Sticky Pi, an AI-powered smart insect trap for community chronoecology

Abstract

Circadian clocks are paramount to insect survival and drive many aspects of their physiology and behaviour. While insect circadian behaviours have been extensively studied in the laboratory, their circadian activity within natural settings is poorly understood. The study of circadian activity necessitates measuring biological variables (e.g., locomotion) at high frequency (i.e., at least several times per hour) over multiple days, which has mostly confined insect chronobiology to the laboratory. In order to study insect circadian biology in the field, we developed the Sticky Pi, a novel, autonomous, open-source, insect trap that acquires images of sticky cards every twenty minutes. Using custom deep-learning algorithms, we automatically and accurately scored where, when and which insects were captured. First, we validated our device in controlled laboratory conditions with a classic chronobiological model organism, Drosophila melanogaster. Then, we deployed an array of Sticky Pis to the field to characterise the daily activity of an agricultural pest, Drosophila suzukii, and its parasitoid wasps. Finally, we demonstrate the wide scope of our smart trap by describing the sympatric arrangement of insect temporal niches in a community, without targeting particular taxa a priori. Together, the automatic identification and high sampling rate of our tool provide biologists with unique data that impacts research far beyond chronobiology; with applications to biodiversity monitoring and pest control as well as fundamental implications for phenology, behavioural ecology, and ecophysiology. We released the Sticky Pi project as an open community resource on https://doc.sticky-pi.com.