Validating metabarcoding-based biodiversity assessments with multi-species occupancy models: A case study using coastal marine eDNA

during field sampling and during lab processing. If imperfect detection is not accounted for, this could lead to biased estimates of species richness and individual species occupancy [10,11]. Accounting for false negatives will improve community-wide species occurrence esti- mates based on eDNA surveys and yield more robust ecological conclusions for making man- agement decisions and informing sampling designs. Optimal sampling designs for eDNA metabarcoding studies are not well-established and differ from traditional ecological sampling methods in the cost and effort required for sample collection [12]. Additionally, there are sev- eral added variables that need to be accounted for in metabarcoding studies compared to tradi- tional sampling approaches, such as sequencing depth and marker selection, which vary between studies and can affect metabarcoding results [5,13,14]. Sampling designs should be experimentally informed and optimized specifically for eDNA metabarcoding methods [15], yet this is seldom practiced, and these added sources of variation during sample processing are seldom considered in the same analysis as sampling design. Occupancy modelling is a powerful tool to account for the additional sources of variation associated with next-generation biomonitoring approaches, and it has been used to assess imperfect detection in terrestrial bioassessment [16–18]. These models include 2-levels: the probability that a species occurs at a site (occupancy; ψ) and the probability of detecting a spe-

cies at a site (probability of detection; p). Recently, occupancy models have been adapted for single-species eDNA studies by adding an additional stochastic level, the probability of capture (θ). Occupancy refers to the probability of a species’ DNA occurring at a site, the probability of capture (θ) refers to the probability of capturing a species’ eDNA in a field sample, and the probability of detection refers to the probability of detecting a species in a PCR replicate [19,20]. The use of occupancy models in single-species eDNA studies is not ubiquitous, but it is increasing [21]. Occupancy modelling can also be applied to whole communities through multi-species occupancy models, which are commonly applied to traditional surveys in terrestrial systems [22,23], yet seldom used in the context of DNA metabarcoding (S1 Table). In the same way that single-species models were adapted for eDNA studies through the inclusion of an addi- tional stochastic level, multi-species models can be adapted for metabarcoding by including this additional level. Modeling communities together in a single multi-species model can improve the accuracy and predictive ability of occupancy models compared to single-species models [24]. The application of multi-species, multi-scale occupancy models to metabarcoding data is rare, focusing on small-scale lab manipulations [25], and no studies have implemented this modelling approach to improve sampling designs in natural systems (but see [26] for a sin- gle species example). Incorporating these models routinely in metabarcoding analysis will

improve ecological inferences and species richness estimates, as well as facilitate the develop- ment of robust sampling designs for a relatively new technique where little thought has been dedicated to developing de novo sampling designs distinct from traditional sampling methods. The inclusion of covariates in occupancy models at each process level extends the application of the model, enabling discrimination between sources of variation in sampling effort and environmental factors. However, making conclusions based on models with covariates requires methods of model assessment and selection for multi-species, multi-scale models. Here, we demonstrate how multi-species occupancy modelling can be used for the analysis of community biodiversity data resulting from eDNA metabarcoding and highlight the poten- tial of these models for both improving methodologies and sound ecological inference. We present methods for model assessment and model comparison adapted for multi-scale, multi- species occupancy models. Finally, we demonstrate how these tools can improve inferential power from eDNA metabarcoding results using a case study in a coastal, marine environment. PLOS ONE Multi-species occupancy models for metabarcoding PLOS ONE | https://doi.org/10.1371/journal.pone.0224119 March 19, 2020 2 / 17 authors and do not necessarily reflect the views of Petroleum Research or its members. B.M. and Z.G. C. are employees of eDNAtec Inc. and M.H. is the founder and Chief Scientific Officer of eDNAtec Inc. The compensations for authors B.M., Z.G.C. and

M.H. were supported by the funder, but the funder did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. Competing interests: B.M. and Z.G.C. are employees of eDNAtec Inc. and M.H. is the founder and Chief Scientific Officer of eDNAtec Inc. This does not alter our adherence to PLOS ONE policies on sharing data and materials.