Assessing the apparent imbalance between geochemical and biochemical indicators of meso- and bathypelagic biological activity: What the @$♯! is wrong with present calculations of carbon budgets?

Burd, Adrian B.; Hansell, Dennis A.; Steinberg, Deborah K.; Anderson, Thomas R.; Arı́stegui, Javier; Baltar, Federico; Beaupré, Steven R.; Buesseler, Ken O.; Dehairs, Frank; Jackson, George A.; Kadko, David; Koppelmann, Rolf; Lampitt, Richard S.; Nagata, Toshi; Reinthaler, Thomas; Robinson, Carol; Robison, Bruce H.; Tamburini, Christian; Tanaka, Tsuneo

doi:10.1016/j.dsr2.2010.02.022

Cited by 258 publications

(258 citation statements)

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“…Among nonbiological processes, gel formation and aggregation are possible sources of macroscopic particles, and this should be considered, because the deep-sea environment provides a favorable physical and chemical environment for increased coagulation rates (21). Indirect evidence for in situ production also comes from budget calculations showing that the amount of particulate organic carbon in the deep sea cannot be derived from sediment-trap fluxes alone (22)(23)(24).…”

Section: Resultsmentioning

confidence: 99%

Role of macroscopic particles in deep-sea oxygen consumption

Bochdansky

Aken²,

Herndl³

2010

Proc. Natl. Acad. Sci. U.S.A.

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Macroscopic particles (>500 μm), including marine snow, large migrating zooplankton, and their fast-sinking fecal pellets, represent primary vehicles of organic carbon flux from the surface to the deep sea. In contrast, freely suspended microscopic particles such as bacteria and protists do not sink, and they contribute the largest portion of metabolism in the upper ocean. In bathy-and abyssopelagic layers of the ocean (2,000-6,000 m), however, microscopic particles may not dominate oxygen consumption. In a section across the tropical Atlantic, we show that macroscopic particle peaks occurred frequently in the deep sea, whereas microscopic particles were barely detectable. In 10 of 17 deep-sea profiles (>2,000 m depth), macroscopic particle abundances were more strongly crosscorrelated with oxygen deficits than microscopic particles, suggesting that biomass bound to large particles dominates overall deepsea metabolism.ecology | video analysis | marine snow | Atlantic ocean P articles have to be of a size visible to the unaided eye before they appreciably contribute to the vertical flux in the ocean (1-3). These particles-dubbed marine snow because they resemble snowflakes in the backscatter of dive lights (4, 5)-are best enumerated by photographic means. However, microscopic and colloidal particles, a large portion of which are freely suspended bacteria and protists (6), are better quantified using optical backscatter (7). Using both techniques in tandem, we can contrast the relative distribution of microscopic and macroscopic particles in the ocean. Most video profiles in the past have been taken to a maximum depth of 1,500 m (8-10), and very few profiles exist to 4,000 m (11, 12). Thus, the data presented here, with a maximum deployment depth of 6,000 m, represent the most comprehensive basin-scale video analyses of deep-sea particles to date. To what extent and at which depth these imaged particles contribute to vertical flux remains unknown; however, a link to oxygen deficits may reflect their significance as hotspots of microbial metabolic activity. Results and DiscussionThe Archimedes III research expedition on the research vessel Pelagia was conducted from December 17, 2007 to January 16, 2008; it followed a cruise track from Fortaleza, Brazil, along the equator to the Sierra Leone Basin and then, headed northwest toward the Cape Verde Islands. The video profiles reported here were collected over a transect of ∼4,000 km (Fig. 1). The Romanche Fracture Zone, the location of the greatest depth in the Mid-Atlantic Ridge, was sampled at a higher resolution than the western and eastern basins of the tropical Atlantic (Fig. 1). Microscopic particle abundance as assessed by optical backscatter generally decreased with depth, except for a pronounced peak in the oxygen minimum zone (250-500 m). Macroscopic particle numbers (>500 μm) as detected by video analysis usually decreased below the oxygen minimum zone to 2,000 m, but then, they frequently increased, forming peaks of high particle numbers ( Figs. 1 and 2). ...

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Section: Resultsmentioning

confidence: 99%

Role of macroscopic particles in deep-sea oxygen consumption

Bochdansky

Aken²,

Herndl³

2010

Proc. Natl. Acad. Sci. U.S.A.

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“…Published estimates of POC flux attenuation with depth are, however, up to 2 orders of magnitude lower than corresponding estimates of heterotrophic metabolism [4][5][6][7] . This discrepancy indicates that either estimates of POC flux and/or community metabolism are unreliable, or that additional, unaccounted for, sources of organic carbon to the twilight zone exist 8 .…”

mentioning

confidence: 99%

Reconciliation of the carbon budget in the ocean’s twilight zone

Giering

Sanders

Lampitt

et al. 2014

Nature

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291

333

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The global carbon cycle is affected by biological processes in the oceans, which export carbon from surface waters in form of organic matter and store it at depth; a process called the 'biological carbon pump'. Most of the exported organic carbon is processed by the water column biota, which ultimately converts it into CO2 via respiration (remineralization). Variations in the resulting decrease in organic flux with depth 9 can, according to models, lead to changes in atmospheric CO 2 of up to 200 ppm 3 , indicating a strong coupling between biological activity in the ocean interior and oceanic storage of CO 2 .A key constraint in the analysis of carbon fluxes in the twilight zone is that, at steady state, the attenuation of particulate organic carbon (POC) flux with depth should be balanced by community metabolism. Published estimates of POC flux attenuation with depth are, however, up to 2 orders of magnitude lower than corresponding estimates of heterotrophic metabolism [4][5][6][7] . This discrepancy indicates that either estimates of POC flux and/or community metabolism are unreliable, or that additional, unaccounted for, sources of organic carbon to the twilight zone exist 8 .We compiled a comprehensive carbon budget of the twilight zone based on an based on the ratio between DOC concentrations and apparent oxygen utilization 15 , and on DOC gradients coupled to turbulent diffusivity measured from previous work at the study site 16 (Methods; Extended Data Fig. 2). DOC was estimated to supply 17% of total export in agreement with previous estimates of 9-20% across the North Atlantic basin 17 . Organic matter input via lateral advection was assumed to be negligible based on analyses of back-trajectories (derived from satellite-derived near-surface velocities over 3 months) of the water masses arriving at the PAP site during the study period, which suggested that the water had not passed over the continental slope (Extended Data Fig. 1b). The final source of DOC, excretion at depth by active flux, was estimated using net samples of zooplankton biomass and allometric equations 6,18 , giving a supply of 3 mg C m -2 d -1 . Defecation and mortality at depth present further sources of organic carbon to the twilight zone, but these were excluded from the budget due to large uncertainties associated with their estimation. Finally, chemolithoautotrophy has been suggested to be a significant source of organic matter in the deep ocean 19 , but without strong evidence that this poorly understood process could provide a major contribution at our study site, we chose to exclude it from our carbon budget.The remineralization of organic carbon by zooplankton and prokaryotes was estimated from zooplankton biomass and prokaryotic activity. It is crucial to note that in a steady state system, such as we assume this to be, organic carbon is lost from the system only by export or by remineralization. We focus entirely on community respiration as a measure of remineralization, a fundamental advance over previous methods to derive...

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“…This ecosystem plays a strong role in the global carbon cycle because it is where a large part of biomineralization made by bacteria occurs [26]. However, a lot of questions concerning the carbon cycle in this ecosystem are still unanswered [27]. Here, we aim at understanding the bacterial contribution to the carbon cycle through dissolved (DOC) and particulate (POC) organic carbon uptakes.…”

Section: A Model To Simplifymentioning

confidence: 99%

Towards a simplification of models using regression trees

Eynaud

Nérini

Baklouti

et al. 2013

J. R. Soc. Interface.

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Over-parametrization in modelling is a well-known issue that makes it hard to identify which part of a model is responsible for a given behaviour. In line with that ascertainment, this work presents the outline of an empirical method to simplify models by decreasing the number of parameters. By using regression trees to classify outputs according to related input parameters, the method provides the modeller with an objective tool to reduce the range of the used parameters and, under certain conditions, to establish relations between them. Thereby, the complexity of the model is reduced on the basis of mathematical arguments. As an example, a dynamic energy budget-based model of a mesopelagic bacterial ecosystem is simplified using the presented method. The main benefits of such a method are thus highlighted: (i) more robust parameter estimations; (ii) less complex formulations; and (iii) fewer modelling assumptions. To conclude, the difficulties encountered are discussed, and several solutions are proposed to deal with them.

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Assessing the apparent imbalance between geochemical and biochemical indicators of meso- and bathypelagic biological activity: What the @$♯! is wrong with present calculations of carbon budgets?

Cited by 258 publications

References 159 publications

Role of macroscopic particles in deep-sea oxygen consumption

Role of macroscopic particles in deep-sea oxygen consumption

Reconciliation of the carbon budget in the ocean’s twilight zone

Towards a simplification of models using regression trees

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