Mapping species’ spatial patterns and understanding their distribution are crucial for informing species conservation strategies, biodiversity management, ecosystem restoration, and species extinction risk assessment1,2,3. As a guide for global biodiversity conservation, the targets in the Kunming-Montreal Global Biodiversity Framework—aiming to reduce biodiversity loss, restore degraded ecosystems and conserve 30% of land, waters and seas—rely heavily on species distribution data4. As one of the parties to both the Convention on Biological Diversity (CBD) and the Kunming-Montreal Global Biodiversity Framework, China has actively aligned its domestic policies with these international commitments. In 2024, the Chinese government published the “China National Biodiversity Conservation Strategy and Action Plan (2023–2030)”, which calls for the establishment of foundational biodiversity datasets, including species distribution data5.
There are various ways for obtaining species distribution data. Species surveys utilizing monitoring technologies based on environmental DNA (eDNA), passive acoustic monitoring, and visual sensors (e.g., camera-trapping) provide the most accurate information on species distributions6. However, these approaches are extremely costly, making them feasible only for investigating a limited number of species or those within specific regions7,8. Species distribution models (SDMs) are also widely used, typically integrating species occurrence points with environmental variables such as climate and topography to evaluate the likelihood of species presence. Common modeling approaches for constructing SDMs include MaxEnt9,10, AIM11, and machine learning algorithms12. However, limitations in the completeness and accuracy of species occurrence data reduce the reliability of the assessment results produced by these models13,14,15.
Area of Habitat (AOH) is defined as the suitable habitat available to a species within its geographic range16. AOH maps are generated by integrating information on the species’ geographic range, suitable habitat types, and elevation limits17. AOH maps can reduce commission errors for geographic ranges of species, especially not well-known and wide-range species. However, it is notable that for well-known species more accurate assessment methods may exist18. AOH maps are useful for identifying priority conservation areas19,20, assessing conservation gap21, supporting ecosystem restoration22, and evaluating species extinction risks1,23. Moreover, overlaying AOH across species enables the creation of species richness maps, which help identify biodiversity hotspots18.
Previous studies have generated multiple sets of AOH maps for terrestrial animals. For example, Rondinini et al.24 and Ficetola et al.17 generated 300 m resolution AOH maps for global terrestrial mammals and amphibians in 2009, respectively. Lumbierres et al.18 generated 100 m resolution AOH maps for global terrestrial mammals and birds in 2015. Similarly, Mi et al.25 generated 300 m resolution AOH maps for National Key Protected Wildlife (NKPW) in China in 2009. However, these datasets are limited to single-year AOH information, making it impossible to track temporal changes in AOH. The International Union for the Conservation of Nature (IUCN) Red List26 serves as an authoritative assessment of species’ threatened status on a global scale, and existing studies primarily use the IUCN Red List to generate AOH maps17,18,24. However, species on the IUCN Red List might not be fully representative of the threatened status of species in China25. For instance, while the global population of Castor fiber exceeds 639,000 individuals and is classified as Least Concern by IUCN, its Chinese population numbers merely 700 individuals, making it one of China’s rarest aquatic mammals26. Notably, nearly half of the terrestrial species on the NKPW List are classified by the IUCN as Least Concern or Data Deficient, yet these species are considered key protected species in China26,27. The NKPW List is based on rigorous scientific assessments of the threatened status of wildlife in China, incorporating input from diverse stakeholders (e.g., governments, scholars, and the public), and is officially published by the Chinese government and protected by Chinese law, which can be used to summarize the threatened status of species in China25.
The NKPW List contains 988 species classified into two protection level accounting to their degree of preciousness and endangerment, with 235 Class I species representing higher protection priority compared to 753 Class II species. We generate 30 m resolution AOH maps for 720 terrestrial species from the NKPW List (2021 version), covering the years 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, and 2022 (Fig. 1). Although the NKPW List contains 988 species (including 869 terrestrial species), we generate AOH maps for 720 terrestrial species sufficient geographic range data are available, excluding marine species and some terrestrial species lacking geographic range data. We used the resulting AOH maps to generate species richness maps for NKPW (Fig. 2). We refined the species distribution data derived from the IUCN Red List by incorporating adjustments to geographic range, elevation, and suitable habitat types (see Methods). These modifications ensured that the generated AOH maps more accurately reflect the actual distribution of species in China.
Area of Habitat (AOH) map of Black Muntjac (Muntiacus crinifrons). (a) The AOH of the species. (b) AOH changes within the Dexing mine (Asia’s largest open pit copper mine). This species’ habitats are forest and shrubland habitats and has an elevation range of 200–1000 m. Long-term temporal sequences of AOH maps can effectively track dynamic habitat changes, providing insights into how specific threats such as mining activities impact species’ habitats over time. However, we remind users to exercise caution when making temporal comparisons of AOH, as the AOH maps are generated based on land cover rather than cover change. Although GLC_FCS30D employs a continuous change-detection method to produce land cover products, there remain certain errors in temporal stability within the time series.
Species richness maps for (a) all species (720 species), (b) class I protected species (195 species) and (c) class II protected species (525 species) in 2020, for example.
High-resolution AOH maps with long-term temporal sequences are vital for biodiversity management and conservation strategy development. Our dataset of high-resolution, long-term temporal sequences of AOH maps of NKPW provide a clear depiction of habitat loss processes over time28, elucidating the relationship between the rate of habitat loss and associated threats (Fig. 1)29. These maps can enable the quantification of species restoration benefits, identification of areas with the greatest potential for species restoration, and prioritization areas for targeted restoration efforts11,22. This information serves as a robust scientific foundation for developing effective biodiversity conservation strategies30. Moreover, the field of corporate sustainability reporting (Environmental, Social, and Governance-ESG) also calls for long-term temporal sequences AOH maps to assess the impacts of corporate activities on biodiversity. For instance, the Environmental Sustainability Reporting Standards (ESRS)31, Taskforce on Nature-related Financial Disclosures (TNFD)32, and the Global Reporting Initiative (GRI)33 all emphasize the importance of focusing on habitat changes, particularly nationally threatened species. Therefore, this dataset has significant research value and broad application potential.
