Executing multi-taxa eDNA ecological assessment via traditional metrics and interactive networks.

Autor: Seymour M; Molecular Ecology and Fisheries Genetics Laboratory, School of Biological Sciences, Bangor University, Bangor, Gwynedd LL57 2UW, UK. Electronic address: mat.seymour@gmail.com., Edwards FK; Centre for Ecology & Hydrology, Wallingford OX10 8BB, UK., Cosby BJ; NERC Centre for Ecology & Hydrology, Environment Centre Wales, Deiniol Road, Bangor, Gwynedd LL57 2UW, UK., Kelly MG; Bowburn Consultancy, 11 Monteigne Drive, Bowburn, Durham DH6 5QB, UK., de Bruyn M; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia., Carvalho GR; Molecular Ecology and Fisheries Genetics Laboratory, School of Biological Sciences, Bangor University, Bangor, Gwynedd LL57 2UW, UK., Creer S; Molecular Ecology and Fisheries Genetics Laboratory, School of Biological Sciences, Bangor University, Bangor, Gwynedd LL57 2UW, UK.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2020 Aug 10; Vol. 729, pp. 138801. Date of Electronic Publication: 2020 Apr 21.
DOI: 10.1016/j.scitotenv.2020.138801
Abstrakt: Current approaches to ecological assessment are limited by the traditional morpho-taxonomic methods presently employed and the inability to meet increasing demands for rapid assessments. Advancements in high throughput sequencing now enable rapid high-resolution ecological assessment using environmental DNA (eDNA). Here we test the ability of using eDNA-based ecological assessment methods against traditional assessment of two key indicator groups (diatoms and macroinvertebrates) and show how eDNA across multiple gene regions (COI, rbcL, 12S and 18S) can be used to infer interactive networks that link to ecological assessment criteria. We compared results between taxonomic and eDNA based assessments and found significant positive associations between macroinvertebrate (p < 0.001 R 2  = 0.645) and diatom (p = 0.015, R 2  = 0.222) assessment metrics. We further assessed the ability of eDNA based assessment to identify environmentally sensitive genera and found an order of magnitude greater potential for 18S, versus COI or rbcL, to determine environmental filtering of ecologically assessed communities. Lastly, we compared the ability of traditional metrics against co-occurrence network properties of our combined 18S, COI and rbcL indicator genera to infer habitat quality measures currently used by managers. We found that transitivity (network connectivity), linkage density and cohesion were significantly associated with habitat modification scores (HMS), whereas network properties were inconsistent with linking to the habitat quality score (HQS) metric. The incorporation of multi-marker eDNA network assessment opens up a means for finer scale ecological assessment, currently limited using traditional methods. While utilization of eDNA-based assessment is recommended, direct comparisons with traditional approaches are difficult as the methods are intrinsically different and should be treated as such with regards to future research. Overall, our findings show that eDNA can be used for effective ecological assessment while offering a wider range of scope and application compared to traditional assessment methods.
Competing Interests: Declaration of competing interest None.
(Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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