Improving taxonomic resolution, biomass and abundance assessments of aquatic invertebrates by combining imaging and DNA megabarcoding

Understanding biodiversity change requires a comprehensive assessment of not only the identity of species inhabiting an ecosystem but also their biomass and abundance. However, assessing biodiversity on the species level with precise biomass information is a time-consuming process and thus rarely applied. While DNA-based approaches like DNA barcoding offer precise species identification, they lack information on specimen size and biomass. In contrast, high-throughput imaging techniques enable rapid measurements of a specimen's size and morphological features but may have low taxonomic resolution. In this study, we combined DNA megabarcoding, <i>i.e.</i>, high-throughput barcoding of single specimens, with semi-automated imaging and deep neural networks to produce accurate taxonomic identifications, abundance, and biomass estimations for insects. In a multiple stressor field experiment, we collected a dataset of 743 specimens from 14 species of the orders Ephemeroptera, Plecoptera, and Trichoptera (EPT), which are routinely used as aquatic biological quality indicator taxa. Each specimen was imaged, weighed, and megabarcoded using the COI barcode gene. From the images captured using the semi-automated imaging device BIODISCOVER, we curated a final dataset of 146,439 images taken from two perpendicular cameras. We trained convolutional neural networks (CNNs) with these pictures for species identification and biomass estimation and evaluated their performance. In addition, we investigated whether models pre-trained for species identification perform better on the biomass estimation task, compared to models trained solely for biomass estimation, thus potentially reducing the need for extensive labelled data in future studies. Our findings demonstrate that combining DNA megabarcoding with automated imaging and deep neural networks enables fast, reliable, but also comprehensive assessment of species composition and biomass on the specimen level, contributing to the urgently needed methods in conservation biology, ecology, and evolution.