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Prospects and challenges of environmental DNA (eDNA) monitoring in freshwater ponds

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Harper, Lynsey R., Buxton, Andrew S., Rees, Helen C., Bruce, Kat, Brys, Rein, Halfmaerten, David, Read, Daniel S., Watson, Hayley V., Sayer, Carl D., Jones, Eleanor P., Priestley, Victoria, Mächler, Elvira, Múrria, Cesc, Garcés-Pastor, Sandra, Medupin, Cecilia, Burgess, Katherine, Benson, Gillian, Boonham, Neil, Griffiths, Richard A., Lawson Handley, Lori, Hänfling, Bernd·2019·Hydrobiologia

Abstract

Environmental DNA (eDNA) analysis is a rapid, non-invasive, cost-efficient biodiversity monitoring tool with enormous potential to inform aquatic conservation and management. Development is ongoing, with strong commercial interest, and new uses are continually being discovered. General applications of eDNA and guidelines for best practice in freshwater systems have been established, but habitat-specific assessments are lacking. Ponds are highly diverse, yet understudied systems that could benefit from eDNA monitoring. However, eDNA applications in ponds and methodological constraints specific to these environments remain unaddressed. Following a stakeholder workshop in 2017, researchers combined knowledge and expertise to review these applications and challenges that must be addressed for the future and consistency of eDNA monitoring in ponds. The greatest challenges for pond eDNA surveys are representative sampling, eDNA capture, and potential PCR inhibition. We provide recommendations for sampling, eDNA capture, inhibition testing, and laboratory practice, which should aid new and ongoing eDNA projects in ponds. If implemented, these recommendations will contribute towards an eventual broad standardisation of eDNA research and practice, with room to tailor workflows for optimal analysis and different applications. Such standardisation will provide more robust, comparable, and ecologically meaningful data to enable effective conservation and management of pond biodiversity.

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Cited by

Metadata

Authors
Harper, L. R., Buxton, A. S., Rees, H. C., Bruce, K., Brys, R., Halfmaerten, D., Read, D. S., Watson, H. V., Sayer, C. D., Jones, E. P., Priestley, V., Mächler, E., Múrria, C., Garcés-Pastor, S., Medupin, C., Burgess, K., Benson, G., Boonham, N., Griffiths, R. A., Lawson Handley, L., & Hänfling, B.
Venue
Hydrobiologia
Published
2018-09-03
Volume
826
Issue
1
Pages
25-41
ISSN
  • 0018-8158 (ISSN-L)
  • 1573-5117
OpenAlex
W2889134269
Cited by
262
References
6
FWCI
15.03
Type
journal article
Language
en
Open access
hybrid
Licence
creativecommons.org/licenses/by/4.0 (tdm)

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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.

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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.