Seasonal variability of meio- and macrobenthic standing stocks and diversity in an Arctic fjord (Adventfjorden, Spitsbergen)

Strong environmental seasonality is a basic feature of the Arctic system, still there are few published records of the seasonal variability of the Arctic marine biota. This study examined the year-round seasonal changes of soft bottom macro- and meiobenthic standing stocks and diversity on a station located in an Arctic fjord (Adventfjorden, Spitsbergen). The seasonality observed in benthic biota was related to the pelagic processes, primarily the seasonal fluxes of organic and inorganic particles. The highest abundance, biomass and richness of benthic fauna occurred in the spring after the phytoplankton bloom. During the summer, when a high load of glacial mineral material was transported to the fiord, the number of both meio- and macrobenthic individuals decreased remarkably. The strong inorganic sedimentation in summer was accompanied by a decline in macrobenthic species richness, but had no effects on evenness. Redundancy analysis (RDA) pointed to granulometric composition of sediments (depended on mineral sedimentation) and organic fluxes as factors best related to meio- and macrobenthic taxonomic composition, but no clear seasonal trend could be observed on the nMDS plots based on meiobenthic higher taxa or macrobenthic species abundances in the samples. This study addresses the possible effects of changes in the winter ice cover on the fjordic benthic systems because it was performed in a year with no ice cover on the fjord

HAUSGARTEN: Multidisciplinary investigations at a deep-sea, long-term observatory in the Arctic Ocean

The marine Arctic has played an essential role in the history of our planet over the past 130 million years and contributes considerably to the present functioning of Earth and its life. The global cycles of a variety of materials fundamental to atmospheric conditions and thus to life depend to a signifi cant extent on Arctic marine processes (Aargaard et al., 1999). The past decades have seen remarkable changes in key Arctic variables. The decrease of sea-ice extent and sea-ice thickness in the past decade is statistically signifi - cant (Cavalieri et al., 1997; Parkinson et al., 1999; Walsh and Chapman, 2001; Partington et al., 2003; Johannessen et al., 2004). There have also been large changes in the upper and intermediate layers of the ocean, which have environmental implications. For instance, the deep Greenland Sea has continued its decadal trend towards warmer and saltier conditions, with a corresponding decrease in oxygen content, refl ecting the lack of effective local convection and ventilation (Dickson et al., 1996; Boenisch et al., 1997). Changes in temperature and salinity and associated shifts in nutrient distributions will directly affect the marine biota on multiple scales from communities and populations to individuals, consequently altering food-web structures and ecosystem functioning (Benson and Trites, 2002; Moore, 2003; Schumacher et al., 2003; Wiltshire and Manly, 2004; Perry et al., 2005). Today, we do not know whether the severe alterations in abiotic parameters represent perturbations due to human impacts, natural long-term trends, or new equilibriums (Bengtson et al., 2004). Because Arctic organisms are highly adapted to extreme environmental conditions with strong seasonal forcing, the accelerating rate of recent climate change challenges the resilience of Arctic life (Hassol, 2004). The entire system is likely to be severely affected by changing ice and water conditions, varying primary production and food availability to faunal communities, an increase in contaminants, and possibly increased UV irradiance. The stability of a number of Arctic populations and ecosystems is probably not strong enough to withstand the sum of these factors, which might lead to a collapse of subsystems. To detect and track the impact of large-scale environmental changes in the transition zone between the northern North Atlantic and the central Arctic Ocean, and to determine experimentally the factors controlling deep-sea biodiversity, the German Alfred Wegener Institute for Polar and Marine Research (AWI) established the deepsea, long-term observatory HAUSGARTEN, representing the fi rst, and by now only, open-ocean, long-term station in a polar region

Diversity of hard-bottom fauna relative to environmental gradients in Kongsfjorden, Svalbard

A baseline study of hard-bottom zoobenthos in relation to environmental gradients in Kongsfjorden, a glacial fjord in Svalbard, is presented, based on collections from 1996 to 1998. The total species richness in 62 samples from 0 to 30 m depth along five transects was 403 species. Because 32 taxa could not be identified to species level and because 11 species are probably new to science, the total number of identified species was 360. Of these, 47 species are new for Svalbard waters. Bryozoa was the most diverse group. Biogeographic composition revealed features of both Arctic and sub-Arctic properties of the fauna. Species richness, frequency of species occurrence, mean abundance and biomass generally decreased towards the tidal glaciers in inner Kongsfjorden. Among eight environmental factors, depth was most important for explaining variance in the composition of the zoobenthos. The diversity was consistently low at shallow depths, whereas the non-linear patterns of species composition of deeper samples indicated a transitional zone between surface and deeper water masses at 15–20 m depth. Groups of “colonial” and “non-colonial” species differed in diversity, biogeographic composition and distribution by location and depth as well as in relation to other environmental factors. “Non-colonial” species made a greater contribution than “colonial” species to total species richness, total occurrence and biomass in samples, and were more influenced by the depth gradient. Biogeographic composition was sensitive to variation of zoobenthic characteristics over the studied depth range. A list of recorded species and a description of sampling sites are presented

Seasonality of bivalve larvae within a high Arctic fjord

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Darwin's Manufactory Hypothesis Is Confirmed and Predicts the Extinction Risk of Extant Birds

In the Origin of Species Darwin hypothesized that the “manufactory” of species operates at different rates in different lineages and that the richness of taxonomic units is autocorrelated across levels of the taxonomic hierarchy. We confirm the manufactory hypothesis using a database of all the world's extant avian subspecies, species and genera. The hypothesis is confirmed both in correlations across all genera and in paired comparisons controlling for phylogeny. We also find that the modern risk of extinction, as measured by “Red List” classifications, differs across the different categories of genera identified by Darwin. Specifically, species in “manufactory” genera are less likely to be threatened, endangered or recently extinct than are “weak manufactory” genera. Therefore, although Darwin used his hypothesis to investigate past evolutionary processes, we find that the hypothesis also foreshadows future changes to the evolutionary tree

Learning biophysically-motivated parameters for alpha helix prediction

<p>Abstract</p> <p>Background</p> <p>Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological "pseudo-energies". Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures.</p> <p>Results</p> <p>Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Q_{<it>α </it>}value of 77.6% and an SOV_{<it>α </it>}value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters.</p> <p>Conclusion</p> <p>The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here.</p