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Automated image-based tracking and its application in ecology
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The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers.
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“ML is used to automate the classification of behaviours/activities (Kabra et al., 2013) and tracking movement trajectories (Dell et al., 2014).”
From Applications of machine learning in animal behaviour studies (2017) · cites this paper as Dell et al., 2014
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Metadata
- Authors
- Dell, A. I., Bender, J. A., Branson, K., Couzin, I. D., de Polavieja, G. G., Noldus, L. P. J. J., Pérez-Escudero, A., Perona, P., Straw, A. D., Wikelski, M., & Brose, U.
- Affiliations
- University of Göttingen (DE)
- Howard Hughes Medical Institute (US)
- Princeton University (US)
- Noldus Information Technology (NL)
- California Institute of Technology (US)
- Research Institute of Molecular Pathology (AT)
- Max Planck Society (DE)
- ORCID
- Venue
- Trends in Ecology & Evolution
- Publisher
- Elsevier BV
- Published
- 2014-07
- Volume
- 29
- Issue
- 7
- Pages
- 417-428
- ISSN
- 0169-5347 (ISSN-L)
- OpenAlex
- W2087262824
- Cited by
- 492
- References
- 110
- Citations by year
- 2026: 24 · 2025: 35 · 2024: 36 · 2023: 43 · 2022: 47 · 2021: 59 · 2020: 44 · 2019: 52 · 2018: 39 · 2017: 43 · 2016: 39 · 2015: 23 · 2014: 6
- FWCI
- 3.96
- Type
- journal article
- Language
- en
- Open access
- green
- nbn-resolving.de
- Locations
- Trends in Ecology & Evolution (publishedVersion)
- PubMed (publishedVersion)
- KOPS (University of Konstanz) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- HAL (submittedVersion, other-oa)
- GRO (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- National Science Foundation (1021010)
- Division of Environmental Biology
- Topics
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Environmental Science › Ecology › Animal Ecology and Behavior Studies
- Keywords
- animal behavior, biologging, telemetry, individual behavior, ecological interactions, life history, remote tracking, environmental drivers, high-throughput analysis
- MeSH terms
- Animals, Behavior, Animal, Ecology, Telemetry, Animal Distribution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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