A simulation-based method for selecting calibration areas for ecological niche models and species distribution models. Machado-Stredel, F., Cobos, M. E., & Peterson, A. T. Frontiers of Biogeography, 2021. ![A simulation-based method for selecting calibration areas for ecological niche models and species distribution models [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Ecological niche models and species distribution models (ENM and SDM, respectively) are tools that have seen massive use and considerable improvement during the last twenty years. The choice of calibration areas for such models has strong effects on model outcomes and model interpretation, as well as on model transfer to distinct environmental settings. However, approaches to selecting these areas remain simple and/or unlinked to biological concepts. Such models should be calibrated within areas that the species of interest has explored throughout its recent history, the accessible area (M). In this paper, we provide a simulation approach for estimating a species’ M considering processes of dispersal, colonization, and extinction in constant current climate or glacial-interglacial climate change frameworks, implemented within a new R package we developed called grinnell. Using the avian genus Aphelocoma, we explored different parameterizations of our simulation, and compared them to current approaches for M selection, in terms of model performance and risk of extrapolation using the algorithm Maxent and mobility-oriented parity analyses. Model calibration exercises from all approaches resulted in at least one model meeting optimal performance criteria for each species; however, we noted high variability among taxa and M selection methods. More importantly, M hypotheses derived directly from simulations of key biological processes, rather than being based on simple proxies of those processes, and as such are better suited to erecting biologically appropriate contrasts in model calibration, and to characterizing the potential for model extrapolation more rigorously. Major factors in our simulations were environmental layer resolution, dispersal kernel characteristics, and the inclusion of a changing framework of climatic conditions. This contribution represents the first simulation-based method for selecting calibration areas for ENM and SDM, offering a quantitative approach to estimate the accessible area of a species while considering its dispersal ability, along with patterns of change in environmental suitability across space and time.
@article{machado-stredel_simulation-based_2021,
title = {A simulation-based method for selecting calibration areas for ecological niche models and species distribution models},
volume = {13},
url = {https://escholarship.org/uc/item/8hq04438},
doi = {10.21425/F5FBG48814},
abstract = {Ecological niche models and species distribution models (ENM and SDM, respectively) are tools that have seen massive use and considerable improvement during the last twenty years. The choice of calibration areas for such models has strong effects on model outcomes and model interpretation, as well as on model transfer to distinct environmental settings. However, approaches to selecting these areas remain simple and/or unlinked to biological concepts. Such models should be calibrated within areas that the species of interest has explored throughout its recent history, the accessible area (M). In this paper, we provide a simulation approach for estimating a species’ M considering processes of dispersal, colonization, and extinction in constant current climate or glacial-interglacial climate change frameworks, implemented within a new R package we developed called grinnell. Using the avian genus Aphelocoma, we explored different parameterizations of our simulation, and compared them to current approaches for M selection, in terms of model performance and risk of extrapolation using the algorithm Maxent and mobility-oriented parity analyses. Model calibration exercises from all approaches resulted in at least one model meeting optimal performance criteria for each species; however, we noted high variability among taxa and M selection methods. More importantly, M hypotheses derived directly from simulations of key biological processes, rather than being based on simple proxies of those processes, and as such are better suited to erecting biologically appropriate contrasts in model calibration, and to characterizing the potential for model extrapolation more rigorously. Major factors in our simulations were environmental layer resolution, dispersal kernel characteristics, and the inclusion of a changing framework of climatic conditions. This contribution represents the first simulation-based method for selecting calibration areas for ENM and SDM, offering a quantitative approach to estimate the accessible area of a species while considering its dispersal ability, along with patterns of change in environmental suitability across space and time.},
language = {en},
number = {4},
urldate = {2023-07-04},
journal = {Frontiers of Biogeography},
author = {Machado-Stredel, Fernando and Cobos, Marlon E. and Peterson, A. Townsend},
year = {2021},
keywords = {Terrestrial Ecoregions (CEC 1997)},
}
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Such models should be calibrated within areas that the species of interest has explored throughout its recent history, the accessible area (M). In this paper, we provide a simulation approach for estimating a species’ M considering processes of dispersal, colonization, and extinction in constant current climate or glacial-interglacial climate change frameworks, implemented within a new R package we developed called grinnell. Using the avian genus Aphelocoma, we explored different parameterizations of our simulation, and compared them to current approaches for M selection, in terms of model performance and risk of extrapolation using the algorithm Maxent and mobility-oriented parity analyses. Model calibration exercises from all approaches resulted in at least one model meeting optimal performance criteria for each species; however, we noted high variability among taxa and M selection methods. More importantly, M hypotheses derived directly from simulations of key biological processes, rather than being based on simple proxies of those processes, and as such are better suited to erecting biologically appropriate contrasts in model calibration, and to characterizing the potential for model extrapolation more rigorously. Major factors in our simulations were environmental layer resolution, dispersal kernel characteristics, and the inclusion of a changing framework of climatic conditions. 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