Modern genetic biocontrol techniques for insect pest management, when compared to chemical insecticide spraying, offer high species specificity and reduced environmental impact. However, some of these methods require the environmental release of genetically modified insects. Because organisms exposed to different environments often show variability in phenotype and gene expression, it is likely that genetically modified insects will also experience environmentally mediated variation, potentially compromising pest control efficiency. This study examines the impact of temperature and nutrition on the early embryonic Tet-off conditional lethality system in Drosophila melanogaster. By independently manipulating parental and offspring environments, we assessed how treatment exposure influenced the probability of larval hatching and the transcript abundance of the transgenic system. Our findings revealed that (1) transgene performance distinctly responds to temperature and nutrition, (2) thermal stress has a greater impact when embryos, rather than parents, are exposed, and (3) extreme nutritional conditions can markedly reduce the penetrance of transgenic lethality. Although changes in transgene transcript abundance were observed, they did not fully explain the phenotypic variation, suggesting that factors downstream of transcription likely drive variation in transgenic lethality.

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.