Abstract: In vivo and in vitro evidence for detoxification of methylmercury (MeHg) as insoluble mercury selenide (HgSe) underlies the central paradigm that mercury exposure is not or little hazardous when tissue Se is in molar excess (Se:Hg > 1). However, this hypothesis overlooks the binding of Hg to selenoproteins, which lowers the amount of bioavailable Se that acts as a detoxification reservoir for MeHg, thereby underestimating the toxicity of mercury. This question was addressed by determining the chemical forms of Hg in various tissues of giant petrels Macronectes spp. using a combination of high energy-resolution X-ray absorption near edge structure and extended X-ray absorption fine structure spectroscopy, and transmission electron microscopy coupled to elemental mapping. Three main Hg species were identified, a MeHg-cysteinate complex, a four-coordinate selenocysteinate complex (Hg(Sec)4), and a HgSe precipitate, together with a minor dicysteinate complex Hg(Cys)2. The amount of HgSe decreases in the order liver > kidneys > brain = muscle, and the amount of Hg(Sec)4 in the order muscle > kidneys > brain > liver. On the basis of biochemical considerations and structural modeling, we hypothesize that Hg(Sec)4 is bound to the carboxy-terminus domain of selenoprotein P (SelP) which contains 12 Sec residues. Structural flexibility allows SelP to form multinuclear Hgx(Se,Sec)y complexes, which can be biomineralized to HgSe by protein self-assembly. Because Hg(Sec)4 has a Se:Hg molar ratio of 4:1, this species severely depletes the stock of bioavailable Se for selenoprotein synthesis and activity to one μg Se/g dry wet in the muscle of several birds. This concentration is still relatively high because selenium is naturally abundant in seawater, therefore it probably does not fall below the metabolic need for essential selenium. However, this study shows that this may not be the case for terrestrial animals, and that muscle may be the first tissue potentially injured by Hg toxicity.

Abstract: Changes in mass, extent, duration, and physical properties of snow are key elements for studying associated climate change feedbacks in northern regions. In this study, we analyzed snowpack physical properties along a ‘mega’ transect from 47°N to 83°N (4,000 km) in northeastern Canada, which includes marked transitions between ecozones from boreal forest to subarctic and arctic ecosystems. Our unique dataset of 391 detailed snowpits acquired over the last 20 years, complemented with snow data from weather stations, shows that snowpack properties such as snow water equivalent, snow depth, density, grain size and basal depth hoar fraction (DHF) are strongly linked to vegetation type. Based on these results, we propose an updated classification of snow types in three classes: boreal forest snow (47–58°N), tundra snow (58–74°N) and polar desert snow (74–83°N), which is more appropriate to the study area than the general north hemisphere classification commonly used. We also show that shrub presence along the transect contributes to a significant increase in DHF development which contributes most strongly to the thermal insulation properties of the snowpack. Overall, our analysis suggests that snow–vegetation interactions have a positive feedback effect on warming at northern latitudes.

Abstract: Studying the effect of global patterns of wind and pressure gradients on the sea level variation (storm surge) is a key issue in understanding the recent climate change effect on the dynamical state of the ocean. The analysis of the spatial and temporal variability of storm surges from observations is a difficult task to accomplish since observations are not homogeneous in time, scarce in space, and moreover, their temporal coverage is limited. A recent global surge database developed by AVISO (DAC, Dynamic Atmospheric Correction) fulfilled the lack of data in terms of spatial coverage, but not regarding time extent, since it only includes the last two decades (1992–2014). In this work, we use the 20th Century Reanalysis V2 (20CR), which spans the years 1871 to 2010, to statistically reconstruct daily maximum surge levels at a global scale. A multivariate linear regression model is fitted between daily mean ERA-interim sea level pressure fields and daily maximum surge levels from DAC. Following, the statistical model is used to reconstruct daily surges using mean sea level pressure fields from 20CR. The verification of the statistical model shows good agreements between DAC levels and the reconstructed surge levels from the 20CR. The validation of the reconstructed surge with tide gauges, distributed throughout the domain, shows good accuracy both in terms of high correlations and small errors. A time series comparison is also depicted at specific tide gauges for the beginning of the 20th century, showing a high concordance. Therefore, this work provides to the scientific community, a daily database of maximum surge levels; which correspond to an extension of the DAC database, from 1871 to 2010. This database can be used to improve the knowledge on historical storm surge conditions, allowing the study of their temporal and spatial variability.