Error rates in wildlife image classification

Gooliaff, TJ; Hodges, Karen E.

doi:10.1002/ece3.5256

Cited by 2 publications

(1 citation statement)

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“…When ecologists use photographs as evidence of species presence, the veracity of the identification depends on a number of factors, namely the quality of the photograph, the experience and training of the identifier, and the taxonomic evidence that is used to classify the species. Studies have investigated the influence of the quality and context of photographs and the experience and training of the identifier, but have failed to consider what taxonomic evidence is used by the identifier (Gooliaff & Hodges, 2018, 2019; Meek et al., 2013; Thornton et al., 2019). These issues are exacerbated when morphologically similar species occur within a dataset, necessitating high‐quality photographs, trained observers, and rigorous taxonomic evidence.…”

Section: Introductionmentioning

confidence: 99%

Linking camera‐trap data to taxonomy: Identifying photographs of morphologically similar chipmunks

McKibben

Frey

2021

Ecology and Evolution

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Remote cameras are a common method for surveying wildlife and recently have been promoted for implementing large-scale regional biodiversity monitoring programs.The use of camera-trap data depends on the correct identification of animals captured in the photographs, yet misidentification rates can be high, especially when morphologically similar species co-occur, and this can lead to faulty inferences and hinder conservation efforts. Correct identification is dependent on diagnosable taxonomic characters, photograph quality, and the experience and training of the observer. However, keys rooted in taxonomy are rarely used for the identification of camera-trap images and error rates are rarely assessed, even when morphologically similar species are present in the study area. We tested a method for ensuring high identification accuracy using two sympatric and morphologically similar chipmunk (Neotamias) species as a case study. We hypothesized that the identification accuracy would improve with use of the identification key and with observer training, resulting in higher levels of observer confidence and higher levels of agreement among observers. We developed an identification key and tested identification accuracy based on photographs of verified museum specimens. Our results supported predictions for each of these hypotheses. In addition, we validated the method in the field by comparing remote-camera data with live-trapping data. We recommend use of these methods to evaluate error rates and to exclude ambiguous records in camera-trap datasets. We urge that ensuring correct and scientifically defensible species identifications is incumbent on researchers and should be incorporated into the camera-trap workflow.

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Section: Introductionmentioning

confidence: 99%

Linking camera‐trap data to taxonomy: Identifying photographs of morphologically similar chipmunks

McKibben

Frey

2021

Ecology and Evolution

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Which mammals can be identified from camera traps and crowdsourced photographs?

Kays

Lasky

Allen

et al. 2022

Journal of Mammalogy

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While museum voucher specimens continue to be the standard for species identifications, biodiversity data are increasingly represented by photographic records from camera traps and amateur naturalists. Some species are easily recognized in these pictures, others are impossible to distinguish. Here we quantify the extent to which 335 terrestrial nonvolant North American mammals can be identified in typical photographs, with and without considering species range maps. We evaluated all pairwise comparisons of species and judged, based on professional opinion, whether they are visually distinguishable in typical pictures from camera traps or the iNaturalist crowdsourced platform on a 4-point scale: (1) always, (2) usually, (3) rarely, or (4) never. Most (96.5%) of the 55,944 pairwise comparisons were ranked as always or usually distinguishable in a photograph, leaving exactly 2,000 pairs of species that can rarely or never be distinguished from typical pictures, primarily within clades such as shrews and small-bodied rodents. Accounting for a species geographic range eliminates many problematic comparisons, such that the average number of difficult or impossible-to-distinguish species pairs from any location was 7.3 when considering all species, or 0.37 when considering only those typically surveyed with camera traps. The greatest diversity of difficult-to-distinguish species was in Arizona and New Mexico, with 57 difficult pairs of species, suggesting the problem scales with overall species diversity. Our results show which species are most readily differentiated by photographic data and which taxa should be identified only to higher taxonomic levels (e.g., genus). Our results are relevant to ecologists, as well as those using artificial intelligence to identify species in photographs, but also serve as a reminder that continued study of mammals through museum vouchers is critical since it is the only way to accurately identify many smaller species, provides a wealth of data unattainable from photographs, and constrains photographic records via accurate range maps. Ongoing specimen voucher collection, in addition to photographs, will become even more important as species ranges change, and photographic evidence alone will not be sufficient to document these dynamics for many species.

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Error rates in wildlife image classification

Abstract: We address the comments made by Thornton et al. (Ecology and Evolution, 2019) in response to our recent article on measuring the agreement among experts in classifying camera images of bobcats and Canada lynx.

Cited by 2 publications

References 9 publications

Linking camera‐trap data to taxonomy: Identifying photographs of morphologically similar chipmunks

Linking camera‐trap data to taxonomy: Identifying photographs of morphologically similar chipmunks

Which mammals can be identified from camera traps and crowdsourced photographs?

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