ArticleEcology and evolution2021
Potential geographical distribution and environmental explanations of rare and endangered plant species through combined modeling: A case study of Northwest Yunnan, China.
Article in Ecology and evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Spatiotemporal Dynamics and Climate Change Impacts on Massive Invasive Plants in Henan Province, China.Ecology and evolution · 2026Article
- Predicting Climate Change-Induced Habitat Contraction ofBiology · 2026Article
- Mountain Riparian Zones as Refugia for Rare and Endangered Plants Under Climate Change.Ecology and evolution · 2026Article
- Patterns of Orchid Diversity and Their Potential Habitat Under Climate Change in Chongqing, China.Biology · 2026Article
- Integrating environmental variables and biotic interactions to predict the future distribution of the endangered Aconitum heterophyllum Wall. ex Royle.BMC plant biology · 2025Article
- Prediction Analysis of Integrative Quality Zones forBiology · 2025Article
- Spatiotemporal Distribution Patterns and Conservation Priorities of Gymnosperms With Different Leaf Shapes in China Under Climate Change.Ecology and evolution · 2025Article
- Modeling Climate Refugia forPlants (Basel, Switzerland) · 2025Article
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- Predicting the potential distribution of three medicinalFrontiers in plant science · 2025Article
- Prediction of the Future Evolution Trends ofBiology · 2024Article
- Endangered status and threatened population ecological factors inEcology and evolution · 2024Article
- Distribution pattern and change prediction ofEcology and evolution · 2023Article
- Differences in the Suitable Distribution Area between Northern and Southern China Landscape Plants.Plants (Basel, Switzerland) · 2023Article
- Modeling ofBiology · 2022Article
- Potential distribution of three types of ephemeral plants under climate changes.Frontiers in plant science · 2022Article
- Potential geographical distribution and environmental explanations of rare and endangered plant species through combined modeling: A case study of Northwest Yunnan, China.Ecology and evolution · 2021Article
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6 authors.
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Abstract
In recent decades, due to the effect of climate change and the interference of human activities, the species habitat index has fallen by 2%. Studying on the geographical distribution pattern and predicting the potential geographical distribution of species are of great significance for developing scientific and effective biodiversity conservation strategies. Plenty of rare and endangered species that need immediate conservation are distributed in Northwest Yunnan. In this regard, this research is conducted in the purpose of predicting the potential geographical distribution of 25 rare and endangered plant species in Northwest Yunnan and analyzing the explanation capabilities of various environmental factors on the potential geographical distribution patterns of these species. Initially, the ecological niche model MaxEnt was employed to predict the potential geographical distribution of target species. Following that, the superposition method was applied to obtain the potential geographical distribution pattern of species richness on the spatial scale of the ecological niche model with a resolution of 0.05° × 0.05°. Ultimately, geographically weighted regression (GWR) model was adopted to investigate the explanation capabilities of various environmental parameters on the potential distribution patterns. The research results showed that the average value of the area under the receiver operating curve (AUC) of each species was between 0.80 and 1.00, which indicated that the simulation accuracy of the MaxEnt model for each species was good or excellent. On the whole, the potential distribution area for each species was relatively concentrated and mainly distributed in the central-western, central-eastern and northern regions of Northwest Yunnan. In addition, the potential distribution areas of these species were between 826.33 km
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