Refinement of behavioral and locomotive data acquisition in caterpillars, through Python programing and 3D modeling software

Larval insects exhibit numerous behaviors to move through their environment in response to a host of different stimuli. Understanding this behavior is important to numerous different fields of study, from ecological conservation to molecular genetics. Specifically, lepidopteran larvae spend the majority of their life in the search of nutrients to support the energy demands of metamorphosis. Previous studies in our lab have demonstrated these tiny organisms respond to environmental stimuli; however, this process is massively time consuming with current imaging software. Our study attempts to refine the process of collecting behavioral data. This study aims to investigate a lepidopteran species, Trichoplusia ni, with specific focus on their change in behavioral patterns when exposed to different stimuli. With water as our control, we predict a change in their behavior when exposed to an appetitive stimulus, food, and an aversive stimulus, regurgitant. This is accomplished by using 3D modeling software to create a circular behavioral chamber that holds the stimuli or controls in the center. In this housing, the larvae can be placed equidistant from the stimulus and then recorded for 30 minutes. Using python programing, we developed the imaging software needed to track the path, distance traveled, and time spent in concentric zones to a near 90% confidence level. This research enhances our comprehension of behaviors exhibited by T. ni, whilst improving data acquisition and lowering the time requirement for future experiments.