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Towards robust and repeatable sampling methods in eDNA‐based studies

DNA‐based techniques are increasingly used for measuring the biodiversity (species presence, identity, abundance and community composition) of terrestrial and aquatic ecosystems. While there are numerous reviews of molecular methods and bioinformatic steps, there has been little consideration of the...

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Bibliographic Details
Published in:Molecular ecology resources 2018-09, Vol.18 (5), p.940-952
Main Authors: Dickie, Ian A., Boyer, Stephane, Buckley, Hannah L., Duncan, Richard P., Gardner, Paul P., Hogg, Ian D., Holdaway, Robert J., Lear, Gavin, Makiola, Andreas, Morales, Sergio E., Powell, Jeff R., Weaver, Louise
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Language:English
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Summary:DNA‐based techniques are increasingly used for measuring the biodiversity (species presence, identity, abundance and community composition) of terrestrial and aquatic ecosystems. While there are numerous reviews of molecular methods and bioinformatic steps, there has been little consideration of the methods used to collect samples upon which these later steps are based. This represents a critical knowledge gap, as methodologically sound field sampling is the foundation for subsequent analyses. We reviewed field sampling methods used for metabarcoding studies of both terrestrial and freshwater ecosystem biodiversity over a nearly three‐year period (n = 75). We found that 95% (n = 71) of these studies used subjective sampling methods and inappropriate field methods and/or failed to provide critical methodological information. It would be possible for researchers to replicate only 5% of the metabarcoding studies in our sample, a poorer level of reproducibility than for ecological studies in general. Our findings suggest greater attention to field sampling methods, and reporting is necessary in eDNA‐based studies of biodiversity to ensure robust outcomes and future reproducibility. Methods must be fully and accurately reported, and protocols developed that minimize subjectivity. Standardization of sampling protocols would be one way to help to improve reproducibility and have additional benefits in allowing compilation and comparison of data from across studies.
ISSN:1755-098X
1755-0998
DOI:10.1111/1755-0998.12907