Projecting shifts in the distributions of Chinese endemic vertebrate species under climate and land-use change

被引:2
|
作者
Deng, Yiming [1 ]
Goodale, Eben [2 ]
Dong, Anran [1 ]
Jiang, Demeng [3 ]
Jiang, Aiwu [1 ]
Zhang, Zhixin [4 ,5 ]
Mammides, Christos [6 ]
机构
[1] Guangxi Univ, Coll Forestry, Guangxi Key Lab Forest Ecol & Conservat, Nanning, Guangxi, Peoples R China
[2] Xian Jiaotong Liverpool Univ, Dept Hlth & Environm Sci, Suzhou, Jiangsu, Peoples R China
[3] Peking Univ, Coll Urban & Environm Sci, Beijing, Peoples R China
[4] Chinese Acad Sci, South China Sea Inst Oceanol, CAS Key Lab Trop Marine Bioresources & Ecol, Guangzhou, Guangdong, Peoples R China
[5] South China Sea Inst Oceanol, Global Ocean & Climate Res Ctr, Guangzhou, Guangdong, Peoples R China
[6] Frederick Univ, Nat Conservat Unit, Nicosia, Cyprus
来源
基金
中国国家自然科学基金;
关键词
biodiversity hotspots; endemic species; global change; IUCN red list; MaxEnt; optimization model; shared socioeconomic pathways; species distribution modeling; DISTRIBUTION MODELS; HABITAT SUITABILITY; RANGE SHIFTS; COVER DATA; IMPACTS; PREDICT; REQUIREMENTS; COMPLEXITY; REPTILE; BIRDS;
D O I
10.3389/fevo.2023.1174495
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Human-induced climate and land-use change impact species' habitats and survival ability. A growing body of research uses species distribution models (SDMs) to predict potential changes in species ranges under global change. We constructed SDMs for 411 Chinese endemic vertebrates using Maximum Entropy (MaxEnt) modeling and four shared socioeconomic pathways (SSPs) spanning to 2100. We compared four different approaches: (1) using only climatic and geographic factors, (2) adding anthropogenic factors (land-use types and human population densities), but only using current data to project into the future, (3) incorporating future estimates of the anthropogenic variables, and (4) processing species occurrence data extracted from IUCN range maps to remove unsuitable areas and reflect each species' area of habitat (AOH). The results showed that the performance of the models (as measured by the Boyce index) improved with the inclusion of anthropogenic data. Additionally, the predicted future suitable area was most restricted and diminished compared to the current area, when using the fourth approach. Overall, the results are consistent with other studies showing that species distributions will shift to higher elevations and latitudes under global change, especially under higher emission scenarios. Species threatened currently, as listed by the IUCN, will have their range decrease more than others. Additionally, higher emission scenarios forecast more threatened species in the future. Our findings show that approaches to optimizing SDM modeling can improve accuracy, predicting more direct global change consequences, which need to be anticipated. We also show that global change poses a significant threat to endemic species even in regions with extensive protected land at higher latitudes and elevations, such as China.
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页数:13
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