Towards a toolkit for global insect biodiversity monitoring

Abstract

Insects are the most diverse group of animals on Earth, yet our knowledge of their diversity, ecology and population trends remains abysmally poor. Four major technological approaches are coming to fruition for use in insect monitoring and ecological research-molecular methods, computer vision, autonomous acoustic monitoring and radar-based remote sensing-each of which has seen major advances over the past years. Together, they have the potential to revolutionize insect ecology, and to make all-taxa, fine-grained insect monitoring feasible across the globe. So far, advances within and among technologies have largely taken place in isolation, and parallel efforts among projects have led to redundancy and a methodological sprawl; yet, given the commonalities in their goals and approaches, increased collaboration among projects and integration across technologies could provide unprecedented improvements in taxonomic and spatio-temporal resolution and coverage. This theme issue showcases recent developments and state-of-the-art applications of these technologies, and outlines the way forward regarding data processing, cost-effectiveness, meaningful trend analysis, technological integration and open data requirements. Together, these papers set the stage for the future of automated insect monitoring. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Keywords: arthropods; ecosystem health; insects; monitoring.

Figure 1.

The complementarity and challenges for the widespread implementation of the four technological approaches to insect monitoring treated in this theme issue: molecular methods, computer vision, autonomous acoustic monitoring and radar-based remote sensing. The six classes of essential biodiversity variables (EBV's) to which each method contributes are shown with increasing opacity based on the assessed contribution of the technology (dark, strong contribution; light, little to no contribution). Temporal resolution refers to the duration of a typical sample, i.e. the aggregation of information to form one sample (minutes between photos, minutes of acoustic recording or radar sweeps, or the days to millennia over which molecular traces aggregate). Spatial coverage indicates the spatial area one sample can cover. For temporal resolution and spatial coverage, the typical range of application is depicted as a solid bar, with the extreme limits of the technology depicted as a black circle. The final column summarizes the most important challenges for widespread implementation; additionally all technologies require specialized skills and training and all face the challenge of handling and processing large data-volumes.