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

Area of Science:

• Zoology
• Physiology
• Bioinformatics

Background:

• Manual scoring of thermal tolerance is time-consuming and prone to observer bias.
• Existing automated methods often rely on costly, proprietary software.
• Balancing accuracy and throughput is a key challenge in physiological measurements.

Purpose of the Study:

• To develop a novel, open-source strategy for automated scoring of insect thermal tolerance videos.
• To infer motor activity using motion detection for accurate thermal limit assessment.
• To provide a cost-effective alternative to existing automated video analysis tools.

Main Methods:

• Utilized a Python command-line application, DIME (detector of insect motion endpoint), for motion detection.
• Applied the DIME strategy to both dynamic and static thermal tolerance assays.
• Validated DIME's performance against manual scoring and visual estimates.

Main Results:

• DIME accurately measures thermal acclimation responses in insects.
• Results from DIME generally agree with visual estimates of thermal limits.
• The DIME application significantly increases throughput compared to manual scoring methods.

Conclusions:

• DIME offers a viable, open-source solution for automated thermal tolerance scoring.
• This method enhances efficiency and reduces observer bias in physiological assays.
• DIME provides a scalable and accurate approach for assessing insect thermal limits.