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Linking regional shifts in microbial genome adaptation with surface ocean biogeochemistry.

ABSTRACT: Linking 'omics measurements with biogeochemical cycles is a widespread challenge in microbial community ecology. Here, we propose applying genomic adaptation as 'biosensors' for microbial investments to overcome nutrient stress. We then integrate this genomic information with a trait-based model to predict regional shifts in the elemental composition of marine plankton communities. We evaluated this approach using metagenomic and particulate organic matter samples from the Atlantic, Indian and Pacific Oceans. We find that our genome-based trait model significantly improves our prediction of particulate C : P (carbon : phosphorus) across ocean regions. Furthermore, we detect previously unrecognized ocean areas of iron, nitrogen and phosphorus stress. In many ecosystems, it can be very challenging to quantify microbial stress. Thus, a carefully calibrated genomic approach could become a widespread tool for understanding microbial responses to environmental changes and the biogeochemical outcomes. This article is part of the theme issue 'Conceptual challenges in microbial community ecology'.

SUBMITTER: Garcia CA

PROVIDER: S-EPMC7133529 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Linking regional shifts in microbial genome adaptation with surface ocean biogeochemistry.

Garcia Catherine A CA Hagstrom George I GI Larkin Alyse A AA Ustick Lucas J LJ Levin Simon A SA Lomas Michael W MW Martiny Adam C AC

Philosophical transactions of the Royal Society of London. Series B, Biological sciences 20200323 1798

Linking 'omics measurements with biogeochemical cycles is a widespread challenge in microbial community ecology. Here, we propose applying genomic adaptation as 'biosensors' for microbial investments to overcome nutrient stress. We then integrate this genomic information with a trait-based model to predict regional shifts in the elemental composition of marine plankton communities. We evaluated this approach using metagenomic and particulate organic matter samples from the Atlantic, Indian and P ...[more]

PMID: 32200740

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Project description:As consequences of global warming sea-ice shrinking, permafrost thawing and changes in fresh water and terrestrial material export have already been reported in the Arctic environment. These processes impact light penetration and primary production. To reach a better understanding of the current status and to provide accurate forecasts Arctic biogeochemical and physical parameters need to be extensively monitored. In this sense, bio-optical properties are useful to be measured due to the applicability of optical instrumentation to autonomous platforms, including satellites. This study characterizes the non-water absorbers and their coupling to hydrographic conditions in the poorly sampled surface waters of the central and eastern Arctic Ocean. Over the entire sampled area colored dissolved organic matter (CDOM) dominates the light absorption in surface waters. The distribution of CDOM, phytoplankton and non-algal particles absorption reproduces the hydrographic variability in this region of the Arctic Ocean which suggests a subdivision into five major bio-optical provinces: Laptev Sea Shelf, Laptev Sea, Central Arctic/Transpolar Drift, Beaufort Gyre and Eurasian/Nansen Basin. Evaluating ocean color algorithms commonly applied in the Arctic Ocean shows that global and regionally tuned empirical algorithms provide poor chlorophyll-a (Chl-a) estimates. The semi-analytical algorithms Generalized Inherent Optical Property model (GIOP) and Garver-Siegel-Maritorena (GSM), on the other hand, provide robust estimates of Chl-a and absorption of colored matter. Applying GSM with modifications proposed for the western Arctic Ocean produced reliable information on the absorption by colored matter, and specifically by CDOM. These findings highlight that only semi-analytical ocean color algorithms are able to identify with low uncertainty the distribution of the different optical water constituents in these high CDOM absorbing waters. In addition, a clustering of the Arctic Ocean into bio-optical provinces will help to develop and then select province-specific ocean color algorithms.

Dataset Information

Linking regional shifts in microbial genome adaptation with surface ocean biogeochemistry.

Publications

Linking regional shifts in microbial genome adaptation with surface ocean biogeochemistry.

Similar Datasets

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