Integrating threat mapping and animal movement data to identify high-risk areas for endangered mobile species

被引:0
|
作者
Curk, T. [1 ]
Melzheimer, J. [1 ]
Aschenborn, O. [1 ]
Amar, A. [2 ]
Kolberg, H. [3 ]
Garbett, R. [4 ]
Maude, G. [4 ]
Reading, R. P. [4 ,5 ]
Selebatso, M. [4 ]
Berzaghi, F. [6 ]
Hempson, G. P. [7 ]
Botha, A. [8 ]
Thomson, R. L. [2 ]
Tate, G. [2 ,8 ]
Spiegel, O. [9 ]
Santangeli, A. [2 ,10 ]
机构
[1] Leibniz Inst Zoo & Wildlife Res, Dept Evolutionary Ecol, Berlin, Germany
[2] Univ Cape Town, FitzPatrick Inst African Ornithol, Cape Town, South Africa
[3] Namibia Bird Club, Vultures Namibia, Windhoek, Namibia
[4] Raptors Botswana, Gaborone, Botswana
[5] Coalit Int Conservat, Denver, CO USA
[6] World Maritime Univ, Ocean Sustainabil Governance & Management, Malmo, Sweden
[7] Univ Glasgow, Sch Biodivers Anim Hlth & Comparat Med, Glasgow, Scotland
[8] Endangered Wildlife Trust, Midrand, South Africa
[9] Tel Aviv Univ, Fac Life Sci, Sch Zool, Tel Aviv, Israel
[10] CSIC UIB, Inst Mediterranean Studies IMEDEA, Anim Demog & Ecol Unit, Esporles, Spain
关键词
GPS tracking; human-wildlife conflict; resource availability; sentinel poisoning; space use; Torgos tracheliotos; vultures; poisoning risk; COMMERCIAL FARMERS; POISON; CONSERVATION; VULTURES; NAMIBIA;
D O I
10.1111/acv.12980
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Given the current biodiversity crisis, understanding how animals move across a landscape dotted with different anthropogenic threats and the consequences of those threats for animals is paramount to devising evidence-based conservation interventions. Vultures roam across large areas and are highly exposed to poisoning, which represents a particularly damaging form of wildlife crime. In this study, we introduce a framework for quantifying the exposure to threats and illustrate an example of poisoning risk as a threat in an endangered African vulture species, the Lappet-faced Vulture (Torgos tracheliotos). We combined GPS tracking data of 19 individuals collected between 2012 and 2022 with food availability and spatial threat maps of both intentional (poachers directly targeting vultures) and unintentional (farmers aiming to kill carnivores, with vultures being secondarily affected) poisoning across most of Southern Africa. We identified poisoning hotspots in northern Botswana and south-eastern Namibia. These areas were also associated with a high number of vulture mortalities, providing additional support for poisoning risk. Northern Botswana and areas at the border between Botswana and South Africa were characterized by high food availability, potentially amplifying the mortality rate by attracting vultures from surrounding areas. Our results offer valuable insights for regional vulture conservation, together with a methodological framework for quantifying and mapping the spatial exposure to threats for mobile species of conservation concern, enabling improved targeting of conservation actions.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Integrating diet and movement data to identify hot spots of predation risk and areas of conservation concern for endangered species
    Ward, Eric J.
    Levin, Phillip S.
    Lance, Monique M.
    Jeffries, Steven J.
    Acevedo-Gutierrez, Alejandro
    CONSERVATION LETTERS, 2012, 5 (01): : 37 - 47
  • [2] Classifying Endangered Species in High-Risk Areas Using Deep Learning
    Brito, Cristian
    Engdahl, Andrea
    Atkinson, John
    ADVANCES AND TRENDS IN ARTIFICIAL INTELLIGENCE: THEORY AND APPLICATIONS, IEA-AIE 2024, 2024, 14748 : 23 - 34
  • [3] Integrating social and ecological information to identify high-risk areas of human-crocodile conflict in the Indonesian Archipelago
    Ardiantiono
    Henkanaththegedara, Sujan M.
    Sideleau, Brandon
    Sheherazade
    Anwar, Yogie
    Haidir, Iding A.
    Amarasinghe, A. A. Thasun
    BIOLOGICAL CONSERVATION, 2023, 280
  • [4] Integrating microbiome analysis and multi-modal data to identify high-risk population for esophageal adenocarcinoma
    Dindar, Duygu Altinok
    Krieger, Madeline
    Palma, Amy
    Cheney, John
    Otaki, Fouad
    Yu, Jessica
    Wood, Stephanie
    Sharpton, Thomas J.
    McGann, James
    Karstens, Lisa
    Yardimci, Gurkan G.
    Zhang, Zhenzhen
    CANCER RESEARCH, 2024, 84 (06)
  • [5] Mapping Diabetes Incidence to Identify High Risk Areas in the United States
    Burrows, Nilka R.
    Kirtland, Karen
    Thompson, Theodore
    Gregg, Edward
    Barker, Lawrence
    Geiss, Linda
    DIABETES, 2013, 62 : A47 - A48
  • [6] Using spatiotemporal analysis to Identify high-risk areas of Dengue in Medellin, Colombia
    Carabali, M.
    Maheu-Giroux, M.
    Calle, D.
    Kaufman, J. S.
    Restrepo, B. N.
    INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES, 2018, 73 : 202 - 203
  • [7] Temporal-spatial risk model to identify areas at high-risk for occurrence of dengue fever
    Galli, Bruno
    Chiaravalloti Neto, Francisco
    REVISTA DE SAUDE PUBLICA, 2008, 42 (04): : 656 - 663
  • [8] Identifying high-risk areas of airborne disease in "movement-contact" network
    Du F.
    Wang J.
    Jin H.
    Dili Xuebao/Acta Geographica Sinica, 2022, 77 (08): : 2006 - 2018
  • [9] Leveraging Healthcare System Data to Identify High-Risk Dyslipidemia Patients
    Nayrana Griffith
    Grace Bigham
    Aparna Sajja
    Ty J. Gluckman
    Current Cardiology Reports, 2022, 24 : 1387 - 1396
  • [10] Leveraging Healthcare System Data to Identify High-Risk Dyslipidemia Patients
    Griffith, Nayrana
    Bigham, Grace
    Sajja, Aparna
    Gluckman, Ty J.
    CURRENT CARDIOLOGY REPORTS, 2022, 24 (10) : 1387 - 1396