Species distribution modelling of the Southern Ocean benthos: a review on methods, cautions and solutions.

• Published in: Antarctic Science
• 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 'Fonds pour la formation a 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 2021
• Subjects: Antarctica; biases; limits; marine benthic invertebrates; modelling performance; [SDE.BE]Environmental Sciences/Biodiversity and Ecology; Southern Ocean; Antarc*; Antarctic Science
• Online Access: https://hal.archives-ouvertes.fr/hal-03347312; https://doi.org/10.1017/S0954102021000183

24 pages International audience Species distribution modelling studies the relationship between species occurrence records and their environmental setting, providing a valuable approach to predicting species distribution in the Southern Ocean (SO), a challenging region to investigate due to its remoteness and extreme weather and sea conditions. The specificity of SO studies, including restricted field access and sampling, the paucity of observations and difficulties in conducting biological experiments, limit the performance of species distribution models. 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 the 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 we provide some guidelines along with potential methods and original solutions that can be used for improving model performance.