Predictive ability of a process-based versus a correlative species distribution model

Title data

Higgins, Steven I. ; Larcombe, Matthew J. ; Beeton, Nicholas J. ; Conradi, Timo ; Nottebrock, Henning:
Predictive ability of a process-based versus a correlative species distribution model.
In: Ecology and Evolution. Vol. 10 (2020) Issue 20 . - pp. 11043-11054.
ISSN 2045-7758
DOI: https://doi.org/10.1002/ece3.6712

Project information

Abstract in another language

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.