Weak association between mountains ranges and risk of extinction from climate change in monotypic genera of rodents
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Giovanni Amori
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Luca Luiselli
Abstract
Monotypic genera (MTGs) hold significant evolutionary value, as their extinction results in the loss of an entire lineage. Given their frequent endemism and restricted distributions, particularly in mountainous regions, MTGs may be disproportionately vulnerable to climate change. In this study, we evaluated the extinction risks of rodent MTGs globally, considering three key factors: (i) proportion of range in mountains, (ii) habitat niche breadth, and (iii) proportion of range in high climate change areas, using a 2080–2099 published scenario. Using IUCN-based assessments and principal component analysis (PCA), we identified 41 MTGs at high risk, with the Oriental region – especially Sulawesi – emerging as a hotspot. While 36.6 % of high-risk MTGs were strictly montane, our multivariate analysis revealed a weak correlation between the proportion of the range within mountainous regions and the percentage of the range located in areas experiencing rapid climatic change, with eleven genera (e.g., Nilopegamys, Eupetaurus, Aeretes, Hodomys) being positioned near the axis representing the proportion of their range situated within high climate change zones. The impact of niche width appeared to be minor with relatively few genera being habitat specialists. The most affected genera were primarily Muridae, occurred predominantly in the Oriental region, and often had extremely narrow distributions. Conservation efforts should prioritize regions with high-risk MTGs, particularly Sulawesi, Sichuan (China), Ethiopia, and Mexico.
Acknowledgments
We are indebted to three anonymous referees for their helpful comments on the submitted draft.
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Research ethics: This work did not require ethical approval from a human subject or animal welfare committee.
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Informed consent: Yes.
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Author contributions: GA and LL: conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing – original draft. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: The authors did not use AI-assisted technologies in creating this article.
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Conflict of interest: The authors declare that they have no conflict of interest.
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Research funding: None declared.
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Data availability: The data are provided in the Online Supplementary Material.
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