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Detecting outliers in species distribution data: Some caveats and clarifications on a virtual species study
Author(s) -
Meynard Christine N.,
Kaplan David M.,
Leroy Boris
Publication year - 2019
Publication title -
journal of biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.7
H-Index - 158
eISSN - 1365-2699
pISSN - 0305-0270
DOI - 10.1111/jbi.13626
Subject(s) - probabilistic logic , outlier , limiting , computer science , threshold model , statistical model , econometrics , ecology , data mining , statistics , statistical physics , mathematics , artificial intelligence , machine learning , biology , physics , engineering , mechanical engineering
Liu et al. (2018) used a virtual species approach to test the effects of outliers on species distribution models. In their simulations, they applied a threshold value over the simulated suitabilities to generate the species distributions, suggesting that using a probabilistic simulation approach would have been more complex and yield the same results. Here, we argue that using a probabilistic approach is not necessarily more complex and may significantly change results. Although the threshold approach may be justified under limited circumstances, the probabilistic approach has multiple advantages. First, it is in line with ecological theory, which largely assumes non‐threshold responses. Second, it is more general, as it includes the threshold as a limiting case. Third, it allows a better separation of the relevant intervening factors that influence model performance. Therefore, we argue that the probabilistic simulation approach should be used as a general standard in virtual species studies.