Winter weather controls net influx of atmospheric CO2 on the north-west European shelf

Shelf seas play an important role in the global carbon cycle, absorbing atmospheric carbon dioxide (CO2) and exporting carbon (C) to the open ocean and sediments. The magnitude of these processes is poorly constrained, because observations are typically interpolated over multiple years. Here, we used 298500 observations of CO2 fugacity (fCO2) from a single year (2015), to estimate the net influx of atmospheric CO2 as 26.2 ± 4.7 Tg C yr-1 over the open NW European shelf. CO2 influx from the atmosphere was dominated by influx during winter as a consequence of high winds, despite a smaller, thermally-driven, air-sea fCO2 gradient compared to the larger, biologically-driven summer gradient. In order to understand this climate regulation service, we constructed a carbon-budget supplemented by data from the literature, where the NW European shelf is treated as a box with carbon entering and leaving the box. This budget showed that net C-burial was a small sink of 1.3 ± 3.1 Tg C yr-1, while CO2 efflux from estuaries to the atmosphere, removed the majority of river C-inputs. In contrast, the input from the Baltic Sea likely contributes to net export via the continental shelf pump and advection (34.4 ± 6.0 Tg C yr-1)

Contrasting resource allocation patterns in Sedum lanceolatum Torr.: Biomass versus energy estimates

Biomass determinations and microbomb calorimetry were used to assess resource allocation in Sedum lanceolatum Torr. between 2,257 and 3,726 m above sea level in the Front Range of the Rocky Mountains, Colorado, USA. In general, energy values did not differ within a tissue among sites, but did differ among tissue types. Flowers and leaves had the greatest energy content per gram ashfree dry weight. Total kilojoules per plant were homogeneous along the elevational gradient.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47753/1/442_2004_Article_BF00379785.pd

Environmental sensing and response genes in cnidaria : the chemical defensome in the sea anemone Nematostella vectensis

Author Posting. © The Author(s), 2008. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Cell Biology and Toxicology 24 (2008): 483-502, doi:10.1007/s10565-008-9107-5.The starlet sea anemone Nematostella vectensis has been recently established as a new model system for the study of the evolution of developmental processes, as cnidaria occupy a key evolutionary position at the base of the bilateria. Cnidaria play important roles in estuarine and reef communities, but are exposed to many environmental stressors. Here I describe the genetic components of a ‘chemical defensome’ in the genome of N. vectensis, and review cnidarian molecular toxicology. Gene families that defend against chemical stressors and the transcription factors that regulate these genes have been termed a ‘chemical defensome,’ and include the cytochromes P450 and other oxidases, various conjugating enyzymes, the ATP-dependent efflux transporters, oxidative detoxification proteins, as well as various transcription factors. These genes account for about 1% (266/27200) of the predicted genes in the sea anemone genome, similar to the proportion observed in tunicates and humans, but lower than that observed in sea urchins. While there are comparable numbers of stress-response genes, the stress sensor genes appear to be reduced in N. vectensis relative to many model protostomes and deuterostomes. Cnidarian toxicology is understudied, especially given the important ecological roles of many cnidarian species. New genomic resources should stimulate the study of chemical stress sensing and response mechanisms in cnidaria, and allow us to further illuminate the evolution of chemical defense gene networks.WHOI Ocean Life Institute and NIH R01-ES01591

Microbial diversity in waters, sediments and microbial mats evaluated using fatty acid-based methods

The review summarises recent advances towards a greater comprehensive assessment of microbial diversity in aquatic environments using the fatty acid methyl esters and phospholipid fatty acids approaches. These methods are commonly used in microbial ecology because they do not require the culturing of micro-organisms, are quantitative and reproducible and provide valuable information regarding the structure of entire microbial communities. Because some fatty acids are associated with taxonomic and functional groups of micro-organisms, they allow particular groups of micro-organisms to be distinguished. The integration of fatty acid-based methods with stable isotopes, RNA and DNA analyses enhances our knowledge of the role of micro-organisms in global nutrient cycles, functional activity and phylogenetic lineages within microbial communities. Additionally, the analysis of fatty acid profiles enables the shifts in the microbial diversity in pristine and contaminated environments to be monitored. The main objective of this review is to present the use of lipid-based approaches for the characterisation of microbial communities in water columns, sediments and biomats

Fluxes of water, sediments, and biogeochemical compounds in salt marshes

Tidal oscillations systematically flood salt marshes, transporting water, sediments, organic matter, and biogeochemical elements such as silica. Here we present a review of recent studies on these fluxes and their effects on both ecosystem functioning and morphological evolution of salt marshes. We reexamine a simplified model for the computation of water fluxes in salt marshes that captures the asymmetry in discharge between flood and ebb. We discuss the role of storm conditions on sediment fluxes both in tidal channels and on the marsh platform. We present recent methods and field instruments for the measurement of fluxes of organic matter. These methods will provide long-term data sets with fine temporal resolution that will help scientists to close the carbon budget in salt marshes. Finally, the main processes controlling fluxes of biogenic and dissolved silica in salt marshes are explained, with particular emphasis on the uptake by marsh macrophytes and diatoms

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets