Analytical phytoplankton carbon measurements spanning diverse ecosystems

Graff, Jason R.; Westberry, T. K.; Milligan, Allen J.; Brown, Matthew B.; Dall’Olmo, Giorgio; Dongen‐Vogels, Virginie van; Reifel, Kristen M.; Behrenfeld, Michael J.

doi:10.1016/j.dsr.2015.04.006

Cited by 203 publications

(285 citation statements)

References 55 publications

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“…To test our choice of carbon conversion parameters we compared direct measurements of C phy with estimates computed using the conversion factors above. In samples from the AMT-22 (N = 15) (Graff et al, 2015), the slope of the regression between direct measurements of C phy and computed C phy , was 0.8 (r 2 = 0.6, p < 0.05). According to this result, the estimates of picoplankton C phy in our dataset are significantly correlated with direct estimates of phytoplankton carbon, and could be an overestimate of direct observations of C phy , which include nanophytoplankton, although a larger sample is required to support this conclusion.…”

Section: In Situ Datasetmentioning

confidence: 99%

“…Bold numbers are the best results for each statistic: highest value for rs and rp, lowest for Ψ , δ, ∆, I and MAPD, and closest to one for S. ratio greater than 100 mg C mg Chla −1 for prymnesiophytes, cyanobacteria and Prochlorococcus sp. Direct observations of pico and nano plankton carbon in the Northern and Southern Atlantic gyres produced carbon-to-chlorophyll ratio estimates, on average, of 106 and 190 mg C mg Chla −1 , respectively (Graff et al, 2015). Marañón et al (2014) also reports values in the range of 80-117 mg C mg Chla −1 for oligotrophic regions.…”

Section: Distribution Of In Situ Data and Accuracy Of Algorithmsmentioning

confidence: 99%

“…Additional data come from a long-term observation program, the Atlantic Meridional Transect (AMT); as well as recently available data collected independently during AMT-22 and in the Pacific (Graff et al, 2015) and from other regions in the Atlantic ocean (Taylor et al, 2013). The dataset assembled consists of cell counts (in cells per milliliter), from water samples originating between 0 and 200 m depth, and collected in the period between 1997 and 2014, to match satellite observations available.…”

Section: In Situ Datasetmentioning

confidence: 99%

“…and picoeukaryotic phytoplankton. For consistency, only information on the same phytoplankton types were extracted from the additional data sources (Zubkov et al, 1998;Tarran et al, 2001Tarran et al, , 2006Taylor et al, 2013; Graff et al, 2015;Tarran, 2015;Tarran and Bruun, 2015) (see Table 1). The carbon concentration (C phy , in mg C m −3 ) for each phytoplankton group (i) and for each sample (j), C phy (i, j), was calculated as follows:…”

Section: In Situ Datasetmentioning

confidence: 99%

“…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. Graff et al (2015) used flow cytometer cell sorting (Graff et al, 2012) to measure phytoplankton carbon in sorted samples, thereby avoiding contamination of results by non-pigmented particles.…”

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 Datasetmentioning

confidence: 99%

Section: Distribution Of In Situ Data and Accuracy Of Algorithmsmentioning

confidence: 99%

Section: In Situ Datasetmentioning

confidence: 99%

Section: In Situ Datasetmentioning

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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Microzooplankton regulation of surface ocean POC:PON ratios

Talmy

Martiny

Hill

et al. 2016

Global Biogeochemical Cycles

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The elemental composition of particulate organic matter in the surface ocean significantly affects the efficiency of the ocean's store of carbon. Though the elemental composition of primary producers is an important factor, recent observations from the western North Atlantic Ocean revealed that carbon‐to‐nitrogen ratios (C:N) of phytoplankton were significantly higher than the relatively homeostatic ratio of the total particulate pool (particulate organic carbon:particulate organic nitrogen; POC:PON). Here we use an idealized ecosystem model to show how interactions between primary and secondary producers maintain the mean composition of surface particulates and the difference between primary producers and bulk material. Idealized physiological models of phytoplankton and microzooplankton, constrained by laboratory data, reveal contrasting autotrophic and heterotrophic responses to nitrogen limitation: under nitrogen limitation, phytoplankton accumulate carbon in carbohydrates and lipids while microzooplankton deplete internal C reserves to fuel respiration. Global ecosystem simulations yield hypothetical global distributions of phytoplankton and microzooplankton C:N ratio predicting elevated phytoplankton C:N ratios in the high‐light, low‐nutrient regions of the ocean despite a lower, homeostatic POC:PON ratio due to respiration of excess carbon in systems subject to top‐down control. The model qualitatively captures and provides a simple interpretation for, a global compilation of surface ocean POC:PON data.

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Annual cycles of phytoplankton biomass in the subarctic Atlantic and Pacific Ocean

Westberry

Schultz

Behrenfeld

et al. 2016

Global Biogeochemical Cycles

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High‐latitude phytoplankton blooms support productive fisheries and play an important role in oceanic uptake of atmospheric carbon dioxide. In the subarctic North Atlantic Ocean, blooms are a recurrent feature each year, while in the eastern subarctic Pacific only small changes in chlorophyll (Chl) are seen over the annual cycle. Here we show that when evaluated using phytoplankton carbon biomass (Cphyto) rather than Chl, an annual bloom in the North Pacific is evident and can even rival blooms observed in the North Atlantic. The annual increase in subarctic Pacific phytoplankton biomass is not readily observed in the Chl record because it is paralleled by light‐ and nutrient‐driven decreases in cellular pigment levels (Cphyto:Chl). Specifically, photoacclimation and iron stress effects on Cphyto:Chl oppose the biomass increase, leading to only modest changes in bulk Chl. The magnitude of the photoacclimation effect is quantified using descriptors of the near‐surface light environment and a photophysiological model. Iron stress effects are diagnosed from satellite chlorophyll fluorescence data. Lastly, we show that biomass accumulation in the Pacific is slower than that in the Atlantic but is closely tied to similar levels of seasonal nutrient uptake in both basins. Annual cycles of satellite‐derived Chl and Cphyto are reproduced by in situ autonomous profiling floats. These results contradict the long‐standing paradigm that environmental conditions prevent phytoplankton accumulation in the subarctic Northeast Pacific and suggest a greater seasonal decoupling between phytoplankton growth and losses than traditionally implied. Further, our results highlight the role of physiological processes in shaping bulk properties, such as Chl, and their interpretation in studies of ocean ecosystem dynamics and climate change.

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Analytical phytoplankton carbon measurements spanning diverse ecosystems

Cited by 203 publications

References 55 publications

Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean

Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean

Microzooplankton regulation of surface ocean POC:PON ratios

Annual cycles of phytoplankton biomass in the subarctic Atlantic and Pacific Ocean

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