Linking metagenomics to aquatic microbial ecology and biogeochemical cycles

Special issue Linking Metagenomics to Aquatic Microbial Ecology and Biogeochemical Cycles.-- 19 pages, 1 figureMicrobial communities are essential components of aquatic ecosystems through their contribution to food web dynamics and biogeochemical processes. Aquatic microbial diversity is immense and a general challenge is to understand how metabolism and interactions of single organisms shape microbial community dynamics and ecosystem‐scale biogeochemical transformations. Metagenomic approaches have developed rapidly, and proven to be powerful in linking microbial community dynamics to biogeochemical processes. In this review, we provide an overview of metagenomic approaches, followed by a discussion on some recent insights they have provided, including those in this special issue. These include the discovery of new taxa and metabolisms in aquatic microbiomes, insights into community assembly and functional ecology as well as evolutionary processes shaping microbial genomes and microbiomes, and the influence of human activities on aquatic microbiomes. Given that metagenomics can now be considered a mature technology where data generation and descriptive analyses are relatively routine and informative, we then discuss metagenomic‐enabled research avenues to further link microbial dynamics to biogeochemical processes. These include the integration of metagenomics into well‐designed ecological experiments, the use of metagenomics to inform and validate metabolic and biogeochemical models, and the pressing need for ecologically relevant model organisms and simple microbial systems to better interpret the taxonomic and functional information integrated in metagenomes. These research avenues will contribute to a more mechanistic and predictive understanding of links between microbial dynamics and biogeochemical cycles. Owing to rapid climate change and human impacts on aquatic ecosystems, the urgency of such an understanding has never been greaterDW was supported by the Canadian National Sciences and Engineering Research Council Discovery and Canada Research Chair programs. KDM acknowledges funding from the United States National Science Foundation Long‐Term Ecological Research Program (NTL–LTER DEB‐1440297) and the Wisconsin Alumni Research Foundation. RM was supported by the EU project SINGEK (H2020‐MSCA‐ITN‐2015‐675752) and the Spanish project ALLFLAGS (CTM2016‐75083‐R, MINECO). HPG was supported by the BMBF project BIBS (01LC1501G) given by the German Ministry of Education and Science (BMBF) and the DFG projects Microprime (GR 1540/23‐1) and APPS (GR 1540/30‐1)With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI