Factors influencing transferability in species distribution models. Issue 7 (22nd April 2022)

• Record Type: Journal Article
• Authors: Rousseau, Josée S.; Betts, Matthew G.
• Is Part Of: Ecography. Volume 2022:Issue 7
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-22
• Subjects: extrapolation -- model transfer -- species distribution models -- stationarity -- traits; Ecology -- Periodicals; Biodiversity -- Periodicals; 574.5
• DOI: 10.1111/ecog.06060
• Languages: English
• ISSNs: 0906-7590

Abstract

Abstract : Species distribution models (SDMs) provide insights into species' ecology and distributions and are frequently used to guide conservation priorities. However, many uses of SDMs require model transferability, which refers to the degree to which a model built in one place or time can successfully predict distributions in a different place or time. If a species' model has high spatial transferability, the relationship between abundance and predictor variables should be consistent across a geographical distribution. We used Breeding Bird Surveys, climate and remote sensing data, and a novel method for quantifying model transferability to test whether SDMs can be transferred across the geographic ranges of 129 species of North American birds. We also assessed whether species' traits are correlated with model transferability. We expected that prediction accuracy between modeled regions should decrease with 1) geographical distance, 2) degree of extrapolation and 3) the distance from the core of a species' range. Our results suggest that very few species have a high model transferability index (MTI). Species with large distributions, with distributions located in areas with low topographic relief, and with short lifespans are more likely to exhibit low transferability. Transferability between modeled regions also decreased with geographical distance and degree of extrapolation. We expect that low transferability in SDMs potentially resulted from both ecological non‐stationarity (i.e. biological differences within a species across its range) and over‐extrapolation. Accounting for non‐stationarity and extrapolation should substantially increase the prediction success of species distribution models, therefore enhancing the success of conservation efforts.