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Environmental DNA (eDNA) as a tool for assessing fish biomass: A review of approaches and future considerations for resource surveys
Open accessAbstract
Environmental DNA (eDNA) has revolutionized our ability to identify the presence and distributions of terrestrial and aquatic organisms. Recent evidence suggests the concentration of eDNA could also provide a rapid, cost‐effective indicator of abundance and/or biomass for fisheries stock assessments. Globally, fisheries resources are under immense pressure, and their sustainable harvest requires accurate information on the sizes of fished stocks. However, in many cases the required information remains elusive because of a reliance on imprecise or costly fishery‐dependent and independent data. Here, we review the literature describing relationships between eDNA concentrations and fish abundance and/or biomass, as well as key influencing factors, as a precursor to determining the broader utility of eDNA for monitoring fish populations. We reviewed 63 studies published between 2012 and 2020 and found 90% identified positive relationships between eDNA concentrations and the abundance and/or biomass of focal species. Key influencing biotic factors included the taxon examined as well as their body size, distribution, reproduction, and migration. Key abiotic factors mostly comprised hydrological processes affecting the dispersal and persistence of eDNA, especially water flow and temperature, although eDNA collection methods were also influential. The cumulative influence of these different factors likely explains the substantial variability observed in eDNA concentrations, both within and among studies. Nevertheless, there is considerable evidence to support using eDNA as an ancillary tool for assessing fish population abundance and/or biomass across discrete spatio‐temporal scales, following preliminary investigations to determine species‐ and context‐specific factors influencing the eDNA abundance/biomass relationship. Advantages of eDNA monitoring relative to other approaches include reduced costs, increased efficiencies, and nonlethal sampling.
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Metadata
- Authors
- Rourke, M. L., Fowler, A. M., Hughes, J. M., Broadhurst, M. K., DiBattista, J. D., Fielder, S., Wilkes Walburn, J., & Furlan, E. M.
- Venue
- Environmental DNA
- Publisher
- Wiley
- Published
- 2021-02-25
- Volume
- 4
- Issue
- 1
- Pages
- 9-33
- ISSN
- 2637-4943 (ISSN-L)
- OpenAlex
- W3133743945
- Cited by
- 435
- References
- 109
- FWCI
- 31.75
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.