Animal biometrics: quantifying and detecting phenotypic appearance
Abstract
Animal biometrics is an emerging field that develops quantified approaches for representing and detecting the phenotypic appearance of species, individuals, behaviors, and morphological traits. It operates at the intersection between pattern recognition, ecology, and information sciences, producing computerized systems for phenotypic measurement and interpretation. Animal biometrics can benefit a wide range of disciplines, including biogeography, population ecology, and behavioral research. Currently, real-world applications are gaining momentum, augmenting the quantity and quality of ecological data collection and processing. However, to advance animal biometrics will require integration of methodologies among the scientific disciplines involved. Such efforts will be worthwhile because the great potential of this approach rests with the formal abstraction of phenomics, to create tractable interfaces between different organizational levels of life.
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The measures yielded by this conversion are sometimes referred to as animal biometrics.
“The measures yielded by this conversion, reviewed in this section, are also sometimes referred to as animal biometrics93.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [93]
As an alternative to gene-based identification, manual tagging can be used to keep track of individual animals.
“As an alternative to gene-based identification, manual tagging can be used to keep track of individual animals10,93.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [93]
The standard for identification of animal species and identity is DNA profiling, which can be difficult to scale to large, distributed populations.
“The standard for identification of animal species and identity is DNA profiling97, which can be difficult to scale to large, distributed populations54,93.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [93]
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- Authors
- Kühl, H. S., & Burghardt, T.
- Affiliations
- Max Planck Institute for Evolutionary Anthropology (DE)
- University of Bristol (GB)
- Corresponding author
- Kühl, Hjalmar S.
- Venue
- Trends in Ecology & Evolution
- Publisher
- Elsevier BV
- Published
- 2013-07
- Volume
- 28
- Issue
- 7
- Pages
- 432-441
- ISSN
- 0169-5347 (ISSN-L)
- OpenAlex
- W2032593695
- Cited by
- 189
- References
- 86
- Citations by year
- 2026: 10 · 2025: 10 · 2024: 8 · 2023: 13 · 2022: 19 · 2021: 25 · 2020: 13 · 2019: 15 · 2018: 15 · 2017: 24 · 2016: 14 · 2015: 12 · 2014: 9 · 2013: 2
- FWCI
- 4.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Trends in Ecology & Evolution (publishedVersion)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- Funders
- University of Bristol
- Engineering and Physical Sciences Research Council (EP/E500110/1)
- Topics
- Physical Sciences › Mathematics › Geometry and Topology › Morphological variations and asymmetry
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Animal Behavior and Reproduction
- Keywords
- animal biometrics, phenomics, phenotypic measurement, morphological characters, pattern recognition, biogeography, population ecology, animal behavior
- MeSH terms
- Animals, Biometry, Ecology, Pattern Recognition, Automated, Phenotype
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