A decade of synthesis and modeling in the US Joint Global Ocean Flux Study

Doney, Scott C.; Ducklow, Hugh W.

doi:10.1016/j.dsr2.2006.01.019

Cited by 17 publications

(10 citation statements)

References 40 publications

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“…We selected 3 open sites for our study. One site was the longterm biogeochemical observatory, Station ALOHA (22.75°N, 158.00°W), the field site for the Hawaii Ocean Time-series (HOT) program, established in the North Pacific Subtropical Gyre in 1988 (Karl et al 2001, Doney & Ducklow 2006. The station is 100 km north of Oahu, Hawaii, USA with a bottom depth of 4700 m. The station is within the southward return flow of the eastern gyre with relatively weak horizontal transport (Karl et al 2001), and thus represents a good site to apply 1-D ecological modeling.…”

Section: Methodsmentioning

confidence: 99%

Oceanic heterotrophic bacterial nutrition by semilabile DOM as revealed by data assimilative modeling

Luo¹,

Friedrichs²,

Doney³

et al. 2010

Aquat. Microb. Ecol.

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Previous studies have focused on the role of labile dissolved organic matter (DOM) (defined as turnover time of ~1 d) in supporting heterotrophic bacterial production, but have mostly neglected semilabile DOM (defined as turnover time of ~100 to 1000 d) as a potential substrate for heterotrophic bacterial growth. To test the hypothesis that semilabile DOM supports substantial amounts of heterotrophic bacterial production in the open ocean, we constructed a 1-dimensional epipelagic ecosystem model and applied it to 3 open ocean sites: the Arabian Sea, Equatorial Pacific and Station ALOHA in the North Pacific Subtropical Gyre. The model tracks carbon, nitrogen and phosphorus with flexible stoichiometry. This study used a large number of observations, including measurements of heterotrophic bacterial production rates and standing stocks, and DOM concentration data, to rigorously test and constrain model output. Data assimilation was successfully applied to optimize the model parameters and resulted in simultaneous representation of observed nitrate, phosphate, phytoplankton and zooplankton biomass, primary production, heterotrophic bacterial biomass and production, DOM, and suspended and sinking particulate organic matter. Across the 3 ocean ecosystems examined, the data assimilation suggests semilabile DOM may support 17 to 40% of heterotrophic bacterial carbon demand. In an experiment where bacteria only utilize labile DOM, and with more of the DOM production assigned to labile DOM, the model poorly represented the observations. These results suggest that semilabile DOM may play an important role in sustaining heterotrophic bacterial growth in diverse regions of the open ocean.

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Section: Methodsmentioning

confidence: 99%

Oceanic heterotrophic bacterial nutrition by semilabile DOM as revealed by data assimilative modeling

Luo¹,

Friedrichs²,

Doney³

et al. 2010

Aquat. Microb. Ecol.

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“…Despite methodological shortcomings such as those outlined in the previous sections, these studies provided a solid basis for the general understanding of the role of bacterioplankton in carbon flux in these ecosystems, as well as their constraints through the supply of substrate derived from phytoplankton primary production, growth limitation by organic carbon and other elements, and mortality losses due to protozoan grazing and virus infection (del Giorgio & Cole 2000, Ducklow 2000, Doney & Ducklow 2006. Today, we have a rather solid general knowledge of the fraction and range of phytoplankton primary production processed via bacterioplankton biomass production and respiration.…”

Section: Refined Functional and Regional Resolutionmentioning

confidence: 99%

Towards a better understanding of microbial carbon flux in the sea*

Gasol¹,

Pinhassi²,

Alonso‐Sáez³

et al. 2008

Aquat. Microb. Ecol.

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We now have a relatively good idea of how bulk microbial processes shape the cycling of organic matter and nutrients in the sea. The advent of the molecular biology era in microbial ecology has resulted in advanced knowledge about the diversity of marine microorganisms, suggesting that we might have reached a high level of understanding of carbon fluxes in the oceans. However, it is becoming increasingly clear that there are large gaps in the understanding of the role of bacteria in regulating carbon fluxes. These gaps may result from methodological as well as conceptual limitations. For example, should bacterial production be measured in the light? Can bacterial production conversion factors be predicted, and how are they affected by loss of tracers through respiration? Is it true that respiration is relatively constant compared to production? How can accurate measures of bacterial growth efficiency be obtained? In this paper, we discuss whether such questions could (or should) be addressed. Ongoing genome analyses are rapidly widening our understanding of possible metabolic pathways and cellular adaptations used by marine bacteria in their quest for resources and struggle for survival (e.g. utilization of light, acquisition of nutrients, predator avoidance, etc.). Further, analyses of the identity of bacteria using molecular markers (e.g. subgroups of Bacteria and Archaea) combined with activity tracers might bring knowledge to a higher level. Since bacterial growth (and thereby consumption of DOC and inorganic nutrients) is likely regulated differently in different bacteria, it will be critical to learn about the life strategies of the key bacterial species to achieve a comprehensive understanding of bacterial regulation of C fluxes. Finally, some processes known to occur in the microbial food web are hardly ever characterized and are not represented in current food web models. We discuss these issues and offer specific comments and advice for future research agendas.

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“…Data availability combined with increasing computational power stimulated a rapid growth in basin to global upper ocean ecosystem-biogeochemistry models (e.g., Sarmiento et al, 1993;Six and Maier-Reimer, 1996;Oschlies and Garcon, 1998;Doney, 1999;Gregg et al, 2003;Aumont et al, 2003;Moore et al, 2004;Le Quéré et al, 2005;Doney and Ducklow, 2006). Such models are now widely applied to questions from seasonal and interannual climate variability (e.g., Le Quéré et al, 2000;McKinley et al, 2004;Wetzel et al, 2005;McKinley et al, 2006;Lovenduski et al, 2007;Le Quéré et al, 2007) to anthropogenic climate change (e.g., Bopp et al, 2001;Boyd and Doney, 2002;Bopp et al et al, 2003).…”

Section: Introductionmentioning

confidence: 99%