Automated insect monitoring with camera traps is transforming ecological understanding

Mark Andrew Kusk Gillespie, Kim Bjerge, Jamie Alison, Hannah Sørine Gerlich , Guillaume Mougeot, Asger Svenning, Simon F. A. Wogram, Toke T. Høye

Addressing global declines in insect biodiversity requires both ecological restoration and high-quality monitoring data. While long-term participatory schemes have been foundational, recent advances in automated recording and AI-based identification offer transformative but undocumented potential. Here, we show how leveraging insect camera traps, deep learning models and statistics drives a step-change in ecological knowledge. We highlight four key areas of ecological understanding: phenology, abundance, richness, and community dynamics, and show how automated data can correct phenological estimates by weeks and improve biodiversity assessments. Data from insect camera traps offer unprecedented resolution and scalability, making them powerful tools for tracking insect communities and informing conservation strategies.