Predictive ability of a process‐based versus a correlative species distribution model. Issue 20 (8th October 2020)

• Record Type: Journal Article
• Authors: Higgins, Steven I.; Larcombe, Matthew J.; Beeton, Nicholas J.; Conradi, Timo; Nottebrock, Henning
• Is Part Of: Ecology and evolution. Volume 10:Issue 20(2020)
• Page Start: 11043
• Page End: 11054
• Publication Date: 2020-10-08
• Subjects: ecological niche model -- extrapolation -- invasive species -- MaxEnt -- mechanistic models -- model transferability -- TTR‐SDM; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.6712
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: Species distribution modeling is a widely used tool in many branches of ecology and evolution. Evaluations of the transferability of species distribution models—their ability to predict the distribution of species in independent data domains—are, however, rare. In this study, we contrast the transferability of a process‐based and a correlative species distribution model. Our case study uses 664 Australian eucalypt and acacia species. We estimate models for these species using data from their native Australia and then assess whether these models can predict the adventive range of these species. We find that the correlative model—MaxEnt—has a superior ability to describe the data in the training data domain (Australia) and that the process‐based model—TTR‐SDM—has a superior ability to predict the distribution of the study species outside of Australia. The implication of this analysis, that process‐based models may be more appropriate than correlative models when making projections outside of the domain of the training data, needs to be tested in other case studies. Abstract : Species distribution modeling is a widely used tool in many branches of ecology and evolution. However, evaluations of the transferability of species distribution models—their ability to predict the distribution of species in independent data domains—are rare. In this study, we show that process‐based models may have better transferability than correlative models.