A Novel Framework to Protect Animal Data in a World of Ecosurveillance
Open accessAbstract
Surveillance of animal movements using electronic tags (i.e., biotelemetry) has emerged as an essential tool for both basic and applied ecological research and monitoring. Advances in animal tracking are occurring simultaneously with changes to technology, in an evolving global scientific culture that increasingly promotes data sharing and transparency. However, there is a risk that misuse of biotelemetry data could increase the vulnerability of animals to human disturbance or exploitation. For the most part, telemetry data security is not a danger to animals or their ecosystems, but for some high-risk cases, as with species’ with high economic value or at-risk populations, available knowledge of their movements may promote active disturbance or worse, potential poaching. We suggest that when designing animal tracking studies it is incumbent on scientists to consider the vulnerability of their study animals to risks arising from the implementation of the proposed program, and to take preventative measures.
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In some cases, it is imperative that the privacy of the data be preserved, for instance to avoid giving poachers access to locations of animals in near-real-time.
“In some cases, it is imperative that the privacy of the data be preserved, for instance to avoid giving poachers access to locations of animals in near-real-time134.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [134]
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Research works printed but not linked: 1
- Taryn S. Murray is affiliated with the South African Institute for Aquatic Biodiversity, in Grahamstown, South Africa. DGR is also affiliated with the Institute of Biology at the University of Neuchâtel, in Neuchâtel, Switzerland. Nathan Young is affiliated with the Department of Sociology and Anthropology at the University of Ottawa, in Ottawa, Ontario, Canada. Downloaded from https://academic.oup.com/bioscience/article/70/6/468/5822090 by guest on 03 June 2024
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Perspectives in machine learning for wildlife conservation
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- Authors
- Lennox, R. J., Harcourt, R., Bennett, J. R., Davies, A., Ford, A. T., Frey, R. M., Hayward, M. W., Hussey, N. E., Iverson, S. J., Kays, R., Kessel, S. T., Mcmahon, C., Muelbert, M., Murray, T. S., Nguyen, V. M., Pye, J. D., Roche, D. G., Whoriskey, F. G., Young, N., & Cooke, S. J.
- Affiliations
- NORCE Research AS (NO)
- Macquarie University (AU)
- Carleton University (CA)
- Zoological Society of London (GB)
- University of British Columbia (CA)
- ETH Zurich (CH)
- Bangor University (GB)
- University of Windsor (CA)
- Dalhousie University (CA)
- North Carolina Museum of Natural Sciences (US)
- Shedd Aquarium (US)
- Instituto de Oceanografia, Rio Grande, Rio Grande de Sul, Brazil
- South African Institute for Aquatic Biodiversity (ZA)
- University of Neuchâtel (CH)
- University of Ottawa (CA)
- Corresponding author
- Lennox, Robert J
- ORCID
- Venue
- BioScience
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-04-29
- Volume
- 70
- Issue
- 6
- Pages
- 468-476
- ISSN
- 0006-3568 (ISSN-L)
- 1525-3244
- OpenAlex
- W3023231788
- Cited by
- 35
- References
- 40
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 3 · 2023: 7 · 2022: 8 · 2021: 9 · 2020: 1
- FWCI
- 3.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- BioScience (publishedVersion, other-oa)
- Repository for Publications and Research Data (ETH Zurich) (publishedVersion, other-oa)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Nelson Mandela University Research Repository (submittedVersion)
- Figshare (submittedVersion)
- Scholarship at UWindsor (University of Windsor) (submittedVersion, cc-by)
- Scholarship at UWindsor (University of Windsor) (submittedVersion)
- NOVA (University of Newcastle Australia) (submittedVersion)
- ETH Zürich Research Collection
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Ocean Tracking Network
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Horizon 2020 Framework Programme (838237)
- Topics
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Conservation, Ecology, Wildlife Education
- Keywords
- animal tracking, biotelemetry, poaching, human disturbance, data sharing, threatened species
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