed as percentage.Range of values of studied landscape descriptors, in a 500m-radius buffer surrounding sampled permanent grasslands for each taxon. Each dot represents a landscape. Proportions are expressed as percentage.Estimate and 95% confidence intervals from model averaging for carabid biodiversity measures. Significant results, i.e. estimates whose confidence interval do not include zero, are in bold. Near significant results are shown with a "X" sign. All models were performed using a negative binomial distribution except for biodiversity measures with *, for which a Gaussian distribution was used and ** for which a Poisson distribution was used. Models with only intercept values indicate a null model, i.e. random forest procedure selected no landscape descriptors (see "2.4. Statistical analyses"). Landscape descriptors: % PG: proportion of permanent grassland, PG connect: permanent grassland connectivity index, PG LP: largest permanent grassland patch (ha), PG edges: permanent grassland edges, % WH: proportion of wooded habitat, WH edges: wooded habitat edges, % crops: proportion of crop. "200m" and "500m" indicate the scale at which the considered landscape descriptor was measured.Estimate and 95% confidence intervals from model averaging for bird biodiversity measures (all species and habitat affinity). Significant results, i.e. estimates whose 95 % confidence interval do not include zero, are in bold. Near significant results are shown with a "X" sign. All models were performed using a Gaussian distribution except for biodiversity measures with *, for which a Poisson distribution was used. Models with only intercept values indicate a null model, i.e. random forest procedure selected no landscape descriptors (see "2.4. Statistical analyses"). Landscape descriptors: % PG: proportion of permanent grassland, PG connect: permanent grassland connectivity index, PG LP: largest permanent grassland patch (ha), PG edges: permanent grassland edges, % WH: proportion of wooded habitat, WH edges: wooded habitat edges, % crops: proportion of crop. "200m" and "500m" indicate the scale at which the considered landscape descriptor was measured.Corrected Akaike information criterion (AICc), AICc difference with best model (ΔAICc), and Akaike weights (wi) of all supported models (ΔAICc < 4) that were included in model averaging for the analysis of plant biodiversity measures. Models are ordered in terms of ΔAICc. In bold are the biodiversity measures with significant effects detected. % PG: proportion of permanent grassland (%), PG connect: permanent grassland connectivity index, PG LP: largest permanent grassland patch (ha), PG edges: permanent grassland edges (km), % WH: proportion of wooded habitat (%), WH edges: wooded habitat edges (km), % crops: proportion of crop (%)

	Appendix 1E Appendix 1F activity-density Appendix 1O abundance of nester (Intercept) (Intercept) grassland richness 12.15 2.08 -1.16 0.82 5.90		-1,11 14,48	-1,834 13,50 0.49 0.31 -1.30	-0,389 15,46	320.5 0.13 -0.49	0 -0.44	0.33 78.6 171.3 222.7	2.12 2.54 1.74	0.12 0.03 0.04
			12.15	% WH 200m WH edges 200m 1.89 -1.09 0.82 5.90		0,51 1,19 0.50 0.14	-0,063 -0,02 0.22 0.65 0.26 -1.15	1,089 2,40 -0.20 -0.37 X X 321.0	0.46 -0.19 171.4 0.26 78.8 0.82 222.9	2.30 2.61 1.99	0.11 0.03 0.04
			12.12	% crops 500m % WH 200m 1.90 -1.00 0.81 5.90		-0,48 0,63 -0.38 -1.41 -1,147 -0,90 0.33	0,192 2,15 0.28 0.16	321.2	0.66 -0.09 171.5 0.23 80.0 222.9	3.57 2.64 2.02	0.06 0.03 0.04
	generalist richness	12.15	WH edges 200m % WH 500m 1.94 4.18 0.81 5.90		0,40 0,67	-0,343 -0,68 0.26 0.67	1,151 2,01 -0.08 -0.96 -1.22 321.7 0.12 -0.23 171.6 1.17 0.18 179.5 223.0	0 2.74 2.05	0.38 0.02 0.03
	ruderal richness	0.50	% WH 500m WH edges 500m 4.18 0.82 5.90		0,30 0,27 0.18 0.21 -1.55 -0,513 -1,269 0.43	1,112 1,818 0.20 -0.28	190.7 0.20	0 0.83 223.0 0.24 180.7 171.7	1.22 2.90 2.11	0.21 0.02 0.03
	Species number farmland forest generalist urban* NA Total Summed species occurrences low medium 2 4 5 8 6 4 3 1 16 17 low medium farmland 23 40 forest 29 90 generalist 112 78 urban* 23 7 NA Total 187 215 Nesting species number low medium farmland 2 4 forest 1 7 generalist 5 4 urban* 3 1 NA Total 11 16 plants birds dimorphic richness * farmland total richness* 0.49 0.47 birds carabids 0 plants carabids birds plants carabids birds plants carabids birds plants carabids birds plants carabids birds 0.0 plants carabids birds carabids 0.50 plants 0 plants carabids birds 0 plants carabids birds plants carabids birds plants carabids birds 5 plants carabids birds 0.0 plants carabids birds biodiversity measure landscape descriptor high long distance* 6 2 6 6 2 5 2 23 6 high long distance* 21 23 61 35 33 22 16 139 72 high long distance* 3 2 5 4 2 2 14 4 0.2 0.4 Proportion of permanent grasslands (%) NA Total 3 17 19 2 20 11 5 5 10 72 NA Total 9 116 180 2 260 68 7 7 18 631 NA Total 1 12 13 15 6 1 1 2 47 0.6 (Intercept) 5,90 (Intercept) 1,55 0.17 4.18 0.82 0.20 5.90 Proportion of permanent grasslands (%) WH edges 200m -1,38 -3,003 0.8 4,737 1,338 PG connect 500m 0,09 -0,116 -0.16 4.18 -0.09 0.80 5.90 0.21 -1.27 1 2 3 4 5 6 7 Grassland connectivity index 2 4 6 8 Area of largest grassland patch (ha) 1 2 3 4 5 Grassland habitat edges (km) 0.1 0.2 0.3 0.4 0.5 Proportion of wooded habitats (%) 1 2 3 4 5 Wooded habitat edges (km) 0.2 0.4 0.6 0.8 Proportion of crops (%) 0.1 0.2 0.3 0.4 0.5 0.6 0.7 % crops 500m 0,94 -0,700 % PG 500m 0,08 -0,132 0.08 4.18 0.48 0.83 0.22 0.18 5.90 10 20 30 40 Grassland connectivity index 5 10 15 20 25 30 35 Area of largest grassland patch (ha) 5 10 15 20 25 Grassland habitat edges (km) 0.1 0.2 0.3 0.4 0.5 Proportion of wooded habitats (%) 10 15 20 25 Wooded habitat edges (km) 0.2 0.4 0.6 0.8 Proportion of crops (%) estimates lower CI upper CI 7,063 1,760 -0.34 -0.29 -1.22 190.8 0.20 -0.18 171.8 0.13 0.23 181.3 0.81 223.2 0,234 0,300 191.0 0.32 0.21 182.1 0.20 0.30 -0.09 172.4 -0.41 1.04 223.3 2,587 0,287 192.4 1.75 0.10 -0.40 182.3 0.10 172.8 -1.49 223.6 allspecies richness (Intercept) 14,74 11,941 17,547 WH edges 200m 2,05 0,123 3,983 PG connect 500m 0,97 -0,734 2,678 WH edges 500m 0,64 -2,046 3,321 forest richness * (Intercept) -0,42 -0,762 -0,074 PG edges 500m 0,33 -0,183 0,839 PG connect 500m 0,32 -0,180 0,821 % crops 500m -0,25 -0,777 0,267 % PG 500m 0,17 -0,394 0,729 grassland richness (Intercept) 12,14 10,261 14,024 WH edges 200m 1,97 0,488 3,443 PG connect 500m 0,66 -0,667 1,986 WH edges 500m 0,12 -1,826 2,072 ruderal richness ** (Intercept) 0,49 0,036 0,940 PG LP 200m 0,15 -0,091 0,400 biodiversity measure landscape descriptor estimates lower CI upper CI allspecies richness (Intercept) 3,02 2,890 3,153 activity-density (Intercept) 1,42 1,224 1,616 % WH 200m 0,18 -0,029 0,391 X crop richness (Intercept) 1,05 0,803 1,289 activity-density ** (Intercept) -0,98 -1,817 -0,514 forest richness (Intercept) 0,79 0,39 1,18 WH edges 200m 0,38 -0,02 0,78 X % WH 200m 0,33 -0,068 0,738 PG edges 200m 0,15 -0,235 0,534 activity-density (Intercept) -1,08 -1,802 -0,367 % WH 200m 0,56 0,015 1,108 % crops 500m -0,42 -1,050 0,203 WH edges 200m 0,45 -0,309 1,200 generalist richness * (Intercept) 4,18 3,456 4,894 % WH 500m -0,62 -2,479 1,243 WH edges 500m -0,76 -2,568 1,058 % WH 200m -0,16 -2,069 1,741 activity-density (Intercept) 0,11 -0,279 0,505 WH edges 200m -0,31 -0,649 0,023 PG LP 200m 0,06 -0,295 0,405 macropterous richness (Intercept) 2,47 2,293 2,645 PG LP 200m -0,14 -0,323 0,049 % WH 500m -0,10 -0,290 0,081 activity-density (Intercept) 0,84 0,583 1,105 WH edges 200m 0,34 0,086 0,586 biodiversity measure Landscape descriptor estimates lower CI upper CI all species total richness (Intercept) 24,29 22,623 25,949 WH edges 200m 1,18 -0,508 2,863 richness of nester (Intercept) 15,87 14,869 16,865 % WH 200m 0,59 -0,270 1,459 WH edges 200m 0,52 -0,381 1,431 abundance of nester (Intercept) 25,81 24,27 27,35 % WH 200m 2,01 -0,31 4,32 WH edges 200m 1,60 -0,76 3,95 % WH 500m -0,39 -2,70 1,92 forest total richness (Intercept) 7,10 6,345 7,845 PG LP 200m -0,76 -1,526 0,009 X WH edges 200m 0,66 -0,155 1,483 PG connect 200m 0,52 -0,338 1,381 WH edges 500m 0,30 -0,666 1,269 richness of nester (Intercept) 4,38 3,934 4,827 richness of nester (Intercept) 2,76 2,330 3,194 WH edges 500m -0,62 -1,096 -0,153 % WH 500m -0,40 -1,058 0,250 PG edges 500m ---abundance of nester (Intercept) 3,14 2,631 3,655 WH edges 500m -0,82 -1,389 -0,248 % WH 500m -0,40 -1,282 0,478 Biodiversity measure Intercept PG LP 200m WH edges 200m % crops 200m % PG 500m PG connect 500m PG edges 500m 0.48 0.11 -0.10 192.7 2.06 0.09 4.18 0.45 -0.14 183.4 activity-density -1.07 0.60 75.6 5.90 0.23 -1.27 -0.95 -0.39 223.8 WH edges 500m AICc ΔAICc wi 0.49 0.17 0.00 193.2 2.55 0.07 0.47 -0.20 -0.05 193.3 2.63 0.07 anemochor richness 1.03 199.7 barochor richness 1.66 0.16 249.6 0 0.38 activity-density 0.17 0.42 124.7 open richness 2.23 0.21 246.5 2.23 -0.18 247.7 2.24 248.7 -1.13 -0.59 76.6 -1.12 0.46 -0.37 76.6 -1.17 0.46 -0.50 77.1 -1.11 0.49 0.25 77.6 5.90 -0.30 -1.09 0.96 223.8 5.90 -0.31 -1.12 -0.44 223.8 5.90 -0.73 -1.04 224.1 5.90 -0.26 -0.87 -1.09 224.2 % crops 500m allspecies richness 14.77 2.03 0.99 345.7 0 0.25 14.76 1.99 0.97 0.08 345.9 0.20 0.23 14.69 1.92 0.61 346.1 0.45 0.20 14.76 2.30 346.2 0.47 0.20 14.75 0.90 1.56 348.6 2.88 0.06 14.68 2.02 348.7 3.04 0.06 forest richness -0.42 0.38 119.6 0 0.19 -0.42 0.38 119.7 0.04 0.19 -0.41 -0.37 120.3 0.68 0.14 -0.40 0.33 120.9 1.25 0.10 -0.43 0.25 -0.17 121.6 1.93 0.07 -0.43 0.20 0.21 121.6 1.94 0.07 -0.42 0.28 -0.11 121.9 2.21 0.06 -0.42 0.04 0.34 122.0 2.31 0.06 -0.42 0.05 0.34 122.0 2.35 0.06 -0.42 0.12 -0.26 122.4 2.80 0.05 1.66 0.13 -0.05 251.5 1.88 0.15 1.68 -0.11 251.7 2.08 0.13 1.66 0.02 0.15 251.9 2.27 0.12 1.69 0.10 252.4 2.81 0.09 1.70 252.7 3.11 0.08 1.66 -0.08 0.15 -0.11 253.6 3.94 0.05 zoochor richness 6.38 1.39 268.5 0 0.70 6.38 1.26 0.34 270.2 1.71 0.30 allspecies richness 3.02 2.23 -0.03 0.18 249.0 -1.13 0.54 77.6 5.90 0.26 -1.28 -0.27 -0.40 0.83 224.3 288.7 activity-density 1.41 0.18 194.2 activity-density 0.55 142.7 -1.10 0.57 77.7 5.90 -0.97 0.92 224.4 1.43 195.4 apterous richness 0.81 0.33 0.24 168.8 -1.09 0.50 0.19 78.0 5.90 -0.25 -0.21 -1.09 0.80 224.6 crop richness 1.05 0.85 0.38 169.0 -1.16 0.45 0.40 78.3 5.90 -1.36 224.7 173.5 activity-density -0.98 0.82 0.40 169.1 -1.02 78.9 5.90 -0.95 -0.84 224.8 69.3 forest richness 0.78 0.43 174.3 0.81 0.35 -0.23 169.1 -1.14 0.32 0.22 -0.39 79.1 5.90 1.32 224.9 0.80 0.40 174.9 0.80 0.27 0.29 169.7 -1.14 0.09 -0.51 79.2 activity-density 0.12 -0.31 123.3 0.78 0.22 0.28 176.0 0.83 0.23 0.24 170.2 -1.11 0.47 -0.10 -0.45 79.3 0.11 124.2 0.78 0.11 0.38 176.6 0.83 0.29 0.19 170.2 dimorphic richness 5.90 -1.45 1.00 220.9 0.11 0.01 -0.31 125.9 0.80 0.14 0.35 177.0 0.81 0.31 -0.26 170.2 5.90 -1.42 -0.91 221.5 0.11 0.11 126.3 0.79 178.0 0.80 0.35 170.3 5.90 -1.42 -0.22 0.81 221.7 macropterous richness 2.47 265.7 0.79 0.27 178.2 0.84 0.31 -0.17 170.4 5.90 -1.16 -0.38 0.98 221.9 2.47 -0.13 266.1 activity-density -1.06 0.61 76.5 0.84 0.29 0.19 170.5 5.90 -1.68 222.0 2.47 -0.09 267.1 -1.09 0.49 -0.30 78.1 0.82 0.37 170.8 5.90 0.15 -1.54 1.04 222.1 2.46 -0.15 -0.12 267.2 -1.10 0.60 78.5 -1.11 -0.55 78.5 0.81 0.11 0.26 0.20 171.3 5.90 0.17 -1.53 -0.96 222.6 0.80 0.24 0.24 0.12 171.3 5.90 -1.15 -0.88 -0.36 222.5 0.81 -0.32 171.3 5.90 -1.25 1.05 222.5 activity-density 0.84 0.34 159.4	1.82 3.04 2.29 2.63 3.58 2.35 2.81 3.95 2.71 3.91 0 2.84 ΔAICc 0 0 1.26 2.25 0.98 0.98 1.42 1.94 2.84 2.91 3.22 3.30 2.59 1.94 3.41 0 2.10 3.45 1.26 0 2.39 3.72 0.21 2.66 3.78 0.25 3.28 3.84 0 0.27 3.45 3.97 0.61 0.91 3.57 0 1.68 1.37 3.71 0.86 2.28 1.39 0 2.62 2.65 1.39 0.56 2.95 3.71 1.46 0.81 0 3.94 1.56 1.02 0.44 0 1.71 1.04 1.40 1.67 1.94 1.22 1.49 2.00 2.01 2.50 1.71 2.50 1.58 2.46 1.55 0	0.15 0.02 0.03 0.10 0.02 0.03 0.09 0.01 0.03 0.05 0.20 0.02 wi 1.00 0.47 0.25 0.15 0.12 0.12 0.10 0.07 0.02 0.02 0.02 0.02 0.13 0.07 0.02 0.07 0.02 0.65 0.10 0.06 0.02 0.35 0.09 0.05 0.01 0.09 0.04 0.01 0.09 0.04 0.01 0.33 0.06 0.03 0.47 0.24 0.05 0.03 0.30 0.14 0.05 0.10 0.13 0.10 0.05 0.07 0.11 0.09 0.05 0.06 0.36 0.05 0.04 0.06 0.29 0.05 0.04 0.06 0.18 0.33 0.04 0.05 0.17 0.14 0.03 0.04 0.12 0.03 0.04 0.12 0.03 0.04 1.00
	% crops 200m WH edges 200m WH edges 200m Summed nesting species occurrences low medium high long distance* -0,15 % PG 500m 0,02 PG connect 500m % WH 200m farmland 21 29 9 8 anemochor richness * (Intercept) 1,03 % PG 500m % WH 500m	-0,413 -0,262 0,85 0,45 0,238 NA -0,248 -0,36 -1,087 0,74 -0,018 0,113 1,155 1,456 0,288 0,366 1,504 Total 1 68 0,849 1,189 -0,11 -0,839 0,629 -0,30 -0,947 0,349	X
	forest barochor richness * 24 activity-density 65 (Intercept) 25 (Intercept) WH edges 500m	1,67	114 1,451 -0,210 -0,19 0,18 -0,840 1,887 0,561 0,455
	generalist urban* abundance of nester 98 66 13 4 WH edges 500m 25 7 WH edges 200m 18 (Intercept) NA Total 156 164 66 26 Nesting species summed abundance low medium high long distance* 0,15 farmland 23 35 9 8 % crops 500m -0,08 PG edges 500m 0,03 open richness (Intercept) % WH 200m zoochor richness (Intercept) 6,38 % crops 500m WH edges 200m WH edges 200m 1,36 % WH 500m % WH 500m PG edges 500m 0,34 activity-density (Intercept) generalist total richness (Intercept) apterous richness (Intercept) WH edges 200m forest 42 126 30 WH edges 200m % WH 200m generalist 182 131 37 21 % WH 500m WH edges 500m	207 -0,006 0,078 7,43 0,42 5,151 24 1 1 2 414 NA Total 1 -0,257 -0,195 2,23 2,050 1,76 0,786 5,208 0,20 -0,028 0,38 -0,79 0,577 -0,13 -0,416 -0,25 -1,432 0,306 0,765 9,710 X 0,089 2,734 2,410 0,248 0,435 1,56 X 7,550 0,157 0,924 2,134 -0,461 0,55 0,204 0,814 10,14 9,639 10,647 1,142 0,82 0,490 1,147 0,81 0,111 1,501 76 0,31 -0,064 0,692 X 0,62 -0,099 1,330 198 371 0,24 -0,113 0,592 -0,44 -1,148 0,268
	urban*	15	4	7 % WH 200m % WH 500m		0,27 -0,03	26	-0,112 -0,699	0,648 0,647
	NA Total	262 richness of nester* 296	83 % crops 500m (Intercept) WH edges 500m 29 WH edges 500m		1 2 -0,21 2,08 0,23 0,05	1 672	-0,563 1,924 -0,178 -0,116	0,147 2,246 0,643 0,210
				% WH 500m		0,04		-0,118	0,207

Map of the study areas. (a) Geographical location of the study region in France. (b) Geographical location of the four study areas in north-western France. In brackets, the number of sampled grasslands in each study area (LSRY = La Roche-sur-Yon).

Acknowledgements

We thank Frédéric Vaidie, Vincent Oury, Jean-Luc Roger and Marie Jagaille for assistance with field and laboratory work. We are also grateful to Mathias Templin, Christian Hof, Matthias Schleuning, Irina Nicholas, Katrin Böhning-Gaese for sharing bird measurements used to calculate the dispersal ability predictor. This study was financed by the French Ministry for the Environment (DIVA 3: public policy, agriculture & biodiversity), the Conseil Régional des Pays de la Loire (URBIO: Biodiversity of Urban Areas), and Angers Loire Métropole (post-doctoral grant).

Authors:

Duflot Rémi a* , Daniel Hervé a , Aviron Stéphanie b , Alignier Audrey b , Beaujouan Véronique a , Burel Françoise c , Cochard Arnaud a , Ernoult Aude c , Pain Guillaume a , Pithon Joséphine A. a Title:

Online appendixes 1

Distribution of plant species among the different biological groups within the sampled permanent grasslands. Species occurrence is the number of species observations across all sampled permanent grasslands (maximum occurrence = 55). Anemochorous: winddispersed, barochorous gravity-dispersed, zoochorous: animal-dispersed. NA: species for which data were not available, others: autochorous and hydrochorous.

Appendix 1M

Estimate and 95% confidence intervals from model averaging for plant biodiversity measures. Significant results, i.e. estimates whose 95 % confidence interval do not include zero, are in bold. Near significant results are shown with a "X" sign. All models were performed using a Gaussian distribution except for biodiversity measures with *, for which


G Chagnon¹

A Stricher
email: arthur.stricher@insa-lyon.fr

G Machado

R Rinaldi
email: renaud.rinaldi@insa-lyon.fr

F Ganachaud
email: francois.ganachaud@insa-lyon.fr

D Favier
email: denis.favier@imag.fr

L Chazeau

Development and characterization of architectured silicone membranes

Keywords: Silicone rubber, crosslink density, bulge test

Some medical applications need the elaboration of membranes with specific anisotropic mechanical behaviour. The present study aims at proposing a solution based on the use of only one material. The idea lies on the creation of architectured membranes by controlling the cross-linking heterogeneities at scale of the representative elementary volume. A silicone rubber is chosen for the elaboration of such membranes, because of its elastomeric properties and its easiness to modify chemistry.

The cross-linking density is locally controlled by the use of a photo-inhibitor UV irradiated before the elastomer crosslinking. Irradiated zones crosslink less during the elastomer curing, and have therefore weaker mechanical properties than the non-irradiated zones. A minimum cross-linking density is imposed to the weak zones to keep an elastomeric behaviour with a moderate residual deformation. A modulus ratio between soft and hard phases of about 3.5 has been reached. An example [1] is proposed in figure 1 during a manual uniaxial tensile test. Different zones can be observed on the specimen due to the different cross-linking degrees. The heterogeneous deformation of the specimen is observed with smaller lateral deformations in the highly cross-linked zones of the specimen. A circular membrane was finally processed. It presents a circular central zone weakly crosslinked and an exterior zone highly cross-linked. A bulge test was performed with this membrane (cf. figure 2). The tests highlight a strong influence of the heterogeneous crosslinking state in the membrane. The stresses of the bulge test are obtained through the method developed in [2]. A simulation of the same test was performed using with two different Mooney-Rivlin (MR) constitutive equations fitted on the uniaxial tests performed on each phase. Large differences are observed between experimental results and simulations at the interface between soft and hard phases in the membrane. Finally an inverse analysis is carried out, identifying which should be the MR parameters in each point of the membrane. The results of the simulation are presented in Figure 3. It can be observed that the centre and the border of the membrane are quite homogeneous whereas an interface of about 3mm width exists.

Figure 1 .

 1 Figure 1. Architectured membrane (a) with no loading (b) manually stretched (A: Hard zones, B: Soft zones).

Figure 2 .

 2 Figure 2. Description of a circular membrane with two zones (a soft and a hard one) and of the bulge test apparatus.

Figure 3 .

 3 Figure 3. Comparison of experimental and numerical circumferential and meridian stresses along the normal radius of the membrane for a bulge test at a pressure of 25kPa.


Prof. Frédéric Lioté
email: frederic.liote@aphp.fr

Objective

Little is known of the clinical features and comorbidity profile of patients presenting with early onset gout (EOG) although international guidelines recommend their treatment rapidly after diagnosis. The objective of this study was to assess specific characteristics and comorbidities of patients suffering from gout having had an early onset.

Methods

Patients from a cross-sectional French national cohort ('GOSPEL'), having suffered their first gout flare before the age of 40 were included in the EOG group and compared to those with an onset after 40, the common gout group.

Results

A total of 120 patients, were included in the EOG group (aged 49.5 (±11.9) years) and 865 patients in the common gout group (aged 64.4 (±10.1) years). Patients with EOGmore often

Accepted Article

. presented with a history of polyarticular flares (p<0.01), but had similar frequency of flares (p=0. [START_REF] Matsuo | Genome-wide association study of clinically defined gout identifies multiple risk loci and its association with clinical subtypes[END_REF]), gout arthropathy (p=0.79) and tophi (p=0.53). Prevalence of each item composing the metabolic syndrome did not differ between groups. In early onset patients, all cardiovascular comorbidities were diagnosed after gout onset. Greater age, low HDL and excessive alcohol intake were associated in multivariate analysis to the common gout group, while familial history of gout, longer urate lowering treatment, higher serum urate levels and the metabolic syndrome were associated to the early onset gout group.

Conclusion

Herein early onset gout patients developed slightly more severe joint involvement and earlier metabolic disorders than common gout patients. Keywords: Gout; metabolic syndrome; early onset; chronic kidney disease

Significance and innovations

 Patients presenting with gout before the age of 40 develop earlier metabolic comorbidities  Patients presenting with gout before the age of 40 develop earlier severe joint involvement  Patients with early onset gout tend to preserve their renal function compared to those developing gout later on

Accepted Article

. Gout is the most common inflammatory arthritis with a recent prevalence estimated at 0.9% in France and 3.9% in the United States [START_REF] Bardin | Prevalence of Gout in the Adult Population of France[END_REF][START_REF] Zhu | Prevalence of gout and hyperuricemia in the US general population: the National Health and Nutrition Examination Survey 2007-2008[END_REF]. The disease is triggered by monosodium urate (MSU) deposition after longstanding hyperuricaemia [START_REF] Pascart | Gout: state of the art after a decade of developments[END_REF]. Unsurprisingly, given the natural history of the disease, epidemiological studies agree that gout incidence increases with age until the age of 70 years and that onset before the forties is unusual [START_REF] Doherty | New insights into the epidemiology of gout[END_REF][START_REF] Kuo | Global epidemiology of gout: prevalence, incidence and risk factors[END_REF].

Nonetheless, this observation does not apply to a significant proportion of patients; the prevalence of gout onset in adults between the age of 30 and 39 years reaching 1.3% in the United States [START_REF] Zhu | Prevalence of gout and hyperuricemia in the US general population: the National Health and Nutrition Examination Survey 2007-2008[END_REF]. This category of patients has been given specific attention by the recent European League Against Rheumatism (EULAR) and British Society of Rheumatology (BSR) guidelines that recommend a rapid initiation of urate lowering therapy (ULT) in patients diagnosed with gout before the age of 40 years [START_REF] Richette | updated EULAR evidence-based recommendations for the management of gout[END_REF][START_REF] Hui | The British Society for Rheumatology Guideline for the Management of Gout[END_REF]. Apart from few studies of Asian patients [START_REF] Lu | Clinical characteristics of and relationship between metabolic components and renal function among patients with early-onset juvenile tophaceous gout[END_REF][START_REF] Yu | Younger age of onset of gout in Taiwan[END_REF][START_REF] Zhang | Clinical characteristics of early-and late-onset gout: A cross-sectional observational study from a Chinese gout clinic[END_REF], little is known of the clinical features of patients presenting with early onset gout (EOG) in other populations, and particularly their comorbidity profile. Profiling these patients is a prerequisite to identifying patients and confirming the need for tailored management of gout in this population.

The GOSPEL cohort included a nation-wide representative population of patients treated for gout in outpatient practice in France [START_REF] Liote | GOSPEL: prospective survey of gout in France. Part I: design and patient characteristics (n = 1003)[END_REF]. The objective of this GOSPEL 4 study was to compare the clinical presentation, evolution, disease characteristics and comorbidities of patients suffering from EOG to the general population of gout patients.

Methods

Study population

This study is part of the GOSPEL survey, completed in 2009, whose design and patient characteristics have been published elsewhere [START_REF] Liote | GOSPEL: prospective survey of gout in France. Part I: design and patient characteristics (n = 1003)[END_REF]. This national cross-sectional

Accepted Article

. epidemiologic survey included 1003 outpatients at least 18 years of age diagnosed by their own physician (private practice (PP) only) as being afflicted by gout. Patients having suffered from their first gout flare prior to the age of 40 were included in the early EOG group and compared to those with onset after 40 (common gout (CG) group).

Patients' clinical features, gout history, comorbidities and treatments prescribed were recorded by physicians (general practitioners (GPs) and PP rheumatologists) at the end of the baseline visit. Particularly, time from the first manifestations of gout was noted as well as time from/to the diagnosis of comorbid conditions. Items of the metabolic syndrome were defined as follows using the latest accepted definition: obesity (elevated waist circumference above 94 cm for men and 80 cm for women), elevated blood pressure (BP) (systolic BP ≥130 mmHg or diastolic BP ≥85 mmHg or on-going anti-hypertensive therapy), elevated triglycerides (triglycerides ≥150 mg/dL or treatment), low HDL-c (HDL-c ≤ 40 mg/dL in men and ≤ 50 mg/dL in women or treatment), hyperglycaemia (≥100 mg/dL or drug treatment for elevated glucose ) [START_REF] Alberti | Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity[END_REF]. Prevalence of the metabolic syndrome was secondarily calculated by the investigators and defined by the presence of at least 3 items of the metabolic syndrome, whether they had been looked for or not [START_REF] Alberti | Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity[END_REF].

Creatinine clearance estimated by the Cockroft and Gault formula gave an estimation of the glomerular filtration rate (GFR). Only significant moderate or worse chronic kidney diseases (CKD) were considered [START_REF] Levey | Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO)[END_REF][START_REF] Levey | The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report[END_REF]. Stage 2 CKD was not determined given that there was no research for proteinuria, renal imaging, or kidney histology findings. Stage 3 CKD was defined as a moderate alteration in eGFR between 30 and 60 mL/min, stage 4 CKD expressed a severe decrease in eGFR between 15 and 30 mL/min, and stage 5 CKD relates to kidney failure with eGFR below 15 mL/min [START_REF] Levey | Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO)[END_REF].

Accepted Article

Statistical analysis

All statistical analyses were performed using R version 3.4. Qualitative variables were described as numbers (%) of each response modality; the number of missing data was recorded. Quantitative variables were described as mean ± standard deviation (SD), semiquantitative variables as median [inter-quartile interval], and number of missing data. The two groups were compared on all variables. For quantitative and semi-quantitative variables, the Student test was used for normal data and the nonparametric Mann-Whitney test otherwise. Qualitative variables were assessed using the Chi2 or exact Fisher test as appropriate.

Multivariate analysis was then applied in order to identify significant associations of the variables with the EOG/CG groups. Using patients with all data available (no missing values), a binary logistic regression model was fitted with the variables exhibiting p-values lower than 0.2 in the bivariate analysis (group comparison). Selection of variables by an automatic step-by-step method based on the Akaike information criterion (AIC) was used.

Adjusted odds ratios and 95% confidence intervals were presented.

Since the sample of patients without any missing value (379/985) was insufficiently representative of the whole study population, a multivariate analysis including a multiple imputation strategy using chained equations (MICE) was implemented. The hypothesis that the process generating missing data is Missing At Random (MAR) was considered. The Mice R package was used with five imputations for each missing data. Five "imputed" samples were obtained. The binary logistic model with automatic variable selection was fitted on each sample and pooled odds ratios and 95% confidence interval were computed.

Accepted Article

. In order to observe the influence of missing data imputation on the estimation model, the results obtained with the complete cases and those obtained using multiple imputations were compared.

All statistical tests were two-sided and P values were considered significant when equal to or less than 0.05.

Results

Bivariate analysis

The age of the first gout flare was known for 985 of the 1003 patients (98.2%) composing the GOSPEL cohort. Out of the 985 patients, 120 (12.2%) were included in the EOG group and were 49.5 (±11.9) years old at time of the study whereas the 865 patients of the CG group were aged 64.4 (±10.1) years (p<0.0001) (Table 1). Age of gout symptom onset is detailed in Figure 1.

Clinical presentation suggested disease to be more severe in the EOG group as compared to patients with CG. There was a significant greater proportion of patients having experienced other arthritis than the first metatarsophalangeal joint (MTP) in the EOG group (53.8%) than in the CG group (40.5%)(p<0.01). Furthermore, more patients of the EOG group versus the CG group had significantly experienced polyarticular flare. Disease activity was similar between groups regarding the past year's number of flares although a lower proportion of patients reached the serum urate (SU) target of below 6.0 mg/dL in the EOG group (19.1% versus 29.8% (p<0.05)) [START_REF] Bursill | Gout, Hyperuricemia and Crystal-Associated Disease Network (G-CAN) consensus statement regarding labels and definitions for disease elements in gout[END_REF]. Severity of the disease was also similar for both arthropathy and clinically palpable tophi (Table 1).

Accepted Article

. Patients from the EOG group reported their general health as being better (75.8% good, 23.3% fair and 0.8% poor) compared to those of the CG group ((63.4% good, 32.6% fair and 4.0% poor) (p=0.02). On a scale of 0 (none) to 100 (worse), gout tended to have a greater impact on the mood of patients of the EOG group of 41.3 (± 33.0) compared to 33.6 (±27.7) (p=0.09) in the CG group. Using a similar scale, all patients considered that gout had a negative impact on their social life at a level of 29.3 (± 30.9) in the EOG group versus 27.9 (±25.5) (p=0.52) in the CG group.

Regarding comorbidities, a greater proportion of patients in the CG group were suffering from moderate to severe chronic kidney disease (CKD) (estimated glomerular filtration rate (eGFR) below 60ml/min, Cockcroft-Gault formula), as 130/611 (21.3%) with available estimated glomerular filtration rate (eGFR) had CKD 3 or 4 in the CG group compared to 5/79 (6.3%) in the EOG group (p<0.01). The metabolic syndrome was significantly more prevalent in the CG group (554/865 (64.0%) patients) than in the EOG group (64/120 (53.3%) patients) (p<0.05). When considering each individual item of the metabolic syndrome, prevalence of each item was not significantly different between groups when they were measured (Table 1). Time from the physician-diagnosis of cardiovascular complications to the first symptoms of gout was significantly different between groups. Gout preceded all cardiovascular events in the EOG group contrary to the CG group where all events were diagnosed at about the same time as gout.

Multivariate analysis

Multivariate analysis was performed using 18 variables exhibiting p-values lower than 0.2 in the bivariate analysis (group comparison). Only 12 variables were retained in at least one of the reduced model (no missing value model and models on the 5 imputed samples). Odd

Accepted Article

. ratios (OR) for these 12 variables are presented for the no missing value only model and pooled OR for the multiple imputations model in Table 2. Overall, 40 patients of the EOG group and 339 patients of the CG had all data available. In the EOG group, the data was available as follows: excessive alcohol consumption (117/120), the metabolic syndrome (70/120), SU level (113/120). Greater age, low HDL and excessive alcohol intake were associated to the CG group, while familial history of gout, longer ULT treatment, higher SU levels and the metabolic syndrome were associated to the EOG group. Association of the EOG group with the metabolic syndrome was highly significant in the model including only patients having all data available with an OR of 7.04 [1.4-47.03], but significance was lost in the model using multiple imputations (1.87 [0.91-3.83] (p=0.09)). Conversely, excessive alcohol intake was not significantly associated to the CG group in the model including only patients with all data available (p=0.07), but was significant in the multiple imputation model (p=0.02). Prevalence of the metabolic syndrome was strongly associated with excessive alcohol consumption (p=0.009). Excessive alcohol consumption was frequently a missing value (19% of all missing data) and its imputed values had therefore a high influence on the significance of the association of the metabolic syndrome.

Discussion

This study provides an assessment of the profile of patients suffering from evolved early onset gout in France. Despite longer disease duration at time of study, these EOG patients presented with fewer renal and physician-identified cardiovascular comorbidities than the common profile of gouty patients followed in clinical practice. Yet, notwithstanding their younger age, patients with EOG presented with joint involvement as severe as patients 15years older with the so-called classical profile. Even more concerning, the 50-year-old

Accepted Article

. patients shared the same prevalence of diabetes mellitus and individual items of the metabolic syndrome as 65-year-old classical gout patients. Whereas the first signs of gout usually appear around the diagnosis of other metabolic comorbidities, in our early onset patients, gout preceded most other comorbidities. These results further suggest the existence of a window of opportunity for the rapid treatment of patients developing gout before the age of 40 advocated by EULAR relying on expert opinion [START_REF] Richette | updated EULAR evidence-based recommendations for the management of gout[END_REF].

Knowledge of gout genetics is growing and it is now known that gene polymorphisms participate in the disease progression. Genetics can account for the development of EOG even in the absence of associated risk factors such as the metabolic syndrome, excessive alcohol intake, drugs or CKD [START_REF] Matsuo | Genome-wide association study of clinically defined gout identifies multiple risk loci and its association with clinical subtypes[END_REF]. The probability is high that such EOG patients developed gout largely because of genetic polymorphisms given the fact that they were not particularly heavy drinkers, had on average better renal function, took fewer diuretics and had less prevalent metabolic syndrome features. Genetic mutations, such as partial HPRT deficiency [START_REF] Lahaye | New mutation affecting hypoxanthine phosphoribosyltransferase responsible for severe tophaceous gout[END_REF], or mostly uric acid transportosome mutations [START_REF] Merriman | An update on the genetic architecture of hyperuricemia and gout[END_REF], are not routinely tested in clinical practice for gout management, and the weight of genetics in the development of gout in younger patients cannot be thoroughly addressed by this study. High frequencies of ABCG2 proteins have been found in a recent retrospective cohort study from China, without difference between EOG and CG [START_REF] Zhang | Clinical characteristics of early-and late-onset gout: A cross-sectional observational study from a Chinese gout clinic[END_REF]. For Matsuo et al, common dysfunction of ABCG2 is a major cause of EOG and its detection might serve to improve earlier prevention and therapy for high-risk individuals (19). However, the higher prevalence of known familial history of gout in the EOG group further supports the hypothesis of a strong underlying genetic basis.

Missing data accounts for discrepancies in performance of the metabolic syndrome and excessive alcohol consumption in the 2 multivariate models. Multiple imputations models consider multiple scenarios in their construction, which widens the range of the OR, providing a possible explanation of why the metabolic syndrome performs differently in the 2

Accepted Article

. models as 42% of values had to be imputed (versus 29% in the CG group). On the contrary, given the very small number of missing data for alcohol consumption, the range of the OR was reduced in the multiple imputation model because the sample was increased and few imputations needed to be performed.

Our study suggests that patients with EOG develop earlier metabolic conditions and, despite longer disease durations, tend to preserve their kidney function. A Taiwanese casecontrolled study of very early gout onset (before the age of 20) suggested that despite their higher body mass index, very EOG tophaceous patients had on average lower lipid and fasting glucose levels when compared to middle-aged onset gout patients. Data from the Chinese cohort studied by Zhang et al. had overall a better cardiovascular profile and particularly had much lower prevalence of metabolic syndrome [START_REF] Zhang | Clinical characteristics of early-and late-onset gout: A cross-sectional observational study from a Chinese gout clinic[END_REF]. Furthermore, Asian patients with very EOG also had preserved renal functions despite longer disease duration in both Asian populations [START_REF] Lu | Clinical characteristics of and relationship between metabolic components and renal function among patients with early-onset juvenile tophaceous gout[END_REF][START_REF] Yamanaka | Gout and hyperuricemia in young people[END_REF]. Our multivariate analysis has also shown that the lipid profile of EOG was better than the one of the CG group, as low-HDL was significantly associated with the latter in all models tested. Yet, the smaller than expected difference between the groups concerning the prevalence of the metabolic syndrome in bivariate analysis was surprising given the age difference [START_REF] Ford | Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey[END_REF]. Multivariate analysis has confirmed that suspicion: once adjusted on age, the metabolic syndrome was significantly associated to the EOG group in the model using only patients with all available data. This result was only a trend in the model using multiple imputations due to the association of excessive alcohol consumption with the CG group. Indeed, given the strong association of the metabolic syndrome and excessive alcohol intake, some of the crude higher prevalence of the metabolic syndrome in the CG group was related to higher alcohol consumption [START_REF] Wakabayashi | Association between alcohol drinking and metabolic syndrome in Japanese male workers with diabetes mellitus[END_REF][START_REF] Jin | Association between alcohol consumption and metabolic syndrome in 19,215 middle-aged and elderly Chinese[END_REF][START_REF] Athyros | Association of drinking pattern and alcohol beverage type with the prevalence of metabolic syndrome, diabetes, coronary heart disease, stroke, and peripheral arterial disease in a Mediterranean cohort[END_REF]. Furthermore, bivariate analysis and the model using multiple imputations provided worse case scenarios of the link between EOG and the metabolic syndrome because missing data which was in proportion

Accepted Article

. more important in the EOG group was considered as an absence of item of the metabolic syndrome, with a higher risk to underestimate its prevalence in the EOG group. The model including only patients with all available data certainly provided the best case scenario and the strength of the link between EOG and the metabolic syndrome probably lies in between.

Although gout has been shown to be implicated in the progression of CKD, our results support the hypothesis that renal function is less impacted by early than late gout [START_REF] Zhang | Clinical characteristics of early-and late-onset gout: A cross-sectional observational study from a Chinese gout clinic[END_REF][START_REF] Richette | Improving cardiovascular and renal outcomes in gout: what should we target?[END_REF].

Taking this into account, specific attention should be given to metabolic conditions in EOG patients, more so than the preservation of kidney function that seems less at stake. Overall, our study suggests that disease activity and severity of the joint involvement of EOG are comparable to those of later-on onset gouts, as confirmed by multivariate analysis. This is surprising given prior data found in the literature. Yu et al. performed a retrospective analysis of 1,079 Taiwanese gout patients showing a younger age of onset than usual (average age of 41.6 years) and far shorter disease duration than our EOG group (4.2 years). Patients presented with more severe gout and recurrent yearly flares (75.9% of patients presented with at least three flares per year), higher SU levels (10.3 mg/dL) and almost as many tophi (16.8%) compared to our cohort [START_REF] Yu | Younger age of onset of gout in Taiwan[END_REF]. Abhishek et al. tried to identify factors associated with recurrent gout flares among untreated gout patients and found that high SU levels and long disease duration predict recurrent flares, but with poor performance [START_REF] Abhishek | Association of Serum Uric Acid and Disease Duration With Frequent Gout Attacks: A Case-Control Study[END_REF]. Higher SU levels in the Taiwanese patients probably explain the more recurrent flares and early tophi than in our GOSPEL cohort EOG group. The three-fold longer disease duration in the EOG group led to similar disease severity in these patients, who on average

had not yet reached their fifties, than in gout patients with the classical profile, who are on average 15 years older. This finding further supports the recent recommendation made by the EULAR and the BSR to treat early patients presenting with early gout, not only to prevent aggravation of comorbidities but also the outcome of severe chronic joint lesions [START_REF] Richette | updated EULAR evidence-based recommendations for the management of gout[END_REF][START_REF] Hui | The British Society for Rheumatology Guideline for the Management of Gout[END_REF].

Accepted Article

. We acknowledge that this study has some limitations. Patients included in the GOSPEL cohort were considered as having gout by their physician but were not crystalproven. A large majority of patients however presented with at least 6 items of the 1977 ACR criteria and their proportion was similar between groups [START_REF] Wallace | Preliminary criteria for the classification of the acute arthritis of primary gout[END_REF]. The new 2015 ACR/EULAR criteria could not be applied retrospectively [START_REF] Neogi | Gout classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative[END_REF]. Secondly, not all patients had recent biological tests which may have impaired to some extent assessment of SU levels, dyslipidemia and hyperglycaemia but this could be corrected by multivariate analysis using the multiple imputation model. Using the multiple imputation model is a robust and stringent statistical analysis which fully takes into account the missing data. In this model, variables that remain significant are reliable. Thirdly, recollection of the date of the first symptoms of gout and diagnosis of comorbidities is subject to imprecision.

In routine practice, patients with EOG present during their evolution with slightly different joint involvement and similar disease severity than the more common middle-age onset gout patients. Despite younger age on average, they present with similar prevalence of diabetes mellitus and metabolic conditions as their older counterparts but benefit from rather preserved renal function. Given these early joint and metabolic complications, this study advocates for an early management of patients with early onset gouts. The metabolic syndrome was defined as the presence of at least 3 of the 5 following items, all item with missing data were considered negative. Abdominal obesity was defined by elevated waist circumference above 94 cm for men and 80 cm for women), high blood pressure was defined by systolic BP ≥ 130 mmHg or diastolic BP ≥ 85 mmHg, or on-going antihypertensive therapy, hypertriglyceridemia defined by triglycerides ≥150mg/dL or treatment, low HDL-c defined by HDL-c ≤ 40mg/dL in men and ≤ 50 mg/dL in women or treatment, hyperglycaemia defined by fasting glucose ≥100 mg/dL or drug treatment for elevated glucose.

Statistical significance defined by p<0.05.

Table 1 :

 1 Characteristics of the early onset and common gout groups. SU: serum urate level; ULT: urate lowering therapy; MTP: metatarsophalangeal; eGFR: Estimated glomerular filtration rate (estimated by the Cockroft-Gault formula); T2D: type 2 diabetes

	Tables and Figures
	Characteristics (number of patients with available	Early onset gout	Common gout
	data)	(n =120)	(n = 865)	p
	Demographics
	Age (years) (n=985)	49.5 (±11.9)	64.4 (±10.1)	<0.0001
	Age at gout onset (years) (n=985)	32.8 (±5.7)	57.2 (±10.9)	<0.0001
	Gout duration (years) (n=985)	16.2 (±13.1)	6.9 (±6.7)	<0.0001
	Male	96,7%	86,6%	0.08
	Known family history of gout (n=980)	38.1%	16.7%	<0.0001
	Renal stone (n=974)	8.5%	3.6%	<0.05
	Excessive alcool consump
	tion (n=793)	41.8%	50.8%	0.12
	Daily alcohol consumption (g/day) (n=793)	27.7 ± 27.2	31.8 ± 31.1	0.1
	Daily consumption of sugar-sweetened beverages
	(n=799)	35.4%	26.6%	0.09
	Body mass index (kg/m 2 )(n=979)	28.7 (± 4.0)	28.3 (±4.1)	0.16
	Clinical tophi (n=985)	17.5%	19.9%	0.54
	Treatment
	Ongoing ULT (n=973)	68.9%	67.9%	0.91
	ULT (allopurinol) duration (years) (n=970)	11.3 (±10.2)	6.6 (±6.0)	<0.001
	SU level at ULT initiation (mg/dL) (n=943)	8.86 (± 1.52)	8.54 (± 1.19)	<0.05
	Last SU level (mg/dL) (n=802)	6.97 (±1.70)	6.77 (±1.97)	0.14
	Last SU level below 6.0mg/dL (n=802)	19.1%	29.8%	<0.05
	Joint disease
	Gout arthropathy (n=985)	22.5%	23.6%	0.79
	Number of flares per year (n=985)	2.14 (±1.75)	1.93 (±1.49)	0.06
	At least 2 flares per year (n=985)	96.7%	92.7%	0.16
	At least one polyarticular attack (n=956)	49.6%	34.8%	<0.01
	Arthritis other than 1 st MTP joint (n=954)	53.8%	40.5%	<0.01
	Comorbidities
	eGFR below 60ml/min (n=690)	6.3%	21.3%	<0.01
	Ischemic heart disease (n=973)	3.4%	9.4%	<0.05
	Physician-indentified dyslipidemia (n=975)	36.4%	48.9%	<0.01
	Physician-identified hypertension (n=979)	30.8%	57.8%	<0.0001
	Cerebrovascular accident (n=970)	1.7%	3.3%	0.57
	Physician-identified diabetes mellitus (n=973)	12%	15.5%	0.38
	Diuretics use (n=985)	9.2%	23.5%	<0.0001
	Metabolic syndrome (n=985)	53.3%	64.0%	<0.05
	High blood pressure (n=984)	85%	91%	0.06

Acknowledgments

We are grateful to Dr Marie-Christine Andro-Delestrain (Mayoly-Spindler), Prof Alain Saraux, Brest, Dr Sabine Lanz (laboratoires Mayoly-Spindler) and Dr Charles Lambert (Ipsen Pharma) for their support and contribution to the project; Dr Pierre Chiarelli and Mr Samy

The GOSPEL project was initially funded thanks to institutional support from LGV, a former Mayoly-Spindler company, and Ipsen Pharma. The current study was funded by the Association pour la Recherche en Pathologie Synoviale (ARPS). The pharmacists and physicians representing the sponsors contributed to discussions on the initial study design.

The sponsors had no role in the decision to submit this manuscript.

Accepted Article

. Sahbane (Vivactis Etudes Cliniques, Courbevoie) for preparing, implementing, and conducting the study's quality controls; Mrs Catherine Delva and Sylvie Lancrenon for preparing preliminary statistical analyses; the general practitioners and rheumatologists who contributed to data collection; and the patients who agreed to participate in the study. The

Accepted Article

Variables

Accepted Article

.