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Journal of Applied Ecology - 2024 - Rast - Death detector Using vultures as sentinels to detect carcasses by combining.pdf 1,04MB
WeightNameValue
1000 Titel
  • Death detector: Using vultures as sentinels to detect carcasses by combining bio‐logging and machine learning
1000 Autor/in
  1. Rast, Wanja |
  2. Portas, Rubén |
  3. Shatumbu, Gabriel Iita |
  4. Berger, Anne |
  5. Cloete, Claudine |
  6. Curk, Teja |
  7. Götz, Theresa Ida |
  8. Aschenborn, Hans Karl Ortwin |
  9. Melzheimer, Joerg |
1000 Erscheinungsjahr 2024
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-11-18
1000 Erschienen in
1000 Quellenangabe
  • 61(12):2936-2945
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1111/1365-2664.14810 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 1. Bio-logging technologies allow scientists to remotely monitor animal behaviour and the environment. In this study, we used the combination of natural abilities of African white-backed vultures Gyps africanus and state-of-the-art bio-logging technology for detecting and locating carcasses in a vast landscape. 2. We used data from two captive and 27 wild vultures to create a reference data set for the training of a support vector machine to distinguish between six behaviour classes based on acceleration data. Next, we combined the classified behaviour of the initial 27 and 7 additional vultures with GPS data and used the ‘Density-Based Spatial Clustering of Applications with Noise’ algorithm to cluster all GPS data to get a position of potential feeding locations. Finally, we used the clustered data set to train a Random Forest algorithm to distinguish between clusters with and without a carcass. The behaviour classifier was trained on 14,682 samples for all behaviour classes, which were classified with a high performance (overall precision: 0.95, recall: 0.89). 3. This enabled a ground team to examine 1900 clusters between May 2022 and March 2023 in the field, 580 linked to a carcass and 1320 without a carcass. The cluster classifier trained on this data set was able to correctly distinguish between carcass and no carcass clusters with high performance (overall precision: 0.92, recall: 0.89). 4. Synthesis and applications. We showed that a carcass detection system using vultures, loggers and artificial intelligence (AI) can be used to monitor the mortality of numerous species in a vast landscape. This method has broad applications, such as studying the feeding ecology of vultures, detecting and monitoring of disease outbreaks, environmental poisoning or illegal killing of wildlife. Similar to vultures and carcasses, our methodological framework can be applied to other species to locate their respective food resources. It could also be applied to other types of resources like temporary water sources, roosting sites and to other behaviours such as marking to locate marking sites.
1000 Sacherschließung
lokal machine learning
lokal gyps africanus
lokal feeding sites
lokal random forest
lokal accelerometry
lokal carcass detection
lokal behaviour classification
lokal support vector machine
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0003-3465-3117|https://orcid.org/0000-0002-0686-0701|https://frl.publisso.de/adhoc/uri/U2hhdHVtYnUsIEdhYnJpZWwgSWl0YQ==|https://orcid.org/0000-0001-5765-8039|https://frl.publisso.de/adhoc/uri/Q2xvZXRlLCBDbGF1ZGluZQ==|https://orcid.org/0000-0002-9082-6433|https://orcid.org/0000-0001-8751-3404|https://orcid.org/0000-0002-7494-3795|https://orcid.org/0000-0002-3490-1515
1000 Hinweis
  • metadata provieded by: LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
1000 Label
1000 Förderer
  1. Bundesministerium für Wirtschaft und Klimaschutz |
  2. Deutsches Zentrum für Luft- und Raumfahrt |
1000 Fördernummer
  1. -
  2. -
1000 Förderprogramm
  1. -
  2. -
1000 Dateien
  1. Death detector: Using vultures as sentinels to detect carcasses by combining bio-logging and machine learning
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Bundesministerium für Wirtschaft und Klimaschutz |
    1000 Förderprogramm -
    1000 Fördernummer -
  2. 1000 joinedFunding-child
    1000 Förderer Deutsches Zentrum für Luft- und Raumfahrt |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6511780.rdf
1000 Erstellt am 2025-06-12T07:42:38.775+0200
1000 Erstellt von 336
1000 beschreibt frl:6511780
1000 Bearbeitet von 355
1000 Zuletzt bearbeitet 2025-08-05T11:23:42.027+0200
1000 Objekt bearb. Tue Aug 05 11:22:52 CEST 2025
1000 Vgl. frl:6511780
1000 Oai Id
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