Biogeochemical cycling of dissolved zinc along the GEOTRACES South Atlantic transect GA10 at 40°S

The biogeochemical cycle of zinc (Zn) in the South Atlantic, at 40°S, was investigated as part of the UK GEOTRACES program. To date there is little understanding of the supply of Zn, an essential requirement for phytoplankton growth, to this highly productive region. Vertical Zn profiles displayed nutrient-like distributions with distinct gradients associated with the watermasses present. Surface Zn concentrations are among the lowest reported for theworld’s oceans (<50 pM). A strong Zn-Si linear relationshipwas observed (Zn (nM)= 0.065 Si (μM), r2=0.97, n = 460). Our results suggest that the use of a global Zn-Si relationship would lead to an underestimation of dissolved Zn in deeper waters of the South Atlantic. By utilizing Si* and a new tracer Zn* our data indicate that the preferential removal of Zn in the Southern Ocean prevented a direct return path for dissolved Zn to the surface waters of the South Atlantic at 40°S and potentially the thermocline waters of the South Atlantic subtropical gyre. The importance of Zn for phytoplankton growth was evaluated using the Zn-soluble reactive phosphorus (SRP) relationship. We hypothesize that the low Zn concentrations in the South Atlantic may select for phytoplankton cells with a lower Zn requirement. In addition, a much deeper kink at ~ 500m in the Zn:SRP ratio was observed compared to other oceanic regions.

Ocean-atmosphere trace gas exchange

The oceans contribute significantly to the global emissions of a number of atmospherically important volatile gases, notably those containing sulfur, nitrogen and halogens. Such gases play critical roles not only in global biogeochemical cycling but also in a wide range of atmospheric processes including marine aerosol formation and modification, tropospheric ozone formation and destruction, photooxidant cycling and stratospheric ozone loss. A number of marine emissions are greenhouse gases, others influence the Earth's radiative budget indirectly through aerosol formation and/or by modifying oxidant levels and thus changing the atmospheric lifetime of gases such as methane. In this article we review current literature concerning the physical, chemical and biological controls on the sea-air emissions of a wide range of gases including dimethyl sulphide (DMS), halocarbons, nitrogen-containing gases including ammonia (NH3), amines (including dimethylamine, DMA, and diethylamine, DEA), alkyl nitrates (RONO2) and nitrous oxide (N2O), non-methane hydrocarbons (NMHC) including isoprene and oxygenated (O)VOCs, methane (CH4) and carbon monoxide (CO). Where possible we review the current global emission budgets of these gases as well as known mechanisms for their formation and loss in the surface ocean.

Iron organic speciation determination in rainwater using cathodic stripping voltammetry

A sensitive method using Competitive Ligand Exchange-Adsorptive Cathodic Stripping Voltammetry (CLE-ACSV) has been developed to determine for the first time iron (Fe) organic speciation in rainwater over the typical natural range of pH. We have adapted techniques previously developed in other natural waters to rainwater samples, using the competing ligand 1-nitroso-2-naphthol (NN). The blank was equal to 0.17 ± 0.05 nM (n = 14) and the detection limit (DL) for labile Fe was 0.15 nM which is 10–70 times lower than that of previously published methods. The conditional stability constant for NN under rainwater conditions was calibrated over the pH range 5.52–6.20 through competition with ethylenediaminetetraacetic acid (EDTA). The calculated value of the logarithm of β′Fe3+3(NN)β′Fe3+(NN)3 increased linearly with increasing pH according to log β′Fe3+3(NN)=2.4±0.6×pH+11.9±3.5log β′Fe3+(NN)3=2.4±0.6×pH+11.9±3.5 (salinity = 2.9, T = 20 °C). The validation of the method was carried out using desferrioxamine mesylate B (DFOB) as a natural model ligand for Fe. Adequate detection windows were defined to detect this class of ligands in rainwater with 40 μM of NN from pH 5.52 to 6.20. The concentration of Fe-complexing natural ligands was determined for the first time in three unfiltered and one filtered rainwater samples. Organic Fe-complexing ligand concentrations varied from 104.2 ± 4.1 nM equivalent of Fe(III) to 336.2 ± 19.0 nM equivalent of Fe(III) and the logarithm of the conditional stability constants, with respect to Fe3+, varied from 21.1 ± 0.2 to 22.8 ± 0.3. This method will provide important data for improving our understanding of the role of wet deposition in the biogeochemical cycling of iron.

Air-Sea Exchange of Marine Trace Gases

Many of the reactive trace gases detected in the atmosphere are both emitted from and deposited to the global oceans via exchange across the air–sea interface. The resistance to transfer through both air and water phases is highly sensitive to physical drivers (waves, bubbles, films, etc.), which can either enhance or suppress the rate of diffusion. In addition to outlining the fundamental processes controlling the air–sea gas exchange, the authors discuss these drivers, describe the existing parameterizations used to predict transfer velocities, and summarize the novel techniques for measuring in situ exchange rates. They review trace gases that influence climate via radiative forcing (greenhouse gases), those that can alter the oxidative capacity of the atmosphere (nitrogen- and sulfur-containing gases), and those that impact ozone levels (organohalogens), both in the troposphere and stratosphere. They review the known biological and chemical routes of production and destruction within the water column for these gases, whether the ocean acts as a source or sink, and whether temporal and spatial variations in saturation anomalies are observed. A current estimate of the marine contribution to the total atmospheric flux of these gases, which often highlights the significance of the oceans in biogeochemical cycling of trace gases, is provided, and how air–sea gas fluxes may change in the future is briefly assessed.

Multiple environmental controls on phytoplankton growth strategies determine adaptive responses of the N : P ratio

The controls on the 'Redfield' N:P stoichiometry of marine phytoplankton and hence the N:P ratio of the deep ocean remain incompletely understood. Here, we use a model for phytoplankton ecophysiology and growth, based on functional traits and resource-allocation trade-offs, to show how environmental filtering, biotic interactions, and element cycling in a global ecosystem model determine phytoplankton biogeography, growth strategies and macromolecular composition. Emergent growth strategies capture major observed patterns in marine biomes. Using a new synthesis of experimental RNA and protein measurements to constrain per-ribosome translation rates, we determine a spatially variable lower limit on adaptive rRNA:protein allocation and hence on the relationship between the largest cellular P and N pools. Comparison with the lowest observed phytoplankton N:P ratios and N:P export fluxes in the Southern Ocean suggests that additional contributions from phospholipid and phosphorus storage compounds play a fundamental role in determining the marine biogeochemical cycling of these elements.

Conceptual Ecological Modelling of Sublittoral Rock Habitats to Inform Indicator Selection

The purpose of this study is to produce a series of Conceptual Ecological Models (CEMs) that represent sublittoral rock habitats in the UK. CEMs are diagrammatic representations of the influences and processes that occur within an ecosystem. They can be used to identify critical aspects of an ecosystem that may be studied further, or serve as the basis for the selection of indicators for environmental monitoring purposes. The models produced by this project are control diagrams, representing the unimpacted state of the environment free from anthropogenic pressures. It is intended that the models produced by this project will be used to guide indicator selection for the monitoring of this habitat in UK waters. CEMs may eventually be produced for a range of habitat types defined under the UK Marine Biodiversity Monitoring R&D Programme (UKMBMP), which, along with stressor models, are designed to show the interactions within impacted habitats, would form the basis of a robust method for indicator selection. This project builds on the work to develop CEMs for shallow sublittoral coarse sediment habitats (Alexander et al 2014). The project scope included those habitats defined as ‘sublittoral rock’. This definition includes those habitats that fall into the EUNIS Level 3 classifications A3.1 Atlantic and Mediterranean high energy infralittoral rock, A3.2 Atlantic and Mediterranean moderate energy infralittoral rock, A3.3 Atlantic and Mediterranean low energy infralittoral rock, A4.1 Atlantic and Mediterranean high energy circalittoral rock, A4.2 Atlantic and Mediterranean moderate energy circalittoral rock, and A4.3 Atlantic and Mediterranean low energy circalittoral rock as well as the constituent Level 4 and 5 biotopes that are relevant to UK waters. A species list of characterising fauna to be included within the scope of the models was identified using an iterative process to refine the full list of species found within the relevant Level 5 biotopes. A literature review was conducted using a pragmatic and iterative approach to gather evidence regarding species traits and information that would be used to inform the models and characterise the interactions that occur within the sublittoral rock habitat. All information gathered during the literature review was entered into a data logging pro-forma spreadsheet that accompanies this report. Wherever possible, attempts were made to collect information from UK-specific peer-reviewed studies, although other sources were used where necessary. All data gathered was subject to a detailed confidence assessment. Expert judgement by the project team was utilised to provide information for aspects of the models for which references could not be sourced within the project timeframe. A multivariate analysis approach was adopted to assess ecologically similar groups (based on ecological and life history traits) of fauna from the identified species to form the basis of the models. A model hierarchy was developed based on these ecological groups. One general control model was produced that indicated the high-level drivers, inputs, biological assemblages, ecosystem processes and outputs that occur in sublittoral rock habitats. In addition to this, seven detailed sub-models were produced, which each focussed on a particular ecological group of fauna within the habitat: ‘macroalgae’, ‘temporarily or permanently attached active filter feeders’, ‘temporarily or permanently attached passive filter feeders’, ‘bivalves, brachiopods and other encrusting filter feeders’, ‘tube building fauna’, ‘scavengers and predatory fauna’, and ‘non-predatory mobile fauna’. Each sub-model is accompanied by an associated confidence model that presents confidence in the links between each model component. The models are split into seven levels and take spatial and temporal scale into account through their design, as well as magnitude and direction of influence. The seven levels include regional to global drivers, water column processes, local inputs/processes at the seabed, habitat and biological assemblage, output processes, local ecosystem functions, and regional to global ecosystem functions. The models indicate that whilst the high level drivers that affect each ecological group are largely similar, the output processes performed by the biota and the resulting ecosystem functions vary both in number and importance between groups. Confidence within the models as a whole is generally high, reflecting the level of information gathered during the literature review. Physical drivers which influence the ecosystem were found to be of high importance for the sublittoral rock habitat, with factors such as wave exposure, water depth and water currents noted to be crucial in defining the biological assemblages. Other important factors such as recruitment/propagule supply, and those which affect primary production, such as suspended sediments, light attenuation and water chemistry and temperature, were also noted to be key and act to influence the food sources consumed by the biological assemblages of the habitat, and the biological assemblages themselves. Output processes performed by the biological assemblages are variable between ecological groups depending on the specific flora and fauna present and the role they perform within the ecosystem. Of particular importance are the outputs performed by the macroalgae group, which are diverse in nature and exert influence over other ecological groups in the habitat. Important output processes from the habitat as a whole include primary and secondary production, bioengineering, biodeposition (in mixed sediment habitats) and the supply of propagules; these in turn influence ecosystem functions at the local scale such as nutrient and biogeochemical cycling, supply of food resources, sediment stability (in mixed sediment habitats), habitat provision and population and algae control. The export of biodiversity and organic matter, biodiversity enhancement and biotope stability are the resulting ecosystem functions that occur at the regional to global scale. Features within the models that are most useful for monitoring habitat status and change due to natural variation have been identified, as have those that may be useful for monitoring to identify anthropogenic causes of change within the ecosystem. Biological, physical and chemical features of the ecosystem have been identified as potential indicators to monitor natural variation, whereas biological factors and those physical /chemical factors most likely to affect primary production have predominantly been identified as most likely to indicate change due to anthropogenic pressures.

Assessing the sensitivity of subtidal sedimentary habitats to pressures associated with marine activities. Phase 1 Report: Rationale and proposed ecological groupings for Level 5 biotopes against which sensitivity assessments would be best undertaken.

The purpose of this study is to produce a series of Conceptual Ecological Models (CEMs) that represent sublittoral rock habitats in the UK. CEMs are diagrammatic representations of the influences and processes that occur within an ecosystem. They can be used to identify critical aspects of an ecosystem that may be studied further, or serve as the basis for the selection of indicators for environmental monitoring purposes. The models produced by this project are control diagrams, representing the unimpacted state of the environment free from anthropogenic pressures. It is intended that the models produced by this project will be used to guide indicator selection for the monitoring of this habitat in UK waters. CEMs may eventually be produced for a range of habitat types defined under the UK Marine Biodiversity Monitoring R&D Programme (UKMBMP), which, along with stressor models, are designed to show the interactions within impacted habitats, would form the basis of a robust method for indicator selection. This project builds on the work to develop CEMs for shallow sublittoral coarse sediment habitats (Alexander et al 2014). The project scope included those habitats defined as ‘sublittoral rock’. This definition includes those habitats that fall into the EUNIS Level 3 classifications A3.1 Atlantic and Mediterranean high energy infralittoral rock, A3.2 Atlantic and Mediterranean moderate energy infralittoral rock, A3.3 Atlantic and Mediterranean low energy infralittoral rock, A4.1 Atlantic and Mediterranean high energy circalittoral rock, A4.2 Atlantic and Mediterranean moderate energy circalittoral rock, and A4.3 Atlantic and Mediterranean low energy circalittoral rock as well as the constituent Level 4 and 5 biotopes that are relevant to UK waters. A species list of characterising fauna to be included within the scope of the models was identified using an iterative process to refine the full list of species found within the relevant Level 5 biotopes. A literature review was conducted using a pragmatic and iterative approach to gather evidence regarding species traits and information that would be used to inform the models and characterise the interactions that occur within the sublittoral rock habitat. All information gathered during the literature review was entered into a data logging pro-forma spreadsheet that accompanies this report. Wherever possible, attempts were made to collect information from UK-specific peer-reviewed studies, although other sources were used where necessary. All data gathered was subject to a detailed confidence assessment. Expert judgement by the project team was utilised to provide information for aspects of the models for which references could not be sourced within the project timeframe. A multivariate analysis approach was adopted to assess ecologically similar groups (based on ecological and life history traits) of fauna from the identified species to form the basis of the models. A model hierarchy was developed based on these ecological groups. One general control model was produced that indicated the high-level drivers, inputs, biological assemblages, ecosystem processes and outputs that occur in sublittoral rock habitats. In addition to this, seven detailed sub-models were produced, which each focussed on a particular ecological group of fauna within the habitat: ‘macroalgae’, ‘temporarily or permanently attached active filter feeders’, ‘temporarily or permanently attached passive filter feeders’, ‘bivalves, brachiopods and other encrusting filter feeders’, ‘tube building fauna’, ‘scavengers and predatory fauna’, and ‘non-predatory mobile fauna’. Each sub-model is accompanied by an associated confidence model that presents confidence in the links between each model component. The models are split into seven levels and take spatial and temporal scale into account through their design, as well as magnitude and direction of influence. The seven levels include regional to global drivers, water column processes, local inputs/processes at the seabed, habitat and biological assemblage, output processes, local ecosystem functions, and regional to global ecosystem functions. The models indicate that whilst the high level drivers that affect each ecological group are largely similar, the output processes performed by the biota and the resulting ecosystem functions vary both in number and importance between groups. Confidence within the models as a whole is generally high, reflecting the level of information gathered during the literature review. Physical drivers which influence the ecosystem were found to be of high importance for the sublittoral rock habitat, with factors such as wave exposure, water depth and water currents noted to be crucial in defining the biological assemblages. Other important factors such as recruitment/propagule supply, and those which affect primary production, such as suspended sediments, light attenuation and water chemistry and temperature, were also noted to be key and act to influence the food sources consumed by the biological assemblages of the habitat, and the biological assemblages themselves. Output processes performed by the biological assemblages are variable between ecological groups depending on the specific flora and fauna present and the role they perform within the ecosystem. Of particular importance are the outputs performed by the macroalgae group, which are diverse in nature and exert influence over other ecological groups in the habitat. Important output processes from the habitat as a whole include primary and secondary production, bioengineering, biodeposition (in mixed sediment habitats) and the supply of propagules; these in turn influence ecosystem functions at the local scale such as nutrient and biogeochemical cycling, supply of food resources, sediment stability (in mixed sediment habitats), habitat provision and population and algae control. The export of biodiversity and organic matter, biodiversity enhancement and biotope stability are the resulting ecosystem functions that occur at the regional to global scale. Features within the models that are most useful for monitoring habitat status and change due to natural variation have been identified, as have those that may be useful for monitoring to identify anthropogenic causes of change within the ecosystem. Biological, physical and chemical features of the ecosystem have been identified as potential indicators to monitor natural variation, whereas biological factors and those physical /chemical factors most likely to affect primary production have predominantly been identified as most likely to indicate change due to anthropogenic pressures.

Ocean acidification with (de)eutrophication will alter future phytoplankton growth and succession.

Human activity causes ocean acidification (OA) though the dissolution of anthropogenically generated CO2 into seawater, and eutrophication through the addition of inorganic nutrients. Eutrophication increases the phytoplankton biomass that can be supported during a bloom, and the resultant uptake of dissolved inorganic carbon during photosynthesis increases water-column pH (bloom-induced basification). This increased pH can adversely affect plankton growth. With OA, basification commences at a lower pH. Using experimental analyses of the growth of three contrasting phytoplankton under different pH scenarios, coupled with mathematical models describing growth and death as functions of pH and nutrient status, we show how different conditions of pH modify the scope for competitive interactions between phytoplankton species. We then use the models previously configured against experimental data to explore how the commencement of bloom-induced basification at lower pH with OA, and operating against a background of changing patterns in nutrient loads, may modify phytoplankton growth and competition. We conclude that OA and changed nutrient supply into shelf seas with eutrophication or de-eutrophication (the latter owing to pollution control) has clear scope to alter phytoplankton succession, thus affecting future trophic dynamics and impacting both biogeochemical cycling and fisheries.

Next Generation Sequencing Reveals the Hidden Diversity of Zooplankton Assemblages

Zooplankton play an important role in our oceans, in biogeochemical cycling and providing a food source for commercially important fish larvae. However, difficulties in correctly identifying zooplankton hinder our understanding of their roles in marine ecosystem functioning, and can prevent detection of long term changes in their community structure. The advent of massively parallel next generation sequencing technology allows DNA sequence data to be recovered directly from whole community samples. Here we assess the ability of such sequencing to quantify richness and diversity of a mixed zooplankton assemblage from a productive time series site in the Western English Channel. Methodology/Principle Findings Plankton net hauls (200 µm) were taken at the Western Channel Observatory station L4 in September 2010 and January 2011. These samples were analysed by microscopy and metagenetic analysis of the 18S nuclear small subunit ribosomal RNA gene using the 454 pyrosequencing platform. Following quality control a total of 419,041 sequences were obtained for all samples. The sequences clustered into 205 operational taxonomic units using a 97% similarity cut-off. Allocation of taxonomy by comparison with the National Centre for Biotechnology Information database identified 135 OTUs to species level, 11 to genus level and 1 to order, <2.5% of sequences were classified as unknowns. By comparison a skilled microscopic analyst was able to routinely enumerate only 58 taxonomic groups. Conclusions Metagenetics reveals a previously hidden taxonomic richness, especially for Copepoda and hard-to-identify meroplankton such as Bivalvia, Gastropoda and Polychaeta. It also reveals rare species and parasites. We conclude that Next Generation Sequencing of 18S amplicons is a powerful tool for elucidating the true diversity and species richness of zooplankton communities. While this approach allows for broad diversity assessments of plankton it may become increasingly attractive in future if sequence reference libraries of accurately identified individuals are better populated.

Phaeocystis colony distribution in the North Atlantic Ocean since 1948, and interpretation of long-term changes in the Phaeocystis hotspot in the North Sea

Monitoring of Phaeocystis since 1948 during the Continuous Plankton Recorder survey indicates that over the last 5.5 decades the distribution of its colonies in the North Atlantic Ocean was not restricted to neritic waters: occurrence was also recorded in the open Atlantic regions sampled, most frequently in the spring. Apparently, environmental conditions in open ocean waters, also those far oVshore, are suitable for complete lifecycle development of colonies (the only stage recorded in the survey). In the North Sea the frequency of occurrence was also highest in spring. Its southeastern part was the Phaeocystis abundance hotspot of the whole area covered by the survey. Frequency was especially high before the 1960s and after the 1980s, i.e., in the periods when anthropogenic nutrient enrichment was relatively low. Changes in eutrophication have obviously not been a major cause of long-term Phaeocystis variation in the southeastern North Sea, where total phytoplankton biomass was related signiWcantly to river discharge. Evidence is presented for the suggestion that Phaeocystis abundance in the southern North Sea is to a large extent determined by the amount of Atlantic Ocean water Xushed in through the Dover Strait. Since Phaeocystis plays a key role in element Xuxes relevant to climate the results presented here have implications for biogeochemical models of cycling of carbon and sulphur. Sea-to-air exchange of CO2 and dimethyl sulphide (DMS) has been calculated on the basis of measurements during single-year cruises. The considerable annual variation in phytoplankton and in its Phaeocystis component reported here does not warrant extrapolation of such figures.