Evaluating 318 continental-scale species distribution models over a 60-year prediction horizon: what factors influence the reliability of predictions?

• Published in: Global ecology and biogeography Vol. 26; no. 3/4; pp. 371 - 384
• Main Authors: Morán-Ordóñez, Alejandra, Lahoz-Monfort, José J., Elith, Jane, Wintle, Brendan A.
• Format: Journal Article
• Language: English
• Published: Oxford John Wiley & Sons Ltd 01.03.2017; Wiley Subscription Services, Inc
• Subjects: AUC; Australia; biodiversity; biogeography; body size; climate; data collection; environmental factors; geographical range; habitat preferences; MACROECOLOGICAL METHODS; MaxEnt; Parameter estimation; prediction; predictions into the future; presence‐only data; rarity; sample size; species distribution models; species traits; Taxonomy; temporal transferability; Australia
• ISSN: 1466-822X; 1466-8238
• DOI: 10.1111/geb.12545

Aim: Species distribution models (SDMs) are currently the most widely used tools in ecology for evaluating the suitability of environments for biodiversity in the face of future environmental change. In this study we seek to provide an assessment of the predictive performance of SDMs over time. How well do SDMs predict for future time periods and what factors influence predictive performance? Innovation: We used a historical spatially explicit database of 1.8 million occurrence records for 318 tetrapod species from across continental Australia over the period 1950-2013. We fitted distribution models for each species to data from four multi-decadal time slices and used these to predict the species distributions up to 60 years after the data collection period for the fitted models. We evaluated predictions against observed data from the relevant time period. Predictions were made assuming either complete knowledge of changes in climatic and environmental conditions or assuming the environment and climate remained unchanged between the fitting and evaluation time periods. We used generalized linear mixed models to model variation in the predictive performance of SDMs over time in relation to a variety of factors, including the length of time between fitting and evaluation, species traits, taxonomic group and attributes of the dataset used to fit models. Main conclusions: We found that most models provided useful predictions even when the period between model fitting and evaluation was 60 years (area under the receiver operator characteristic curve >0.7 in 80% of the species evaluated). Variation in predictive performance over time was strongly related to the species range breadth (models for species with broad geographical ranges teftded to perform worse than models for locally restricted species) and to the environmental coverage of occupancy data. Conversely, taxonomic group, habitat preferences and body size were not highly influential in describing the variation in predictive performance over time.