Inexpensive monitoring of flying insect activity and abundance using wildlife cameras

Abstract The ability to measure flying insect activity and abundance is important for ecologists, conservationists and agronomists alike. However, existing methods are laborious and produce data with low temporal resolution (e.g. trapping and direct observation), or are expensive, technically complex, and require vehicle access to field sites (e.g. radar and lidar entomology).We propose a method called “camfi” for long-term non-invasive monitoring of the activity and abundance of low-flying insects using images obtained from inexpensive wildlife cameras, which retail for under USD$100 and are simple to operate. We show that in certain circumstances, this method facilitates measurement of wingbeat frequency, a diagnostic parameter for species identification. To increase usefulness of our method for very large monitoring programs, we have developed and implemented a tool for automatic detection and annotation of flying insect targets based on the popular Mask R-CNN framework. This tool can be trained to detect and annotate insects in a few hours, taking advantage of transfer learning.We demonstrate the utility of the method by measuring activity levels and wingbeat frequencies in Australian Bogong moths Agrotis infusa in the Snowy Mountains of New South Wales, and find that these moths have log-normally distributed wingbeat frequencies (mean = 49.4 Hz, std = 5.25 Hz), undertake dusk flights in large numbers, and that the intensity of their dusk flights is modulated by daily weather factors. Validation of our tool for automatic image annotation gives baseline performance metrics for comparisons with future annotation models. The tool performs well on our test set, and produces annotations which can be easily modified by hand if required. Training completed in less than 2 h on a single machine, and inference took on average 1.15 s per image on a laptop.Our method will prove invaluable for ongoing efforts to understand the behaviour and ecology of the iconic Bogong moth, and can easily be adapted to other flying insects. The method is particularly suited to studies on low-flying insects in remote areas, and is suitable for very large-scale monitoring programs, or programs with relatively low budgets.