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Measuring agreement among experts in classifying camera images of similar species

Camera trapping and solicitation of wildlife images through citizen science have become common tools in ecological research. Such studies collect many wildlife images for which correct species classification is crucial; even low misclassification rates can result in erroneous estimation of the geogr...

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Autores principales: Gooliaff, TJ, Hodges, Karen E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6262731/
https://www.ncbi.nlm.nih.gov/pubmed/30519423
http://dx.doi.org/10.1002/ece3.4567
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author Gooliaff, TJ
Hodges, Karen E.
author_facet Gooliaff, TJ
Hodges, Karen E.
author_sort Gooliaff, TJ
collection PubMed
description Camera trapping and solicitation of wildlife images through citizen science have become common tools in ecological research. Such studies collect many wildlife images for which correct species classification is crucial; even low misclassification rates can result in erroneous estimation of the geographic range or habitat use of a species, potentially hindering conservation or management efforts. However, some species are difficult to tell apart, making species classification challenging—but the literature on classification agreement rates among experts remains sparse. Here, we measure agreement among experts in distinguishing between images of two similar congeneric species, bobcats (Lynx rufus) and Canada lynx (Lynx canadensis). We asked experts to classify the species in selected images to test whether the season, background habitat, time of day, and the visible features of each animal (e.g., face, legs, tail) affected agreement among experts about the species in each image. Overall, experts had moderate agreement (Fleiss’ kappa = 0.64), but experts had varying levels of agreement depending on these image characteristics. Most images (71%) had ≥1 expert classification of “unknown,” and many images (39%) had some experts classify the image as “bobcat” while others classified it as “lynx.” Further, experts were inconsistent even with themselves, changing their classifications of numerous images when they were asked to reclassify the same images months later. These results suggest that classification of images by a single expert is unreliable for similar‐looking species. Most of the images did obtain a clear majority classification from the experts, although we emphasize that even majority classifications may be incorrect. We recommend that researchers using wildlife images consult multiple species experts to increase confidence in their image classifications of similar sympatric species. Still, when the presence of a species with similar sympatrics must be conclusive, physical or genetic evidence should be required.
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spelling pubmed-62627312018-12-05 Measuring agreement among experts in classifying camera images of similar species Gooliaff, TJ Hodges, Karen E. Ecol Evol Original Research Camera trapping and solicitation of wildlife images through citizen science have become common tools in ecological research. Such studies collect many wildlife images for which correct species classification is crucial; even low misclassification rates can result in erroneous estimation of the geographic range or habitat use of a species, potentially hindering conservation or management efforts. However, some species are difficult to tell apart, making species classification challenging—but the literature on classification agreement rates among experts remains sparse. Here, we measure agreement among experts in distinguishing between images of two similar congeneric species, bobcats (Lynx rufus) and Canada lynx (Lynx canadensis). We asked experts to classify the species in selected images to test whether the season, background habitat, time of day, and the visible features of each animal (e.g., face, legs, tail) affected agreement among experts about the species in each image. Overall, experts had moderate agreement (Fleiss’ kappa = 0.64), but experts had varying levels of agreement depending on these image characteristics. Most images (71%) had ≥1 expert classification of “unknown,” and many images (39%) had some experts classify the image as “bobcat” while others classified it as “lynx.” Further, experts were inconsistent even with themselves, changing their classifications of numerous images when they were asked to reclassify the same images months later. These results suggest that classification of images by a single expert is unreliable for similar‐looking species. Most of the images did obtain a clear majority classification from the experts, although we emphasize that even majority classifications may be incorrect. We recommend that researchers using wildlife images consult multiple species experts to increase confidence in their image classifications of similar sympatric species. Still, when the presence of a species with similar sympatrics must be conclusive, physical or genetic evidence should be required. John Wiley and Sons Inc. 2018-10-30 /pmc/articles/PMC6262731/ /pubmed/30519423 http://dx.doi.org/10.1002/ece3.4567 Text en © 2018 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Gooliaff, TJ
Hodges, Karen E.
Measuring agreement among experts in classifying camera images of similar species
title Measuring agreement among experts in classifying camera images of similar species
title_full Measuring agreement among experts in classifying camera images of similar species
title_fullStr Measuring agreement among experts in classifying camera images of similar species
title_full_unstemmed Measuring agreement among experts in classifying camera images of similar species
title_short Measuring agreement among experts in classifying camera images of similar species
title_sort measuring agreement among experts in classifying camera images of similar species
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6262731/
https://www.ncbi.nlm.nih.gov/pubmed/30519423
http://dx.doi.org/10.1002/ece3.4567
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