Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling

被引:89
|
作者
Thapa, Arjun [1 ,2 ]
Wu, Ruidong [3 ]
Hu, Yibo [1 ]
Nie, Yonggang [1 ]
Singh, Paras B. [1 ,2 ]
Khatiwada, Janak R. [2 ,4 ]
Yan, Li [1 ]
Gu, Xiaodong [5 ]
Wei, Fuwen [1 ]
机构
[1] Chinese Acad Sci, Inst Zool, Key Lab Anim Ecol & Conservat Biol, Beijing, Peoples R China
[2] Univ Chinese Acad Sci, Int Coll, Beijing, Peoples R China
[3] Yunnan Univ, Inst Int Rivers & Ecosecur, Kunming, Yunnan, Peoples R China
[4] Chinese Acad Sci, Chengdu Inst Biol, Chengdu, Sichuan, Peoples R China
[5] Wildlife Conservat Div, Sichuan Forestry Dept, Chengdu, Sichuan, Peoples R China
来源
ECOLOGY AND EVOLUTION | 2018年 / 8卷 / 21期
关键词
habitat; Himalaya; predictive model; red panda; LANGTANG NATIONAL-PARK; SPECIES DISTRIBUTION; AILURUS-FULGENS; CLIMATE-CHANGE; GIANT PANDAS; MICROHABITAT SEPARATION; SAMPLING BIAS; CONSERVATION; HABITAT; SUITABILITY;
D O I
10.1002/ece3.4526
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
An upsurge in anthropogenic impacts has hastened the decline of the red panda (Ailurus fulgens). The red panda is a global conservation icon, but holistic conservation management has been hampered by research being restricted to certain locations and population clusters. Building a comprehensive potential habitat map for the red panda is imperative to advance the conservation effort and ensure coordinated management across international boundaries. Here, we use occurrence records of both subspecies of red pandas from across their entire range to build a habitat model using the maximum entropy algorithm (MaxEnt 3.3.3k) and the least correlated bioclimatic variables. We found that the subspecies have separate climatic spaces dominated by temperature-associated variables in the eastern geographic distribution limit and precipitation-associated variables in the western distribution limit. Annual precipitation (BIO12) and maximum temperature in the warmest months (BIO5) were major predictors of habitat suitability for A. f. fulgens and A. f. styani, respectively. Our model predicted 134,975 km(2) of red panda habitat based on 10 percentile thresholds in China (62% of total predicted habitat), Nepal (15%), Myanmar (9%), Bhutan (9%), and India (5%). Existing protected areas (PAs) encompass 28% of red panda habitat, meaning the PA network is currently insufficient and alternative conservation mechanisms are needed to protect the habitat. Bhutan's PAs provide good coverage for the red panda habitat. Furthermore, large areas of habitat were predicted in cross-broader areas, and transboundary conservation will be necessary.
引用
收藏
页码:10542 / 10554
页数:13
相关论文
共 50 条
  • [41] Predicting the potential nationwide distribution of the snail vector, Oncomelania hupensis quadrasi, in the Philippines using the MaxEnt algorithm
    Loida M. Recopuerto-Medina
    Andrea Bernice M. Aguado
    Bianca Manuela M. Baldonado
    Rica Nikki B. Bilasano
    Sophia Miel L. Dullano
    Justine Marie R. Molo
    Nikki Heherson A. Dagamac
    Parasitology Research, 2024, 123
  • [42] Predicting the potential distribution of the Asian citrus psyllid, Diaphorina citri (Kuwayama), in China using the MaxEnt model
    Wang, Rulin
    Yang, Hua
    Luo, Wei
    Wang, Mingtian
    Lu, Xingli
    Huang, Tingting
    Zhao, Jinpeng
    Li, Qing
    PEERJ, 2019, 7
  • [43] Predicting the geographic distribution habitats of Schizomyia buboniae (Diptera: Cecidomyiidae) and its host plant Deverra tortuosa (Apiaceae) in Egypt by using MaxEnt modeling
    Mohamed Kamel
    Ahmed S. Bream
    Mohamed M. Moursy
    Sanad H. Ragab
    The Journal of Basic and Applied Zoology, 82 (1):
  • [44] MaxEnt Modeling for Predicting the Potential Wintering Distribution of Eurasian Spoonbill (Platalea leucorodia leucorodia) under Climate Change in China
    Fu, Aihua
    Gao, Erhu
    Tang, Xiaoping
    Liu, Zengli
    Hu, Faxiang
    Zhan, Zhenjie
    Wang, Jiadong
    Luan, Xiaofeng
    ANIMALS, 2023, 13 (05):
  • [45] Comparison between optimized MaxEnt and random forest modeling in predicting potential distribution: A case study with Quasipaa boulengeri in China
    Zhao, Ziyi
    Xiao, Nengwen
    Shen, Mei
    Li, Junsheng
    SCIENCE OF THE TOTAL ENVIRONMENT, 2022, 842
  • [46] Mapping the potential distribution and invasion risk of Watermelon mosaic virus using MaxEnt ecological niche modelingMapping the potential distribution and invasion risk of Watermelon mosaic virus using MaxEnt ecological niche modeling
    Kayo Heberth de Brito Reis
    Mayara Moledo Picanço
    Poliana Silvestre Pereira
    Hugo Daniel Dias de Souza
    Mônica Carvalho de Sá
    George Correa Amaro
    Ricardo Siqueira da Silva
    Marcelo Coutinho Picanço
    Renato Almeida Sarmento
    Theoretical and Applied Climatology, 2025, 156 (1)
  • [47] Predicting the geographic distribution habitats of Schizomyia buboniae (Diptera: Cecidomyiidae) and its host plant Deverra tortuosa (Apiaceae) in Egypt by using MaxEnt modeling
    Kamel, Mohamed
    Bream, Ahmed S.
    Moursy, Mohamed M.
    Ragab, Sanad H.
    JOURNAL OF BASIC AND APPLIED ZOOLOGY, 2021, 82 (01):
  • [48] Maxent modeling for predicting potential distribution of goitered gazelle in central Iran: the effect of extent and grain size on performance of the model
    Khosravi, Rasoul
    Hemami, Mahmoud-Reza
    Malekian, Mansoureh
    Flint, Alan L.
    Flint, Lorraine E.
    TURKISH JOURNAL OF ZOOLOGY, 2016, 40 (04) : 574 - 585
  • [49] Predicting Quercus gilva distribution dynamics and its response to climate change induced by GHGs emission through MaxEnt modeling
    Shi, Jingye
    Xia, Muxuan
    He, Guoqin
    Gonzalez, Norela C. T.
    Zhou, Sheng
    Lan, Kun
    Ouyang, Lei
    Shen, Xiangbao
    Jiang, Xiaolong
    Cao, Fuliang
    Li, He
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2024, 357
  • [50] Predicting current and future potential distribution of Cynaeus angustus (Coleoptera: Tenebrionidae) in global scale using the MaxEnt model
    Zhao, Chao
    Bai, Chunqi
    Wang, Dianxuan
    JOURNAL OF STORED PRODUCTS RESEARCH, 2023, 101