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bioRxiv · 10.1101/2025.05.23.655794

NeMO: a flexible R package for nested multi-species occupancy modelling and eDNA study optimization

Mace, B.·Manel, S.·Valentini, A.·Rocle, M.·Roset, N.·Delrieu-Trottin, E.
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Abstract

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 Rhone 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. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/655794v3_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@3a8f2borg.highwire.dtl.DTLVardef@8d8375org.highwire.dtl.DTLVardef@b1976forg.highwire.dtl.DTLVardef@14cfde5_HPS_FORMAT_FIGEXP M_FIG 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. C_FIG

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Mace, B., Manel, S., Valentini, A., Rocle, M., Roset, N., Delrieu-Trottin, E.. 2025-05-28. NeMO: a flexible R package for nested multi-species occupancy modelling and eDNA study optimization. https://doi.org/10.1101/2025.05.23.655794

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