Abstract
Environmental DNA (eDNA) metabarcoding was used to characterize finfish communities in the nearshore estuarine environment. Monthly sampling was conducted June - August 2017 at two sites with structured habitats: a natural rock reef and a shellfish aquaculture farm within the same coastal embayment of Long Island Sound, CT, USA. Seventeen common and 25 rare finfish taxa were detected using eDNA metabarcoding. Incomplete status of reference sequence databases for finfish species was identified as a methodological challenge. Confidence in molecular identification was improved appreciably through the use of publicly-available data obtained from local trawling and seining surveys. Comparison between eDNA metabarcoding and trawling surveys on 6/27/2017, the only day when both data types were available, revealed more finfish species detected by eDNA metabarcoding. The high sensitivity of eDNA metabarcoding detected finfish species rarely observed in traditional surveys and showed the potential for this methodology to augment existing literature for finfish species distribution patterns and invasive species detection. Non-metric multidimensional scaling (NMS) analysis of finfish communities achieved a low-stress, 2D solution, and revealed greater variation between samples collected from different months than samples collected from the two habitats. Similarly, permutational analysis of variance (PERMANOVA) found both month and the interaction term (month x site) significant, with the latter identifying site as significant only in July and August. Different finfish assemblages were significantly associated with each axis, axes representing temporal and spatial variations, respectively. Additionally, polycarbonate and nylon filters were compared to optimize the sampling method; finfish communities retrieved using the 2 types of filters were statistically indistinguishable by NMS analysis, although the filtration time for nylon filters was shorter. If the objective is to detect rare species, nylon filters are recommended over polycarbonate filters because of higher capture rates of rare taxa. Our study demonstrates the potential for applying eDNA metabarcoding as a stand-alone method to conduct finfish surveys with high sensitivity.
Highlights
Coastal ecosystems are among the most productive and diverse ecosystems on the planet, hosting about 80% of the 13,200 known marine fish species (Costa-Pierce, 2016)
While this study benefited greatly from recent contribution of local reference sequences into GenBank (Stoeckle et al, 2018), and by validation of Environmental DNA (eDNA) identification using historical Department of Energy and Environmental Protection (DEEP) trawl survey data, we suggest that the incomplete status of reference sequence databases for finfish species remains an important research gap
As reference sequences of under-documented northwestern Atlantic finfish became available in GenBank (Stoeckle et al, 2018), the 3 rare amplicon sequence variants (ASVs) were re-classified as striped cusk-eel (Ophidion marginatum), northern stargazer (Astroscopus guttatus), and fourspot flounder (Paralichthys oblonga) with 99, 100, and 100% match
Summary
Coastal ecosystems are among the most productive and diverse ecosystems on the planet, hosting about 80% of the 13,200 known marine fish species (Costa-Pierce, 2016). One promising new technology collects traces of organismal DNA in the environment and uses DNA sequences to provide identification. Such DNA sources are referred to collectively as environmental DNA (eDNA), and include whole microorganisms, cellular materials such as tissue, feces, or scales, and free DNA released from cytoplasm. PCR-based eDNA methods include q-PCR for single species quantification and eDNA-metabarcoding for community analysis. The former approach relies upon taxon-specific primers to generate quantitative information on taxon-specific genes, an index of taxon abundance; the latter combines DNA-based identification with high-throughput, next-generation sequencing to characterize complex communities. EDNA metabarcoding was first applied to survey microbial diversity (Sogin et al, 2006) and more recently to survey macroorganisms (Thomsen et al, 2012; Valentini et al, 2016)
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