The genomic revolution has fundamentally changed how we survey biodiversity on earth. High-throughput sequencing ("HTS") platforms now enable the rapid sequencing of DNA from diverse kinds of environmental samples (termed "environmental DNA" or "eDNA"). Coupling HTS with our ability to associate sequences from eDNA with a taxonomic name is called "eDNA metabarcoding" and offers a powerful molecular tool capable of noninvasively surveying species richness from many ecosystems. Here, we review the use of eDNA metabarcoding for surveying animal and plant richness, and the challenges in using eDNA approaches to estimate relative abundance. We highlight eDNA applications in freshwater, marine and terrestrial environments, and in this broad context, we distill what is known about the ability of different eDNA sample types to approximate richness in space and across time. We provide guiding questions for study design and discuss the eDNA metabarcoding workflow with a focus on primers and library preparation methods.We additionally discuss important criteria for consideration of bioinformatic filtering of data sets, with recommendations for increasing transparency. Finally, looking to the future, we discuss emerging applications of eDNA metabarcoding in ecology, conservation, invasion biology, biomonitoring, and how eDNA metabarcoding can empower citizen science and biodiversity education.--

Environmental DNA (eDNA) monitoring is a novel molecular technique to detect species in natural habitats. Many eDNA studies in aquatic systems have focused on lake or ponds, and/or on large vertebrate species, but applications to invertebrates in river systems are emerging. A challenge in applying eDNA monitoring in flowing waters is that a species' DNA can be transported downstream. Whether and how far eDNA can be detected due to downstream transport remains largely unknown. In this study we tested for downstream detection of eDNA for two invertebrate species, Daphnia longispina and Unio tumidus, which are lake dwelling species in our study area. The goal was to determine how far away from the source population in a lake their eDNA could be detected in an outflowing river. We sampled water from eleven river sites in regular intervals up to 12.3 km downstream of the lake, developed new eDNA probes for both species, and used a standard PCR and Sanger sequencing detection method to confirm presence of each species' eDNA in the river. We detected D. longispina at all locations and across two time points (July and October); whereas with U. tumidus, we observed a decreased detection rate and did not detect its eDNA after 9.1 km. We also observed a difference in detection for this species at different times of year. The observed movement of eDNA from the source amounting to nearly 10 km for these species indicates that the resolution of an eDNA sample can be large in river systems. Our results indicate that there may be species' specific transport distances for eDNA and demonstrate for the first time that invertebrate eDNA can persist over relatively large distances in a natural river system.

DNA sampled from the environment (eDNA) is a useful way to uncover biodiversity patterns. By combining a conceptual model and empirical data, we test whether eDNA transported in river networks can be used as an integrative way to assess eukaryotic biodiversity for broad spatial scales and across the land–water interface. Using an eDNA metabarcode approach, we detect 296 families of eukaryotes, spanning 19 phyla across the catchment of a river. We show for a subset of these families that eDNA samples overcome spatial autocorrelation biases associated with the classical community assessments by integrating biodiversity information over space. In addition, we demonstrate that many terrestrial species are detected; thus suggesting eDNA in river water also incorporates biodiversity information across terrestrial and aquatic biomes. Environmental DNA transported in river networks offers a novel and spatially integrated way to assess the total biodiversity for whole landscapes and will transform biodiversity data acquisition in ecology.