Guillaumot Charlène
email: charleneguillaumot21@gmail.com

Danis Bruno

Saucède Thomas

Species Distribution Modelling of the Southern Ocean benthos: a review on methods, cautions and solutions Running title: Review of methods and limits for Southern Ocean SDM

Keywords: Antarctica, marine benthic invertebrates, modelling performance, biases, limits

Species Distribution Modelling (SDM) studies the relationship between species occurrence records and their environmental setting, providing a valuable approach to predict species distribution in the Southern Ocean (SO), a challenging region to investigate due to remoteness and extreme weather and sea conditions. The specificity of SO studies, including restricted field access and sampling, the paucity of observations and difficulties to conduct biological experiments, limit the performance of SDMs.

In this review, we discuss some issues that may influence model performance when preparing datasets and calibrating models, namely the selection and quality of environmental descriptors, the spatial and temporal biases that may affect the quality of occurrence data, the choice of modelling algorithms, and the spatial scale and limits of the projection area.

We stress the importance of evaluating and communicating model uncertainties, and most common evaluation metrics are reviewed and discussed accordingly. Based on a selection of case studies on SO benthic invertebrates, we highlight important cautions to take, and pitfalls to avoid when modelling the distribution of SO species, and provide some guidelines along with potential methods and original solutions that can be used for improving model performance.

Introduction

Due to its remoteness and extreme weather and sea conditions, the Southern Ocean (SO) is a challenging region to carry out biological studies [START_REF] Kaiser | Patterns, processes and vulnerability of Southern Ocean benthos: A decadal leap in knowledge and understanding[END_REF][START_REF] Gutt | Cross-disciplinarity in the advance of Antarctic ecosystem research[END_REF]. It is is also one of Earth's regions where we observe the most rapid and dramatic environmental changes in marine ecosystems, motivating the study of these marine communities [START_REF] Turner | Antarctic climate change and the environment: an update[END_REF][START_REF] Ashton | Warming by 1 C drives species and assemblage level responses in Antarctica's marine shallows[END_REF][START_REF] Clark | Lack of long-term acclimation in Antarctic encrusting species suggests vulnerability to warming[END_REF]. Ecological modelling approaches are now well-established and can be used to predict spatial patterns of organisms', populations' and species' distributions and assess their environmental drivers [START_REF] Peterson | Ecological niches and geographic distributions (MPB-49)[END_REF]. Based on field observations and experimental datasets, ecological modelling encompasses valuable approaches to help analyse biological data and interpolate our knowledge of species distribution in relation to environmental descriptors [START_REF] Kennicutt | Six priorities for Antarctic science[END_REF].

Species distribution models (SDMs) are ecological models that study the statistical relationship between species occurrence records and environmental factors, determining the set of environmental conditions that is suitable to species distribution [START_REF] Elith | Novel methods improve prediction of species' distributions from occurrence data[END_REF], Elith and Leathwick 2009, Peterson et al. 2011). They represent the species realised niche [START_REF] Pearson | Species' distribution modeling for conservation educators and practitioners[END_REF][START_REF] Sillero | What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods[END_REF], being the ensemble of abiotic conditions for which the species survives and reproduces, adding into consideration the influence of biotic interactions (competition, predation, parasitism, symbiosis…) [START_REF] Hutchinson | Concluding remarks Cold Spring Harbor[END_REF]. SDMs have been widely used in various fields of ecology, such as conservation biology, biogeography, paleoecology and global change biology [START_REF] Pearson | Species' distribution modeling for conservation educators and practitioners[END_REF]). In the recent years, a growing number of ecological studies have been using

SDMs to analyse the distribution of marine pelagic and benthic species in the SO (e.g. marine invertebrates, fish, sea birds and marine mammals) and determine species environmental preferences [START_REF] Loots | Habitat modelling of Electrona antarctica (Myctophidae, Pisces) in Kerguelen by generalized additive models and geographic information systems[END_REF][START_REF] Pierrat | Large-scale distribution analysis of Antarctic echinoids using ecological niche modelling[END_REF][START_REF] Xavier | Biogeography of Cephalopods in the Southern Ocean using habitat suitability prediction models[END_REF][START_REF] Nachtsheim | Habitat modelling of crabeater seals (Lobodon carcinophaga) in the Weddell Sea using the multivariate approach Maxent[END_REF], compare ecological niche predictions as a response to changing environments [START_REF] Basher | The past, present and future distribution of a deep-sea shrimp in the Southern Ocean[END_REF][START_REF] Gallego | On the need to consider multiphasic sensitivity of marine organisms to climate change: A case study of the Antarctic acorn barnacle[END_REF][START_REF] Guillaumot | Benthic species of the Kerguelen Plateau show contrasting distribution shifts in response to environmental changes[END_REF][START_REF] Jerosch | Ensemble modelling of Antarctic macroalgal habitats exposed to glacial melt in a polar fjord[END_REF] or identify diversity hotspots for conservation purposes [START_REF] Pinkerton | Spatial and seasonal distribution of adult Oithona similis in the Southern Ocean: Predictions using boosted regression trees[END_REF][START_REF] Hibberd | Describing and predicting the spatial distribution of benthic biodiversity in the sub-Antarctic and Antarctic[END_REF][START_REF] Thiers | Important marine sectors for the top predator community around Kerguelen Archipelago[END_REF].

However, the quality of ocean-wide models is often limited by the heterogeneity, amount and spatial distribution of data, along with limited temporal and spatial resolutions. For all these reasons, both modelling methods and model construction should be tested for accuracy and robustness prior to interpretation, and these indicators should be transparently communicated to ensure that model outputs are relevant given the specificities of datasets used for modelling.

In the present paper, we review the most common methodological issues encountered in species distribution modelling applied to the SO, following the above flowchart (Fig. 1).

Challenges regarding occurrence and environmental datasets peculiarities are described. The choice of SDM algorithm, and procedures to implement and evaluate models are addressed.

Based on benthic invertebrate case studies, we stress important precautions to take, and pitfalls to avoid at common steps of SDM implementation. Finally, we aim to provide some guidelines with a set of potential methods and original solutions that can be used for improving model performance.

Quality of datasets

Environmental datasets: field data.

Preparing environmental datasets is the first encountered challenge when generating models [START_REF] Gutt | Correlative and dynamic species distribution modelling for ecological predictions in the Antarctic: a cross-disciplinary concept[END_REF][START_REF] De Broyer | Biogeographic atlas of the Southern Ocean[END_REF]). The SO, here defined as waters south of 45°S latitude, covers an extensive area of over 20 million km² [START_REF] Breitzke | Overview of seismic research activities in the Southern Ocean-quantifying the environmental impact[END_REF]. Having access to environmental data with good temporal and spatial resolutions at such a broad scale is challenging, an issue common to all broad scale oceanographic studies [START_REF] Robinson | A systematic review of marine-based Species Distribution Models (SDMs) with recommendations for best practice[END_REF].

Broad scale is defined here as the entire SO, regional scale as smaller areas of a few hundreds square kilometers and local scale as a few square kilometers to square meters [START_REF] Gage | Diversity in deep-sea benthic macrofauna: the importance of local ecology, the larger scale, history and the Antarctic[END_REF].

Oceanographic data acquisition in the field is strongly conditioned by weather and sea conditions along with the seasonality of polar regions (polar night and dense sea ice coverage in winter) that prompt recurring gaps in the acquisition of environmental data in the SO. Data are also much more sampled close to research stations and along main sailing routes [START_REF] Guillaumot | Broad-scale species distribution models applied to data-poor areas[END_REF]. This is particularly striking in regions such as the southwestern Weddell Sea, along the shores of the Western Antarctic Peninsula, and in the Bellingshausen and Amundsen seas [START_REF] Clarke | How well do we know the Antarctic marine fauna? A preliminary study of macroecological and biogeographical patterns in Southern Ocean gastropod and bivalve molluscs[END_REF], Griffiths et al. 2014).

Environmental datasets: satellite derived-data.

Satellite derived-data form a significant source of information for SO oceanographic studies.

Providing valuable environmental indicators at broad spatial scale, they can give details about continuous and long-term measurements of water masses including sea-ice coverage, extent, and duration, sea surface temperatures and salinities, biogeochemical parameters, sea level, primary production, and typical meteorological parameters [START_REF] El Mahrad | Contribution of remote sensing technologies to a holistic coastal and marine environmental management framework: A review[END_REF].

The accuracy of satellite data however should be considered with care, given detection limits, interpolations that reduce the influence of atmospheric particulate scatter and the use of interpolation and gap filling methods that smooth raw data at broad spatial and temporal scales [START_REF] Pope | Community review of Southern Ocean satellite data needs[END_REF][START_REF] Stock | Comparison of Cloud-Filling Algorithms for Marine Satellite Data[END_REF].

Whenever possible, it is recommended to validate environmental data derived from satellite products at regional and local scales, by comparing pixels on a satellite image with 'real' field observation data [START_REF] Henson | Variability in efficiency of particulate organic carbon export: A model study[END_REF][START_REF] Trull | Distribution of planktonic biogenic carbonate organisms in the Southern Ocean south of Australia: a baseline for ocean acidification impact assessment[END_REF]. Simple correlation analyses, or more complex ground-truth processes are available to compare satellite and in situ data and secure the interpretation of satellite-derived products [START_REF] White-Newsome | Validating satellite-derived land surface temperature with in situ measurements: A public health perspective[END_REF][START_REF] Allan | Remote sensing, numerical modelling and ground truthing for analysis of lake water quality and temperature[END_REF]. This however constitutes a huge task and is not performed generally before implementing SDMs.

Environmental datasets: access to datasets.

Environmental data generated at the scale of the entire SO can be accessed for free through different web portals such as the NASA's OceanColor Web (https://oceancolor.gsfc.nasa.gov/), where satellite-derived data, averaging different temporal measurements down to 4 km resolution are available at the scale of the entire SO, since 2000. These images are postprocessed to characterise sea surface temperature or ocean color as a proxy of surface productivity.

The NOAA's data center (WOCE2013, https://www.nodc.noaa.gov/OC5/woa13/woa13data.html) also makes available post-processed data of ocean temperature, salinity, oxygen concentration and nutrients at different grid formats, down to 0.25° resolution, averaging six decades (from 1955 to 2012). Bio-Oracle (https://www.bio-oracle.org/) compiles a large panel of marine data layers at 1° spatial resolution for different depth layers and time periods, for the present (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) and the future (2040-2050; 2090-2100) [START_REF] Assis | BioORACLE v2. 0: Extending marine data layers for bioclimatic modelling[END_REF]. Finally, GEBCO (https://www.gebco.net/) is the reference platform for very high-resolution bathymetry data (~500 m resolution) of the world's ocean.

Several works also make available compilation of these SO datasets dedicated to ecological modelling in the SO; they represent a valuable source of information to start with data preparation and modelling [START_REF] Raymond | Polar Environmental Data Layers, Ver. 3, Australian Antarctic Data Centre[END_REF], Fabri-Ruiz et al. 2017b, Guillaumot et al. 2018c).

An increasing number of environmental data collected during SO oceanographic campaigns were made accessible for regional-scale studies.

Environmental datasets: spatial and temporal resolutions.

Most environmental data are accessible through broad-scale maps from the aforementioned data portals and available with a finest spatial resolution of ~ 4 km, if not coarser [START_REF] Raymond | Polar Environmental Data Layers, Ver. 3, Australian Antarctic Data Centre[END_REF][START_REF] De Broyer | Biogeographic atlas of the Southern Ocean[END_REF], Fabri-Ruiz et al. 2017b, Guillaumot et al. 2018c). This low resolution strongly hampers the precise assessment of relationships between species occurrences and environmental descriptors (Pittman 2017, [START_REF] Staveley | Seascape structure and complexity influence temperate seagrass fish assemblage composition[END_REF]) and consequently the accuracy of model predictions [START_REF] Connor | Effects of grain size and niche breadth on species distribution modeling[END_REF], because the relevance of environmental descriptors is a trade-off between their resolution and their spatial and temporal coverage [START_REF] Guisan | Sensitivity of predictive species distribution models to change in grain size[END_REF][START_REF] Seo | Scale effects in species distribution models: implications for conservation planning under climate change[END_REF][START_REF] Lauzeral | Spatial range shape drives the grain size effects in species distribution models[END_REF][START_REF] Vale | Predicting species distribution at range margins: testing the effects of study area extent, resolution and threshold selection in the Sahara-Sahel transition zone[END_REF]. It is recommended that the resolution of environmental descriptors used in SDM should be in line with the scale of ecological processes at play and for which species ecophysiological responses show the highest variations, if models are expected to capture most of species-environment relationships [START_REF] Austin | Improving species distribution models for climate change studies: variable selection and scale[END_REF].

The published environmental datasets are often averaged over relatively long periods of time (from years to decades for WOCE2013 or Bio-Oracle). The analysis of inter-annual variations can complement the interpretation of model predictions: the absence of such information does not preclude running models but this should be kept in mind when it comes to interpreting model outputs (Guillaumot et al. 2018a). Important environmental variations within a reference time period may not satisfy the equilibrium criterium between species distribution and environmental conditions, which is a strong prerequisite of SDM [START_REF] Elith | Novel methods improve prediction of species' distributions from occurrence data[END_REF]) and may affect the relevance and accuracy of model predictions (Guillaumot et al. 2018a). In this respect, an alternative to improve modelling performance would be using seasonal averages or extreme values as environmental descriptors, rather than pluri-annual to annual averages (Franklin 2009, Bradie and[START_REF] Bradie | A quantitative synthesis of the importance of variables used in MaxEnt species distribution models[END_REF].

Environmental datasets: cartographic projections.

Considering the poles in numerical analyses has long been a source of difficulties in spatial modelling as the convergence of meridians distorts shapes, surfaces, angles or distances towards high latitudes when using standard cylindric representations such as the Mercator projection [START_REF] Deleersnijder | An orthogonal curvilinear coordinate system for a world ocean model[END_REF][START_REF] Eby | Grid transformation for incorporating the Arctic in a global ocean model[END_REF][START_REF] Murray | Explicit generation of orthogonal grids for ocean models[END_REF]). Working with conical or azimutal projections (e.g. polar stereographic system) helps maintain the consistency of angles and shapes, and therefore better meets requirements of SO studies, although areas and distances are progressively distorted when moving away from the pole [START_REF] Mulcahy | Symbolization of map projection distortion: a review[END_REF].

Mapping environmental descriptors and projecting model predictions can be carried out with either square or hexagonal pixels. Each option does not alter image quality and hexagonal shapes may even offer some advantages [START_REF] Kamgar-Parsi | Quantization error in spatial sampling: comparison between square and hexagonal pixels[END_REF]Sander 1989, Tirunelveli et al. 2002). However, some contrasts may be present between images using square or hexagonal pixels, as each pixel measure the average environmental conditions in the considered surface [START_REF] Vanden Berghe | Minimum required number of specimen records to develop accurate species distribution models[END_REF].

Subdividing the study area into subregions and using different pixel shapes can be a good solution to improve the relevance of representations [START_REF] Vanden Berghe | Minimum required number of specimen records to develop accurate species distribution models[END_REF][START_REF] Cryer | Progress on predicting the distribution of Vulnerable Marine Ecosystems and options for designing spatial management areas for bottom fisheries within the SPRFMO Convention Area[END_REF]. It can also be suggested to evaluate the accuracy of environmental values captured both in square and hexagonal pixels using baseline in situ, field measurements. This is yet to be tested for ecological modelling studies for the SO.

2.1.6. Environmental datasets: future forecasts. The relevance of using future predictions based on the global assessment scenarios for marine studies has been widely questioned [START_REF] Flato | Evaluation of climate models[END_REF][START_REF] Frölicher | Sources of uncertainties in 21st century projections of potential ocean ecosystem stressors[END_REF][START_REF] De La Hoz | OCLE: A European open access database on climate change effects on littoral and oceanic ecosystems[END_REF], including their use for SDM, given that, climate models mainly rely on untestable assumptions [START_REF] Beaumont | Why is the choice of future climate scenarios for species distribution modelling important?[END_REF][START_REF] Gotelli | Climate change, genetic markers and species distribution modelling[END_REF], Cavanagh et al. 2017[START_REF] Freer | Predicting ecological responses in a changing ocean: the effects of future climate uncertainty[END_REF], future layers are not always available for oceanographic studies (Fabri-Ruiz 2018, Guillaumot et al. 2018a[START_REF] Guillaumot | Benthic species of the Kerguelen Plateau show contrasting distribution shifts in response to environmental changes[END_REF], discrepancies between present observations and future predictions can be problematic [START_REF] Jiménez-Valverde | Climate data source matters in species distribution modelling: the case of the Iberian Peninsula[END_REF], and models are based on a representation of the climate system that has a complex cascading effect on ecological processes [START_REF] Cavanagh | A synergistic approach for evaluating climate model output for ecological applications[END_REF]. [START_REF] Cavanagh | A synergistic approach for evaluating climate model output for ecological applications[END_REF] examined how well IPCC-class models reproduced sea-ice conditions. By subsetting CMIP5 models that best describe spatial extent and temporal ice cover, they improved the precision of projected future sea ice distribution which better suited to ecological analyses. Extending this method to other key oceanographic parameters should contribute to improving the accuracy of future climate models for the SO and their relevance to ecological studies.

Occurrence datasets

Occurrence datasets: historical compilation.

Biological sampling in the SO began with the first expeditions of the HMS Challenger (1873)(1874)(1875)(1876). Sampling effort has considerably increased over the second part of the 20 th century and during the last decades in particular, following technological advances that have enabled the access to remote regions and sample processing (Fig. 2). SDMs. All these side effects were reviewed in detail by [START_REF] Newbold | Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models[END_REF]. The impact on species niche definition and SDM predictions have been reported in many works [START_REF] Ensing | Taxonomic identification errors generate misleading ecological niche model predictions of an invasive hawkweed[END_REF][START_REF] Lahoz-Monfort | Imperfect detection impacts the performance of species distribution models[END_REF][START_REF] Monk | How long should we ignore imperfect detection of species in the marine environment when modelling their distribution?[END_REF][START_REF] Aguiar | Effect of chronological addition of records to species distribution maps: The case of Tonatia saurophila maresi (Chiroptera, Phyllostomidae) in South America[END_REF][START_REF] Tessarolo | Temporal degradation of data limits biodiversity research[END_REF], Guillaumot et al. 2018a) that all advice to thoroughly check datasets for quality management prior to running models. Spatial aggregation can affect model accuracy, as aggregated presence records do not fully and homogeneously represent the entire environment that is occupied by given species. This aggregation also violates an initial assumption of SDM that requires independence between records (Araújo andGuisan 2006, Hijman 2012). This may bias model predictions [START_REF] Luoto | Uncertainty of bioclimate envelope models based on the geographical distribution of species[END_REF][START_REF] Segurado | Consequences of spatial autocorrelation for niche-based models[END_REF][START_REF] Dormann | Effects of incorporating spatial autocorrelation into the analysis of species distribution data[END_REF][START_REF] Kühn | Incorporating spatial autocorrelation may invert observed patterns[END_REF][START_REF] Crase | A new method for dealing with residual spatial autocorrelation in species distribution models[END_REF], leading to statistical artefacts and generating inaccurate patterns (Bahn andMcGill 2007, Currie 2007).

Occurrence datasets: spatial aggregation.

Spatial aggregation of data and the effect of this spatial aggregation on model outputs can be quantified using the Moran's I index, which estimates the spatial autocorrelation between presence records used to build the model and predicted presence probabilities [START_REF] Luoto | Uncertainty of bioclimate envelope models based on the geographical distribution of species[END_REF]. This spatial autocorrelation implies that close pixels are expected to present more similar predicted probabilities than distant ones, due to the short geographic distance between records rather than to environmental similarities alone. Testing and correcting for this bias should help reduce its impact on model predictions (see section 3.3) [START_REF] Diniz-Filho | Spatial autocorrelation and red herrings in geographical ecology[END_REF][START_REF] Kühn | Incorporating spatial autocorrelation may invert observed patterns[END_REF].

Occurrence datasets: presence-only records.

SDMs based on presence/absence data are recognized as having better predictive performance than models using presence-only data [START_REF] Zaniewski | Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns[END_REF], Brotons et al. 2004[START_REF] Lobo | The uncertain nature of absences and their importance in species distribution modelling[END_REF][START_REF] Wisz | Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data[END_REF][START_REF] Smith | On evaluating species distribution models with random background sites in place of absences when test presences disproportionately sample suitable habitat[END_REF][START_REF] Carvalho | Ecological niche modelling predicts southward expansion of Lutzomyia (Nyssomyia) flaviscutellata (Diptera: Psychodidae: Phlebotominae), vector of Leishmania (Leishmania) amazonensis in South America, under climate change[END_REF][START_REF] Peel | Reliable species distributions are obtainable with sparse, patchy and biased data by leveraging over species and data types[END_REF]. However, except for some local-scale studies (e.g. [START_REF] Robinson | Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities[END_REF], in most oceanographic studies species absence records are usually not available for SDM, and working with presence-only records is the only alternative [START_REF] Lobo | The uncertain nature of absences and their importance in species distribution modelling[END_REF]. SDMs are then built by associating presenceonly records to a random selection of background records that will be used to characterise the full environmental conditions [START_REF] Franklin | Moving beyond static species distribution models in support of conservation biogeography[END_REF][START_REF] Barbet-Massin | Selecting pseudo-absences for species distribution models: how, where and how many?[END_REF]. Background records should not be mistaken with pseudo-absence records that are artificial absence data, where the species is supposed (but not confirmed) to be absent. Pseudo-absence records do not represent the overall conditions of the study area. Presence/pseudo-absence models consist in another modelling approach, rather predicting occupied and unoccupied habitats than suitable and less suitable habitats for presence-background modelling (Sillero and Barbosa 2020).

Presence-only datasets may contain several uncertainties that can bias model predictions.

(1)

Working on rare or cryptic species is generally prone to taxonomic misidentifications that may either contract or, alternatively, expand the extent of predicted species distributions [START_REF] Costa | Impacts of species misidentification on species distribution modeling with presence-only data[END_REF][START_REF] Aubry | The importance of data quality for generating reliable distribution models for rare, elusive, and cryptic species[END_REF]. Such biases due to taxonomic errors were shown to be highly variable and depend on experts identifying specimens as suggested by [START_REF] Beale | Incorporating uncertainty in predictive species distribution modelling[END_REF] who worked on a compilation of several collections. (2) Sampling gear may have an impact on species detection. Inaccurate species observations may generate false positives (species predicted as present while it was not sampled or observed in the field) and false negatives (species predicted as absent while it was sampled or observed in the field) during model initialization (Guillera-Arroita 2016). Species presence records should be carefully scrutinized prior to modelling [START_REF] Lozier | Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling[END_REF] or at least, records should be catagorized into different subsets of data verifiability [START_REF] Aubry | The importance of data quality for generating reliable distribution models for rare, elusive, and cryptic species[END_REF]). ( 3) Georeferencing errors are a frequent issue in databases [START_REF] Murphey | Georeferencing of museum collections: A review of problems and automated tools, and the methodology developed by the Mountain and Plains Spatio-Temporal Database-Informatics Initiative (Mapstedi)[END_REF], Maldonaldo et al. 2015). This is especially the case in large databases compiling independent datasets using species presences recorded with varying levels of precision [START_REF] Graham | The influence of spatial errors in species occurrence data used in distribution models[END_REF][START_REF] Bloom | Why georeferencing matters: introducing a practical protocol to prepare species occurrence records for spatial analysis[END_REF]. Several studies simulated virtual random georeferencing errors and showed that these errors lead to a significant drop in model performances, inconsistencies in the respective contributions of environmental descriptors contribution also influencing model interpretation [START_REF] Graham | The influence of spatial errors in species occurrence data used in distribution models[END_REF][START_REF] Osborne | Effects of species and habitat positional errors on the performance and interpretation of species distribution models[END_REF][START_REF] Naimi | Spatial autocorrelation in predictors reduces the impact of positional uncertainty in occurrence data on species distribution modelling[END_REF]. These side effects seem to be minimized in local-scale models, here again advocating for the use of local-scale models whenever possible [START_REF] Mitchell | Sensitivity of fine-scale species distribution models to locational uncertainty in occurrence data across multiple sample sizes[END_REF]).

Occurrence datasets: dealing with small datasets.

Usually, the number of species presence records available for modelling is relatively limited considering the wide geographic extent of the SO [START_REF] De Broyer | Biogeographic atlas of the Southern Ocean[END_REF]). Generating SDMs with small datasets may include many pitfalls. (1) It reduces the potential of SDMs to transfer in space and time [START_REF] Hernandez | The effect of sample size and species characteristics on performance of different species distribution modeling methods[END_REF][START_REF] Raes | Partial versus full species distribution models[END_REF], (2) it truncates predicted distribution and niche definition [START_REF] Hortal | Limitations of biodiversity databases: case study on seed-plant diversity in Tenerife, Canary Islands[END_REF], 2008, Rocchini et 6) on the application of evaluation metrics [START_REF] Pearson | Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar[END_REF]), ( 7) it complicates the identification of model optimal complexity [START_REF] Galante | The challenge of modeling niches and distributions for data-poor species: a comprehensive approach to model complexity[END_REF]) and ( 8) it leads to a reduction in model accuracy because presence and background datasets would not differ markedly [START_REF] Luoto | Uncertainty of bioclimate envelope models based on the geographical distribution of species[END_REF].

Alternatives are being developed to produce more accurate models based on a limited amount of presence records. One solution is generating sereval models performed on restricted areas and datasets with more detailed information and then averaging them with a weighted ensemble approach. This "ensemble of small models' approach had higher performance compared to single models [START_REF] Lomba | Overcoming the rare species modelling paradox: a novel hierarchical framework applied to an Iberian endemic plant[END_REF][START_REF] Breiner | Overcoming limitations of modelling rare species by using ensembles of small models[END_REF][START_REF] Fabri-Ruiz | Modèles de distribution et changements environnementaux: Application aux faunes d'échinides de l'océan Austral et écorégionalisation[END_REF].

Another alternative is to restrict the prediction area according to where occurrence records are found and ensuring upstream that the number of records is sufficient to precisely characterise the species environmental preferences: a trivial advice surprisingly neglected as recently pointed out by [START_REF] Morales | MaxEnt's parameter configuration and small samples: are we paying attention to recommendations? A systematic review[END_REF] and [START_REF] Araújo | Standards for distribution models in biodiversity assessments[END_REF].

Occurrence datasets: definition of species occupied environmental space.

Spatial aggregation, along with heterogeneity, limited size and uncertainties in datasets can strongly bias the quantification of the species occupied environmental space [START_REF] Hortal | Historical bias in biodiversity inventories affects the observed environmental niche of the species[END_REF][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 | Temporal degradation of data limits biodiversity research[END_REF]). However, accurately defining species occupied space is the cornerstone of SDM initialization [START_REF] Elith | Novel methods improve prediction of species' distributions from occurrence data[END_REF][START_REF] Boulanger | Model-specification uncertainty in future area burned by wildfires in Canada[END_REF].

Moreover, SDM supposes that species are in equilibrium with the environmental conditions they inhabit. SDM does not take into consideration potential vagrants that have dispersed out of their usual environmental range or populations that could momentarily survive in unsuitable habitats, because it violates the equilibrium assumption between species distribution and environmental conditions [START_REF] Beale | Incorporating uncertainty in predictive species distribution modelling[END_REF]. These elements should be cautiously considered when preparing datasets prior to generating models, by removing any atypical record.

Over the last two decades, field data acquisition was expanded by the use of biologging technology with electronic devices attached on seabirds and marine mammals to access the position of species all year long [START_REF] Raymond | Important marine habitat off east Antarctica revealed by two decades of multi-species predator tracking[END_REF], Ropert-Coudert et al. 2020). These data uncover the hidden behaviour of marine animals, constitute a powerful way of better estimating species occupied space; they can also be used to validate and refine the understanding of environmental conditions prevailing in those species distribution areas [START_REF] Arthur | Winter habitat predictions of a key Southern Ocean predator, the Antarctic fur seal (Arctocephalus gazella)[END_REF][START_REF] Nachtsheim | Habitat modelling of crabeater seals (Lobodon carcinophaga) in the Weddell Sea using the multivariate approach Maxent[END_REF], Hindell et al. 2020).

Adapting model implementation to datasets

The choice of modelling algorithms

To run performant SDMs, several assumptions must be tested and computing methods adapted to each case study [START_REF] Austin | Spatial prediction of species distribution: an interface between ecological theory and statistical modelling[END_REF][START_REF] De La Hoz | Temporal transferability of marine distribution models: The role of algorithm selection[END_REF]). Among them, the choice of the modelling algorithm should be of major concern, since no algorithm works best for all species, in all areas, at all spatial scales, and all time periods [START_REF] Jarnevich | Caveats for correlative species distribution modeling[END_REF][START_REF] Qiao | No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation[END_REF]. The selection and parameterization of modelling algorithms were proved to be major causes of variation between SDM predictions [START_REF] Diniz-Filho | Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change[END_REF][START_REF] Dormann | Components of uncertainty in species distribution analysis: a case study of the great grey shrike[END_REF][START_REF] Buisson | Uncertainty in ensemble forecasting of species distribution[END_REF], Watling et al. 2015[START_REF] Boulanger | Model-specification uncertainty in future area burned by wildfires in Canada[END_REF]. Each algorithm is particularly suited to deal with a specific type and quality of data [START_REF] Guisan | Predictive habitat distribution models in ecology[END_REF][START_REF] Austin | Spatial prediction of species distribution: an interface between ecological theory and statistical modelling[END_REF][START_REF] Elith | Novel methods improve prediction of species' distributions from occurrence data[END_REF], Peterson 2011, Guisan et al. 2017), which will determine final model outputs [START_REF] Aguirre-Gutiérrez | Fit-for-purpose: species distribution model performance depends on evaluation criteria-Dutch hoverflies as a case study[END_REF][START_REF] Beaumont | Which species distribution models are more (or less) likely to project broadscale, climate-induced shifts in species ranges?[END_REF].

When modelling species distribution, it is necessary to select appropriate algorithms that have good transferability performances that is, have good abilities to correctly transfer predictions to other geographic space and time periods [START_REF] Randin | Are niche-based species distribution models transferable in space?[END_REF], and that they limit overfitting (i.e. mitigate model complexity) while being flexible in integrating complex environmental relationships. Machine-learning algorithms (e.g. MaxEnt, Boosted Regression Trees BRT, Random Forests RF, support vector machines SVM, [START_REF] Vapnik | Statistical learning theory Wiley[END_REF][START_REF] Breiman | Random forests[END_REF], Elith et al. 2008, 2011) give access to important computing performances [START_REF] Zhou | Ensemble methods: foundations and algorithms[END_REF], and are relevant approaches for handling complex relationships between species occurrences and the environment (Olden et al. 2008, Elith andLeathwick 2009). The BRT and RF algorithms are particularly suited to complex and heterogeneous datasets (Fig. 4, Guillaumot et al. 2020a).

They were proved efficient in generating performant models with limited overfitting [START_REF] Elith | Novel methods improve prediction of species' distributions from occurrence data[END_REF][START_REF] Wisz | Effects of sample size on the performance of species distribution models[END_REF][START_REF] Wenger | Assessing transferability of ecological models: an underappreciated aspect of statistical validation[END_REF]. They can automatically select the most informative features among a large set [START_REF] Merow | What do we gain from simplicity versus complexity in species distribution models?[END_REF][START_REF] García-Callejas | The effects of model and data complexity on predictions from species distributions models[END_REF], Guillaumot et al. 2020a) and perform well to generalize predictions in the absence of information or conversely, to deal with redundant information provided by correlated factors [START_REF] Breiman | Classification and regression trees[END_REF][START_REF] De'ath | Classification and regression trees: a powerful yet simple technique for ecological data analysis[END_REF][START_REF] Friedman | Greedy function approximation: a gradient boosting machine[END_REF]). vertebrates of Florida) or (4) using virtual species [START_REF] Meynard | Predicting species distributions: a critical comparison of the most common statistical models using artificial species[END_REF], García-Callejas and Araujo 2016[START_REF] Qiao | No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation[END_REF].

However, to generate such comparisons (Fig. 4) it is important to specifically adjust each algorithm on the case study. Algorithms all perform differently with regards to overfitting, spatial aggregation, and transferability, and comparing model performances using different parameter settings is challenging [START_REF] Merow | What do we gain from simplicity versus complexity in species distribution models?[END_REF] given that model parameterization has strong effects on the quality of model outputs [START_REF] Anderson | Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent[END_REF][START_REF] Rodda | Challenges in identifying sites climatically matched to the native ranges of animal invaders[END_REF][START_REF] Warren | Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria[END_REF][START_REF] Yackulic | Presence-only modelling using MAXENT: when can we trust the inferences?[END_REF][START_REF] Radosavljevic | Making better Maxent models of species distributions: complexity, overfitting and evaluation[END_REF][START_REF] Moreno-Amat | Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data[END_REF][START_REF] Halvorsen | How important are choice of model selection method and spatial autocorrelation of presence data for distribution modelling by MaxEnt?[END_REF][START_REF] Galante | The challenge of modeling niches and distributions for data-poor species: a comprehensive approach to model complexity[END_REF][START_REF] Lieske | Ensembles of Ensembles: Combining the Predictions from Multiple Machine Learning Methods[END_REF].

Initially developped in the 1990's, ensemble modelling has been increasingly used since then [START_REF] Hansen | Neural network ensembles[END_REF]Salamon 1990, Schapire 1990). Ensemble modelling consists in combining several algorithms [START_REF] Zhou | Ensemble methods: foundations and algorithms[END_REF], input datasets (occurrence or environmental descriptors datasets) or parameterizations [START_REF] Araújo | Ensemble forecasting of species distributions[END_REF], and see [START_REF] Hao | A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD[END_REF] for a review of applications).

The approach seems interesting as it can provide predictions that take into account the variability of several models (Araújo andNew 2007, Hao et al. 2019).

Ensemble modelling has been used for various studies in SDM [START_REF] Araújo | Ensemble forecasting of species distributions[END_REF][START_REF] Marmion | Evaluation of consensus methods in predictive species distribution modelling[END_REF], Thuiller et al. 2009[START_REF] Buisson | Uncertainty in ensemble forecasting of species distribution[END_REF][START_REF] Luedeling | Agroforestry systems in a changing climate-challenges in projecting future performance[END_REF][START_REF] Trolle | Advancing projections of phytoplankton responses to climate change through ensemble modelling[END_REF][START_REF] Carvalho | Ecological niche modelling predicts southward expansion of Lutzomyia (Nyssomyia) flaviscutellata (Diptera: Psychodidae: Phlebotominae), vector of Leishmania (Leishmania) amazonensis in South America, under climate change[END_REF][START_REF] Scales | Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models[END_REF][START_REF] Jerosch | Ensemble modelling of Antarctic macroalgal habitats exposed to glacial melt in a polar fjord[END_REF]) and benefited from the The main benefits of using ensemble models lie in the fact that the different algorithms will perform differently for various input cases (despite their overall performance). The models thus complement each other, avoiding some biases that may have resulted from using a single algorithm [START_REF] Marmion | Evaluation of consensus methods in predictive species distribution modelling[END_REF][START_REF] Knutti | The end of model democracy?[END_REF][START_REF] Zhou | Ensemble methods: foundations and algorithms[END_REF]). However, model interpretation is much more difficult when mixing algorithms implemented differently, with contrasting ways of presenting outputs [START_REF] Sillero | What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods[END_REF]) and different definitions of thresholds for identifying habitat suitability (Perrault-Hébert 2019), requiring the normalization of predictions that is rarely applied [START_REF] Zhang | Ensemble machine learning models for aviation incident risk prediction[END_REF]. This is the main limitation to the approach and could offset the gain in model performance [START_REF] Crimmins | Evaluating ensemble forecasts of plant species distributions under climate change[END_REF][START_REF] Zhu | Do consensus models outperform individual models? Transferability evaluations of diverse modeling approaches for an invasive moth[END_REF][START_REF] Hao | Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models[END_REF], that was contested as it was assessed without using an independent evaluation dataset [START_REF] Hao | A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD[END_REF]. Combining predictions of different models generated with contrasting assumptions is therefore tricky when interpreting the results (Perrault-Hébert 2019). Optimizing the parameterization of one single algorithm (that could be correctly evaluated) may therefore constitute a more valuable option (Perrault-Hébert 2019). Comparing the performance of different algorithms can be helpful in a first stage of the modelling process, in order to select the most suited algorithm and calibrate the models [START_REF] Massada | Wildfire ignition-distribution modelling: a comparative study in the Huron-Manistee National Forest, Michigan, USA[END_REF].

The choice of environmental descriptors

The selection of environmental descriptors is also a crucial step of the modelling process [START_REF] Franklin | Moving beyond static species distribution models in support of conservation biogeography[END_REF][START_REF] Austin | Improving species distribution models for climate change studies: variable selection and scale[END_REF][START_REF] Petitpierre | Selecting predictors to maximize the transferability of species distribution models: lessons from crosscontinental plant invasions[END_REF]. Ideally, environmental descriptors should be selected for their ecological relevance to the studied organisms [START_REF] Austin | Improving species distribution models for climate change studies: variable selection and scale[END_REF][START_REF] Dormann | Collinearity: a review of methods to deal with it and a simulation study evaluating their performance[END_REF][START_REF] Bradie | A quantitative synthesis of the importance of variables used in MaxEnt species distribution models[END_REF], they must capture environmental discontinuities and constraints in the distribution area [START_REF] Jarnevich | Caveats for correlative species distribution modeling[END_REF], and should also be detailed enough to represent the habitat complexity and variability to allow good SDM accuracy and performance [START_REF] Elith | Species distribution models: ecological explanation and prediction across space and time[END_REF][START_REF] Barbet-Massin | Selecting pseudo-absences for species distribution models: how, where and how many?[END_REF], Bucklin et al. 2015[START_REF] Petitpierre | Selecting predictors to maximize the transferability of species distribution models: lessons from crosscontinental plant invasions[END_REF].

In most studies, the final number of descriptors selected to depict species environment is generally close to 10 ( Selecting environmental descriptors therefore implies that several tests should be performed upstream to determine the best set to be used depending on research objectives. Fois et al.

(2018) recommended to first calibrate models with a large set of descriptors of different nature (proximal vs. distal descriptors) that will be stepwise pruned, after analysing their ability to accurately describe the habitat and testing collinearity (El-Gabbas and Dormann 2018).

Generating, testing, and comparing several sets of descriptors is a widespread strategy to stepwise target the set that allows the best predictive accuracy [START_REF] Snickars | Species-environment relationships and potential for distribution modelling in coastal waters[END_REF], Bucklin et al. 2015[START_REF] Bradie | A quantitative synthesis of the importance of variables used in MaxEnt species distribution models[END_REF][START_REF] Petitpierre | Selecting predictors to maximize the transferability of species distribution models: lessons from crosscontinental plant invasions[END_REF]. Replacing environmental descriptors by Principal Components of a Factorial Analysis was also proved to be efficient, because complex environmental gradients of the study area are simplified in fewer, orthogonalized components [START_REF] Kühn | Incorporating spatial autocorrelation may invert observed patterns[END_REF][START_REF] Petitpierre | Selecting predictors to maximize the transferability of species distribution models: lessons from crosscontinental plant invasions[END_REF]. So far, this latter method has never been applied to SO case studies and should be tested to evaluate the interpretability of model results.

Correcting spatial biases

Generating a model based on spatially aggregated presence-only records may bias predictions with higher probability of occurrence predicted in highly sampled areas [START_REF] Dormann | Effects of incorporating spatial autocorrelation into the analysis of species distribution data[END_REF], Guillaumot et al. 2018a). To compensate for such a bias, a first approach is to sample background records according to the spatial bias introduced by the aggregated presence records themselves [START_REF] Phillips | Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data[END_REF]). The background dataset is used to define the environmental background: its boundaries and variability constitutes essential information to build and project model outputs (Wisz andGuisan 2009, Barbet-Massin et al. 2012). The choice of the number of background records to be sampled and the extent of their distribution should be considered carefully when calibrating a model, because it can strongly influence model predictions [START_REF] Chefaoui | Assessing the effects of pseudo-absences on predictive distribution model performance[END_REF][START_REF] Lobo | The uncertain nature of absences and their importance in species distribution modelling[END_REF][START_REF] Barbet-Massin | Selecting pseudo-absences for species distribution models: how, where and how many?[END_REF][START_REF] Jarnevich | Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection[END_REF]). This number should respect the prevalence score, being the ratio between the species occupied space (represented by presence record locations) and the total surface of the study area (represented by background locations) [START_REF] Mcpherson | The effects of species' range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact[END_REF]). Some advice are provided in [START_REF] Barbet-Massin | Selecting pseudo-absences for species distribution models: how, where and how many?[END_REF] for selecting a correct number of background records according to prevalence scores.

Targeting background records has been extensively tested and several procedures were developed to significantly improve the relevance of models (Fig. 5). Background records can be sampled within pre-defined areas (i.e. 'disks' or 'buffers') close to presence records [START_REF] Hengl | Spatial prediction of species' distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging[END_REF][START_REF] Phillips | Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data[END_REF][START_REF] Fourcade | Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias[END_REF][START_REF] Bertrand | 3-D habitat suitability of jack mackerel Trachurus murphyi in the Southeastern Pacific, a comprehensive study[END_REF], following the presence or absence of other species [START_REF] Phillips | Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data[END_REF][START_REF] Syfert | The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models[END_REF][START_REF] Iturbide | A framework for species distribution modelling with improved pseudo-absence generation[END_REF][START_REF] Molloy | Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll[END_REF][START_REF] Phillips | Applying species distribution modelling to a data poor, pelagic fish complex: The ocean sunfishes[END_REF][START_REF] Ranc | Performance tradeoffs in target-group bias correction for species distribution models[END_REF], according to probabilities given by a kernel density estimator (KDE) of the sampling frequency [START_REF] Fourcade | Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias[END_REF][START_REF] Jarnevich | Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection[END_REF], Guillaumot et al. 2018a[START_REF] Fabri-Ruiz | Can we generate robust species distribution models at the scale of the Southern Ocean?[END_REF] or according to additive descriptors of accessibility and sampling effort (El-Gabbas and Dormann 2018). Once again, the selected method should be adapted to each case study and its efficiency tested prior to model interpretation [START_REF] Støa | Sampling bias in presence-only data used for species distribution modelling: theory and methods for detecting sample bias and its effects on models[END_REF]. A second method consists in filtering available presence data to reduce the influence of the clustering of species records [START_REF] Segurado | Consequences of spatial autocorrelation for niche-based models[END_REF][START_REF] Kramer-Schadt | The importance of correcting for sampling bias in MaxEnt species distribution models[END_REF], Boria et al. 2014). This is an efficient method compared to the background targeted sampling approach detailed above but the remaining number of presence records after filtering should be sufficient to correctly determine species occupied space [START_REF] Kramer-Schadt | The importance of correcting for sampling bias in MaxEnt species distribution models[END_REF]. Reliable information should be also available to characterise the bias in species occurrence data (Aiello-Lammens et al. 2015, Sillero andBarbosa 2020). The filtering protocol requires meeting many pre-requisites, but the priority is given to keeping presence data independent and minimizing records clustering (Alagador D. pers. comm).

Overall, if several methods were developed to correct for the effect of spatial aggregation on model outputs, it is recommended to interpret model projections performed for poorly sampled areas with great caution [START_REF] Phillips | Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data[END_REF][START_REF] Iturbide | Background sampling and transferability of species distribution model ensembles under climate change[END_REF].

Model outputs

Taxonomic bias and population variability

SDMs are usually parameterized using all presence records available for a species and all environmental conditions prevailing at the species records [START_REF] Elith | Species distribution models: ecological explanation and prediction across space and time[END_REF]. When modelling species distribution at broad spatial scale, it is often assumed that all populations of a species have the same relationship to environmental conditions over the entire distribution area [START_REF] Pierrat | Large-scale distribution analysis of Antarctic echinoids using ecological niche modelling[END_REF][START_REF] Xavier | Biogeography of Cephalopods in the Southern Ocean using habitat suitability prediction models[END_REF][START_REF] Guillaumot | Benthic species of the Kerguelen Plateau show contrasting distribution shifts in response to environmental changes[END_REF][START_REF] Fabri-Ruiz | Can we generate robust species distribution models at the scale of the Southern Ocean?[END_REF]. However, occurrence datasets may include a set of populations with different phenotypic plasticities [START_REF] Chevin | Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory[END_REF], transgenerational adaptations [START_REF] Dixon | Genomic determinants of coral heat tolerance across latitudes[END_REF] or simply different habitat selection in the case of vagile species. Therefore, the modelled species can actually present different abilities to respond to environmental changes. In particular, physiological performances of populations are likely to vary in marine species with wide distribution range and high dispersal capabilities over long distances [START_REF] Thatje | Effects of capability for dispersal on the evolution of diversity in Antarctic benthos[END_REF]. This is particularly relevant with regards to future predictions that do not integrate inter-population variability in the potential acclimation of species, and may lead models to alternatively over-or under-estimate the distribution of species suitable environments [START_REF] Cacciapaglia | Marine species distribution modelling and the effects of genetic isolation under climate change[END_REF]van Woesik 2017, Thyrring et al. 2017).

Phylogeographic studies have also regularly revealed the existence of cryptic species in the SO benthos, which show similar morphologies for distinct genotypes and potentially, distinct ecological requirements and geographic distributions [START_REF] Lozier | Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling[END_REF]. Such studies often stressed the need of taxonomic revisions [START_REF] González-Wevar | Systematic revision of Nacella (Patellogastropoda: Nacellidae) based on a complete phylogeny of the genus, with the description of a new species from the southern tip of South America[END_REF][START_REF] Ocaranza-Barrera | Molecular divergence between Iridaea cordata (Turner) Bory de Saint-Vincent from the Antarctic Peninsula and the Magellan Region[END_REF][START_REF] Moreau | The overlooked diversity of Southern Ocean sea stars (Asteroidea) reveals original evolutionary pathways[END_REF]. SDMs can be generated based on a spatial subdivision of presence records according to the genetic structure of taxa, and in a second step, the different predictions be merged together to the broader scale [START_REF] Knowles | Coupling genetic and ecological-niche models to examine how past population distributions contribute to divergence[END_REF][START_REF] Marcer | Tackling intraspecific genetic structure in distribution models better reflects species geographical range[END_REF][START_REF] Cacciapaglia | Marine species distribution modelling and the effects of genetic isolation under climate change[END_REF][START_REF] Ikeda | Genetically informed ecological niche models improve climate change predictions[END_REF][START_REF] Roberts | Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure[END_REF][START_REF] Pardo-Gandarillas | Phylogeography and species distribution modelling reveal the effects of the Pleistocene ice ages on an intertidal limpet from the south-eastern Pacific[END_REF]).

However, defining the genetic structure of benthic species in the SO is a long-term endeavour that requires a constantly renewed, sampling effort considering the extent and complexity of the study area [START_REF] Moreau | Reproductive strategy as a piece of the biogeographic puzzle: a case study using Antarctic sea stars (Echinodermata, Asteroidea)[END_REF][START_REF] Fraser | Antarctica's ecological isolation will be broken by storm-driven dispersal and warming[END_REF][START_REF] Moore | The Antarctic Circumpolar Current isolates and connects: Structured circumpolarity in the sea star Glabraster antarctica[END_REF]. Awaiting for taxonomic revisions and enhanced sampling effort to best depict relationships between genetic units and environmental conditions [START_REF] Vandersteen | Detecting gene expression profiles associated with environmental stressors within an ecological context[END_REF], combining SDM with experimental data or mechanistic approaches can be an alternative to take into account the possible physiological contrasts between populations [START_REF] Kearney | Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges[END_REF], Kearney et al. 2010[START_REF] Buckley | Can mechanism inform species' distribution models?[END_REF][START_REF] Fordham | Population dynamics can be more important than physiological limits for determining range shifts under climate change[END_REF][START_REF] Briscoe | Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia[END_REF], Feng and Papes 2017, Lopez/Guillaumot et al. in press.).

Definition of the projection area

The limitations in the current knowledge of species distribution also affect the quality of information available to estimate their potential distribution [START_REF] Thuiller | Large-scale environmental correlates of forest tree distributions in Catalonia (NE Spain)[END_REF]. When the limits of species environmental ranges are not fully captured, this uncertainty can significantly impact the accuracy of SDM predictions [START_REF] Hortal | Limitations of biodiversity databases: case study on seed-plant diversity in Tenerife, Canary Islands[END_REF][START_REF] Lobo | More complex distribution models or more representative data?[END_REF][START_REF] Rocchini | Accounting for uncertainty when mapping species distributions: the need for maps of ignorance[END_REF][START_REF] Sánchez-Fernández | Species distribution models that do not incorporate global data misrepresent potential distributions: a case study using Iberian diving beetles[END_REF][START_REF] Titeux | The need for large-scale distribution data to estimate regional changes in species richness under future climate change[END_REF][START_REF] El-Gabbas | Wrong, but useful: regional species distribution models may not be improved by range-wide data under biased sampling[END_REF]. It reduces the applicability of models for predictive purposes [START_REF] Thuiller | Effects of restricting environmental range of data to project current and future species distributions[END_REF]), induces model overfitting [START_REF] Tsoar | A comparative evaluation of presence-only methods for modelling species distribution[END_REF][START_REF] Barve | The crucial role of the accessible area in ecological niche modeling and species distribution modeling[END_REF][START_REF] Guillaumot | Benthic species of the Kerguelen Plateau show contrasting distribution shifts in response to environmental changes[END_REF]) and can lead to overestimating the extent of suitable areas [START_REF] Anderson | The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela[END_REF].

This bias can be partly overcome by reducing the extent of the projection area to the known distribution of occurrence records available [START_REF] Anderson | The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela[END_REF], and by increasing knowledge on species ecology and physiology to identify the environmental conditions that are unsuitable for their survival or development [START_REF] Byrne | From pole to pole: the potential for the Arctic seastar Asterias amurensis to invade a warming Southern Ocean[END_REF].

Model extrapolation

Models are said to extrapolate when a portion of the predicted area includes environmental conditions that are outside the range of values for which the model was calibrated. Model extrapolation may occur when model predictions are transfered, either in space or time. When extrapolated, model predictions are in non-analog conditions compared to initial, calibration conditions, because calibration data may not encompass the entire environmental range of each of the predictors [START_REF] Guillaumot | Extrapolation in species distribution modelling. Application to Southern Ocean marine species[END_REF]). The set of projected environmental conditions can otherwise still be within the range of conditions, but specific combinations of environmental descriptors may become new, leading to extrapolation too [START_REF] Mesgaran | Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models[END_REF]). In such conditions, predictions might be ecologically and statistically invalid and model interpretation inaccurate [START_REF] Randin | Are niche-based species distribution models transferable in space?[END_REF], Williams and Jackson 2007, Williams et al. 2007[START_REF] Fitzpatrick | The projection of species distribution models and the problem of non-analog climate[END_REF][START_REF] Owens | Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas[END_REF]).

Among different approaches, [START_REF] Elith | The art of modelling range-shifting species[END_REF] propose to estimate and quantify model extrapolation using the Multivariate Environmental Similarity Surface (MESS) index to identify the most influencial descriptors that lead to extrapolation. Grid-cell pixels for which at least one environmental descriptor has a value outside the range of environmental values defined by presence-only records (calibration range) is considered to be extrapolation. In these cases, MESS gets negative values and the ensemble of pixels containing negative values defines the extrapolation area [START_REF] Elith | The art of modelling range-shifting species[END_REF][START_REF] Guillaumot | Extrapolation in species distribution modelling. Application to Southern Ocean marine species[END_REF]). Most often, for SDMs performed at the scale of the SO, the number of records available to define the environmental space occupied by species is limiting, and the resolution of environmental descriptors relatively low (see section 2). As a consequence, SDM projections sometimes include wide extrapolation areas that may cover over 75% of the predicted regions (Fig. 6) [START_REF] Guillaumot | Extrapolation in species distribution modelling. Application to Southern Ocean marine species[END_REF]. In addition to quantifying the overall extrapolation area (Fig. 6), it is possible to fine-tune the analysis and define which environmental descriptors and areas are concerned with extrapolation [START_REF] Owens | Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas[END_REF][START_REF] Guillaumot | Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities[END_REF]) (Fig. 7). Such information could be used to resample the environmental descriptors implemented in the model.

In any case, it has been recommended to provide information on model extrapolation and more generally to other concepts of uncertainties (species detection, errors…) along with model predictions, because they are essential to accurate interpretation [START_REF] Beale | Incorporating uncertainty in predictive species distribution modelling[END_REF][START_REF] Addison | Practical solutions for making models indispensable in conservation decision-making[END_REF], Guisan et al. 2013). Limiting model projections to "realistic" depth ranges or some other environmental limiting factor based on a robust knowledge of species ecology (i.e. some expert-driven decision) was proved efficient to reduce extrapolation [START_REF] Kearney | Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges[END_REF][START_REF] Hare | Projecting range limits with coupled thermal tolerance-climate change models: an example based on gray snapper (Lutjanus griseus) along the US east coast[END_REF][START_REF] De Villiers | Combining field phenological observations with distribution data to model the potential distribution of the fruit fly Ceratitis rosa Karsch (Diptera: Tephritidae)[END_REF][START_REF] Guillaumot | Extrapolation in species distribution modelling. Application to Southern Ocean marine species[END_REF]. Such a strategy is transitional before complementary samples and more comprehensive occurrence datasets are made available to better define the species occupied space [START_REF] Guillaumot | Extrapolation in species distribution modelling. Application to Southern Ocean marine species[END_REF].

Model validation and accuracy of model predictions

Some usual metrics for the evaluation of model predictions

Once models are generated, the accuracy of predictions must be assessed to evaluate the validity of models with regards to scientific issues to address, compare different model outputs and allow the formulation of reliable interpretations [START_REF] Zurell | A standard protocol for reporting species distribution models[END_REF]. Several metrics were developed to evaluate the performance of models [START_REF] Fielding | A review of methods for the assessment of prediction errors in conservation presence/absence models[END_REF]Bell 1997, Allouche et al. 2006).

Most of them are based on the calculation of an error matrix (or confusion matrix) that displays the proportion of presence and absence records that are correctly predicted by the model [START_REF] Allouche | Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS)[END_REF].

In most biological studies focused on the SO benthos, absence records are usually unavailable and SDMs are generated based on a set of presence/background records (see section 2.2). As a consequence, statistics that are commonly used for presence/absence datasets may not be appropriate for model evaluation [START_REF] Wiley | Niche modeling perspective on geographic range predictions in the marine environment using a machine-learning algorithm[END_REF][START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF], Braunish et al. 2013), such as the Kappa statistic [START_REF] Allouche | Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS)[END_REF]. In contrast, the AUC, the short for Area Under the Curve, or Area Under the Receiver Operating Curve, is one of the most used and appropriate metrics for measuring the performance of model predictions based on presencebackground data [START_REF] Hand | Measuring classifier performance: a coherent alternative to the area under the ROC curve[END_REF]. The AUC is an objective measure that remains stable with lowprevalence datasets (i.e. low frequency of occurrences with regards to the projection space) and is not sensible to threshold effects [START_REF] Manel | Evaluating presence-absence models in ecology: the need to account for prevalence[END_REF][START_REF] Hand | Measuring classifier performance: a coherent alternative to the area under the ROC curve[END_REF], van Proosdij et al. 2016).

However, for presence/background models, specificity (the fraction of correctly predicted absences) might be overestimated when the number of background records is much higher than the number of presence-only records, or when background and presences are associated to very different environmental values. This incidentally inflates AUC scores [START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF][START_REF] Raes | A null-model for significance testing of presence-only species distribution models[END_REF][START_REF] Lobo | More complex distribution models or more representative data?[END_REF][START_REF] Jiménez-Valverde | Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling[END_REF] and invalidates the relevance of the AUC metrics (van Proosdij et al. 2016).

Even when properly employed, the AUC cannot be used to compare models when SDMs are generated for different species, based on different environmental descriptors, or projected on distinct regions, because values depend on the relative size of suitable areas, and prevalence scores may contrast (see section 3.3) (Wisz et al. 2008, Anderson and[START_REF] Anderson | Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent[END_REF]. The AUC metrics must be used as a simple measure of the relative ranking of model predictions associated with a specific dataset (El-Gabbas and Dormann 2018). Overall, each statistics is characterised by specific advantages and potential biases, so that it is recommended to use several statistics for evaluating model predictions [START_REF] Allouche | Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS)[END_REF].

The accuracy of model predictions can also be evaluated by testing the classification of independent test data, where the available occurrence dataset can be split into independent subsets to train or test the model (see [START_REF] Fielding | A review of methods for the assessment of prediction errors in conservation presence/absence models[END_REF] for a review).

Cross-validation procedures

Cross-validation procedures are aimed at evaluating model predictions using a subset of presence or absence records retrieved from the initial dataset used for modelling, in order to assess how well test data match with modelled predictions [START_REF] Bahn | Testing the predictive performance of distribution models[END_REF]. When working with presence-only datasets, two subsets of presence records are used: one subset is used to train the model (the training group), and the second subset is used to test the model (the test group). Test data and training data must be spatially independent from each other (Hijmans 2012, Bahn and[START_REF] Bahn | Testing the predictive performance of distribution models[END_REF]. In most modelling exercises, standard cross-validation procedures are commonly used, in which the initial presence dataset is randomly split into a training and test subset. Frequently, as previously discussed, presence data are spatially aggregated in SO datasets and the necessary condition of independence between training and test data is seldom met, making the model accuracy evaluation over-optimistic [START_REF] Telford | Evaluation of transfer functions in spatially structured environments[END_REF][START_REF] Hijmans | Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model[END_REF][START_REF] Radosavljevic | Making better Maxent models of species distributions: complexity, overfitting and evaluation[END_REF]. In contrast to random procedures, spatial cross-validation procedures improve the performance of the validation step by spatially segregating training and test subsets, ensuring the spatial independence between data even when they are spatially aggregated in initial datasets [START_REF] Dhingra | Global mapping of highly pathogenic avian influenza H5N1 and H5Nx clade 2.3. 4.4 viruses with spatial cross-validation[END_REF][START_REF] Roberts | Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure[END_REF][START_REF] Guillaumot | Broad-scale species distribution models applied to data-poor areas[END_REF], see also http://cran.rapporter.net/web/packages/blockCV/vignettes/BlockCV_for_SDM.html).

Several spatial cross-validation procedures were proposed (Fig. 8), and the most appropriate one can be determined by comparing the different procedures, define the one that suits the most for the study [START_REF] Muscarella | ENM eval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models[END_REF][START_REF] Radosavljevic | Making better Maxent models of species distributions: complexity, overfitting and evaluation[END_REF][START_REF] Valavi | blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models[END_REF][START_REF] Guillaumot | Broad-scale species distribution models applied to data-poor areas[END_REF], depending on the spatial scale of the analysis, on the number and spatial distribution of presence data, and on the selected algorithm (and its associated complexity) used for modelling [START_REF] El-Gabbas | Wrong, but useful: regional species distribution models may not be improved by range-wide data under biased sampling[END_REF]Dormann 2018, Hao et al. 2020).

Conclusions and future prospects

This review summarizes some points and issues to be considered during SDM construction for modelling the distribution of SO species (Fig. 1). It shows that accurate and efficient SDMs can be produced for SO species when considering common potential biases and issues, and correcting for their side effects. Proposed corrections must be adjusted to each case study: no consensus method, nor implementation procedure always perform best, each case study requiring proper analyses to generate the most relevant and accurate predictions. This entails that, for each model, several procedures to implement the model should be tested to select the most suitable one, ideally giving priority to the availability of independent datasets to evaluate models. We showed that SDMs perform best when the species occupied space is accurately described, using extensive occurrence datasets with both presence and absence records, and when data are checked for positioning and georeferencing errors. A good knowledge of species' ecology, life history traits, and populational variations within the overall species distribution and environmental range help improve model quality [START_REF] Fois | Using species distribution models at local scale to guide the search of poorly known species: Review, methodological issues and future directions[END_REF]. The compilation, examination, and preparation of datasets prior to modelling are essential steps to generate efficient models. Estimating and communicating uncertainties associated to model predictions is also an important task to be highlighted. This may include a "simple" interpretation of the ecological relevance of SDM outputs by experts [START_REF] Merow | Integrating occurrence data and expert maps for improved species range predictions[END_REF] to the mapping of model extrapolation as illustrated here. Model uncertainties are part of model outputs and should not be omitted [START_REF] Guisan | Predicting species distributions for conservation decisions[END_REF][START_REF] Grimm | Towards better modelling and decision support: documenting model development, testing, and analysis using TRACE[END_REF][START_REF] Grimm | Robustness analysis: Deconstructing computational models for ecological theory and applications[END_REF].

Remaining challenges to perform relevant SDMs for SO studies include more efforts in data collection outside of the main sampling hotspots and to fill knowledge gaps in SO species taxonomy. Some methodological perspectives, developed in other regions, address the integration of physiological information into SDMs. This completes the understanding of species environmental preferences and help better estimating species realised niche [START_REF] Kearney | Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges[END_REF][START_REF] Talluto | Cross-scale integration of knowledge for predicting species ranges: a metamodelling framework[END_REF][START_REF] Mathewson | Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current, past, and future climates[END_REF][START_REF] Rodríguez | Integration of physiological knowledge into hybrid species distribution modelling to improve forecast of distributional shifts of tropical corals[END_REF][START_REF] Gamliel | Incorporating physiology into species distribution models moderates the projected impact of warming on selected Mediterranean marine species[END_REF]). Such studies have been recently developed for SO benthic species: in (López-Farrán / approach, were information of both SDM and physiological models are fully integrated, using the physiological information as a prior to inform the SDM [START_REF] Gamliel | Incorporating physiology into species distribution models moderates the projected impact of warming on selected Mediterranean marine species[END_REF]. Recently applied to an endemic sea urchin of the Kerguelen Plateau (Guillaumot et al. in prep.), the method allows to more precisely predicting the effect of seasonal variations on species habitat suitability.

Other interesting methodological insights are the consideration of biotic interaction information, dispersal capacities estimates or population dynamics in complement to SDM predictions to generalize the understanding of the main drivers of species distribution [START_REF] Pellissier | Species distribution models reveal apparent competitive and facilitative effects of a dominant species on the distribution of tundra plants[END_REF][START_REF] Meier | Cooccurrence patterns of trees along macro-climatic gradients and their potential influence on the present and future distribution of Fagus sylvatica L[END_REF][START_REF] Pagel | Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics[END_REF][START_REF] Conlisk | Uncertainty in assessing the impacts of global change with coupled dynamic species distribution and population models[END_REF][START_REF] Pellissier | Combining food web and species distribution models for improved community projections[END_REF][START_REF] Leach | Modelling the influence of biotic factors on species distribution patterns[END_REF][START_REF] Anderson | When and how should biotic interactions be considered in models of species niches and distributions[END_REF]. This however necessitates a deep knowledge of the species ecology and of this surrounding environment, suggesting the first applications to be rather expected in local or regional scale studies.

A final take-home message is that model outputs should be interpreted carefully and model predictions always considered with a critical eye. Models are simple representations of complex systems and should be used in complement to other approaches to support conservation strategies or address fundamental research objectives (Porfirio et al. 2014, Kampichler andSierdsema et al. 2017).

Figure 1 .

 1 Figure 1. Flow chart of the SDM construction process. Steps 1 to 4 concern data collection, and treatment. Steps 5 to 7 integrate procedures for model implementation and evaluation. Dashed rectangles allow for a possible step backward when assessing model uncertainties or evaluating model performance.

Figure 2 .

 2 Figure 2. Cumulative number of Antarctic species described over time, according to data available in the Register of Antarctic Marine species (until March 2010). From De Broyer and Danis (2011).

Figure 3 .

 3 Figure 3. Distribution of benthos sampling sites (red dots) in the Southern Ocean (SO, < 45°S). Sampling sites are not evenly distributed in the SO, showing important spatial aggregation in the Scotia Arc region and Western Antarctic Peninsula with several clusters along the Antarctic shelf, and over the Kerguelen and Campbell plateaus. In contrast, deep-sea regions and remote areas of the Antarctic shelf are undersampled. From Guillaumot et al. (2019), updated from Griffiths et al. (2014).

Figure 4 .

 4 Figure 4. Compared Area Under the Curve (AUC) performances of SDMs generated with different algorithms (ANN=Artificial Neural Network, BRT=Boosted Regression Trees, CTA=Classification Tree Analysis, FDA=Flexible Discriminant Analysis, GAM=Generalised Additive Model, GLM=Generalised Linear Model, MARS=Multivariate Adaptive Regression Splines, MAXENT=Maximum Entrpoy, RF=Random Forest, SRE=Surface Range Envelope) to predict the distribution of the sea urchin Sterechinus diadema in the Southern Ocean. Results show a good performance for BRT and RF, adapted to small, historically compiled datasets (temporally heterogeneous) and spatially aggregated presenceonly data. Models were calibrated with presence-only data and 200 background data randomly sampled in the study area. Average scores of 100 model replicates. See Guillaumot et al. (2018b) for details.

  development of R packages to implement them (Biomod:[START_REF] Thuiller | BIOMOD-a platform for ensemble forecasting of species distributions[END_REF], BiodiversityR: Kindt et al. 2017, biomod2: Thuiller et al. 2018, sdm: Naimi et al. 2019).

Figure 5 .

 5 Figure 5. Comparison of predicted distribution probabilities (between 0 and 1) of the sea urchin Ctenocidaris nutrix on the Kerguelen Plateau: (A) without compensating for sampling bias; (B) with a kernel density estimator (KDE) correction: more background data are sampled in highly sampled areas. The spatial aggregation of presence-only records near the shoreline of the Kerguelen Islands strongly biases model predictions. The KDE correction was proved efficient to correct for such a bias and provides more relevant predictions. From Guillaumot et al. (2018a).

Figure 6 .

 6 Figure 6. Extrapolation map of the SDM generated for the sea star Acodontaster hodgsoni, with all presence-only records available. Extrapolation corresponds here to the ensemble of environmental conditions that are outside of the boundaries of the calibration range. The extrapolation area is displayed in black and covers 78.6% of the entire projection area; coloured pixels (yellow-red color palette) show distribution probabilities (included between 0 and 1). Extracted from Guillaumot et al. (2020b).

Figure 7 .

 7 Figure 7. Extrapolation map of the SDM generated for the sea star Acodontaster hodgsoni indicating environmental descriptors responsible for extrapolation (black pixels of Fig. 6 are here coloured according to the descriptor responsible for extrapolation; i.e. for each pixel, the predictor in question lies outside the calibration range). In this case study, 14 environmental descriptors are responsible for extrapolation, depth being the main contributor. White pixels correspond to areas where the model does not extrapolate (the corresponding model predictions are shown in Fig.6). POC stands for 'Particulate Organic Carbon', Chla is the concentration in chlorophyll a in the seasurface. Generated from[START_REF] Guillaumot | Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities[END_REF].

Figure 8 .

 8 Figure 8. Different cross-validation procedures based on the study of the sea star Odontaster validus, showing presence-only records and a random set of 1,000 background data selected according to a Kernel Density Estimation weighting scheme from the dataset of Griffiths et al. (2014) on sampling effort of the SO benthos. Data are split into training (pink) and test (green) subsets. The blue background corresponds to bathymetry and grey areas to emerged lands. (A) Random cross-validation procedure, with a random split into 75% training and 25% test data. (B) '2-fold CLOCK' clustering by random spatial

Figure 1 .

 1 Figure 1. Flow chart of the SDM construction process. Steps 1 to 4 concern data collection, and treatment. Steps 5 to 7 integrate procedures for model implementation and validation. Dashed rectangles allow for a possible step backward when assessing model uncertainties or evaluating model performance.

Figure 2 .

 2 Figure 2. Cumulative number of Antarctic species described over time, according to data available in the Register of Antarctic Marine species (until March 2010). From De Broyer and Danis (2011).

Figure 3 .

 3 Figure 3. Distribution of benthos sampling sites (red dots) in the Southern Ocean (SO, <45°S). Sampling sites are not evenly distributed in the SO, showing important spatial aggregation in the Scotia Arc region and Western Antarctic Peninsula with several clusters along the Antarctic shelf, and over the Kerguelen and Campbell plateaus. In contrast, deep-sea regions and remote areas of the Antarctic shelf are undersampled. From Guillaumot et al. (2019), updated from Griffiths et al. (2014).

Figure 4 .

 4 Figure 4. Compared Area Under the Curve (AUC) performances of SDMs generated with different algorithms (ANN=Artificial Neural Network, BRT=Boosted Regression Trees, CTA=Classification Tree Analysis, FDA=Flexible Discriminant Analysis, GAM=Generalised Additive Model, GLM=Generalised

Figure 5 .

 5 Figure 5. Comparison of predicted distribution probabilities (between 0 and 1) of the sea urchin Ctenocidaris nutrix on the Kerguelen Plateau: (A) without compensating for sampling bias; (B) with a kernel density estimator (KDE) correction: more background data are sampled in highly sampled areas. The spatial aggregation of presence-only records near the shoreline of the Kerguelen Islands strongly biases model predictions. The KDE correction was proved efficient to correct for such a bias and provides more relevant predictions. From Guillaumot et al. (2018a).

Figure 6 .

 6 Figure 6. Extrapolation map of the SDM generated for the sea star Acodontaster hodgsoni, with all presence-only records available. Extrapolation corresponds here to the ensemble of environmental conditions that are outside of the boundaries of the calibration range. The extrapolation area is displayed in black and covers 78.6% of the entire projection area; coloured pixels (yellow-red color palette) show distribution probabilities (included between 0 and 1). Extracted from Guillaumot et al. (2020b).

Figure 7 .

 7 Figure 7. Extrapolation map of the SDM generated for the sea star Acodontaster hodgsoni indicating environmental descriptors responsible for extrapolation (black pixels of Fig. 6 are here coloured according to the descriptor responsible for extrapolation; i.e. for each pixel, the predictor in question lies outside the calibration range). In this case study, 14 environmental descriptors are responsible for extrapolation, depth being the main contributor. White pixels correspond to areas where the model does not extrapolate (the corresponding model predictions are shown in Fig.6). POC stands for 'Particulate Organic Carbon', Chla is the concentration in chlorophyll a in the seasurface. Generated from[START_REF] Guillaumot | Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities[END_REF].

Figure 8 .

 8 Figure 8. Different cross-validation procedures based on the study of the sea star Odontaster validus, showing presence-only records and a random set of 1,000 background data selected according to a Kernel Density Estimation weighting scheme from the dataset of Griffiths et al. (2014) on sampling effort of the SO benthos. Data are split into training (pink) and test (green) subsets. The blue background corresponds to bathymetry and grey areas to emerged lands. (A) Random cross-validation procedure, with a random split into 75% training and 25% test data. (B) '2-fold CLOCK' clustering by random spatial

  al. 2011[START_REF] Sánchez-Fernández | Species distribution models that do not incorporate global data misrepresent potential distributions: a case study using Iberian diving beetles[END_REF][START_REF] Titeux | The need for large-scale distribution data to estimate regional changes in species richness under future climate change[END_REF][START_REF] El-Gabbas | Wrong, but useful: regional species distribution models may not be improved by range-wide data under biased sampling[END_REF], (3) it reduces modelling goodness-of-it as the model may wrongly represent the reality(Stockwell and Peterson 2002, McPherson et al.

2004,

[START_REF] Pearson | Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar[END_REF][START_REF] Wisz | Effects of sample size on the performance of species distribution models[END_REF][START_REF] Liu | The effect of sample size on the accuracy of species distribution models: considering both presences and pseudo-absences or background sites[END_REF]

, (4) it increases instability between model replicates

(Guillaumot et al. 2018a

), (

5

) it gives rise to methological constraints on threshold selection

[START_REF] Jiménez-Valverde | Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling[END_REF] Lobo 2007, Bean et al. 2012)

or (

  [START_REF] Pierrat | Large-scale distribution analysis of Antarctic echinoids using ecological niche modelling[END_REF][START_REF] Mormède | Chapter 2.3. Distribution modelling[END_REF], Guillaumot et al. 2018a, Fabri-

[START_REF] Fabri-Ruiz | Can we generate robust species distribution models at the scale of the Southern Ocean?[END_REF]

. Overall, a small number of descriptors will allow generating less complex models and facilitate interpretation

[START_REF] Austin | Improving species distribution models for climate change studies: variable selection and scale[END_REF][START_REF] Braunisch | Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change[END_REF], Bucklin et al. 2015[START_REF] Petitpierre | Selecting predictors to maximize the transferability of species distribution models: lessons from crosscontinental plant invasions[END_REF]

. In contrast, increasing the number of descriptors potentially increases the effect of collinearity between them (i.e. correlation between values of descriptors), which may lead to statistical artefacts in model predictions if algorithms cannot handle information redundancy

[START_REF] Dormann | Collinearity: a review of methods to deal with it and a simulation study evaluating their performance[END_REF][START_REF] Merow | What do we gain from simplicity versus complexity in species distribution models?[END_REF]

. Therefore, collinearity is usually tested beforehand and collinear descriptors are adjusted (practically, one descriptor of a pair is removed) before running the model

[START_REF] Dormann | Collinearity: a review of methods to deal with it and a simulation study evaluating their performance[END_REF][START_REF] Merow | A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter[END_REF][START_REF] Fois | Using species distribution models at local scale to guide the search of poorly known species: Review, methodological issues and future directions[END_REF]

). However,

Guillaumot et al. (2020a)

showed that model complexity, transferability, and accuracy do not significantly change between models generated with different sets including from 4 to 58 collinear descriptors when using the BRT algorithm. BRT automatically keep the most relevant descriptors to describe species distribution and can deal with redundant information

[START_REF] De'ath | Classification and regression trees: a powerful yet simple technique for ecological data analysis[END_REF][START_REF] Whittingham | Why do we still use stepwise modelling in ecology and behaviour?[END_REF], Elith et al. 2008)

, which is not the case for all algorithms

[START_REF] Merow | What do we gain from simplicity versus complexity in species distribution models?[END_REF]

).

  Guillaumot et al. in press) the combination of physiological experimental results and SDM projections assessed the invading potential of the patagonian crab Halicarcinus planatus (Fabricius, 1775) on Antarctic coasts, as similarly done in[START_REF] Byrne | From pole to pole: the potential for the Arctic seastar Asterias amurensis to invade a warming Southern Ocean[END_REF] for the Arctic sea star Asterias amurensis Lutken, 1871. Hybrid modelling approaches constitute another exciting

Acknowledgements

This work was supported by a "Fonds pour la formation à la Recherche dans l'Industrie et l'Agriculture" (FRIA) and "Bourse fondation de la mer" grants to C. Guillaumot. This is contribution no. 47 to the vERSO project (www.versoproject.be), funded by the Belgian Science Policy Office (BELSPO, contract n°BR/132/A1/vERSO). Research was also financed by the "Refugia and Ecosystem Tolerance in the Southern Ocean" project (RECTO; BR/154/A1/RECTO) funded by the Belgian Science Policy Office (BELSPO), this study being contribution number 24.