Integrating machine learning and species distribution models for predicting the potential hazard areas of Marmota baibacina in Xinjiang, China

作者：Qianying Shao, Kaihui Li, Mardan Aghabey Turghan, Anming Bao, Alimujiang Kasimu, Yanming Gong, Jie Bai, Jun Lin, X. Li, Jin Zhao · 发表于：Frontiers in Ecology and Evolution · 年份：2025 · DOI：10.3389/fevo.2025.1608071 · 被引用次数：3 · 研究领域：Species Distribution and Climate Change、Remote Sensing in Agriculture、Ecosystem dynamics and resilience

Introduction Under global climate change and intensified human activities, species distributions are undergoing significant shifts. Marmota baibacina , a representative keystone species among Central Asian high-altitude species, exacerbates vegetation degradation and soil erosion through herbivory and burrowing activities. As the primary reservoir of Yersinia pestis, it poses a significant public health threat. Methods This study integrated five machine learning models (XGBoost, RF, SVM, LogBoost) and the MaxEnt model to predict the current (1970–2000) and future (2041–2100) distribution of Marmota baibacina under three climate scenarios (SSP126, SSP370, SSP585), utilizing 111 occurrence records and 29 environmental variables spanning climatic, topographic, edaphic, and vegetation dimensions. Results The results indicated that (1) All five models demonstrated high predictive accuracy with AUC values exceeding 0.9. After screening 29 environmental variables, machine learning models identified 10 key variables with high feature importance, while MaxEnt selected 16 environmental variables; (2) Dominant drivers revealed that Bio18 (warmest quarter precipitation), Bio2 (diurnal temperature range), Bio11 (coldest quarter temperature), and Bio15 (precipitation seasonality) collectively contributed >70% to machine learning models, whereas MaxEnt prioritized slope, NDVI, and Bio18; (3) Under current climatic conditions, the potential suitable habitats of Marmota baibacina in Xi...