New Preprint: Scalable Insect Monitoring Systems

We are pleased to share a new preprint co-authored by Jacob Kamminga and colleagues:

“Insect monitoring without pitfalls: seven steps for robust insect sensing systems”
Published on EcoEvoRxiv (2026)
🔗 https://lnkd.in/eUtz-B9n

In the context of ongoing discussions surrounding global insect biodiversity decline, a critical challenge remains the lack of long-term, large-scale, and unbiased monitoring data. Insects play an essential role in ecosystem functioning, yet current monitoring approaches often suffer from spatial, temporal, and methodological limitations.

This paper proposes seven practical steps for transitioning from proof-of-concept sensing solutions to scalable, reliable, and transparent insect monitoring infrastructures. The work integrates expertise from ecology, artificial intelligence, and sensor engineering, addressing both technical and operational challenges involved in automated biodiversity monitoring.

Main contributions

• Sensor-based insect detection (e.g., camera traps, radar systems, optical sensors)
• AI-driven image classification and signal processing for species recognition
• Methodological guidance for reducing bias and improving robustness
• Strategies for responsible scaling and long-term deployment of monitoring systems
• Operational considerations for real-world implementation

By outlining a structured framework for robust insect sensing systems, this work contributes to the development of reliable data infrastructures that can support evidence-based biodiversity research and conservation policy.

We congratulate Jacob and the full author team on this important interdisciplinary contribution!