Open AccessEnvironmental DNA provides quantitative estimates of Pacific hake abundance and distribution in the open ocean
Author(s) -
Andrew Olaf Shelton,
Ana RamónLaca,
Abigail Wells,
Julia Clemons,
Dezhang Chu,
Blake E. Feist,
Ryan P. Kelly,
Sandra L. ParkerStetter,
Rebecca Thomas,
Krista M. Nichols,
Linda Park
Publication year - 2022
Publication title -
proceedings - royal society. biological sciences/proceedings - royal society. biological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.342
H-Index - 253
eISSN - 1471-2954
pISSN - 0962-8452
DOI - 10.1098/rspb.2021.2613
Subject(s) - environmental dna , abundance (ecology) , fishery , habitat , merluccius , hake , spatial ecology , geography , ecology , biodiversity , biology , fish <actinopterygii>
All species inevitably leave genetic traces in their environments, and the resulting environmental DNA (eDNA) reflects the species present in a given habitat. It remains unclear whether eDNA signals can provide quantitative metrics of abundance on which human livelihoods or conservation successes depend. Here, we report the results of a large eDNA ocean survey (spanning 86 000 km2 to depths of 500 m) to understand the abundance and distribution of Pacific hake (Merluccius productus ), the target of the largest finfish fishery along the west coast of the USA. We sampled eDNA in parallel with a traditional acoustic-trawl survey to assess the value of eDNA surveys at a scale relevant to fisheries management. Despite local differences, the two methods yield comparable information about the broad-scale spatial distribution and abundance. Furthermore, we find depth and spatial patterns of eDNA closely correspond to acoustic-trawl estimates for hake. We demonstrate the power and efficacy of eDNA sampling for estimating abundance and distribution and move the analysis eDNA data beyond sample-to-sample comparisons to management relevant scales. We posit that eDNA methods are capable of providing general quantitative applications that will prove especially valuable in data- or resource-limited contexts.