气候变化领域集成服务门户(CCPortal): Predicting the Spatial Distribution of the Mangshan Pit Viper (Protobothrops mangshanensis) under Climate Change Scenarios Using MaxEnt Modeling

CCPortal

DOI	10.3390/f15040723
	Predicting the Spatial Distribution of the Mangshan Pit Viper (Protobothrops mangshanensis) under Climate Change Scenarios Using MaxEnt Modeling
	Deng, Zeshuai; Xia, Xin; Zhang, Mu; Chen, Xiangying; Ding, Xiangyun; Zhang, Bing; Deng, Guoxing; Yang, Daode
发表日期	2024
EISSN	1999-4907
起始页码	15
结束页码	4
卷号	15 期号:4
英文摘要	This study explores the critical issue of understanding the potential impacts of climate change on the habitat suitability of the highly endangered forest-dwelling Mangshan pit viper (Protobothrops mangshanensis) in China. Through the application of the MaxEnt model, high-resolution bioclimatic datasets, and species occurrence data, the research aims to elucidate the spatial and temporal dynamics of P. mangshanensis distribution from the present to the years 2050 and 2070. Through the integration of three climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and exploring different shared socioeconomic pathway (SSP) scenarios (SSP126, SSP370, and SSP585), the study seeks to provide comprehensive insights into the potential variations in habitat suitability under diverse future climate conditions. The methodology employed involves the construction of the MaxEnt model utilizing the BioClim dataset and 83 species occurrence points. The SSP scenarios mentioned above represent future climate change scenarios, and the accuracy of the model is evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Key findings reveal that the MaxEnt model exhibits high accuracy (AUC = 0.998), pinpointing the current suitable habitat for P. mangshanensis to be confined to the Mangshan area within the Nanling Mountains, covering an approximate area of 1023.12 km(2). However, projections based on future climate scenarios suggest notable shifts in habitat suitability dynamics. While potential suitable habitats may emerge in the northwest of the current range, the existing suitable habitats are anticipated to undergo significant reduction or even complete disappearance. Notably, precipitation during the driest month emerges as a critical determinant influencing the distribution of the species. In conclusion, the study underscores the exacerbating impact of climate change on habitat deterioration and survival risks for P. mangshanensis, emphasizing the urgent need for conservation measures to safeguard the remaining suitable habitats for this endangered species. The implications of these findings are far-reaching, with the anticipated contraction of the snake's range potentially leading to its disappearance and increased habitat fragmentation. By shedding light on the potential distributional changes of P. mangshanensis in Mangshan, the research provides valuable insights for informing targeted conservation strategies and policy interventions aimed at mitigating the adverse effects of climate change on endangered species.
英文关键词	spatial distribution; Mangshan pit viper; Protobothrops mangshanensis; MaxEnt model; critically endangered species; habitat changes; wildlife conservation; climate change
语种	英语
WOS研究方向	Forestry
WOS类目	Forestry
WOS记录号	WOS:001211170000001
来源期刊	FORESTS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/307689
作者单位	Central South University of Forestry & Technology
推荐引用方式 GB/T 7714	Deng, Zeshuai,Xia, Xin,Zhang, Mu,et al. Predicting the Spatial Distribution of the Mangshan Pit Viper (Protobothrops mangshanensis) under Climate Change Scenarios Using MaxEnt Modeling[J],2024,15(4).
APA	Deng, Zeshuai.,Xia, Xin.,Zhang, Mu.,Chen, Xiangying.,Ding, Xiangyun.,...&Yang, Daode.(2024).Predicting the Spatial Distribution of the Mangshan Pit Viper (Protobothrops mangshanensis) under Climate Change Scenarios Using MaxEnt Modeling.FORESTS,15(4).
MLA	Deng, Zeshuai,et al."Predicting the Spatial Distribution of the Mangshan Pit Viper (Protobothrops mangshanensis) under Climate Change Scenarios Using MaxEnt Modeling".FORESTS 15.4(2024).