Denis Lafage
email: lafage.denis@gmail.com

Sébastien Maugenest

Jan-Bernard Bouzillé

Julien Pétillon

Disentangling the

Keywords: connectivity, variance partitioning, nestedness, spiders, carabids

come

Introduction

A large number of studies have tried to understand the determinants of local species richness, i.e. αdiversity [START_REF] Jiménez-Valverde | Climate and regional beta-diversity gradients in spiders: dispersal capacity has nothing to say?[END_REF]. Studies dealing with β-diversity are considerably less numerous [START_REF] Mcknight | Putting beta-diversity on the map: broad-scale congruence and coincidence in the extremes[END_REF] but their number has increased in the recent years (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][START_REF] Boieiro | Spatial factors play a major role as determinants of endemic ground beetle Beta diversity of Madeira island laurisilva[END_REF][START_REF] Braaker | Habitat connectivity shapes urban arthropod communities -the key role of green roofs[END_REF]. α-Diversity describes within-habitat diversity [START_REF] Macarthur | The theory of island Biogeography[END_REF] and is mainly driven by local processes [START_REF] Whittaker | Evolution and measurement of species diversity[END_REF]. β-Diversity, or between-habitat diversity [START_REF] Magurran | Measuring biological diversity[END_REF]) is believed to respond to two models: i) the niche model, which states that species sorting is linked to species requirements; ii) the model which states that species sorting is linked to dispersal across the landscape [START_REF] Nekola | The distance decay of similarity in biogeography and ecology[END_REF]. Thus β-diversity is generally thought to be driven by both local and landscape factors, yet the impact of local and landscape factors on α-and βdiversities seems to vary among groups (e.g. [START_REF] Báldi | Effects of grazing and biogeographic regions on grassland biodiversity in Hungary -analysing assemblages of 1200 species[END_REF][START_REF] Duflot | Landscape heterogeneity as an ecological filter of species traits[END_REF]) and habitats studied (e.g. Jeanneret et al. 2003a). For instance, plant species richness response to landscape diversity has been found to be negative by [START_REF] Purtauf | Landscape context of organic and conventional farms: Influences on carabid beetle diversity[END_REF], positive by [START_REF] Weibull | Species composition in agroecosystems: The effect of landscape, habitat, and farm management[END_REF] and null by Dauber et al. (2003) and Krauss et al. (2004). It is generally hard to draw a precise picture of the factors shaping diversity, and especially β-diversity, of diverse groups at different scales because most studies either investigated one model-group or used different explanatory variables in diverse habitats, making hard to properly compare the influence of similar variables on different taxa.

In agro-ecosystems, meadows are considered to be the most species-rich habitat [START_REF] Noordijk | Effects of vegetation management by mowing on ground-dwelling arthropods[END_REF][START_REF] Woodcock | Effects of seed addition on beetle assemblages during the re-creation of species-rich lowland hay meadows[END_REF] and have been proven to be an important source of diversity for crops [START_REF] Benton | Farmland biodiversity: is habitat heterogeneity the key?[END_REF][START_REF] Purtauf | Landscape context of organic and conventional farms: Influences on carabid beetle diversity[END_REF]. Plants and arthropods are a key component of meadow ecosystems, particularly spider and carabid species that can play a role in pest control [START_REF] Symondson | Can generalist predators be effective biocontrol agents?[END_REF].

However, the intensification of agricultural practices has led to a serious impoverishment of species diversity with the introduction of damaging practices [START_REF] Marshall | Impacts of an agri-environment field margin prescription on the flora and fauna of arable farmland in different landscapes[END_REF]) and landscape homogenisation (e.g. [START_REF] Schmidt | The role of perennial habitats for Central European farmland spiders[END_REF][START_REF] Strijker | Marginal lands in Europe-causes of decline[END_REF]). In Europe, agri-environment schemes (AES) have been implemented to counteract these effects by providing financial incentives for farmers to adopt extensive agricultural practices. AES seem to have varying success [START_REF] Kleijn | Mixed biodiversity benefits of agri-environment schemes in five European countries[END_REF]Lafage and Pétillon 2015) depending on the AES type and the model studied.

Indeed, some studies recently suggested that landscape factors could have a higher impact on species diversity than management practices [START_REF] Weibull | Species composition in agroecosystems: The effect of landscape, habitat, and farm management[END_REF][START_REF] Batáry | Are spiders reacting to local or landscape scale effects in Hungarian pastures?[END_REF], lowering the effect of agricultural practices improvement. Understanding the relative importance of local and landscape factors on α-and β-diversities is thus essential to design effective measures to promote biodiversity in agroecosystems. Further, assessing the relative contributions of local versus regional processes is considered essential in understanding global patterns of species diversity [START_REF] Huston | Local processes and regional patterns: appropriate scales for understanding variation in the diversity of plants and animals[END_REF]).

Here, using variance partitioning, we disentangled the influence of both local and landscape (composition and connectivity) variables on α-and β-diversities of three biological model groups varying in (shortdistance) mobility and (long-distance) dispersal: two groups of macro-arthropods (spiders and carabids) and plants in a single habitat, some meadows of the Loire Valley.

Due to the lack of mobility of plants, we first expected α-diversity of plants to be more driven by local factors than that of spiders and carabids. In wet meadows, seed dispersal is reported to be low outside of flood events, and we consequently expected β-diversity of plants to be more determined by local than landscape factors (well-known role of local filters: [START_REF] Keddy | Assembly and response rules: two goals for predictive community ecology[END_REF], and also to be higher than that of the two other groups (due to increased turnover). Lastly, due to their long-distance dispersal abilities, we expected diversities of spiders and carabids to be mostly driven by landscape factors, but mainly shaped by landscape connectivity for carabids (active dispersal) and by landscape composition for spiders (passive dispersal). The relationship between structuring factors and biological groups was also investigated with an explanatory approach using multivariate analyses of species composition of the three studied taxa.

Due to differing dispersion abilities and mobility, we expected the relative importance of local and landscape factors to differ among groups, especially regarding β-diversity. For plants, little is known about the influence of the surrounding landscape and its relative importance [START_REF] Marini | Patterns of plant species richness in Alpine hay meadows: Local vs. landscape controls[END_REF].

For animals, the ability of individuals to disperse among local communities may be an important determinant of species turnover and nestedness, where species with low dispersal ability are strongly influenced by increasing habitat isolation [START_REF] Hendrickx | Pervasive effects of dispersal limitation on withinand among-community species richness in agricultural landscapes[END_REF]). Thus, we expected spider and carabid β-diversities to be mainly influenced by landscape connectivity, and plant β-diversity by local conditions. Indeed, [START_REF] Weibull | Species composition in agroecosystems: The effect of landscape, habitat, and farm management[END_REF] found species richness of butterflies, carabids, rove beetles, and spiders to generally increase with landscape heterogeneity on a farm scale. On the other hand, Öberg et al. (2007) found Linyphiidae (a spider family) diversity to be more sensitive to local habitat type. Finally, due to differences in dispersal capacities, we expected spider and carabid β-diversities to be lower than plant βdiversity.

Methods

Study area and sampling design

The study site covered 200 km of the Loire Valley in Western France (Figure 1). Land is mainly covered by hay meadows and poplar groves. The hedgerow network is prominent. Hay meadows are cut in early or mid-summer and usually grazed by cattle in autumn.

Carabids and spiders were sampled in 77 hay meadows along the study area. Sampling was carried out on 1-28 June 2013, between 10.00 and 17.00, when weather was dry. Suction sampling was realised using a suction sampler with a 12.5-cm diameter intake placed on the ground. At each sampling site, 5 samples were taken. Each sample consisted of 10-15-s suctions (total area: 0.12 m²/sample). Samples were stored in 70% alcohol in the field and taken back to the laboratory for sorting and identification to species level. Suction sampling is a standard technique to provide quantitative data [START_REF] Brook | Experimental verification of suction sampler capture efficiency in grasslands of differing vegetation height and structure[END_REF].

One plant survey per site was performed from May to June 2013. Each relevé survey sampled homogeneous herbaceous vegetation in a 16-m² plot [START_REF] Chytrý | Plot sizes used for phytosociological sampling of European vegetation[END_REF]. Within each plot, a cover value (6 classes: <1%; 1-5%; 5-25%; 25-50%; 50-75%; 75-100%) was attributed to each plant species.

Environmental characteristics

Three variable sets likely to affect arthropod and plant diversities were defined. The first variable set comprised eight variables describing local conditions. Two variables relevant for spiders and carabids described habitat conditions determined by vegetation: mean litter depth (ten measurements, to the next cm, per sampling plot) and mean vegetation height (ten measurements, to the next cm, per sampling plot). Soil characteristics were defined using mean Ellenberg indicator values [START_REF] Ellenberg | Zeigerwerte von Pflanzen in Mitteleuropa, 2nd ed[END_REF] for moisture, nutrient level, and exposure to light. One factor described whether the site had been flooded during spring 2013 (i.e. a binary variable for the short-term effects of flooding) as flooding has been proven to have a strong impact on carabid and spider assemblages [START_REF] Lafage | Short term recolonisation by arthropod after a spring flood, with a focus on spiders and carabids[END_REF]. Two variables described local meteorological conditions (mean temperature and precipitation during the sampling month).

The second set of variables corresponded to landscape composition regarding land-cover and geology. It included the four dominant land-cover types (grasslands, woods, water, and urbanisation) defined at four different scales, using buffers.

Given the landscape configuration, buffers were limited to 400 m to avoid including hills or too large amounts of water surface (buffers: 100, 200, 300, and 400 m). It also included bedrock type (arena, granite, gneiss, mica, clay, shale, chalk). Land cover types and hedgerows (see after) were retrieved using photo-interpretation of satellite imagery (BD Ortho IGN, 2008 and 200il9) [START_REF] Vaudelet | Le val de Loire: Cartographie de l'occupation du sol et du bocage de 2007 et analyses de leurs évolutions depuis[END_REF]. We retrieved bedrock type from the Simplified Lithographic Map provided by the 'Bureau de Recherches Géologiques et Minières' (BRGM).

The third variable set characterised connectivity. It included three variables describing meadows localisation: distance to the sea (longitudinal connectivity), minimum distance to the river Loire (lateral connectivity), and distance to a hedgerow. Longitudinal and lateral connectivity were included because of their role in shaping plant and arthropod distribution (Johanson et al. 1996;[START_REF] Bonn | The significance of flood regimes for carabid beetle and spider communities in riparian habitats -a comparison of three major rivers in germany[END_REF]. It also included landscape heterogeneity, estimated using the Landscape Shannon Diversity Index (SHDI), using the same four buffers used for the landscape data set. SHDI was calculated using the QGIS plugin LecoS [START_REF] Jung | LecoS -A QGIS plugin to conduct landscape ecology statistics[END_REF]. Landscape closure was estimated using the beta index (BI) of connectivity (beta = number of hedges/number of nodes). Area of the field was also included in this dataset. Correlations between predictive variables of each set were tested using Pearson tests but were weak or not significant (Table S1, S2 andS3). Finally, variables describing spatial patterns in arthropod communities at different scales were researched. We used Moran's eigenvector maps (MEM) [START_REF] Legendre | PCNM: MEM spatial eigenfunction and principal coordinate analyses[END_REF]; see [START_REF] Braaker | Habitat connectivity shapes urban arthropod communities -the key role of green roofs[END_REF] for a detailed description.

Statistical analyses

Spider and carabid species richness (α-diversity) were estimated for each sampling site using the average of four non-parametric estimators based on species incidence: Chao1, Jacknife1, Jacknife2, and Bootstrap. β-Diversity was estimated through a dissimilarity matrix (corresponding to Sørensen pair-wise dissimilarity) partitioned into its two components-species turnover (βt) and nestedness (βn)-following [START_REF] Baselga | Partitioning the turnover and nestedness components of beta diversity[END_REF] and using the betapart R package [START_REF] Baselga | betapart : an R package for the study of beta diversity[END_REF].

To ensure the buffer scalefits to group requirements, we performed simple regressions on each variable and selected the radius with lowest AIC value for each land cover or connectivity variable, and each response variable (Table S4).

Variables shaping arthropod and plant α-diversities were defined using multiple regressions with a stepwise model selection procedure by AIC [START_REF] Akaike | A new look at the statistical model identification[END_REF], implemented in the MASS R package [START_REF] Venables | Modern Applied Statistics with S, Fourth[END_REF]. Linear, logarithmic, inverse, quadratic, cubic, power, compound, logistic, growth, and exponential regression were compared and the model with the highest R² was selected.

Relative contribution of explicative variables to the model was estimated using the relaimpo package [START_REF] Grömping | Relative Importance for Linear Regression in R: The Package relaimpo[END_REF] with the R² contribution averaged over orderings among regressors [START_REF] Lindeman | Introduction to bivariate and multivariate analysis[END_REF][START_REF] Chevan | Hierarchical partitioning[END_REF].

To test for differences in β-diversities among groups, multiple-site dissimilarity matrices were computed using the betapart package for R [START_REF] Baselga | betapart : an R package for the study of beta diversity[END_REF] and the Simpson dissimilarity index. We then performed a re-sampling procedure in the β-diversity matrix (50 pairs of sites were randomly sampled 50 times) to perform multiple comparison tests.

To identify variables significantly explaining arthropod and plant β-diversities, similarity matrices corresponding to species turnover were regressed against environmental variables using the Canonical Analysis of Principal Coordinates (CAP) implemented in the vegan package for R [START_REF] Oksanen | vegan: Community Ecology Package[END_REF]).

The environmental model was built with a forward selection procedure, using CAP, on the environmental dataset to select significant variables (P = 0.05 after 9999 random permutations) to explain variation in dissimilarity matrices. Variance partitioning was then performed on the selected variables using the vegan package. Variables expressed in proportion (land cover) were arcsine-transformed and surface variables were log-transformed to fit with normal distribution.

To investigate possible differences between groups, spider, carabid, and plant drivers of species assemblages were investigated using constrained analysis. Following [START_REF] Legendre | Ecologically meaningful transformations for ordination of species data[END_REF], species activity-densities were transformed to a Bray-Curtis distance matrix prior to analyses.

The choice between redundancy analysis (RDA) and constrained correspondence analysis (CCA) was made according to the axis length of a detrended correspondence analysis (DCA). In the analyses, the distance matrix was the response variable and the environmental variables were the predictors. Monte Carlo tests with 999 permutations were carried out to test the significance of the selected environmental factors and constrained analyses axes.

Results

A total of 6036 spiders belonging to 97 species, 383 carabids belonging to 43 species, and 181 plant species were sampled. Spider and carabid assemblages were dominated by small aerial dispersers: 78% of spiders were Linyphiidae and 78% of carabids were small winged species. Spider assemblages were dominated by six Linyphiid species Tenuiphantes tenuis (29.4% of individuals), Meioneta rurestris (7.0% of individuals), Erigone dentipalplis (4.8%), Bathyphantes gracilis (4.8%), Oedothorax fuscus (4.4%) and Meioneta mollis (4.2%). Carabid assemblages were dominated by two Harpalin species (Acupalpus exiguus: 51.0% and Syntomus obscuroguttatus: 15.4%) and one Bembidin species (Bembidion biguttatus: 7.8%). Plant assemblages were more balanced with ten species totalising a cumulative cover of 35%, with a frequency ranging from 6.1% (Lolium perenne) to 2.3% (Plantago lanceolata).

The MEM analysis did not reveal any significant spatial pattern in the spider (P = 0.15), carabid (P = 0.68) or plant (P = 0.242) diversities and thus were not included in the connectivity data set.

Variables affecting arthropod and plant α-diversities Overall, our models significantly explained plant, spider and carabid α-diversities (R² = 0.63, P < 0.001; R² = 0.52, P < 0.001; and R² = 0.30, P < 0.001, respectively).

Local variables explained most α-diversity variance in plants (54.90%, Figure 2). Mean nutrient index was the main local factor (39.42% of variance explained) and was negatively linked to α-diversity (Table 1). The fraction of spider and carabid α-diversities explained by local factors was moderate to null (22.86% and 0%, respectively). Spider α-diversity was mostly negatively affected by moisture (15.8% of variance explained).

Landscape composition variables were the best predictors of spider and carabid α-diversities (54.09% and 57.93% of relative variance explained, respectively; see Figure 2). Bedrock type was the main factor explaining spider, carabid, and plant α-diversities (32.5%, 44.01%, and 18.28% of variance explained, respectively).

Finally, connectivity moderately explained α-diversity of spiders and plants (23.05%, and 21.70%, respectively), with the BI (beta index of connectivity) being the most important factor positively affecting spider α-diversity and negatively affecting plant α-diversity. Connectivity explained 42.07% of relative variance in carabid α-diversity, with the BI as the most important factor with a positive effect.

Among-group comparison of β-diversities

Spider β-diversity was 0.97, corresponding to βt = 0.95 and βn = 0.02. Carabid β-diversity was 0.95, corresponding to βt = 0.92 and βn = 0.03. Plant β-diversity was 0.96, corresponding to βt = 0.95 βn = 0.01. βn of the three groups was thus considered negligible and was not included in further analysis.

We did not find any significant difference in β-diversity between the three groups (χ² = 46.34, df = 47, P = 0.499).

Variables affecting arthropod and plant β-diversities

The first nine axes of the CAP on plant β-diversity were significant and explained 51.43% of species turnover. The first four axes of the CAP on spider β-diversity were significant and explained 20.55% of species turnover. The first three axes of the CAP on carabid β-diversity were significant and explained 31.46% of species turnover.

Variance partitioning revealed that local factors mainly affected plant β-diversity (43.75% of relative variance, Figure 3). Plant β-diversity was negatively affected by precipitation, by all Ellenberg indicators tested, and by flooding (Table 2). 2). When including interactions with local and landscape composition variables, connectivity was the second driver of spider β-diversity (30.76% of relative variance). Landscape heterogeneity also positively influenced spider βdiversity (Table 2). Carabid β-diversity was influenced by local factors (38.46% of variance explained) and positively determined by mean temperature.

When including interactions with local and landscape composition variables, connectivity was the second driver of carabid β-diversity (38.46% of relative variance). Distance to the river negatively influenced carabid β-diversity (Table 2).

Variables determining species assemblage composition CCA on spider assemblages explained 42.9% of the total variance, with the first three axes of the CCA significant (all with P = 0.005). Variables explaining spider species composition were mainly moisture and distance to nearest hedge and bedrock type (Figure 4).

RDA on carabid assemblages explained 57.59% of total variance, with the first two axes of the RDA significant (P = 0.051 and P = 0.024, respectively). The main variables explaining carabid species composition were flooding, moisture, and distance to nearest hedge (Figure 5).

RDA on plant assemblages explained 54.8% of total variance, with the first six axes significant (P = 0.005, P = 0.005, P = 0.005, P = 0.005, P = 0.01, P = 0.013, respectively). Main variables explaining plant species composition were moisture, nutrient, distance to the river, and distance to the nearest hedge (Figure 6).

Discussion

By comparing different groups in a same habitat, our study demonstrated that local factors better explained plant α-diversity, whereas spider and carabid α-diversities were more determined by landscape factors (composition and connectivity) in a floodplain. Nevertheless, variables affecting species assemblages were quite similar for the three groups and consistently included soil moisture and distance to the nearest hedge.

Plant α-diversity was primarily explained by soil nutrient index. This is in accordance with the numerous studies demonstrating that soil enrichment negatively influences plant α-diversity (e.g. [START_REF] Grace | The factors controlling species density in herbaceous plant communities: an assessment[END_REF]).

Furthermore, local characteristics such as abiotic environment [START_REF] Sebastia | Role of topography and soils in grassland structuring at the landscape and community scales[END_REF][START_REF] Bennie | Influence of slope and aspect on long-term vegetation change in British chalk grasslands[END_REF]) and other soil characteristics [START_REF] Critchley | Association between lowland grassland plant communities and soil properties[END_REF] have been demonstrated to influence plant species richness.

Among local variables, spider α-diversity was most sensitive to the moisture index. This is also in accordance with numerous studies demonstrating that soil moisture is an important driver of spider assemblages [START_REF] Entling | Niche properties of Central European spiders: shading, moisture and the evolution of the habitat niche[END_REF][START_REF] Pétillon | Influence of abiotic factors on spider and ground beetle communities in different salt-marsh systems[END_REF]). In floodplains with regular flooding events, the Ellenberg index of moisture reflects the flood duration that is known to have negative effects on arthropod species richness (e.g. Guerish et al. 2012).

Spider and carabid α-diversities were best explained by landscape composition. α-Diversity of the two groups was mainly affected by bedrock type. In floodplains, bedrock type influence soil moisture, granulometry and nutrient content, which in turn affects arthropods and plants. Arthropod diversity is indeed negatively affected by flooding (Guerish et al. 2012), carabid diversity is affected by soil structure because of their edaphic larval phase [START_REF] Blake | The relationship between the classification of Scottish ground beetle assemblages (Coleoptera, Carabidae) and the National Vegetation Classification of British plant communities[END_REF], and plant diversity is negatively affected by nutrient content [START_REF] Grace | The factors controlling species density in herbaceous plant communities: an assessment[END_REF]). Nevertheless, our findings for spiders were in opposition to previous studies, which did not find any influence of bedrock type on spider communities [START_REF] Coulson | The spider communities on peat and upland grasslands In northern England[END_REF][START_REF] Sanderson | Soil, vegetation and space: an analysis of their effects on the invertebrate communities of a moorland in north-east England[END_REF]. In intensive agro-ecosystems, landscape composition is usually considered a limiting factor for spider species richness [START_REF] Benton | Farmland biodiversity: is habitat heterogeneity the key?[END_REF][START_REF] Purtauf | Landscape context of organic and conventional farms: Influences on carabid beetle diversity[END_REF].

Conversely, in a landscape extensively dominated by hay meadows, we found spider diversity being positively affected by urban cover, although to a lesser extent. Sites with highest urban covers presented a high number of unique singleton that appear to be species associated with dry meadows. In floodplains, urbanisation takes place in zones excluded from flooding. Thus, the apparent positive influence of urbanisation could be explained by the highest altitude of urbanized zones.

As a large part of spider assemblage was composed by Linyphiidae (78% of individuals), which passively disperses through ballooning, it follows that ubiquitous species usually found in urbanised zones could have successfully colonised meadows.

Landscape effect on spider diversity is still under debate as it may depend on the habitat under study (Jeanneret et al. 2003a). For instance, [START_REF] Batáry | Are spiders reacting to local or landscape scale effects in Hungarian pastures?[END_REF] found no impact of landscape on spider richness in Hungarian pasture, whereas other studies have positively demonstrated its effect on species richness in crop fields, fallow lands, and woodlots [START_REF] Schmidt | The role of perennial habitats for Central European farmland spiders[END_REF][START_REF] Schmidt | The role of perennial habitats for Central European farmland spiders[END_REF][START_REF] Öberg | Influence of habitat type and surrounding landscape on spider diversity in Swedish agroecosystems[END_REF][START_REF] Drapela | Spider assemblages in winter oilseed rape affected by landscape and site factors[END_REF].

Carabids are usually considered more sensitive to landscape than spiders (e.g Jeanneret et al. 2003b).

However, our results suggest that spider α-diversity is more influenced by landscape composition, whereas carabid α-diversity is more influence by landscape connectivity, which was also suggested in a spider-carabid comparative study (Barabaro et al. 2005). For the three groups, the beta index of connectivity was the main variable affecting species diversity, among the connectivity variables tested. This index, reflecting the landscape's enclosure by a hedgerow network, was expected to have a positive impact on arthropod and plant α-diversities by allowing the presence of woody species and facilitation of movements between suitable habitats [START_REF] Burel | Landscape structure effects on carabid beetles spatial patterns in western France[END_REF][START_REF] Weibull | Species composition in agroecosystems: The effect of landscape, habitat, and farm management[END_REF][START_REF] Purtauf | Landscape context of organic and conventional farms: Influences on carabid beetle diversity[END_REF][START_REF] Miyashita | Landscape heterogeneity at multiple spatial scales enhances spider species richness in an agricultural landscape[END_REF]. In this instance, our hypotheses were confirmed for spiders and carabids but not for plants.

This might be a result of a limited capacity of woody plants to colonise grasslands. For instance, [START_REF] Łukasz | Edge effect in different groups of organisms: vascular plant, bryophyte and fungi species richness across a forest-grassland border[END_REF] found an optimum diversity 3 m away from the edge between forest and grassland.

We also observed that Linyphiidae and Harpalini dominated the assemblages of spiders and carabids respectively. This dominance of small species could first be attributed to a sampling effect, although suction sampling is usually recommended to quickly obtain a representative sample of epigeic arthropod communities (e.g. Duffey 1974). In fact, Mommertz et al. (1996) considered it an inefficient way to sample large arthropods (such as Carabidae and Lycosidae). However, [START_REF] Brook | Experimental verification of suction sampler capture efficiency in grasslands of differing vegetation height and structure[END_REF] considered suction sampling an efficient technique to sample arthropods, including Carabidae, pending a sufficient sampling effort. We can thus consider that our results were not biased by the sampling technique, and that the dominance of small aerial dispersers was a consequence of an environmental filter, here the stochasticity due to flooding (Zulka 1994).

In our study, the β-diversity link to nestedness was very weak for the three groups, and contrary to our expectations, we did not find any difference in species turnover rate between the three groups.

Interestingly, the main driver of plant, spider, and carabid species assemblages was linked to hydrological conditions (moisture and flooding). This could induce similar β-diversities of the three groups, with such an important driver acting on assemblages in the same way. In addition, the importance of species turnover suggests either low landscape connectivity for the groups studied, or similar dispersion capacities of the three groups in this particular landscape. Considering the well-known (long-distance) dispersal abilities of plants (Soons et al. 2004), spiders [START_REF] Bell | Ballooning dispersal using silk: world fauna, phylogenies, genetics and models[END_REF]) and carabids [START_REF] Kotze | Species decline--but why? Explanations of carabid beetle (Coleoptera, Carabidae) declines in Europe[END_REF], which have also been reported in alluvial meadows, we argue that they were not limiting, and that the high turnover is more likely explained by weak landscape connectivity. Here, the distance to the nearest hedge was indeed one of the main factors influencing species assemblages of the three groups.

Whereas the hedgerow network could be considered a movement facilitator for some animals [START_REF] Beier | Do habitat corridors provide connectivity[END_REF], it can also act as a physical barrier for dispersal [START_REF] Larrivée | Diversity of canopy and understorey spiders in north-temperate hardwood forests[END_REF], especially of ballooning spiders (which represent 78% of individuals cached) and small carabids (85% of individuals cached) as demonstrated in the Loire valley by Lafage et al. (in press). Interestingly plant species assemblages were also driven by the distance from the meadow to the Loire river, suggesting the occurence of hydrochory. In any case, as we only sampled hay meadows, we did not expect a high level of nestedness in β-diversity. Given the very low contribution of nestedness in total β-diversity, we chose not to include nestedness in further analysis. This decision was also motivated by the ongoing debate on β-diversity partitioning. Indeed, [START_REF] Almeida-Neto | Rethinking the relationship between nestedness and beta diversity: a comment on Baselga (2010)[END_REF] suggested that only species turnover estimated by [START_REF] Baselga | Partitioning the turnover and nestedness components of beta diversity[END_REF] is usable.

Species turnover was mainly influenced by local factors for carabids and plants. As expected, local abiotic conditions (assessed by Ellenberg indicator values) explained plant β-diversity, with moisture as the dominant factor. Logically, mean precipitation and flooding were also variables negatively affecting plant β-diversity. Among local variables, carabid β-diversity was only affected by mean temperature, which is in accordance with previous studies (e.g. [START_REF] Gillingham | The relative importance of climate and habitat in determining the distributions of species at different spatial scales: a case study with ground beetles in Great Britain[END_REF]).

Among landscape composition variables, bedrock type influenced all group β-diversities. Nevertheless, spiders were the most sensitive group to landscape composition, including urbanisation, which had a positive effect on species turnover. This is in agreement with the numerous studies which have found bedrock type to be an important driver of arthropod and plant species turnover (carabids: [START_REF] Blake | The relationship between the classification of Scottish ground beetle assemblages (Coleoptera, Carabidae) and the National Vegetation Classification of British plant communities[END_REF]spiders: Churchill 1998;plants: Janssens et al. 1998;[START_REF] Gabriel | Beta diversity at different spatial scales: plant communities in organic and conventional agriculture[END_REF].

As expected, connectivity was an important driver of carabid and spider β-diversity. Indeed, landscape openness is considered a dispersal trait filter for carabids, with highly mobile (small, macropterous) carabid species selected with increasing connectivity [START_REF] Duflot | Landscape heterogeneity as an ecological filter of species traits[END_REF]. As previous studies found ballooning behaviour to be dependent of landscape heterogeneity [START_REF] Bonte | Geographical variation in wolf spider dispersal behaviour is related to landscape structure[END_REF], we expected spider β-diversity to be positively influenced by landscape heterogeneity. Pure connectivity variables did not explain a significant portion of spider β-diversity. Nevertheless, interactions between the BI and local and/or landscape variables explained an important part of spider β-diversity, confirming the results of [START_REF] Bonte | Geographical variation in wolf spider dispersal behaviour is related to landscape structure[END_REF]. Our findings are also in accordance with [START_REF] Jonsen | Response of generalist and specialist insect herbivores to landscape spatial structure[END_REF] which found generalist insect diversity to increase with habitat diversity.

Overall, spider diversity appeared more sensitive to landscape composition, carabid diversity to landscape connectivity, and plant diversity to local factors. Thus, managing biodiversity in meadows implies acting at both the local and landscape scales, the former with an eye toward plants, and the latter toward arthropods. However, managers have to be aware that improving connectivity through hedgerow networks could also be a brake to biodiversity enhancement, as we found it could be for spiders and carabids.

Fig. 3

 3 Fig. 3 Venn diagram for variance partitioning of spider, carabid, and plant b-diversity. Values correspond to relative variance explained

Table 1 :

 1 Variables selected by the multiple regression program, stepAIC, for spider, carabid and plant αdiversity. F and P values are given only for the variables kept in the model by AIC. Direction corresponds to global trends of the best model fitting each variable to α-diversity. Letters in bracket indicate best model type (l: log; c: cubic; L: linear; p: power; q: quadratic, ns: no significant).

				Spiders			Carabids			Plants
			F	P	Direction	F	P	Direction	F	P	Direction
	Local	Mean vegetaion height
		Litter depth
		Mean temperature	20.54 < 0.001	-(l)				8.36	0.005	ns
		Precipitations				1.78	0.187
		Mean moisture index 13.95 < 0.001	-(c)	2.14	0.149
		Mean continentality
		index	2.20	0.143					11.59 < 0.001	-(q)
		Mean light index							15.94 < 0.001	+ (q)
		Mean nutrient index							43.66 < 0.001	-(q)
		Flooding	0.57	0.455		1.48	0.229		1.87	0.176
	Landscape	Grassland cover	5.17	0.026	-(L)	5.86	0.019	-(l)
		Urban cover	10.54 0.002	+ (L)
		Water cover				2.36	0.129		4.64	0.036	+ (q)
		Wood cover	1.59	0.212
		Lithography	4.25 < 0.001		2.43	0.036		2.89	0.015
		Area				0.65	0.424
	Connectivity	Distance to the sea				4.37	0.041	ns	5.25	0.026	ns
		Distance to the river	9.96	0.002	-(p)	0.78	0.381		3.86	0.054
		Min distance to hedge							0.05	0.822
		Beta index of
		connectivity	7.37	0.009	+ (L)	13.42 < 0.001	+ (L)	22.54 < 0.001	-(l)
		Index of
		heterogeneity							13.24 < 0.001	ns

Table 2 :

 2 Variables selected by the forward selection after canonical analysis of principal coordinates (CAP) on the β-diversity matrix for spiders, carabids, and plants. F and P values are given only for the variables kept in the model by AIC.

				Spiders		Carabids		Plants
			F	P	Direction	F	P	Direction	F	P	Direction
		Mean vegetaion
	Local	height
		Litter depth
		Mean temperature				2.45	0.010	+
		Precipitations	2.56 0.010	-				3.67	0.010	-
		Mean moisture index 2.64 0.010	-				4.21	0.010	-
		Mean continentality
		index							3.35	0.010	+
		Mean light index							4.21	0.010	+
		Mean nutrient index							2.19	0.010	-
		Flooding				4.78	0.010	-	3.39	0.010	-
	Landscape Grassland cover
		Urban cover	2.99 0.010	+				1.83	0.030	+
		Water cover
		Wood cover
		bedrock type	1.37 0.049		1.73	0.010		1.59	0.010
		Area
	Connectivity Distance to the sea				2.21	0.060		4.11	0.010	+
		Distance to the river				2.09	0.020	-	5.27	0.010	-
		Min distance to hedge							4.44	0.010	-
		Bocage length							2.79	0.020	+
		Beta index of
		connectivity							2.09	0.030	+
		Index of
		heterogeneity	2.43 0.010	+				2.31	0.010	+

Acknowledgments

We would like to thank the CORELA for its support,Charlotte Papin and Thomas Joubin for field and laboratory assistance, Arnaud Horellou for his help in identifying problematic carabids and Editage for editing the English. This study was funded by 'Plan Loire Grandeur Nature' (FEDER), 'Région Pays de la Loire' and 'Agence de l'Eau Loire-Bretagne'.

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

The authors declare that all appropriate ethics and approvals were obtained for the study.


Christopher P Ewels

Xavier Rocquefelte

Harold W Kroto

Mark J Rayson

Patrick R Briddon

Malcolm I Heggie

Predicting experimentally stable allotropes: Instability of Pentagraphene

In recent years a plethora of theoretical carbon allotropes have been proposed, none of which have been experimentally isolated. We discuss here criteria that should be met for a new phase to be potentially experimentally viable. We take as examples Haeckelites, 2D networks of sp 2 -carbon containing pentagons and heptagons, and "Penta-graphene", consisting of a layer of pentagons constructed from a mixture of sp 2 -and sp 3 -coordinated carbon atoms. In 2D-projection appearing as the "Cairo pattern", pentagraphene is elegant and aesthetically pleasing. However we dispute the author's claims of its potential stability and experimental relevance.

SIGNIFICANCE STATEMENT

We describe criteria that should be applied when evaluating whether theoretically proposed carbon allotropes may be experimentally isolated. We discuss the importance of energetic isomeric "funnels" centered on a stable allotropic form, the role of defects in catalyzing structural transformations to lower energy isomers, and chemical stability. This is demonstrated with literature examples such as C60 and B80. We apply these criteria to a recently proposed carbon allotrope, Penta-graphene, demonstrating with the aid of density functional calculations that it will not be experimentally attainable. A second example, Haeckelites, are unlikely to be experimentally achievable when neutral, but may be stabilized through significant charge transfer. The principals discussed here are general and can be applied to any theoretically proposed materials.

INTRODUCTION

One of the joys of carbon research is the huge flexibility of carbon bonding (1-4), resulting in many varied allotropes that have already been experimentally identified. Computational modeling opens the floor to predicting many more, and tools such as graph theory (5) and evolutionary algorithms ( 6) allow systematic exploration of potential bonding networks. New computationally proposed phases are typically identified as metastable via positive phonon modes, and sometimes via molecular dynamics simulations showing lattice coherence at experimental operating temperatures. However there are common criteria beyond these two tests which link those allotropes which have been experimentally isolated.

Firstly they occupy deep potential wells in the surrounding energetic landscape. Additionally the surrounding energy wells are all higher in energy, "funneling" towards the stable structural form. Finally, barriers to subsequent conversion to alternative structures are typically high. Buckminsterfullerene, Ih-C60, is a good example. The disconnectivity graph for C60 connecting the 1,812 isomers with pentagonal and hexagonal faces via branches whose height indicates the transformation barrier has a 'willow tree pattern', with a gentle funnel running towards the stable Ih-C60 isomer (7) (Figure 1a). The relatively high barriers are accessible during high temperature growth, and alternatively can be catalysed via the presence of impurities or carbon interstitial atoms (8-10).

In contrast, attempts to experimentally isolate higher order boron fullerenes have been largely unsuccessful to date. For the proposed fullerene B80 this can be understood since the energy landscape was shown to feature many closely related isomers with similar (and sometimes lower) energies (11). In contrast calculations for B40, for which there are first experimental indications (12), show a single (D2d, 1A1) cage isomer, energetically well separated from alternative isomers (Figure 1b). This behavior is consistent with the rules discussed above.

We apply here a similar analysis for experimental viability to other proposed phases, starting with "Penta-Graphene", a twodimensional carbon allotrope proposed by Zhang et al (4). The structure can be viewed as a series of out-of-plane distorted ethylene units connected via tetrahedral sp 3 -carbon linkers. The result is a corrugated layer which in projection matches the "Cairo pattern" of distorted pentagons (Figure 2a).

RESULTS AND DISCUSSION

Thermodynamic Stability: Relative Energy

The first test of any new proposed structure is of its thermodynamic stability. Considering Penta-graphene, while real phonon energies (positive eigenvalues from the Hessian matrix) indicate that it is at least a local structural minimum (4), its formation enthalpy shows that it is a very high energy structure. We have performed a number of calculations on pentagraphene and structural derivatives, using DFT/LDA calculations (see Method). Penta-graphene is 0.761 eV per atom less stable than graphene. This is significantly less stable than amorphous carbons (0.16eV) (13) most nanotubes and Ih-C60 (0.39eV) ( 14), and places it in a similar energy range to experimentally unconfirmed isomers such as .

Transforming to Graphene. Secondly, penta-graphene is not the stable centre of a "funnel" of isomeric structures, but instead forms part of an energetic funnel of structures centred on graphene. Figure 2 shows an example sequence of bond rotations and bondbreakages by which penta-graphene can be transformed directly to graphene. Each step is exothermic, but only if the unit cell vectors are allowed to geometrically relax along with all atoms. This does not appear to have been done in the original paper by Zhang et al. The unit cell area increases 19.6% from penta-graphene (25.96Å 2 ) to graphene (31.05Å 2 ). This is because the sp 3 bonding and corresponding non-planarity in penta-graphene give it a relatively dense 2D-projected basal plane compared to graphene. Constraining the unit cell dimensions to those of penta-graphene therefore energetically disadvantages planar sp 2 restructuring.

The periodic boundary conditions for the calculations necessarily require that the set of rotations at each step, e.g. from (a) to (b) be simultaneous as shown, and this may be argued to be improbable. However, we have also tested the equivalent single isolated bond rotation for a 2×2 repeated supercell of penta-graphene. Rotating just one of the C-C bonds marked in Figure 2a releases 0.812eV, showing that transformation from penta-graphene to graphene could occur stepwise.

Thus since Penta-graphene forms part of a continuous energetic funnel towards Graphene, it will not be possible to isolate experimentally on thermodynamic grounds.

The above analysis is consistent with molecular dynamics simulations which have been used to map out carbon energy landscapes in terms of structural crystallinity and density, as a function of applied pressure and temperature (16). Consistent with experiment, these landscapes predict that stable crystalline carbon polytypes contain only one hybridization state of carbon (either sp 2 or sp 3 ), with mixed phases stable in amorphous configurations. They also show energy funneling towards fully sp 2 or sp 3 phases, depending on system pressure.

Metastable structures may still nonetheless be isolated if their conversion to stable phases is kinetically inhibited due to high interconversion barriers, so we next consider kinetic stability criteria.

Kinetic Stability and the importance of defects

A classical stability test is the use of molecular dynamics (MD) simulations at experimental temperatures. Penta-graphene was shown in this way to maintain its structure for 1ps at 300K (4). However this is not sufficient to demonstrate kinetic stability.

Structural distortion in materials is typically a localized process commencing at defective sites, often catalysed by impurities and defects. The 90° carbon-carbon bond rotation processes shown in Figure 2a are an example of this. Each bond rotation step represents the annihilation of a dislocation dipole in the underlying graphene lattice, and requires the simultaneous breaking and reformation of two carbon bonds. Similar carbon-carbon bond rotation in graphene has an extremely high calculated enthalpy barrier of 8.99eV (9). For the single bond rotation step in penta-graphene discussed above we have calculated the reaction barrier to be in the range 2. 33-3.04 eV (17). This is already significantly lower than that of graphene.

However the C-C bond rotation barrier in graphene is reduced by a factor of four in the presence of defects such as carbon adatoms (9), and similarly introducing a carbon adatom to penta-graphene also drops the calculated barrier by 35% to only 1.51 eV. Additionally the local energy release associated with this restructuring will also likely render the process auto-catalytic. Thus in the presence of any defects penta-graphene is unlikely to be stable. We note that the authors did indeed run MD simulations for point defects in Penta-Graphene (4), but these were for only 5ps and with fixed lattice constants as discussed above.

Chemical Stability and Oxidation

While chemical stability is not strictly a criterion for experimental viability, it is nonetheless an important indicator. In order to examine potential environmental stability of Penta-graphene, we calculated its reaction with O 2. In the triplet state O2 oxidation of the surface is highly exothermic, releasing 2.24eV per O2 molecule, with a barrier to chemisorption of only 0.16eV. Accounting for spin conversion to an eventual singlet state (surface crossing) releases a further 2.34eV. Oxygen forms epoxides at the distorted localized surface C=C bonds. Thus Penta-graphene would undergo highly exothermic spontaneous oxidation on the slightest exposure to air.

Haeckelites and related sp 2 -layered structures

Our next example is the Haeckelites (1-3), a family of layered sp 2 -carbon structures constructed from pentagons, heptagons, and optionally hexagons. Despite extensive theoretical investigation since their first proposition nearly twenty years ago (1), these structures have never been unambiguously experimentally isolated.

Figure 3a-c shows three Haeckelite structures. In terms of thermodynamic stability all three are less stable than graphene, (Fig. 3a) Oblique O5,6,7 by 0.377eV/C, (Fig. 3b) rectangular R5,7 by 0.244eV/C and (Fig. 3c) Hexagonal H5,6,7 by 0.251eV/C, although the energies relative to graphene are significantly lower than that of Penta-graphene. Structures 3a and 3b can both be converted to graphene via 90° bond rotations of the bonds labeled 'X' in Figure 3, and hence lie on the thermodynamic 'funnel' terminating at graphene. As discussed previously, the bond rotation barrier can also be lowered through the presence of defects. Structure 3c cannot reconstruct via bond rotations to Graphene, but if produced through a sequential edge growth process could still be replaced by thermodynamically preferable graphene. Thus from the arguments above, we would not expect these structures to be experimentally viable.

If the system is charged however, the thermodynamic picture changes. Figure 4 shows the energy of Haeckelite-3c and Pentagraphene relative to graphene at different charge states. While Penta-graphene becomes increasingly unstable with charging, the Haeckelites become increasingly stable, and for high charge accumulation, Haeckelite-3c becomes more stable than graphene. The effect is even more marked for the Octahedral-square structure (Figure 3e) which shifts from 0.57eV/C less stable than neutral graphene, to comparable energy when charged 0.25e/C. Structural analogues that have indeed been experimentally isolated are metal borocarbides MB 2C2, where M=Mg, Sc, Ce, Ca, Y, La, Lu (18,19). These are layered B2C2 structures intercalated with metal ions (Figure 3). Hexagon, pentagon-heptagon (Figure 3b) and octagon-square lattices (Figure 3d) have been experimentally identified depending on the metal cation (19). Among these phases MgB2C2 exhibits a stable hexagonal graphene-like lattice. This can be understood since the Mg 2+ cation donates sufficient electrons to compensate the electron deficiency of boron with respect to carbon, i.e. 2 electrons for each B 2C2 motif. In contrast M 3+ cations (La 3+ , Ce 3+ , Y 3+ , Sc 3+ , Lu 3+ ) donate one extra electron compared to Mg 2+ , leading to an extra charge of 0.25e/lattice atom. These cations form non-hexagonal borocarbide lattices, in agreement with our analysis of thermal stability vs charge state for carbon sheets, where at 0.25e/carbon atom Haeckelite and octahedron-square lattices become thermodynamically competitive with graphene (20).

Thus while the calculations suggest that isolated Haeckelites are unlikely to be formed experimentally, in situations of heavy doping, such as metal-ion intercalation in layered crystals, or ion overlayers on surfaces, experimental isolation of Haeckelites may be possible.

CONCLUSIONS

The calculations presented here strongly suggest that penta-graphene will not be an experimentally achievable allotrope of carbon. Not only would penta-graphene be difficult to isolate from the plethora of alternative isomers with similar energies, it should rapidly restructure towards graphene in the presence of even a few catalytic impurities. Even were it to form, it would not be environmentally stable. Haeckelites, while unlikely to be experimentally attainable in isolation, may still be experimentally achievable in the presence of charge transfer ions.

In general when determining the experimental feasibility of synthesizing new carbon phases, it is not sufficient to establish the pristine material as a metastable minimum on a local energy surface. We have highlighted here the importance that the structure lie at the apex of a disconnectivity graph of related isomers, occupying a unique energetically isolated position. Chemical and kinetic stability, notably in the presence of catalytic defects (both intrinsic and extrinsic) are also critical. This analysis could easily be extended to other proposed carbon allotropes, such as the various graphyne phases ( 21), and indeed is general beyond its simple application to carbon.

METHOD

Quantum Chemical Calculations.

DFT calculations were performed using the AIMPRO code ( 22), with a basis set containing 22 independent Gaussian-based functions per carbon atom and 40 per oxygen atom. Hartwigsen-Goedecker-Hutter relativistic pseudopotentials were used (23). Finite temperature smearing was used for the electronic state population with temperature kT=0.01eV. Lattice vectors were relaxed simultaneously with atom positions and lattice symmetry broken. For Pentagraphene and related 12 atom unit cells a 4×4 Monkhorst-Pack (24) k-point grid was used, for the 2×2 48 atom cell a 2×2 k-point grid was used. Saddle points were determined using the climbing nudged elastic band algorithm (25). Orthorhombic octahedron-square cells contained 4 atoms, 8×8×1 k-points, hexagonal H567 cells had 16 atoms, 4×4×1 k-points, R5,7 cells contained 48 atoms, 2×2×1 k-point grid, and O5,6,7 cells 12 atoms, 4×4×1 k-point grid. • The exception is Ca 2+ which also favours a square-octahedral lattice, however this is because of additional effects (ionic radius). 2a) and Hex-Haeckelite (Figure 3a) compared to graphene, as a function of charge state per C atom. While Penta-Graphene is increasingly unstable with charging, Haeckelites and Square-Octahedral structures are increasingly stabilized, becoming comparable with graphene or even thermodynamically favoured at 0.25e/carbon.

•

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Figure 1 .

 1 Figure 1. (a) "Willow Tree" pattern of different C60 isomers, with the lower points of each vertical bar representing the calculated formation enthalpy relative to Ih-C60, bar heights representing the calculated barrier to transformation. This shows that Ih-C60 is significantly more stable than other isomers and lies at the centre of an "energetic funnel". Adapted from (7). (b) Calculated formation enthalpies of B40, showing the D2d cage structure is significantly more stable than alternative isomers. Taken from Reference (12).

Figure 2 .

 2 Figure 2. A calculated structural transformation route from (a) penta-graphene to (d) graphene, each step is exothermic. Red arrows indicate direction of motion of atoms for 90° rotation of carbon-carbon bonds. Red (blue) lines indicate C-C bonds that are broken (formed). Note that structures (a-c) were constrained within orthogonal unit cells, this constraint was lifted for step (c) to (d). The final structure, graphene, is 0.761 eV per atom more stable than (a). Unit cells marked with dotted lines, calculated cell dimensions are (a) 5.095Å×5.095Å, (b) 4.769Å×5.510Å, (c) 4.888Å×5.318Å, and (d) 4.883Å×6.476Å, α=100.88°.

Figure 3 .

 3 Figure 3. Three Haeckelite structures (a) Oblique O5,6,7, (b) a rectangular R5,7 and (c) Hexagonal H5,6,7 (nomenclature from Ref [1b]), and (d) octagon-square structure. In MC2B2 borocarbides, lighter carbon atoms marked with red circles are replaced with boron in (b) M=Sc and (d) M= Ce, Y, Ca, Ln (metal ions located above the centre of heptagons and octagons respectively). Bonds marked 'x', when rotated, convert a 5-7-7-5 patch into four hexagons.

Figure 4 .

 4 Figure 4. Relative stability of Penta-Graphene (Figure2a) and Hex-Haeckelite (Figure3a) compared to graphene, as a function of charge state per C atom. While Penta-Graphene is increasingly unstable with charging, Haeckelites and Square-Octahedral structures are increasingly stabilized, becoming comparable with graphene or even thermodynamically favoured at 0.25e/carbon.

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•

ACKNOWLEDGMENTS. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642742, National Science Foundation (NSF) through the Division of Materials Research (DMR) and Division of Chemistry (CHE), grants NSFDMR-1157490 and CHE-1019193, the Florida State University Research Foundation, and the Swedish Research Council (Reg. no. 2012-3174).

Author contributions: C.P.E., H.W.K., X.R. M.I.H. designed research; C.P.E., X.R. performed research; M.J.R., P.R.B. developed calculation tools; C.P.E., X.R., H.W.K., M.I.H. wrote the paper.

The authors declare no conflict of interest. 1 To whom correspondence may be addressed. E-mail: chris.ewels@cnrs-imn.fr, kroto@chem.fsu.edu