Centering Ecological Goals in Automated Identification of Individual Animals
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918461980016640 |
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| author | Picek, Lukas Haucke, Timm Adam, Lukáš Nepovinnykh, Ekaterina Otarashvili, Lasha Papafitsoros, Kostas Berger-Wolf, Tanya Brown, Michael B. Burghardt, Tilo Cermak, Vojtech Hedwig, Daniela Kitzes, Justin Lapp, Sam Maji, Subhransu Rubenstein, Daniel Subramonian, Arjun Stewart, Charles Zuffi, Silvia Beery, Sara |
| author_facet | Picek, Lukas Haucke, Timm Adam, Lukáš Nepovinnykh, Ekaterina Otarashvili, Lasha Papafitsoros, Kostas Berger-Wolf, Tanya Brown, Michael B. Burghardt, Tilo Cermak, Vojtech Hedwig, Daniela Kitzes, Justin Lapp, Sam Maji, Subhransu Rubenstein, Daniel Subramonian, Arjun Stewart, Charles Zuffi, Silvia Beery, Sara |
| contents | Recognizing individual animals over time is central to many ecological and conservation questions, including estimating abundance, survival, movement, and social structure. Recent advances in automated identification from images and even acoustic data suggest that this process could be greatly accelerated, yet their promise has not translated well into ecological practice. We argue that the main barrier is not the performance of the automated methods themselves, but a mismatch between how those methods are typically developed and evaluated, and how ecological data is actually collected, processed, reviewed, and used. Future progress, therefore, will depend less on algorithmic gains alone than on recognizing that the usefulness of automated identification is grounded in ecological context: it depends on what question is being asked, what data are available, and what kinds of mistakes matter. Only by centering these questions can we move toward automated identification of individuals that is not only accurate but also ecologically useful, transparent, and trustworthy. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_20626 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Centering Ecological Goals in Automated Identification of Individual Animals Picek, Lukas Haucke, Timm Adam, Lukáš Nepovinnykh, Ekaterina Otarashvili, Lasha Papafitsoros, Kostas Berger-Wolf, Tanya Brown, Michael B. Burghardt, Tilo Cermak, Vojtech Hedwig, Daniela Kitzes, Justin Lapp, Sam Maji, Subhransu Rubenstein, Daniel Subramonian, Arjun Stewart, Charles Zuffi, Silvia Beery, Sara Populations and Evolution Artificial Intelligence Recognizing individual animals over time is central to many ecological and conservation questions, including estimating abundance, survival, movement, and social structure. Recent advances in automated identification from images and even acoustic data suggest that this process could be greatly accelerated, yet their promise has not translated well into ecological practice. We argue that the main barrier is not the performance of the automated methods themselves, but a mismatch between how those methods are typically developed and evaluated, and how ecological data is actually collected, processed, reviewed, and used. Future progress, therefore, will depend less on algorithmic gains alone than on recognizing that the usefulness of automated identification is grounded in ecological context: it depends on what question is being asked, what data are available, and what kinds of mistakes matter. Only by centering these questions can we move toward automated identification of individuals that is not only accurate but also ecologically useful, transparent, and trustworthy. |
| title | Centering Ecological Goals in Automated Identification of Individual Animals |
| topic | Populations and Evolution Artificial Intelligence |
| url | https://arxiv.org/abs/2604.20626 |