Charlène Guillaumot
email: charlene.guillaumot@ulb.ac.be

Salomé Fabri-Ruiz

Alexis Martin

Marc Eléaume

Bruno Danis

Jean-Pierre Feral

Thomas Saucède

Jean-Pierre Féral

Benthic species of the Kerguelen Plateau show contrasting distribution shifts in response to environmental changes

Keywords: conservation, echinoid, future predictions, historical changes, Kerguelen Plateau, species distribution modeling

come

Physiological responses of marine species to changes in seawater temperature, salinity, and more acidic conditions have been assessed by ecophysiological experiments [START_REF] Collard | Could the acid-base status of Antarctic sea urchins indicate a better-than-expected resilience to near-future ocean acidification?[END_REF][START_REF] Karelitz | Ocean acidification has little effect on developmental thermal windows of echinoderms from Antarctica to the tropics[END_REF][START_REF] Peck | Juveniles are more resistant to warming than adults in four species of Antarctic marine invertebrates[END_REF][START_REF] Suckling | Adult acclimation to combined temperature and pH stressors significantly enhances reproductive outcomes compared to short-term exposures[END_REF], which are usually confronted with practical limitations due to substantial technical and funding issues [START_REF] Suckling | Adult acclimation to combined temperature and pH stressors significantly enhances reproductive outcomes compared to short-term exposures[END_REF]. Species geographic responses to the multiple effects of climate change may include resilience, distribution range shift toward the pole, where they would find more suitable conditions, and local extinction [START_REF] Doney | Climate change impacts on marine ecosystems[END_REF][START_REF] Walther | Ecological responses to recent climate change[END_REF]. Species distribution models (SDM) have been currently used to address these biogeographic issues for conservation purposes [START_REF] Marshall | Species distribution modelling to support marine conservation planning: The next steps[END_REF][START_REF] Reiss | Benthos distribution modelling and its relevance for marine ecosystem management[END_REF][START_REF] Ross | Use of predictive habitat modelling to assess the distribution and extent of the current protection of 'listed' deep-sea habitats[END_REF][START_REF] Zucchetta | Modelling the spatial distribution of the seagrass Posidonia oceanica along the North African coast: Implications for the assessment of Good Environmental Status[END_REF], assess the direct impact of human activities on ecosystems [START_REF] Vázquez-Luis | Spatial distribution modelling of the endangered bivalve Pinna nobilis in a marine protected area[END_REF][START_REF] Vierod | Predicting the distribution of vulnerable marine ecosystems in the deep sea using presence-background models[END_REF], and predict species distribution range shifts in response to climate change [START_REF] Guillera-Arroita | Is my species distribution model fit for purpose? Matching data and models to applications[END_REF][START_REF] Tingley | Realized niche shift during a global biological invasion[END_REF]. SDM relate species occurrence records to abiotic environmental predictors (Elith & Leathwick, 2009;[START_REF] Elith | Novel methods improve prediction of species' distributions from occurrence data[END_REF] to identify species suitable areas [START_REF] Phillips | Applying species distribution modelling to a data poor, pelagic fish complex: The ocean sunfishes[END_REF]. Species distribution range shifts can be modeled by projecting species suitable areas in geography using key predictive environmental descriptors and different climate scenarios for the near future [START_REF] Guisan | Predicting species distribution: Offering more than simple habitat models[END_REF][START_REF] Reiss | Benthos distribution modelling and its relevance for marine ecosystem management[END_REF].

Applied SDM studies to marine species [START_REF] Duffy | Explicitly integrating a third dimension in marine species distribution modelling[END_REF][START_REF] Marshall | Species distribution modelling to support marine conservation planning: The next steps[END_REF][START_REF] Robinson | Pushing the limits in marine species distribution modelling: Lessons from the land present challenges and opportunities[END_REF][START_REF] Ross | Use of predictive habitat modelling to assess the distribution and extent of the current protection of 'listed' deep-sea habitats[END_REF] may be also confronted with substantial limitations. Sampling bias, data availability, quality, and heterogeneous distribution are common issues [START_REF] Guillera-Arroita | Is my species distribution model fit for purpose? Matching data and models to applications[END_REF][START_REF] Robinson | Pushing the limits in marine species distribution modelling: Lessons from the land present challenges and opportunities[END_REF][START_REF] Tessarolo | Uncertainty associated with survey design in species distribution models[END_REF]. However, protocols have been developed to address these methodological issues and provide robust and relevant distribution predictions [START_REF] Barbet-Massin | Selecting pseudo-absences for species distribution models: How, where and how many?[END_REF]Guillaumot, Martin, Eléaume, & Saucède, in press;[START_REF] Phillips | Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data[END_REF].

In the Southern Ocean, echinoids are common components of marine benthic communities. Nearly 200 species with contrasting ecological niches [START_REF] David | Synopses of the Antarctic benthos[END_REF] were recorded [START_REF] Fabri-Ruiz | Southern Ocean Echinoids database. An updated version of Antarctic, Sub-Antarctic and cold temperate echinoid database[END_REF]. They also constitute substantial elements of trophic networks [START_REF] Marina | The food web of Potter Cove (Antarctica): Complexity, structure and function[END_REF][START_REF] Raymond | A Southern Ocean dietary database[END_REF], can promote benthic diversity [START_REF] Linse | Biodiversity of echinoids and their epibionts around the Scotia Arc, Antarctica[END_REF][START_REF] Pabis | Distribution patterns in the biomass of macrozoobenthic communities in Admiralty Bay (King George Island, South Shetlands, Antarctic)[END_REF], and play a key role in benthic ecosystems [START_REF] Brey | The genus Sterechinus neumayeri (Echinodermata: Echinoidea) on the Weddell Sea shelf and slope (Antarctica): Distribution, abundance and biomass[END_REF][START_REF] David | Synopses of the Antarctic benthos[END_REF].

For instance, cidaroids were listed as vulnerable marine ecosystem (VME) indicator taxa by CCAMLR (Convention for the Conservation of Antarctic Marine Living Resources, CVD code) because of their rich epibiont assemblages [START_REF] Hardy | Ectosymbiosis associated with cidaroids (Echinodermata: Echinoidea) promotes benthic colonization of the seafloor in the Larsen Embayments, Western Antarctica[END_REF][START_REF] Linse | Biodiversity of echinoids and their epibionts around the Scotia Arc, Antarctica[END_REF], brooding behavior, lack of motility, and sensitivity to fishing activities (CCAMLR 2009).

In this work, SDM were used to predict the geographic response to environmental changes of four common echinoid species of the Kerguelen Plateau with contrasting ecological niches: Abatus cordatus, Brisaster antarcticus, Ctenocidaris nutrix, and Sterechinus diadema. Recent observations indicate that the Kerguelen Plateau is a highly dynamic region currently undergoing significant ecological changes in response to climate warming [START_REF] Fe R E N C E S Allan | Critical indirect effects of climate change on sub-Antarctic ecosystem functioning[END_REF][START_REF] Byrne | From pole to pole: The potential for the Arctic seastar Asterias amurensis to invade a warming Southern Ocean[END_REF][START_REF] Molinos | Climate velocity and the future global redistribution of marine biodiversity[END_REF]. The objectives were to test whether changing environmental conditions might really impact local species distribution range in a near future, a critical issue for conservation strategies. To address this issue, SDM were generated for each species separately and for three different environmental conditions belonging to different time periods: past conditions [1955][1956][1957][1958][1959][1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970][1971][1972][1973][1974], present-day situation [2005][2006][2007][2008][2009][2010][2011][2012], and near-future predictions .

| MATERIAL S AND ME THODS

| Studied area

The Kerguelen Plateau is a vast and remote area of the Southern Ocean that displays unique oceanographic features and proximity between marine fronts generating strong latitudinal temperature and salinity gradients [START_REF] Moore | Surface chlorophyll concentrations in relation to the Antarctic Polar Front: Seasonal and spatial patterns from satellite observations[END_REF][START_REF] Park | Polar Front around the Kerguelen Islands: An up-todate determination and associated circulation of surface/subsurface waters[END_REF].

Important micronutrient releases, including iron, and high chlorophyll a concentrations are present on the eastern margin of the plateau. This contrasts with the "High Nutrient Low Chlorophyll" waters reported in most of the Southern Ocean [START_REF] Koubbi | Ecoregionalisation of the Kerguelen and Crozet islands oceanic zone. Part II: The Crozet oceanic zone[END_REF][START_REF] Park | Polar Front around the Kerguelen Islands: An up-todate determination and associated circulation of surface/subsurface waters[END_REF]. High diversity levels in pelagic and benthic ecosystems are also described on the Kerguelen Plateau in comparison with the surrounding oceanic areas [START_REF] Féral | Kerguelen, un archipel sous haute surveillance[END_REF][START_REF] Koubbi | Ecoregionalisation of the Kerguelen and Crozet islands oceanic zone. Part II: The Crozet oceanic zone[END_REF]. The Kerguelen Plateau makes part of the French and Australian protected areas and aggregates substantial conservation issues for marine biodiversity due to fast environmental changes and the impact of fisheries activities (CCAMLR 2008[START_REF] Koubbi | Ecoregionalisation of the Kerguelen and Crozet islands oceanic zone. Part II: The Crozet oceanic zone[END_REF][START_REF] Welsford | The Heard Island and McDonald Islands marine reserve and conservation zone -A model for Southern Ocean marine reserves[END_REF][START_REF] Welsford | Demersal fishing interactions with marine benthos in the Australian EEZ of the southern ocean: An assessment of the vulnerability of benthic habitats to impact by demersal gears[END_REF]. It was proposed that the recorded climate changes may correspond to a southward shift of the Antarctic Circumpolar Current [ACC] and of its frontal systems, in particular the sub-Antarctic Front [SAF] and the Polar Front [PF] [START_REF] Fe R E N C E S Allan | Critical indirect effects of climate change on sub-Antarctic ecosystem functioning[END_REF]. This makes marine biodiversity of the region particularly at risk with regard to environmental changes. Alteration in marine biodiversity and ecosystem functioning is particularly expected to impact coastal marine areas of the Kerguelen Islands (CCAMLR 2008, 2013[START_REF] Hureau | Marine research on the Kerguelen Plateau: From early scientific expeditions to current surveys under the CCAMLR objectives[END_REF]. The relative low and direct anthropogenic impacts on the Kerguelen marine ecosystems make this archipelago a relevant sentinel to assess the direct effects of actual environmental changes on sub-Antarctic marine habitats. In this context, the National Nature Reserve of the French Southern Territories was recently extended to most of the French Exclusive Economic Zone around the Kerguelen Islands over around 400,000 km 2 (decree issued on 12 December 2016) following the commitments of France to the COP21 meeting held in Paris in 2015 (http://www.gouvernement.fr/action/la-cop-21, accessed on 29 September 2017). This makes the reserve the sixth world's largest Marine Protected Area (https://gis.ccamlr.org/ home, accessed on 29 September 2017).

| Biological records

Echinoid species records were obtained from [START_REF] Fabri-Ruiz | Southern Ocean Echinoids database. An updated version of Antarctic, Sub-Antarctic and cold temperate echinoid database[END_REF].

The dataset contains presence-only data of 201 echinoid species collected in the Southern Ocean from the Antarctic coasts to 45°S latitude. This dataset is a compilation of data collected during oceanographic campaigns undertaken between 1872 and 2015. Four species with contrasting ecological requirements were selected in the dataset for this study. The four selected species are known by a sufficient number of presence records to perform robust species distribution models. These four species are common on the Kerguelen Plateau and constitute substantial representatives of Antarctic benthic ecosystems (De Ridder, [START_REF] De Ridder | Antarctic and subantarctic echinoids from Marion Dufresne' expeditions MD03, MD04, MD08 and from the Polarstern expedition Epos III[END_REF][START_REF] Díaz | Evolutionary pathways among shallow and deep-sea echinoids of the genus Sterechinus in the Southern Ocean[END_REF][START_REF] Hardy | Ectosymbiosis associated with cidaroids (Echinodermata: Echinoidea) promotes benthic colonization of the seafloor in the Larsen Embayments, Western Antarctica[END_REF][START_REF] Linse | Biodiversity of echinoids and their epibionts around the Scotia Arc, Antarctica[END_REF][START_REF] Moya | Environmental control on the structure of echinoid assemblages in the Bellingshausen Sea (Antarctica)[END_REF][START_REF] Poulin | Pattern of spatial distribution of a broodprotecting schizasterid echinoid, Abatus cordatus, endemic to the Kerguelen Islands[END_REF]. Namely, we selected Abatus cordatus and Brisaster antarcticus, two species endemic to sub-Antarctic regions, and Ctenocidaris nutrix and Sterechinus diadema that present broader distribution ranges in the Southern Ocean (Figure 1).

Duplicate records that fell on one single grid-cell pixel of environment layers were removed from the dataset.

| Environmental data

The environmental descriptors used in this study were generated and described by [START_REF] Fabri-Ruiz | Southern Ocean Echinoids database. An updated version of Antarctic, Sub-Antarctic and cold temperate echinoid database[END_REF]. They are available as raster layers collected from different sources and modified to fulfill modeling requirements at the scale of the Southern Ocean.

Environmental data cover the extent of the Southern Ocean (<45°S) at a grid-cell resolution of 0.1 degrees (around 10 km). Neighborjoining interpolation was applied to correct for missing values that may interfere with certain distribution modeling approaches. The dataset contains environmental descriptors for the six decades included between 1955 and 2012. Environmental data were processed using the functions proposed by the R (R Core Team 2015)

SDMPlay [START_REF] Guillaumot | SDMPlay: Species Distribution Modelling Playground. R package version 1[END_REF] and raster packages [START_REF] Hijmans | raster: Geographic Data Analysis and Modelling. R package version 2[END_REF].

Predictor selection is a major concern that can alter modeling performances [START_REF] Braunisch | Selecting from correlated climate variables: A major source of uncertainty for predicting species distributions under climate change[END_REF][START_REF] Petitpierre | Selecting predictors to maximize the transferability of species distribution models: Lessons from cross-continental plant invasions[END_REF]. Here, we tested for collinearity between predictors and removed one descriptor from the initial dataset for VIF > 5 (variance inflation factor with the stepwise F I G U R E 1 Distribution of presence-only records available for the four studied species (occurrence duplicates were removed)

Sterechinus diadema n = 528

Abatus cordatus

Brisaster antarcƟcus

Ctenocidaris nutrix n = 160 n = 93 n = 48

procedure of the usdm R package [START_REF] Naimi | Where is positional uncertainty a problem for species distribution modelling?[END_REF]) and Spearman correlation >0.85 [START_REF] Dormann | Collinearity: A review of methods to deal with it and a simulation study evaluating their performance[END_REF]. A total of 15 descriptors were selected to run the models (Table S1).

Environmental descriptors were declined for three time periods: [1955][1956][1957][1958][1959][1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970][1971][1972][1973][1974], [2005][2006][2007][2008][2009][2010][2011][2012], and the future projection of the IPCC scenario RCP 8.5 (IPCC Fifth Report, 2013) for .

Oxygen concentration and POC export layers were not available for future projections. Similarly, chlorophyll a, oxygen concentration, POC export, and ice coverage layers were not available for past projections. Therefore, these environmental parameters were considered constant and similar to the present values (Table S1).

| Distribution modeling

Species distributions were modeled using the boosted regression trees (BRT) modeling technique [START_REF] Breiman | Random forests[END_REF]. BRT was shown to present high stability and transferability performances for limited and biased datasets [START_REF] Reiss | Species distribution modelling of marine benthos: A North Sea case study[END_REF][START_REF] Heikkinen | Does the interpolation accuracy of species distribution models come at the expense of transferability?[END_REF][START_REF] Guo | Uncertainty in ensemble modelling of large-scale species distribution: Effects from species characteristics and model techniques[END_REF]Guillaumot et al. in press.). BRT was calibrated according to [START_REF] Elith | A working guide to boosted regression trees[END_REF]. The combination of parameters that minimizes the predictive deviance of the models (learning rate lr, tree complexity tc, and bag fraction bf) were set at, respectively, lr = 0.0001, tc = 2, and bf = 0.75. Modeling computation was performed using the gbm R package [START_REF] Ridgeway | gbm: Generalized Boosted Regression Models[END_REF].

Presence-only methods imply using background data to be selected in the study area. In the Southern Ocean, sampling effort is spatially contrasted and such spatial heterogeneities can alter modeling performances [START_REF] Araújo | Five (or so) challenges for species distribution modelling[END_REF]. We corrected for spatial bias using a target-background sampling method [START_REF] Phillips | Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data[END_REF]. The kernel density estimation (KDE) of visited pixels was estimated (i.e., grid cells in which at least one echinoid is recorded, according to the database compiled by Fabri-Ruiz et al. ( 2017)). KDE layer is a proxy for the survey effort that is used to spatially weight background sampling and compensates for the weight of frequently visited sites in the models. As suggested by [START_REF] Barbet-Massin | Selecting pseudo-absences for species distribution models: How, where and how many?[END_REF], we selected a number of background records similar to the number of presence-only records available and applied a 100-fold replication of the background sampling in each case.

To take into account the limitations of model extrapolation, modeling areas were limited in geography and depth. On the basis of species records and ecology [START_REF] David | Synopses of the Antarctic benthos[END_REF], we considered 1,000 m depth as a maximal extrapolation threshold for A. cordatus and S. diadema, and 1,500 m depth for B. antarcticus and C. nutrix. Boundaries in latitude and longitude were species-specific and determined according to the most extreme positions of species records.

The area under the receiver operating characteristic curve (AUC) was used to evaluate models' robustness. AUC is an unbiased metric (i.e., threshold independence) that is unaffected by low-prevalence datasets, which are typical in presence-only data samples [START_REF] Fielding | A review of methods for the assessment of prediction errors in conservation presence/absence models[END_REF][START_REF] Hand | Measuring classifier performance: A coherent alternative to the area under the ROC curve[END_REF][START_REF] Proosdij | Minimum required number of specimen records to develop accurate species distribution models[END_REF].

Distribution models were generated for the present time period defined as decade [2005][2006][2007][2008][2009][2010][2011][2012] using environmental descriptors available for decade [2005][2006][2007][2008][2009][2010][2011][2012] and presence-only records collected between 1974 and 2015, considering the minimum number of records needed to build robust models. This was considered an acceptable trade-off as main temperature and salinity shifts occurred before 1974 in Antarctic and sub-Antarctic regions [START_REF] Frenot | Azorella selago Hook used to estimate glacier fluctuations and climatic history in the Kerguelen Islands over the last two centuries[END_REF][START_REF] Meredith | Rapid climate change in the ocean west of the Antarctic Peninsula during the second half of the 20th century[END_REF][START_REF] Whitehouse | Rapid warming of the ocean around South Georgia, Southern Ocean, during the 20th century: Forcings, characteristics and implications for lower trophic levels[END_REF]. Based on these models, projections were made for the historical period covering the couple of decades [1955][1956][1957][1958][1959][1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970][1971][1972][1973][1974] and for the predicted future period (RCP 8.5, [2050(RCP 8.5, [ -2099]]), using the respective environmental descriptors available for the two periods (Table S1). To assess the magnitude of environmental shifts that occurred between the three time periods, the significance of environmental shifts was tested between the environmental layers used for modeling using a Wilcoxon paired test.

| Species distribution changes

Three indices were used to analyze model outputs and compare distribution maps between periods. They were adapted from [START_REF] Crase | Modelling both dominance and species distribution provides a more complete picture of changes to mangrove ecosystems under climate change[END_REF] (Table 1) to ( 1) quantify the extent of the distribution area as the sum of the presence probabilities (E occupied ), (2) compare

Formula Description [2005][2006][2007][2008][2009][2010][2011][2012] Relative raw difference between distribution maps indicating the overall change in the distribution of presence probabilities E overlap [2005][2006][2007][2008][2009][2010][2011][2012] Relative overlap between distribution maps indicating any change in the distribution of suitable areas i corresponds to a grid-cell pixel, and p and q are the species distribution maps produced for the three time periods ([1955-1974], [2005][2006][2007][2008][2009][2010][2011][2012], and ). E instability : High values indicate important overall changes in the distribution between periods. E overlap : High values indicate similar highly suitable areas between periods.

E occupied ∑ n i=1 p i Sum of presence probabilities E instability [
TA B L E 1 Indices adapted from [START_REF] Crase | Modelling both dominance and species distribution provides a more complete picture of changes to mangrove ecosystems under climate change[END_REF] for our case study scaled by E occupied [2005][2006][2007][2008][2009][2010][2011][2012] providing possible scores included between 0 and 1 for the two indices.

∑ n i=1 �q i -p i �∕ ∑ n i=1 p i ∑ n i=1 p i ⋅ q i ∕ ∑ n i=1 p i
In each SDM, we used the MaxSSS index (maximum sensitivity plus specificity) as threshold value between suitable and nonsuitable areas and turned raw distribution maps into red (suitable) and yellow (nonsuitable) binary maps. The MaxSSS was suggested to be the best index for presence-only datasets [START_REF] Liu | Selecting thresholds for the prediction of species occurrence with presence-only data[END_REF]. It was measured using the dismo R package [START_REF] Hijmans | dismo: Species Distribution Modelling. R package version 1[END_REF]. Species distribution maps and changes between periods were analyzed on species-specific extents, the four species being recorded on the Kerguelen Plateau.

To analyze the magnitude of environmental shifts that occurred between periods, the environmental subspace modeled as suitable for each species was estimated on the extent of the Kerguelen Plateau only (-56° to -46°S, +63° to +81°E). The total environmental space was defined as the total set of values present on the Kerguelen Plateau for the parameters that most contribute to the models. The portion of space occupied by each species (i.e., the occupied environmental subspace or species realized niche) was delineated by convex hulls and positioned based on the convex hull centroids using the SIBER R package [START_REF] Jackson | Comparing isotopic niche widths among and within communities: SIBER -Stable isotope Bayesian ellipses in R[END_REF]. Convex hulls correspond to polygons that delineate threshold values and comprise each species suitable environment. Size and position of convex hulls were compared between the three time periods.

| RE SULTS

| Environmental changes through time

Environmental differences were tested between the three time periods of reference, for the eight environmental descriptors available for each period specifically (Table S1). Differences in the mean and amplitude of seafloor salinity and temperature were tested for significance (Wilcoxon test, p < .05) between [1955][1956][1957][1958][1959][1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970][1971][1972][1973][1974], [2005][2006][2007][2008][2009][2010][2011][2012], and

| Environmental contributions and the spatial scale

All SDM generated for the present period [2005][2006][2007][2008][2009][2010][2011][2012] show high AUC values (AUC > 0.879; Figure 2), indicating high modeling relevance. The modeled species distributions show different distribution patterns, also contrasting in the extent of the predicted suitable area (Figure 2a-d) and the contribution of the environmental descriptors to each model (Figure 2a'-d'). The extent of suitable areas ranges from the regional scale to the entire Southern Ocean. The distribution of A. cordatus is exclusively predicted in coastal shallow waters (depth <200 m) of the Kerguelen Islands (Figure 2a). In contrast, the distribution of S. diadema is modeled from the sub-Antarctic area to the Antarctic coasts (Figure 2d). The same holds true for the widely distributed S. diadema with a high contribution of mean seafloor temperatures (30.6%) to the SDM (Figure 2d').

| Species modeled ecological niches

The modeled environmental spaces occupied by the four studied species indicate contrasting realized niches. Results show that A. cordatus is a shallow-water species (Figure 3) endemic to coastal waters of the Kerguelen Islands with suitable areas characterized by high temperature amplitudes, low salinity values (33.6 to 33.9 PSU), and temperatures contained between 3 and 4°C (Figure 3;

| Species distribution range shifts

For each species, SDM were produced with past [1955][1956][1957][1958][1959][1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970][1971][1972][1973][1974] and future environmental predictions to assess the magnitude of species distribution range shift with changing environments between periods (Figures 4 and5; Table 2). For all species, distribution range shifts are relatively weak between [1955][1956][1957][1958][1959][1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970][1971][1972][1973][1974] and [2005][2006][2007][2008][2009][2010][2011][2012], with E instability being included between 0.02 ± 0.01 (for S. diadema) and 0.10 ± 0.04 (for B. antarcticus) (Table 2). Projections for [1955][1956][1957][1958][1959][1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970][1971][1972][1973][1974] indicate smaller distribution range compared to the present-day conditions, A. cordatus excepted. The distribution of B. antarcticus is restricted to the central and eastern parts of the Kerguelen Plateau (Figure 4). High E overlap values (0.44 ± 0.03), however, indicate that the most suitable areas have not markedly changed between [1955-1974] and [2005-2012]. For [1955][1956][1957][1958][1959][1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970][1971][1972][1973][1974],

C. nutrix is predicted on the northern Kerguelen Plateau and other sub-Antarctic archipelagoes (Figure 4). Comparison between [1955][1956][1957][1958][1959][1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970][1971][1972][1973][1974] and [2005][2006][2007][2008][2009][2010][2011][2012] indicates an increase in habitat suitability in [2005][2006][2007][2008][2009][2010][2011][2012] with a poleward shift of suitable environments and more suitable areas in South Georgia and in the northern part of the Antarctic Peninsula (Figure 4). For S. diadema, suitable areas are also predicted to increase in the southern part of the Kerguelen Plateau and near the western Antarctic Peninsula region in [2005][2006][2007][2008][2009][2010][2011][2012] compared to [1955][1956][1957][1958][1959][1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970][1971][1972][1973][1974] (Figure 4).

The predicted distribution shifts are much more marked between [2005][2006][2007][2008][2009][2010][2011][2012] and than between [1955][1956][1957][1958][1959][1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970][1971][1972][1973][1974] and [2005][2006][2007][2008][2009][2010][2011][2012], with E instability values being included between 0.12 ± 0.02 and 0.42 ± 0.12. However, high E overlap values between [1955-1974] and [2050-2099] 5).

This marked reduction is in line with the high contribution of seafloor temperature amplitude to the model (32.9%), the preference of A. cordatus for high amplitudes (Figure 2e; Figure S2), and conversely, predicted decrease in these values for (Figure S1). B. antarcticus shows the highest E instability (0.42 ± 0.12) and E overlap (0.61 ± 0.06) values, which predict a high stability of suitable areas in [2005][2006][2007][2008][2009][2010][2011][2012] and their expansion in over the entire Kerguelen Plateau (Figure 5). This is in line with predictions of higher and more widespread chlorophyll a concentrations F I G U R E 3 Scaled density probability of environmental occupied space, modeled as suitable for the four species on the extent of the Kerguelen Plateau (-46° to -56°S, +63° to +81°E). Suitability is defined by the MaxSSS threshold over the Kerguelen Plateau according to scenario RCP 8.5 (Figure S1). For C. nutrix and S. diadema, the high overlap values (between 0.42 ± 0.01 and 0.45 ± 0.04, respectively) indicate minor changes in the extent of suitable areas, but binary maps (Figure 5) indicate noticeable distribution shifts. The distribution of C. nutrix is predicted to shift poleward, with a decrease in suitable areas on the northern Kerguelen Plateau and in lower latitudes (<-50°S), and an increase along the western Antarctic Peninsula and in the eastern Weddell Sea (Figure 5). The future distribution of S. diadema is also predicted to decrease in the lower latitudes, with a poleward shift of preferential areas (Figure 5), in coastal waters of the northwestern Antarctic Peninsula in particular, but suitability is also predicted to decrease in the southernmost areas of the Peninsula (Figure 5).

| Environmental shifts and evolution of ecological niche space

To compare the size of species realized niches between the three periods (Figure 6), the environmental subspace occupied by each species was delineated by plotting the values of environmental descriptors that contribute the most to the models (Figure 2a'-d').

A. cordatus is the species that differs the most from the three other echinoids, with a niche space restricted to a narrow range of seafloor temperature amplitudes and mean salinity values (between 33.6 and 33.9 PSU) (Figure 6a). Projections for indicate unsuitable conditions for the species on the Kerguelen Plateau (Figure S1), with values of seafloor temperature amplitudes and mean salinity values plotted outside of the species niche (Figure 6a), suggesting local potential extinction. In contrast, the niche space occupied by other species almost extends to the total environmental conditions available, suggesting wide fundamental niches (Figure 6b-d).

| D ISCUSS I ON

| SDM limitations and perspectives

Modeling species distribution in the Southern Ocean is challenging.

The paucity of available data is a major limitation to analyses that are restricted to presence-only data models, usually considered less reliable and less efficient than presence-absence or abundance data models [START_REF] Brotons | Presenceabsence versus presence-only modelling methods for predicting bird habitat suitability[END_REF]. In addition, presence-only datasets can be heterogeneous in space and time (compilation of 150 years of sampling in the present case study), which can influence modeling performances (Guillaumot et al., in press;[START_REF] Newbold | Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models[END_REF][START_REF] Tessarolo | Uncertainty associated with survey design in species distribution models[END_REF]. SDM performed with spatially biased presence-only data must consider these limitations and apply appropriate algorithms, protocols, and corrections [START_REF] Barbet-Massin | Selecting pseudo-absences for species distribution models: How, where and how many?[END_REF]Guillaumot et al., in press;[START_REF] Phillips | Sample selection bias and presence-only distribution models: Implications for background and pseudo-absence data[END_REF][START_REF] Proosdij | Minimum required number of specimen records to develop accurate species distribution models[END_REF].

Strengthening sampling effort to characterize and model the entire distribution area of widely distributed species is one of the main priorities of Antarctic science, as recently pointed out by [START_REF] Kennicutt | Six priorities for Antarctic science[END_REF]. Improving our knowledge of the Southern Ocean marine life also includes the development of efficient and robust modeling approaches. For instance, other environmental descriptors should be added to models in order to better characterize and depict species niches [START_REF] Austin | Improving species distribution models for climate change studies: Variable selection and scale[END_REF][START_REF] Bradie | A quantitative synthesis of the importance of variables used in MaxEnt species distribution models[END_REF].

Integrating biotic interactions [START_REF] Leach | Modelling the influence of biotic factors on species distribution patterns[END_REF] and combining correlative and mechanistic approaches [START_REF] Briscoe | Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia[END_REF][START_REF] Buckley | Can mechanism inform species' distribution models?[END_REF][START_REF] Gutt | Correlative and dynamic species distribution modelling for ecological predictions in the Antarctic: A cross-disciplinary concept[END_REF][START_REF] Morin | Comparing niche-and process-based models to reduce prediction uncertainty in species range shifts under climate change[END_REF]) also constitute promising perspectives to improve our understanding of species distribution and potential response to changing environments.

Over the past decades, significant environmental changes were recorded on the Kerguelen Plateau, including a decrease in salinity and an increase in water temperatures between [1955][1956][1957][1958][1959][1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970][1971][1972][1973][1974] and [2005][2006][2007][2008][2009][2010][2011][2012] [START_REF] Frenot | Azorella selago Hook used to estimate glacier fluctuations and climatic history in the Kerguelen Islands over the last two centuries[END_REF]. According to IPCC scenario RCP 8.5 (IPCC Fifth Report, 2013), predicted changes for imply an important water warming and freshening [START_REF] Fe R E N C E S Allan | Critical indirect effects of climate change on sub-Antarctic ecosystem functioning[END_REF][START_REF] Féral | Kerguelen, un archipel sous haute surveillance[END_REF][START_REF] Gutt | The Southern Ocean ecosystem under multiple climate change stresses: An integrated circumpolar assessment[END_REF]. However, such predictions might be locally imprecise. The Kerguelen Plateau constitutes a major barrier to the flow of the Antarctic Circumpolar Current (ACC) [START_REF] Park | Polar Front around the Kerguelen Islands: An up-todate determination and associated circulation of surface/subsurface waters[END_REF]. Latitudinal heat flux and marine fronts' position are still debated, which could lead to different climatic scenarios [START_REF] Vivier | Variability of the Antarctic Circumpolar Current transport through the Fawn Trough, Kerguelen Plateau[END_REF] and potentially different SDM for the region.

| Model performance and species niche width

AUC values differ between SDM, with high and stable values for A. cordatus and lower scores for B. antarcticus and S. diadema. In previous works, [START_REF] Qiao | No silver bullets in correlative ecological niche modelling: Insights from testing among many potential algorithms for niche estimation[END_REF] discussed the link between species niche width and the evaluation of SDM performances using true skill statistics and kappa indices. SDM produced for narrowniche species were proved to be associated with high sensitivity and high specificity scores. This is in line with the present results that show the highest AUC values for models performed for the narrowniche species, A. cordatus, and the lowest values for the wide-niche species, S. diadema. When using AUC to evaluate the performance of models performed with presence-only data, the maximum AUC value is given by the formula AUC = 1α/2, in which α corresponds to the fraction of the study area occupied by the species [START_REF] Phillips | Maximum entropy modelling of species geographic distributions[END_REF][START_REF] Proosdij | Minimum required number of specimen records to develop accurate species distribution models[END_REF][START_REF] Raes | A null-model for significance testing of presence-only species distribution models[END_REF].

Considering the extent of the species respective distribution areas, A. cordatus is the species with the smallest fraction of area coverage (α), which can account for the high AUC values of the model. TA B L E 2 Comparison of model outputs using the metrics developed by [START_REF] Crase | Modelling both dominance and species distribution provides a more complete picture of changes to mangrove ecosystems under climate change[END_REF]; see equation details in Table 1. Values correspond to mean and standard deviation of 100 model replicates. Suitable area defined as the number of pixels for which species distribution probabilities are higher than the MaxSSS threshold value ± 1,648 13,054.2 ± 228.4 13,427.6 ± 222.6 13,895.4 ± 631.4 0.02 ± 0.01 0.12 ± 0.02 0.44 ± 0.01 0.45 ± 0.02

| Major environmental drivers

Environmental descriptors that contribute the most to SDM vary between species according to the different ecological niches. For instance, the distribution of the deposit-feeder B. antarcticus is strongly correlated with chlorophyll a concentrations and the species distribution is mainly predicted in regions with chlorophyll a blooms such as in the northeast of the Kerguelen Islands, in the vicinity of the Polar Front, and near the coasts of Crozet Islands [START_REF] Park | Polar Front around the Kerguelen Islands: An up-todate determination and associated circulation of surface/subsurface waters[END_REF]. In contrast, the nearshore species A. cordatus is mainly correlated with the values of seafloor temperature amplitudes, which are the highest in the shallow waters of the Kerguelen Islands.

The present results show the importance of using amplitude values in SDM, in association with other parameter metrics. They contribute to the SDM performed for the four echinoid species of this study as major descriptors. [START_REF] Bradie | A quantitative synthesis of the importance of variables used in MaxEnt species distribution models[END_REF] already discussed the importance of including descriptors of seasonal means and extremes in models. These descriptors were proved to further account for species distribution patterns than annual means, considering their stronger relationship with species niche width and ecological traits (i.e., growth and survival; see [START_REF] Franklin | Mapping species distributions: Spatial inference and prediction[END_REF].

| Species responses to environmental change

In the present work, we could generate robust models to assess the influence of changing environmental conditions on species distribution range, both in the geography and in the environment.

The results confirm the sensitivity of species to changing environmental conditions. However, the modeled distribution maps and environmental spaces show diverging responses between species and time period. All species distributions were not proved to be significantly different between [1955-1974] and [2005-2012].

The occupied environmental spaces and suitable areas are quite similar both on the extent of the Southern Ocean and at the scale of the Kerguelen Plateau. Distribution range shifts are predicted F I G U R E 6 Occupied environmental subspaces modeled for [1955][1956][1957][1958][1959][1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970][1971][1972][1973][1974], [2005][2006][2007][2008][2009][2010][2011][2012], and Narrow-niche species that are limited by environmental barriers, such as A. cordatus, cannot modify the occupied niche space while facing environmental changes. A. cordatus is endemic to coastal areas of the Kerguelen Plateau. It is a brooding species that is with no larval dispersal stage, which is a limiting factor to its dispersal capabilities and distribution range [START_REF] David | Synopses of the Antarctic benthos[END_REF]. Therefore, A. cordatus occupies the entire environmental space available around the Kerguelen Islands and cannot disperse to colonize other areas and find a climatic refuge. This corresponds to the "Wallace Dream" category as formalized by [START_REF] Saupe | Variation in niche and distribution model performance: The need for a priori assessment of key causal factors[END_REF]. In such a situation, the predicted changes in seafloor temperature amplitude and mean seafloor salinity are beyond the limits of the species niche, suggesting the potential extinction of A. cordatus in a near future.

The other studied species present a broader distribution range with no environmental barrier. Distributions are only limited by species environmental requirements, a situation formalized as the "Hutchinson Dream" category [START_REF] Saupe | Variation in niche and distribution model performance: The need for a priori assessment of key causal factors[END_REF]. Species environmental suitability includes a large range of conditions, which encompass all the environmental conditions present on the Kerguelen Plateau. The distribution range of B. antarcticus is predicted to increase in the future, while the species suitable area remains constant due to the high chlorophyll-a concentrations predicted in the Kerguelen and Crozet areas. Seafloor temperatures are predicted to increase according to scenario RCP 8.5, but they will remain within the range of the species niche and environmental suitability is not predicted to decrease. Despite a narrow distribution range in the present day, these results clearly suggest that the species has a large niche compared to A. cordatus and shows a higher potential resilience while facing predicted future changes.

The present distribution of C. nutrix extends over the entire Southern Ocean. However, the species preference for seafloor temperature is included between 1 and 4°C. Mean seafloor temperatures were predicted to increase between [1955-1974] and [2050-2099],

leading to the progressive poleward shift of C. nutrix. In [1955][1956][1957][1958][1959][1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970][1971][1972][1973][1974],

the species was predicted to be mainly distributed in the northern part of the Kerguelen Plateau. According to future predictions, the distribution should be reduced in the northern Kerguelen Plateau and should reach the coasts of the western Antarctic Peninsula to the south, where temperature increase would match with the species preferences.

Sterechinus diadema is the Antarctic echinoid with the broadest distribution range. Between [1955-1974] and [2005-2012], suitable areas remain almost unchanged in both the sub-Antarctic and Antarctic regions. In the future, southward distribution shifts are predicted on the Kerguelen Plateau due to the preference of S. diadema for water temperatures below 4°C. However, the species suitable area will not expand over the western Antarctic Peninsula due to the prevalence of sea ice and low chlorophyll a concentrations that limit the species distribution.

| Effects of environmental change and conservation strategies

Water seafloor temperature and salinity were shown to have significantly varied since the 1950s, but major changes are still to come in a near future according to IPCC scenario RCP 8.5 (IPCC Fifth Report, 2013). Modifications in the seasonal amplitude of temperature and salinity variations should have decisive effects on costal marine ecosystems (CCAMLR 2008(CCAMLR , 2013;;[START_REF] Féral | Kerguelen, un archipel sous haute surveillance[END_REF][START_REF] Gutt | Cross-disciplinarity in the advance of Antarctic ecosystem research[END_REF][START_REF] Hureau | Marine research on the Kerguelen Plateau: From early scientific expeditions to current surveys under the CCAMLR objectives[END_REF][START_REF] Sahade | Climate change and glacier retreat drive shifts in an Antarctic benthic ecosystem[END_REF][START_REF] Schram | Testing Antarctic resilience: The effects of elevated seawater temperature and decreased pH on two gastropod species[END_REF][START_REF] Smale | Likely responses of the Antarctic benthos to climate-related changes in physical disturbance during the 21st century, based primarily on evidence from the West Antarctic Peninsula region[END_REF]. Predictions also suggest that other parameters of importance for marine life, such as chlorophyll a concentrations, should significantly vary in the future (Schram, Schoenrock, McClintock, Amsler, & Angus, 2015a,b;[START_REF] Turner | Antarctic climate change and the environment: An update[END_REF]. Based on the available abiotic and biotic data, the present results suggest that the impact of these future changes on benthic organisms will have no equivalent in comparison with what happened in the past century. Results also illustrate that the response of benthic organisms may vary according to speciesspecific physiology, life traits, biogeography, and ecological niche width, which is in line with previous works [START_REF] Burrows | Geographical limits to species-range shifts are suggested by climate velocity[END_REF][START_REF] Clarke | Spatial variation in seabed temperatures in the Southern Ocean: Implications for benthic ecology and biogeography[END_REF][START_REF] Peck | A cold limit to adaptation in the sea[END_REF][START_REF] Peck | Acclimation and thermal tolerance in Antarctic marine ectotherms[END_REF]. Hence, benthic species of coastal areas might be particularly at risk, especially if they have narrow ecological niche and are endemic to the island. This net, Féral, Saucède et al., 2016) for long-term field observations of physical processes and ecosystem changes. In particular, the effects of temperature and salinity variations on coastal benthic species are monitored in the field [START_REF] Féral | Kerguelen, un archipel sous haute surveillance[END_REF]. The approach is coupled with ecophysiological experiments in the laboratory.

Such a program is very complementary to the modeling approach presented in this study, as new field data should help improve the relevance of predictive models. Both approaches should provide scientific grounds to conservation managers of the French Southern Territories to develop efficient conservation plans in coastal areas of the Kerguelen archipelago.

  the relative raw difference between distribution maps indicating the overall change in the distribution of presence probabilities (E instability ), and (3) analyze the relative overlap between distribution maps indicating any change in the distribution of suitable areas (E overlap ). To compare index values between species, E instability and E overlap were

  according to scenario RCP 8.5. Differences in chlorophyll a values were also tested for significance between [2005-2012] and [2055-2099] (Wilcoxon test, p < .05). These results indicate that significant environmental changes have occurred in the recent past and might happen in a near future, potentially affecting species geographic range.

  Contributions of environmental parameters differ between species models and are dependent on the extent of species distribution range. This is to be related to the geographic scale of descriptor variations. chlorophyll a values vary at the local scale, with high concentrations in certain areas of the Kerguelen Plateau and Crozet Islands. Coastal waters are also characterized by high temperature and salinity amplitudes in contrast to offshore areas (FigureS1). At broader scale, contrasts between descriptor values are less marked, but latitudinal gradients are noticeable in sea ice concentration and temperature values between Antarctic and sub-Antarctic regions.The SDM produced for B. antarcticus shows a high contribution of chlorophyll a concentrations, mean seafloor temperatures, and mean sea surface salinities (Figure2b'). The predicted distribution of this deposit-feeder is obviously determined by the occurrence of high chlorophyll a blooms during the austral summer on the Kerguelen Plateau and in Crozet Islands (Figure2b; FigureS1). In contrast, C. nutrix is more widely distributed (Figures1 and 2c

  ) and high contributions to the models are represented by environmental descriptors that vary at broader scale, such as geomorphology, mean sea surface salinities, and mean seafloor temperatures (Figure2c').

Figure

  Figure S2). Environmental preferences are quite narrow and suitable habitats restricted to a small area. Environmental preferences of the three other species are wider, although B. antarcticus distribution is restricted to the Kerguelen and Crozet archipelagoes (Figures 1 and 3). B. antarcticus is deposit-feeder and shows preferences for areas with high chlorophyll a enriched waters. The species shows a heterogeneous depth distribution that can be explained by the recurrence of a deep plateau and slopes in the area (Figure S1). C. nutrix and S. diadema have both a wide distribution range extending from sub-Antarctic to Antarctic regions and wide ecological niches (Figure 1). Mean seafloor salinity values are bimodal for C. nutrix, which is distributed in coastal waters of low salinity values in the Kerguelen Islands (33.7 to 34 PSU) as well as in offshore areas, in waters of high salinity values (Figure 3; Figure S1). Temperature tolerance of

F

  I G U R E 4 Binary plots of modeled species distribution projected on[1955][1956][1957][1958][1959][1960][1961][1962][1963][1964][1965][1966][1967][1968][1969][1970][1971][1972][1973][1974]. Yellow pixels: suitable area; red pixels: nonsuitable area. The map size is representative of the modeling boundaries for each species. (a) Abatus cordatus, (b) Brisaster antarcticus, (c) Ctenocidaris nutrix, and (d) Sterechinus diadema

F

  I G U R E 5 Binary plots of modeled species distribution projected on [2050-2099], scenario RCP 8.5. Yellow pixels: suitable area; red pixels: nonsuitable area. The map size is representative of the modeling boundaries for each species. (a) Abatus cordatus, (b) Brisaster antarcticus, (c) Ctenocidaris nutrix, and (d) Sterechinus diadema

  for (a) Abatus cordatus, (b) Brisaster antarcticus, (c) Ctenocidaris nutrix, and (d) Sterechinus diadema. Environmental spaces were defined by predictors that most contribute to the respective SDM (Figure 2e). Background dots: environmental values present on the Kerguelen Plateau. Convex hulls delineate the environmental subspace preferentially occupied by species (environmental values of pixels for which values are higher than the MaxSSS value). Triangles: centroid position of the convex hulls. Colors correspond to the different time periods more important in the future, but the importance depends on (1) species niche width, (2) species distribution, (3) species sensitivity to seasonal variations, and (4) geographic features that may influence species distribution. Narrow-niche, coastal, and endemic species such as A. cordatus should be the most affected by the predicted changes compared to wide-niche echinoids such as B. antarcticus, C. nutrix, and S. diadema. The three last species are also predicted to show different distribution range shifts, from expansion (B. antarcticus) to poleward shift (C. nutrix) and latitudinal reduction (S. diadema).

  stresses the need for implementing long-term observing systems and monitoring the specific effects of environmental changes on coastal species. The National Nature Reserve of the French Southern Territories contains areas in which fisheries activities are allowed and enhanced MPA devoted to conservation and research activities. The coastal areas of the Kerguelen Islands are now entirely included in this new enhanced MPA. The contribution of scientists has led to the implementation of the French Long-Term Ecological Research PROTEKER observatory (IPEV program no. 1044, www.proteker.

Funding information

French Polar Institute, Grant/Award Number: 1044; Belgian Science Policy Office, Grant/Award Number: BR/132/A1/ vERSO environmental changes and the direct

ACK N OWLED G M ENTS

This work is a contribution to the PROTEKER program (www.proteker. net) funded by the French polar institute (IPEV, program no.1044) and contribution no. 22 to the vERSO project (www.versoproject.be) funded by the Belgian Science Policy Office (BELSPO, contract no. BR/132/ A1/ vERSO). The authors are grateful to the master, crew and scientific staff (PI Guy Duhamel, MNHN) of the FV Austral for collection of specimens during POKER II cruise. Work at sea was supported by the Terres Australes et Antarctiques Françaises (TAAF), the Syndicat des Armateurs Réunionnais de Palangriers Congélateurs (SARPC), and the Direction des Pêches Maritimes et de l'Aquaculture, Ministère de l'Agriculture et de l'Alimentation (DPMA). Specimens were curated at the Muséum national d'Histoire naturelle, Paris (MNHN) by N. Vasset and made available for study by N. Améziane and M. Eléaume. CO N FLI C T O F I NTE R E S T None declared. AUTH O R CO NTR I B UTI O N S CG, AM, ME, and TS conceived the ideas and designed methodology;

S U PP O RTI N G I N FO R M ATI O N

Additional supporting information may be found online in the Supporting Information section at the end of the article.

How to cite this article