Archive for oxygen

Identifying the water mass composition of a sample has never been so easy!

Posted by mmaheigan 
· Thursday, August 31st, 2023 

When we collect seawater in any point of the ocean, we are collecting a mix of water masses from different origin that traveled until there keeping their salinity and temperature properties. The Atlantic Ocean is likely the most complex basin in term of water masses containing more than 15 in its depths. Some of them were “born” in the North Atlantic Ocean, others in the Southern Ocean, even in the Mediterranean Sea! And when we collect a seawater sample we can know which water masses are there, where they come from, what happened to each of them during their journey to us, what story can they tell us.

The variation of any non-conservative property (such as dissolved organic carbon or nutrients) in the deep open ocean depends on the mixing of those water masses and on the biogeochemical processes affecting it (such as heterotrophic respiration). But the effect of the water mass mixing is usually very high, so in order to study the biogeochemical processes, it is necessary to remove that effect.

On the other hand, estimating the contribution of the water masses composing a sample is useful to trace the distribution of each water mass identifying the depth of maximum water mass contribution or the depth-range where the water mass is dominant contributing > 50%. Ocean biogeochemists and microbiologists can get more out of their data estimating the impact of water mass mixing on the variability of any chemical (e.g. inorganic nutrients and dissolved organic carbon) or biological (e.g. prokaryotic heterotrophic abundance and production) property.

Knowing the contribution of each water mass to each sample was not an easy task and required expertise on the origin, circulation and mixing patterns of the water masses present in the study area. This could be even harder in very complex oceanic basin such as the deep Atlantic Ocean. The most commonly used methodology is the Optimum Multi-Parameter (OMP) analysis that was first applied by Tomczak in 1981. However, this methodology is time consuming and requires availability of a large set of quality-controlled chemical variables (e.g. nutrients, oxygen,..) together with a deep knowledge of the oceanography of the studied area. Those chemical variables are not always available or do not have the required quality by contrast to potential temperature and salinity that are high standard core variables in any cruise or database. In a recent research article, we applied multi-regression machine learning models to solve ocean water mass mixing. The models tested were trained using the solutions from OMP analyses previously applied to samples from cruises in the Atlantic Ocean. Extremely Randomized Trees algorithm yielded the highest score (R2 = 0.9931; mse = 0.000227). The model allows solving the mixing of water masses in the Atlantic Ocean using potential temperature, salinity, latitude, longitude and depth. Potential temperature and salinity are the most commonly collected and curated variables in oceanography both from oceanographic cruises and autonomous vehicles (e.g. ARGO) avoiding the use of less commonly measured chemical variables which also require longer and time-consuming analyses of both the water samples and the data.

Figure 1. A16 section for the contribution of the water masses (A) AAIW5, (B) ENACW12, (C) AAIW3, (D) MW, (E) LSW, (F) ISOW, (G) CDW and (H) WSDW obtained with the Extremely Randomized Trees algorithm. Ocean Data View software (Schlitzer, 2015).

We also provide the code with instructions where any user can easily introduce the required variables (latitude, longitude, depth, temperature and salinity) of the chosen Atlantic samples and obtain the water mass proportion of each one in a fast and easy way. Actually, it would allow the user to obtain this information in real time during a cruise.

New research using other methods like OMP and its variants can be incorporated to the existing model increasing its accuracy and prediction capacity. Help us to improve the model and increase its spatial resolution!

Ocean biogeochemists and microbiologists can benefit from this tool even if they do not have a deep knowledge of the oceanography of the studied area. Identifying the water masses composition of a sample has never been so easy!

Author
Cristina Romera-Castillo (Instituto de Ciencias del Mar-CSIC, Barcelona, Spain)

Twitter: @crisrcas

Towards using historical oxygen observations to reconstruct the air-sea flux of biological oxygen

Posted by mmaheigan 
· Tuesday, December 13th, 2022 

Dissolved oxygen (O2) is a central observation in oceanography with a long history of relatively high precision measurements and increasing coverage over the 21st century. O2 is a powerful tracer of physical, chemical and biological processes (e.g., photosynthesis and respiration, wave-induced bubbles, mixing, and air-sea diffusion). A commonly used approach to partition the processes controlling the O2 signal relies on concurrent measurements of argon (an inert gas), which has solubility properties similar to O2. However, only a limited fraction of O2 measurements have paired argon measurements.

Figure 1. (a) The newly developed empirical model to parameterize the physical oxygen saturation anomaly (ΔO2[phy]) in order to separate the biological contribution from total oxygen, and (b-c) regional, inter-annual, and decadal variability of air-sea gas flux of biological oxygen (F[O2]bio as) reconstructed from the historical dissolved oxygen record.

A recent study published in the Journal of Global Biogeochemical Cycles presents semi-analytical algorithms to separate the biological and physical O2 oxygen signals from O2 observations. Among the approaches, a machine-learning algorithm using ship-based measurements and historical records of physical parameters from reanalysis products as predictors shows encouraging performance. The researchers leveraged this new algorithm to reconstruct regional, inter-annual, and decadal variability of the air-sea flux of biological oxygen (from historical O2 records.

The long-term objective of this proof-of-concept effort is to estimate from historical oxygen records and a rapidly growing number of O2 measurements on autonomous platforms. In regions where vertical and horizontal mixing is weak, the projected  approximates net community production, providing an independent constraint on the strength of the biological carbon pump.

 

Authors:
Yibin Huang (Duke University)
Rachel Eveleth (Oberlin College)
David (Roo) Nicholson (Woods Hole Oceanographic Institution)
Nicolas Cassar (Duke University)

Linking the calcium carbonate and alkalinity cycles in the North Pacific ocean

Posted by mmaheigan 
· Tuesday, December 13th, 2022 

The marine carbon and alkalinity cycles are tightly coupled. Seawater stores so much carbon because of its high alkalinity, or buffering capacity, and the main driver of alkalinity cycling is the formation and dissolution of biologically produced calcium carbonate (CaCO3). In a recent publication in GBC, the authors conducted novel carbon-13 tracer experiments to measure the dissolution rates of biologically produced CaCO3 along a transect in the North Pacific Ocean. They combined these experiment data with shipboard analyses of the dissolved carbonate system, the 13C-content of dissolved inorganic carbon, and CaCO3 fluxes, to constrain the alkalinity cycle in the upper 1000 meters of the water column. Dissolution rates were too slow to explain alkalinity production or CaCO3 loss from the particulate phase. However, driving dissolution with the metabolic consumption of oxygen brings alkalinity production and CaCO3 loss estimates into quantitative agreement (Figure). The authors argue that a majority of CaCO3 production is likely dissolved through metabolic processes in the upper ocean, including zooplankton grazing, digestion, and egestion, and microbial degradation of marine particle aggregates that contain both organic carbon and CaCO3. This hypothesis stems from the basic fact that almost all marine CaCO3 is biologically produced, placing CaCO3 at the source of the acidifying process (metabolic consumption of organic matter). This process is important because it puts an emphasis on biological processing for the cycling of not only carbon, but also alkalinity, the main buffering component in seawater. These results should help both scientists and stakeholders to understand the fundamental controls on calcium carbonate cycling in the ocean, and therefore the processes that distribute alkalinity throughout the world’s oceans.

Figure Caption: Sinking-dissolution model results compared with tracer-based alkalinity regeneration rates (TA*-CFC, Feely et al., 2002). We also plot alkalinity regeneration rates using updated time transit distribution ages (TA*- and Alk*-TTD). The modeled alkalinity regeneration rate uses our measured dissolution rates for biologically produced calcite and aragonite, and is driven by a combination of background saturation state and metabolic oxygen consumption. The dissolution rate is split up into a calcite component (produced mainly by coccolithophores) and an aragonite component (produced mainly by pteropods). Aragonite does not contribute significantly to the overall dissolution rate. Driving dissolution by metabolic oxygen consumption produces alkalinity regeneration rates that are in quantitative agreement with tracer-based estimates.

 

Authors:
Adam Subhas (Woods Hole Oceanographic Institution) et al.

 

Also see Eos highlight here

Integrated analysis of carbon dioxide and oxygen concentrations as a quality control of ocean float data

Posted by mmaheigan 
· Friday, August 26th, 2022 

A recent study in Communications Earth & Environment, examined spatiotemporal patterns of the two dissolved gases CO2 and O2 in the surface ocean, using the high-quality global dataset GLODAPv2.2020. We used surface ocean data from GLODAP to make plots of carbon dioxide and oxygen relative to saturation (CORS plots). These plots of CO2 deviations from saturation (ΔCO2) against oxygen deviations from saturation (ΔO2) (Figure 1) provide detailed insight into the identity and intensity of biogeochemical processes operating in different basins.

Figure 1: Relationships between ΔCO2 and ΔO2 in the global ocean basins based on surface data in the GLODAPv2.2020 database. The black dashed lines are the least-squares best-fit lines to the data; unc denotes the uncertainty in the y-intercept value with 95% confidence; r is the associated Pearson correlation coefficient; n is the number of data points.

In addition, data in all basins and all seasons shares some common behaviors: (1) negative slopes of best fit lines to the data, and (2) near-zero y-intercepts of those lines. We utilized these findings to compare patterns in CORS plots from GLODAP with those from BGC-Argo float data from the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) program. Given that the float O2 data is likely to be more accurate than the float pH data (from which the float CO2 is calculated), CORS plots are useful for detecting questionable float CO2 data, by comparing trends in float CORS plots (e.g. Figure 2) to trends in GLODAP CORS plots (Figure 1). As well as the immediately detected erroneous data, we discovered significant discrepancies in ΔCO2-ΔO2 y-intercepts compared to the global reference (i.e., GLODAPv2.2020 y-intercepts, Figure 1). The y-intercepts of 48 floats with QCed O2 and CO2 data (at regions south of 55°S) were on average greater by 0.36 μmol kg−1 than the GLODAP-derived ones, implying the overestimations of float-based CO2 release in the Southern Ocean.

Figure 2. CORS plots from data collected by SOCCOM floats F9096 and F9099 in the high-latitude Southern Ocean. Circles with solid edges denote data flagged as ‘good’, whereas crosses denote data flagged as ‘questionable’.

Our study demonstrates CORS plots’ ability to identify questionable data (data shown to be questionable by other QC methods) and to reveal issues with supposed ‘good’ data (i.e., quality issues not picked up by other QC methods). CORS plots use only surface data, hence this QC method complements existing methods based on analysis of deep data. As the oceanographic community becomes increasingly reliant on data collected from autonomous platforms, techniques like CORS will help diagnose data quality, and immediately detect questionable data.

 

Authors:
Yingxu Wu (Polar and Marine Research Institute, Jimei University, Xiamen, China; University of Southampton)
Dorothee C.E. Bakker (University of East Anglia)
Eric P. Achterberg (GEOMAR Helmholtz Centre for Ocean Research Kiel)
Amavi N. Silva (University of Southampton)
Daisy D. Pickup (University of Southampton)
Xiang Li (George Washington University)
Sue Hartman (National Oceanography Centre, Southampton)
David Stappard (University of Southampton)
Di Qi (Polar and Marine Research Institute, Jimei University, Xiamen, China)
Toby Tyrrell (University of Southampton)

Introducing the Coastal Ocean Data Analysis Product in North America (CODAP-NA)

Posted by mmaheigan 
· Friday, October 22nd, 2021 

Coastal ecosystems are hotspots for commercial and recreational fisheries, and aquaculture industries that are susceptible to change or economic loss due to ocean acidification. These coastal ecosystems support about 90% of the global fisheries yield and 80% of the known marine fish species, and sustain ecosystem services worth $27.7 Trillion globally (a number larger than the U.S. economy). Despite the importance of these areas and economies, internally-consistent data products for water column carbonate and nutrient chemistry data in the coastal ocean—vital to understand and predict changes in these systems—currently do not exist. A recent study published in Earth Syst. Sci. Data compiled and quality controlled discrete sampling-based data—inorganic carbon, oxygen, and nutrient chemistry, and hydrographic parameters collected from the entire North American ocean margins—to create a data product called the Coastal Ocean Data Analysis Product for North America (CODAP-NA) to fill the gap. This effort will promote future OA research, modeling, and data synthesis in critically important coastal regions to help advance the OA adaptation, mitigation, and planning efforts by North American coastal communities; and provides a foothold for future synthesis efforts in the coastal environment.

Figure caption. Sampling stations of the CODAP-NA data product.

 

Authors:
Li-Qing Jiang (University of Maryland; NOAA NCEI)
Richard A. Feely (NOAA PMEL)
Rik Wanninkhof (NOAA AOML)
Dana Greeley (NOAA PMEL)
Leticia Barbero (University of Miami; NOAA AOML)
Simone Alin (NOAA PMEL)
Brendan R. Carter (University of Washington; NOAA PMEL)
Denis Pierrot (NOAA AOML)
Charles Featherstone (NOAA AOML)
James Hooper (University of Miami; NOAA AOML)
Chris Melrose (NOAA NEFSC)
Natalie Monacci (University of Alaska Fairbanks)
Jonathan Sharp (University of Washington; NOAA PMEL)
Shawn Shellito (University of New Hampshire)
Yuan-Yuan Xu (University of Miami; NOAA AOML)
Alex Kozyr (University of Maryland; NOAA NCEI)
Robert H. Byrne (University of South Florida)
Wei-Jun Cai (University of Delaware)
Jessica Cross (NOAA PMEL)
Gregory C. Johnson (NOAA PMEL)
Burke Hales (Oregon State University)
Chris Langdon (University of Miami)
Jeremy Mathis (Georgetown University)
Joe Salisbury (University of New Hampshire)
David W. Townsend (University of Maine)

Using BGC-Argo to obtain depth-resolved net primary production

Posted by mmaheigan 
· Friday, July 23rd, 2021 

Net primary production (NPP)—the organic carbon produced by the phytoplankton minus the organic carbon respired by phytoplankton themselves—serves as a major energy source of the marine ecosystem. Traditional methods for measuring NPP rely on ship-based discrete sampling and bottle incubations (e.g., 14C incubation), which introduce potential artifacts and limit the spatial and temporal data coverage of the global ocean. The global distribution of NPP has been estimated using satellite observations, but the satellite remote sensing approach cannot provide direct information at depth.

Figure 1. Panel A. Trajectories of 5 BGC-Argo and 1 SOS-Argo with the initial float deployment locations denoted by filled symbols. The dash-line at 47° N divided the research area into the northern (temperate) and southern (subtropical) regions. Stars indicate ship stations where 14C NPP values were measured during NAAMES cruises and compared with NPP from nearby Argo floats. Panels B and C. Monthly climatologies of net primary production (NPP, mmol m-3 d-1) profiles in the northern and southern regions of the research area, derived from BGC-Argo measurements using the PPM model. The shadings indicate one standard deviation. The red dotted line indicates mixed layer depth (MLD, m), and the yellow dashed line shows euphotic depth (Z1%, m).

To fill this niche, a recent study in Journal of Geophysical Research: Biogeosciences, applied bio-optical measurements from Argo profiling floats to study the year-round depth-resolved NPP of the western North Atlantic Ocean (39° N to 54° N). The authors calculated NPP with two bio-optical models (Carbon-based Productivity Model, CbPM; and Photoacclimation Productivity Model, PPM). A comparison with NPP profiles from 14C incubation measurements showed advantages and limitations of both models. CbPM reproduced the magnitude of NPP in most cases, but had artifacts in the summer (a large NPP peak in the subsurface) due to the subsurface chlorophyll maximum caused by photoacclimation. PPM avoided the artifacts in the summer from photoacclimation, but the magnitude of PPM-derived NPP was smaller than the 14C result. Latitudinally varying NPP were observed, including higher winter NPP/lower summer NPP in the south, timing differences in NPP seasonal phenology, and different NPP depth distribution patterns in the summer months. With a 6-month record of concurrent oxygen and bio-optical measurements from two Argo floats, the authors also demonstrated the ability of Argo profiling floats to obtain estimates of the net community production (NCP) to NPP ratio (f-ratio), ranging from 0.3 in July to -1.0 in December 2016.

This work highlights the utility of float bio-optical profiles in comparison to traditional measurements and indicates that environmental conditions (e.g. light availability, nutrient supply) are major factors controlling the seasonality and spatial (horizontal and vertical) distributions of NPP in the western North Atlantic Ocean.

 

Authors:
Bo Yang (University of Virginia, UM CIMAS/NOAA AOML)
James Fox (Oregon State University)
Michael J. Behrenfeld (Oregon State University)
Emmanuel S. Boss (University of Maine)
Nils Haëntjens (University of Maine)
Kimberly H. Halsey (Oregon State University)
Steven R. Emerson (University of Washington)
Scott C. Doney (University of Virginia)

Air-sea gas disequilibrium drove deoxygenation of the deep ice-age ocean

Posted by mmaheigan 
· Thursday, March 18th, 2021 

During the Last Glacial Maximum (~20,000 years ago, LGM) sediment data show that the deep ocean had lower dissolved oxygen (O2) concentrations than the preindustrial ocean, despite cooler temperatures of this period increasing O2 solubility in sea water.

Figure 1. a) Whole ocean inventory of the O2 components in the preindustrial control (PIC): total O2 (O2); the preformed components equilibrium O2 (O2 equilibrium), physical disequilibrium O2 (O2 diseq phys) and biologically-mediated disequilibrium (O2 diseq bio); and O2 respired from soft-tissue (O2 soft). b) The difference in whole ocean inventory of O2 components between the LGM and PIC simulations.

In a study published in Nature Geoscience, the authors provide one of the first explanations for glacial deoxygenation. The authors combined a data-constrained model of the preindustrial (PIC) and LGM ocean with a novel decomposition of O2 to assess the processes affecting the oceanic distribution of oxygen. The decomposition allowed for the preformed disequilibrium O2—the amount of oxygen that deviates from its solubility equilibrium value when at the surface—to be tracked, along with other contributions such as the O2 consumed by bacterial respiration of organic matter. In the preindustrial ocean, a third of the subsurface oxygen deficit was a result of disequilibrium rather than oxygen consumed by bacteria. This contradicts previous assumptions (Figure 1a). Nearly 80% of the disequilibrium resulted from upwelling waters, depleted in O2 due to respiration, not fully equilibrating before re-subduction into the ocean interior. This effect was even greater during the LGM (Figure 1b). The authors attributed this largely to the widespread presence of sea ice—which acts as a cap on the surface preventing the water from gaining oxygen from the atmosphere—in the ocean around Antarctica, with a smaller contribution from iron fertilization.

This study provides one of the first mechanistic explanations for LGM deep ocean deoxygenation. As the ocean is currently losing oxygen due to warming, the effect of other processes, including sea ice changes, could prove important for understanding long-term ocean oxygenation changes.

Authors
Ellen Cliff (University of Oxford)
Samar Khatiwala (University of Oxford)
Andreas Schmittner (Oregon State University)

Joint highlight with GEOTRACES International Project Office

Timing matters: Correcting float-based measurements of diurnal oxygen variability

Posted by mmaheigan 
· Friday, November 6th, 2020 

Despite its fundamental importance to the global carbon cycle, climate, and marine ecosystems, oceanic primary production is grossly under-sampled. Autonomous platforms represent an important frontier for expanding measurements of marine primary productivity in time and space, but this requires the establishment of robust, standardized methods to obtain reliable data from these platforms. Using data from profiling floats deployed in the northern Gulf of Mexico, authors of a recent study published in Biogeosciences demonstrated, for the first time, that daily cycles of dissolved oxygen can be observed with Argo-type profiling floats. The floats were instructed to profile continuously, resulting in about one profile every three hours. The floats recorded data both on the ascent (upcast) and the descent (downcast). Adjacent casts showed hysteresis in gradient areas, i.e. a lag in the concentration measurement, due to the slow response time of oxygen sensors.

Figure 1: Example of raw oxygen measurements from a downcast (dark purple line) and an upcast (dark green line) and corrected profiles (lighter purple and green lines) in (a) density and (b) pressure coordinates. (c) Upcasts and downcasts (top 150 m) plotted against each other with raw data (purple) and data corrected according to the new method (red). (d) The root-mean-square difference (RMSD) between the upcast and downcast after correcting casts for a range of time constants (τ), showing an optimal τ value in this case of 76 s (red dot).

To correct for these measurement errors, the authors developed a method to determine sensor response time in situ, using an established process for correcting sensor response time errors. This method requires a timestamp associated with each observation. The response time parameter (τ) was determined by correcting consecutive profiles taken in opposite directions using a range of possible values and finding the minimum root-mean-square-difference between them (Figure 1). In light of these findings, future oxygen measurements from Argo floats should be transmitted with time stamps for a calibration period during which up- and downcasts are recorded to facilitate response time correction. The method developed here will contribute to more accurate measurement of dissolved oxygen, thus improving the quality of derived quantities such as primary productivity.

 

Authors
Christopher Gordon (Dalhousie University)
Katja Fennel (Dalhousie University)
Clark Richards (Fisheries and Oceans Canada)
Nick Shay (University of Miami)
Jodi Brewster (University of Miami)

Estuarine sediment resuspension drives non-local impacts on biogeochemistry

Posted by mmaheigan 
· Friday, September 18th, 2020 

Sediment processes, including resuspension and transport, affect water quality in estuaries by altering light attenuation, primary productivity, and organic matter remineralization, which then influence oxygen and nitrogen dynamics. In a recent paper published in Estuaries and Coasts, the authors quantified the degree to which sediment resuspension and transport affected estuarine biogeochemistry by implementing a coupled hydrodynamic-sediment transport-biogeochemical model of the Chesapeake Bay. By comparing summertime model runs that either included or neglected seabed resuspension, the study revealed that resuspension increased light attenuation, especially in the northernmost portion of the Bay, which subsequently shifted primary production downstream (Figure 1). Resuspension also increased remineralization in the central Bay, which experienced higher organic matter concentrations due to the downstream shift in primary productivity. When combined with estuarine circulation, these resuspension-induced shifts caused oxygen to increase and ammonium to increase throughout the Bay in the bottom portion of the water column. Averaged over the channel, resuspension decreased oxygen by ~25% and increased ammonium by ~50% for the bottom water column. Changes due to resuspension were of the same order of magnitude as, and generally exceeded, short-term variations within individual summers, as well as interannual variability between wet and dry years. This work highlights the importance of a localized process like sediment resuspension and its capacity to drive biogeochemical variations on larger spatial scales. Documenting the spatiotemporal footprint of these processes is critical for understanding and predicting the response of estuarine and coastal systems to environmental changes, and for informing management efforts.

Figure 1: Schematic of how resuspension affects biogeochemical processes based on HydroBioSed model estimates for Chesapeake Bay.

Authors:
Julia M. Moriarty (University of Colorado Boulder)
Marjorie A. M. Friedrichs (Virginia Institute of Marine Science)
Courtney K. Harris (Virginia Institute of Marine Science)

 

Also see the Geobites piece “Muddy waters lead to decreased oxygen in Chesapeake Bay” on this publication, by Hadley McIntosh Marcek

Turning a spotlight on grazing

Posted by mmaheigan 
· Thursday, July 23rd, 2020 

Microscopic plankton in the surface ocean make planet Earth habitable by generating oxygen and forming the basis of marine food webs, yielding harvestable protein. For over 100 years, oceanographers have tried to ascertain the physical, chemical, and biological processes governing phytoplankton blooms. Zooplankton grazing of phytoplankton is the single largest loss process for primary production, but empirical grazing data are sparse and thus poorly constrained in modeling frameworks, including assessments of global elemental cycles, cross-ecosystem comparisons, and predictive efforts anticipating future ocean ecosystem function. As sunlight decays exponentially with depth, upper-ocean mixing creates dynamic light environments with predictable effects on phytoplankton growth but unknown consequences for grazing.

Figure caption: Rates (d−1) of phytoplankton growth (μ), grazing mortality (g), and biomass accumulation (r) under four mixed layer scenarios simulated using light as a proxy of (a) sustained deep mixing, (b) rapid shoaling, (c) sustained shallow mixing, and (d) rapid mixed layer deepening. Error bars represent one standard deviation of the mean of duplicate experiments. Grazing was measured but not detected in the sustained deep mixing and rapid shoaling conditions, denoted with x.

Using data from a spring cruise in the North Atlantic, authors of a recent study published in Limnology & Oceanography compared the influences of microzooplankton predation and fluctuations in light availability—representative of a mixing water column—on phytoplankton standing stock. Data from at-sea incubations and light manipulation experiments provide evidence that phytoplankton’s instantaneous and zooplankton’s delayed responses to light fluctuations are key modulators of the balance between phytoplankton growth and grazing rates (Figure 1). These results suggest that light is a potential, remotely retrievable predictor of when and where in the ocean zooplankton grazing may represent an important loss term of phytoplankton production. If broadly verified, this approach could be used to systematically assess sparsely measured grazing across spatial and temporal gradients in representative regions of the ocean. Such data will be essential for enhancing our predictive capacity of ocean food web function, global biogeochemical cycles and the many derived processes, including fisheries production and the flow of carbon through the oceans.

Authors:
Françoise Morison (University of Rhode Island)
Gayantonia Franzè (University of Rhode Island, currently Institute of Marine Research, Norway)
Elizabeth Harvey (University of Georgia, currently University of New Hampshire)
Susanne Menden-Deuer (University of Rhode Island)

 

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