Fish community assessment with eDNA metabarcoding: effects of sampling design and bioinformatic filtering

Author:

Evans Nathan T.12,Li Yiyuan1,Renshaw Mark A.13,Olds Brett P.13,Deiner Kristy14,Turner Cameron R.15,Jerde Christopher L.16,Lodge David M.14,Lamberti Gary A.1,Pfrender Michael E.1

Affiliation:

1. University of Notre Dame, Department of Biological Sciences and Environmental Change Initiative, Notre Dame, IN, USA.

2. Florida International University, Southeast Environmental Research Center, Miami, FL, USA.

3. Oceanic Institute at Hawai’i Pacific University, Shrimp Department, Waimanalo, HI, USA.

4. Cornell University, Department of Ecology and Evolutionary Biology and Atkinson Center for a Sustainable Future, Ithaca, NY, USA.

5. EcoSystem Genetics LLC, South Bend, IN, USA.

6. University of California, Santa Barbara, Marine Science Institute, Santa Barbara, CA, USA.

Abstract

Species richness is a metric of biodiversity that represents the number of species present in a community. Traditional fisheries assessments that rely on capture of organisms often underestimate true species richness. Environmental DNA (eDNA) metabarcoding is an alternative tool that infers species richness by collecting and sequencing DNA present in the ecosystem. Our objective was to determine how spatial distribution of samples and “bioinformatic stringency” affected eDNA-metabarcoding estimates of species richness compared with capture-based estimates in a 2.2 ha reservoir. When bioinformatic criteria required species to be detected only in a single sample, eDNA metabarcoding detected all species captured with traditional methods plus an additional 11 noncaptured species. However, when we required species to be detected with multiple markers and in multiple samples, eDNA metabarcoding detected only seven of the captured species. Our analysis of the spatial patterns of species detection indicated that eDNA was distributed relatively homogeneously throughout the reservoir, except near the inflowing stream. We suggest that interpretation of eDNA metabarcoding data must consider the potential effects of water body type, spatial resolution, and bioinformatic stringency.

Publisher

Canadian Science Publishing

Subject

Aquatic Science,Ecology, Evolution, Behavior and Systematics

Reference47 articles.

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2. Bailey, R.M., Latta, W.C., and Smith, G.R. 2004b. An atlas of Michigan fishes with keys and illustrations for their identification. Museum of Zoology, Unversity of Michigan, Miscellaneous Publications No. 192, Ann Arbor, Mich.

3. An Approach to Estimate Probability of Presence and Richness of Fish Species

4. Environmental DNA for wildlife biology and biodiversity monitoring

5. Bonar, S.A., Hubert, W.A., and Willis, D.W. 2009. Standard methods for sampling North American freshwater fishes. American Fisheries Society, Bethesda, Md.

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