Amur tiger (Panthera tigris altaica) is critically endangered and also the subspecies of the tiger with the most restoration potential in China. It is challenging to protect large-ranging carnivores like tigers under increasing pressure of human development. To provide a more technically robust foundation for tiger habitat conservation prioritization, we conducted a comprehensively empirical analysis based on a broadly collected occurrence dataset of tigers and their prey. We modeled tiger distribution by running an ensemble model integrating nine different algorithms. We found that the ensemble model performed well and outperformed any individual model regarding the discrimination ability. We used cumulative resistant kernel analysis to identify core habitats as with high predicted movement density and used factorial least-cost paths to model corridors among tiger occurrence locations. We found core habitats for Amur tigers are distributed in three mountain areas, namely eastern Wanda Mountain, southern Zhangguangcailing, and Laoyeling-Dalongling. We found significant protection gaps as existing protected areas only cover less than 1/4 of predicted core habitats, but this proportion will rise significantly with the establishment of the Northeast China Tiger and Leopard National Park. Furthermore, we ranked spatial priorities for the expansion of the protected area network, simultaneously considering biological and socioeconomic dimensions under the Zonation framework. Our study presented the most up-to-date and detailed maps of the predicted potential distribution and area of the most important habitats for the Amur tigers in China, which can provide quantitative guidance in the effort to maximize the efficiency of conservation initiatives at a regional scale.

This dataset contains the environmental variables used to model ungulate richness and Amur tiger habitat suitability and the R code to conduct these analyses.