A practical overview of transferability in species distribution modeling

• Published in: Environmental reviews Vol. 25; no. 1; pp. 127 - 133
• Main Authors: Werkowska, Wioletta, Márquez, Ana L., Real, Raimundo, Acevedo, Pelayo
• Format: Journal Article
• Language: English
• Published: NRC Research Press 01.03.2017
• Subjects: Biodiversity; biogeography; Biological diversity; downscaling; epidemiology; modèle de répartition des espèces; modèle spatialement explicite; multicolinéarité; multicollinearity; réduction d’échelle; spatial transferability; spatially explicit model; species distribution model; temporal transferability; transférabilité spatiale; transférabilité temporelle
• ISSN: 1181-8700; 1208-6053; 1208-6053
• DOI: 10.1139/er-2016-0045

Species distribution models (SDMs) are basic tools in ecology, biogeography, and biodiversity. The usefulness of SDMs has expanded beyond the realm of ecological sciences, and their application in other research areas is currently frequent, e.g., spatial epidemiology. In any research area, the principal interest in these models resides in their capacity to predict species response in new scenarios, i.e., the models’ transferability. Although the transferability of SDMs has been the subject of interest for many years, only in the 2000s did this topic gain particular attention. This article reviews the concept of the transferability of SDMs to new spatial scenarios, temporal periods, and (or) spatial resolutions, along with the potential constraints of the model’s transferability, and more specifically: (i) the type of predictors and multicollinearity, (ii) the model complexity, and (iii) the species’ intrinsic traits. Finally, we describe a practicable analytical protocol to be assessed before transferring a model to a new scenario. This protocol is based on three fundamental pillars: the environmental equilibrium of the species with the environment, the environmental similarity between the new scenario, and the areas used to model parametrisation and the correlation structure among predictors.