TY - JOUR

T1 - Environmental DNA metabarcoding for fish diversity assessment in a macrotidal estuary: A comparison with established fish survey methods

AU - Gibson, Thomas

AU - Carvalho, Gary

AU - Ellison, Amy

AU - Gargiulo, Enrica

AU - Hatton-Ellis, Tristan

AU - Handley, Lori Lawson

AU - Mariani, Stefano

AU - Collins, Rupert A.

AU - Sellers, Graham

AU - Distaso, Marco

AU - Zampieri, Carlo

AU - Creer, Simon

PY - 2023/11/1

Y1 - 2023/11/1

N2 - Fishes are a dominant component of the macrofauna in estuaries and are important for assessing the health of these threatened ecosystems. Several studies have applied environmental DNA (eDNA) metabarcoding to assess the biodiversity of fishes in estuaries. However, none have combined measurement of physicochemical variables with a spatially extensive sampling design across the full salinity gradient. This study aimed to compare spatial fish assemblage composition detected via eDNA metabarcoding of surface water samples with conventional fishing gear surveys in a macrotidal estuary (river Dee, North Wales, UK). In addition, eDNA assemblage composition across seasons was investigated. In autumn 2018, triplicate eDNA samples were taken at 13 stations in a spatially systematic design alongside seine, fyke and beam trawl sampling. In summer 2019, eDNA samples from eight of the 13 original stations were collected again in the upper and lower estuary. DNA was extracted from samples and subjected to metabarcoding analysis using an established assay targeting teleost fishes. The key findings were that in autumn, eDNA detected 17 of the 26 (71%) species caught by fishing gears, which included the most abundant species. Overall, eDNA detected a greater species richness, per 30 samples, than seine or fyke nets (but not beam trawling). Additionally, there was a clear correlation between salinity and assemblage composition, which was consistent across seasons. Overall, the study indicates that eDNA metabarcoding could enhance existing fish sampling methods, by generating a more comprehensive picture of estuarine fish biodiversity and providing additional information for ecological inference and management actions.

AB - Fishes are a dominant component of the macrofauna in estuaries and are important for assessing the health of these threatened ecosystems. Several studies have applied environmental DNA (eDNA) metabarcoding to assess the biodiversity of fishes in estuaries. However, none have combined measurement of physicochemical variables with a spatially extensive sampling design across the full salinity gradient. This study aimed to compare spatial fish assemblage composition detected via eDNA metabarcoding of surface water samples with conventional fishing gear surveys in a macrotidal estuary (river Dee, North Wales, UK). In addition, eDNA assemblage composition across seasons was investigated. In autumn 2018, triplicate eDNA samples were taken at 13 stations in a spatially systematic design alongside seine, fyke and beam trawl sampling. In summer 2019, eDNA samples from eight of the 13 original stations were collected again in the upper and lower estuary. DNA was extracted from samples and subjected to metabarcoding analysis using an established assay targeting teleost fishes. The key findings were that in autumn, eDNA detected 17 of the 26 (71%) species caught by fishing gears, which included the most abundant species. Overall, eDNA detected a greater species richness, per 30 samples, than seine or fyke nets (but not beam trawling). Additionally, there was a clear correlation between salinity and assemblage composition, which was consistent across seasons. Overall, the study indicates that eDNA metabarcoding could enhance existing fish sampling methods, by generating a more comprehensive picture of estuarine fish biodiversity and providing additional information for ecological inference and management actions.

U2 - 10.1016/j.ecss.2023.108522

DO - 10.1016/j.ecss.2023.108522

M3 - Article

VL - 294

JO - Estuarine, Coastal and Shelf Science

JF - Estuarine, Coastal and Shelf Science

SN - 0272-7714

M1 - 108522

ER -