Abstract
A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or "in development", hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (A) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (B) De novo bioindicator analyses; (C) Structural community metrics including inferred ecological networks; and (D) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programs that leverage recent analytical advancements, while pointing out current limitations and future research needs.
| Original language | English (US) |
|---|---|
| Journal | Molecular ecology |
| DOIs | |
| State | Published - May 17 2020 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledgements: TC,JP and LAPG were supported by the Swiss National Science Foundation (grant 31003A_179125). TC, JP, LAPG and AB were supported by the European Cross-Border Cooperation Program (Interreg France-Switzerland 2014-2020, SYNAQUA project). LAS was funded by a ‘Ramón y Cajal’ contract (RYC-2012-11404) from the Spanish Ministry of Economy and Competitiveness. EA is funded by the Saudi Aramco-KAUST Center for Marine Environmental Observations. DAB would like to acknowledge the financial support of the French Agence Nationale de la Recherche project NGB (ANR-17-CE32-011) and the ERA-NET C-IPM BioAWARE. AB and FK were supported by the Office Français de la Biodiversité (OFB). SC benefitted from the UK Natural Environment Research Council Grants NE/N003756/1 and NE/N006216/1. TS and LF were supported by the German Science Foundation (DFG) under grant STO414/15-1. XP is supported by the New Zealand Ministry for Business, Innovation and Employment contracts CAWX1904 (Biosecurity Toolbox) and C05X1707 (Lakes380). AB and FL were supported by DNAqua-Net COST Action CA15219 ‘Developing new genetic tools for bioassessment of aquatic ecosystems in Europe’ funded by the European Union. AL is supported by IKERBASQUE (Basque Foundation for Science) and the Basque Government (project microgAMBI). We thank the members of DNAqua-net COST Action for helpful discussions.