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Optimizing automated photo identification for population assessments
Journal article   Peer reviewed

Optimizing automated photo identification for population assessments

Philip T. Patton, Krishna Pacifici, Robin W. Baird, Erin M. Oleson, Jason B. Allen, Erin Ashe, Aline Athayde, Charla J. Basran, Elsa Cabrera, John Calambokidis, …
Conservation biology, Vol.39(4), pp.e14436-n/a
08/2025
DOI: https://doi.org/10.1111/cobi.14436
PMID: 39807876
Appears in  Recent Faculty of Science and Engineering Publications

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Abstract

artificial intelligence captura-recaptura cetacean Jolly-Seber misidentification optimization stock assessment
enThis link goes to a English sectionesThis link goes to a Spanish sectionzhThis link goes to a Chinese section Several legal acts mandate that management agencies regularly assess biological populations. For species with distinct markings, these assessments can be conducted noninvasively via capture-recapture and photographic identification (photo-ID), which involves processing considerable quantities of photographic data. To ease this burden, agencies increasingly rely on automated identification (ID) algorithms. Identification algorithms present agencies with an opportunity—reducing the cost of population assessments—and a challenge—propagating misidentifications into abundance estimates at a large scale. We explored several strategies for generating capture histories with an ID algorithm, evaluating trade-offs between labor costs and estimation error in a hypothetical population assessment. To that end, we conducted a simulation study informed by 39 photo-ID datasets representing 24 cetacean species. We fed the results into a custom optimization tool to discern the optimal strategy for each dataset. Our strategies included choosing between truly and partially automated photo-ID and, in the case of the latter, choosing the number of suggested matches to inspect. True automation was optimal for datasets for which the algorithm identified individuals well. As identification performance declined, the optimization recommended that users inspect more suggested matches from the ID algorithm, particularly for small datasets. False negatives (i.e., individual was resighted but erroneously marked as a first capture) strongly predicted estimation error. A 2% increase in the false negative rate translated to a 5% increase in the relative bias in abundance estimates. Our framework can be used to estimate expected error of the abundance estimate, project labor effort, and find the optimal strategy for a dataset and algorithm. We recommend estimating a strategy's false negative rate before implementing the strategy in a population assessment. Our framework provides organizations with insights into the conservation benefits and consequences of automation as conservation enters a new era of artificial intelligence for population assessments.

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