Author Archive for mmaheigan – Page 32

What controls the distribution of dissolved organic carbon in the surface ocean?

Posted by mmaheigan 
· Friday, November 11th, 2016 

Around 662 billion tons of organic carbon are dissolved in the ocean, making the pool one of Earth’s major, exchangeable carbon reservoirs. Dissolved organic carbon (DOC) has many ecological functions. It can form complexes with metals (1); absorb UV and visible light, acting as a “sunscreen” for marine microorganisms and controlling primary production in the upper water column (2); it has antioxidant activity, reacting with free radicals in the media (3); but most importantly, it serves as substrate for the microbial loop and as a vehicle for carbon sequestration in the ocean. Therefore, DOC plays an important role in climate on geological time scales.

Because the amount of atmospheric CO2 is of the same magnitude as the DOC pool, and is closely linked to it through exchange, variations in one of these reservoirs can affect the other, impacting the carbon cycle with consequences for climate. Significant net DOC remineralization would lead to an increase of atmospheric CO2, enhancing greenhouse warming at the surface of the Earth. Net oxidation of only 1% of the seawater DOC pool within 1 year would be sufficient to generate a CO2 flux of 7 PgC/yr, comparable to that produced annually by fossil fuel combustion (4). It has also been proposed that a large-scale oxidation of DOC may have prevented a dramatic global glaciation (‘snowball earth’) in the Neoproterozoic period (5).

Despite its importance, knowledge about DOC dynamics is relatively limited; in fact, it was considered highly inert until about three decades ago when a new analytical technique for measuring it via high-temperature catalytic oxidation stimulated new interest (6). The technique eventually provided more accurate DOC values, showing that it was more involved in the carbon cycle than previously thought and that its concentrations vary with depth, time, and location. Considering DOC distributions observed in the surface Atlantic Ocean (Fig. 1), we see values in the subtropical gyres of 65-70 µmol Kg-1, the highest concentrations in the tropics (> 70 µmol Kg-1), the lowest in the Southern Ocean (< 50 µmol Kg-1), and moderate concentrations in the northern North Atlantic (55-60 µmol Kg-1); this pattern is consistent in other ocean basins. So what controls this distribution and can we predict it? Even with improved analytical techniques, DOC is not a variable that can be measured easily at sea, and the sampling must be done carefully since it is easy to contaminate. Therefore, DOC data are typically fewer than those of other more readily determined variables such as nutrients and oxygen. If we could predict DOC from variables for which much greater global ocean coverage exists, we could fill the very large spatial and temporal gaps in the DOC fields.

DOC is produced in the upper water column by phytoplankton (primary producers). Actually, half of the inorganic carbon that is fixed by phytoplankton is transformed to DOC. Heterotrophic microbes consume most of that DOC, but ~ 4% of global annual net primary production (~ 2 Pg C y-1) (7) accumulates as DOC, much of which is exported to the mesopelagic via vertical mixing and convergence, thus contributing to the biological carbon pump.

New primary production, the foundation of a system’s net community production (NCP), depends on new nutrients reaching the euphotic zone, which happens primarily via upwelling in divergence zones and winter vertical mixing. NCP is the balance of the carbon generated by primary producers minus that lost through heterotrophic respiration (prokaryotes and animals). It can be estimated either by a loss of reactants (CO2 or nutrients) or a gain in products (suspended POC, DOC, and export production) (8).

In our work, we needed to establish the fraction of NCP that was present in dissolved form (i.e., the net DOC production ratio, or NDPr). For that, we simply estimated NCP from the nitrate (NO3–) that is consumed in the euphotic zone (DNO3–):

ΔNO3– = new NO3– (introduced from deeper layers) – remaining NO3– (at surface) (Eq. 1)

In the same way, we also calculated net accumulated DOC, or ΔDOC:

ΔDOC = DOC in euphotic zone – DOC introduced from deeper layers (Eq. 2)

The ratio between ΔDOC and ΔNO3– gave us the NDPr:

NDPr = ΔDOC/ΔNO3– (Eq. 3)

NDPr was calculated throughout the Atlantic Ocean using observations of DOC and NO3– from >15 international oceanographic cruises over the last decade, including those occupied by the US Repeat Hydrography program (Fig. 1). Values of NDPr mostly varied between 0.1 and 0.4 (Fig. 2), with the exception of the North Atlantic Subtropical Gyre (NASG), where NDPr values reach >0.8 at times. After sensitivity testing, we applied a NDPr value of 0.17 to the entire basin, which yielded the smallest error between calculated and observed DOC concentrations. Applying this NDPr value to ΔNO3– (i.e. NCP) obtained from cruise data, we estimate ΔDOC (Eq. 4), in which 6.6 is the molar conversion from N to C units:

ΔDOC= ΔNO3– * 6.6 * 0.17 = NCP * 0.17 (Eq. 4)

To obtain the calculated DOC concentration (DOCcalculated), we added the DOC concentration of underlying source waters (DOCsource) to ΔDOC (Eq. 5):

DOCcalculated = DOCsource + ΔDOC (Eq. 5)

When comparing calculated vs. observed DOC (Fig. 3), we found significant agreement (R2 = 0.64; p < 0.001; n=268) throughout the Atlantic, except in the western North Atlantic, where observed DOC > estimated DOC, especially in the southern sector. After this validation of our approach using nutrients and DOC observations, we applied the method to the more extensive NO3– distributions available in the World Atlas Ocean (WOA) climatology to develop a DOCcalculated map for the entire Atlantic (Fig. 4a). The calculated values agree well with the observations, with a total error of 8.94%.

How much DOC is annually produced in the surface Atlantic Ocean? Total organic carbon export (considered equivalent to NCP) in the Atlantic has been estimated to be 4.15-4.3 Pg C y-1 (9, 10). Applying the 0.17 NDPr (equation 3) indicates that 0.70-0.75 Pg C y-1 accumulates in the Atlantic surface as DOC; as such, the Atlantic accounts for ~36% of the global net DOC production ~2 Pg C y-1.

In permanently stratified areas like the southern sectors of the NASG, our approach is invalid since there is little nutrient input from underlying depths. Also, the static view of our approach does not take into account advection that will modify the DOC distributions, nor does it account for eventual removal of accumulated and advected DOC by microbes. To account for these influences on distributions, we applied the ΔNO3– measurements to a steady-state ocean circulation model including terrestrial DOC inputs and DOC remineralization (Fig. 4b). In the model, zonal advection is evident through enrichment of DOC in the Caribbean Sea. Also, inputs of terrestrial DOC are observed near the outflow of the Amazon River. However, the model only slightly improved the match between observations and modeled DOC, with a total error of 8.71% vs. the 8.94% obtained before the model application.

The correspondence between observations and modeled values was good, considering that we are comparing observations of DOC from cruises during specific seasons with estimates based on more idealized nutrient climatology. The main mismatch is found in the western NASG, where observations can reach 13 µmol Kg-1 higher than calculated values. Local production and/or allochthonous inputs of either new nutrients or DOC must be considered. Local production of DOC could result from addition of nitrogen from sources beyond vertical mixing such as diazotrophic N2 fixation, atmospheric deposition, and river runoff. Alternatively, DOC can be concentrated by evaporation, as is sea salt. However, none of these explain the high DOC values observed in the NASG. DOC flux estimated from dissolved organic nitrogen (DON) released by N2 fixation (11) is too low to explain the extra DOC. Regarding the atmospheric deposition, aerosol optical depth data suggest higher deposition in the eastern than in the western North Atlantic (11), and no excess of DOC is observed there. According to salinity distributions from the World Ocean Atlas, advection of DOC from the closest major rivers (Amazon and Orinoco) does not extend far enough northward to explain the NASG anomaly. Salinity normalization of DOC does not erase the feature, indicating that evaporation is not the cause. Those elevated values of carbon are found during cruises from 2003 in the same area (12), so it appears to be a persistent feature. The anomaly also coincides with a DON maximum and a light stable isotope (δ15N) composition in the particulate organic carbon based on measurements recorded in 2004 (13). An explanation for these anomalies has not been confirmed.

 

Conclusions

New nutrients are the fundamental driver of net DOC accumulation in the surface Atlantic Ocean. As such, climate-driven changes in ocean dynamics, which will affect the supply of nutrients to the euphotic zone, will affect the DOC inventory. The effects of climate change on the nutrient supply to the upper water column are not well known, but they will depend on the opposing influences of thermal stratification and upwelling intensification. Some authors predict an intensification and spatial homogenization of coastal upwelling systems (14, 15). Such would increase the nutrient input to the euphotic zone and the net DOC production. In contrast, others have reported that ocean warming should intensify thermal stratification, reducing nutrient flux by vertical mixing in regions not affected by coastal upwelling systems (16, 17). Depending on which of these phenomena dominate, the nutrient supply will change, in turn changing the DOC budget and its distribution. Furthermore, the percentage of NCP accumulating as DOC (i.e. NDPr), found here to be ~17%, could change in response to a shift in the balance of autotrophs and heterotrophs. This multitude of influencing factors will undoubtedly impact the future course of the oceanic DOC budget.

 

Authors

Cristina Romera-Castillo (Univ. of Vienna) and Dennis A. Hansell (RSMAS, Univ. Miami)

Acknowledgments

The authors thank the other co-author, Robert T. Letscher, from the more extended version of this published work. Also to Dr. X.A. Álvarez-Salgado for the use of DOC data he collected during cruises supported by the Spanish government. Data collection on US CLIVAR sections and involvement by C.R.-C. and D.A.H. were supported by US National Science Foundation OCE1436748.

References

  1. Midorikawa, T., E. Tanoue, 1998. Mar. Chem. 62, 219-239.
  2. Arrigo, K. R., C. W., Brown, 1996. Mar. Ecol. Prog. Ser. 140, 207-216.
  3. Romera-Castillo, C., R. Jaffé, 2015. Mar. Chem. 177, 668–676.
  4. Hedges, J. I. 2002. In: Hansell, D., Carlson, C. (Eds.), 2002. Biogeochemistry of marine dissolved organic matter. Academic Press, San Diego, pp. 1-33.
  5. Peltier, W. R. et al., 2007. Nature 450, 813-819.
  6. Hansell, D. A., C. A. Carlson, 2015. Eos, 96, doi:10.1029/2015EO033011.
  7. Hansell, D. A., et al., 2009. Oceanography 22, 202-211.
  8. Hansell, D. A., C. A. Carlson, 1998. Global Biogeochem. Cycles 12, 443-453.
  9. Laws, E. A., et al., 2000. Global Biogeochem. Cycles 14, 1231-1246.
  10. Dunne, J. P., et al., 2007. Global Biogeochem. Cycles 21, GB4006.
  11. Benavides, M., et al., 2013. J. Geophys. Res.: Oceans 118, 3406-3415.
  12. Carlson, C. A. et al., 2010. Deep-Sea Res. Pt II 57, 1433-1445.
  13. Landolfi, A. et al., 2016. Deep-Sea Res. Part I 111, 50-60.
  14. Sydeman, W. J. et al., 2014. Science 345, 77-80.
  15. Wang, D. et al., 2015. Nature 518, 390-394.
  16. Cermeño, P. et al., 2008. PNAS 105, 20344-20349.
  17. Bopp L, et al., 2013. Biogeosciences 10, 6225-6245.
  18. Schlitzer, R., 2015. Ocean Data View. Available at https://odv.awi.de
  19. Romera-Castillo, C. et al., 2016. PNAS 113, 10497–10502.

IMBER ClimECO5: Towards more resilient oceans: Predicting and projecting future changes in the ocean and their impacts on human societies

Posted by mmaheigan 
· Wednesday, November 9th, 2016 

 

OCB-sponsored participants of IMBER ClimECO5 in August 2016.

Tayler Clarke completed her MSc at the University of Costa Rica in 2013. Her master’s thesis focused on the spatial distribution and reproduction of shark and ray bycatch in shrimp trawl fisheries. Currently, she is a second year PhD student in William Cheung’s lab at the Institute for the Oceans and Fisheries, University of British Columbia. Tayler continues to study shrimp trawl fisheries in her dissertation, this time focusing on climate change impacts. She hopes to work with Costa Rican universities and the national fishery management institute to integrate her PhD thesis into current fisheries management initiatives.

“I loved participating in the IMBER summer school because it gave me the opportunity to interact with so many motivated graduate students and early career researchers. The classes and workshops exposed us to interesting new tools and ideas. The best part was being able to collaborate on a research project with the instructors’ guidance. Lisa and the instructors created a very positive, exciting environment that stimulated learning and collaborations. I am very grateful for the opportunity to have participated in IMBER summer school 2016!”

 

Daniel Kaufman is a PhD student in marine science at the Virginia Institute of Marine Science, College of William and Mary. He received his BS in physics from the University of Maryland, College Park. His current research examines phytoplankton dynamics and climate-induced impacts in the Ross Sea, Antarctica, using gliders and biogeochemical modeling. He also contributes to an investigation of effects of anthropogenic watershed use on the Chesapeake Bay using the Regional Ocean Modeling System.

“The atmosphere in the course was both really positive and energizing throughout. The highlight for me was meeting, exchanging ideas, and having fun with participants from such a wide variety of academic and personal backgrounds. The practical exercises and group project gave me a chance to gain valuable practice with new modeling approaches under the supervision of topic experts. Lectures during ClimEco5 introduced me to new ideas and expanded on ideas with which I had previously only passing familiarity, and this all made it a wonderful learning experience over a broad range of topics.”

 

 

Arnault Le Bris was a postdoctoral research associate at the Gulf of Maine Research Institute (GMRI) when he attended the ClimEco5 summer school. Since then, Arnault has started a new research scientist position at the Centre for Fisheries Ecosystems Research at The Marine Institute of Memorial University of Newfoundland, Canada. During his postdoctoral research at GMRI, Arnault worked on understanding and forecasting the impacts of climate change and fishing on New England lobster fisheries. In his research, Arnault has paid special attention to the mechanisms underlying the responses in lobster life history traits to climate change.

“The ClimEco5 summer school delivered a unique interdisciplinary approach to address the issues that oceans, and the human societies depending on oceans, are facing. I was impressed by the diversity and quality of the lectures, which married perfectly with the diversity of the participants. I especially appreciated that all lecturers stayed the entire course, which allowed for deeper interactions between participants and lecturers, and contributed to numerous fun movements that we shared…”

 

 

Kathy Mills is an associate research scientist at the Gulf of Maine Research Institute in Portland, Maine. She is a quantitative fisheries ecologist, and her work focuses on understanding how environmental variability and climate change affect fish populations, fisheries, and fishing communities. Her recent work focuses on climate vulnerability assessment and adaptation approaches in marine fisheries.

“IMBER’s ClimECO5 summer school was a great opportunity to gain an overview of tools and approaches that can be used to understand the ecological, social, and coupled social-ecological impacts of climate change on marine ecosystems. It was exciting to dig into these topics with lecturers who are international leaders in this field. It was also invigorating to meet so many students and early career scientists from across the world who are working on similar questions using a wide range of interesting approaches.”

 

 

Mark Morales received his BS in Environmental Systems: Ecology, Behavior and Evolution at University of California San Diego. During his BS, Mark was involved with various research groups at the Scripps Institution of Oceanography where he studied coastal biological oceanography, Antarctic ecology and fisheries science. As a recipient of the NSF GRFP, Mark is now a 2nd year PhD student in Ecology and Evolutionary Biology at the University of California Santa Cruz. For his PhD, Mark is interested in teasing apart the relationships between interannual environmental variability and recruitment success of central California rockfish species. Mark is using state-of-the-art statistical and mathematical frameworks such as species distribution models, ocean circulation models, nutrient-phytoplankton-zooplankton models and individual-based movement and bioenergetics models to address his questions.

“The level of participant and instructor engagement during IMBER ClimEco5 exceeded my expectations. The amount of scientific and cultural diversity housed within the summer school was the most fascinating part to me. I have acquired many new collaborators, and more importantly friendships that will last throughout my career and beyond. The quality of instruction was terrific and I went home armed with many new tools under my belt that will surely allow me to tackle some of the most pressing questions in marine science. IMBER ClimEco5 is hands down the best event that I have attended as an early career scientist. Without the kind financial support from OCB, none of this would have been possible.”

 

Ellen Willis-Norton is a PhD student in Ecology and Evolutionary Biology at the University of California, Santa Cruz. Her research seeks to understand how the California Current Ecosystem will respond to climate change. She is currently working with colleagues at NOAA to publish a climate vulnerability assessment for 65 commercially important west coast fisheries. The assessment will be used as a starting point for her research examining how west coast fish species’ habitat will shift with climate change and whether certain management tools can increase their resilience to a changing climate.

“The ClimEco5 summer school was truly an interdisciplinary experience led by renowned lecturers. The in-depth lectures allowed me to develop my species distribution modeling skills and experiment with new model approaches. Additionally, I learned about qualitative and agent-based modeling tools that I had never been exposed to before. The summer school also provided invaluable networking opportunities; I now am collaborating with PhD students and Post-Docs from around the world. I hope to maintain the relationships I made at the summer school throughout my career!”

A Report from the 2016 OCB Summer Workshop July 25-28, 2016 (Woods Hole, MA)

Posted by mmaheigan 
· Wednesday, October 19th, 2016 

The 11th annual Ocean Carbon & Biogeochemistry summer workshop, sponsored by NSF and NASA, convened 186 participants from July 25-28, 2016 at the Woods Hole Oceanographic Institution in Woods Hole, MA.

This year’s summer workshop featured the following six plenary sessions:

Plenary 1. EXport Processes in the Ocean from RemoTe Sensing (EXPORTS)
Plenary 2. The biology of carbon export – New processes and approaches
Plenary 3. Recent advances in quantifying ocean carbon uptake
Plenary 4. Quantifying ocean carbon, oxygen, and nutrient cycles
Plenary 5. The Indian Ocean – Monsoon-driven biogeochemical processes
Plenary 6. Marine ecosystem thresholds and regime shifts

Day 1 kicked off with a presentation on the projected instrumentation and scientific capabilities of the NASA Plankton, Aerosol, Cloud, and ocean Ecosystems (PACE) Mission, which segued into the first plenary session on the proposed NASA EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) field campaign. Speakers in this session provided an overview of both the EXPORTS Science Plan and the Implementation Plan. The session also featured three scientific overview talks on the EXPORTS science questions, the first of which highlighted the influence of ecosystem characteristics such as plankton community structure on organic matter export from the euphotic zone. The second talk focused on key processes in the mesopelagic zone that affect vertical transfer of organic matter to depth.

The third talk focused on how data generated by EXPORTS will reduce uncertainties in current and future estimates of export, including an overview of current modeling capabilities for different export pathways. The second plenary session of Day 1 was convened by  organizers of a recent NSF workshop and white paper on novel biological processes and pathways regulating organic matter export and degradation. Speakers in this session explored potential contributions of mixotrophs, marine microgels, and episodic events (e.g., jelly falls) to biological pump function, and provided an overview of our current observational capacity to quantify carbon export and monitor changes in the biological pump over a range of temporal and spatial scales. The plenary session closed with a community-sharing presentation describing the Carbon Flux Explorer, an autonomous float that can quantify and photograph particulate carbon fluxes. After the plenary sessions, graduate students provided short presentations about their research interests and then all participants convened for a welcome reception and poster session.

Day 2 opened with agency updates from NSF, NASA, and NOAA representatives. Speakers in plenary session 3 then described data- and model-based approaches for studying internal variability (interannual to decadal) and anthropogenic change in ocean carbon uptake, and explored the role of physical processes (e.g., subduction, mesoscale and submesoscale processes, etc.) in modulating ocean carbon uptake. A presentation on the Coupled Model Intercomparison Project (CMIP) provided an overview of CMIP5 simulations of the ocean carbon cycle and how well these simulations reproduce anthropogenic CO2 uptake and natural variability in ocean CO2 associated with  the biological pump. To provide a broader range of spatial and temporal perspectives, the session included talks on land-ocean exchanges of dissolved carbon across coastal, estuarine, wetland, and riverine systems and differences in ocean carbon storage during the last ice age, as constrained by paleo-proxies of ocean ventilation and deep-sea oxygen concentrations. This session concluded with a community-sharing presentation on Carbon Hot Spot, a nascent process study to characterize biophysical interactions and quantify ocean carbon uptake in Western Boundary Current regions such as the Kuroshio Extension.

During the afternoon of day 2, speakers in plenary session 4 provided an overview of the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) project and newly emerging seasonally resolved ocean carbon data sets from biogeochemical sensor-equipped Argo floats, which are providing unprecedented constraints on wintertime air-sea CO2 dynamics in the Southern Ocean. The session concluded with a presentation on the rationale and plan for a global biogeochemical observing network based on Argo floats equipped with biogeochemical sensors to more effectively monitor changing ocean conditions. Immediately following the plenary session, communication professionals from COMPASS led interactive communication training workshops to help participants share their science across a broad range of audiences using various tools, outlets, and communication strategies. Participants reconvened in the evening hours for the inaugural OCB ocean festival, featuring recent documentaries on the Palmer LTER in Antarctica and the CARIACO time-series.

After a morning poster session on day 3, participants enjoyed a presentation and Q&A session on the 2015 UN Climate Conference in Paris. Then speakers in plenary session 5 delivered a series of talks on the complex physical oceanographic and climatic drivers that influence biogeochemistry and biological processes in the Indian Ocean, where scientists are amidst planning for the second International Indian Ocean Expedition (IIOE2). The session opened with an overview presentation on key physical oceanographic features and observing resources in the Indian Ocean. Speakers then addressed links between physics, climate, and biogeochemical processes such as dissolved organic carbon (DOC) distribution and dynamics, iron limitation, trace metal cycling and measurements from GEOTRACES, nitrogen fixation, and oxygen deficiency in different parts of the basin and associated effects on biology. The final presentation of the session focused on seasonally variable Indian Ocean boundary currents and their impacts on local ecology and biogeochemistry. Day 3 wrapped up with a presentation by the new US SOLAS (Surface Ocean Lower Atmosphere Study) representative to initiate discussion and brainstorm ideas to facilitate scientific exchanges and new collaborations on topical areas of interest to both OCB and SOLAS.
The final day of the workshop opened with a presentation on the proposed NASA field campaign Arctic-COLORS. The Arctic-COLORS science plan is undergoing revisions, so authors were seeking input from the OCB community. The final plenary session of the workshop, plenary 6, featured a series of talks on marine ecosystem thresholds and regime shifts. In this session, speakers explored phytoplankton response to natural climate variability and anthropogenic climate change (phenology, biogeography, community composition, etc.), implications of climate-driven changes in bloom phenology for higher trophic levels, ecological changes and associated shifts in benthic communities of the Pacific Arctic, and the development of tools such as early warning systems to identify and predict nonlinear shifts in ocean ecosystems. A separate 1.5-day meeting on Arctic-COLORS immediately following the OCB workshop provided an opportunity for more in-depth discussions and opportunities to gather feedback.

For more information, including links to plenary talks and webcast footage, please visit the workshop archive page or contact Heather Benway.

Biogeochemical Cycling of Trace Elements Within the Ocean: A Synthesis Workshop

Posted by mmaheigan 
· Sunday, September 18th, 2016 

  

Over 100 scientists from 12 nations met at the Lamont-Doherty Earth Observatory in Palisades New York, USA, on 1 – 4 August 2016 for a synthesis workshop on the Biogeochemical Cycling of Trace Elements within the Ocean. The workshop focused on setting priorities for utilizing GEOTRACES trace element and isotope (TEI) data sets to advance scientific objectives at the interface of marine biogeochemistry and ecology, and was jointly sponsored by the GEOTRACES and OCB Programs.

Workshop activities were organized around three scientific themes:
1. Biological uptake and trace element bioavailability,
2. Abiotic cycling and scavenging, including particulate and dissolved speciation, and
3. Export, recycling and regeneration

Following a series of plenary talks designed to stimulate discussion on these topics, participants spent the remainder of the workshop in smaller group discussions to identify knowledge gaps and develop ideas for synthesis activities and products that combine GEOTRACES TEI data with other biogeochemical and biological data sets.

Tentative activities and products include:
• estimating bioavailability of iron (Fe)
• testing hypothesis for Fe and light co-limitation in the deep chlorophyll maxima;
• exploring Redfieldian concepts using GEOTRACES data and ocean models;
• calculating community trace metal demand vs. supply;
• developing a synthesis paper on existing methods and current state of knowledge on ligand composition and cycling;
• comparing radionuclide-based tracer methods for estimating downward flux of carbon, nutrients and trace metals;
• combining TEI distributions with AOU and preformed TEI concentrations to differentiate biotic (e.g., respiration) and abiotic (e.g., scavenging, physical transport) removal processes;
• estimating elemental scavenging using partition coefficients (Kd);
• combining particulate TEI and beam transmission data to develop algorithms for particle distributions that affect TEI scavenging; and
• developing synthesis paper on TEIs in nepheloid layers.

To learn more about and/or contribute to these activities, please contact Heather Benway (OCB) or Bob Anderson (LDEO). For more information, visit the workshop website or view the plenary presentations.

Trace metal uptake and remineralization and their impact on upper ocean stoichiometry

Posted by mmaheigan 
· Wednesday, July 6th, 2016 

1. Stoichiometry of metals in the ocean

The close relationship between the stoichiometry of nutrients dissolved in the upper ocean and the planktonic organisms that grow in these waters has long been recognized (1, 2). The stoichiometry of 106 C:16 N:1 P first summarized by Redfield has become a fundamental concept of marine biogeochemistry, with numerous studies using the ratio as a benchmark to assess ecosystem function. Decades after the work of Redfield, with the implementation of trace metal-clean techniques, oceanographers produced the first meaningful measurements of dissolved trace metals in the open ocean (3-5), and they found that many of the bioactive metals such as Fe, Zn, Ni, Cu and Cd are also depleted in surface waters and enriched at depth, similar to the macronutrients. Such nutrient-like behavior supported not only a growing understanding of the physiological roles that these metals play in phytoplankton physiology (6), but it also indicated that biological uptake and sub-surface remineralization were important processes for controlling the distributions of these bioactive metals in the ocean. Thus, the biogeochemical behavior of the micronutrient metals is in many ways analogous to that of the macronutrients N, P and Si.

In the open ocean far from coastal and shelf influences, dissolved concentrations of bioactive metals increase with depth at relatively consistent ratios to macronutrients (5), and these metal:nutrient remineralization ratios have been used to approximate the composition of sinking biogenic material and euphotic zone phytoplankton (7, 8). These ‘extended Redfield ratios’ have been compared to average compositions of marine phytoplankton species grown in culture (9-12),  and the general agreement between these approaches further supports the importance of biological uptake and subsequent remineralization of trace metals in the upper ocean as key processes impacting trace metal geochemistry. Average metal:nutrient stoichiometries for phytoplankton have also been compared to dissolved stoichiometries in the ambient water, and relationships between these fractions have been used to estimate nutrient limitation and deficiency in the ocean (13). Thus, there is significant interest in controls on upper ocean metal stoichiometries, as well as the relationships between cellular/biological, particulate and dissolved fractions.

Analogous to macronutrients, there are also relationships between metal stoichiometries in phytoplankton and those in deeper waters of the ocean. Departures from these relationships are likely to provide insights into the internal biogeochemical cycling of metals in the ocean. Morel and Hudson (7) noted differences in the extended stoichiometries of plankton and the water column and concluded that they must reflect the relative efficiency of remineralization of the elements, as well as the propensity of elements to be scavenged onto sinking particles in the sub-surface ocean. Similarly, the rapid remineralization of trace metals from sinking plankton was addressed in seminal work by Collier and Edmond (14). Using carefully collected data on surface plankton material, and with more computational rigor than (7), they compared surface particle stoichiometries to deep water dissolved stoichiometries and calculated the relative remineralization of plankton-associated elements in sinking biogenic material. They noted significant differences among the behaviors of biogenic metals such as Cd, Ni and Fe due to their scavenging and remineralization behaviors. More recently, Morel (15) mused about these processes and their relationships to cellular biochemistry and evolution of phytoplankton physiology and ocean biogeochemistry.

Through the GEOTRACES program, the data to test and extend these early, relatively simple box models and stoichiometric comparisons are now available. Metal concentrations and stoichiometries for phytoplankton, bulk and size-fractionated particulate material, and co-located dissolved species have been measured in the North Atlantic and South Pacific Oceans thus far. Combined with data for non-bioactive metals such as Ti and Th, these data also provide the opportunity to discern the behavior and contributions of lithogenic vs. biogenic matter, as well as the processes of remineralization and scavenging.

2. Processes affecting dissolved and particulate stoichiometries of trace metals

Vertical profiles of dissolved macronutrients show characteristic depletion at the surface and enrichment at depth due to remineralization, and dissolved micronutrients often show the same behavior. However, the internal cycling of metals in the ocean is expected to differ from that of macronutrients for a few salient reasons. Some metals such as Fe are significantly less soluble than macronutrients and are prone to abiotic adsorption onto particulate surfaces (16). This process is driven by thermodynamics, and the accompanying process of desorption also occurs; the net observed process is typically called ‘scavenging’ (Fig. 1). Scavenging in the deep ocean causes concentrations of less soluble metals such as Fe and Al to decrease along the path of thermohaline circulation, in contrast to macronutrients and more soluble metals that may mimic macronutrient behavior such as Cd and Zn (17). In the absence of significant lateral nutrient inputs, the balance of scavenging and remineralization will influence the resulting vertical profiles of dissolved elements (18).

Another key difference between macronutrients and metals is the importance of abiotic particulate fractions such as lithogenic (e.g., aeolian dust and sediment) and authigenic (e.g., Fe- and Mn-oxyhydroxide) phases. While biogenic phases are almost universally produced at the surface and remineralized with depth, abiotic phases can exhibit very different and dynamic internal cycles (19). Dust events, lateral transport and poorly constrained scavenging processes can both deliver and remove specific metals alongside biological processes. Lithogenic phases are generally denser and more refractory than biogenic particles and detritus and are thought to sink more rapidly and remineralize more slowly and at greater depth (Fig. 1; 20). Lithogenic particles may also (re)scavenge metals differently than biogenic material. Efforts to examine these processes in sinking material have been extremely limited to date, with only a few studies examining metals in trace metal-clean sediment traps (21, 22). However, recently published datasets from the GEOTRACES program are shedding new light on the multiple facets of metal partitioning and how they affect subsurface remineralization and scavenging.

A comparison of metal:phosphorus ratios in the upper ocean illuminates some of these processes. Figure 2 displays Cd:P, Fe:P, Co:P and Ni:P ratios in particles in the upper 100m, 100-300m, and 300-1,000m of the water column in the middle of the North Atlantic basin. Particulate material is sub-divided into ratios for phytoplankton cells and non-lithogenic particles (corrected for lithogenic minerals using Ti; 19). Also plotted are dissolved remineralization ratios (that is, the slope of a linear regression between the dissolved metal and phosphate) for these upper ocean depth ranges. The close coupling of Cd and P biogeochemistry has long been recognized (4), and indeed we observe very close agreement (within a factor of about 2) between dissolved Cd:P remineralization and Cd:P in surface ocean particles, as well subsurface particles. Clearly these elements are remineralizing from sinking particles at similar rates. Such comparisons of particulate and dissolved constituents need to carefully consider the different residence times of these fractions and the likelihood for lateral inputs. Here, we have chosen to focus on stations from the mid-North Atlantic gyre, where the upper 700m of the water column consists primarily of a single water mass (23).

In contrast, the remineralization of Fe and P are quickly decoupled in the water column (Fig. 2). Between 100 and 300m, typically the depth of most rapid regeneration of sinking organic material, labile particulate Fe:P has more than doubled from that in surface waters, and the Fe:P ratio of remineralized dissolved elements (0.98 mmol/mol) is more than 10-fold below that of the labile material that is sinking into these waters. Looking deeper into the water column, Fe and P continue to decouple in labile (i.e., non-lithogenic) particulates, with Fe:P of 300-1,000m particles increasing 10-fold and the dissolved remineralization ratio being nearly 1,000-fold lower (0.35 mmol/mol). Additionally, organic ligands play an important role in stabilizing dissolved Fe (24), so dissolved Fe and P ratios may be further decoupled by biological processes impacting the production and fate of these ligands (20).

A strength of GEOTRACES datasets is their wide coverage of the periodic table, and additional insights can be gained from looking at the behaviors of other bioactive trace metals that are also incorporated into sinking biogenic material. Co:P ratios in particles and remineralized dissolved fractions in the water column follow the same trend as Fe, but the decoupling of Co and P is much more subtle than with Fe, presumably due to differences in ligand coordination and Co co-oxidation with Mn (25, 26). Dissolved Co:P remineralization ratios at 100-300m generally match those found in phytoplankton and drop only 3-fold below 300m. Similarly, labile particulate Co:P ratios don’t change between 0-100m and 100-300m, also indicating that Co and P remineralize in tandem in the upper 300m. Below 300m, labile particulate Co:P increases approximately 3-fold (in contrast with Fe:P, which increases 12-fold), and this depth effect matches the effect in dissolved remineralization ratios. Thus, even though Fe and Co are considered hybrid metals that display both biological uptake and scavenging, there are clear differences in the behaviors of these metals. Nickel provides yet another perspective on the coupling of metals and P. Dissolved remineralization ratios in both subsurface depth ranges closely resemble surface ocean labile particles, supporting the biological coupling of Ni and P (5). However, residual labile particulate Ni:P increases 2- to 4-fold in successive depth ranges, indicating that remineralization is rather decoupled. Given that Ni seems to be associated with both organic material and opal frustules in diatoms (27), it may be that Ni and P are remineralized from particulate organic matter in tandem, but some Ni remains associated with sinking biogenic silica in the ocean.

3. Additional tools to explore and differentiate remineralization processes

The GEOTRACES program has welcomed the application of new analytical approaches that further enable us to study the cycling of metals in the ocean. Spectroscopy and quantitative imaging methods using synchrotron radiation have become more common in the past decade (28), and these allow us to analytically distinguish the behaviors of different fractions of particle assemblages. During the FeCycle II project, a GEOTRACES process study, the fate of Fe was tracked during a spring diatom bloom (29). Diatom cells from the dominant bloom species (Asterionellopsis glacialis) were collected in surface waters and from trace-metal clean sediment traps at 100m and 200m in the 48h following the decline of the bloom. Synchrotron x-ray fluorescence (SXRF) analyses of individual cells showed that constituent elements were lost from sinking cells at notably different rates (Fig. 3). Phosphorus was rapidly released from sinking cells, with mean P quotas decreasing 55% and 73% from surface values by 100m and 200m, respectively (30). However, only 25% of cellular Fe was lost from cells sinking through the upper 200m, while 61% of cellular Ni was remineralized. This supports the story told by the bulk biogeochemical data from the North Atlantic: Ni is remineralized largely to a similar degree as P, while Fe is lost more slowly from sinking biogenic material.

Application of microanalytical techniques such as SXRF can be combined with bulk approaches to further advance understanding of subsurface metal remineralization and cycling. In FeCycle II, Fe:P of sinking A. glacialis cells increased, on average, only 2.3-fold in the upper 200m, while Fe:P in bulk particulate matter increased more than 13-fold (30). This indicates that the behavior of sinking cells was not representative of the full particle assemblage. Iron and P were likely more completely decoupled in sinking fecal pellets and detrital material (which appears to have contributed significantly to the particulate Fe pool during FeCycle II; 31) than in intact sinking cells. Further application of this approach will allow us to not only distinguish between the behavior of biogenic and lithogenic fractions (Fig. 1), but potentially also between detrital particles. By considering metals such as Mn that are prone to oxidation and scavenging in the subsurface ocean (32, 33), it may also be possible to separate abiotic scavenging from net biological remineralization (Fig. 1). Additionally, 2D (and potentially 3D) mapping of elements within cells and particles also provides information about the spatial and potentially chemical associations of elements with particles (30, 34).

The GEOTRACES program is generating unprecedented data, both in terms of quality and quantity, regarding the cycling of bioactive trace metals in the ocean. Syntheses of these data, and integration of insights from novel microanalytical tools, as well as transcriptomic and proteomic approaches, are resulting in substantial advances in our understanding of metal biogeochemistry. No longer are we limited to a few painstakingly collected dissolved metal profiles. There is now painstakingly collected full-depth coverage of most ocean basins, including in many cases dissolved and particulate fractions of nearly all biogenic elements, enabling testing of early hypotheses about trace metal cycling and parameterization of these processes into next-generation ocean biogeochemical models.

Authors

Benjamin S. Twining, Daniel C. Ohnemus, Renee L. Torrie (Bigelow Laboratory for Ocean Sciences)

Acknowledgments

This work was funded by NSF grant OCE-1232814 to BST. RLT was funded by NSF REU grant 1460861 to Bigelow Laboratory for Ocean Sciences.

References

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Marine particles: Distribution, composition, and role in scavenging of TEIs

Posted by mmaheigan 
· Sunday, July 3rd, 2016 

GEOTRACES and particles in the ocean

GEOTRACES is an international program to study the global marine biogeochemical cycles of trace elements and their isotopes (TEIs). The program’s guiding mission is to “identify processes and quantify fluxes that control the distributions of key TEIs in the ocean” (1).

Particles represent a key parameter for the GEOTRACES program because of their role as sources, sinks, and in the internal cycling of so many TEIs (1, 2). Particles in the ocean fall into two classes: 1. Those that have sources external to the system such as lithogenic material carried by atmospheric transport, river, or lateral transport from continental margin sediments; and 2. those that are produced internally in the system, primarily by biological production, but also by authigenic mineral precipitation (2).

External particle sources such as mineral dust deposition and sediment resuspension act as sources of dissolved TEIs when they partially dissolve in seawater. Conversely, dissolved TEIs are removed by active biological uptake or passive adsorption onto particles surfaces, followed by particle removal by aggregation and sinking. Indeed, the biological and abiotic interactions of dissolved TEIs with particles determine the residence time of a dissolved TEI.

In most open ocean basins away from ocean floor boundaries, external sources of particles are dwarfed by the much greater biological production and destruction of particles. Particle cycling in most open ocean basins is thus dominated by the biological pump, the processes by which suspended particles are produced by photosynthesis in the euphotic zone at the surface, and are then abiotically or biologically aggregated into larger particles that can sink into the abyss (3).

As particulate organic carbon (POC) cycles through processes such as aggregation, disaggregation, remineralization, and sinking (collectively referred to here as particle dynamics), other particle phases are swept along for the ride, including other major components such as biologically precipitated minerals (especially CaCO3 and opal), as well as lithogenic and authigenic particles, and scavenged TEIs adsorbed to the surfaces of other particles (Fig. 1).

In this article, I will briefly review the role of particle composition on the scavenging of TEIs.

Scavenging: A two-step removal process

Most adsorption of TEIs likely occurs onto small, suspended particles, which are usually more abundant, have more available surface area, and have a longer residence time in the water column than large, sinking particles. For TEIs to be removed from the water column, the suspended particles must then be aggregated into larger, sinking particles. There are thus two distinct steps for the removal of a dissolved TEI by scavenging: 1) adsorption onto suspended particle surfaces, followed by 2) removal via the aggregation of suspended particles onto larger particles that sink out of the water column. Fig. 1 shows a very simple schematic illustrating these basic processes. The adsorption step is governed by the affinity of a TEI for a particular particle surface, and the removal step is governed by the particle dynamics that package suspended particles into large, sinking aggregates, and are the focus of studies of the biological pump. The removal of TEIs by scavenging thus intimately links one of OCB’s scientific goals, the understanding of the biological carbon pump, to GEOTRACES’s mission to identify processes and quantify fluxes that control the distributions of key TEIs in the ocean.

Particle concentration and composition: horizontal and vertical variations

Particles collected in the ocean are a heterogeneous mixture of biogenic, lithogenic, and authigenic (precipitated in-situ) components. The relative proportions of these different components vary geographically and with depth. Fig. 2 shows the distribution of total particle concentration from GA03, the U.S. GEOTRACES North Atlantic Zonal Transect cruise in 2010/2011, as well as the changing composition of small (<51 mm) particles at three stations along the transect (4). Particle concentrations are highest at the surface and at the margins, where biological production is highest. It is clear that particulate organic matter (POM) dominates particle composition in the upper 100 m, making up more than 70% of the suspended particle mass at all three stations. The balance in the upper 100 m is mostly made of other biogenic components such as CaCO3 and opal, with a small contribution from lithogenic particles directly under the Saharan dust  plume. At all stations, the inorganic components (everything except for POM) become relatively more important with depth as POM is remineralized. In the eastern half of the basin, lithogenic particles make up the largest fraction of particle mass, accounting for >50% of particle mass below 1500 m. In the western half of the basin, further from the Saharan dust source, lithogenic particles are not as important, and CaCO3 makes up the largest fraction (~50%) of particle mass between 500 – 3000 m. A special case is found in a station over the Mid-Atlantic Ridge, where iron oxyhydroxides from the hydrothermal plume make up ~50% of the particle mass. Iron and manganese oxyhydroxides are rarely dominant components of particle mass, except in special situations such as hydrothermal plumes, but may exert a particularly large influence on TEI adsorption (5, 6 ).

Studies suggest that particle composition may affect both the affinity of dissolved TEIs for adsorbing onto particle surfaces (2), and the vertical flux of particles from the water column (7-9). Horizontal and vertical changes in particle composition thus allow us to test hypotheses of the importance of particle composition on both steps in the scavenging of TEIs.

Effect of particle composition on adsorption of TEIs

The affinity of TEIs to particles has typically been characterized by a partition coefficient, Kd, which is calculated empirically as:

Prior to the GEOTRACES program, the effect of particle composition on TEI adsorption affinity had been studied in the field using sediments and sinking particles collected in sediment traps. The affinity of trace metals to marine sediments of different compositions varied: Some trace metals (Cs, Be, Sn, and Fe) had a higher affinity to sediments dominated by aluminosilicate clay minerals, and others (Ba, Cd, Zn, Mn, and Co) had a higher affinity to sediments enriched in Mn oxyhydroxides (10). In the water column, correlations between the partition coefficient of 230Th and particle composition in sediment trap particles from around the world have variously implied that the scavenging efficiency of 230Th is controlled by CaCO3 (11, 12), lithogenic material (13, 14), and/or Mn oxyhydroxides (15). Studies that span strong opal gradients across the Polar Front in the Southern Ocean show higher partition coefficients for 231Pa scavenging in areas of high opal content (11, 16). 231Pa is generally not as particle-reactive as 230Th in the open ocean, but is often removed with equal efficiency as 230Th in near-margin areas (e.g., 17), presumably because opal is more important in margin settings. The Arctic, on the other hand, displays the opposite 230Th/231Pa removal signal, with 231Pa removal less efficient relative to 230Th at the margins compared to the open ocean (18).

Since TEIs adsorb primarily onto suspended particles rather than sinking particles, studying the correlations between partition coefficients and suspended particles may resolve some of the discrepancies observed in the sediment trap studies (c.f., 2).

The GEOTRACES GA03 North Atlantic Zonal Transect has provided the first opportunity to investigate the correlation between partition coefficients of various TEIs and the particle composition of suspended particles in the ocean. Thus far, this has been done for 230Th and 231Pa partition coefficients, with Mn and Fe oxyhydroxides emerging as key controlling phases and opal having no controlling effect (5). The North Atlantic is very opal-poor (Fig. 2), so particles collected from more diatom-rich regions are needed to examine the potential of opal as a controlling phase. Other studies are underway to study the  particle affinities of Hg (19), Po (20), and Pb (6) on this same North Atlantic transect. Subsequent U.S. GEOTRACES sections (GP16—Eastern Tropical South Pacific Zonal Transect and GN01—Western Arctic) will also have full ocean depth size-fractionated particle concentration and composition, allowing us to examine samples from different biogeochemical provinces, and hopefully expanding the range of particle compositions.

TEIs as tracers of scavenging rates and particle dynamics

The unprecedented data sets from GEOTRACES are also allowing us to estimate adsorption and desorption rate constants (Fig. 1) from inverse modeling of the observations of dissolved and particulate TEIs and particle concentrations (19, 21, 22). This gives us a kinetic view of the scavenging process to complement the empirically-derived partition coefficients, which are often viewed as representing equilibrium constants. Applying inverse modeling approaches to observations of the distributions of size-fractionated particles and particulate TEIs can also allow us to estimate rates of particle remineralization, aggregation, disaggregation, and sinking (21, 23). This approach requires only that a conceptual model relating the suspended and sinking particle size fractions be applied to observations of particle mass and particulate TEIs, and does not require knowledge about which specific physical or biological processes are responsible for particle transformations. For example, Fig. 1 illustrates a simple conceptual model in which a pool of suspended particles can be lost to the dissolved phase through remineralization, or by aggregation into sinking particles; conversely, sinking particles can sink, or can be disaggregated back into suspended particles. By assuming that particulate TEIs are simply part of the overall particle pool (e.g., a coating on organic particles in the case of radiogenic TEIs such as 230Th or as part of a lithogenic particle in the case of a TEI such as Ti) and thus are subject to the same rates of particle transformations as the major phases such as POC, we can apply the same conceptual model to observations of particle mass and to observations of particulate TEI to better constrain the rates of these transformations (23). As some of these rates such as aggregation and disaggregation are notoriously difficult to measure directly, these inverse approaches offer a way forward to quantify these important processes.

Particle composition and the biological pump

In addition to its effect on scavenging efficiency, particle composition has also been implicated as an important factor in the strength and efficiency of the biological pump. Several meta-analyses of global deep (>1000 m) sediment trap data showed strong correlations between POC flux and mineral flux (7-9), leading to the development of the “ballast hypothesis.” The mechanisms to explain the correlations, which are still being debated (24, 25), range from mineral protection of POC (8), mineral contribution to particle excess density (7), scavenging of mineral particles by POC (26), and minerals as proxies for particle packaging, POC lability, and ecosystem structure (9, 27-29).

Although the GA03 dataset is based on size-fractionated particle samples collected by in-situ filtration rather than sinking particles collected by sediment traps, we can nonetheless examine whether there is a correlation between POC and ballast minerals in small or large particle size fractions. We found that POC concentration in large (>51 mm) particles was not consistently correlated with any of the potential ballast minerals CaCO3, opal, and lithogenic particles (4).The lack of strong correlations within this regional dataset is consistent with the idea that ballast mineral correlations with POC may only emerge in global datasets that combine different biogeochemical provinces (25).

Outlook

The GEOTRACES program is not only rapidly expanding global observations of dissolved TEIs, but it is also the latest major program to systematically sample particle distributions since JGOFS and GEOSECS (2). These particle measurements are not only helping us understand the processes controlling TEI distributions, but the TEI measurements can also be used as tracers for quantifying key processes of particle cycling. Both GEOTRACES and OCB can benefit from the insights gained in each program.

Author

Phoebe J. Lam (Department of Ocean Sciences, University of California, Santa Cruz)

References

1. GEOTRACES, Scientific Committee on Oceanic Research, Ed. (Baltimore, Maryland, 2006).
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4. P. J. Lam et al., Deep Sea Research Part II: Topical Studies in Oceanography 116, 303 (6//, 2015).
5. C. T. Hayes et al., Marine Chemistry 170, 49 (3/20/, 2015).
6. E. A. Boyle et al., paper presented at the 2016 Ocean Sciences Meeting, New Orleans, LA, USA, 2016.
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19. C. H. Lamborg et al., Philos T R Soc A, (accepted).
20. Y. Tang et al., paper presented at the 2016 Ocean Sciences Meeting, New Orleans, LA, USA, 2016.
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22. P. Lerner et al., Deep Sea Research Part I: Oceanographic Research Papers 113, 57 (7//, 2016).
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24. P. Boyd, T. Trull, Progress in Oceanography 72, 276 (2007).
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Using GEOTRACES data to appraise iron cycling as represented within global ocean models

Posted by mmaheigan 
· Thursday, June 23rd, 2016 

We rely on global ocean models to predict how climate change might affect the evolution of ocean productivity, acidification, and deoxygenation (1). Such platforms are also used to test hypotheses regarding the controls on ocean biogeochemical cycling and to understand past change (both on historical and geologic timescales). Ocean biogeochemistry models began with relatively simple formulations of a carbon export flux that involved restoring to observed phosphate distributions, but have more recently evolved into complex multi-element representations of the ocean. In line with our understanding that the trace micronutrient iron (Fe) limits phytoplankton productivity over large areas of the world ocean (2), most global models that aim to project future change also explicitly represent the Fe cycle.

Datasets regarding the major limiting nutrients (nitrate, phosphate, and silicate) have been available as gridded ‘climatologies’ since the early 1990s (3). This has greatly facilitated the development and evaluation of modelled distributions over the past two decades. However, over this period there has been little comprehensive evaluation of how different models represent the ocean Fe cycle. Over recent years, there has been a marked increase in the availability of iron measurements in the ocean (4), largely driven by the international GEOTRACES effort to conduct full depth, basin-scale surveys. This led us to initiate the first-ever attempt to critically compare a range of global ocean iron models against the largest global datasets, as well as against the newly emerging ocean section data (5).

The Fe Model Intercomparison Project (FeMIP) sought to be as inclusive as possible in this first step and therefore did not seek to standardize the underlying ocean circulation or external inputs. Instead, we simply asked each of the thirteen models to provide their best representation of dissolved iron in three dimensions at monthly resolution. We then compared these models against each other, a global iron database of over 20,000 observations and against five unique basin-scale sections from the GEOTRACES intermediate data product 2014 (IDP2014) (6).

Firstly, it is apparent that even when the underlying iron cycles of the different models are evaluated, a substantial degree of inter-model discord exists. The total iron input varies from around 2 to 200 Gmol yr-1 across the thirteen models (Fig. 1a). Even for ‘well known’ sources like atmospheric deposition, the inter-model variability is around an order of magnitude. On the other hand, the average concentrations of dissolved Fe between the models is much less variable and ranges from 0.35 to 0.81 nmol L-1 (Fig. 1b), or an average of 0.58±0.14 nmol L-1. This apparent constancy reflects an initial view of the ocean Fe cycle in which interior Fe concentrations were held at a quasi-constant value of 0.6 nmol L-1 assuming a constant concentration of Fe-binding ligands (7). Thus the FeMIP models are balancing widely varying Fe input fluxes against relatively constant overall Fe concentrations by tuning the Fe scavenging rate, which is a crucial but poorly known parameter. This results in residence times for Fe that range from <5 to >500 years across the FeMIP models (Fig 1c). This difference is important, as it represents substantial inter-model deviation concerning the timescales over which the different models respond to a perturbation in Fe supply.

When compared statistically against the global dataset, similar levels of variability arise. Some models display correlation coefficients of >0.5, whereas others are slightly anti-correlated. When the FeMIP models are compared against the five GEOTRACES sections, it becomes apparent that those models that represent the newly emerging features of the iron cycle perform much better. For instance, having Fe scavenging rates that vary in space and time, including variable Fe:carbon (C) stoichiometry, multiple Fe sources, and representing ligand concentrations in a dynamic manner, all act to improve the representation of different observed features in the models. Importantly, the IDP2014 provided the opportunity to demonstrate that the issues at hand were specific to Fe, since the models could represent the observed distributions of major nutrients with a much greater degree of skill (5).

The next stage of FeMIP will be a deeper comparison of the processes themselves. Of particular interest is whether GEOTRACES datasets can provide broader assessments of the rates of Fe scavenging – e.g., using other particle-reactive, non-biological tracers such as thorium (8, 9). Equally, the emerging database of GEOTRACES process studies provides an important opportunity to appraise the way different models represent biological iron cycling and in particular, the often-observed importance of recycled and remineralized sources of Fe (10, 11). Finally, the new GEOTRACES intermediate data product 2017 will also facilitate further evaluation of models, providing new section data from the Atlantic, Pacific and Arctic Oceans.

Author

Alessandro Tagliabue (Dept. of Earth, Ocean and Ecological Sciences, School of Environmental Sciences, University of Liverpool)

References

1. L. Bopp et al., Biogeosci. 10(10), 6225-6245, doi:10.5194/bg-10-6225-2013 (2013).
2. C. M. Moore et al., Nature Geosci., doi:10.1038/ngeo1765 (2013).
3. S. Levitus et al., Prog. Oceanogr. 31(3), 245-273, doi:10.1016/0079-6611(93)90003-v (1993).
4. A. Tagliabue et al., Biogeosci. 9(6), 2333-2349, doi:10.5194/bg-9-2333-2012 (2012).
5. A. Tagliabue, A. et al., Glob. Biogeochem. Cycles, doi:10.1002/2015gb005289 (2016).
6. E. Mawji et al., Marine Chem. 177, 1-8. doi:10.1016/j. marchem.2015.04.005 (2015).
7. K. S. Johnson, R. M. Gordon, K. H. Coale, Marine Chem. 57(3-4), 137-161, doi:10.1016/s0304-4203(97)00043-1 (1997).
8. C. T. Hayes et al., Geochim. Cosmochim. Acta 169, 1-16, doi:10.1016/j.gca.2015.07.019 (2015).
9. N. Rogan et al., Geophys. Res. Lett. 43(6), 2732-2740. doi:10.1002/2016gl067905 (2016).
10. P. W. Boyd et al., Glob. Biogeochem. Cycles 29(7), 1028-1043, doi:10.1002/2014gb005014 (2015).
11. R. F. Strzepek et al., Glob. Biogeochem. Cycles 19(4), GB4S26, doi:10.1029/2005gb002490 (2005).

Exploring molecular methods for assessing trace element bioavailability in phytoplankton

Posted by mmaheigan 
· Saturday, June 4th, 2016 

This summer OCB and GEOTRACES are co-sponsoring a synthesis workshop on the biogeochemical cycling of trace elements in the ocean. The overall goal of the workshop is to bring together expertise from across the field of oceanography to take advantage of the growing datasets of trace elements in the ocean and explore biological-chemical-physical underpinnings of trace element cycling within the ocean. One of the three main themes that will be addressed at the workshop is “biological uptake and trace element bioavailability.” Part of this theme will include a discussion of how molecular markers have been used to address questions of trace element bioavailability in the past and the exciting future for continued efforts in this area given the growing molecular toolkit.

As a starting point to open the door to this broad discussion topic, let us turn our attention to how molecular methods have been used to evaluate iron (Fe) bioavailability to certain phytoplankton groups. Marine phytoplankton play a key role in the global carbon cycle by performing a significant fraction of global primary production (1). Since John Martin’s groundbreaking work introduced the concept that Fe is a limiting nutrient for phytoplankton growth (2), numerous studies have shown that insufficient Fe limits primary productivity in the major high-nitrate, low-chlorophyll (HNLC) regions of the ocean, and that Fe availability can regulate phytoplankton processes in many other oceanic settings (3, 4). Fe is a critical micronutrient required by phytoplankton for a Exploring molecular methods for assessing trace element bioavailability in phytoplankton Dreux Chappell (Old Dominion Univ.) multitude of cellular tasks, including electron transfer in photosynthesis and respiration, as well as macronutrient acquisition and assimilation (5). Studies of Fe limitation in marine environments beyond the traditional HNLC regions suggest that Fe limitation may be driven not simply by low Fe concentrations, but a combination of low Fe bioavailability coupled with high macronutrient supply (e.g., 6, 7). In an admittedly overly simplified summary, the different pools of Fe present in the ocean are defined based on filter pore-size cut-offs and chemical interactions with organic compounds (3). Knowledge of the distribution of these various forms of Fe in the oceans has increased dramatically in recent years thanks to coordinated sampling efforts like GEOTRACES.

While the bioavailability of Fe to phytoplankton is believed to be different for the various Fe pools, there is still no clear consensus as to which, if any, pools of Fe are always bioavailable and which, if any, are completely unavailable (8, 9). Additionally, it is known that all phytoplankton are not created equal with respect to their ability to persist under low-Fe conditions (10) and access different Fe pools (9). These factors make it difficult to accurately predict how changing concentrations of the different pools of Fe may impact phytoplankton productivity in a changing ocean. One way to address questions about biological availability is to use phytoplankton themselves as in situ indicators of Fe stress. An approach that appears promising in addressing these questions of bioavailability of Fe to individual phytoplankton species is the development of species-specific molecular markers of Fe limitation such as those that have been developed for the oceanic diatom Thalassiosira oceanica (11), and for the two main groups of the nitrogen-fixing cyanobacterial genus Trichodesmium (12). These assays in particular follow the expression of genes that encode flavodoxin, a non-Fe-containing protein that phytoplankton are known to substitute for the Fe-containing ferrodoxin protein to maintain photosynthetic electron transport under Fe-limiting conditions (13). They focus on gene expression analysis rather than protein analysis, as many phytoplankton have multiple genes that encode for flavodoxin proteins and, at least in diatoms, not all gene copies are sensitive to Fe (14).

The Trichodesmium assays were calibrated using cultures grown with six different concentrations of Fe in the media. In laboratory cultivation experiments, gene expression was shown to be inversely proportional to Fe present in the media and expression was downregulated when Fe was fed back to Fe-limited cultures (12). The Trichodesmium thiebautii assay was further used to evaluate field populations from open ocean samples collected globally (15), providing insights into Fe bioavailability to wild populations of Trichodesmium. Overall, there was an inverse correlation between gene expression and total dissolved Fe concentrations (Fig. 1). Comparing the results with the laboratory calibration led to the conclusion that most of the dissolved Fe, including organically bound Fe, was available to T. thiebautii. An intriguing result from this study was that one sample collected in the plume of the Amazon River had significantly higher gene expression than would be expected based on the measured dissolved Fe at that site (Fig. 1; 15). These findings suggest that there is a fraction of the dissolved Fe in the Amazon River plume that is not bioavailable to T. thiebautii, the clade of Trichodesmium that is more abundant and active in the open ocean (15, 16).

The T. oceanica assay is also highly sensitive to Fe with high gene expression in cultures that were Fe-limited, a rapid reduction of gene expression following an Fe pulse to Fe-limited cultures, and no induction of expression by macronutrient limitation (11). Using this method on field samples from the northeast Pacific Ocean, T. oceanica flavodoxin expression was found to be highest in samples with low measured dissolve Fe and vice versa (11). Two notable exceptions to this trend were samples collected along the shallow shelf of Haida Gwaii, stations 26 and 27, which showed anomalously high expression of both genes despite high measured dissolved Fe (Fig. 2), suggesting that something about the dissolved Fe in these shallow coastal stations made it unavailable to the T. oceanica in these waters. It should be noted that T. oceanica is an oceanic diatom species, so both sets of findings suggest that there is a fraction of dissolved Fe from a terrestrially influenced water sample that was not bioavailable to a species of oceanic phytoplankton.

While these two datasets are intriguing, they are limited in scope and admittedly raise more questions than they answer. There are a variety of questions that stem from these results, including what, if anything, is different about the dissolved Fe at these stations? Was this coastal/terrestrially sourced Fe unavailable because only oceanic phytoplankton, which rarely encounter dissolved Fe from terrestrial sources, were queried? Did coastal phytoplankton simply outcompete the oceanic phytoplankton for access to the dissolved Fe perhaps because they have different or more efficient Fe uptake mechanisms? Would an assay targeting coastal phytoplankton reveal the same results? We know that coastal diatoms have significantly higher Fe requirements (10). What if coastal and oceanic phytoplankton also differ in their abilities to access Fe from different sources? A recent study in the Sea of Okhotsk yielded a significant correlation between a bulk diatom community indicator of Fe stress and dissolved Fe with increasing distance from the mouth of the Amur River, suggesting that the riverine Fe was bioavailable to the community that was dominated by coastal diatoms (17).

A number of recent advances in molecular microbial oceanography are making it increasingly possible to start answering these types of questions on a broader scale. A major sequencing effort that was completed in 2014, the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP), added a wealth of data on the functional genetic diversity of marine microeukaryotes (18). The project resulted in over 650 publicly available transcriptomes from over 250 genera of marine microeukaryotes. Even before all the transcriptomes from the project were released, data mining of a limited portion of the MMETSP publically available dataset yielded valuable information as to the varied Fe management strategies utilized by different species of marine diatoms, creating a list of additional potential molecular markers for evaluating Fe nutritional status of diatoms in the field (19). Another data mining technique that has proven useful in identifying coordinated transcriptional responses in diatoms has involved the application of clustering algorithms to evaluate publically available microarray data for two of the more commonly studied diatom species, Phaeodactylum tricornutum and Thalassiosira pseudonana, grown under a wide variety of conditions (20). New insights into the diatom Fe stress response and Fe uptake mechanisms have also been gained through combining physiological experiments with genetic knockdowns of previously uncharacterized Fe responsive genes (21). These new molecular advances are providing a suite of new potential targets for querying the physiological status of phytoplankton present throughout the global ocean. At the OCB/GEOTRACES synthesis workshop on the biogeochemical cycling of trace elements in the ocean, the conversation will include a discussion of how to effectively combine these new analyses with the growing datasets of bioactive trace elements to answer questions regarding the biological availability of different trace element pools. While this mini-review has focused on Fe, additional trace elements will be discussed at the meeting in August.

Author

Dreux Chappell (Old Dominion University)

References

1. C. B. Field et al., Science 281, 237-240 (1998).
2. J. H. Martin, S. Fitzwater, Nature 331, 341-343 (1988).
3. P. W. Boyd, M. J. Ellwood, Nature Geoscience 3, 675-682 (2010).
4. P. W. Boyd et al., Science 315, 612-617 (2007).
5. F. M. M. Morel, N. M. Price, Science 300, 944-947 (2003).
6. K. W. Bruland, E. L. Rue, G. J. Smith, Limnol. Oceanogr. 46, 1661-1674 (2001).
7. A. L. King, K. N. Buck, K. A. Barbeau, Marine Chem. 128-129, 1-12 (2012).
8. E. Breitbarth et al., Biogeosci. 7, 1075-1097 (2010).
9. Y. Shaked, H. Lis, Frontiers Microbiol. 3 (2012).
10. W. G. Sunda, S. A. Huntsman, Marine Chem. 50, 189-206 (1995).
11. P. D. Chappell et al., ISME Journal 9, 592-602 (2015).
12. P. D. Chappell, E. A. Webb, Envir. Microbiol. 12, 13-27 (2010).
13. J. LaRoche et al., J. Phycol. 31, 520-530 (1995).
14. L. P. Whitney et al., Frontiers Microbiol. 2 (2011).
15. P. D. Chappell et al., ISME Journal 6, 1728-1739 (2012).
16. A. M. Hynes, Massachusetts Institute of Technology (MIT) (2009).
17. K. Suzuki et al., Biogeosci. 11, 2503-2517 (2014).
18. P. J. Keeling et al., Plos Biol. 12 (2014).
19. R. D. Groussman, M. S. Parker, E. V. Armbrust, PloS One 10 (2015).
20. J. Ashworth et al., Marine Genom. 26, 21-28 (2016).
21. J. Morrissey et al., Curr. Biol. 25, 364-371 (2015).

Biogeochemical cycling of organic iron-binding ligands: Insights from GEOTRACES data in the Atlantic Ocean

Posted by mmaheigan 
· Friday, June 3rd, 2016 

Iron is a limiting nutrient for phytoplankton in nearly half of the global surface ocean, and much attention has been paid to the biogeochemical cycling of iron in seawater since suitable trace metal-clean sampling and analysis procedures were developed (1). The organic complexation of dissolved iron, in particular, has emerged as an inherent feature of iron chemistry in the oceans (2-5), and iron speciation measurements are increasingly incorporated into field studies (Fig. 1). The integration of organic iron-binding ligands into biogeochemical models improves their ability to reproduce global dissolved iron distributions (6), and changes in ligand concentrations in the Southern Ocean can have a more pronounced impact on atmospheric CO2 in model studies than changes in iron supply terms from hydrothermal and dust sources (7). Indeed, field distribution measurements, targeted experimental studies and modeling efforts over the last twenty years have left little doubt that organic ligands are a critical factor in the global biogeochemical cycling of iron. Here we highlight some of the features of iron-binding ligand distributions in the Atlantic from the unprecedented basin-scale datasets coming out of the GEOTRACES program and attempt to elucidate some of the sources and sinks of iron-binding organic ligands in the oceans.

Dissolved iron speciation

The speciation of dissolved iron (Fe), which describes the chemical forms or species of iron in a filtered (typically <0.2 μm) sample, includes both inorganic (Fe´) and organic (FeL) components. In the oceans, iron speciation studies consistently report that nearly all dissolved iron (>99.9%) is organically complexed (8). This is perhaps not surprising given that the inorganic speciation of iron  in oxygenated seawater is dominated by hydrolysis reactions leading to iron precipitation and very low inorganic iron solubility (~0.1 nmol L-1) under most ocean conditions (9).

Studies of organic iron speciation in seawater use an electrochemical technique, competitive ligand exchange-adsorptive cathodic stripping voltammetry (CLE-ACSV), to determine the concentrations and conditional stability constants of iron-binding organic ligands. This is accomplished by titrating iron-binding ligands in a sample with additions of iron and competing against any natural iron-ligand complexes with an added well-characterized ‘competitive’ ligand, which forms an electroactive complex with iron that can be measured at the surface of a hanging mercury drop electrode. Titrations often depict no measurable iron bound to the competitive ligand for the first few iron additions, reflecting the presence of excess strong iron-binding ligands in most seawater samples (8). These ligands are described as ligand classes, L1, L2, L3, L4, defined by the conditional stability constants determined by CLE-ACSV, with L1 and L2-type ligands the strongest iron-binding organic ligands

Iron speciation in the Atlantic: Observations from recent GEOTRACES efforts

The iron speciation datasets emerging from the GEOTRACES program allow a first look at basin-scale distributions of iron-binding ligands in the oceans. Iron speciation datasets from GEOTRACES Sections GA02 (10) and GA03 (11) from the Dutch and U.S. GEOTRACES programs, respectively, document the ubiquitous nature of iron-binding ligands in the Atlantic basin. In particular, both datasets evince the presence of strong, L1-type ligands throughout the water column and no discernible trend with depth in the conditional stability constants for these ligands (10, 11). These observations support the emerging picture from many other field studies of a strong iron-binding ligand pool that is not necessarily restricted to the surface ocean or euphotic zone (Fig. 2) (8).

Elevated dissolved iron and aluminum concentrations in surface waters across the GA03 zonal section, particularly near the center of the basin, demonstrate the widespread contribution of dust deposition to the iron inventory in the Atlantic (12). Iron isotope studies indicate that 71-87% of the dissolved iron along the entire GEOTRACES GA03 section was attributable to dust (13). Water-soluble organic matter characterized from the surfaces of aerosols collected on GA03 exhibited structural differences between aerosol sources that were consistent with their iron solubilities (14), and organic complexation of some of the leached iron was observed in seawater leaches of these aerosols (15). Recent studies using model ligands highlight the particular importance of stronger iron-binding ligands in the stabilization of iron leached from natural aerosols (16).

In the Atlantic GEOTRACES sections, the highest concentrations of ligands in excess of dissolved iron ([L]-[Fe], or L´) were often measured at the surface, where dissolved iron concentrations were low (10, 11). The overall complexation capacity for iron, which is a function of both ligand concentration and the conditional stability constant, also tended to be high in the upper water column. Antarctic Intermediate Water (AAIW) stands out in both datasets as exhibiting higher complexation capacity for dissolved iron than the surrounding water masses (10, 11). These waters originate from highly productive surface waters, and elevated ligand concentrations subducted with these water masses may be the result of higher strong iron-binding ligand concentrations commonly observed in and around chlorophyll maxima and in incubation experiments of iron-stressed diatom communities (see (8) and references therein). In a compilation of three iron speciation datasets that extend from the Arctic (17) down through the Western Atlantic (10) and into the Antarctic (18), higher ligand concentrations were reported at high latitudes relative to low latitudes, with the strongest (highest conditional stability constants) excess ligands measured in the Antarctic (10), and larger excesses of weaker ligands in the Arctic (10, 17).

Excess ligand concentrations in the Atlantic usually decreased with depth as dissolved iron concentrations increased, consistent with saturation of excess ligands with iron (10, 11). A north-south trend of decreasing ligands and excess ligands was reported in the GA02 Western Atlantic meridional section, which was clearly depicted in the samples collected from the North Atlantic Deep Water (NADW) along the section (10). In the GA03 zonal section of the North Atlantic (11), excess ligand concentrations in the water column were on the high end of the two datasets, consistent with the northern end of the GA02 meridional section (10). If anything, organic matter remineralization appeared to be a source of weaker L3-type iron-binding ligands in the GA03 zonal section dataset (11).

Excess ligand concentrations in the North Atlantic exhibited local minima in the heart of the oxygen minimum zone west of Mauritania (11), possibly due to scavenging of ligand complexes on sinking particles (19), or elevated reduced iron(II) concentrations complexing the excess ligands (20). It is unclear how much of the iron-binding ligand pool measured by CLE-ACSV may also bind iron(II), or what chemical form of iron(II) is present in these samples, though some may be biogenic (21). One of the most pronounced features in the iron(II) data from GA03 is the exceedingly high iron(II) concentrations in the TAG hydrothermal plume samples, where elevated dissolved iron was ~80% colloidal-sized (0.02-0.2 μm size fraction) iron(II) species (20, 22). These iron(II) colloids are likely pyrite nanoparticles (23), which themselves may be stabilized by organic matter (24).

In the TAG plume samples collected along GA03, excess ligands were at a minimum in the highest iron samples of the plume, but the conditional stability constants of the excess ligands that were detected in these samples were among the highest in the dataset, leading to an elevated complexation capacity for iron around the vent. It is likely that some of the elevated dissolved iron in these samples was not exchangeable with the added competitive ligand during the voltammetry measurements, which would lead to an overestimation of ligand parameters (8). It is also possible that some of this observed increase in complexation capacity in the plume reflects a microbial response to the iron-enriched plume (25). Several studies have now reported varying degrees of organic complexation of iron in hydrothermal vent plumes (10, 11, 26, 27), and while the cycling of ligands in these systems remains unclear, organic stabilization must be a key factor in determining the chemical speciation and transport of iron from these vents into the deep sea (28-30).

The increasingly rich database of iron-binding ligand distributions from individual field studies and the GEOTRACES program show the widespread organic complexation of iron in the oceans. These datasets also display the inherent complexity of ligand cycling, since ligands are at the interface between the dynamic biogeochemical cycles of both trace metals and organic matter in seawater.

Identity of iron-binding ligands

Electrochemistry (CLE-ACSV) measures ligand concentrations and conditional stability constants from a combination of titration and competition, and is the basis of most of our insights to date into the sources, sinks, and cycling of organic iron-binding ligands in the oceans. Mass spectrometry-based techniques, on the other hand, are increasingly being employed to identify the chemical structures of natural iron-ligand complexes in the oceans. Results from both of these approaches appear to be converging on similar descriptions of the iron-binding ligand pool, one which comprises a mixture of defined biomolecules with high affinities for iron (e.g., siderophores, heme) and weaker iron-binding, ill-defined compounds with high chemical heterogeneity (e.g., humic substances, polysaccharides) (Fig. 3; (8)).

Siderophores are small iron-binding ligands widely produced by bacteria to acquire iron from the environment (31), including the marine environment (32, 33). Model siderophores are typically, though not exclusively, characterized as among the strongest L1-type ligands measured in CLE-ACSV (3, 8). Iron complexed by these discrete biomolecules can usually be chromatographically resolved, although isolation of these compounds from seawater is notoriously difficult. Ferrioxamines and amphibactins have been the most widely reported from the water column (Figure 3; (34-36)) and shipboard incubations (37), though marine bacteria cultures have produced a broader suite (32). The limited diversity and only picomolar concentrations of siderophores extracted from seawater compared to marine bacteria cultures likely reflects limitations in sampling, the extraction procedures available (8, 32), and detection of certain siderophore functional groups that are preferentially photodegraded in surface waters (38). Importantly, siderophores have been observed to persist throughout the water column below the euphotic zone (36) and are not restricted to low-iron waters (34). Additional discrete biomolecules, like heme or intracellular iron storage proteins, are also expected to contribute to the strong iron-binding ligand pool in seawater, though they may be prone to particle adsorption and aggregation processes, making them more likely to be found in the colloidal and particulate phases (8, 39). Because of their role as intracellular iron-binding ligands, these molecules are Science usually released to the extracellular environment as iron complexes, rather than as free ligands. Similarly, viruses may even constitute a component of colloidal organically complexed iron (40).

Unlike siderophores, humic substances and polysaccharides are complex molecules with high heterogeneity and complexity (Fig. 3), which cannot generally be resolved chromatographically, but represent a large component of the natural organic matter pool (41). Some of these molecules actually form electroactive complexes with iron and have been directly measured by electrochemistry in estuarine, coastal, and deep open ocean waters (42-44). Suwannee River Fulvic Acid (SRFA), a suspected component of the refractory dissolved organic matter pool (45), has been identified as a model ligand that can be used to reproduce the peak of natural electroactive iron complexes (46); exopolymeric substances can similarly form electroactive iron complexes (47). The conditional stability constants for exopolysaccharides and SRFA determined by CLE-ACSV generally fall under the L2 to L4 ligand class definitions (8).

Ligand processes at ocean interfaces (Fig. 4)

Photochemical degradation of natural iron-binding ligands is variable in field studies (48, 49), and may account for sea surface minima in ligand concentrations observed in some profiles (8). Experimental studies indicate that siderophore photolability depends on chemical structure and whether the siderophore is bound to iron (FeL) or not (L´) (38). Humic substances, on the other hand, are universally photoreactive by the nature of their molecular structure (50), though the iron-binding ability of their photoproducts is unknown. Dust deposition and rainfall may serve as ligand sources if depositing strong iron-binding ligands in addition to their iron loads (14, 51-54), or stimulating ligand production by surface microbial communities (55, 56), which may be critical for stabilizing atmospherically-derived iron in surface waters (16). Inorganic iron additions in mesoscale fertilization experiments have also been shown to stimulate ligand production in the fertilized waters (8, 57). Similarly, a microbial iron cycle fueled by hydrothermal iron inputs at the crust-ocean interface has recently been suggested (25) to support the organic stabilization and transport of dissolved iron in plumes extending remarkable distances from vent systems (28, 30).

Along the coastal margins, organically complexed iron is delivered to the coastal ocean from river plumes, estuaries, and shelf sediments, often along with excess weaker iron-binding ligands, including humic substances (8, 42, 44, 58). Elevated excess iron-binding ligands were observed in bottom waters of several of the GA03 stations in the North Atlantic (11) that were also local maxima in excess copper-binding ligands (59), indicating overlap in the ligand pool between these two bioactive elements (44).

Internal ligand cycling (Fig. 4)

The low solubility of inorganic iron, and the overwhelming organic complexation of the dissolved iron pool by a diverse suite of ligands has significant implications for iron bioavailability to marine phytoplankton (60, 61). The high biological demand for iron by phytoplankton and heterotrophic bacteria in turn supports a myriad of iron acquisition strategies, which are largely mediated by organic complexation (33, 61). High excess ligand concentrations in low-iron waters may potentially result from ligand production, iron uptake from a ligand complex, or both. Production of excess iron-binding ligands has been observed under a range of iron conditions, with iron additions in large-scale iron fertilization experiments (57), and in iron-stressed diatom communities (49, 62, 63), perhaps indicative of a community iron cycle including diatom-associated bacterial communities (64).

Grazing, viral lysis, and organic matter remineralization are likely important sources of weaker iron-binding ligands (e.g., humic-like substances, exopolysaccharides, or intracellular organic iron complexes like heme) to the ocean interior (8, 19). Mounting evidence points to bacterial production of the strongest ligands observed in seawater as an iron uptake strategy (8, 49, 57). Organic matter remineralization may similarly be a source of strong iron-binding ligands to the entire water column, given that siderophore production by heterotrophic bacteria is not necessarily restricted to the surface ocean and may be associated with ‘hot spots’ of sinking organic matter in the deep sea (65).

The extent to which iron-binding ligands are remineralized themselves is unknown. Excess ligand concentrations tended to decrease with depth in the Western North Atlantic as dissolved iron concentrations increased (10, 11). Decreasing total ligand concentrations in NADW samples were negatively correlated, albeit weakly, with apparent oxygen utilization (AOU) along the GA02 meridional section (10), implying some ligand remineralization during circulation since dilution was not expected to impact ligand concentrations. Gerringa et al. (2015) calculated a residence time on the order of 103 years for iron-binding ligands in the NADW, up to four times longer than that of dissolved iron, suggesting that particles must scavenge iron from strong organic complexes in the deep sea (10). Overall, the interactions between iron-binding ligands and sinking particles, whether lithogenic or biogenic in origin, are largely uncharacterized. These particles likely serve as both sources and sinks of iron-binding ligands (19) depending on the nature of the ligands, particles and biological communities involved.

Conclusions

Detailed large-scale datasets from the Atlantic Ocean have given us an unparalleled view of ligand cycling in this basin. These studies have enabled us to take a holistic look at ligand sources and sinks and internal cycling for the first time, and new paradigms have emerged. Biological contributions to the ligand pool are clear across nearly all ligand datasets. Although the direct connection between the organisms responsible for ligand production and the compounds they produce is still uncertain, marine microorganisms appear to be active producers of strong iron-binding ligands that influence iron cycling through the water column. Expansion of basin-scale datasets to the other basins and collaborative experimental studies to elucidate the mechanisms of ligand cycling behind the basin-scale distributions, some of which have been described here for the Atlantic, will improve under  standing of the cycling of organic iron-binding ligands and inform global biogeochemical models of iron and carbon cycles.

Authors

Kristen N. Buck (Univ. of South Florida, College of Marine Science), Chelsea Bonnain (Univ. of South Florida, College of Marine Science), and Randelle M. Bundy (Woods Hole Oceanographic Institution)

Acknowledgments

The authors thank Kathy Barbeau for her helpful comments on the text. This work was funded in part by the National Science Foundation through an award to KNB, OCE-0927453.

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Mesodinium rubrum: An Old Bug Meets New Technology

Posted by mmaheigan 
· Tuesday, April 12th, 2016 

Blooms of red water associated with the remarkable ciliate Mesodinium rubrum have been observed at least since Darwin’s time (1). This ciliate retains the chloroplasts from ingested prey and is able to use them for photosynthesis (reviewed in 2). Recent studies have shown that the plastids can reproduce within the ciliate and that nuclei from the original algal prey remain
transcriptionally active (3). It is very likely that there are at least two different species of Mesodinium that perform this feat, the original M. rubrum and a recently described larger species, M. major (4). Both species have in common certain species of cryptophyte
algae as their preferred food, and hence are colored deep red by their prey’s phycoerythrin pigment and characteristic yellow fluorescence (Fig. 1). Mesodinium is believed to hold the ciliate swimming speed record, with short jumps of up to 1.2 cm s-1, and can change its position vertically in the water column to access nutrients (5). Along with rapid growth, its impressive motility probably contributes to the large aggregations obvious to the naked eye, in which concentrations of >106 cells l-1 have been observed (6 ) (Fig. 1). Even outside of bloom conditions, they are a regular component of estuarine and coastal plankton assemblages and can contribute significantly to primary productivity (7). However, as mixotrophs (organisms capable
of both photosynthesis and ingestion), they are undersampled and underappreciated by phytoplankton and zooplankton ecologists alike.

Red water has been reported in Long Island Sound on occasion by other observers. While Mesodinium was present in >80% of all samples examined in >10 years of monthly plankton monitoring data, no sample ever exceeded 2.6 x 104 cells l-1. In Fall 2012, Univ. Connecticut personnel servicing a moored array observed and sampled red water in western Long Island Sound (40.9°N 73.6°W). Microscopy and DNA sequencing confirmed that the bloom was due to Mesodinium (100% identical by small subunit rDNA to the larger M. major), and we subsequently reported on our efforts to document the bloom using satellite imagery (8). Here, we summarize those results and discuss the promise of new sensors for quantifying blooms of specific plankton groups by their pigment signatures, especially when coarsely resolved monitoring samples are inadequate.

Ocean color satellites provide a means to assess such red tides, but the standard chlorophyll products are inaccurate in the optically complex waters of Long Island Sound, which contain river runoff with colored dissolved organic matter (cDOM) and suspended sediments (9, 10) (Fig 2). Imagery from the MODIS sensor of fluorescence line height (Fig. 2A) indicated the presence of an unspecified bloom in Western Long Island Sound coincident with the bloom, but the spatial resolution (1-km pixels) did not allow us to gauge the bloom extent adequately, and the spectral bands of that sensor are not sufficient to discriminate the type of bloom.

Serendipitously, an image was available for the western Sound from the novel Hyperspectral Imager for the Coastal Ocean (HICO) instrument aboard the International Space Station. This sensor contains >100 channels in the visible and near infrared regions of the spectrum and hence has the capability to resolve multiple peaks and valleys due to fluorescence and absorbance of the chlorophylls and accessory pigments found in various phytoplankton groups. It also has the higher spatial resolution (110-m pixels) needed to quantify the extent of the bloom and variation in ciliate abundance within it. Because the red water we observed appeared (microscopically) to be almost exclusively due to Mesodinium, the HICO reflectance spectrum was an almost pure example of the in situ optical signature of this unique organism (i.e. an “endmember” in remote sensing terminology).

In addition to phycoerythrin, the cryptophyte chloroplasts that the ciliate retains contain chlorophyll-a, chlorophyll-c2, phycocyanin, and the carotenoid alloxanthin. The reflectance spectrum measured with the HICO sensor revealed features related to the fluorescence and absorption associated with these pigments that can be used as a spectral “fingerprint” of this specific organism (Fig. 3A). With reflectance measured across the full visible spectrum, small dips in the spectrum can be revealed with a 4th derivative analysis and related to the associated pigments (11) (Fig. 3B). In addition to absorbing green light, phycoerythrin also fluoresces yellow light (12) (Fig. 1B) and a peak in reflectance was observed at ~565 nm associated with this feature. This unique fluorescence feature allowed us to map the surface distribution of Mesodinium in Long Island Sound. Traditional ocean color satellites do not measure reflectance of light at this waveband, but yellow fluorescence (band depth at 565 nm) could be detected from the hyperspectral measurements of HICO and related to the relative amount of Mesodinium up to the measured 106 cells L-1 with distinctly red colored water (Fig. 4).

The fine-scale distribution of the HICO imagery reveals that Mesodinium was found in small 100-m patches along the sea surface rather than distributed throughout a single multi-kilometer patch as suggested by the 1-km MODIS imagery (Fig 2A). Such high spatial resolution from aircraft has been used to assess concentration mechanisms of blooms, including internal waves (13) and Langmuir circulation (14). Further research is underway to assess the observed patterns with hydrographic and air-sea processes local to this region. Understanding the spatial distribution may also lead to a better understanding of the environmental factors that lead to these episodic blooms of Mesodinium. Generally, Mesodinium is more abundant in lower salinity estuarine water, but the causes of bloom initiation and demise are not well known (15).

Though now defunct, the HICO sensor should serve as a model for remote sensing in the coastal zone. With its high spectral and spatial resolution, images from HICO could be used to assess coastal processes, as highlighted here, but only at infrequent intervals. While possible with airborne technology, no existing or planned satellite sensor can sample at high spectral, spatial, and temporal resolution for adequate monitoring of the coastal zone. Providing near-daily coverage for much of the globe, the next generation NASA ocean color sensor, Pre-Aerosol, Cloud and ocean Ecosystems (PACE), is slated to have the unique hyperspectral capabilities to allow for better discrimination of marine blooms and habitats, but with a larger km-scale resolution. International sensors with new capabilities will also help to fill this gap (16). With new hyperspectral technology in space, autonomous and routine differentiation of phyto- and mixotrophic plankton blooms in surface waters may be possible and could provide an important tool for resource managers. Improved monitoring of bloom-forming plankton will also lead to more refined estimates of coastal primary productivity and mechanisms for their episodic growth and decline. If future sensors or sensor constellations combine high repeat sampling with the hyperspectral capabilities and high spatial resolution of HICO, we will be able to understand not only the composition and extent of blooms, but also the sub-mesoscale processes that drive their persistence and spatial structure.

Authors

Heidi Dierssen and George McManus (University of Connecticut)

Acknowledgments

We thank Kay Howard-Strobel, Senjie Lin, and the NOAA Phytoplankton Monitoring Network for images of the bloom and of Mesodinium. Dajun Qiu verified the genetic identity of the ciliate. Adam Chlus and Bo-Cai Gao contributed to the image processing. We also thank the HICO Science Team and NASA Ocean Biology Distributed Active Archive Center for providing satellite imagery.

References

  1. Darwin, C., 1909. The Voyage of the Beagle, P.F. Collier.
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  8. Dierssen, H. et al., 2015. Proc. Natl. Acad. Sci., doi:10.1073/pnas.1512538112.
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  10. Aurin, D. A. et al., 2010. J. Geophys. Res. 115, 1–11.
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  16. International Ocean Colour Coordinating Group (IOCCG). www.ioccg.org
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