Accumulation of aluminium by the freshwater crustacean Asellus aquaticus in neutral water

This study examined the accumulation of aluminium (Al), mostly as the insoluble (Al(OH)(3)) species, by the freshwater crustacean Asellus aquaticus at neutral pH. Animals were exposed to a range of Al concentrations (5-356 microg l(-1)) in three experiments. The first two were of 30 and 50 days duration, respectively, followed by transfer of the A. aquaticus to water containing no Al for 20 days. The third used live and dead animals in order to investigate the contribution made by surface adsorption of Al to the total accumulated. Significant accumulation of Al in the whole tissues occurred by day 10 in all animals in the 30- and 50- day experiment. Peak concentrations of Al were measured in animals between days 10 and 20 with high concentration factors ranging from 1.4 x 10(4) to 5.5 x 10(3). By day 30, accumulated Al had fallen but was still significantly greater than the control in the 50- day exposure experiment. This 30- day increase followed by decreased accumulation of Al was repeated over the remaining exposure period (i.e. 30-50 days) although rates of uptake and loss and peak tissue levels of Al were higher. Proportionality between environmental (water) and tissue concentrations of Al occurred at day 20 but not at day 45. Significantly more Al was accumulated by dead animals than live animals at all Al exposure concentrations. These results suggest that Al is available to the crustacean at neutral pH and that the cuticle may provide an important site of uptake.     

Where the wild things are: predicting hotspots of seabird aggregations in the California Current System

Marine protected areas (MPAs) provide an important tool for conservation of marine ecosystems. To be most effective, these areas should be strategically located in a manner that supports ecosystem function. To inform marine spatial planning and support strategic establishment of MPAs within the California Current System, we identified areas predicted to support multispecies aggregations of seabirds ("hotspots"). We developed habitat-association models for 16 species using information from at-sea observations collected over an 11-year period (1997-2008), bathymetric data, and remotely sensed oceanographic data for an area from north of Vancouver Island, Canada, to the USA/Mexico border and seaward 600 km from the coast. This approach enabled us to predict distribution and abundance of seabirds even in areas of few or no surveys. We developed single-species predictive models using a machine-learning algorithm: bagged decision trees. Single-species predictions were then combined to identify potential hotspots of seabird aggregation, using three criteria: (1) overall abundance among species, (2) importance of specific areas ("core areas") to individual species, and (3) predicted persistence of hotspots across years. Model predictions were applied to the entire California Current for four seasons (represented by February, May, July, and October) in each of 11 years. Overall, bathymetric variables were often important predictive variables, whereas oceanographic variables derived from remotely sensed data were generally less important. Predicted hotspots often aligned with currently protected areas (e.g., National Marine Sanctuaries), but we also identified potential hotspots in Northern California/Southern Oregon (from Cape Mendocino to Heceta Bank), Southern California (adjacent to the Channel Islands), and adjacent to Vancouver Island, British Columbia, that are not currently included in protected areas. Prioritization and identification of multispecies hotspots will depend on which group of species is of highest management priority. Modeling hotspots at a broad spatial scale can contribute to MPA site selection, particularly if complemented by fine-scale information for focal areas.