Environmental DNA provides quantitative estimates of Pacific hake abundance and distribution in the open ocean

Shelton, Andrew O.; Ramón-Laca, Ana; Wells, Abigail; Clemons, Julia; Chu, Dezhang; Feist, Blake E.; Kelly, Ryan P.; Parker‐Stetter, Sandra L.; Thomas, Rebecca; Nichols, Krista M.; Park, Linda

doi:10.1098/rspb.2021.2613

Cited by 55 publications

(45 citation statements)

References 58 publications

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“…Over the past decade, rapid technological advances in the collection and analysis of trace genetic material from sampled environmental media (water (Ficetola et al 2008, Thomsen et al 2012), soil (Andersen et al 2012), feces (Pompanon et al 2012), or even air (Lynggaard et al 2022); hereafter environmental DNA [eDNA]) have opened new frontiers for environmental surveillance. Studies using eDNA have focused on diverse topics including monitoring biodiversity (Creer et al 2016), managing invasive species (Jerde et al 2013), characterizing diet (Deagle et al 2013), and supporting fisheries management (Fukaya et al 2021, Shelton et al 2022), in habitats from tropical forests (Lopes et al 2017) to the deep sea (Everett and Park 2018).…”

Section: Introductionmentioning

confidence: 99%

Toward quantitative metabarcoding

Shelton

Gold

Jensen

et al. 2022

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

Toward quantitative metabarcoding

Shelton

Gold

Jensen

et al. 2022

Preprint

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“…Importantly, we demonstrate that even species with an amplification efficiency slightly below average (e.g., a = 0.7) exhibit high rates of non-detections at DNA concentrations far higher than from typical eDNA field samples (e.g. λ > 100 copies/μL; [45]). Together these simulations indicate that the probability of non-detection is dominated by the subsampling process at low template DNA concentrations while the probability of non-detection is driven primarily by the PCR process (i.e., differences in amplification efficiencies) at higher template DNA concentrations.…”

Section: Resultsmentioning

confidence: 99%

Distinguishing Signal from Noise: Understanding Patterns of Non-Detections to Inform Accurate Quantitative Metabarcoding

Gold

Shelton

Casendino

et al. 2022

Preprint

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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 is the presence of non-detections, where a technical PCR replicate fails to detect a species observed in other replicates. Such non-detections are an important special case of variability among technical replicates in metabarcoding data, particularly in environmental samples. 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) develop a qualitative understanding of how non-detections 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 in 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.

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“…Future autonomous sampling research should focus on the evolution of joint survey platforms that combine multiple collection methods without substantially increasing vessel days or labor requirements (e.g., simultaneous collection of acoustic, video, oceanographic, and environmental DNA, eDNA, data). For example, recent pilot studies demonstrate promise for combining eDNA and acoustic-midwater trawl sampling (Shelton et al 2022 ).…”

Section: Novel Data To Stimulate Improvements In Scientific Advicementioning

confidence: 99%

Oceans of plenty? Challenges, advancements, and future directions for the provision of evidence-based fisheries management advice

Goethel

Omori

Punt

et al. 2022

Rev Fish Biol Fisheries

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Marine population modeling, which underpins the scientific advice to support fisheries interventions, is an active research field with recent advancements to address modern challenges (e.g., climate change) and enduring issues (e.g., data limitations). Based on discussions during the ‘Land of Plenty’ session at the 2021 World Fisheries Congress, we synthesize current challenges, recent advances, and interdisciplinary developments in biological fisheries models (i.e., data-limited, stock assessment, spatial, ecosystem, and climate), management strategy evaluation, and the scientific advice that bridges the science-policy interface. Our review demonstrates that proliferation of interdisciplinary research teams and enhanced data collection protocols have enabled increased integration of spatiotemporal, ecosystem, and socioeconomic dimensions in many fisheries models. However, not all management systems have the resources to implement model-based advice, while protocols for sharing confidential data are lacking and impeding research advances. We recommend that management and modeling frameworks continue to adopt participatory co-management approaches that emphasize wider inclusion of local knowledge and stakeholder input to fill knowledge gaps and promote information sharing. Moreover, fisheries management, by which we mean the end-to-end process of data collection, scientific analysis, and implementation of evidence-informed management actions, must integrate improved communication, engagement, and capacity building, while incorporating feedback loops at each stage. Increasing application of management strategy evaluation is viewed as a critical unifying component, which will bridge fisheries modeling disciplines, aid management decision-making, and better incorporate the array of stakeholders, thereby leading to a more proactive, pragmatic, transparent, and inclusive management framework–ensuring better informed decisions in an uncertain world. Supplementary Information The online version contains supplementary material available at 10.1007/s11160-022-09726-7.

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Environmental DNA provides quantitative estimates of Pacific hake abundance and distribution in the open ocean

Cited by 55 publications

References 58 publications

Toward quantitative metabarcoding

Toward quantitative metabarcoding

Distinguishing Signal from Noise: Understanding Patterns of Non-Detections to Inform Accurate Quantitative Metabarcoding

Oceans of plenty? Challenges, advancements, and future directions for the provision of evidence-based fisheries management advice

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