Environmental DNA metabarcoding of lake fish communities reflects long-term data from established survey methods

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Authors Bernd Hänfling, Lori Lawson Handley, Daniel S. Read, Christoph Hahn, Jianlong Li, Paul Nichols, Rosetta C. Blackman, Anna Oliver, Ian J. Winfield
Journal/Conference Name Molecular Ecology
Paper Category , ,
Paper Abstract Organisms continuously release DNA into their environments via shed cells, excreta, gametes and decaying material. Analysis of this ‘environmental DNA’ (eDNA) is revolutionizing biodiversity monitoring. eDNA outperforms many established survey methods for targeted detection of single species, but few studies have investigated how well eDNA reflects whole communities of organisms in natural environments. We investigated whether eDNA can recover accurate qualitative and quantitative information about fish communities in large lakes, by comparison to the most comprehensive long-term gill-net data set available in the UK. Seventy-eight 2L water samples were collected along depth profile transects, gill-net sites and from the shoreline in three large, deep lakes (Windermere, Bassenthwaite Lake and Derwent Water) in the English Lake District. Water samples were assayed by eDNA metabarcoding of the mitochondrial 12S and cytochrome b regions. Fourteen of the 16 species historically recorded in Windermere were detected using eDNA, compared to four species in the most recent gill-net survey, demonstrating eDNA is extremely sensitive for detecting species. A key question for biodiversity monitoring is whether eDNA can accurately estimate abundance. To test this, we used the number of sequence reads per species and the proportion of sampling sites in which a species was detected with eDNA (i.e. site occupancy) as proxies for abundance. eDNA abundance data consistently correlated with rank abundance estimates from established surveys. These results demonstrate that eDNA metabarcoding can describe fish communities in large lakes, both qualitatively and quantitatively, and has great potential as a complementary tool to established monitoring methods.
Date of publication 2016
Code Programming Language R
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