The alarming declines of freshwater biodiversity call for efficient biomonitoring at fine spatiotemporal scales, such that conservation measures be grounded upon accurate biodiversity data. Here, we show that combining environmental DNA (eDNA) extracted from stream water samples with models based on hydrological first principles allows upscaling biodiversity estimates for aquatic insects at very high spatial resolution. Our model decouples the diverse upstream contributions to the eDNA data, enabling the reconstruction of taxa distribution patterns. Across a 740-km 2 basin, we obtain a space-filling biodiversity prediction at a grain size resolution of 1-km long stream sections. The model’s accuracy in matching direct observations of aquatic insects’ local occurrence ranges between 57–100%. Our results demonstrate how eDNA can be used for high-resolution biodiversity assessments in rivers with minimal prior knowledge of the system. Our approach allows identification of biodiversity hotspots that could be otherwise overlooked, enabling implementation of focused conservation strategies.

Assessing individual components of biodiversity, such as local or regional taxon richness, and differences in community composition is a long‐standing challenge in ecology. It is especially relevant in spatially structured and diverse ecosystems. Environmental DNA (eDNA) has been suggested as a novel technique to detect taxa and therefore may allow to accurately measure biodiversity. However, we do not yet fully understand the comparability of eDNA‐based assessments to classical morphological approaches. We assessed may‐, stone‐, and caddisfly genera with two contemporary methods, namely eDNA sampling followed by molecular identification and kicknet sampling followed by morphological identification. We sampled 61 sites distributed over a large river network, allowing a comparison of various diversity measures from the catchment to site levels and providing insights into how these measures relate to network properties. We extended our data with historical morphological records of total diversity at the catchment level. At the catchment scale, identification based on eDNA and kicknet samples detected similar proportions of the overall and cumulative historically documented richness (gamma diversity), 42% and 46%, respectively. We detected a good overlap (62%) between genera identified from eDNA and kicknet samples at the regional scale. At the local scale, we found highly congruent values of local taxon richness (alpha diversity) between eDNA and kicknet samples. Richness of eDNA was positively related to discharge, a descriptor of network position, while kicknet was not. Beta diversity, a measure of dissimilarity between sites, was comparable for the two contemporary methods and is driven by species replacement and not by nestedness. Although eDNA approaches are still in their infancy and optimization regarding sampling design and laboratory work is still needed, our results indicate that it can capture different components of diversity, proving its potential utility as a new tool for large sampling campaigns across hitherto understudied complete river catchments.

Regular monitoring of ecosystems can be used for the early detection of invasive alien species (IAS), and provide information for management and preventing them from becoming established or advancing into new areas. Current methods of monitoring freshwater systems for IAS can be both financially costly and time‐consuming, with routine monitoring often carried out at low intensity and at only a small number of sites. In this study, we evaluate how environmental DNA (eDNA) metabarcoding for monitoring freshwater macroinvertebrate IAS compares to traditional kick‐net sampling as part of a national (Switzerland) and a catchment monitoring programme. Kick‐net sampling was more fruitful for the detection of several well‐known target macroinvertebrate IAS. However, eDNA samples proved complementary for the detection of IAS that belong to species often being unnoticed by traditional sampling due to methodological or taxonomic reasons. Specifically, the invasive jellyfish Craspedacusta sowerbii, hardly detectable using classic kick‐net sampling, was found to be widespread in both the national and the catchment‐scale monitoring with the eDNA method only. Our study shows that IAS detection using eDNA is easily implemented in both national‐ and catchment‐scale monitoring campaigns. However, successful detection of target IAS is still highly dependent on primer choice, species' biology, and availability of adequate markers. Specifically, multiple markers should be considered for detecting IAS from several different taxonomic groups, such as those under the ‘freshwater macroinvertebrate’ umbrella term. While eDNA is still developing in terms of these fundamental methodological requirements, surveillance for both target and non‐target IAS using eDNA is likely to increase efficiency in early detection of IAS in freshwater systems.