Towards reliable estimates of abundance trends using automated non‐lethal moth traps

Abstract

Monitoring insect abundance or species richness at high spatial and temporal resolution is difficult due to personnel, maintenance, and post‐processing costs as well as ethical considerations. Non‐invasive automated insect monitoring systems could provide a solution to address these constraints. However, every new insect monitoring design needs to be evaluated with respect to reliability and bias based on comparisons with conventional methods. In this study, we evaluate the effectiveness of an automated moth trap (AMT), built from off‐the‐shelf‐hardware, in capturing declines in moth abundance, by comparing it to a conventional, lethal trap. Both trap types were operated five times on 16 plots from the beginning of July 2021 to the end of August 2021. On average AMTs recorded fewer individuals than conventional traps. However, both trap types depicted the same seasonal decline of approximately 3% per day, which corresponded to a total difference of ~85% over the sampling period. Given our sample size, both trap types had the same limitations in their reliability to detect smaller changes in abundance trends. This first proof of concept demonstrated that AMTs depict large magnitude events such as phenological patterns just as well as conventional, lethal traps. Therefore, AMTs are a promising tool for future autonomous and non‐lethal monitoring, which paves the way for high temporal coverage and resolution in insect monitoring. However, this initial quantitative field test revealed that its long‐term applicability must be preceded by several adjustments to the image quality, power supply and to data transfer.

Large uncertainty in trait responses across insects among overall declines in a subtropical city

“…This approach should integrate traditional taxonomic methods, contemporary molecular approaches and advanced automated monitoring and identification tools (Slade & Ong, 2023). Despite current limitations in taxonomic, geographic and life history knowledge, the ongoing development of methods like metabarcoding (e.g., Theodorou et al, 2020) and automated insect sensors (e.g., Möglich et al, 2023), coupled with comprehensive trait assembly (e.g., Coleman et al, 2023; Shirey et al, 2021), presents promising avenues for expanding and scaling future research.…”

Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects

“…Nonetheless, direct field observations on the presence and behaviour of individuals on and around the camera system could help refine sampling strategies and assess the validity of model assumptions and predictions. Valuable information could come from mark–recapture or removal of individuals attracted to the system or by alternative sampling methods deployed in the vicinity of the camera system [ 55 ].…”

“…lethal moth traps) for validation or calibration purposes (e.g. [ 55 ]). Informal comparisons may, however, help justify the suboptimal, yet sometimes essential pooling of data collected by different methods over time.…”

Nature 4.0: A networked sensor system for integrated biodiversity monitoring

“…In the Nature 4.0 project, we developed several low‐cost automated moth traps as tools for monitoring moth abundance (Mielke Möglich et al., 2023; Figure 9). Our automated moth trap is built from off‐the‐shelf components, which are powered by batteries or solar panels.…”