Optimising sampling and analysis protocols in environmental DNA studies.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
02 06 2021
Historique:
received: 19 02 2021
accepted: 18 05 2021
entrez: 3 6 2021
pubmed: 4 6 2021
medline: 6 11 2021
Statut: epublish

Résumé

Ecological surveys risk incurring false negative and false positive detections of the target species. With indirect survey methods, such as environmental DNA, such error can occur at two stages: sample collection and laboratory analysis. Here we analyse a large qPCR based eDNA data set using two occupancy models, one of which accounts for false positive error by Griffin et al. (J R Stat Soc Ser C Appl Stat 69: 377-392, 2020), and a second that assumes no false positive error by Stratton et al. (Methods Ecol Evol 11: 1113-1120, 2020). Additionally, we apply the Griffin et al. (2020) model to simulated data to determine optimal levels of replication at both sampling stages. The Stratton et al. (2020) model, which assumes no false positive results, consistently overestimated both overall and individual site occupancy compared to both the Griffin et al. (2020) model and to previous estimates of pond occupancy for the target species. The inclusion of replication at both stages of eDNA analysis (sample collection and in the laboratory) reduces both bias and credible interval width in estimates of both occupancy and detectability. Even the collection of > 1 sample from a site can improve parameter estimates more than having a high number of replicates only within the laboratory analysis.

Identifiants

pubmed: 34079031
doi: 10.1038/s41598-021-91166-7
pii: 10.1038/s41598-021-91166-7
pmc: PMC8172848
doi:

Substances chimiques

DNA, Environmental 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

11637

Références

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Auteurs

Andrew Buxton (A)

Durrell Institute of Conservation and Ecology, School of Anthropology and Conservation, University of Kent, Marlowe Building, Canterbury, Kent, CT2 7NR, UK. A.S.Buxton@kent.ac.uk.

Eleni Matechou (E)

School of Mathematics, Statistics and Actuarial Science, University of Kent, Sibson Building, Canterbury, Kent, CT2 7FS, UK.

Jim Griffin (J)

Department of Statistical Science, University College London, 196-199 Tottenham Court Rd, Bloomsbury, London, W1T 7PJ, UK.

Alex Diana (A)

School of Mathematics, Statistics and Actuarial Science, University of Kent, Sibson Building, Canterbury, Kent, CT2 7FS, UK.

Richard A Griffiths (RA)

Durrell Institute of Conservation and Ecology, School of Anthropology and Conservation, University of Kent, Marlowe Building, Canterbury, Kent, CT2 7NR, UK.

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Classifications MeSH