CV.eDNA: A hybrid approach to invertebrate biomonitoring using computer vision and DNA metabarcoding

Abstract

O_LIAutomated invertebrate classification using computer vision has shown significant potential to improve specimen processing efficiency. However, challenges such as invertebrate diversity and morphological similarity among taxa can make it difficult to infer fine-scale taxonomic classifications using computer vision. As a result, many invertebrate computer vision models are forced to make classifications at coarser levels, such as at family or order. C_LIO_LIHere we propose a novel modular method to combine computer vision and bulk DNA metabarcoding specimen processing pipelines to improve the accuracy and taxonomic granularity of individual specimen classifications. To improve specimen classification accuracy, our methods use multimodal fusion models that combine image data with DNA-based assemblage data. To refine the taxonomic granularity of the models classifications, our methods cross-references the classifications with DNA metabarcoding detections from bulk samples. We demonstrated these methods using a continental-scale, invertebrate bycatch dataset collected by the National Ecological Observatory Network. We also introduce the CV.eDNA R package, which aims to assist practitioners looking to implement our methods. C_LIO_LIUsing our methods, we reached a classification accuracy of 79.6% across the 17 taxa using real DNA assemblage data, and 83.6% when the assemblage data was "error-free", resulting in a 2.2% and 6.2% increase in accuracy when compared to a model trained using only images. After cross-referencing with the DNA metabarcoding detections, we improved taxonomic granularity in up to 72.2% of classifications, with up to 5.7% reaching species-level. C_LIO_LIBy providing computer vision models with coincident DNA assemblage data, and refining individual classifications using DNA metabarcoding detections, our methods the potential to greatly expand the capabilities of biological computer vision classifiers. Our methods allow computer vision classifiers to infer taxonomically fine-grained classifications when it would otherwise be difficult or impossible due to challenges of morphologic similarity or data scarcity. These methods are not limited to terrestrial invertebrates and could be applied in any instance where image and DNA metabarcoding data are concurrently collected. C_LI