Mammal suitable habitats are increasingly threatened by human disturbance, yet quantifying the performance of protected areas (PAs) in conserving these habitats remains challenging. Additionally, research on the relationship between the representativeness of PAs and their effectiveness in conserving mammal suitable habitats remains limited. To address this gap, we predicted the potential suitable habitats (PSHs) of terrestrial mammals across China using species distribution models. We then developed a rapid assessment method to evaluate the effectiveness of PAs in mitigating human footprints (HFP) within the mammal PSHs. Locally Weighted Scatterplot Smoothing (Lowess) was applied to identify the optimal proportion of mammal PSHs represented by PAs that maximizes conservation effectiveness. Our analysis revealed that 99.6 % of mammal PSHs experienced a significant increase in HFP from 2011 to 2020, with narrow-range species showing the most rapid growth. Despite these pressures, PAs effectively mitigated HFP within the PSHs of 92 % of species. For mammal species with varying PSH sizes, the highest effectiveness in PA representativeness was observed at 29 % for species with PSH < 100,000 km2, 19 % for species with 100,000 km2 ≤ PSH ≤ 250,000 km2, and 17 % for species with PSH > 250,000 km2. Our findings emphasize the need to enhance the representativeness of PAs for mammal suitable habitats, particularly for narrow-range species. This study will provide scientific guidance for the conservation of suitable habitats for terrestrial mammals in China and globally, as well as for the strategic planning of PAs.
Conservation effectiveness of terrestrial mammals and its relationship with representativeness of protected areas in China.
Chunting Feng,Hao Wang,Xiuxiang Meng,Mei Luo,Ruocheng Hu,Bingran Ma,Weiyang Zhao,Wei Wang
Published 2025 in Journal of Environmental Management
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- Publication year
2025
- Venue
Journal of Environmental Management
- Publication date
2025-05-12
- Fields of study
Biology, Medicine, Environmental Science
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- Source metadata
Semantic Scholar, PubMed
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