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Adaptive carbon export response to warming in the Sargasso Sea - PubMed

  • ️Sat Jan 01 2022

Adaptive carbon export response to warming in the Sargasso Sea

Michael W Lomas et al. Nat Commun. 2022.

Abstract

Ocean ecosystem models predict that warming and increased surface ocean stratification will trigger a series of ecosystem events, reducing the biological export of particulate carbon to the ocean interior. We present a nearly three-decade time series from the open ocean that documents a biological response to ocean warming and nutrient reductions wherein particulate carbon export is maintained, counter to expectations. Carbon export is maintained through a combination of phytoplankton community change to favor cyanobacteria with high cellular carbon-to-phosphorus ratios and enhanced shallow phosphorus recycling leading to increased nutrient use efficiency. These results suggest that surface ocean ecosystems may be more responsive and adapt more rapidly to changes in the hydrographic system than is currently envisioned in earth ecosystem models, with positive consequences for ocean carbon uptake.

© 2022. The Author(s).

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Time series of surface ocean temperature (@ 10 m), mixed layer depth, and seasonal mixing amplitude.

A CTD temperature recorded at 10 ± 2 m (thin black line), and seasonally detrended anomaly (red circles). The thick line is the Model 1 linear regression to the anomaly data. B Mixed layer depths estimated from each CTD cast in the BATS record using the 0.2 oC variable sigma—θ criterion. C Difference between average maximum winter/spring (February/March) and minimum summer (July/August) mixed layer depths for each year. Note: 2010 is highlighted in a different color for reasons discussed in the text.

Fig. 2
Fig. 2. Time series of surface ocean macronutrient inventories and integrated daily and annual net primary production (NPP).

A Nitrate and B high-sensitivity phosphate nutrient inventories (0–140 m; mmol m−2), and C integrated (0–140 m) daily and annual NPP. Black lines in each panel are individual cruise values, while the red lines and open diamonds are annual values. Note, annual NPP is on a different scale (right axis).

Fig. 3
Fig. 3. Time series of vertical elemental fluxes at 150 m.

A Particulate organic carbon (Cf) flux; B particulate organic nitrogen (Nf) flux; C particulate phosphorus (Pf) flux.

Fig. 4
Fig. 4. Time series of flow cytometry-derived, euphotic zone integrated phytoplankton carbon biomass (g m−2) and phytoplankton growth rates.

A Biomass of cyanobacteria Prochlorococcus and Synechococcus; B biomass of picoeukaryotes and nanoeukaryotes; C total phytoplankton carbon biomass derived from the product of bulk POC:Chl slope and integrated Chl, and summed flow cytometry-derived carbon biomass estimates. D Phytoplankton growth rates estimated by dividing integrated in situ primary production by the two estimates of phytoplankton carbon biomass as described in (C).

Fig. 5
Fig. 5. Time series of elemental flux stoichiometric ratios at 150 m.

A Particulate Cf:Nf flux ratio (thin black line); B particulate Cf:Pf flux ratio; and C particulate Nf:Pf flux ratio. Red triangles represent the annual mean flux ratio, and the blue dashed line represents the Redfield Ratio.

Fig. 6
Fig. 6. Time series of seston elemental stoichiometric ratios in the upper (0–60 m) euphotic zone layer.

A POC:PON; B POC:PP; C PON:PP. Red triangles represent the annual mean stoichiometric ratio, and the blue dashed line represents the Redfield Ratio.

Fig. 7
Fig. 7. Time series of phytoplankton-specific C:P ratios calculated from the trait-based model approach of Tanioka et al..

Model phytoplankton C:P in the surface 100 m is computed as a function of growth rate and POC:Chl-a ratio derived from MODIS-Aqua combined with a map of temperature-dependent nutrient limitation.

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References

    1. Falkowski PG, Barber RT, Smetacek V. Biogeochemical controls and feedbacks on ocean primary productivity. Science. 1998;281:200–206. - PubMed
    1. Harris, G. Phytoplankton Ecology: Structure, Function and Flucuation (Springer Science and Business Media, 2012).
    1. Boyce D, Lewis M, Worm B. Global phytoplankton decline over the past century. Nature. 2010;466:591–596. - PubMed
    1. Martinez E, Antoine D, D’Ortenzio F, Gentili B. Climate-driven basin-scale decadal oscillations of oceanic phytoplankton. Science. 2009;326:1253–1256. - PubMed
    1. Bopp L, et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences. 2013;10:6225–6245.

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