Biogeochemical characterization of pennate diatom and Melosira arctica ice algal aggregates in the Central Arctic Ocean collected in summer 2011 and 2012

Sea-ice diatoms are known to accumulate in large aggregates in and under the sea ice including melt ponds. In the Arctic, they can contribute substantially to particle export when sinking from the ice. The role and regulation of microbial aggregation in the highly seasonal, nutrient- and light-limited Arctic sea-ice ecosystem is not yet well understood, and may vary in relation to the fate of the Arctic sea-ice cover. To elucidate the mechanism controlling the formation and export of algal aggregates from sea ice, we investigated samples taken in late summer 2011 and 2012, during two cruises to the Eurasian Basin of the Central Arctic Ocean. Dense, spherical aggregates composed mainly of pennate diatoms, and filamentous aggregates formed by Melosira arctica were found in different degradation stages, with carbon to Chlorophyll a ratios ranging from 110 to 66700, and carbon to nitrogen molar ratios of 8-35 and 9-40, respectively. Fresh sub-ice algal aggregate densities ranged between 1 and 17 aggregates/m**2, corresponding to a net primary production of 0.4-40 mg C/m**2/d, contributing 3-80% of total biomass and up to 94% of total production at a local scale. A key factor controlling buoyancy of the aggregates was light intensity, regulating photosynthetic oxygen production and flotation by gas bubbles trapped within the mucous matrix, even at low ambient nutrient concentrations. Our data was used to evaluate the factors regulating the distribution and importance of the Arctic algal aggregates as carbon source for pelagic and benthic communities.

Isotopic ratios and concentrations for mineral-associated and particulate organic fractions of sediment core Wilson_Lake samples

The flux of sediment and organic carbon from continents to the coastal ocean is an important factor governing organic burial in coastal sediments, and these systems preserve important records of environmental and biogeochemical conditions during past global change events. Burial of organic materials in coastal systems can be promoted by chemical resilience or through protection by association with mineral surfaces, but the role and influence of these processes on organic records from ancient sediments is poorly known. We studied sediment and organic matter burial as particulate organic matter (POM) and mineral-bound organic matter (MOM) in near-shore marine sediments from the Wilson Lake core (New Jersey, USA) that span the Paleocene-Eocene thermal maximum (PETM), a climatic perturbation 55.9 Myr ago. Our results show that distinct POM and MOM fractions can be isolated from sediments. Both fractions appear to be dominated by terrestrial material, but POM consisted primarily of recently synthesized material whereas MOM included a significant fraction of pre-aged organic matter from soils or ancient sediments. Variation in organic burial through the PETM is associated with changes in inorganic nitrogen burial, clay mineralogy, and clastic grain size that we associate with enhanced continental weathering, erosion and redeposition of ancient kaolinites, and eustatic sea level variation, respectively. These results provide a new perspective on factors governing carbon burial and carbon isotope records in ancient marine margin settings and offer information on rate and phasing of late Paleocene/early Eocene Earth system changes that may constrain interpretations of the cause of the PETM climate change event.

Sedimentology, geochemistry and ostracodes from Lake Ohrid

Ancient Lake Ohrid, located in the southern Balkan Peninsula in Macedonia and Albania is characterized by a high degree of endemism and it is considered to be the oldest lake in Europe. But its exact age (between one and ten million years) and also its origin are so far not known. To unravel these uncertainties an ICDP (International Continental Scientific Drilling Program) drilling project (Scientific Collaboration On Past Speciation Conditions in Ohrid (SCOPSCO)), started in April 2013. In addition to the investigations about the age and origin, other paleolimnological studies, e.g., the reconstruction of past climate and of past lake level changes, should be performed with the drilled cores. Used proxies in such paleolimnological studies are, e.g., ostracodes because they respond sensitively to environmental changes but an accurate knowledge of their preferences and tolerances to specific environmental conditions is necessary for this purpose. So far, this knowledge about the, mostly endemic, Ohrid ostracodes was limited. Thus, within the framework of this thesis, ostracodes and a multiplicity of environmental data were collected in Lake Ohrid and its adjacent waters during four field campaigns. In a total of 47 ostracode species could be detected in the entire study area and 32 of them were found alive in Lake Ohrid. Multivariate statistic identified that water depth, salinity, conductivity, pH, and dissolved oxygen were the main determining factors for ostracode distribution in the entire study area. In Lake Ohrid, the distribution was mainly controlled by water depth, water temperature, and pH. Some ostracodes were identified as strong indicator species for important environmental variables, e.g., water temperature and water depth. A distinctive feature of Lake Ohrid was the finding of the ostracode genus Amnicythere whose species normally inhabit oligo-(meso-)haline waters and this could point to a marine origin of the lake. So far, the specialized endemic ostracodes show the highest abundances and the greatest spatial distribution in Lake Ohrid but during the sampling eight widespread species were found for the first time in the lake. They inhabited mainly the northern part of the lake, where two cities are located and industry and agriculture play a major role, and they were limited to water depths above 50 m and this could be an evidence for an increasing anthropogenic pressure because widespread ostracode species often replace endemic species. To unravel the human impact on Lake Ohrid during the last decades short sediment cores were taken and the multi-proxy study indicated that the lake productivity between the early 1920s and the late 1980s was relatively low. Diatom assemblages indicate a rising productivity in the southern part of Lake Ohrid since the mid 1970s and geochemical proxies and ostracodes point to an increasing productivity since the late 1980s in the southern and in the northern part. A slight increase in the productivity continued until 2009. Noticeable is the fact that since the early 1990s, the increasing productivity and the increasing concentrations of heavy metals correspond to a decreasing number of ostracodes in the northern part of Lake Ohrid. Perhaps, this indicates that living conditions in this lake part became less favorable for the mostly endemic ostracode species. Furthermore, the sediment samples from the cores show relatively high concentrations of arsenic, iron, and nickel. Fluctuations in ostracode assemblages from three longer sediment cores, the longest spans approximately 136 ka, taken in Lake Ohrid, correspond to fluctuations in the productivity, in the carbonate content, of the lake level, and of climate changes. Between the marine isotope stage (MIS) 6 and MIS 2 the number of ostracode valves is very low or the valves were completely absent. This corresponds to a low lake productivity, a low carbonate content, and a low lake level. At the onset of the Holocene, the number of valves increased markedly and this correlates with an increased productivity and carbonate content and a warmer climate. But during the Little Ice Age (LIA), the number of valves dropped again and species which prefer warmer waters disappeared completely. This drop corresponds also to a low productivity. After the LIA, the number of species increased again but since 1895 AD a strong and abrupt decrease is visible. A reason for this could be an increase in the heavy metal concentrations.

Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.