The ISME JournalEcologyPeer-reviewed journal article
Ocean carbon export can be predicted from ocean color-based phytoplankton communities
Sasha J. KrameriD, Erin L. JonesiD, Margaret EstapaiD, Nicola L. PauliD, Tatiana A. RynearsoniD, Alyson E. SantoroiD, Sebastian Sudek, Colleen A. DurkiniD
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- 18
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- top 1%
Abstract
Carbon flux to the deep sea can be dictated by surface ocean phytoplankton community composition, but translating surface ocean observations into quantitative predictions of carbon export requires additional consideration of the underlying ecosystem drivers. Here, we used genetic tracers of phytoplankton detected in surface seawater and within sinking particles collected in the mesopelagic ocean to identify mechanistic links between surface communities and carbon export in the North Pacific and North Atlantic Oceans. Phytoplankton 18S rRNA sequences were sampled over a one-month period in surface seawater and within bulk-collected and individually-isolated sinking particles using mesopelagic sediment traps (100-500m). Nearly all phytoplankton amplicon sequence variants (ASVs) exported from the surface were packaged in large (>300 {micro}m) particles. Individually, these particles contained only a few distinct phytoplankton ASVs, but collectively, large particles transported about half of the surface taxonomic diversity into the mesopelagic. The relative sequence abundances of the surface community detected within particles were quantitatively related to measured POC fluxes: a linear model based on the relative sequence abundance of just two pigment-based phytoplankton taxa, diatoms and photosynthetic Hacrobia, was predictive of POC flux magnitude. These two taxa were also enriched within the ecologically-distinct particle classes that had the greatest influence on carbon export magnitude. As global, hyperspectral ocean color satellites begin to quantify these taxonomic groups in the surface ocean, the relationship of these taxa to carbon fluxes demonstrated here may help generate more accurate global estimates of export.
- assay: metagenomics
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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