Journal article
Anna Thonis, Alyssa M. Stansfield, H. Resit Akcakaya
Global Change Biology, vol. 30, 2024, pp. e17232
Global Change Biology, vol. 30, 2024, pp. e17232
DOI:
10.1111/gcb.17232
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Cite
APA
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Thonis, A., Stansfield, A. M., & Akcakaya, H. R. (2024). Unravelling the role of tropical cyclones in shaping present species distributions. Global Change Biology, 30, e17232. https://doi.org/10.1111/gcb.17232
Chicago/Turabian
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Thonis, Anna, Alyssa M. Stansfield, and H. Resit Akcakaya. “Unravelling the Role of Tropical Cyclones in Shaping Present Species Distributions.” Global Change Biology 30 (2024): e17232.
MLA
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Thonis, Anna, et al. “Unravelling the Role of Tropical Cyclones in Shaping Present Species Distributions.” Global Change Biology, vol. 30, 2024, p. e17232, doi:10.1111/gcb.17232.
BibTeX Click to copy
@article{anna2024a,
title = {Unravelling the role of tropical cyclones in shaping present species distributions},
year = {2024},
journal = {Global Change Biology},
pages = {e17232},
volume = {30},
doi = {10.1111/gcb.17232},
author = {Thonis, Anna and Stansfield, Alyssa M. and Akcakaya, H. Resit}
}
Abstract
Driven by climate change, tropical cyclones (TCs) are predicted to change in intensity and frequency through time. Given these forecasted changes, developing an understanding of how TCs impact insular wildlife is of heightened importance. Previous work has shown that extreme weather events may shape species distributions more strongly than climatic averages; however, given the coarse spatial and temporal scales at which TC data are often reported, the influence of TCs on species distributions has yet to be explored. Using TC data from the National Hurricane Center, we developed spatially and temporally explicit species distribution models (SDMs) to examine the role of TCs in shaping present-day distributions of Puerto Rico's 10 Anolis lizard species. We created six predictor variables to represent the intensity and frequency of TCs. For each occurrence of a species, we calculated these variables for TCs that came within 500 km of the center of Puerto Rico and occurred within the 1-year window prior to when that occurrence was recorded. We also included predictor variables related to landcover, climate, topography, canopy cover and geology. We used random forests to assess model performance and variable importance in models with and without TC variables. We found that the inclusion of TC variables improved model performance for the majority of Puerto Rico's 10 anole species. The magnitude of the improvement varied by species, with generalist species that occur throughout the island experiencing the greatest improvements in model performance. Rangerestricted species experienced small, almost negligible, improvements but also had more predictive models both with and without the inclusion of TC variables compared to generalist species. Our findings suggest that incorporating data on TCs into SDMs may be important for modeling insular species that are prone to experiencing these types of extreme weather events.
Driven by climate change, tropical cyclones (TCs) are predicted to change in intensity and frequency through time. Given these forecasted changes, developing an understanding of how TCs impact insular wildlife is of heightened importance. Previous work has shown that extreme weather events may shape species distributions more strongly than climatic averages; however, given the coarse spatial and temporal scales at which TC data are often reported, the influence of TCs on species distributions has yet to be explored. Using TC data from the National Hurricane Center, we developed spatially and temporally explicit species distribution models (SDMs) to examine the role of TCs in shaping present-day distributions of Puerto Rico's 10 Anolis lizard species. We created six predictor variables to represent the intensity and frequency of TCs. For each occurrence of a species, we calculated these variables for TCs that came within 500 km of the center of Puerto Rico and occurred within the 1-year window prior to when that occurrence was recorded. We also included predictor variables related to landcover, climate, topography, canopy cover and geology. We used random forests to assess model performance and variable importance in models with and without TC variables. We found that the inclusion of TC variables improved model performance for the majority of Puerto Rico's 10 anole species. The magnitude of the improvement varied by species, with generalist species that occur throughout the island experiencing the greatest improvements in model performance. Rangerestricted species experienced small, almost negligible, improvements but also had more predictive models both with and without the inclusion of TC variables compared to generalist species. Our findings suggest that incorporating data on TCs into SDMs may be important for modeling insular species that are prone to experiencing these types of extreme weather events.
