ArticleEcology and evolution2020
Predictive ability of a process-based versus a correlative species distribution model.
Article in Ecology and evolution, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Rethinking Model Transferability: Validity Domains as a New Approach to Delineate the Limits of Bloom Date Projections.Global change biology · 2026Article
- Systematic conservation planning for nature recovery.Bioscience · 2025Article
- Do savanna trees mast? Phenological dynamics of flowering and fruiting in savanna tree species.Oecologia · 2025Article
- Time-lagged genomic erosion and future environmental risks in a bird on the brink of extinction.Proceedings. Biological sciences · 2025Article
- Paleorecords Reveal Biological Mechanisms Crucial for Reliable Species Range Shift Projections Amid Rapid Climate Change.Ecology letters · 2025Article
- Global climate change-driven impacts on the Asian distribution ofEcology and evolution · 2024Article
- Forecasting the Expansion ofInsects · 2024Article
- Reassessment of the risks of climate change for terrestrial ecosystems.Nature ecology & evolution · 2024Article
- Modeling the potential global distribution of the Egyptian cotton leafworm, Spodoptera littoralis under climate change.Scientific reports · 2023Article
- Ecological Niche Modelling Approaches: Challenges and Applications in Vector-Borne Diseases.Tropical medicine and infectious disease · 2023Review
- Transferability of correlative and process-based species distribution models revisited: A response to Booth.Ecology and evolution · 2021Article
- A problem with variable selection in a comparison of correlative and process-based species distribution models: Comments on Higgins et al., 2020.Ecology and evolution · 2021Article
- Predictive ability of a process-based versus a correlative species distribution model.Ecology and evolution · 2020Article
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Authors and funding
5 authors.
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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.
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