Modelled spatial and seasonal distribution of Blue Whiting (Micromesistius poutassou) larvae in the North-East Atlantic (1951 to 2005)

Blue whiting (Micromesistius poutassou, http://www.marinespecies.org/aphia.php?p=taxdetails&id=126439) is a small mesopelagic planktivorous gadoid found throughout the North-East Atlantic. This data contains the results of a model-based analysis of larvae captured by the Continuous Plankton Recorder (CPR) during the period 1951-2005. The observations are analysed using Generalised Additive Models (GAMs) of the the spatial, seasonal and interannual variation in the occurrence of larvae. The best fitting model is chosen using the Aikaike Information Criteria (AIC). The probability of occurrence in the continous plankton recorder is then normalised and converted to a probability distribution function in space (UTM projection Zone 28) and season (day of year). The best fitting model splits the distribution into two separate spawning grounds north and south of a dividing line at 53 N. The probability distribution is therefore normalised in these two regions (ie the space-time integral over each of the two regions is 1). The modelled outputs are on a UTM Zone 28 grid: however, for convenience, the latitude ("lat") and longitude ("lon") of each of these grid points are also included as a variable in the NetCDF file. The assignment of each grid point to either the Northern or Southern component (defined here as north/south of 53 N), is also included as a further variable ("component"). Finally, the day of year ("doy") is stored as the number of days elapsed from and included January 1 (ie doy=1 on January 1) - the year is thereafter divided into 180 grid points.

Abundance, biomass and secondary production of benthic megafauna on the Barents Sea shelf in 2008 and 2009

Megabenthos plays a major role in the overall energy flow on Arctic shelves, but information on megabenthic secondary production on large spatial scales is scarce. Here, we estimated for the first time megabenthic secondary production for the entire Barents Sea shelf by applying a species-based empirical model to an extensive dataset from the joint Norwegian? Russian ecosystem survey. Spatial patterns and relationships were analyzed within a GIS. The environmental drivers behind the observed production pattern were identified by applying an ordinary least squares regression model. Geographically weighted regression (GWR) was used to examine the varying relationship of secondary production and the environment on a shelfwide scale. Significantly higher megabenthic secondary production was found in the northeastern, seasonally ice-covered regions of the Barents Sea than in the permanently ice-free southwest. The environmental parameters that significantly relate to the observed pattern are bottom temperature and salinity, sea ice cover, new primary production, trawling pressure, and bottom current speed. The GWR proved to be a versatile tool for analyzing the regionally varying relationships of benthic secondary production and its environmental drivers (R² = 0.73). The observed pattern indicates tight pelagic? benthic coupling in the realm of the productive marginal ice zone. Ongoing decrease of winter sea ice extent and the associated poleward movement of the seasonal ice edge point towards a distinct decline of benthic secondary production in the northeastern Barents Sea in the future.

Biogenic opal, biogenic barium, calcium carbonate and U/Th isotope data from core-top sediments of SONNE cruise SO202 (INOPEX)

Sedimentary proxies used to reconstruct marine productivity suffer from variable preservation and are sensitive to factors other than productivity. Therefore, proxy calibration is warranted. Here we map the spatial patterns of two paleoproductivity proxies, biogenic opal and barium fluxes, from a set of core-top sediments recovered in the Subarctic North Pacific. Comparisons of the proxy data with independent estimates of primary and export production, surface water macronutrient concentrations and biological pCO2 drawdown indicate that neither proxy shows a significant correlation with primary or export productivity for the entire region. Biogenic opal fluxes, when corrected for preservation using 230Th-normalized accumulation rates, show a good correlation with primary productivity along the volcanic arcs (tau = 0.71, p = 0.0024) and with export productivity throughout the western Subarctic North Pacific (tau = 0.71, p = 0.0107). Moderate and good correlations of biogenic barium flux with export production (tau = 0.57, p = 0.0022) and with surface water silicate concentrations (tau = 0.70, p = 0.0002) are observed for the central and eastern Subarctic North Pacific. For reasons unknown, however, no correlation is found in the western Subarctic North Pacific between biogenic barium flux and the reference data. Nonetheless, we show that barite saturation, uncertainty in the lithogenic barium corrections and problems with the reference datasets are not responsible for the lack of a significant correlation between biogenic barium flux and the reference data. Further studies evaluating the factors controlling the variability of the biogenic constituents in the sediments are desirable in this region.