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Test responses: Species identification by experts and non-experts: comparing images from field guides

Austen, Gail E. and Bindemann, Markus and Griffiths, Richard A. and Roberts, David L. (2023) Test responses: Species identification by experts and non-experts: comparing images from field guides. [Data Collection]

Description

Accurate species identification is fundamental when recording ecological data. However, the ability to correctly identify organisms visually is rarely questioned. We investigated how experts and non-experts compared in the identification of bumblebees, a group of insects of considerable conservation concern. Experts and non-experts were asked whether two concurrent bumblebee images depicted the same or two different species. Overall accuracy was below 60% and comparable for experts and non-experts. However, experts were more consistent in their answers when the same images were repeated, and more cautious in committing to a definitive answer. Our findings demonstrate the difficulty of correctly identifying bumblebees using images from field guides. Such error rates need to be accounted for when interpreting species data, whether or not they have been collected by experts. We suggest that investigation of how experts and non-experts make observations should be incorporated into study design, and could be used to improve training in species identification.

Additional information: The original location of this dataset was https://kar.kent.ac.uk/57048
Subjects: Q Science > QH Natural history
Q Science > QL Zoology
DOI: 10.22024/UniKent/01.01.191
Divisions: Divisions > Division of Human and Social Sciences > School of Anthropology and Conservation
Divisions > Division of Human and Social Sciences > School of Anthropology and Conservation > DICE (Durrell Institute of Conservation and Ecology)
Divisions > Division of Human and Social Sciences > School of Psychology
Depositing User: Gail Austen
Collection period:
From
To
UNSPECIFIED
2016
Last Modified: 08 Aug 2023 10:18
Publication Date: 8 August 2023
URI: https://data.kent.ac.uk/id/eprint/191

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