Beyond static species distribution models: temporally dynamic, spatially nested models reduce extrapolation for future projections
Abstract
Species distribution models are widely used to project biodiversity responses to climate change, but geographically restricted training data can truncate species’ climatic niches, increasing reliance on extrapolation. Spatially nested models can reduce this truncation by incorporating climatic information from species’ wider distributions. However, how accounting for interannual environmental variation rather than temporal averages affects extrapolation remains unclear. Using occurrence records for 28 butterfly species in Denmark, we compared four approaches combining regional versus spatially nested training data with static (long-term average) versus dynamic (annual) predictors. Regional models used climatic information for Denmark, whereas nested models incorporated European-scale climatic suitability. Static models used temporally averaged predictors, while dynamic models matched observations to year-specific conditions. We assessed climatic niche coverage, model’s predictive performance under spatial block cross-validation and a common forward temporal holdout, and future climatic conditions outside the training domain. Regional training excluded ~ 30% of the climatic space occupied across species’ European distributions. Temporally averaged predictors captured nearly all climatic space represented by annual conditions across Europe, but only ~ 37% within Denmark. In the forward evaluation for 2010–2018, dynamic models outperformed static models. Under spatial cross-validation, static regional models outperformed dynamic regional models, whereas static and dynamic nested models did not differ significantly. Spatial nesting and temporal matching substantially reduced future extrapolation, with their combination consistently requiring the least extrapolation. Model choice should therefore reflect the intended application. Static models may be adequate for mapping contemporary suitability within well-represented environmental conditions when supported by spatial validation. For projections under environmental change, broadening training domain across space and time reduces extrapolation, even without improving spatial discrimination. Predictive discrimination and environmental support for transfer should be assessed jointly, with temporal validation and extrapolation risk receiving particular attention for climate-change projections.
Related
- Social cues drive the evolution of plastic pathogen virulence in spatially and temporally variable environments
- Warming responses of microbial respiration emerge from spatially structured species interactions
- Species Distribution, Antifungal Susceptibility, and Machine Learning-Based Prediction of Optimal Therapies Against Candida Species
- Distribution dataset of chondrichthyan species occurring in French metropolitan waters and across the North-East Atlantic
- Species distribution modelling of three Aacia species in the Negev Desert: predicting future range shifts and identifying climatic drivers
BibTeX
@misc{rocabado2026beyond,
title = {{Beyond static species distribution models: temporally dynamic, spatially nested models reduce extrapolation for future projections}},
author = {Camila Rocabado and Derek Corcoran and Trine Bilde and Samuel Gascoigne and Signe Normand and Marjo Saastamoinen and Santiago Jose Elias Velazco and Damaris Zurell and Roslyn Henry and Greta Bocedi},
howpublished = {EcoEvoRxiv},
year = {2026},
month = oct,
doi = {10.32942/x2v974},
url = {https://ecoevorxiv.org/repository/view/15694/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Beyond static species distribution models: temporally dynamic, spatially nested models reduce extrapolation for future projections
AU - Camila Rocabado
AU - Derek Corcoran
AU - Trine Bilde
AU - Samuel Gascoigne
AU - Signe Normand
AU - Marjo Saastamoinen
AU - Santiago Jose Elias Velazco
AU - Damaris Zurell
AU - Roslyn Henry
AU - Greta Bocedi
PY - 2026
DA - 2026/10/23
PB - EcoEvoRxiv
DO - 10.32942/x2v974
UR - https://ecoevorxiv.org/repository/view/15694/
AB - Species distribution models are widely used to project biodiversity responses to climate change, but geographically restricted training data can truncate species’ climatic niches, increasing reliance on extrapolation. Spatially nested models can reduce this truncation by incorporating climatic information from species’ wider distributions. However, how accounting for interannual environmental variation rather than temporal averages affects extrapolation remains unclear. Using occurrence records for 28 butterfly species in Denmark, we compared four approaches combining regional versus spatially nested training data with static (long-term average) versus dynamic (annual) predictors. Regional models used climatic information for Denmark, whereas nested models incorporated European-scale climatic suitability. Static models used temporally averaged predictors, while dynamic models matched observations to year-specific conditions. We assessed climatic niche coverage, model’s predictive performance under spatial block cross-validation and a common forward temporal holdout, and future climatic conditions outside the training domain. Regional training excluded ~ 30% of the climatic space occupied across species’ European distributions. Temporally averaged predictors captured nearly all climatic space represented by annual conditions across Europe, but only ~ 37% within Denmark. In the forward evaluation for 2010–2018, dynamic models outperformed static models. Under spatial cross-validation, static regional models outperformed dynamic regional models, whereas static and dynamic nested models did not differ significantly. Spatial nesting and temporal matching substantially reduced future extrapolation, with their combination consistently requiring the least extrapolation. Model choice should therefore reflect the intended application. Static models may be adequate for mapping contemporary suitability within well-represented environmental conditions when supported by spatial validation. For projections under environmental change, broadening training domain across space and time reduces extrapolation, even without improving spatial discrimination. Predictive discrimination and environmental support for transfer should be assessed jointly, with temporal validation and extrapolation risk receiving particular attention for climate-change projections.
ER -