Technical Note: Ensuring consistent, global measurements of short-lived halocarbon gases in the ocean and atmosphere

Very short-lived halocarbons are significant sources of reactive halogen in the marine boundary layer, and likely in the upper troposphere and lower stratosphere. Quantifying ambient concentrations in the surface ocean and atmosphere is essential for understanding the atmospheric impact of these trace gas fluxes. Despite the body of literature increasing substantially over recent years, calibration issues complicate the comparison of results and limit the utility of building larger-scale databases that would enable further development of the science (e.g. sea-air flux quantification, model validation, etc.). With this in mind, thirty-one scientists from both atmospheric and oceanic halocarbon communities in eight nations gathered in London in February 2008 to discuss the scientific issues and plan an international effort toward developing common calibration scales (http://tinyurl.com/c9cg58). Here, we discuss the outputs from this meeting, suggest the compounds that should be targeted initially, identify opportunities for beginning calibration and comparison efforts, and make recommendations for ways to improve the comparability of previous and future measurements.

Short-term responses of the cold water coral Lophelia pertusa to predicted rises in atmospheric CO2

Cold-water corals are associated with high local biodiversity, but despite their importance as ecosystem engineers, little is known about how these organisms will respond to projected ocean acidification. Since preindustrial times, average ocean pH has decreased from 8.2 to ~8.1, and predicted CO2 emissions will decrease by up to another 0.3 pH units by the end of the century. This decrease in pH may have a wide range of impacts upon marine life, and in particular upon calcifiers such as cold-water corals. Lophelia pertusa is the most widespread cold-water coral (CWC) species, frequently found in the North Atlantic. Here, we present the first short-term (21 days) data on the effects of increased CO2 (750 ppm) upon the metabolism of freshly collected L. pertusa from Mingulay Reef Complex, Scotland, for comparison with net calcification. Over 21 days, corals exposed to increased CO2 conditions had significantly lower respiration rates (11.4±1.39 SE, µmol O2 g−1 tissue dry weight h−1) than corals in control conditions (28.6±7.30 SE µmol O2 g−1 tissue dry weight h−1). There was no corresponding change in calcification rates between treatments, measured using the alkalinity anomaly technique and 14C uptake. The decrease in respiration rate and maintenance of calcification rate indicates an energetic imbalance, likely facilitated by utilisation of lipid reserves. These data from freshly collected L. pertusa from the Mingulay Reef Complex will help define the impact of ocean acidification upon the growth, physiology and structural integrity of this key reef framework forming species.

Late winter under ice pelagic microbial communities in the high Arctic Ocean and the impact of short-term exposure to elevated CO2 levels

Polar Oceans are natural CO2 sinks because of the enhanced solubility of CO2 in cold water. The Arctic Ocean is at additional risk of accelerated ocean acidification (OA) because of freshwater inputs from sea ice and rivers, which influence the carbonate system. Winter conditions in the Arctic are of interest because of both cold temperatures and limited CO2 venting to the atmosphere when sea ice is present. Earlier OA experiments on Arctic microbial communities conducted in the absence of ice cover, hinted at shifts in taxa dominance and diversity under lowered pH. The Catlin Arctic Survey provided an opportunity to conduct in situ, under-ice, OA experiments during late Arctic winter. Seawater was collected from under the sea ice off Ellef Ringnes Island, and communities were exposed to three CO2 levels for 6 days. Phylogenetic diversity was greater in the attached fraction compared to the free-living fraction in situ, in the controls and in the treatments. The dominant taxa in all cases were Gammaproteobacteria but acidification had little effect compared to the effects of containment. Phylogenetic net relatedness indices suggested that acidification may have decreased the diversity within some bacterial orders, but overall there was no clear trend. Within the experimental communities, alkalinity best explained the variance among samples and replicates, suggesting subtle changes in the carbonate system need to be considered in such experiments. We conclude that under ice communities have the capacity to respond either by selection or phenotypic plasticity to heightened CO2 levels over the short term.

The effects of short-term acidification on the intertidal macroalgae Palmaria palmate and Saccharina latissima

Ocean acidification, the result of increased dissolution of carbon dioxide (CO2) in seawater, is a leading subject of current research. The effects of acidification on non-calcifying macroalgae are, however, still unclear. The current study reports two 1-month studies using two different macroalgae, the red alga Palmaria palmata (Rhodophyta) and the kelp Saccharina latissima (Phaeophyta), exposed to control (pHNBS = ∼8.04) and increased (pHNBS = ∼7.82) levels of CO2-induced seawater acidification. The impacts of both increased acidification and time of exposure on net primary production (NPP), respiration (R), dimethylsulphoniopropionate (DMSP) concentrations, and algal growth have been assessed. In P. palmata, although NPP significantly increased during the testing period, it significantly decreased with acidification, whereas R showed a significant decrease with acidification only. S. latissima significantly increased NPP with acidification but not with time, and significantly increased R with both acidification and time, suggesting a concomitant increase in gross primary production. The DMSP concentrations of both species remained unchanged by either acidification or through time during the experimental period. In contrast, algal growth differed markedly between the two experiments, in that P. palmata showed very little growth throughout the experiment, while S. latissima showed substantial growth during the course of the study, with the latter showing a significant difference between the acidified and control treatments. These two experiments suggest that the study species used here were resistant to a short-term exposure to ocean acidification, with some of the differences seen between species possibly linked to different nutrient concentrations between the experiments.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.