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Freshwater ecosystems are the most vulnerable worldwide and freshwater bivalves rank amongst the most threatened animals in the world. Surveying and monitoring freshwater bivalves are difficult tasks: they are difficult to find, hard to identify (taxonomic expertise is needed), and working underwater is technically challenging. It is therefore crucial to find more efficient methods to survey and monitor these species. Here, we present the first metabarcoding approach for freshwater bivalves and compare environmental DNA (eDNA) and traditional surveys. We describe two sets of primers (for Unionida and Venerida) developed for freshwater bivalves eDNA metabarcoding. These primers have been tested in the field, with about 300 studied sites. Results were compared to freshwater bivalves’ surveys using traditional methods, with eDNA always detecting more species than traditional surveys, especially when Sphaerids were taken into account. While our study initially focused on Western Palearctic freshwater bivalve species, our primers were confronted in silico with available sequences and have proven to be effective at a global scale. The results show that eDNA metabarcoding, with our developed primers, is a remarkable tool allowing for non-invasive surveys, detection of rare and inconspicuous species, absence data and overall freshwater bivalves routine monitoring.

Despite the ecological and societal importance of large rivers, fish sampling remains costly and limited to specific habitats (e.g., river banks). Using an eDNA metabarcoding approach, we regularly sampled 500 km of a large river (Rhône River). Comparisons with long-term electrofishing surveys demonstrated the ability of eDNA metabarcoding to qualitatively and quantitatively reveal fish assemblage structures (relative species abundance) but eDNA integrated a larger space than the classical sampling location. Combination of a literature review and field data showed that eDNA behaves in the water column like fine particulate organic matter. Its detection distance varied from a few km in a small stream to more than 100 km in a large river. To our knowledge, our results are the first demonstration of the capacity of eDNA metabarcoding to describe longitudinal fish assemblage patterns in a large river, and metabarcoding appears to be a reliable, cost-effective method for future monitoring.

Biodiversity monitoring using environmental DNA (eDNA) metabarcoding has expanded rapidly, providing a non-invasive tool widely adopted by ecologists and stakeholders. However, eDNA surveys are prone to imperfect detection, and non-detections are often misinterpreted as true absences – a critical issue when monitoring rare or elusive species. Despite its implications for biodiversity assessments, detection uncertainty is rarely quantified in eDNA-based studies. Occupancy modeling offers a powerful solution to this limitation but remains underused, partly due to a lack of accessible and flexible tools. We developed NeMO (Nested eDNA Metabarcoding Occupancy), a user-friendly R package for fitting multi-species occupancy models in a Bayesian framework. NeMO explicitly accounts for the nested structure of eDNA metabarcoding workflows – typically involving multiple replication steps such as field samples and PCR replicates – while accommodating presence/absence or read-count data. The framework estimates species occupancy, eDNA collection probability, amplification probability, and expected read counts, and allows users to assess the influence of environmental or methodological covariates on each process. Crucially, NeMO helps rigorously assess detectability and optimize resource allocation in eDNA surveys. It estimates the minimum number of eDNA samples, PCR replicates, and sequencing depth required to reliably detect species when present, thereby guiding study design. We illustrate its utility using a fish biodiversity dataset from the Rhône River (France). NeMO integrates key modelling features into a single streamlined framework, providing researchers and practitioners with an accessible and effective tool to assess detectability and optimize resource allocation in eDNA metabarcoding surveys. Our results highlight the importance of quantifying detection uncertainty, which has major implications for conservation monitoring and for designing cost-effective and reliable eDNA strategies. NeMO provides a flexible framework for modeling multi-species site occupancy from eDNA data, estimating species presence and detectability despite imperfect detection. It supports diverse experimental designs, enables model comparison, and guides study design optimization by calculating minimal sampling effort to detect species, helping ecologists balance detection accuracy and resource allocation.