Assessing metabarcoding-based identifications for monitoring beetle communities in temperate forests

Abstract

With accelerating global change, there is an urgent need for rapid and comprehensive species monitoring programs to assess the status of insect assemblages; knowledge that is indispensable for the development of strategies that counteract insect declines. With the advent of high-throughput sequencing technologies, DNA metabarcoding has evolved into a particularly useful tool for speedy identification of species from bulk samples. Here, we evaluated the suitability of both tissue- and preservative-based DNA metabarcoding approaches for potential use in large-scale forest insect monitoring initiatives with a focus on beetle communities. For this purpose, we investigated the contents of pitfall (Barber) traps and corresponding cross-window traps for the presence of Coleoptera species. We also compared the performance of three common preservative liquids in terms of species and DNA preservation and determined the optimal time interval for trap replacement. To assess how well the investigated forest beetle community can actually be represented by metabarcoding, we identified ground beetles (Carabidae) of each Barber trap sample using (a) morphological identification; (b) DNA metabarcoding of the homogenised tissues; and (c) DNA metabarcoding of the preservative liquids used in the traps. Finally, we evaluated the influence of the number of DNA extraction and PCR replicates on taxon detection. Even though our study was limited to a single location and peak season, we succeeded in detecting the DNA of 389 mostly plausible beetle species across a total of 54 samples. Effects of preservative liquids were small, although more species were captured by ethanol-filled traps. We further observed an increase in detected beetle species with increasing length of trapping intervals. Overall, we found tissue-based metabarcoding approaches employing a well-designed DNA extraction (and possibly PCR) replication strategy to represent a powerful option for monitoring forest beetles and potentially other insect communities. The preservative-based approach we used needs further optimisation.