Spatio-Temporal Changes and Habitats of Rare and Endangered Species in Yunnan Province Based on MaxEnt Model

Yiwei Lian, Yang Bai (), Zhongde Huang, Maroof Ali, Jie Wang and Haoran Chen
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Yiwei Lian: Center for Integrative Conservation & Yunnan Key Laboratory for Conservation of Tropical Rainforests and Asian Elephants, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China
Yang Bai: Center for Integrative Conservation & Yunnan Key Laboratory for Conservation of Tropical Rainforests and Asian Elephants, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China
Zhongde Huang: Center for Integrative Conservation & Yunnan Key Laboratory for Conservation of Tropical Rainforests and Asian Elephants, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China
Maroof Ali: Center for Integrative Conservation & Yunnan Key Laboratory for Conservation of Tropical Rainforests and Asian Elephants, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China
Jie Wang: Center for Integrative Conservation & Yunnan Key Laboratory for Conservation of Tropical Rainforests and Asian Elephants, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China
Haoran Chen: Institute of International Rivers and Ecological Security, Yunnan University, Kunming 650500, China

Land, 2024, vol. 13, issue 2, 1-19

Abstract: Biodiversity is crucial for ecosystem functioning, but it is rapidly declining due to human activities and climate change. Protecting biodiversity has become a key priority for global environmental conservation actions. Rare and endangered species have a great impact on the ecosystem, yet due to their limited survival capacity, they are more prone to extinction, thus exerting a significant impact on biodiversity. However, current research reveals a lack of information concerning the potential distribution and changes of these species. This study used the maximum entropy model to predict the present and future potential habitats of rare and endangered species in Yunnan Province. After superimposing model results, four richness regions are divided by the natural breakpoint method and analyzed. Existing protected areas are compared with hotspots, and the land-use composition of hotspots is also analyzed. The results revealed that, in both current and future scenarios, rare and endangered species in Yunnan Province are primarily found in the western mountainous region, the Xishuangbanna–Wenshan high temperature area, and the Kunming–Qujing dense vegetation cover area. These species are also expanding their distribution towards the western mountainous area. However, under the low carbon emission scenario (RCP2.6), these species will spread from the high abundance regions to the low altitude hotspots by 2070. In the high carbon emissions scenario (RCP8.5), there will be fewer high abundance areas in 2070 than in 2050. The transfer matrix analysis reveals regional richness variations over time. Furthermore, the analysis revealed significant conservation gaps and found that existing hotspot areas were heavily affected by human activities. To improve conservation efficiency, it is necessary to enhance the protection of existing hotspots in Yunnan Province. Climate change plays a significant role in species migration, with precipitation levels being a key factor. The necessary actions should be taken to address the insufficient protection, resolve conflicts between human activities and land use in critical areas, and formulate effective strategies for adapting to future climate changes. Yunnan Province, with its rich species resources, has the potential to become a global innovator in biodiversity conservation by implementing improved conservation strategies.

Keywords: rare and endangered species; MaxEnt model; biodiversity prediction; climate change; gap analysis (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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