Do camera light traps for moths provide similar data as conventional funnel light traps?

Abstract Insects are of crucial importance for terrestrial ecosystems, but many populations decline rapidly. Conventional collecting methods are usually time‐consuming, resulting in a low temporal, spatial and taxonomic resolution of data. Automated camera light traps (CLTs) allow non‐lethal monitoring of species‐rich moths (Lepidoptera) and other nocturnal insects, but so far little is known about their performance compared to conventional collecting methods. By observing the behaviour of moths in previous field work, we hypothesised that CLTs perform well in moth groups in which species tend to sit down quietly after approaching the lamp (such as Geometridae) but worse in moth groups in which species are persistently active (such as Sphingidae). We tested the performance of two CLTs, equipped with Sony alpha 7II (24 megapixel sensor) cameras that resulted in images with approx. Four hundred twenty dpi resolution. The study was carried out in a forested area near Bielefeld in NW Germany for 196 nights in a row from March to October 2023, and photos were taken every 2 min during the night. All macromoths recognisable in the photographs were identified and counted individually. We directly compared the data from the CLTs with moth samples obtained from conventional funnel light traps (FLTs) during 12 nights which were spread across the flight season. The resulting images from the CLTs allowed reliable species identification of all observed macromoths with only a few exceptions due to technical problems. In direct comparison during 12 nights, CLTs recorded 39 species exclusively, FLTs recorded 48 species exclusively, and 53 species were recorded by both methods equally. During the whole sampling period of 196 nights in a row, a total of 225 moth species were recorded by CLTs. We found six indicator species for CLTs (all Geometridae) and one species for FLT, a hawkmoth species. Families differed in the length to which they remained on the screen of the CLTs. Our study was the first to systematically compare the methods and it shows that CLTs perform overall very well. Results from CLTs differ to a certain extent from conventional trapping methods because they seem to perform worse in groups with highly active species and perform better in calmer groups like geometrid moths. CLTs are promising devices for insect monitoring since they deliver data with high resolution in time, space and taxonomy. The use of artificial intelligence (AI) for the analysis of images is intended as the next logic step