NeMO : A Flexible R Package for Nested Multi‐Species Occupancy Modeling and eDNA Study Optimization

ABSTRACT

Biodiversity monitoring using environmental DNA (eDNA) metabarcoding has expanded rapidly, providing a noninvasive 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 to 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 modeling 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.