Environmental DNA (eDNA) sampling is increasingly used in surveys of species distribution as a potentially sensitive and efficient monitoring method. Yet access to modelling tools designed specifically for interpreting this new data type lags behind its ubiquity. While occupancy modelling software has dominated the analytical landscape for eDNA data analysis of single species, this type of model may not always be the most appropriate. The rate of eDNA detection often corresponds to species density, rather than just occupancy, and researchers often have access to observations from non‐genetic sampling methods at the same sites.

To provide users access to a modelling framework designed to maximize the use of all available data, we developed an R package, eDNAjoint . The package provides an easy‐to‐use interface for fitting a ‘joint’ model that integrates data from paired or semi‐paired eDNA and traditional surveys in a Bayesian framework. The model can be used to estimate parameters like the probability of a false positive eDNA detection and mean catch rate at a site, and the package allows access to multiple model variations and Bayesian prior customization. Additional functionality can be used for model selection, summarising posteriors and comparing the relative sensitivities of the two survey methods.

We demonstrate the use of eDNAjoint by fitting a variation of the model with site‐level covariates that scale the sensitivity of eDNA sampling relative to traditional sampling. The example workflow uses binary eDNA and seine count data for the endangered tidewater goby ( Eucyclogobius newberryi ) from a study by Schmelzle and Kinziger (2016). This use case includes a prior sensitivity analysis and an evaluation of the relationship between detection rates and environmental variables.

eDNAjoint has the potential to greatly increase the range of users who will be able to rigorously analyse eDNA and traditional survey data in a Bayesian framework, understand if and how eDNA can improve monitoring practices, and gain confidence in the interpretability of eDNA data.

Non-native species have the potential to cause ecological and economic harm to coastal and estuarine ecosystems. Understanding which habitat types are most vulnerable to biological invasions, where invasions originate, and the vectors by which they arrive can help direct limited resources to prevent or mitigate ecological and socio-economic harm. Information about the occurrence of non-native species can help guide interventions at all stages of invasion, from first introduction, to naturalization and invasion. However, monitoring at relevant scales requires considerable investment of time, resources, and taxonomic expertise. Environmental DNA (eDNA) metabarcoding methods sample coastal ecosystems at broad spatial and temporal scales to augment established monitoring methods. We use COI mtDNA eDNA sampling to survey a diverse assemblage of species across distinct habitats in the Salish Sea in Washington State, USA, and classify each as non-native, native, or indeterminate in origin. The non-native species detected include both well-documented invaders and species not previously reported within the Salish Sea. We find a non-native assemblage dominated by shellfish and algae with native ranges in the temperate western Pacific, and find more-retentive estuarine habitats to be invaded at far higher levels than better-flushed rocky shores. Furthermore, we find an increase in invasion level with higher water temperatures in spring and summer across habitat types. This analysis contributes to a growing understanding of the biotic and abiotic factors that influence invasion level, and underscores the utility of eDNA surveys to monitor biological invasions and to better understand the factors that drive these invasions.

Considerable resources are spent to track fish movement in marine environments, often with the intent of estimating behavior, distribution, and abundance. Resulting data from these monitoring efforts, including tagging studies and genetic sampling, often can be siloed. For Pacific salmon in the Northeast Pacific Ocean, predominant data sources for fish monitoring are coded wire tags (CWTs) and genetic stock identification (GSI). Despite their complementary strengths and weaknesses in coverage and information content, the two data streams rarely have been integrated to inform Pacific salmon biology and management. Joint, or integrated, models can combine and contextualize multiple data sources in a single statistical framework to produce more robust estimates of fish populations.

The analysis of environmental DNA (eDNA) is a rapidly developing field with great potential and as-yet-undreamed-of uses in conservation and ecology^1,2,3. Residual genetic information from environmental samples can reveal species distributions and genotypes^4,5,6, among other information about the living elements of ecosystems. However, as with all tools, eDNA techniques can be misused and, in particular, the analysis of human DNA from environmental samples has raised ethical concerns about the information that might be derived from those samples.

Metabarcoding is a powerful molecular tool for simultaneously surveying hundreds to thousands of species from a single sample, underpinning microbiome and environmental DNA (eDNA) methods. Deriving quantitative estimates of underlying biological communities from metabarcoding is critical for enhancing the utility of such approaches for health and conservation. Recent work has demonstrated that correcting for amplification biases in genetic metabarcoding data can yield quantitative estimates of template DNA concentrations. However, a major source of uncertainty in metabarcoding data stems from non-detections across technical PCR replicates where one replicate fails to detect a species observed in other replicates. Such non-detections are a special case of variability among technical replicates in metabarcoding data. While many sampling and amplification processes underlie observed variation in metabarcoding data, understanding the causes of non-detections is an important step in distinguishing signal from noise in metabarcoding studies. Here, we use both simulated and empirical data to 1) suggest how non-detections may arise in metabarcoding data, 2) outline steps to recognize uninformative data in practice, and 3) identify the conditions under which amplicon sequence data can reliably detect underlying biological signals. We show with both simulations and empirical data that, for a given species, the rate of non-detections among technical replicates is a function of both the template DNA concentration and species-specific amplification efficiency. Consequently, we conclude metabarcoding datasets are strongly affected by (1) deterministic amplification biases during PCR and (2) stochastic sampling of amplicons during sequencing—both of which we can model—but also by (3) stochastic sampling of rare molecules prior to PCR, which remains a frontier for quantitative metabarcoding. Our results highlight the importance of estimating species-specific amplification efficiencies and critically evaluating patterns of non-detection in metabarcoding datasets to better distinguish environmental signal from the noise inherent in molecular detections of rare targets.

Harmful algae can have profound economic, environmental, and social consequences. As the timing, frequency, and severity of harmful algal blooms (HABs) change alongside global climate, efficient tools to monitor and understand the current ecological context of these taxa are increasingly important. Here we employ environmental DNA metabarcoding to identify patterns in a wide variety of potentially harmful algae and associated ecological communities in the Hood Canal of Puget Sound in Washington State, USA. Tracking trends of occurrence in a series of water samples over a period of 19 months, we find algal sequences from genera with harmful members in a majority of samples, suggesting that these groups are routinely present in local waters. We report patterns in variants of the economically important genus Pseudo-nitzschia (of which some members produce domoic acid; family Bacillariaceae), as well as multiple potentially harmful algal taxa previously unknown or poorly documented in the region, including a cold-water variant from the genus Alexandrium (of which some members produce saxitoxin; family Gonyaulacaceae), two variants from the genus Karlodinium (of which some members produce karlotoxins; family Kareniaceae), and one variant from the parasitic genus Hematodinium (family Syndiniaceae). We then use data on environmental variables and the biological community surrounding each algal taxon to illustrate the ecological context in which they are commonly found. Environmental DNA metabarcoding thus simultaneously (1) alerts us to potential new or cryptic occurrences of algae from harmful genera, (2) expands our knowledge of the co-occurring conditions and species associated with the growth of these organisms in changing marine environments, and (3) suggests a pathway for multispecies monitoring and management moving forward.

Environmental DNA (eDNA) detection employing quantitative PCR (qPCR) and droplet digital PCR (ddPCR) offers a non-invasive and efficient approach for monitoring aquatic organisms. Accurate and sensitive quantification of eDNA is crucial for tracking rare and invasive species and understanding the biodiversity abundance and distribution of aquatic organisms. This study compares the sensitivity and quantification precision of qPCR and ddPCR for eDNA surveys through Bayesian inference using latent parameters from both known concentration (standards) and environmental samples across three teleost fish species assays. The results show that ddPCR offers higher sensitivity and quantification precision, particularly at low DNA concentrations (< 1 copy/μL reaction), than qPCR. These findings highlight the superior performance of ddPCR for eDNA detection at low concentrations, guiding researchers towards more reliable methods for effective species monitoring. Additionally, this study indicates that a two-step (detection and concentration) model increased the precision of qPCR results, useful for enhancing the robustness of eDNA quantification. Furthermore, we investigated the lower limit of quantification for ddPCR, providing insights on how such limit can be extended, which could also be applied to qPCR.

Quantifying the biomass, or number of individuals, diversity, and distribution of marine species is a critical aspect of understanding and managing marine ecosystems. In recent years, there has been growing interest in using environmental DNA (eDNA) for marine ecosystem management and biodiversity assessment. However, the main challenge hindering eDNA applicability has been the inability to infer absolute species abundances from multispecies analysis (eDNA metabarcoding). In this study, we demonstrate a way forward by estimating the abundance of commercially important fish species in a Norwegian fjord using a joint Bayesian statistical model of traditional trawl-catch data and molecular data derived from eDNA. Using this model, we accurately predict out-of-sample trawl catches using eDNA alone. Moreover, our model provides empirical estimates for key processes linking marine eDNA concentration to the fish population abundance estimated from trawl observations, including trawl catchability, DNA shedding, degradation, dilution, transport, recovery rate, and isolation efficiency. These processes, including amplification efficiencies correcting for Polymerase Chain Reaction (PCR) bias, are species-specific and enable the translation of eDNA metabarcoding data into abundances. These findings have broad implications for the use of eDNA in marine ecosystem management and conservation efforts.

Predation by invasive species can threaten local ecosystems and economies. The European green crab (Carcinus maenas), one of the most widespread marine invasive species, is an effective predator associated with clam and crab population declines outside of its native range. In the U.S. Pacific Northwest, green crab has recently increased in abundance and expanded its distribution, generating concern for estuarine ecosystems and associated aquaculture production. However, regionally-specific information on the trophic impacts of invasive green crab is very limited. We compared the stomach contents of green crabs collected on clam aquaculture beds versus intertidal sloughs in Willapa Bay, Washington, to provide the first in-depth description of European green crab diet at a particularly crucial time for regional management. We first identified putative prey items using DNA metabarcoding of stomach content samples. We compared diet composition across sites using prey presence/absence and an index of species-specific relative abundance. For eight prey species, we also calibrated metabarcoding data to quantitatively compare DNA abundance between prey taxa, and to describe an ‘average’ green crab diet at an intertidal slough versus a clam aquaculture bed. From the stomach contents of 61 green crabs, we identified 54 unique taxa belonging to nine phyla. The stomach contents of crabs collected from clam aquaculture beds were significantly different from the stomach contents of crabs collected at intertidal sloughs. Across all sites, arthropods were the most frequently detected prey, with the native hairy shore crab (Hemigrapsus oregonensis) the single most common prey item. Of the eight species calibrated with a quantitative model, two ecologically-important native species–the sand shrimp (Crangon franciscorum) and the Pacific staghorn sculpin (Leptocottus armatus)–had the highest average DNA abundance when detected in a stomach content sample. In addition to providing timely information on green crab diet, our research demonstrates the novel application of a recently developed model for more quantitative DNA metabarcoding. This represents another step in the ongoing evolution of DNA-based diet analysis towards producing the quantitative data necessary for modeling invasive species impacts.

Environmental DNA (eDNA) includes a set of rapidly emerging technologies that have the potential to support environmental monitoring and biodiversity conservation through novel, non-invasive, cost-effective and democratic methods and tools. Meanwhile, eDNA researchers are developing international standards for eDNA technologies, methods and data outputs. For eDNA technologies to be accessible, useful and appropriate, we must ensure that any standards developed include a broad conception of users from around the world, a diversity of ecological contexts and locations and, most importantly, a realistic outlook on research capacities and infrastructure. In this article, we assemble perspectives on international standardisation of eDNA from a diverse and global group of users and experts from Africa, South America and the Pacific Islands. The authors of this article collaborated by answering and discussing a set of open-ended questions aimed at eliciting hopes, concerns and experiences regarding eDNA standards. The result is a set of emergent themes and a generative consensus to highlight the need for the creation of adaptable standards, the development of regional capacity, increased sensitising to data sovereignty and the viewing of standardisation as a global capacity-building activity.

Environmental DNA (eDNA) analysis has matured to the point that it is ready for deployment in many applications, particularly in aquatic environments. But public agencies have yet to adopt eDNA methods into their environmental decision making routines at scale, even when eDNA offers clear advantages to those now in use. This article provides a perspective on this gap by considering adoption of a new technology as a path-dependent, social process in which some paths lead to outcomes that provide far greater benefits than others.

Environmental DNA (eDNA) data make it possible to measure and monitor biodiversity at unprecedented resolution and scale. As use-cases multiply and scientific consensus grows regarding the value of eDNA analysis, public agencies have an opportunity to decide how and where eDNA data fit into their mandates. Within the United States, many federal and state agencies are individually using eDNA data in various applications and developing relevant scientific expertise. A national strategy for eDNA implementation would capitalize on recent scientific developments, providing a com- mon set of next-generation tools for natural resource management and public health protection.

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.

Federal and state agencies have begun using residual genetic information taken from the environment—environmental DNA or eDNA—to help make management and regulatory decisions. Environmental DNA can provide information from water, soil, or air samples about the living parts of ecosystems with unprecedented scope, in some cases providing broad surveys of the species present and in others pinpointing hard-to-find species. However, standards for analysis and interpretation have only recently begun to arise in the nascent field of eDNA analysis. As this new and valuable source of information begins to influence the implementation of environmental laws, we survey existing federal uses of eDNA and review federal information requirements relevant to natural resource management—in particular, under the data-hungry Endangered Species Act and National Environmental Policy Act. We find that some agencies are already using eDNA data, and, for the most part, these uses are likely to meet the legal requirements of the controlling statutes and regulations. Though legally acceptable, social factors influence the degree to which a technology becomes widespread in agency practice. We survey likely future scenarios for eDNA uptake and offer recommendations for driving broader adoption of this useful technology and enabling management and regulatory decisions grounded in eDNA as a data source.