Abstract Insects represent the most diverse group of organisms on Earth and comprise the majority of known species; yet they are seldom accounted for in large‐scale biodiversity monitoring systems and conservation planning. We have developed the Mothbox —an open source automated light trap that makes insect monitoring accessible to non‐specialists and scalable for scientific and conservation purposes—and the Mothbot —a machine learning tool and user interface for processing data from automated light traps. The Mothbox is portable, durable and low cost, while Mothbot prioritizes human‐in‐the‐loop data validation of computer vision outputs to ensure data quality and aid in developing custom image reference libraries. Mothbox has been extensively tested in field conditions, with >185 deployments, spanning >450 nights and >100 sampling locations. We present proof‐of‐concept studies that examine shifts in insect activity and richness across (i) time intervals throughout the night at a single location, and (ii) different habitat types, to assess whether these patterns can be captured through automated sampling with Mothbox hardware and Mothbot processing. As the field of automated insect monitoring continues to develop, Mothbox provides an affordable entry point for a range of stakeholders. It enables entomologists to explore new research questions by tracking high temporal resolution changes in insect activity, as well as systematically monitoring insects across landscapes. Mothbox will help build global insect‐monitoring capacity via autonomous sampling, improving our ability to detect biodiversity loss and guide effective conservation action.
Szczygieł et al. (Fri,) studied this question.