Sediment organic matter, grain size, and results of prediction models from northern North Atlantic and Arctic Seas

Sediment samples and hydrographic conditions were studied at 28 stations around Iceland. At these sites, Conductivity-Temperature-Depth (CTD) casts were conducted to collect hydrographic data and multicorer casts were conductd to collect data on sediment characteristics including grain size distribution, carbon and nitrogen concentration, and chloroplastic pigment concentration. A total of 14 environmental predictors were used to model sediment characteristics around Iceland on regional geographic space. For these, two approaches were used: Multivariate Adaptation Regression Splines (MARS) and randomForest regression models. RandomForest outperformed MARS in predicting grain size distribution. MARS models had a greater tendency to over- and underpredict sediment values in areas outside the environmental envelope defined by the training dataset. We provide first GIS layers on sediment characteristics around Iceland, that can be used as predictors in future models. Although models performed well, more samples, especially from the shelf areas, will be needed to improve the models in future.

Biogenic and lithogenic silica, silicic acid, production of biogenic silica, and irradiance measurements during the Ross Sea Bloom Project 1994-1996

The development of the seasonal phytoplankton bloom in the Ross Sea was studied during two cruises. The first, conducted in November-December 1994, investigated the initiation and rapid growth of the bloom, whereas the second (December 1995-January 1996) concentrated on the bloom's maximum biomass period and the subsequent decline in biomass. Central to the understanding of the controls of growth and the summer decline of the bloom is a quantitative assessment of the growth rate of phytoplankton. Growth rates were estimated over two time scales with different methods. The first estimated daily growth rates from isotropic incorporation under simulated in situ conditions, including 14C, 15N and 32Si uptake measurements combined with estimates of standing stocks of particulate organic carbon, nitrogen and biogenic silica. The second method used daily to weekly changes in biomass at selected locations, with net growth rates being estimated from changes in standing stocks of phytoplankton. In addition, growth rates were estimated in large-volume experiments under optimal irradiances. Growth rates showed distinct temporal patterns. Early in the growing season, short-term estimates suggested that growth rates of in situ assemblages were less than maximum (relative to the temperature-limited maximum) and were likely reduced due to low irradiance regimes encountered under the ice. Growth rates increased thereafter and appeared to reach their maximum as biomass approached the seasonal peak, but decreased markedly in late December. Differences between the major taxonomic groups present were also noted, especially from the isotopic tracer experiments. The haplophyte Phaeocystic antarctica was dominant in 1994 throughout the growing season, and it exhibited the greatest growth rates (mean 0.41/day) during spring. Diatom standing stocks were low early in the growing season, and growth rates averaged 0.100/day. In summer diatoms were more abundant, but their growth rates remained much lower (mean of 0.08/day) than the potential maximum. Understanding growth rate controls is essential to the development of predictive models of the carbon cycle and food webs in Antarctic waters.

Secchi depth maps for the Baltic Sea, 2003-2012, with links to GeoTIFs

Secchi depth is a measure of water transparency. In the Baltic Sea region, Secchi depth maps are used to assess eutrophication and as input for habitat models. Due to their spatial and temporal coverage, satellite data would be the most suitable data source for such maps. But the Baltic Sea's optical properties are so different from the open ocean that globally calibrated standard models suffer from large errors. Regional predictive models that take the Baltic Sea's special optical properties into account are thus needed. This paper tests how accurately generalized linear models (GLMs) and generalized additive models (GAMs) with MODIS/Aqua and auxiliary data as inputs can predict Secchi depth at a regional scale. It uses cross-validation to test the prediction accuracy of hundreds of GAMs and GLMs with up to 5 input variables. A GAM with 3 input variables (chlorophyll a, remote sensing reflectance at 678 nm, and long-term mean salinity) made the most accurate predictions. Tested against field observations not used for model selection and calibration, the best model's mean absolute error (MAE) for daily predictions was 1.07 m (22%), more than 50% lower than for other publicly available Baltic Sea Secchi depth maps. The MAE for predicting monthly averages was 0.86 m (15%). Thus, the proposed model selection process was able to find a regional model with good prediction accuracy. It could be useful to find predictive models for environmental variables other than Secchi depth, using data from other satellite sensors, and for other regions where non-standard remote sensing models are needed for prediction and mapping. Annual and monthly mean Secchi depth maps for 2003-2012 come with this paper as Supplementary materials.

Mineral and oxygen isotopic compositions in a profile through the altered lower oceanic crust, ODP Hole 176-735B

ODP Hole 735B located on the Southwest Indian Ridge at 57°E is an in situ sampled long, continuous section of lower oceanic crust. Oxygen isotope compositions of constituent minerals of Leg 176 gabbros have been measured by UV-laser oxygen isotope microprobe. Together with existing data from Leg 118, a complete oxygen isotope profile through the lower oceanic crust has been obtained. Most clinopyroxenes and olivines have normal mantle values of ~5.5 per mil and ~5.2 per mil, respectively, while plagioclases show slight d18O enrichment relative to its mantle value of 6.1per mil. Down-hole variations of Hole 735B gabbro indicate a downward decreasing d18O profile, with a kink at a depth of about 800 m below sea floor. Above this depth, gabbros are depleted in 18O relative to unaltered basalts, while below ~800 m they show nearly unmodified d18O values. Abundant seawater penetration appears to be limited to the upper part of the lower crust at ODP site 735 (~800 m into the gabbroic layer and ~2-2.5 km into the oceanic crust from the top of pillow basalts). Mass balance calculations show that the lower crust formed under this ultra-slow-spreading ridge has an average d18O value of 5.5 per mil. The whole crust at Site 735 has an overall 18O enrichment with d18O values of 6.0 per mil to 7.8 per mil, depending on the possible variation of the d18O values of the upper pillow basalts and sheeted dykes. The apparent difference in oxygen isotope compositions of ocean crusts formed with different spreading rates has important implications on the buffering of ocean water over geological time, as well as on the oxygen recycling between crust and mantle through subduction. The difference of seawater penetration between fast- and slow-spreading ridges could be related to their particular magmatic-tectonic history during the formation and aging of the crust. However, more analyses on continuous sections through oceanic and ophiolitic crust in different tectonic settings are required to derive any predictive models.

Density of alkyl esters and its mixtures

Biodiesel density is a key parameter in biodiesel simulations and process development. In this work we selected, evaluated and improved two density models, one theoretical (Rackett-Soave) and one empirical (Lapuerta's method) for methanol based biodiesels (FAME) and ethanol based biodiesel (FAEE). For this purpose, biodiesel was produced from vegetable oils (sunflower, rapeseed, soybean, olive, safflower and other two commercial mixtures of vegetable oils) and animal fats (edible and crude pork fat and beef tallow) using both methanol and ethanol for the transesterification reactions, and blended to get 21 FAME and 21 FAEE, reporting their density and detailed composition. Bibliographic data have also been used. The Rackett-Soave method has been improved by the use of a new acentric factor correlation, whereas the parameters of the empirical one are improved by considering a bigger density data bank. Results show that the evaluated models could be used to estimate the biodiesel density with a good grade of accuracy but the performed modifications improve the accuracy of the models: ARD (%) for FAME; 0.33, and FAEE; 0.26, both calculated with the modification of Rackett-Soave method and ARD (%) for FAME; 0.40 calculated with the modification of the Lapuerta's method).

Coral-algae metabolism and diurnal changes in the CO2-carbonate system of bulk sea water

Precise measurements were conducted in continuous flow seawater mesocosms located in full sunlight that compared metabolic response of coral, coral-macroalgae and macroalgae systems over a diurnal cycle. Irradiance controlled net photosynthesis (Pnet), which in turn drove net calcification (Gnet), and altered pH. Pnet exerted the dominant control on [CO3]2- and aragonite saturation state (Omega arag) over the diel cycle. Dark calcification rate decreased after sunset, reaching zero near midnight followed by an increasing rate that peaked at 03:00 h. Changes in Omega arag and pH lagged behind Gnet throughout the daily cycle by two or more hours. The flux rate Pnet was the primary driver of calcification. Daytime coral metabolism rapidly removes dissolved inorganic carbon (DIC) from the bulk seawater and photosynthesis provides the energy that drives Gnet while increasing the bulk water pH. These relationships result in a correlation between Gnet and Omega arag, with Omega arag as the dependent variable. High rates of H+ efflux continued for several hours following mid-day peak Gnet suggesting that corals have difficulty in shedding waste protons as described by the Proton Flux Hypothesis. DIC flux (uptake) followed Pnet and Gnet and dropped off rapidly following peak Pnet and peak Gnet indicating that corals can cope more effectively with the problem of limited DIC supply compared to the problem of eliminating H+. Over a 24 h period the plot of total alkalinity (AT) versus DIC as well as the plot of Gnet versus Omega arag revealed a circular hysteresis pattern over the diel cycle in the coral and coral-algae mesocosms, but not the macroalgae mesocosm. Presence of macroalgae did not change Gnet of the corals, but altered the relationship between Omega arag and Gnet. Predictive models of how future global changes will effect coral growth that are based on oceanic Omega arag must include the influence of future localized Pnet on Gnet and changes in rate of reef carbonate dissolution. The correlation between Omega arag and Gnet over the diel cycle is simply the response of the CO2-carbonate system to increased pH as photosynthesis shifts the equilibria and increases the [CO3]2- relative to the other DIC components of [HCO3]- and [CO2]. Therefore Omega arag closely tracked pH as an effect of changes in Pnet, which also drove changes in Gnet. Measurements of DIC flux and H+ flux are far more useful than concentrations in describing coral metabolism dynamics. Coral reefs are systems that exist in constant disequilibrium with the water column.

Results of wave modelling for Moreton Bay, Southeast Queensland, and a reef lagoon off Lizard Island, Great Barrier Reef, Australia

The distribution, abundance, behaviour, and morphology of marine species is affected by spatial variability in the wave environment. Maps of wave metrics (e.g. significant wave height Hs, peak energy wave period Tp, and benthic wave orbital velocity URMS) are therefore useful for predictive ecological models of marine species and ecosystems. A number of techniques are available to generate maps of wave metrics, with varying levels of complexity in terms of input data requirements, operator knowledge, and computation time. Relatively simple "fetch-based" models are generated using geographic information system (GIS) layers of bathymetry and dominant wind speed and direction. More complex, but computationally expensive, "process-based" models are generated using numerical models such as the Simulating Waves Nearshore (SWAN) model. We generated maps of wave metrics based on both fetch-based and process-based models and asked whether predictive performance in models of benthic marine habitats differed. Predictive models of seagrass distribution for Moreton Bay, Southeast Queensland, and Lizard Island, Great Barrier Reef, Australia, were generated using maps based on each type of wave model. For Lizard Island, performance of the process-based wave maps was significantly better for describing the presence of seagrass, based on Hs, Tp, and URMS. Conversely, for the predictive model of seagrass in Moreton Bay, based on benthic light availability and Hs, there was no difference in performance using the maps of the different wave metrics. For predictive models where wave metrics are the dominant factor determining ecological processes it is recommended that process-based models be used. Our results suggest that for models where wave metrics provide secondarily useful information, either fetch- or process-based models may be equally useful.