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

sampling effort is often assessed using accumulation curves (e.g. [48]). However, these meth- ods do not account for imperfect detection and cannot accommodate the many variables in the field and the lab that can impact these results. From an ecological perspective, environmen- tal variables (e.g. temperature, salinity, turbidity) can be included at the level of occupancy to determine their effects on community diversity and the presence of individual species. From a methodological perspective, environmental and experimental variables (e.g. sample volume, sequencing depth) can be included at the level of field sampling and technical replication to understand how these factors affect metabarcoding results. Understanding the effects of these covariates facilitates the development of more robust experimental and survey designs. Fur- thermore, simulations using occupancy models can be used to optimize sampling effort, enabling practitioners to fine-tune the trade-off between field sampling and lab work [21]. The number of sites, biological samples, and technical replicates can all be optimized to maximize the species richness recovered from eDNA samples while minimizing effort. PCR level sto- chasticity, which is known to affect sequencing results [46,49], was not considered in our case study (i.e., PCR replicates were pooled before sequencing) but PCR replicates can easily be included as technical replicates in the model described here. PCR replicates are commonly included separately in single-species occupancy models for eDNA data [19,20,27]. By includ-

ing PCR replicates as technical replicates, additional stochasticity in the sampling process can be accounted for, further improving inferences. A key advantage of the occupancy modeling framework demonstrated here is its flexibility. Modifications to the model can allow several additional factors to be included, and a priori information can be used to guide model development. For example, multiple sampling periods have been included in dynamic, multi-season occupancy models to quantify temporal changes in community structure (e.g. [22]). Repeated eDNA sampling for metabarcoding could be modelled similarly to account for local extinction and colonization events between sampling periods. In addition to accounting for false negatives, several studies have developed methods for including false positives in occupancy models [50–52]. False positives may potentially arise from metabarcoding data through sequencing errors, PCR errors, and poor reference database coverage or quality [15,53,54]. Strict bioinformatic filtering helps to minimize the inclusion of these errors in resulting data sets; however, the possibility of false positives cannot be elimi- nated. Our model did not consider false positives, and, to our knowledge, these have yet to be incorporated into multi-species occupancy models. Following current protocols, abundance estimates from metabarcoding data are not reliable [55,56], but occupancy models can provide a means to estimate trends in abundance from the presence-absence data generated by meta-

barcoding based on documented relationships between occupancy and abundance [57–59]. The hierarchical modeling framework used in occupancy modeling can also be adapted to include or estimate taxa abundances [31]. As more research is done to understand the relation- ship between sequence read counts and species biomass, read counts could potentially be used as taxon abundances in a hierarchical modelling framework to estimate species biomass. We demonstrate for the first time how a multi-scale, multi-species occupancy modelling framework can be used in a natural system to account for imperfect detection and allow for critical assessment of experimental and environmental factors influencing biodiversity data from eDNA metabarcoding. Despite the utility of these models for improving detection and targeting areas of variation in the pipeline from sample collection to sample processing, this approach has been underutilized in DNA metabarcoding studies (S1 Table; but see [25]). This multi-species occupancy modelling framework will be particularly useful for bioassessment studies using DNA metabarcoding because it will improve estimates of occupancy and species richness, aid in optimizing sampling efforts in the field and lab, and, using the model assess- ment methods described here, identify ecological and environmental factors affecting PLOS ONE Multi-species occupancy models for metabarcoding PLOS ONE | https://doi.org/10.1371/journal.pone.0224119 March 19, 2020 12 / 17