Climate change is predicted to result in elevational and latitudinal shifts in species distributions. Among different taxa, high-elevation specialist species are likely to suffer the greatest impact from climate change due to the limited ability to track their niches. Although much work has been undertaken on predicting the effects of climate change on the range contraction/expansion of mountain species, one important but fairly neglected issue is to consider the impacts of nonclimate data in the projections of Species Distribution Models (SDMs). We evaluated the degree to which incorporating non-climatic data into climate-based SDMs would change the predicted vulnerability of the Caspian Snowcock (Tetraogallus caspius), a poorly known high-elevation specialist species, to climate change. We first optimized the MaxEnt model for the current species distribution using: (1) only climatic variables; and, (2) both climatic and non-climatic data. We then projected the optimized model for two future time periods under different climate scenarios. Finally, we calculated differences in the mean elevation and lower and upper range limits for the species. We predicted that with changing climatic conditions, Caspian Snowcock will undergo significant elevational and some latitudinal shifts in its distribution and will face a drastic decrease in suitable habitat in the next 50 years. Including non-climatic data in the models increased model performance and resulted in reduced predictions of habitat loss under future climate scenarios. Terrain roughness was the most important predictor in this model, suggesting that more complex topography will retain favourable microclimates for the species in the future. The results thus highlight the importance of including topographic variables in climate-based SDMs. Our findings can guide biodiversity managers in prioritizing protected areas and adopting proactive measures to mitigate the negative impacts of climate change.
Impacts of climate change on a high elevation specialist bird are ameliorated by terrain complexity
Chamberlain, Dan
2024-01-01
Abstract
Climate change is predicted to result in elevational and latitudinal shifts in species distributions. Among different taxa, high-elevation specialist species are likely to suffer the greatest impact from climate change due to the limited ability to track their niches. Although much work has been undertaken on predicting the effects of climate change on the range contraction/expansion of mountain species, one important but fairly neglected issue is to consider the impacts of nonclimate data in the projections of Species Distribution Models (SDMs). We evaluated the degree to which incorporating non-climatic data into climate-based SDMs would change the predicted vulnerability of the Caspian Snowcock (Tetraogallus caspius), a poorly known high-elevation specialist species, to climate change. We first optimized the MaxEnt model for the current species distribution using: (1) only climatic variables; and, (2) both climatic and non-climatic data. We then projected the optimized model for two future time periods under different climate scenarios. Finally, we calculated differences in the mean elevation and lower and upper range limits for the species. We predicted that with changing climatic conditions, Caspian Snowcock will undergo significant elevational and some latitudinal shifts in its distribution and will face a drastic decrease in suitable habitat in the next 50 years. Including non-climatic data in the models increased model performance and resulted in reduced predictions of habitat loss under future climate scenarios. Terrain roughness was the most important predictor in this model, suggesting that more complex topography will retain favourable microclimates for the species in the future. The results thus highlight the importance of including topographic variables in climate-based SDMs. Our findings can guide biodiversity managers in prioritizing protected areas and adopting proactive measures to mitigate the negative impacts of climate change.| File | Dimensione | Formato | |
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