Mathieu Cusson
email: mathieu.cusson@uqac.ca

Tasman P Crowe

Rita Araújo

Francisco Arenas

Rebbecca Aspden

Fabio Bulleri

Dominique Davoult

Kirstie Dyson

Simonetta Fraschetti

Kristjan Herkül

Cédric Hubas

Stuart Jenkins

Jonne Kotta

Patrik Kraufvelin

Aline Migné

Markus Molis

Olwyen Mulholland

-L J Noël

David M Paterson

James Saunders

Paul J Somerfield

Isabel Sousa-Pinto

Nicolas Spilmont

Antonio Terlizzi

Lisandro Benedetti-Cecchi

Relationships between biodiversity and the stability of marine ecosystems: Comparisons at a European scale using meta-analysis

Keywords: Biodiversity ecosystem functioning, temporal variability, diversity-stability relationships, community variability, benthic marine coastal habitats. Highlights

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Introduction

For a long time, ecologists (e.g. [START_REF] Macarthur | Fluctuations of animal populations and a measure of community stability[END_REF][START_REF] Elton | Stability Increases with Diversity in Plant Communities: Empirical Evidence from the 1988 Yellowstone Drought[END_REF] have suggested that more diverse communities are more stable and diversity-stability relationships have been explored using various theoretical models (e.g. [START_REF] May | Stability and complexity in model ecosystems[END_REF][START_REF] Raffaelli | How extinction patterns affect ecosystems[END_REF][START_REF] Solan | Extinction and Ecosystem Function in the Marine Benthos[END_REF][START_REF] Loreau | Biodiversity and ecosystem stability: a synthesis of underlying mechanisms[END_REF], laboratory and field studies (e.g. [START_REF] Tilman | Biodiversity and stability in grasslands[END_REF][START_REF] Mcgrady-Steed | Biodiversity, density compensation, and the dynamics of populations and functional groups[END_REF][START_REF] Petchey | Species richness, environmental fluctuations, and temporal change in total community biomass[END_REF]. Interest in these relationships has resurged in recent years due to concern about the potential consequences of changing biodiversity for ecosystem functioning (e.g. [START_REF] Stachowicz | Understanding the Effects of Marine Biodiversity on Communities and Ecosystems[END_REF][START_REF] Donohue | On the dimensionality of ecological stability[END_REF].

Many of the theoretical and experimental studies have produced idiosyncratic results [START_REF] Cottingham | Biodiversity may regulate the temporal variability of ecological systems[END_REF][START_REF] Balvanera | Quantifying the evidence for biodiversity effects on ecosystem functioning and services[END_REF]. Empirical support for relationships between biodiversity and stability across different ecological systems and spatial scales is still limited and contrasting, partly because of the practical limitations of empirical studies in encompassing long-term community dynamics. Indeed, individual studies have shown that increasing diversity may reduce [START_REF] Li | Diversity-stability relationships revisited: scaling rules for biological communities near equilibrium[END_REF][START_REF] Ives | General relationships between species diversity and stability in competitive systems[END_REF][START_REF] Loreau | Biodiversity and ecosystem stability: a synthesis of underlying mechanisms[END_REF], increase (e.g. [START_REF] Tilman | Biodiversity: Population versus ecosystem stability[END_REF][START_REF] Mccann | The diversity-stability debate[END_REF][START_REF] Isbell | Biodiversity, productivity and the temporal stability of productivity: patterns and processes[END_REF], or have little or no effect on the stability of some community attributes (e.g. [START_REF] Mcgrady-Steed | Biodiversity, density compensation, and the dynamics of populations and functional groups[END_REF]. While no widespread consensus has been reached in the literature on which mechanisms are important in relating stability to biodiversity, a number of factors are known to affect the relationship. Among others, these include the scale of observation, historical effects of sites and species' life-histories, direct and indirect effects of disturbance (e.g. [START_REF] Bertocci | Contrasting effects of mean intensity and temporal variation of disturbance on a rocky seashore[END_REF] including speed and asynchrony of responses: [START_REF] Loreau | Biodiversity and ecosystem stability: a synthesis of underlying mechanisms[END_REF], biodiversity and productivity [START_REF] Kondoh | Unifying the relationships of species richness to productivity and disturbance[END_REF]. Other factors that may prevent determining relationships are pitfalls in experimental design (e.g. [START_REF] Loreau | Biodiversity and ecosystem functioning: current knowledge and future challenges[END_REF][START_REF] Hector | Biodiversity and ecosystem functioning: reconciling the results of experimental and observational studies[END_REF], calculation method and bias in estimating temporal variability [START_REF] Mcardle | Variation in the Size of Animal Populations -Patterns, Problems and Artifacts[END_REF][START_REF] Cottingham | Biodiversity may regulate the temporal variability of ecological systems[END_REF] and unappreciated statistical properties of these variables [START_REF] Doak | The statistical inevitability of stability -diversity relationships in community ecology[END_REF].

Studies on diversity and stability relationships have focussed largely on community aggregated variables (i.e. total biomass, production) or population abundances (see also [START_REF] Mykrä | The stability-diversity relationship in stream macroinvertebrates: influences of sampling effects and habitat complexity[END_REF]. Conversely, the analysis of stability of diversity per se within assemblage has received less attention. Temporal stability (inversely proportional to variability) in richness is expected to decrease with increasing average in number of taxa due to a pure statistic argument (positive scaling relationship between mean and variance). On the other hand, temporal variability in richness and changes in species structure within assemblages are important properties of communities. Disturbance regimes [START_REF] Connell | Diversity in tropical rain forest and coral reefs[END_REF][START_REF] Hughes | Reciprocal relationships and potential feedbacks between biodiversity and disturbance[END_REF] and resource availability may contribute to maintain high and relatively stable numbers of taxa at certain temporal and spatial scales. Several studies have shown that rich assemblages are locally organized in complex networks with varying interaction strengths and are prone to be generally more resistant to compositional turnover than less complex systems [START_REF] Elton | Stability Increases with Diversity in Plant Communities: Empirical Evidence from the 1988 Yellowstone Drought[END_REF][START_REF] Levine | Elton revisited: A review of evidence linking diversity and invasibility[END_REF][START_REF] Shurin | Diversity-stability relationship varies with latitude in zooplankton[END_REF]. If assemblage complexity begets stability via increased networks of interactions that prevent local extinctions, then rich (or more generally, diverse) assemblages should be compositionally more stable through time as compared with less diverse assemblages, despite the expected positive relationship between mean and variance. Also, intrinsic community properties such as negative covariance in species occurrence could lead to lower temporal variation at the more diverse sites offsetting the mean-variance scaling effect.

The role of evenness in diversity-stability relationships is not well understood [START_REF] Hillebrand | Consequences of dominance: A review of evenness effects on local and regional ecosystem processes[END_REF] and its use can provide different information not considered in the other diversity indices [START_REF] Wilsey | Relationships among indices suggest that richness is an incomplete surrogate for grassland biodiversity[END_REF]. Evenness within assemblages may enhance compositional stability [START_REF] Elton | Stability Increases with Diversity in Plant Communities: Empirical Evidence from the 1988 Yellowstone Drought[END_REF] and reduce the risk of local extinction and invasion provided that no strong dominant can prevent further colonization. [START_REF] Polley | Plant functional traits improve diversity-based predictions of temporal stability of grassland productivity[END_REF] have shown that, in some circumstances, evenness in plant abundances and functional traits contributes as much as species richness to reduce temporal variability in productivity.

Moreover, low dominance intensifies the stabilizing effect of richness on aggregated variables (e.g. total abundance): their variability becomes less affected by the scaling coefficient, z, determining the strength of the relationship between the mean and the variance [START_REF] Doak | The statistical inevitability of stability -diversity relationships in community ecology[END_REF][START_REF] Vogt | Species richness-variability relationships in multi-trophic aquatic microcosms[END_REF].

Ecological mechanisms that govern diversity, resource availability and species interactions are scale-dependent, so the prevalence of one mechanism at a given scale does not exclude the potential influence of other mechanisms at other scales (e.g. [START_REF] Whittaker | Scale and species richness: towards a general, hierarchical theory of species diversity[END_REF][START_REF] Raffaelli | Biodiversity and ecosystem functioning: issues of scale and trophic complexity[END_REF]. This justifies the need to examine diversity-stability relationships at multiple scales. In this study, existing data sets were used to examine diversity-stability relationships and test whether they were different among habitats and between different European marine systems. This approach tests the general hypothesis that diversity measures (species richness and evenness) can be used as predictors of temporal stability within assemblages. Temporal stability implies lower variability that was measured as temporal variance in total community abundance, taxa number and community structure. Our specific hypotheses are that temporal variability in univariate and multivariate measures reflecting changes in species (or higher taxa) abundance and composition within assemblages is related to biodiversity measures (i) at the scale of small patches (quadrats or grabs; ~ 0.10 m 2 ); (ii) at the scale of shores (site; ~ 100s of m 2 ); and (iii) relationships between temporal variability and biodiversity at either scale varies according to the type of habitats and regions (marine systems). We are aware that the above hypotheses tested with observational data sets remain strictly correlative, not causal.

Methods

Data bases description

Existing data sets of macrobenthic communities (n = 28) have been compiled (see list in Table 1). Each data set consists of multi-sites temporal series (6 minimum) of macrobenthic community abundances (densities or % cover) of algae and fauna and covered most European regions (Fig. 1). Data sets had median values of 12 sites per data set, 4 sampling dates and 6 samples per date. The data sets cover diverse marine benthic habitats (emergent rock: n = 20; rock pool: n = 3; sediment: n = 4) with the addition of one data set using subsurface artificial panels (discarded for categorical habitat analyses).

-Table 1

- -Figure 1-

Estimation of temporal variation

The temporal variability in species richness (number of species/taxa within quadrats/grabs) and total abundance (as density or % cover, within quadrats/grabs) of macrobenthic algae and fauna were used as surrogate measures of the community stability (where low variability corresponds to high stability). Due to differences in sampling design among data sets, the temporal variability was estimated as follows: (i) For randomised spatial samples at each sampling date, temporal variability (σ t 2 ) in targeted variables were estimated using the Mean Squares (MS) obtained from a one-way ANOVA with time as independent factor, as [START_REF] Anderson | PERMANOVA: a FORTRAN computer program for permutational multivariate analysis of variance[END_REF]. For fixed quadrats the average Bray-Curtis dissimilarity for each replicate quadrat over time was used. For analyses of variation in community structure, all abundances were square-root transformed while for variation in community composition, data was transformed to presenceabsence.

σ t 2  (MS time -MS residual )/n,

Diversity estimates

In each region, organisms were identified to the lowest taxonomic level possible in the field or in the lab (usually species). Organisms were identified at the same level of taxonomic resolution across data sets. Diversity was quantified in terms of species richness (S, number of species, taxa, or morphological groups) and Pielou's evenness (J'). Separate analyses were done using estimates from two different scales of observation: at the scale of the quadrat/grab (~ 0.10 m 2 ) and at the scale of the site (~ 100s m 2 ). Estimates at the scale of the quadrat refer to the average values of variables within quadrats (i.e. all dates pooled) while estimates at scale of the site (i.e. all dates and quadrats pooled) refer to the total number of species and to the evenness of species densities averaged by site. Analyses were also performed using the rarefaction index E(Sn) in order to address the comparability of richness by standardizing abundances (see Appendix 1 for details).

Data analysis

All relationships between dependent (univariate and multivariate measures of temporal variability) and independent variables (diversity measures: S and J') were separately investigated using linear regression. Specifically, it was examined if average species richness could be a predictor of temporal variations (as a response variable) in: a) species richness; b) community structure; and c) composition. Average evenness was also used as a predictor of temporal variation in: d) average species richness; e) community structure; and f) composition. Finally, it was tested if h) average species richness and i) average evenness were potential predictors of temporal variation in community abundance. It is worth noting that the analysis in a) represents a test for mean-variance relationship of species richness and this is discussed further in the text. The rarefaction index E(Sn) was also used as a predictor of temporal variation in average species richness, community structure and composition (see supplementary results in Appendix 1). The correlation coefficient (r) and the slope parameter (), reflecting the strength and steepness of the relationship between dependent and independent variables, respectively, were used in meta-analyses (see Table A1 in supplemental material). A standard meta-analytical effect size was used to determine whether there is a significant general trend in the strength of the relationship among all the data sets [START_REF] Hedges | Statistical methods for meta-analysis[END_REF][START_REF] Gurevitch | Meta-analysis in ecology[END_REF]. Fisher Z-transformed correlation coefficients

    r r r z     1 / 1 ln 5 . 0
were used, with sampling variance ν z = 1 /(N-3), where r is the correlation coefficient from the linear regression and N is the sample size. The slope parameter () along with its variance estimate SE  was used as size effect [START_REF] Hillebrand | Differences in species richness patterns between unicellular and multicellular organisms[END_REF]; see also [START_REF] Hillebrand | On the generality of the latitudinal diversity gradient[END_REF] to test for general trends. It was also investigated with categorical meta-analyses if the results were significant when aggregated within habitats (emergent rock; rock pool; and sediment) or regions (North Atlantic and Mediterranean locations; no test for Baltic region) and to test whether categories differ from each other.

Sediment habitat includes both subtidal and shore soft sediment. The analysis of heterogeneity (Q-statistic) of effect sizes for different groups was also tested (Q-statistic [START_REF] Hedges | Statistical methods for meta-analysis[END_REF]. This test discriminates between the total heterogeneity (Q T ) into heterogeneity between and within categories (respectively Q B and Q W ) that are comparable to the SS terms in a standard ANOVA. Mixed model meta-analyses were used (with MetaWin 2 [START_REF] Rosenberg | MetaWin: Statistical Software for Meta-Analysis[END_REF] and effect sizes were considered significant if their confidence interval did not bracket zero. Bootstrap 95% confidence intervals were built using 999 iterations. Potential effects of the duration (average in month) of sampling at each study site as well as the sampling effort (composite variable of averaged number of dates and samples per date for each data set sites) on effect sizes r z and  were examined by continuous model meta-analysis [START_REF] Rosenberg | MetaWin: Statistical Software for Meta-Analysis[END_REF]. A significance level α of 0.05 was adopted for all tests.

Results

Analyses of species richness variations

Species richness as independent variable

Significant positive correlation coefficients were observed between temporal variation in species richness and species richness levels at both quadrat (~ 0.10 m 2 ) and site (~ 100s m 2 ) scales as the overall effect sizes (grand means) were positive and did not bracket zero (Fig. 2a,b). However, no significant trends were observed for emerged rock (ER) habitat and western Mediterranean (ME) region at both scales. At the scale of the site, the relationship strength r z values were significantly higher for rock pool (RP) than ER habitat and significantly higher in North Atlantic (NA) than in ME region (Fig. 2b). No difference of ER effect sizes between NA (n = 4) and ME (n = 16) regions were observed in all tests (results not shown). No significant overall size effects or relationship between temporal changes in community structure and composition within assemblages with species richness was found (Fig. 2c-f). The strength and the slope of the relationships followed similar patterns for these analyses. Relationship analyses using expected species richness E(Sn) (or rarefaction index) at the scale of the site as an independent variable depicted some differences with observed species richness (see Fig. A1 in supplemental materials).

-Figure2-

Evenness as independent variable

Overall, positive r z effect size of the relationship between temporal variation in species number and evenness was observed only at the scale of the site (Fig. 3a,b). Positive strength r z values were observed for soft sediment (SD) habitats and NA region at the quadrat scale, while at the scale of the site, positive r z -values were observed for ER habitats and for both NA and ME regions. Slope β of the relationships followed similar trends as for the strength r z , except from the NA region where β values were not significantly different from zero (Fig. 3a,b). When considering relationships between temporal changes within assemblages with evenness values, positive overall r z was only observed with community structure analyses at the scale of the site (Fig. 3c-f). ER habitats as well as the ME region showed positive r z for the latter analysis (Fig. 3d) while data from SD habitats always showed positive r z values for all multivariates analyses (both in structure and composition data at both scales; Fig. 3c-f).

Inversely, temporal changes in community composition were negatively related to evenness for ER habitat and ME region (Fig. 3e). All multivariate analyses for r z and β followed same trends (Fig. 3c-f) with an exception for SD habitats at the scale of the quadrat (Fig 3c ), where r z was positive but β not.

-Figure 3-

Analyses of total community abundance variations

Diversity was not linked to temporal variation in total community abundance (total % cover or density). Overall strength r z and slope β effect sizes of relationships between temporal variations in community total abundance (density or % cover) and both species richness (S) and evenness (J') were not significant (Fig. 4a-d) at all scales. Positive strength r z and slope β with S was however observed for SD habitat (both scales) and NA region (scale of the site only) (Fig. 4a,b). Temporal variation in total abundance was positively correlated with J' for rock pool habitat at both scales (Fig. 4c,d) as shown by positive r z and slope β effect sizes.

-Figure 4-

Heterogeneity among data set (sampling effort and duration effect)

The duration of the studies did not affect the values of strength r z and slope β in any of the analyses (see Supplemental material, appendix 3). The sampling effort, determined as composite variable of number of dates and samples per date, negatively affected r z from analyses of temporal changes in community structure (quadrat: p-value = 0.0255) and composition (quadrat: p = 0.0114; site: p = 0.0049) that used species richness as an independent variable. Sampling effort did not affect effect sizes in analyses of temporal variation in richness with evenness as the independent variable and all analyses of temporal variation in total abundance. Slope β-values were not affected by sampling effort in any of the analyses (results not shown).

Diversity indices measured at the scale of the quadrat versus indices measured at that of the site were correlated (average ±SE of Pearson's r coefficient per data set: species richness: 0.71 ± 0.04; evenness: 0.71 ± 0.07). However, richness and evenness measures were weakly positively correlated at the scale of quadrat (r = 0.36 ± 0.08) and at the scale of the site (r = 0.11 ± 0.08).

Discussion

This study has highlighted that, in most cases, temporal variability in the number of taxa is positively related to diversity measures in marine benthic coastal assemblages. These results suggest that greater diversity leads to less stability (inversely related to temporal variability).

Variations in species abundance and composition within communities and temporal variation in total community abundance were, however, generally not linked to species richness and evenness. In addition, the diversity-stability relationships were scale dependent and varied across type of habitats and regions considered. The potential underlying mechanisms linking diversity measurements to stability are discussed below.

Temporal variation in species richness

Our results suggest that the overall stability in species richness is negatively related to diversity estimates (species richness, evenness and rarefaction index). To some extent, the observed negative relationship between species richness and stability in species richness may be explained by simple mean-variance scaling effect. However, positive relationships between other diversity indices (evenness and rarefaction index) and richness assessed at the scale of the site were consistent in our analysis. This strengthens the hypothesis that fluctuations within assemblages are closely controlled by their constituent species and their dominance structure.. Empirical and theoretical studies have generally shown that diversity levels affect variations in relative abundances, patterns of colonization and extinction rates (e.g. [START_REF] Inchausti | On the relation between temporal variability and persistence time in animal populations[END_REF][START_REF] Solan | Extinction and Ecosystem Function in the Marine Benthos[END_REF], which, in turn, determine species richness variability.

The cycle of colonization and local extinction of species, variation in species richness, are affected by processes that influence average population sizes and their temporal stability. Indeed, small or highly variable populations are more likely to become locally extinct [START_REF] Shaffer | Minimum Population Sizes for Species Conservation[END_REF][START_REF] Pimm | On the risk of extinction[END_REF][START_REF] Inchausti | On the relation between temporal variability and persistence time in animal populations[END_REF][START_REF] Melbourne | Extinction risk depends strongly on factors contributing to stochasticity[END_REF].

The identity of species within communities undoubtedly plays an important role since more diverse communities are more likely to include species or functional groups [START_REF] Mccann | The diversity-stability debate[END_REF] that can affect the function or properties of the whole community (i.e. sampling effect, [START_REF] Huston | Hidden treatments in ecological experiments: Re-evaluating the ecosystem function of biodiversity[END_REF][START_REF] Tilman | Plant Diversity and Ecosystem Productivity: Theoretical Considerations[END_REF]. Outcomes from various studies of temporal variation in species richness have led to different results. Simulation studies have demonstrated that species variation (turnover) is reduced with increasing richness when high number of taxa may either facilitate colonization or reduce extinction of present species, or when environmental conditions are variable [START_REF] Shurin | How is diversity related to species turnover through time?[END_REF]. In contrast, higher temporal stability (assessed as low values of the coefficient of variation) in species richness was associated with low richness and evenness values in New Zealand sandflat sites [START_REF] Thrush | Complex positive connections between functional groups are revealed by neural network analysis of ecological time series[END_REF]. These results were explained by strong connections between functional groups in species-poor communities.

Temporal variation in abundance

Stability in total abundance of community was generally not linearly linked to species richness nor evenness indices. [START_REF] Balvanera | Quantifying the evidence for biodiversity effects on ecosystem functioning and services[END_REF] also failed to observe significant relationships between stability (as natural variation) and diversity, although using different measure of stability and data sets that study did not exclusively represent marine habitat.

Temporal variability of aggregated community (total abundance, total biomass, etc.) or population (density, biomass) properties are preferred response variables used in studies of diversity-stability relationships and most of the relationships were negative [START_REF] Stachowicz | Understanding the Effects of Marine Biodiversity on Communities and Ecosystems[END_REF][START_REF] Valdivia | Observational evidence of a negative biodiversity-stability relationship in intertidal epibenthic communities[END_REF]. Many mechanistic approaches were identified to interpret theoretical and empirical outcomes from relationships between variability of such aggregated variable and diversity measures (e.g. [START_REF] Petchey | Species richness, environmental fluctuations, and temporal change in total community biomass[END_REF][START_REF] De Mazancourt | Predicting ecosystem stability from community composition and biodiversity[END_REF]. In particular, different non-exclusive mechanisms were reported to regulate the link between diversity and stability: the statistical averaging [START_REF] Doak | The statistical inevitability of stability -diversity relationships in community ecology[END_REF]"portfolio effect" Tilman et al., 1998); negative covariances among populations [START_REF] Tilman | Diversity-stability relationships: Statistical inevitability or ecological consequence?[END_REF]; asynchrony in response to environmental fluctuation [START_REF] Ives | General relationships between species diversity and stability in competitive systems[END_REF] and overyielding [START_REF] Tilman | The ecological consequences of changes in biodiversity: A search for general principles[END_REF]. These mechanisms have been considered important to shape the relationship between diversity and stability of above-ground biomass [START_REF] Grman | Mechanisms contributing to stability in ecosystem function depend on the environmental context[END_REF], total abundances in marine hard bottom communities [START_REF] Benedetti-Cecchi | Mechanisms underpinning diversity-stability relationships in hard bottom assemblages[END_REF][START_REF] Bulleri | Temporal stability of European rocky shore assemblages: variation across a latitudinal gradient and the role of habitat-formers[END_REF] and production in grassland plants [START_REF] Isbell | Biodiversity, productivity and the temporal stability of productivity: patterns and processes[END_REF][START_REF] De Mazancourt | Predicting ecosystem stability from community composition and biodiversity[END_REF]. Increasing the number of taxa present in a community would reduce mean and variance in the total community abundance and, then statistically reduce community variance (see [START_REF] Cottingham | Biodiversity may regulate the temporal variability of ecological systems[END_REF]. On the contrary, rich communities may also increase average strength among species favouring competitive exclusion and enhancing abundance fluctuations. Even if meanvariance scaling effects were present, intrinsic community properties such as negative covariance in species occurrence could lead to lower temporal variation at sites with higher species richness. Several empirical studies have highlighted the role of dominant species traits for the function of the whole community. For example, lower variability of dominant species than subordinate species may affect the whole community stability [START_REF] Polley | Dominant species constrain effects of species diversity on temporal variability in biomass production of tallgrass prairie[END_REF][START_REF] Grman | Mechanisms contributing to stability in ecosystem function depend on the environmental context[END_REF][START_REF] Sasaki | Dominant species, rather than diversity, regulates temporal stability of plant communities[END_REF][START_REF] Valdivia | Mesoscale Variation of Mechanisms Contributing to Stability in Rocky Shore Communities[END_REF].

The results of the present study also suggest that communities from different habitats exhibit different diversity-stability relationships. While data from emergent rocky habitats exhibited no relationship, richness decreased stability of sandy community abundances while evenness decreased stability in rock pool community abundances (see Fig. 4). If poor correlation between richness and evenness measures at each scale may partly explain this, inherent differences exist in forces that structure communities among habitats. Strong interactions, mainly competition, among species in soft sediments are largely limited to the provision of biogenic habitat communities that are commonly maintained in early successional stages by frequent physical and biological disturbances. Competitive displacement and exclusion are generally less frequent in sediment habitats compared to hard-bottom ones [START_REF] Peterson | Predation, competitive exclusion, and diversity in the soft-sediment benthic communities of estuaries and lagoons[END_REF][START_REF] Black | Absence of preemption and interference competition for space between large suspension-feeding bivalves and smaller infaunal macroinvertebrates[END_REF]. Following the work of [START_REF] Danovaro | Exponential decline of deep-sea ecosystem functioning linked to benthic biodiversity loss[END_REF] in deep sea sediments, [START_REF] Loreau | Biodiversity and ecosystem functioning: The mystery of the deep sea[END_REF] suggested that infaunal species, through the reworking of sediments could generate a prevalence of positive species interactions in soft sediments (in contrast to hard-bottoms, cf [START_REF] Noël | Species interactions during succession in rockpools: Role of herbivores and physical factors[END_REF][START_REF] Benedetti-Cecchi | Mechanisms underpinning diversity-stability relationships in hard bottom assemblages[END_REF] leading to complementarity effects [START_REF] Loreau | Biodiversity and ecosystem functioning: Recent theoretical advances[END_REF]. Positive covariance, which is observed when species fluctuate synchronously in response to environmental change, is widespread [START_REF] Houlahan | Compensatory dynamics are rare in natural ecological communities[END_REF][START_REF] Valone | An empirical evaluation of the insurance hypothesis in diversity-stability models[END_REF] and contributes to increase variability in total abundance. However when present, the compensatory dynamics among intertidal species that contribute to stability has a lower effect in high latitude where environmental forcing may prevail on biological interactions [START_REF] Bulleri | Temporal stability of European rocky shore assemblages: variation across a latitudinal gradient and the role of habitat-formers[END_REF]. While rock pools are benign environments compared to emergent rock in term of physical stress (i.e. desiccation, see [START_REF] Noël | Species interactions during succession in rockpools: Role of herbivores and physical factors[END_REF], they can be much more heavily grazed (e.g. [START_REF] Benedetti-Cecchi | Interactive effects of spatial variance and mean intensity of grazing on algal cover in rock pools[END_REF]. This high grazing pressure observed in rock pools may change the nature of interactions between species from competition to facilitation [START_REF] Bertness | Positive interactions in communities[END_REF] and increase the number of grazer resistant-species [START_REF] Noël | Grazing dynamics in intertidal rockpools: Connectivity of microhabitats[END_REF]. If stabilizing effects of species richness on community abundances were observed in rock pool mesocosms [START_REF] Romanuk | Environmental variability alters the relationship between richness and variability of community abundances in aquatic rock pool microcosms[END_REF], the effect of evenness still remains unclear.

Temporal variation within communities

Using multivariate analyses, we found that stability (measured with Bray-Curtis dissimilarities) of species abundance and composition within communities are generally not correlated with diversity indices. Our results suggest that relationships between diversity and community stability may be governed by evenness rather than the number of species (cf. Fig. 2 and3). Moreover, contrasting results among habitats exist, with sediment communities with high evenness being less stable, perhaps from prevalence of positive species interactions in this habitat previously discussed. Theoretical studies have revealed that relationships between community variability in composition and number of taxa may increase, decrease or remain unchanged mainly due to the type of calculation of variability used but also stochastic processes that alter patterns of dominance and total abundance among species [START_REF] Stevens | Stochastic relations between species richness and the variability of species composition[END_REF]. Among other results, [START_REF] Stevens | Stochastic relations between species richness and the variability of species composition[END_REF] reported that simulated community variation in composition, given that total abundance stay equal, would show positive correlation with evenness. Our empirical observations would give only little support to these predictions (see Fig. 3e). At high dominance (or low evenness), it was observed that stability in species composition within a community may be either enhanced on emergent rocky shores or become reduced on sediment shores (see Fig 3e). Results from empirical studies have also contradictory outcomes showing that various measures of grassland diversity (including species richness and evenness) can enhance [START_REF] Elton | Stability Increases with Diversity in Plant Communities: Empirical Evidence from the 1988 Yellowstone Drought[END_REF] or decrease stability [START_REF] Rodriguez | Stability may decrease with diversity in grassland communities: Empirical evidence from the 1986 Cantabrian Mountains (Spain) drought[END_REF], while no effect was recorded for J') measured by temporal variance in compositional richness. Moreover, in contrast to our study, [START_REF] Mykrä | The stability-diversity relationship in stream macroinvertebrates: influences of sampling effects and habitat complexity[END_REF] observed that stability within assemblage in streams is promoted by species richness, although this relationship disappeared when compositional stability was related to species richness estimated with a rarefaction index that standardized abundance.

Scale of observation

Many rich benthic communities are actually composed by rare species [START_REF] Gray | The measurement of marine species diversity, with an application to the benthic fauna of the Norwegian continental shelf[END_REF][START_REF] Gray | The impact of rare species on natural assemblages[END_REF][START_REF] Ellingsen | Rare species, habitat diversity and functional redundancy in marine benthos[END_REF]. Uncommon species are theoretically important to maintain ecosystem functions in the context of the insurance hypothesis [START_REF] Yachi | Biodiversity and ecosystem productivity in a fluctuating environment: The insurance hypothesis[END_REF] and are important to ensure community persistence and resilience [START_REF] Hillebrand | Consequences of dominance: A review of evenness effects on local and regional ecosystem processes[END_REF]. Spatial species distributions are highly heterogeneous at various scales (e.g. [START_REF] Chapman | Complex and inconsistent patterns of variation in benthos, micro-algae and sediment over multiple spatial scales[END_REF][START_REF] Kraufvelin | Scale-dependent distribution of soft-bottom infauna and possible structuring forces in low diversity systems[END_REF]. Indeed, variations in the number of taxa may be influenced by a combination of random spatial and temporal sampling errors that cause species, particularly those that are either sparse or rare, to be included or not in a patch [START_REF] Mcardle | Variation in the Size of Animal Populations -Patterns, Problems and Artifacts[END_REF][START_REF] De Juan | Spatial and temporal variability in species richness in a temperate intertidal community[END_REF]. The recent work of de Juan and Hewitt (2014) illustrated how seasonality and inter annual sampling schemes may affect variability in species composition and species accumulation profiles. In our study, care was taken to select data sets with sampling dates spread among seasons or within years. There was no effect of the duration of the studies on effect sizes measured, but seasonal variations within studies may have influenced our overall results. Patterns of diversity in small patches have been identified as potential contributors to ecological stability [START_REF] Elton | Stability Increases with Diversity in Plant Communities: Empirical Evidence from the 1988 Yellowstone Drought[END_REF], but the consistency seen in our results at both quadrat and site scales indicates that mechanisms not related to heterogeneity among patches may dominate and create the observed patterns. It has been generally accepted that regional species pools may regulate the species richness seen at smaller scales (e.g. [START_REF] Ricklefs | Community Diversity: Relative Roles of Local and Regional Processes[END_REF][START_REF] Witman | The relationship between regional and local species diversity in marine benthic communities: A global perspective[END_REF][START_REF] Kotta | Diversity patterns and their causes. Regional scale patterns[END_REF].

When data sets were analysed separately, a large proportion of the observed relationships between stability and biodiversity were weak or not significant. For example, in the analyses shown in Fig 2a, only 5 data sets out of 28 showed significant relationships and 6 show correlation coefficients over 0.5 (Table A1). The observed significant results with combined data sets illustrate the importance of using robust meta-analytical tools to investigate such hypotheses. Nonetheless, more data from soft sediments and rock pool habitats are needed to generate more conclusive results. The available data sets in this study were to some extent over-represented in the Mediterranean region and in the emergent rock habitat. Indeed, the Mediterranean region was solely represented by studies on emergent rock. On the other hand, consistent results between Mediterranean and North Atlantic results for emergent rock were seen. Large scale comparison of diversity effects on ecosystem processes may be masked systematically by the effects of variation in environmental variables on these processes and may lead to erroneous conclusions [START_REF] Loreau | Biodiversity and ecosystem functioning: a mechanistic model[END_REF][START_REF] Loreau | Biodiversity and ecosystem functioning: The mystery of the deep sea[END_REF].

Concluding remarks

This study provides one of the few comprehensive assessments of large spatial scale variation in the relationship between diversity and temporal stability across different marine systems.

Our results suggest that diverse assemblages enhance variability in species richness without affecting variability in species abundance and composition within community. The use of complementary diversity indices (e.g. richness and evenness) over various time series warrants the generation of robust stability-diversity analyses. Despite the caveat resulting from incomplete and unevenly distributed data, it has been highlighted that the scale of observation needs to be considered in diversity-stability studies and outcomes may also depend on the habitats and the biogeographic systems considered (e.g. North-Atlantic or western Mediterranean). Conversely, there are needs to extend the analyses shown here to more sites (and time series) in order to generate better pictures across habitats. Targeted longterm observations and experiments are undoubtedly important to unravel effects of environmental variables, species interaction strength within assemblages and potential effect of climate changes on biodiversity and the functioning of ecosystems. Nevertheless, where sufficient data sets exist, a meta-analysis like the one presented here can provide a cost-effective approach to clarifying and generating further hypotheses about diversity-stability relationships.

Figure captions

Electronic Supplemental Material (see methods section).

Appendix 1.

Analyses with expected number of taxa as independent variable Methods 40

We used the normalised expected number of taxa rarefaction method [START_REF] Sanders | Marine benthic diversity -A comparative study[END_REF], as modified by [START_REF] Hurlbert | Nonconcept of species diversity -critique and alternative parameters[END_REF]) as an independent variable in order to address the comparability of richness by standardizing abundances (see [START_REF] Gotelli | Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness[END_REF]. This method calculates the expected number of taxa, E(Sn), in a reduced standardised sample of n individuals selected from the given sample. For this rarefaction approach a reduced number of 45 individuals (n) were chosen which took into account the lowest abundances at the scale of site (i.e. all quadrats and dates pooled) for each data set. Abundances (in density or % cover) within quadrat were not consistent or sufficient to perform quadrat scale analyses. The average of n used was 125 and a minimum was set at n = 40 to ensure satisfactory assessment.

In a very few cases (7 out of 336 sites) the total abundances at the site scale show numbers 50 slightly below the minimum of 40.

Results

We observed positive overall relationships between temporal variations in observed species richness and expected number of taxa E(Sn) (Fig. A1a). All categories, except for rock pool 55 (RP) habitats, showed positive strength r z and slope β. Changes in community structure also showed positive trends, while only the effect size for emergent rock habitat was positively significant (Fig. A1b). We did not observed significant general trend with compositional community analysis, except for sediment habitat (Fig. A1c).

60

Difference when using observed species richness and E(Sn)

The most notable difference we observed between results using observed species richness and E(Sn) as independent variables was that temporal changes in species abundance within the assemblage become positively correlated with E(Sn) values (cf. Figs. 2d andA1b). Minors differences were also observed using E(Sn): Temporal variation in species richness: emergent 65 rock (ER) habitat and Mediterranean (ME) region became positive while RP habitat become non-significant (Fig A1a). Temporal variation in community: ER habitat (community structure) and sediment habitat (community composition) became positive (Fig. A1b,c).

Figure 1 .

 1 Figure 1. Approximate location of sampling areas (with data set number, see Table 1) included in this study. Multiple sites were sampled in each area. Full circle: emergent rock habitat; open circle: rock pool; triangle: soft sediment; diamond: artificial substrata (PVCpanels).

Figure 2 .

 2 Figure 2. Mean effect size (± 95% CI) for overall effect (All data: All), by habitat (Sediment: SD; Emergent rock: ER; Rock pool: RP), and by region (North Atlantic: NA; Mediterranean: ME) of the relationship strength r z (black circle) and slope β (open circle) between temporal variability and species richness. Effect sizes are displayed by type of analyses (species richness variability: A, B; community assemblage variability in: structure C, D and composition E, F) and scale of richness measure (average by patch scale as Quadrat: A, C, E; total by shore as Site: B, D, F). Brackets with * indicate significant (P < 0.05) heterogeneity of effect sizes among categories. Significant effects where CIs do not overlap with zero line. Note that negative values indicate a positive stability-diversity relationship while positive values do the opposite.

Figure 3 .

 3 Figure 3. Mean effect size (± 95% CI) for overall effect, by habitat and by region of the relationship strength r z (black circle) and slope β (open circle) between temporal variability and evenness J'. Effect sizes are displayed by type of analyses (species richness variability: A, B; community assemblage variability in: structure C, D and composition E, F) and scale of evenness measure (average by patch scale as Quadrat: A, C, E; reassessed by shore as Site: B, D, F). See Figure 2 for abbreviations.

Figure 4 .

 4 Figure 4. Mean effect size (± 95% CI) for overall effect, by habitat, and by region of the relationship strength r z (black circle) and slope β (open circle) between temporal variability in total abundance (density or % cover) and: species richness (A, B), and evenness J' (C, D).

Figure 1 .

 1 Figure 1. Approximate location of sampling areas (with data set number, see Table 1) 5

Table 1

 1

	)

Table A1 .

 A1 Pearson correlation coefficient observed in the meta-analysis. # refers to dataset # in Table1. Habitat (H) category: Sediment: SD; Emergent rock: ER; Rock pool: RP. Region (R) category: NA: North Atlantic; ME: Mediterranean. Heather of each column refers to graphs in the MS Figures. For example, 2D corresponds to a correlation seen in Figure2Dbetween community assemblage variability in structure with species richness S at the site scale. Correlations with p < 0.05 are in Bold.

	Appendix 2. # H	R	2A	2B	2C	2D	2E	2F	3A	3B	3C	3D	3E	3F	4A	4B	4C	4D
	1	SD	Baltic	0.75	0.94	0.31	0.26	0.41	0.39	-0.05	0.36	0.88	0.92	0.85	0.90	0.43	0.56	0.28	0.02
	2	SD	NA	0.75	0.91	-0.80	-0.61	-0.10	0.15	0.37	-0.62	-0.33	0.71	-0.03	0.30	0.30	0.10	0.05	-0.72
	3	AR	NA	0.34	0.62	-0.71	-0.17	-0.88	-0.47	0.37	0.49	-0.48	-0.10	-0.64	-0.43	-0.18	0.18	0.17	0.15
	4	SD	NA	0.30	0.14	0.28	0.03	-0.06	-0.21	0.60	0.48	0.88	0.80	0.70	0.61	0.52	0.27	0.66	0.55
	5	ER	ME	-0.34	-0.41	-0.05	0.22	-0.71	0.00	-0.40	-0.27	-0.04	-0.05	-0.56	-0.74	-0.41	0.11	-0.55	-0.62
	6	ER	ME	-0.26	0.03	-0.29	-0.39	-0.34	-0.46	-0.39	-0.18	-0.23	-0.15	-0.19	-0.14	-0.21	-0.15	-0.67	-0.53
	7	ER	ME	0.22	0.08	-0.22	-0.26	-0.47	-0.37	0.43	0.40	-0.45	-0.17	-0.54	-0.42	-0.38	-0.34	0.31	-0.05
	8	ER	ME	0.17	-0.14	-0.32	-0.22	-0.31	-0.11	0.22	0.08	-0.45	-0.18	-0.47	-0.28	0.36	0.17	0.05	0.27
	9	ER	ME	0.26	-0.10	0.00	-0.19	0.25	-0.09	-0.20	-0.05	0.42	0.64	0.44	0.62	-0.33	-0.53	0.45	0.14
	10	ER	ME	0.54	0.51	0.14	0.15	-0.33	0.07	0.02	0.09	0.02	0.11	-0.44	-0.39	0.18	-0.34	0.72	0.69
	11	ER	ME	-0.17	-0.09	0.33	0.21	-0.36	-0.03	-0.22	-0.19	0.51	0.59	-0.18	-0.29	0.13	0.37	0.34	0.30
	12	ER	ME	0.10	-0.05	-0.37	0.04	-0.27	0.09	-0.22	0.24	-0.38	0.41	-0.52	0.15	-0.32	0.05	0.03	0.14
	13	ER	ME	-0.20	-0.37	-0.09	0.10	-0.22	0.04	0.17	0.40	-0.10	0.46	-0.09	0.34	-0.05	-0.37	0.34	0.22
	14	ER	ME	-0.19	-0.05	-0.39	-0.22	-0.18	-0.10	-0.35	-0.11	-0.29	0.22	-0.30	-0.12	0.33	0.43	-0.65	-0.53
	15	ER	ME	0.18	-0.01	0.31	0.33	-0.34	-0.12	-0.23	-0.19	-0.06	0.17	-0.71	-0.52	-0.40	0.05	-0.49	-0.58
	16	ER	ME	0.08	0.22	0.51	0.35	0.73	0.57	0.32	0.34	-0.19	0.10	0.09	0.27	-0.28	-0.47	-0.56	-0.53
	17	ER	ME	-0.40	0.03	-0.43	-0.34	-0.33	-0.40	-0.38	0.22	-0.23	0.39	-0.25	0.25	0.42	0.05	0.29	-0.08
	18	ER	ME	0.12	0.05	0.38	0.51	0.39	0.36	0.14	0.16	0.49	0.20	0.41	0.31	0.16	0.22	0.16	0.14
	19	ER	ME	-0.05	0.31	0.21	0.37	-0.31	-0.20	-0.06	0.17	0.15	0.59	-0.08	0.12	-0.46	-0.49	0.65	0.42
	20	ER	ME	0.51	0.34	0.56	0.62	-0.31	0.32	0.63	0.68	0.71	0.71	-0.21	-0.16	0.72	0.40	0.79	0.82
	21	ER	ME	0.25	0.44	0.12	0.07	-0.04	-0.06	0.27	0.25	0.13	0.09	-0.18	-0.29	0.59	0.68	0.60	0.59
	22	ER	ME	0.45	0.35	-0.42	-0.16	-0.75	-0.60	0.16	-0.01	-0.13	-0.23	-0.22	-0.24	-0.75	-0.64	-0.38	-0.21
	23	RP	NA	0.35	0.29	-0.14	-0.05	-0.12	0.12	0.20	0.24	0.07	0.26	-0.06	0.00	0.07	0.18	0.20	0.08
	24	ER	NA	0.19	0.13	0.33	0.20	0.39	0.17	0.51	0.45	-0.29	-0.01	-0.07	0.16	0.00	0.10	-0.43	-0.07
	25	RP	NA	0.63	0.69	0.89	0.87	0.51	0.60	0.62	0.69	0.75	0.85	0.61	0.52	0.72	0.76	0.91	0.90
	26	ER	NA	0.74	0.66	0.68	0.69	0.74	0.81	-0.02	0.38	0.20	0.65	-0.21	0.33	0.49	0.50	-0.19	0.13
	27	RP	NA	0.49	0.77	-0.53	-0.30	-0.75	-0.53	-0.57	-0.50	-0.87	-0.96	-0.70	-0.89	-0.20	-0.09	0.39	0.18
	28	SD	NA	-0.02	0.12	-0.82	-0.51	-0.77	-0.51	0.22	0.37	0.58	0.21	0.52	0.29	0.42	0.48	-0.38	-0.49

Acknowledgments

We are gratefully indebted to Carlo Heip for his outstanding achievements in the leadership of European research into marine biodiversity. We would like to acknowledge all participating institutions in BIOFUSE project and all participants that helped in field sampling, identifying species as well as by providing data and advice and comments on the manuscript. The BIOFUSE project, led by TP Crowe and L Benedetti-Cecchi, has been carried out in the framework of the MarBEF Network of Excellence 'Marine Biodiversity and Ecosystem Functioning' which was funded by the Sustainable Development, Global Change and Ecosystems Programme of the European Community's Sixth Framework Programme (contract #GOCE-CT-2003-505446), which was coordinated by Carlo Heip. Additional funding was provided by partner institutions. During the writing, M Cusson was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC; Discovery Grants).

Effect sizes are displayed by scale of diversity measure (average by patch scale as quadrat: A, C; reassessed at site scale: B, D). See Figure 2 for abbreviations.


Claire Passarelli
email: claire.passarelli@mnhn.fr

Tarik Meziane

Najet Thiney

Dominique Boeuf

Bruno Jesus

Mickael Ruivo

Christian Jeanthon

Cédric Hubas

Seasonal variations of the composition of microbial biofilms in sandy tidal flats: Focus of

Keywords: biofilms, biogenic structures, EPS, monosaccharide composition, fatty acids, pigments

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

In coastal soft-bottom areas, a large diversity of microbial mats exists; their composition depends on environmental conditions such as salinity, tidal height and duration of exposure, light, grain size, and organic matter input [START_REF] Brotas | Spatio-temporal distribution of the microphytobenthic biomass in intertidal flats of Tagus Estuary (Portugal)[END_REF][START_REF] Franks | Flat laminated microbial mat communities[END_REF][START_REF] Paterson | Microphytobenthos in Contrasting Coastal Ecosystems: Biology and Dynamics[END_REF]. Additionally, they are sensitive to anthropic actions such as eutrophication or antibiotic release in the environment [START_REF] Lubarsky | Impairment of the Bacterial Biofilm Stability by Triclosan[END_REF][START_REF] Sawall | Effects of Eutrophication, Seasonality and Macrofouling on the Diversity of Bacterial Biofilms in Equatorial Coral Reefs[END_REF]. These biofilms are often very productive and they also provide different ecosystem services, such as nutrient recycling, degradation of pollutants and sediment stabilisation, and they are essential for the recruitment of numerous benthic organisms [START_REF] Decho | Microbial biofilms in intertidal systems: an overview Cont[END_REF].

Biofilms consist of microorganisms embedded in a matrix of extracellular polymeric substances (EPS), which are produced by the different organisms of the biofilm. These EPS are chemically diverse molecules but are mainly composed of carbohydrates and protein, and contain very few lipids. EPS have various roles both for microorganisms: they protect from pollutants and desiccation [START_REF] Decho | Microbial exopolymer secretions in ocean environments: their role(s) in food webs and marine processes[END_REF][START_REF] Potts | Desiccation Tolerance of Prokaryotes[END_REF] and they allow locomotion, especially for diatoms, and attachment to sediment [START_REF] Wotton | The essential role of exopolymers (EPS) in aquatic systems[END_REF]. They are also important for coastal systems, as they are for instance responsible for sediment stabilisation in intertidal areas [START_REF] Decho | Microbial exopolymer secretions in ocean environments: their role(s) in food webs and marine processes[END_REF].

Because their composition varies depending on their environment, microbial mats are useful as proxies of environmental conditions. Therefore, various markers have been developed to characterise the microbial composition of biofilms, and, more recently, their EPS composition. Quantification of carbohydrates and proteins has been a first step, and GC-MS and LC-MS (gas and liquid chromatography coupled with mass spectrometry) now allow a further analysis of these molecules. Carbohydrates in particular have been investigated as they are crucial for sediment stabilisation by biofilms [START_REF] De Brouwer | Biogenic stabilization of intertidal sediments: The importance of extracellular polymeric substances produced by benthic diatoms[END_REF]; these molecules have been characterised through their monosaccharide composition, which has been shown to vary according to organisms and environmental conditions [START_REF] De Brouwer | Daily fluctuations of exopolymers in cultures of the benthic diatoms Cylindrotheca closterium and Nitzschia sp. (Bacillariophyceae)[END_REF][START_REF] Hanlon | Dynamics of extracellular polymeric substance (EPS) production and loss in an estuarine, diatom-dominated, microalgal biofilm over a tidal emersion-immersion period[END_REF][START_REF] Underwood | Environmental effects on exopolymer production by marine benthic diatoms: Dynamics, changes in composition, and pathways of production[END_REF].

Due to the numerous roles of EPS in coastal systems, and given that these roles depend on EPS composition, characterising EPS composition and variations is an important challenge to understand the provision of ecosystem services by microbial mats in varying environments. Therefore, this study was designed to test the hypothesis that the EPS composition of biofilms: (1) varies seasonally and is influenced by other environmental conditions; and (2) does not only depend on the microorganisms forming the microbial mats.

Biofilms were followed over the year on-field in an intertidal area, in three experimental conditions. Artificial tubes mimicking polychaete tubes were used to modify biofilm development, as such tubes influence both microbial expansion and EPS production [START_REF] Passarelli | Impacts of biogenic structures on benthic assemblages: microbes, meiofauna, macrofauna and related ecosystem functions[END_REF]. Biofilms were sampled monthly and the microbial assemblage was characterised by their fatty acid and pigment contents. EPS were characterised by the monosaccharide composition of their carbohydrates.

Material and methods

Study site

Experiments were carried out from March 2011 to April 2012 in the Chausey archipelago (France) within the Normand-Breton Gulf (English Channel), which is characterized by a megatidal regime (tidal range up to 14 m). The Chausey archipelago includes 1500 ha of soft sediment tidal flats.

Experimental design

Twelve cylindrical benthic chambers of 300 mm inner diameter and 60 mm depth were placed in the intertidal sandy area of Chausey archipelago (Normand-Breton Gulf, France). Solid PVC cylindrical rods were fixed to the bottom of each benthic chamber, simulating tubes of large polychaete "builder" species, such as Lanice conchilega or Melinna cristata (5 mm diameter, 95 mm length). Three treatments were used (4 replicate chambers per treatment); a control treatment (Ctrl), without tubes; then 2 test treatments with varying densities of evenly spaced tubes: a low density (LD) treatment (637 tubes•m -2 ) and a high density (HD) treatment (2505 tubes•m -2 ). The LD treatment potentially destabilised the sediment (i. e. initiated interactive flow; [START_REF] Friedrichs | Skimming flow induced over a simulated polychaete tube lawn at low population densities[END_REF], whereas the HD treatment most likely had a stabilising effect on it (i. e. initiated skimming flow; [START_REF] Friedrichs | Skimming flow induced over a simulated polychaete tube lawn at low population densities[END_REF].

These densities were both within the range of natural densities for polychaete tubes (up to 5000 ind•m -2 ; e.g. [START_REF] Friedrichs | Skimming flow induced over a simulated polychaete tube lawn at low population densities[END_REF] for Melinna cristata).

Before positioning the chambers in the field, they were filled with defaunated sand (washed with 1 mol•l -1 HCl, frozen and thawed) collected at the study site (medium sand, median grain size between 260 and 300 µm). They were then placed into the intertidal area on 21 March 2011, with a 2 m gap between chambers. Sampling was carried out approximately every month (14 times in the experiment, depending on tidal height and accessibility of the study site) from the beginning of the experiment to 8 April 2012.

Sampling locations within the chambers were randomly chosen for each sampling time, taking care that a single location was never sampled twice in a 3-month period.

Visual observation of the plots confirmed that any cavities created by sampling were filled by natural sediment within 24 h, after which no trace of the sampling was apparent.

Sampling and measurements

Fatty acids composition of the sediment

One sample was taken from each benthic chamber at every sampling date (2 cm inner diameter cut off syringe, 1 cm depth). Sediment cores were frozen and transported back to the laboratory where they were freeze-dried, and stored at -26 °C. 400 to 500 mg of sediment were weighted and used for the analysis. Fatty acid (FA) analysis was performed following the modified method of [START_REF] Bligh | A rapid method of total lipid extraction and purification[END_REF] as described in [START_REF] Mortillaro | Fatty acid and stable isotope (δ13C, δ15N) signatures of particulate organic matter in the lower Amazon River: Seasonal contrasts and connectivity between floodplain lakes and the mainstem[END_REF]. Before extraction, an internal standard (FA 23:0) was added to every sample for quantification purpose. Lipids were extracted with a 20 min ultrasonication in a mixture of distilled water, chloroform and methanol in ratio 1:1:2 (v:v:v). Lipids were concentrated under N 2 flux, and saponified, in order to separate FAs, with a mixture of NaOH (2 mol•l -1 ) and methanol (1:2, v:v) at 90 °C during 90 min. Saponification was stopped with hydrochloric acid. FAs were then derivatized with BF 3 -methanol (boron-trifluoride methanol) at 90 °C during 10 min and kept frozen in chloroform. Just before analysis, samples were dried under N 2 flux and transferred to hexane. One µl of the mixture was injected in a gas chromatograph (GC, Varian CP-3800 equipped with flame ionization detector), which allowed separation and quantification of FAs. Separation was performed with a Supelco OMEGAWAX 320 column (30 m × 0.32 mm i.d., 0.25 µm film thickness) with He as carrier gas. The following temperature program was used: 60 °C for 1 min, then raise to 150 °C at 40 °C•min -1 (held 3 min), then raise to 240 °C at 3 °C•min -1 (held 7 min). Peaks of FAs were identified by comparison of the retention time with analytical standards (Supelco™ 37, PUFA-1 Marine Source, and Bacterial Mix, Supelco Inc., USA) and analysis of the sample in a gas chromatograph coupled to mass spectrometer (GC-MS, Varian 450GC with Varian 220-MS).

FAs are designated as X:YωZ, where X is the number of carbons, Y the number of double bonds and Z the position of the ultimate double bond from the terminal methyl.

Pigment composition of the sediment Three samples were taken from each benthic chamber at every sampling date (2 cm inner diameter cut off syringe, 1 cm depth), and pooled. Samples were kept frozen at -26 °C until analysis. Pigments were extracted from a sub-sample of approximately 0.2 g of freezedried sediment with 2 ml of 95 % cold buffered methanol (2 % ammonium acetate) for 15 min at -20 °C, in the dark. Samples were sonicated for 30 s at the beginning of the extraction period. Extracts were filtered with Whatman membrane filters (0.2 mm) immediately before HPLC analysis. Pigment extracts were analysed using a Shimadzu HPLC comprised of a solvent delivery module (LC-10ADVP) with system controller (SCL-10AVP), a photodiode array (SPD-M10AVP) and a fluorescence detector (RF-10AXL). Chromatographic separation was carried out using a C18 column for reverse phase chromatography (Supelcosil, 25 cm long, 4.6 mm in diameter, and 5 µm particles). The solvents used were 0.5 mol⋅l -1 ammonium acetate in methanol and water (85:15, v:v), acetonitrile and water (90:10, v:v), and 100 % ethyl acetate. The solvent gradient followed [START_REF] Kraay | Separation of chlorophylls c1, c2 and c3 of marine phytoplankton by reversed-phase-C18-high-performance liquid chromatography[END_REF] with a flow rate of 0.6 ml•min -1 and an injection volume of 100 µl. Identification and calibration of the HPLC peaks was confirmed with chlorophyll a, chlorophyll b and b-carotene standards from Sigma and chlorophyll c, fucoxanthin, diadinoxanthin , diatoxanthin, lutein, zeaxanthin, and pheophytin a standards from DHI. Pigments were identified by the absorption spectra and retention times and the concentrations calculated from the signals in the photodiode array (chlorophylls and carotenoids) or fluorescence detector (pheophorbides and pheophythins).

As samples were not frozen in situ but transported in the dark before being frozen, diatoxanthin had time to revert to diadinoxanthin. Therefore, concentrations of diatoxanthin and diadinoxanthin were added and analysed together as xanthophyll concentration.

EPS composition in monosaccharides

Three samples were taken from each benthic chamber at every sampling date (2 cm inner diameter cut off syringe, 1 cm depth), pooled onto one sample per chamber, and frozen until analysis. Monosaccharide composition of carbohydrates was determined using gas chromatography (GC), following a method adapted from [START_REF] Pierre | Biochemical characterization of extracellular polymeric substances extracted from an intertidal mudflat using a cation exchange resin[END_REF]. Briefly, EPS were extracted, and carbohydrates decomposed into monosaccharides with acidic hydrolysis.

Sugars were thereafter silylated and separated with GC.

Colloidal EPS were extracted by rotating the samples in artificial sea water (30 g⋅l -1 ) 1h at 4 °C. Bound EPS were thereafter recovered by adding resin (Dowex Marathon C, sodium form, Sigma) to the remaining sediment and performing a second extraction. Both fractions were dialysed against distilled water (12 -14 kDa) and freeze-dried.

EPS were dissolved in 2 mol⋅l -1 HCl, and heated 4 h at 90 °C. The solution then mainly contained monosaccharides, and was freeze-dried again.

Silylation was performed with a mixture of BSTFA:TMCS (N,Obis(trimethylsilyl)trifluoroacetamide and trimethylchlorosilane, 100:1, Sigma) and pyridine (1:1, v:v), 2 h at room temperature. After the silylation, 1 µl of the sample was injected in GC (GC, Varian CP-3800 equipped with flame ionization detector), which allowed separation and relative quantification of sugars. Separation was performed with an Agilent Technologies VF-1701ms column (30 m × 0.32 mm i.d., 0.25 µm film thickness) with He as carrier gas. The following temperature program was used: start at 150 °C, raise to 200 °C at 7 °C•min -1 (held 5 min). Injector temperature was set at 250 °C. Peaks of monosaccharides were identified by comparison of the retention time with analytical standards (rhamnose, fucose, xylose, mannose, galactose, glucose, scyllo-and myo-inositol, galacturonic and glucuronic acid), which had been prepared (silylation and injection only) as samples.

Carbohydrate and protein concentrations in EPS were measured by colorimetry following methods from [START_REF] Lubarsky | The stabilisation potential of individual and mixed assemblages of natural bacteria and microalgae[END_REF].

Statistical analyses

Non-parametric statistics under the R statistical framework were used for univariate analyses as data violated assumptions of homogeneity of variance and normality. Differences between treatments were assessed by Kruskal-Wallis tests (KW), while differences between sampling time for each treatment were assessed by Friedman tests (considering data from each chamber as paired data).

Non-metric MultiDimensional Scaling (nMDS) analyses (500 iterations) were performed with similarity matrix constructed with Bray Curtis distances for FAs, pigments and sugars. These graphical representations show similarity between samples: the closer the symbols are in the representation, the closer the characteristics of the samples are. 2-factor ANOSIMs (crossed factors: Time and Treatments) were then used to assess the difference between assemblages from different sampling time (Primer 5 software, 10000 iterations), and variables responsible for such differences were investigated with SIMPER analyses.

Multiple Factor Analysis (MFA) was performed to compare chambers at different sampling time using FA, pigment and sugar datasets. MFA studies several groups of variables defined on the same set of individuals. The core of the method is a factor analysis applied to the whole set of variables, in which each group of variables is weighted so that no group of variables become dominant over the others [START_REF] Escofier | Multiple factor analysis (AFMULT package)[END_REF]. Data were scaled to unit variance.

Results

Composition of the microbial community

Fatty acid contents

Overall compositions of microbial communities and organic matter in sediment was assessed through their fatty acid (FA) content. Quantitative data (Fig. S1) showed accumulation of FAs in sediment in early August and late September. FAs found in our sediment samples are listed on table S1. Most abundant FAs were saturated FAs (SFAs) 14:0, 16:0, 18:0, mono-unsaturated FA (MUFA) 16:1ω7 and polyunsaturated FA (PUFA) 20:5ω3, suggesting a large abundance of diatoms in these biofilms. Moreover, branched FAs (BFAs) reached a 2 % relative contribution, showing the presence of heterotrophic bacteria.

Data showed a dramatic change in FA composition of sediment through time (Fig. 1; ANOSIM: R = 0.767, p < 0.001). Samples from two following months were significantly different one from each other's, except in winter where samples were similar (ANOSIM posthoc pairwise tests, Table 1). SIMPER analysis showed that FAs 16:1ω7 and 20:5ω3, as well as saturated FAs (SFAs; mainly 14:0, 16:0 and 18:0) were responsible for the differences between time groups. Namely, proportions of 16:1ω7 and 20:5ω3 varied significantly through time in every treatment (Friedman tests, p < 0.001); they were present in higher proportions in April, May, at the beginning of August, in some samples of November and from January to March 2012, compared to other months. On the contrary, the ratio between saturated and polyunsaturated FAs (SFA/PUFA ratio) was lower in these months.

Samples were also significantly different between treatments (ANOSIM, R = 0.284, p < 0.001; ANOSIM post-hoc pairwise tests Table 2), although the time effect seemed predominant (Fig. 1). Main differences were explained by MUFA 16:1ω7, PUFA 20:5ω3 and SFA 18:0, which were more abundant in Ctrl treatments compared to others; by SFA 14:0 which was more abundant in both treatments with tubes; and by SFA 16:0 which accumulated more in LD chambers.

Pigment contents

Overall composition of the photosynthetic communities in sediment was assessed through their pigment content. Total pigment concentrations (Fig. S1) showed accumulation in summer (LD treatments) or after summer (other treatments). Sediment samples contained mainly chlorophyll a, fucoxanthin, alloxanthin and chlorophyll c (Table S2), showing the preponderance of diatoms and cryptophytes.

Pigment composition of sediment also changed through time (Fig. 2; ANOSIM: R = 0.393, p < 0.001). Significant differences between two following month mostly appear in late summer, at the beginning of autumn and in winter (ANOSIM post-hoc pairwise tests, Table 1). Regardless of the sampling time, chlorophyll a accounted for approximately 50 % of pigments in sediment, while proportions of fucoxanthin and alloxanthin exhibited significant differences among time (Friedman tests, p > 0.05 for chlorophyll a and p < 0.05 for fucoxanthin and alloxanthin). Pigments contributing highly to dissimilarities between seasons were chlorophylls a and c, which were in higher proportions in autumn and winter, and chlorophyllide a and xanthophylls, which were in higher proportions in spring and summer.

Pigment composition of sediment changed weakly between treatments (ANOSIM: R = 0.08, p = 0.016): chambers with tubes had significantly different compositions compared to control chambers (ANOSIM post-hoc pairwise tests, Table 2). Chlorophyllide a was indeed in lower proportion and alloxanthin in higher proportion in Ctrl chambers compared to other chambers.

EPS

General composition

EPS total concentrations in both carbohydrates and proteins showed variations through time, with accumulation of EPS at the beginning of summer (Fig. S3). Glucose was the most abundant sugar, followed by mannose and galactose (Table S3). Glucose percentages varied significantly through time in each treatment, for colloidal and bound fractions (Friedman tests, p < 0.001), with highest contributions in summer.

EPS fractions

Colloidal and bound fractions had slightly different monosaccharide compositions (ANOSIM: R = 0.083, p < 0.001). SIMPER analysis showed that 4 sugars explained more than 70 % of dissimilarities between fractions: glucose was more abundant in colloidal EPS while mannose, xylose and scyllo-inositol were more abundant in bound EPS.

Therefore, the analysis was made separately for colloidal and bound EPS fractions.

Colloidal EPS

Monosaccharide composition of colloidal EPS changed through time, with significant differences between sampling months (Fig. 3; ANOSIM: R = 0.375, p < 0.001). Periods of variations were distributed along the year (ANOSIM post-hoc tests, Table 1). Sugars explaining most dissimilarity between sampling times were the most abundant: glucose, mannose and galactose, as well as scyllo-inositol when comparing winter months with other sampling times.

No significant differences between treatments were found (ANOSIM, R = 0.049, p = 0.068).

Bound EPS

Monosaccharide composition of bound EPS changed through time, with significant differences between sampling months (Fig. 4; ANOSIM, R = 0.376, p < 0.001). Significant evolution of bound EPS composition was mainly found between August and February (Table 1). Sugars explaining most dissimilarity between sampling times were also glucose, mannose and galactose.

Bound EPS composition in monosaccharides also changed slightly between treatments (ANOSIM: R = 0.079, p = 0.017): Ctrl and LD had significantly different compositions compared to HD chambers (ANOSIM post-hoc pairwise tests, Table 2).

Multi-marker approach

Results of MFA combining datasets of FAs, pigments, colloidal and bound EPS are displayed in Fig. 8. Glucose and galactose percentages in both EPS fractions contribute most to the first axis, while pigments β-caroten, xanthophylls, pheophytin, percentage of myoinositol in bound EPS, and FA 16:1ω7 contribute most to the second axis. Results showed correlations between different types of variables (Fig. 5

Discussion

EPS composition over the course of the experiment

Composition of carbohydrates in colloidal and bound EPS EPS were divided in two fractions according to their extraction protocol: the colloidal fraction was extracted with water only whereas bound EPS were collected with a strong cationic resin [START_REF] Takahashi | Optimising extraction of extracellular polymeric substances (EPS) from benthic diatoms: comparison of the efficiency of six EPS extraction methods[END_REF]. This resin traps the cations maintaining the links between bound EPS, therefore allowing their release in the water. Our results demonstrate that these two EPS fractions are chemically different, as their monosaccharide general compositions are significantly different. These two compositions of colloidal and bound EPS do not change simultaneously along the year (Table 1), suggesting that their control is specific to each EPS fraction. These different compositions are consistent with literature data showing that both fractions have different production and degradation pathways [START_REF] Staats | Isolation and characterization of extracellular polysaccharides from the epipelic diatoms Cylindrotheca closterium and Navicula salinarum[END_REF][START_REF] Underwood | The Importance of Extracellular Carbohydrate Production by Marine Epipelic Diatoms[END_REF].

Most abundant sugars were the same, but in different proportions, in both fractions.

EPS carbohydrates consisted mostly in glucose, which always reached percentages over 50 %.

Colloidal EPS contained more glucose than bound EPS, which is consistent with literature data [START_REF] Hofmann | Dynamics and compositional changes in extracellular carbohydrates in estuarine sediments during degradation[END_REF][START_REF] Pierre | Biochemical characterization of extracellular polymeric substances extracted from an intertidal mudflat using a cation exchange resin[END_REF][START_REF] Taylor | The quantitative variability and monosaccharide composition of sediment carbohydrates associated with intertidal diatom assemblages[END_REF]). Yet, [START_REF] Pierre | Biochemical characterization of extracellular polymeric substances extracted from an intertidal mudflat using a cation exchange resin[END_REF] obtained lower percentages of glucose, especially in bound fractions were percentage reached 20 % of total monosaccharide only. This difference could originate from a different producing community, for instance epipelic assemblages instead of epipsammic ones, as they analysed mud instead of sand in our study. Also, our sampling protocol, which includes a freezing step, might prompt release of intracellular material, especially of some chrysolaminarin, which is an intracellular storage glucan in diatoms, mainly composed of glucose [START_REF] Chiovitti | The glucans extracted with warm water from diatoms are mainly derived from intracellular chrysolaminaran and not extracellular polysaccharides[END_REF][START_REF] Underwood | Environmental effects on exopolymer production by marine benthic diatoms: Dynamics, changes in composition, and pathways of production[END_REF]. Other abundant monosaccharides were mannose and galactose, which is consistent with observation made on Cylindrotheca closterium cultures [START_REF] Underwood | Environmental effects on exopolymer production by marine benthic diatoms: Dynamics, changes in composition, and pathways of production[END_REF]). Yet, the large amounts of glucose, mannose and galactose present in our sediment samples suggest that organic matter was mostly fresh, as these three sugars are particularly labile [START_REF] Oakes | Composition, production, and loss of carbohydrates in subtropical shallow subtidal sandy sediments: Rapid processing and long-term retention revealed by C-13-labeling[END_REF].

Variations in EPS compositions

Both bound and colloidal carbohydrates displayed seasonal variations in their monosaccharide composition. Glucose was mainly responsible for seasonal differences; it accumulated in summer and was recorded in lower proportions in other seasons. Higher percentages of glucose in EPS have been correlated with photosynthetic production [START_REF] Bellinger | Biofilm polymers: relationship between carbohydrate biopolymers from estuarine mudflats and unialgal cultures of benthic diatoms[END_REF][START_REF] De Brouwer | Short-term dynamics in microphytobenthos distribution and associated extracellular carbohydrates in surface sediments of an intertidal mudflat[END_REF][START_REF] Hanlon | Dynamics of extracellular polymeric substance (EPS) production and loss in an estuarine, diatom-dominated, microalgal biofilm over a tidal emersion-immersion period[END_REF]. Also, in the studied sediments, the increase in glucose proportion in summer may originate from the degradation of intracellular chrysolaminarin, released in the environment by senescent diatoms. Other seasons displayed a higher diversity of sugars, which could be explained by the preferential degradation of glucose by heterotrophic bacteria [START_REF] Bellinger | Biofilm polymers: relationship between carbohydrate biopolymers from estuarine mudflats and unialgal cultures of benthic diatoms[END_REF][START_REF] Hofmann | Dynamics and compositional changes in extracellular carbohydrates in estuarine sediments during degradation[END_REF]: particularly, fucose, rhamnose and galactose are known to be more difficult for them to assimilate than glucose [START_REF] Giroldo | Relative increase of deoxy sugars during microbial degradation of an extracellular polysaccharide released by a tropical freshwater Thalassiosira sp (Bacillariophyceae)[END_REF]. Finally, nutrient concentrations in pore water and light exposure of biofilms also vary seasonally, and both factors have been demonstrated to alter EPS carbohydrate composition [START_REF] De Brouwer | Daily fluctuations of exopolymers in cultures of the benthic diatoms Cylindrotheca closterium and Nitzschia sp. (Bacillariophyceae)[END_REF][START_REF] Underwood | Environmental effects on exopolymer production by marine benthic diatoms: Dynamics, changes in composition, and pathways of production[END_REF], potentially strengthening the effects of photosynthesis and EPS degradation in shaping carbohydrate composition.

Colloidal EPS compositions were not influenced by experimental conditions, while the composition of bound carbohydrates appeared to vary between treatments: chambers with high tube density had different sediment composition compared to others chambers. Such effect might result from a direct influence of hydrodynamics on EPS production, or from an indirect influence of tubes on benthic assemblages, or from a combination of both. As microbial assemblages and bound EPS compositions do not display similar temporal evolutions (Table 1), a direct effect of hydrodynamics on the production or degradation of bound EPS is a likely hypothesis. With such mechanisms, microorganisms could better adapt their adhesion to the local current conditions. For instance, desoxy-monosaccharides like rhamnose and pentoses like xylose are able to promote adhesion to sediment because of their hydrophobicity [START_REF] Bellinger | Significance of diatomderived polymers in carbon flow dynamics within estuarine biofilms determined through isotopic enrichment[END_REF]. Functionality of different sugars [START_REF] Pierre | Biochemical characterization of extracellular polymeric substances extracted from an intertidal mudflat using a cation exchange resin[END_REF] revealed the presence of myo-inositol in carbohydrates of marine sediment; this sugar is crucial for the functioning of many eukaryotic cells and is also a growth factor for various organisms. Our sediment samples also contained this sugar, and additionally its stereoisomer: scyllo-inositol. To our knowledge, this monosaccharide has never been found in EPS carbohydrates. In diatoms, it may originate from myo-inositol after transformation by the myo-inositol deshydrogenase enzyme [START_REF] Gross | Distribution of myo-inositol dehydrogenase in algae[END_REF], which may explain its presence in the biofilm. Also, this monomer was an important sugar in separating winter from other seasons, which might be explained by variations in the functioning of this enzyme, although its role is still under investigation [START_REF] Gross | Distribution of myo-inositol dehydrogenase in algae[END_REF]. In soils, scyllo-inositol is associated to phosphate in scyllo-inositol hexakisphosphate, which forms an important part of the organic phosphorus content [START_REF] Turner | Quantification and bioavailability of scyllo-inositol hexakisphosphate in pasture soils[END_REF].

When studying different mudflats, [START_REF] De Brouwer | Distribution of extracellular carbohydrates in three intertidal mudflats in Western Europe[END_REF] showed that EPS mainly originated from refractory dissolved organic matter. This is probably not the case here, as monosaccharide composition revealed a high proportion of glucose in every sample. Sugar composition can therefore be interpreted in terms of functionality for microorganisms. For instance, uronic acids have acidic groups which provide sites for sediment -organism interactions, so that they facilitate the fixation of cells to sediment [START_REF] Bellinger | Significance of diatomderived polymers in carbon flow dynamics within estuarine biofilms determined through isotopic enrichment[END_REF]. Also, deoxy-sugars such as fucose and rhamnose, and pentoses seem to promote biostabilisation of the sediment by enhancing the hydrophobicity of the biofilm [START_REF] Bellinger | Significance of diatomderived polymers in carbon flow dynamics within estuarine biofilms determined through isotopic enrichment[END_REF][START_REF] Zhou | The role of surface-active carbohydrates in the formation of transparent exopolymer particles by bubble adsorption of seawater[END_REF]. Finally, differences in monosaccharide composition have been shown to promote the development of different bacterial communities, depending on the ability of different bacterial groups to degrade these carbohydrates [START_REF] Taylor | Microphytobenthic extracellular polymeric substances (EPS) in intertidal sediments fuel both generalist and specialist EPS-degrading bacteria[END_REF].

Changes in EPS composition and bacterial population will in turn provide recruitment cues for diverse organisms and can therefore facilitate or hinder the resilience of ecosystem engineers [START_REF] Nelson | Coral and macroalgal exudates vary in neutral sugar composition and differentially enrich reef bacterioplankton lineages[END_REF].

EPS and microbial communities

Fatty acid biomarkers

In the studied environment, external factors varying seasonally such as temperature, photoperiod, and hydrodynamics have a strong structuring influence on EPS composition.

EPS composition is also impacted by the composition of producing assemblages [START_REF] Decho | Microbial exopolymer secretions in ocean environments: their role(s) in food webs and marine processes[END_REF], which were in the present work characterised by their fatty acid (FA) and pigment compositions. FAs ubiquitous to all living organisms, but most taxonomic or functional groups are characterised by a given association of some FAs. For instance, branched FAs are only produced by bacteria [START_REF] Arts | Lipids in Freshwater Ecosystems[END_REF]. Description of FA content of sediment provides, together, information on taxonomic or functional groups and on the degradation state of organic matter. The increase of SFA percentages, and of its ratio to PUFAs, demonstrates an accumulation of degraded OM in sediments in summer and autumn [START_REF] Grossi | Impact of feeding by Arenicola marina (L.) and ageing of faecal material on fatty acid distribution and bacterial community structure in marine sediments: An experimental approach[END_REF][START_REF] Sun | Rates and mechanisms of fatty acid degradation in oxic and anoxic coastal marine sediments of Long Island Sound, New York, USA[END_REF]. On the other hand, spring and winter months, as well as the beginning of August, are characterised by higher MUFA and PUFAs percentages; in particular, MUFA 16:1ω7 and PUFA 20:5ω3 (or EPA for eicosapentaenoic acid) relative contributions showed a drastic increase in these months. These FAs are mainly produced by microphytobenthos in these environments, especially diatoms, dinoflagellates and cyanobacteria [START_REF] Arts | Lipids in Freshwater Ecosystems[END_REF][START_REF] Volkman | Microbial lipids of an inter-tidal sediment. 1. Fatty-acids and hydrocarbons[END_REF]. Yet the association of MUFA 16:1ω7 and PUFA 20:5ω3 together with high amounts of SFAs 14:0 and 16:0 suggest a dominance of diatoms [START_REF] Dijkman | Composition and heterogeneity of the microbial community in a coastal microbial mat as revealed by the analysis of pigments and phospholipid-derived fatty acids[END_REF].

Pigment biomarkers

Pigments were used to characterise the abundance and taxonomic composition of microphytobenthic communities. The abundance of fucoxanthin, together with chlorophylls a and c, is consistent with results of FA analysis in demonstrating the prevalence of diatoms in this environment [START_REF] Jeffrey | Phytoplankton pigments in oceanography: guidelines to modern methods[END_REF]. Also, the presence of alloxanthin shows the occurrence of cryptophytes [START_REF] Jeffrey | Phytoplankton pigments in oceanography: guidelines to modern methods[END_REF]. Proportions of chlorophylls c and fucoxanthin displayed a seasonal pattern, being higher in autumn and winter compared to spring and summer; on the contrary, chlorophyllide a showed a reversed pattern. This last pigment reveals the occurrence of senescent diatoms [START_REF] Jeffrey | Recent advances in HPLC pigment analysis of phytoplankton[END_REF], which suggests a higher predation rate in these months. Also, the occurrence of chlorophyllide a might be related to an exogenous supply of pigment. Xanthophylls displayed the same seasonal pattern than chlorophyllide a, which can be explained by their role in the xanthophyll cycle, which act as a photoprotection mechanism [START_REF] Jeffrey | Phytoplankton pigments in oceanography: guidelines to modern methods[END_REF]; in spring and summer months, when light intensity increases, xanthophyll production increases, and diadinoxanthin in transformed into diatoxanthin.

Multi-marker approach and general dynamic of the biofilm When combining datasets of FAs and pigments together with those on EPS composition (Fig. 5), samples from every month clearly gathered together, showing a welldefined temporal evolution. Also, this analysis highlights the association between variables and samples, which allows following the dynamics of the biofilm in the course of year and to link EPS composition with microorganisms.

In winter time, the sediment contained high proportions of fucoxanthin and chlorophyll c, which indicated the development of diatoms [START_REF] Jeffrey | Phytoplankton pigments in oceanography: guidelines to modern methods[END_REF]; in the meantime, proportions of most sugars except glucose, particularly galactose and mannose in both EPS fractions, and scyllo-inositol in colloidal EPS, increased. In spring, the abundance of diatoms increased, as demonstrated by increase in 16:1ω7 and 20:5ω3 biomarkers [START_REF] Dalsgaard | Fatty acid trophic markers in the pelagic marine environment[END_REF]. Also, the increase of alloxanthin proportions together with 18:4ω3 FAs mirrored the development of cryptophytes. In late spring, xanthophyll proportions increased, as a protection against light intensity for diatoms [START_REF] Jeffrey | Phytoplankton pigments in oceanography: guidelines to modern methods[END_REF]. Summer was characterised by high amounts of most saturated FAs, which indicates the accumulation of degraded organic matter [START_REF] Balzano | Changes in fatty acid composition in degrading algal aggregates[END_REF][START_REF] Lü | Decomposition of algal lipids in clay-enriched marine sediment under oxic and anoxic conditions[END_REF][START_REF] Sun | Rates and mechanisms of fatty acid degradation in oxic and anoxic coastal marine sediments of Long Island Sound, New York, USA[END_REF]. This degradation is emphasised by the presence of chlorophyllide a which indicates the presence of senescing diatoms [START_REF] Jeffrey | Recent advances in HPLC pigment analysis of phytoplankton[END_REF]; these organisms may then release in the environment high amounts of intracellular chrysolaminarin, explaining the increase of glucose proportions in both EPS fraction in summer. Some glucose may also originate from higher photosynthetic production in late spring and in the beginning of summer, when light intensity, photoperiod and temperature increase. Finally, autumn is characterised by high levels of markers of bacterial degradation: pheophytin a which results from the degradation of chlorophyll [START_REF] Lorenzen | Determination of Chlorophyll and Pheo-Pigments: Spectrophotometric Equations[END_REF] as well as most branched FAs, which characterise heterotrophic bacteria [START_REF] Arts | Lipids in Freshwater Ecosystems[END_REF].

Results of analyses on separate datasets showed no correlation between the periods where microbial assemblages change and those where EPS composition does so (Table 1). However, our multi-marker analysis suggested links between EPS composition and biological processes taking place in the sediment, such as photosynthesis, which depend on microbial assemblages. For instance, the large increase of glucose contribution in summer was linked to the degradation of senescing diatoms evidenced by chlorophyllide a. Finally, it seems that EPS composition of sediment is influenced at the same time by the succession of microbial assemblages taking place in tidal flats and directly by environmental conditions.

Conclusion

In this study, association of FA and pigment biomarkers together with monosaccharide composition of EPS complemented each other to allow a fine characterisation of the biofilm in the course of year, describing its composition and functioning. Furthermore, as different monosaccharides may be associated with different ecosystem functions (stabilisation, recruitment cues for instance; [START_REF] Bellinger | Significance of diatomderived polymers in carbon flow dynamics within estuarine biofilms determined through isotopic enrichment[END_REF][START_REF] Nelson | Coral and macroalgal exudates vary in neutral sugar composition and differentially enrich reef bacterioplankton lineages[END_REF][START_REF] Zhou | The role of surface-active carbohydrates in the formation of transparent exopolymer particles by bubble adsorption of seawater[END_REF], this set of variables may indicate diverse roles of biofilms for the ecosystem. Fig. S3

Supplementary figures captions

  .A); for instance, pigment pheophytin a was associated with most of branched FAs. Combination of the four datasets allowed the segregation of samples from different months along a clear seasonal cycle, with a specific combination of biomarkers for each season (Fig 5.B).

FigureFig. 4 :

 4 Figure(s) captions

Fig. 5 :

 5 Fig. 5: Results of MFA analysis on sediment samples over the course of the experiment: a.

Fig. S2 :

 S2 Fig. S2: evolution of total pigment concentrations in sediment over the course of the experiment. The x axis displays the number of day from the beginning of the experiment (March 21st, 2011). Treatments were : no tubes (Ctrl, grey circles); low tube density (LD, empty square) and high tube density (HD, black triangles).

Fig. S3 :

 S3 Fig.S3: evolution of total EPS concentrations: a. carbohydrates and b. proteins in sediment over the course of the experiment. The x axis displays the number of day from the beginning of the experiment(March 21st, 2011). Treatments were : no tubes (Ctrl, grey circles); low tube density (LD, empty square) and high tube density (HD, black triangles).

  Fig. S1Supplementary figures

  Fig.S2

Table 1 :

 1 Results of ANOSIM pairwise post-hoc tests comparing consecutive months (e. g. May with April and June) using 4 sets of markers: fatty acids, pigments, colloidal and bound sugars. All 12 samples (pooled from all 3 treatments) from each sampling times were used. Test

	significance: ns: not significant; *: p < 0.05; **: p < 0.01; ***: p < 0.001. Coll: colloidal. Aug: August; Sept: September; Oct: October; Nov:
	November; Jan: January; Feb: February; Mar: March; Apr: April.
	Apr	May	June	July		01Aug 31Aug Sept	Oct	Nov		Jan12	Fev12	Mar12	Apr12
	Fatty acids	***	***	***	***	***	***	**	***	***	ns	Ns	***
	Pigments	ns	ns	ns	ns	*	**	***	ns	ns	***	***	***
	Coll. sugars	*	**	**	*	ns	***	ns	ns	**	ns	**	ns
	Bound sugars	*	ns	ns	ns	**	**	*	*	***	**	Ns	ns

Table ( s) Click here to download Table(s): Revised tables -Passarelli et al..docx

 (

Table 2 :

 2 Results of ANOSIM pairwise post-hoc tests comparing experimental treatments using 4 sets of markers: fatty acids, pigments, colloidal and bound sugars. All replicates (all samples pooled from all sampling dates) were used for each treatment. Test significance: ns: not significant; Non-metric Multidimensional Scaling (nMDS) plot of Bray Curtis similarities of the fatty acid (FA) compositions of sediment over the course of the experiment. For each date, all

	*: p < 0.05; **: p < 0.01; ***: p < 0.001. Treatments were: Ctrl: no tubes; LD: low tube density; HD: high tube density. Tests for colloidal sugars
	(Coll.sugars) were not performed as global test was not significant.
		Ctrl LD	Ctrl HD	LD HD
	Fatty acids	***	***	***
	Pigments	*	**	ns
	Coll. sugars	-	-	-
	Bound sugars	ns	*	*

Fig. 2: Non-metric Multidimensional Scaling (nMDS) plot of Bray Curtis similarities of the pigment compositions of sediment over the course of the experiment. For each date, all replicates from each treatment are displayed. Data were transformed with Hellinger transformation prior to analysis. The acceptable value of stress indicates that the grouping representation is acceptable: distances between symbols correlate with dissimilarities between the pigment compositions of sediment. Ctrl treatment: circles; LD treatment: squares; HD treatments: triangles. Apr: April; Jun: June; Jul: July; Aug: August; Sept: September; Oct:

October; Nov: November; Jan: January; Feb: February; Mar: March. treatments: triangles. Apr: April; Jun: June; Jul: July; Aug: August; Sept: September; Oct: October; Nov: November; Jan: January; Feb: February; Mar: March.

Acknowledgment

The authors thank two anonymous reviewers whose advice and comments greatly improved the manuscript. We the Conservatoire du Littoral, the Syndicat Mixte des Espaces Littoraux de la Manche and the Syndicat Mixte Littoral Normand for their involvement in the project. We thank M.-F. Bernard, Y. Turgis, A. Guigny, E. Oulhen, A. Migné and the crew of Gwen Ha Ruz for their valuable help with fieldwork. This work received support from the "ATM Biodiversité des microorganismes", and their support is gratefully acknowledged.

Table S1: fatty acid percentages in sediment samples (mean over all samples from each treatment and sampling time; ± standard error).

12:0 0.59 ± 0.07 16:2w4 0.51 ± 0.05 13:0 0.05 ± 0.01 16:2w6 0.19 ± 0.02 14:0 6.02 ± 0.17 16:3w4 0.69 ± 0.09 15:0 3.15 ± 0.13 16:4w1 0.03 ± 0.01 16:0 45.38 ± 1.08 16:4w3 0.09 ± 0.02 17:0 0.87 ± 0.03 18:2w6 0.33 ± 0.04 18:0 6.43 ± 0.27 18:2w9 0.08 ± 0.02 19:0 0.07 ± 0.02 18:3w3 0.06 ± 0.01 20:0 0.24 ± 0.02 18:3w6 0.79 ± 0.13 22:0 0.17 ± 0.02 18:4w3 0.17 ± 0.03 24:0 0.