Predictive spatial niche and biodiversity hotspot models for small mammal communities in Alaska: applying machine-learning to conservation planning
CONTEXT: Changing global environmental conditions, especially at northern latitudes, are threatening to shift species distributions and alter wildlife communities. OBJECTIVE: We aimed to establish current distributions and community arrangements of small mammals to provide important baselines for monitoring and conserving biodiversity into the future. METHODS: We used 4,408 archived museum and open-access records and the machine learning algorithm, RandomForests, to create high-resolution spatial niche models for 17 species of rodents and shrews in Alaska.