Scoring thermal tolerance traits live or with recorded video can be time consuming and susceptible to observer bias, and as with many physiological measurements, there can be trade-offs between accuracy and throughput. Recent studies show that automated particle tracking is a viable alternative to manually scoring videos, although some of the software options are proprietary and costly. In this study, we present a novel strategy for automated scoring of thermal tolerance videos by inferring motor activity with motion detection using an open-source Python command line application called DIME (detector of insect motion endpoint). We apply our strategy to both dynamic and static thermal tolerance assays, and our results indicate that DIME can accurately measure thermal acclimation responses, generally agrees with visual estimates of thermal limits, and can significantly increase throughput over manual methods.

Department of Ecology Faculty of Environmental Sciences Czech University of Life Sciences Prague 165 00 Praha Czech Republic

Department of Entomology University of Kentucky Lexington KY 40508 USA

Department of Mechanical Engineering University of Kentucky Lexington KY 40508 USA

Citace poskytuje Crossref.org

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Dryad
10.5061/dryad.cfxpnvxc2