In the face of severe environmental crises that threaten insect biodiversity, new technologies are imperative to monitor both the identity and ecology of insect species. Traditionally, insect surveys rely on manual collection of traps, which provide abundance data but mask the large intra- and inter-day variations in insect activity, an important facet of their ecology. Although laboratory studies have shown that circadian processes are central to insects' biological functions, from feeding to reproduction, we lack the high-frequency monitoring tools to study insect circadian biology in the field. To address these issues, Geissmann et al. developed the Sticky Pi, a novel, autonomous, open-source, insect trap that acquires images of sticky cards every 20 minutes. Using custom deep learning algorithms, Sticky Pi automatically and accurately scored where, when, and which insects were captured. The automatic identification and high sampling rate of this tool provide biologists with unique data with the potential to impact research far beyond chronobiology, with applications to biodiversity monitoring and pest control as well as fundamental implications for phenology, behavioural ecology, and ecophysiology. The Sticky Pi project was released as an open community resource on https://doc.sticky-pi.com. The image shows Sticky Pi in a strawberry field at the University of British Columbia farm.

Image Credit: Quentin Geissmann