Temporal and along-shelf distribution of the larval fish assemblage at Gran Canaria, Canary Islands

Temporal and spatial variations of the larval fish community off the island of Gran Canaria (Canary Islands) were studied in weekly surveys from October 2005 to June 2006. A total of 156 taxa, belonging to 51 families and 15 orders, were identified. Myctophidae was by far the most abundant family (30%), followed by Sparidae (11%), Clupeidae (9%) and Gonostomatidae (7%). As expected for an oceanic island, neritic and oceanic taxa contributed in similar proportions. Leeward and windward retention areas were found for total egg and neritic larval abundance. However, seasonality showed a stronger influence on the annual larval assemblage than sampling site, as the latter was not significant on a long time scale. Results suggest that there are two seasonal larval assemblages corresponding to the two main characteristic periods of the water column in these waters: mixing (winter) and stratification (summer). In addition, a significant relationship was recorded between lunar illumination and small mesozooplankton biomass, suggesting that this relationship may be extended to certain neritic families. The most abundant neritic larvae (Sparidae) showed this lunar pattern, which partially supports a recent hypothesis about the effect of lunar illumination on larval fish survival and development in subtropical waters.

Estimating the monthly pCO2 distribution in the North Atlantic using a self-organizing neural network

[EN] Here we present monthly, basin-wide maps of the partial pressure of carbon dioxide (pCO2) for the North Atlantic on a latitude by longitude grid for years 2004 through 2006 inclusive. The maps have been computed using a neural network technique which reconstructs the non-linear relationships between three biogeochemical parameters and marine pCO2. A self organizing map (SOM) neural network has been trained using 389 000 triplets of the SeaWiFSMODIS chlorophyll-a concentration, the NCEP/NCAR reanalysis sea surface temperature, and the FOAM mixed layer depth. The trained SOM was labelled with 137 000 underway pCO2 measurements collected in situ during 2004, 2005 and 2006 in the North Atlantic, spanning the range of 208 to 437atm. The root mean square error (RMSE) of the neural network fit to the data is 11.6?atm, which equals to just above 3 per cent of an average pCO2 value in the in situ dataset. The seasonal pCO2 cycle as well as estimates of the interannual variability in the major biogeochemical provinces are presented and discussed. High resolution combined with basin-wide coverage makes the maps a useful tool for several applications such as the monitoring of basin-wide air-sea CO2 fluxes or improvement of seasonal and interannual marine CO2 cycles in future model predictions. The method itself is a valuable alternative to traditional statistical modelling techniques used in geosciences.