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Modeling misidentification errors in capture–recapture studies using photographic identification of evolving marks

Modeling misidentification errors in capture–recapture studies using photographic identification... Misidentification of animals is potentially important when naturally existing features (natural tags) are used to identify individual animals in a capture–recapture study. Photographic identification (photoID) typically uses photographic images of animals' naturally existing features as tags (photographic tags) and is subject to two main causes of identification errors: those related to quality of photographs (non‐evolving natural tags) and those related to changes in natural marks (evolving natural tags). The conventional methods for analysis of capture–recapture data do not account for identification errors, and to do so requires a detailed understanding of the misidentification mechanism. Focusing on the situation where errors are due to evolving natural tags, we propose a misidentification mechanism and outline a framework for modeling the effect of misidentification in closed population studies. We introduce methods for estimating population size based on this model. Using a simulation study, we show that conventional estimators can seriously overestimate population size when errors due to misidentification are ignored, and that, in comparison, our new estimators have better properties except in cases with low capture probabilities (<0.2) or low misidentification rates (<2.5%). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecology Wiley

Modeling misidentification errors in capture–recapture studies using photographic identification of evolving marks

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References (21)

Publisher
Wiley
Copyright
"© Society for Community Research and Action"
ISSN
0012-9658
eISSN
1939-9170
DOI
10.1890/08-0304.1
Publisher site
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Abstract

Misidentification of animals is potentially important when naturally existing features (natural tags) are used to identify individual animals in a capture–recapture study. Photographic identification (photoID) typically uses photographic images of animals' naturally existing features as tags (photographic tags) and is subject to two main causes of identification errors: those related to quality of photographs (non‐evolving natural tags) and those related to changes in natural marks (evolving natural tags). The conventional methods for analysis of capture–recapture data do not account for identification errors, and to do so requires a detailed understanding of the misidentification mechanism. Focusing on the situation where errors are due to evolving natural tags, we propose a misidentification mechanism and outline a framework for modeling the effect of misidentification in closed population studies. We introduce methods for estimating population size based on this model. Using a simulation study, we show that conventional estimators can seriously overestimate population size when errors due to misidentification are ignored, and that, in comparison, our new estimators have better properties except in cases with low capture probabilities (<0.2) or low misidentification rates (<2.5%).

Journal

Ecology – Wiley

Published: Jan 1, 2009

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