Selecting environmental descriptors is critical for modelling the distribution of Antarctic benthic species.

• Published in: Polar Biology
• Main Authors: Guillaumot, Charlène, Danis, Bruno, Saucède, Thomas
• Other Authors: Laboratoire de Biologie Marine (LBM), Université libre de Bruxelles (ULB), Biogéosciences UMR 6282 Dijon (BGS), Centre National de la Recherche Scientifique (CNRS)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Work supported by a “Fonds pour la formation à la Recherche dans l’Industrie et l’Agriculture” (FRIA) and “Bourse Fondation de la mer” grants.
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2020
• Subjects: Environmental descriptors; Conservation; Asteroidea; Collinearity; Southern ocean; Boosted regression trees (BRT); Species distribution models (SDMs); [SDE.BE]Environmental Sciences/Biodiversity and Ecology; [SDV.BID]Life Sciences [q-bio]/Biodiversity; Antarctic; Southern Ocean; Antarc*; Polar Biology
• Online Access: https://hal.archives-ouvertes.fr/hal-02985328; https://doi.org/10.1007/s00300-020-02714-2

19 pages International audience Species distribution models (SDMs) are increasingly used in ecological and biogeographic studies by Antarctic biologists, including for conservation and management purposes. During the modelling process, model calibration is a critical step to ensure model reliability and robustness, especially in the case of SDMs, for which the number of selected environmental descriptors and their collinearity is a recurring issue. Boosted regression trees (BRT) was previously considered as one of the best modelling approach to correct for this type of bias. In the present study, we test the performance of BRT in modelling the distribution of Southern Ocean species using different numbers of environmental descriptors, either collinear or not. Models are generated for six sea star species with contrasting ecological niches and wide distribution ranges over the entire Southern Ocean. For the six studied species, overall modelling performance is not affected by the number of environmental descriptors used to generate models, BRT using the most informative descriptors and minimizing model overfitting. However, removing collinear descriptors also helps reduce model overfitting. Our results confirm that BRTs may perform well and are relevant to deal with complex and redundant environmental information for Antarctic biodiversity distribution studies. Selecting a limited number of non-collinear descriptors before modelling may generate simpler models and facilitate their interpretation. The modelled distributions do not differ noticeably between the different species despite contrasting species ecological niches. This unexpected result stresses important limitations in using SDMs for broad scale spatial studies, based on limited, spatially aggregated data, and low-resolution descriptors.