Alpine permafrost index map

The objective of this study is the production of an Alpine Permafrost Index Map (APIM) covering the entire European Alps. A unified statistical model that is based on Alpine-wide permafrost observations is used for debris and bedrock surfaces across the entire Alps. The explanatory variables of the model are mean annual air temperatures, potential incoming solar radiation and precipitation. Offset terms were applied to make model predictions for topographic and geomorphic conditions that differ from the terrain features used for model fitting. These offsets are based on literature review and involve some degree of subjective choice during model building. The assessment of the APIM is challenging because limited independent test data are available for comparison and these observations represent point information in a spatially highly variable topography. The APIM provides an index that describes the spatial distribution of permafrost and comes together with an interpretation key that helps to assess map uncertainties and to relate map contents to their actual expression in terrain. The map can be used as a first resource to estimate permafrost conditions at any given location in the European Alps in a variety of contexts such as research and spatial planning. Results show that Switzerland likely is the country with the largest permafrost area in the Alps, followed by Italy, Austria, France and Germany. Slovenia and Liechtenstein may have marginal permafrost areas. In all countries the permafrost area is expected to be larger than the glacier-covered area.

Whale sightings, group sizes and krill biomass in West Greenland in 2005

This study combined data on fin whale Balaenoptera physalus, humpback whale Megaptera novaeangliae, minke whale B. acutorostrata, and sei whale B. borealis sightings from large-scale visual aerial and ship-based surveys (248 and 157 sightings, respectively) with synoptic acoustic sampling of krill Meganyctiphanes norvegica and Thysanoessa sp. abundance in September 2005 in West Greenland to examine the relationships between whales and their prey. Krill densities were obtained by converting relationships of volume backscattering strengths at multiple frequencies to a numerical density using an estimate of krill target strength. Krill data were vertically integrated in 25 m depth bins between 0 and 300 m to obtain water column biomass (g/m**2) and translated to density surfaces using ordinary kriging. Standard regression models (Generalized Additive Modeling, GAM, and Generalized Linear Modeling, GLM) were developed to identify important explanatory variables relating the presence, absence, and density of large whales to the physical and biological environment and different survey platforms. Large baleen whales were concentrated in 3 focal areas: (1) the northern edge of Lille Hellefiske bank between 65 and 67°N, (2) north of Paamiut at 63°N, and (3) in South Greenland between 60 and 61° N. There was a bimodal pattern of mean krill density between depths, with one peak between 50 and 75 m (mean 0.75 g/m**2, SD 2.74) and another between 225 and 275 m (mean 1.2 to 1.3 g/m**2, SD 23 to 19). Water column krill biomass was 3 times higher in South Greenland than at any other site along the coast. Total depth-integrated krill biomass was 1.3 x 10**9 (CV 0.11). Models indicated the most important parameter in predicting large baleen whale presence was integrated krill abundance, although this relationship was only significant for sightings obtained on the ship survey. This suggests that a high degree of spatio-temporal synchrony in observations is necessary for quantifying predator-prey relationships. Krill biomass was most predictive of whale presence at depths >150 m, suggesting a threshold depth below which it is energetically optimal for baleen whales to forage on krill in West Greenland.

Concentrations of brominated and chlorinated contaminants in adipose tissue of polar bears (Ursus maritimus) between 1991-2007

Adipose tissue was sampled from the western Hudson Bay (WHB) subpopulation of polar bears at intervals from 1991 to 2007 to examine temporal trends of PCB and OCP levels both on an individual and sum-contaminant basis. We also determined levels and temporal trends of emerging polybrominated diphenyl ethers (PBDEs), hexabromocyclododecane (HBCD), polybrominated biphenyls (PBBs) and other current-use brominated flame retardants. Over the 17-year period, sum DDT (and p,p'-DDE, p,p'-DDD, p,p'-DDT) decreased (-8.4%/year); alpha-hexachlorocyclohexane (alpha-HCH) decreased (-11%/year); beta-HCH increased ( + 8.3%/year); and sum PCB and sum chlordane (CHL), both contaminants at highest concentrations in all years (>1 ppm), showed no distinct trends even when compared to previous data for this subpopulation dating back to 1968. Some of the less persistent PCB congeners decreased significantly (-1.6%/year to -6.3%/year), whereas CB153 levels tended to increase (+ 3.3%/year). Parent CHLs (c-nonachlor, t-nonachlor) declined, whereas non-monotonic trends were detected for metabolites (heptachlor epoxide, oxychlordane). sum chlorobenzene, octachlorostyrene, sum mirex, sum MeSO2-PCB and dieldrin did not significantly change. Increasing sum PBDE levels (+13%/year) matched increases in the four consistently detected congeners, BDE47, BDE99, BDE100 and BDE153. Although no trend was observed, total-(alpha)-HBCD was only detected post-2000. Levels of the highest concentration brominated contaminant, BB153, showed no temporal change. As long-term ecosystem changes affecting contaminant levels may also affect contaminant patterns, we examined the influence of year (i.e., aging or "weathering" of the contaminant pattern), dietary tracers (carbon stable isotope ratios, fatty acid patterns) and biological (age/sex) group on congener/metabolite profiles. Patterns of PCBs, CHLs and PBDEs were correlated with dietary tracers and biological group, but only PCB and CHL patterns were correlated with year. DDT patterns were not associated with any explanatory variables, possibly related to local DDT sources. Contaminant pattern trends may be useful in distinguishing the possible role of ecological/diet changes on contaminant burdens from expected dynamics due to atmospheric sources and weathering.