Moudry V. auteur aut IRD Bazzichetto M. auteur aut IRD Remelgado R. auteur aut IRD Devillers Rodolphe auteur aut IRD Lenoir J. auteur aut IRD Mateo R. G. auteur aut IRD Lembrechts J. J. auteur aut IRD Sillero N. auteur aut IRD Lecours V. auteur aut IRD Cord A. F. auteur aut IRD Barták V. auteur aut IRD Balej P. auteur aut IRD Rocchini D. auteur aut IRD Torresani M. auteur aut IRD Arenas-Castro S. auteur aut IRD Man M. T. auteur aut IRD Prajzlerová D. auteur aut IRD Gdulová K. auteur aut IRD Prosek J. auteur aut IRD Marchetto E. auteur aut IRD Zarzo-Arias A. auteur aut IRD Gábor L. auteur aut IRD Leroy F. auteur aut IRD Martini M. auteur aut IRD Malavasi M. auteur aut IRD Gatti R. C. auteur aut IRD Wild J. auteur aut IRD Símová P. auteur aut IRD text journalArticle eng text/pdf reformatted digital access Species distribution models (SDMs) have proven valuable in filling gaps in our knowledge of species occurrences. However, despite their broad applicability, SDMs exhibit critical shortcomings due to limitations in species occurrence data. These limitations include, in particular, issues related to sample size, positional uncertainty, and sampling bias. In addition, it is widely recognised that the quality of SDMs as well as the approaches used to mitigate the impact of the aforementioned data limitations depend on species ecology. While numerous studies have evaluated the effects of these data limitations on SDM performance, a synthesis of their results is lacking. However, without a comprehensive understanding of their individual and combined effects, our ability to predict the influence of these issues on the quality of modelled species-environment associations remains largely uncertain, limiting the value of model outputs. In this paper, we review studies that have evaluated the effects of sample size, positional uncertainty, sampling bias, and species ecology on SDMs outputs. We build upon their findings to provide recommendations for the critical assessment of species data intended for use in SDMs. specialized data quality ecological niche modelling filtering sampling spatial scale validation 020 021 082 2024 12 e07294 [20 ] 2024 0906-7590 https://www.documentation.ird.fr/hor/fdi:010091209 10.1111/ecog.07294 0906-7590 [F B010091209] https://www.documentation.ird.fr/hor/fdi:010091209 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2024-09/010091209.pdf IRD - Base Horizon / Pleins textes 2024-09-27 2025-11-06 fdi:010091209 fre