Changes in partitioning of carbon amongst photosynthetic pico- and nano-plankton groups in the Sargasso Sea in response to changes in the North Atlantic Oscillation

Casey, John R.; Aucan, Jérôme; Goldberg, Stacey R.; Lomas, Michael W.

doi:10.1016/j.dsr2.2013.02.002

Cited by 73 publications

(85 citation statements)

References 94 publications

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“…and 1319 fgC cell −1 for picoeukaryotic phytoplankton. These values of ε(i) are comparable to values from the Bermuda Atlantic Timeseries Study (BATS) (Casey et al, 2013), for Prochlorococcus spp. and Synechococcus spp., whereas picoeukaryotic phytoplankton ε(i) values are lower than in BATS.…”

Section: In Situ Datasetsupporting

confidence: 68%

“…Subsequently, the carbon concentration is inferred from measurements of the proxy, which would typically be easier to measure than the carbon concentration itself. The proxies include adenosine triphosphate (ATP) (Sinclair et al, 1979); the refractive index of phytoplankton cells (Stramski, 1999); and the forward light scatter by phytoplankton cells in a flow cytometer (Casey et al, 2013). Redalje and Laws (1981) used chlorophyll-a labeling and showed that the specific activity of carbon in chlorophylla became equivalent to that of total phytoplankton carbon in incubations of 6-12 h, and so chlorophyll-a labeling could be used to infer phytoplankton carbon and growth rates.…”

Section: Introductionmentioning

confidence: 99%

“…Various methods for in situ measurements of phytoplankton carbon in the laboratory or in the field have been reviewed by Casey et al (2013). Some of the in situ methods require a proxy measurement, which is then calibrated against phytoplankton carbon.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, there has been a growing interest in monitoring the standing stock of phytoplankton in carbon units (CEOS, 2014), in addition to chlorophyll units. There are many reasons for this interest, which include calculation of primary production using carbon-based models (Behrenfeld et al, 2005;Westberry et al, 2008); estimating phytoplankton loss rates (Zhai et al, 2008(Zhai et al, , 2010; comparison with estimates of phytoplankton biomass in carbon units from marine ecosystem models (Dutkiewicz et al, 2015); and establishing the budget of the pools of carbon in the ocean (CEOS, 2014), their turnover rates (Casey et al, 2013), and their exchanges with the atmospheric and terrrestrial domains (CEOS, 2014). With increasing appreciation of the different roles of various phytoplankton functional types in the oceanic biogeochemical cycles (Le Quéré et al, 2005), there is a corresponding need to know the pools of carbon associated with the different phytoplankton types, rather than just the total phytoplankton carbon.…”

Section: Introductionmentioning

confidence: 99%

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Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean

et al. 2017

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The differences among phytoplankton carbon (C phy ) predictions from six ocean color algorithms are investigated by comparison with in situ estimates of phytoplankton carbon. The common satellite data used as input for the algorithms is the Ocean Color Climate Change Initiative merged product. The matching in situ data are derived from flow cytometric cell counts and per-cell carbon estimates for different types of pico-phytoplankton. This combination of satellite and in situ data provides a relatively large matching dataset (N > 500), which is independent from most of the algorithms tested and spans almost two orders of magnitude in C phy . Results show that not a single algorithm outperforms any of the other when using all matching data. Concentrating on the oligotrophic regions (Chlorophyll-a concentration, B, less than 0.15 mg Chl m −3 ), where flow cytometric analysis captures most of the phytoplankton biomass, reveals significant differences in algorithm performance. The bias ranges from −35 to +150% and unbiased root mean squared difference from 5 to 10 mg C m −3 among algorithms, with chlorophyll-based algorithms performing better than the rest. The backscatteringbased algorithms produce different results at the clearest waters and these differences are discussed in terms of the different algorithms used for optical particle backscattering coefficient (b bp ) retrieval.

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Section: In Situ Datasetsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean

et al. 2017

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“…POC : PON : POP ratios in Prochlorococcus, Synechococcus and picoeukaryote are 234 : 33 : 1, 181 : 33 : 1 and 118 : 15 : 1, respectively, at the BATS site (Martiny et al, 2013, and Lomas et al, unpublished data), which clearly suggests imprints of a mixture of Prochlorococcus and Synechococcus on the observed POM stoichiometry presented in Table 1. The biomass of Prochlorococcus, Synechococcus and picoeukaryotes together contributes ∼ 40 % to the POC pool (Casey et al, 2013) and ∼ 75 % to the PON pool (Fawcett et al, 2011), with major contributions from each group varying seasonally. Hence, variability in biological parameters could potentially explain a significant fraction of the variability in the POM and TOM ratios but not all of it.…”

Section: Linkages Of Concentrations and Ratios Of Pom And Tom To Chlomentioning

confidence: 99%

C : N : P stoichiometry at the Bermuda Atlantic Time-series Study station in the North Atlantic Ocean

et al. 2015

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Abstract. Nitrogen (N) and phosphorus (P) availability, in addition to other macro-and micronutrients, determine the strength of the ocean's carbon (C) uptake, and variation in the N : P ratio of inorganic nutrient pools is key to phytoplankton growth. A similarity between C : N : P ratios in the plankton biomass and deep-water nutrients was observed by Alfred C. Redfield around 80 years ago and suggested that biological processes in the surface ocean controlled deepocean chemistry. Recent studies have emphasized the role of inorganic N : P ratios in governing biogeochemical processes, particularly the C : N : P ratio in suspended particulate organic matter (POM), with somewhat less attention given to exported POM and dissolved organic matter (DOM). Herein, we extend the discussion on ecosystem C : N : P stoichiometry but also examine temporal variation in stoichiometric relationships. We have analyzed elemental stoichiometry in the suspended POM and total (POM + DOM) organic-matter (TOM) pools in the upper 100 m and in the exported POM and subeuphotic zone (100-500 m) inorganic nutrient pools from the monthly data collected at the Bermuda Atlantic Time-series Study (BATS) site located in the western part of the North Atlantic Ocean. C : N and N : P ratios in TOM were at least twice those in the POM, while C : P ratios were up to 5 times higher in TOM compared to those in the POM. Observed C : N ratios in suspended POM were approximately equal to the canonical Redfield ratio (C : N : P = 106 : 16 : 1), while N : P and C : P ratios in the same pool were more than twice the Redfield ratio. Average N : P ratios in the subsurface inorganic nutrient pool were ∼ 26 : 1, squarely between the suspended POM ratio and the Redfield ratio. We have further linked variation in elemental stoichiometry to that of phytoplankton cell abundance observed at the BATS site. Findings from this study suggest that elemental ratios vary with depth in the euphotic zone, mainly due to different growth rates of cyanobacterial cells. We have also examined the role of the Arctic Oscillation on temporal patterns in C : N : P stoichiometry. This study strengthens our understanding of the variability in elemental stoichiometry in different organic-matter pools and should improve biogeochemical models by constraining the range of non-Redfield stoichiometry and the net relative flow of elements between pools.

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Long‐term variability of phytoplankton carbon biomass in the Sargasso Sea

Wallhead

Garçon

Casey

et al. 2014

Global Biogeochemical Cycles

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Time series of phytoplankton carbon biomass are scarce yet may provide important insights into ocean productivity and carbon export to depth via the oceanic biological pump. We combine recent flow-cytometric measurements with pigment concentrations and other standard measurements to reconstruct taxon-specific phytoplankton carbon biomass in the Sargasso Sea over 22 years, using a multiple regression approach. The reconstructed series reveal an increasing trend (~3% per year) in total phytoplankton carbon, apparently driven by increasing nutrient supply by vertical mixing associated with a shift to a negative phase in the winter North Atlantic Oscillation index. Also, the reconstructed eukaryote biomass fraction shows a multiannual shift from~45% in the early 1990s/late 2000s to~70% in the late 1990s/early 2000s. We hypothesize that a multiannual shift in the seasonal pattern of mixing may have stimulated and restructured the eukaryote community while suppressing prokaryote populations by increasing photodamage and grazing mortality.

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Changes in partitioning of carbon amongst photosynthetic pico- and nano-plankton groups in the Sargasso Sea in response to changes in the North Atlantic Oscillation

Cited by 73 publications

References 94 publications

Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean

Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean

C : N : P stoichiometry at the Bermuda Atlantic Time-series Study station in the North Atlantic Ocean

Long‐term variability of phytoplankton carbon biomass in the Sargasso Sea

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