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MEPS 594:149-164 (2018)  -  DOI:

Methods for improving species distribution models in data-poor areas: example of sub-Antarctic benthic species on the Kerguelen Plateau

Charlène Guillaumot1,*, Alexis Martin2, Marc Eléaume3, Thomas Saucède4

1Laboratoire de Biologie Marine, Université Libre de Bruxelles, Avenue FD Roosevelt 50, CP 160/15, 1150 Bruxelles, Belgique
2Département adaptation du vivant, Muséum national d’Histoire naturelle, UMR BOREA 7208, 57 rue Cuvier, 75231 Paris Cedex 05, France
3Département Origine et Évolution, Muséum national d’Histoire naturelle, UMR ISYEB 7205, 57 rue Cuvier, 75231 Paris Cedex 05, France
4Biogéosciences, UMR 6282, Université Bourgogne Franche-Comté, CNRS, 6 bd Gabriel 21000 Dijon, France
*Corresponding author:

ABSTRACT: Species distribution models (SDMs) are essential tools to aid conservation biologists in evaluating the combined effects of environmental change and human activities on natural habitats and for the development of relevant conservation plans. However, modeling species distributions over vast and remote regions is often challenging due to poor and heterogeneous data sets, and this raises questions regarding the relevance of the modeling procedures. In recent years, there have been many methodological developments in SDM procedures using virtual species and broad data sets, but few solutions have been proposed to deal with poor or heterogeneous data. In the present work, we address this methodological challenge by studying the performance of different modeling procedures based on 4 real species, using presence-only data compiled from various oceanographic surveys on the Kerguelen Plateau (Southern Ocean). We followed a practical protocol to test for the reliability and performance of the models and to correct for limited and aggregated data, as well as accounting for spatial and temporal sampling biases. Our results show that producing reliable SDMs is feasible as long as the amount and quality of available data allow testing and correcting for these biases. However, we found that SDMs could be corrected for spatial and temporal heterogeneities in only 1 of the 4 species we examined, highlighting the need to consider all potential biases when modeling species distributions. Finally, we show that model reliability and performance also depend on the interaction between the incompleteness of the data and species niches, with the distribution of narrow-niche species being less sensitive to data gaps than species occupying wider niches.

KEY WORDS: Species distribution modeling · Model performance · Historical datasets · Kerguelen Plateau · Presence-only data

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Cite this article as: Guillaumot C, Martin A, Eléaume M, Saucède T (2018) Methods for improving species distribution models in data-poor areas: example of sub-Antarctic benthic species on the Kerguelen Plateau. Mar Ecol Prog Ser 594:149-164.

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