Description: Sharing and secondary analysis of data have become increasingly important for research. Especially in geography, the collection of digital data has grown due to technological changes. Responsible handling and proper documentation of research data have therefore become essential for funders, publishers and higher education institutions. To achieve this goal, universities offer support and training in research data management. This article presents the experiences of a pilot workshop in research data management, especially for geographers. A discipline-specific approach to research data management training is recommended. The focus of this approach increases researchers’ interest and allows for more specific guidance. The instructors identified problems and challenges of research data management for geographers. In regards to training, the communication of benefits and reaching the target groups seem to be the biggest challenges. Consequently, better incentive structures as well as communication channels have to be established.

Description: Lake Urmia, the second largest saline Lake on earth and a highly endangered ecosystem, is on the brink of a serious environmental disaster similar to the catastrophic death of the Aral Sea. Progressive drying has been observed during the last decade, causing dramatic changes to Lake Urmia’s surface and its regional water supplies. The present study aims to improve monitoring of spatiotemporal changes of Lake Urmia in the period 1975–2015 using the multi-temporal satellite altimetry and Landsat (5-TM, 7-ETM+ and 8-OLI) images. In order to demonstrate the impacts of climate change and human pressure on the variations in surface extent and water level, Lake Sevan and Van Lake with different characteristics were studied along with the Urmia Lake. Normalized Difference Water Index-Principal Components Index (NDWI-PCs), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), Automated Water Extraction Index (AWEI), and MultiLayer Perceptron Neural Networks (MLP NNs) classifier were investigated for the extraction of surface water from Landsat data. The presented results revealed that MLP NNs has a better performance in the cases where the other models generate poor accuracy. The results show that the area of Lake Sevan and Van Lake have increased while the area of Lake Urmia has decreased by ~65.23% in the past decades, far more than previously reported (~25% to 50%). Urmia Lake’s shoreline has been receding severely between 2010 and 2015 with no sign of recovery, which has been partly blamed on prolonged droughts, aggressive regional water resources development plans, intensive agricultural activities, and anthropogenic changes to the system. The results also indicated that (among the proposed factors) changes in inflows due to overuse of surface water resources and constructing dams (mostly during 1995–2005) are the main reasons for Urmia Lake’s shoreline receding. The model presented in this manuscript can be used by managers as a decision support system to find the effects of building new dams or other infrastructures

Description: Arctic and subarctic regions are sensitive to climate change and, reversely, provide dramatic feedbacks to the global climate. With a focus on discovering paleoclimate and paleoceanographic evolution in the Arctic and Northwest Pacific Oceans during the last 20,000 years, we proposed this German–Sino cooperation program according to the announcement “Federal Ministry of Education and Research (BMBF) of the Federal Republic of Germany for a German–Sino cooperation program in the marine and polar research”. Our proposed program integrates the advantages of the Arctic and Subarctic marine sediment studies in AWI (Alfred Wegener Institute) and FIO (First Institute of Oceanography). For the first time, the collection of sediment cores can cover all climatological key regions in the Arctic and Northwest Pacific Oceans. Furthermore, the climate modeling work at AWI enables a “Data-Model Syntheses”, which are crucial for exploring the underlying mechanisms of observed changes in proxy records.

Description: The Antarctic deep-sea sponge Latrunculia (Latrunculia) biformis Kirkpatrick, 1908 (Class Demospongiae Sollas, Order Poecilosclerida Topsent, Latrunculiidae Topsent) was selected for chemical analyses due to its potent anticancer activity. Metabolomic analysis of its crude extract by HRMS/MS-based molecular networking showed the presence of several clusters of pyrroloiminoquinone alkaloids, i.e., discorhabdin and epinardin-type brominated pyridopyrroloquinolines and tsitsikammamines, the non-brominated bis-pyrroloiminoquinones. Molecular networking approach combined with a bioactivity-guided isolation led to the targeted isolation of the known pyrroloiminoquinone tsitsikammamine A (1) and its new analog 16,17-dehydrotsitsikammamine A (2). The chemical structures of the compounds 1 and 2 were elucidated by spectroscopic analysis (one-dimensional (1D) and two-dimensional (2D) NMR, HR-ESIMS). Due to minute amounts, molecular modeling and docking was used to assess potential affinities to potential targets of the isolated compounds, including DNA intercalation, topoisomerase I-II, and indoleamine 2,3-dioxygenase enzymes. Tsitsikammamines represent a small class of pyrroloiminoquinone alkaloids that have only previously been reported from the South African sponge genus Tsitsikamma Samaai & Kelly and an Australian species of the sponge genus Zyzzya de Laubenfels. This is the first report of tsitsikammamines from the genus Latrunculia du Bocage and the successful application of molecular networking in the identification of comprehensive chemical inventory of L.biformis followed by targeted isolation of new molecules. This study highlights the high productivity of secondary metabolites of Latrunculia sponges and may shed new light on their biosynthetic origin and chemotaxonomy

Description: We investigated the plankton community composition and abundance in the urban marine environment of Thessaloniki Bay. We collected water samples weekly from March 2017 to February 2018 at the coastal front of Thessaloniki city center and monthly samples from three other inshore sites along the urban front of the bay. During the study period, conspicuous and successive phytoplankton blooms, dominated by known mucilage-producing diatoms alternated with red tide events formed by the dinoflagellates Noctiluca scintillans and Spatulodinium pseudonoctiluca, and an extensive mucilage aggregate phenomenon, which appeared in late June 2017. At least 11 known harmful algae were identified throughout the study, with the increase in the abundance of the known harmful dinoflagellate Dinophysis cf. acuminata occurring in October and November 2017. Finally, a red tide caused by the photosynthetic ciliate Mesodinium rubrum on December 2017 was conspicuous throughout the sampling sites. The above-mentioned harmful blooms and red tides were linked to high nutrient concentrations and eutrophication. This paper provides an overview of eutrophication impacts on the response of the unicellular eukaryotic plankton organisms and their impact on water quality and ecosystem services

Description: Underwater photogrammetry and in particular systematic visual surveys of the deep sea are by far less developed than similar techniques on land or in space. The main challenges are the rough conditions with extremely high pressure, the accessibility of target areas (container and ship deployment of robust sensors, then diving for hours to the ocean floor), and the limitations of localization technologies (no GPS). The absence of natural light complicates energy budget considerations for deep diving flash-equipped drones. Refraction effects influence geometric image formation considerations with respect to field of view and focus, while attenuation and scattering degrade the radiometric image quality and limit the effective visibility. As an improvement on the stated issues, we present an AUV-based optical system intended for autonomous visual mapping of large areas of the seafloor (square kilometers) in up to 6000 m water depth. We compare it to existing systems and discuss tradeoffs such as resolution vs. mapped area and show results from a recent deployment with 90,000 mapped square meters of deep ocean floor.