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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.

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  15. Herfort, L. et al., 2011. Estuar. Coast. Shelf Sci. 95, 440–446.
  16. International Ocean Colour Coordinating Group (IOCCG). www.ioccg.org

Marine and Human Systems: Addressing Multiple Scales and Multiple Stressors

Posted by mmaheigan 
· Sunday, April 3rd, 2016 

Eileen Hofmann (Old Dominion University, Norfolk, VA, USA)
Lisa Maddison (IMBER IPO, Institute of Marine Research, Bergen, Norway)
Ingrid van Putten (CSIRO, Hobart, Tasmania, Australia)
Javier Arístegui (Universidad de Las Palmas de Gran Canaria, Islas Canarias, Spain)

The Integrated Marine Biogeochemistry and Ecosystem Research Project (IMBER) is developed around four research themes, which include: Key interactions in marine ecosystems; sensitivity to global change; feedbacks to the Earth system; and responses of society. When IMBER was initiated in 2005, the responses of society theme represented a new direction for global environmental change programs because it explicitly acknowledged the role of humans as both drivers and recipients of change in marine ecosystems. IMBER project-wide activities, regional programs and working groups have advanced the science associated  with each research theme. However, the strength of these activities has been in the identification of theoretical and methodological overlap among the themes, facilitating integration of ideas and synthesis of research outcomes, and highlighting new research directions.

The biennial IMBIZO (Zulu word for a gathering) is an important IMBER-wide activity for assessing current understanding of theoretical and empirical research at the local, regional and global scale, and pointing to future research needs. IMBIZO IV, held in October 2015 in Trieste, Italy, addressed linkages between marine ecosystems and human systems (Fig. 1). In particular, emphasis was on current systems understanding and approaches to predict the effects of multiple stressors, at multiple scales, on marine ecosystems and dependent human populations. A novel aspect of this IMBIZO was the focus on exposing the need for human systems to respond to changes and for governance systems to adequately guide these responses.

IMBIZO IV was developed around four workshops (Fig. 1) that addressed i) marine ecosystem-based governance, ii) upwelling systems as models for interdisciplinary global change studies, iii) integrated modeling to support marine socio-ecological systems under global change, and iv) regime shifts and their socio-ecological implications. Although each workshop had distinct objectives, all addressed aspects of climate, ecosystems and societies with a view towards integrating and synthesizing current understanding and highlighting approaches for developing innovative societal responses to changing marine ecosystems. The workshops were supplemented with plenary presentations that provided overviews of the state of understanding and research needs and joint sessions and debates that allowed cross-workshop interactions (Fig. 2).

Within the context of each workshop, questions were addressed that considered the challenges of multiple stressors, pressures, and drivers,  existing knowledge gaps, and the type of expertise needed to move forward. Some workshops also evaluated the need for paradigm shifts to adequately address particular research questions. The overall goal of each workshop was to determine how integration of the diverse array of knowledge and different  research outcomes for marine systems could be done to provide useful advice for policy and management.

The results of the individual workshops are being summarized in a variety of ways including white papers, synthesis papers, short communications, and special issues. However, the workshop results have common components with perhaps the clearest message being the need for continued conversations and exchange of information between scientists from different disciplinary backgrounds. To enable this dialogue to take place collaboratively and ultimately to develop workable solutions will mean that a common understanding of language will need to be developed and that jargon be avoided. Facilitating cross-disciplinary communication by domain experts will also help crucially important communication to management authorities and decision makers.

Aside from the need for good communication between scientists that straddle the physical, ecological and human domains, the different workshops considered the linkages and interactions between the driving forces (pressures-state-impacts-responses, DPSIR) and how these are understood and represented. For most marine systems, the system state, how much of what is present and where, can be described with differing degrees of certainty depending on location and factors such as monitoring intensity and accessibility. The connectivity and linkages between marine system components and driving forces are known from a theoretical perspective and for many systems these have been described quantitatively using different modeling approaches. However, there is considerable empirical uncertainty about how marine systems might respond to continued and cumulative anthropogenic stresses and how in turn, this may feed back to the human domain and affect, for instance, future food security.

Marine systems may not be generalizable, sometimes cannot be simply scaled up, or may not respond linearly to anthropogenic stressors. Regime shifts may occur that are not easily (or not at all) reversible, thus requiring adaptation by resource users. The governance system is crucially important in this context as it provides links to management, policy and regulatory systems that influence use of and access to marine resources. Governance institutions are ultimately responsible for the sustainable management of marine resources and any necessary reduction in the pressure exerted on the resources. These governance systems in essence close the loop between the natural and human systems. Natural, socio-economic, and governance systems need to be central to continued research efforts and inform all levels of decision making to ensure informed steps are taken.

Global environmental change is happening and will continue to affect ecosystems and alter the ecosystem services provided to humanity. The need for timely detection and attribution of these changes remains, especially where change is irreversible. Human systems and society at large are both creators of the many stressors that drive change in marine ecosystems as well as recipients of these changes. Human systems can drive positive changes through good governance aimed at reducing vulnerability, and enhancing adaptive capacity and resilience. It is clear that many knowledge gaps remain, in particular the way in which multiple drivers and stressors interact. Much work also remains to be done in further detailing and modeling the crucial dependencies between human and ocean systems. All of these uncertainties place limitations on the predictability of governance outcomes and risk unintended consequences and maladaptation if not addressed adequately. Outcomes from IMBIZO IV will provide guidance for these important research efforts for the next decade of IMBER research.

IMBER gratefully acknowledges the support provided by the OCB Program for IMBIZO IV and its ongoing support of IMBER activities.

New Satellites Paint a Portrait of Plankton Spatial Variability

Posted by mmaheigan 
· Saturday, April 2nd, 2016 

The newest generation of satellites reveals plankton variability changes in character from uniform to chaotic at different spatial scales, reviving a classic question in oceanography. How does plankton variability change at different spatial scales, and why?

New satellites, new insights

Satellite technologies can now collect images with resolution down to the scale of meters, presenting oceanographers data with unprecedented information about the fine-scale structure of plankton communities in the surface ocean. In August 2015, there was significant media attention after two of the world’s most advanced satellites, Landsat 8 and Sentinel-2, published images of a cyanobacteria (algal) bloom in the Baltic sea (Fig. 1). For scale, the images conveniently have boats in them (you really have to squint, or just zoom in – a little game of Where’s Waldo at sea).

While these images are beautiful in their own right, to an oceanographer they also illustrate the complexity of the biophysical interactions that drive plankton distributions. When we run computer models to simulate e.g., how plankton communities might respond to a changing climate, we can’t replicate all of this variability, so we typically represent an X km × Y km square of ocean with a single value (e.g., plankton concentration), which we consider as the average for that box; one peek at an image like this demonstrates that it’s difficult to justify this approach as doing full justice to the system it’s simulating. Similarly, when we take samples out in the field, we often fill bottles with seawater and assume that sample represents a X km × Y km area around it. This image suggests that taking a measurement off one side of the boat might give you a very different representation of that region than if you had taken it off the other side! These approaches are further complicated by studies indicating that the variability we see in these images persists at microscopic scales.

This is not meant to needlessly criticize these approaches; oceanography is a challenging science, and we do the best we can. Often, these approaches can yield wonderful insights. These images just draw attention to the fact that plankton spatial variability remains a fascinating and open problem in oceanography, which present-day technology puts us in good position to start addressing.

Characterizing variability

One way we can characterize such variability is by using a power spectral density (PSD), which allows us to quantify how much variability is contained at each scale in an image. Computing the PSD for each of the above images is a straightforward exercise, thanks to modern computational capabilities. To draw an analogy, we can also compute the PSD for a painting by each of Rothko and Pollock (Figs. 2a. and 2b., respectively); we might take the former to represent ’homogeneity’ and the latter to represent ’chaos’ (as Pollock’s paintings have been thought of for years). That is, imagine a satellite looks down on a plankton bloom and sees a rather gargantuan painting of each type; how do these paintings compare with observed blooms, in terms of spatial variability?

Methods

The PSD has been computed for the red band of the RGB image of the Rothko painting, a black and white conversion of the Pollock painting, and for the green band of each of the satellite images. Computing the PSD for other configurations did not change the result. The wavenumber k = 1 in this case corresponds to a wavelength λ ≈ 50 km. Wavenumbers have been rescaled to those of the Sentinel-2 image, and PSDs have been normalized to their L2 norm.

Comparing power spectral densities

When we computed the PSDs for these four images (Figs. 1a, b and 2a, b), we found remarkable consistency (almost identical PSDs) between the two satellite images (Figs. 1a and b), which were taken four days apart. This suggests that 1) the satellites are accurately and reproducibly capturing spatial bloom variability, and 2) bloom PSDs don’t change significantly from day to day. The PSDs from the satellite images matched the Pollock spectrum at smaller spatial scales (i.e. high wavenumber) and the Rothko spectrum at larger spatial scales (i.e. low wavenumber) (Fig. 3). This raises the question: why might this be happening? Also, at what scale does the ’Rothko-Pollock’ transition occur and why?

Significance

If the distribution of plankton was purely that of Brownian (random) motion, we’d expect a flatter PSD (i.e. a line with slope = -2). Another null hypothesis is that the distribution of plankton might be set passively by advection of oceanic currents. In this case, we’d expect plankton distributions to have the same signature as temperature, which also has a PSD slope of -2. However, these spectra (Fig. 3) have slopes that are steeper than -2 (closer to -2.5 or -3), so clearly there’s more afoot. The steeper slope of -3 at larger scales means that variability falls off faster as we look at smaller scales, i.e. something about the plankton distribution is ’homogenizing’ at larger scales. Then, the PSDs get shallower at wavelengths of ~1 km, indicating that something kicks in at sub-kilometer scales that introduces more variability. One way to think about this transition, which has been hypothesized since the 1970s (1), is that different processes can dominate at different spatial scales. The specifics of the 70s manner of thinking aren’t quite compatible with these data, but the general concept is plausible. Plankton grow in response to light and nutrient conditions, but also live in a turbulent environment. At large scales, growth occurs somewhat uniformly and is dominated by ambient light and nutrient conditions, whereas smaller-scale biophysical interactions can introduce an additional source of variability in plankton growth. Biophysical variability can occur in many ways, including small-scale horizontal motions that can stir plankton patches into filaments and small-scale vertical motions that can enhance growth locally by bringing up nutrients. In either case, these biophysical interactions are only observable at smaller scales.

Thus, at larger scales, the plankton will be distributed relatively homogeneously as uniform (light-/temperature-driven) growth wins out (. la Rothko), and at smaller scales, they will be distributed heterogeneously as advective processes come into play (à la Pollock). The spatial scale at which this transition occurs is controversial and depends on many factors, though was originally hypothesized to be ~1 km, which here appears plausible. See the vertical line in Fig. 3, which corresponds to a 1-km wavelength and appears to agree well with the scale of the observed transition from Rothko-type to Pollock-type behavior.

Another thing to note is that these cyanobacterial mats (Fig. 1) are very thin and form just at the ocean surface –zoom in and you can see how the boat tracks cut through them. Thus, these patterns may be representative of a different set of physical processes occurring only in the uppermost layer of the ocean.

While two satellite images of the same bloom may not be enough to verify the growth vs. turbulence hypothesis, ’Rothko-type’ versus ’Pollock-type’ behavior may not be quantitative enough descriptions to satisfy any oceanographer, and the equally-complex third dimension isn’t included in these pictures, there is still a clear message here. The spatial resolution available from the newest generation of satellites provides a novel opportunity to approach problems of scale in oceanography.

Author

B. B. Cael (MIT Earth, Atmosphere and Planetary Sciences, Woods Hole Oceanographic Institution)

Acknowledgments

It is a pleasure to thank Bror Jonsson, Mick Follows, Bryan Kaiser, and Amala Mahadevan for useful discussion of this topic.

References

  1. Denman, K.L., T. Platt, 1976. J. Marine Res. 34, 593-601.
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