Andrew Olaf Shelton

Datasets derived from mixed‐template DNA amplification and sequencing (‘metabarcoding’) are common in environmental DNA (eDNA), microbiome or microbial ecology analyses. Such metabarcoding data have been broadly used to inform estimates of biodiversity, distribution and abundance across a wide range of species and habitats. Generally, data from metabarcoding consist of a table of read‐counts assigned to each of a set of taxa or sequence variants. As such, they appear to contain quantitative information about the environment under study, immediately eligible for traditional community ecology analyses. In fact, metabarcoding data are compositional, can be biased by variation in PCR amplification rates among taxa, and have other statistical attributes that are distinct from many other common ecological observations. The difficulty in understanding and accounting for the complexity of metabarcoding observations has hindered their ecological interpretation and limited their integration with other ecological data types. Here, we use both simulations and empirical observations to show that the variability among replicate metabarcoding observations is closely tied to the absolute quantities of DNA present in a sample. We then develop a general statistical model for estimating DNA abundance from metabarcoding observations using the relationship between mean and variance of metabarcoding reads. We demonstrate that the model can effectively estimate DNA abundances using simulations and from experimentally created DNA samples. The model can be applied to a single marker or to multiple markers simultaneously, allowing the direct integration of information from multiple genetic markers derived from a common biological sample. Estimates of absolute DNA abundance are the logical unit to connect DNA observations to organismal abundance or biomass. Our methods can be used to link together multiple types of DNA observations or to connect DNA observations with non‐genetic ecological methods for surveying ecosystems. However, important aspects of the ‘ecology of DNA’ remain unresolved and may limit our ability to link observed concentrations to organismal abundances in a general way.

Environmental DNA (eDNA) samples capture mixtures of DNA from a wide range of organisms and often yield metabarcoding datasets that include taxa beyond the intended scope of the primer set used. The presence of this non-target DNA (termed “molecular bycatch”) can make it difficult to interpret trends in the primary targets. Here, we present an example in which including non-target detections alters the ecological interpretation of a metabarcoding time series and highlight a quantitative approach that can account for molecular bycatch in a biologically meaningful way. We collected high-resolution temporal samples from an estuary (Hood Canal, Washington, USA), using a surface-stationed autonomous sampler over a 48-hour period. We metabarcoded samples with a primer set that targets fish, but also amplifies (non-target) marine mammals with lower efficiency. We then transformed sequence read proportions into absolute DNA concentrations using a mock community–based bias correction and single-species concentrations via droplet digital PCR (ddPCR). We found that non-target mammals were substantially under-represented in the raw metabarcoding data relative to their biased-corrected proportions. Cross-validation of this quantitative metabarcoding approach showed that predicted DNA concentrations closely matched independent ddPCR measurements for total 12S DNA and two single-species concentrations (Atlantic bottlenose dolphin and Pacific herring) in both mock communities and environmental samples. After estimating absolute species concentrations for all species, we revealed species-specific patterns that were not apparent in the observed read counts alone. Our results underscore the importance of correcting for species-specific amplification bias when evaluating community composition from metabarcoding data, particularly in the context of non-target detections.

Between 1989 and 2007, 28 Distinct Population Segments (DPS) of Pacific salmon (Oncorhynchus spp.) spawning in rivers in California and the Pacific Northwest (Oregon, Washington, Idaho) were listed (protected) under the US Endangered Species Act (ESA). In the roughly 25 years since then, considerable efforts have been made to recover these populations, but no DPS has increased sufficiently to be delisted. We evaluated abundance trends of ESA‐listed Pacific salmon DPS, along with DPS that were not ESA‐listed. Our goal was to evaluate whether protected DPS increased in abundance during the period of protection (nominally 1995–2020 in our study), either in absolute terms or relative to the unprotected DPS. A majority of the protected DPS had increasing abundance trends over this time period, and protected populations had higher median trends than non‐protected populations of the same species. Geographically, populations in the Pacific Northwest had higher median trends than those in California. Among species of protected populations, Chinook salmon (O. tshawytscha ), chum salmon (O. keta ) and sockeye salmon (O. nerka ) had higher median trends than coho salmon (O. kisutch ) and steelhead (anadromous O. mykiss ). For most DPS (listed and unlisted), trends in harvest rates and hatchery releases were relatively stable during the same time period, whereas trends in indicators related to freshwater and marine climate were generally negative for salmon. Our results suggest that salmon recovery actions may have helped to stabilise and increase protected DPS, but most remain far below their recovery goals.

The ‘omics revolution’ has advanced scientific understanding of marine ecosystems and led to a rapid increase in data that can inform species' population structure, distribution, and abundance. Of the 'omics data types, environmental DNA (eDNA) may present the most cost‐effective opportunity for developing quantitative estimates of abundance trends, a key input for stock assessment models. However, eDNA has yet to be widely adopted for stock status determinations within regional fisheries management organisations. We review progress towards addressing key challenges that limited the application of eDNA in marine fisheries management, including advances in (1) the quantitative relationship between eDNA observations and species biomass, (2) reducing false‐negative and false‐positive detections, (3) defining the spatial scale of eDNA, (4) collecting biological data from eDNA surveys, (5) quantifying uncertainty in eDNA surveys, and (6) responding to scepticism of new survey methods. We use a case study with Pacific hake (Merluccius productus) to demonstrate the development of an eDNA index and its direct integration into an age‐structured stock assessment model. Given the many ways in which the field of eDNA has matured, we propose that eDNA can meaningfully inform a range of fisheries management needs, and outline a roadmap for using eDNA in stock assessment models in data‐limited to data‐rich species. A primary impediment to operationalising eDNA as stock assessment model inputs is the lack of interdisciplinary research teams, including geneticists, ecological modellers, and stock assessment scientists, which are necessary to interpret methods and results across scientific disciplines and ensure data are used appropriately.

Environmental laws around the world require some version of an environmental‐impact assessment surrounding construction projects and other discrete instances of human development. Information requirements for these assessments vary by jurisdiction, but nearly all require an analysis of the biological elements of ecosystems. Amplicon‐sequencing—also called metabarcoding—of environmental DNA (eDNA) has made it possible to sample and amplify the genetic material of many species present in those environments, providing a tractable, powerful, and increasingly common way of doing environmental‐impact analysis for development projects. Here, we analyze an 18‐month time series of water samples taken before, during, and after two culvert removals in a salmonid‐bearing freshwater stream. We also sampled multiple control streams to develop a robust background expectation against which to evaluate the impact of this discrete environmental intervention in the treatment stream. We generate calibrated, quantitative metabarcoding data from amplifying the 12s MiFish mtDNA locus and complementary species‐specific quantitative PCR data to yield multispecies estimates of absolute eDNA concentrations across time, creeks, and sampling stations. We then use a linear mixed effects model to reveal patterns of eDNA concentrations over time, and to estimate the effects of the culvert removal on salmonids in the treatment creek. We focus our analysis on four common salmonid species: cutthroat trout (Oncorhynchus clarkii), coho salmon (Oncorhynchus kisutch), rainbow trout (Oncorhynchus mykiss), and sockeye salmon (Oncorhynchus nerka). We find that one culvert in the treatment creek seemed to have no impact while the second culvert had a large impact on fish passage. The construction itself seemed to have only transient effects on salmonid species during the two construction events. In the context of billions of dollars of court‐mandated road culvert replacements taking place in Washington State, USA, our results suggest that culvert replacement can be conducted with only minimal impact of construction to key species of management concern. Furthermore, eDNA methods can be an effective and efficient approach for monitoring hundreds of culverts to prioritize culverts that are required to be replaced. More broadly, we demonstrate a rigorous, quantitative method for environmental‐impact reporting using eDNA that is widely applicable in environments worldwide.

Environmental DNA (eDNA) has gained popularity as a tool for ecosystem biomonitoring and biodiversity assessment. Although much progress has been made regarding laboratory and fieldwork protocols, the issue of sampling efficiency requires further investigation, particularly in three‐dimensional marine systems. This study focuses on fish community composition in marine ecosystems and aims to analyze the efficiency of sampling design given the sampling effort for distinguishing between different communities. We sampled three fjords in Northern Norway, taking samples along fjord transects and at three different depths, and amplified a fragment of the mitochondrial 12S rRNA gene of bony fishes using the MiFish primers. We evaluated the effect of (i) the number of sampling stations, (ii) samples' spatial distribution, and (iii) the data treatment approach (presence/absence versus semiquantitative) for maximizing the efficiency of eDNA metabarcoding sampling when inferring differences of fish community compositions between fjords. We found that the manner of data treatment strongly affected the minimum number of sampling stations required to detect differences among communities; because the semiquantitative approach retained some information about abundance of the underlying reads, it was the most efficient. Furthermore, we found little‐to‐no difference of fish communities in samples from intermediate depths when comparing vertical fish communities. Lastly, we found that the differences between fish communities at the surface were the highest across the horizontal distance and overall, samples ~30 km apart showed the highest variation in the horizontal distribution. Boosting sampling efficiency (reducing sampling effort without compromising ecological inferences) can significantly contribute to enhanced biodiversity management and efficient biomonitoring plans.

Amplicon‐sequence data from environmental DNA (eDNA) and microbiome studies provide important information for ecology, conservation, management, and health. At present, amplicon‐sequencing studies—known also as metabarcoding studies, in which the primary data consist of targeted, amplified fragments of DNA sequenced from many taxa in a mixture—struggle to link genetic observations to the underlying biology in a quantitative way, but many applications require quantitative information about the taxa or systems under scrutiny. As metabarcoding studies proliferate in ecology, it becomes more important to develop ways to make them quantitative to ensure that their conclusions are adequately supported. Here we link previously disparate sets of techniques for making such data quantitative, showing that the underlying polymerase chain reaction mechanism explains the observed patterns of amplicon data in a general way. By modeling the process through which amplicon‐sequence data arise, rather than transforming the data post hoc, we show how to estimate the starting DNA proportions from a mixture of many taxa. We illustrate how to calibrate the model using mock communities and apply the approach to simulated data and a series of empirical examples. Our approach opens the door to improve the use of metabarcoding data in a wide range of applications in ecology, public health, and related fields.

Many ecological data sets are proportional, representing mixtures of constituent elements such as species, populations, or strains. Analyses of proportional data are challenged by categories with zero observations (zeros), all observations (ones), and overdispersion. In lieu of ad hoc data adjustments, we describe and evaluate a zero‐and‐one inflated Dirichlet regression model, with its corresponding R package (zoid), capable of handling observed data consisting of three possible categories: zeros, proportions, or ones. Instead of fitting the model to observations of single biological units (e.g., individual organisms) within a sample, we sum proportional contributions across units and estimate mixture proportions using one aggregated observation per sample. Optional estimation of overdispersion and covariate influences expand model applications. We evaluate model performance, as implemented in Stan, using simulations and two ecological case studies. We show that zoid successfully estimates mixture proportions using simulated data with varying sample sizes and is robust to overdispersion and covariate structure. In empirical case studies, we estimate the composition of a mixed‐stock Chinook salmon (Oncorhynchus tshawytscha) fishery and analyze the stomach contents of Atlantic cod (Gadus morhua). Our implementation of the model as an R package facilitates its application to varied ecological data sets composed of proportional observations.

Amplicon-sequence data from environmental DNA (eDNA) and microbiome studies provides important information for ecology, conservation, management, and health. At present, amplicon-sequencing studies – known also as metabarcoding studies, in which the primary data consist of targeted, amplified fragments of DNA sequenced from many taxa in a mixture – struggle to link genetic observations to underlying biology in a quantitative way, but many applications require quantitative information about the taxa or systems under scrutiny. As metabarcoding studies proliferate in ecology following decades of microbial and microbiome work using similar techniques, it becomes more important to develop ways ot make them quantitative to ensure that their conclusions are adequately supported. Here we link previously disparate sets of techniques for making such data quantitative, showing that the underlying PCR mechanism explains observed patterns of amplicon data in a general way. By modeling the process through which amplicon-sequence data arises, rather than transforming the data post-hoc, we show how to estimate the starting DNA proportions from a mixture of many taxa. We illustrate how to calibrate the model using mock communities and apply the approach to simulated data and a series of empirical examples. Our approach opens the door to improve the use of metabarcoding data in a wide range of applications in ecology, public health, and related fields.

All species inevitably leave genetic traces in their environments, and the resulting environmental DNA (eDNA) reflects the species present in a given habitat. It remains unclear whether eDNA signals can provide quantitative metrics of abundance on which human livelihoods or conservation successes depend. Here, we report the results of a large eDNA ocean survey (spanning 86 000 km²to depths of 500 m) to understand the abundance and distribution of Pacific hake (Merluccius productus), the target of the largest finfish fishery along the west coast of the USA. We sampled eDNA in parallel with a traditional acoustic-trawl survey to assess the value of eDNA surveys at a scale relevant to fisheries management. Despite local differences, the two methods yield comparable information about the broad-scale spatial distribution and abundance. Furthermore, we find depth and spatial patterns of eDNA closely correspond to acoustic-trawl estimates for hake. We demonstrate the power and efficacy of eDNA sampling for estimating abundance and distribution and move the analysis eDNA data beyond sample-to-sample comparisons to management relevant scales. We posit that eDNA methods are capable of providing general quantitative applications that will prove especially valuable in data- or resource-limited contexts.