Scoring thermal limits in small insects using open-source, computer assisted motion detection

Scoring large amounts of thermal tolerance traits live or with recorded video can be time consuming and susceptible to investigator bias, and as with many physiological measurements, there can be trade-offs between accuracy and throughput. Recent studies show that particle tracking is a viable alternative to manually scoring videos, although it may not detect subtle movements, and many 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 the throughput over manual methods.

Methods
Data is collected from observing videos from bioassays which are scored by humans or by an algorithm using the proposed tool DIME.