Description: Recent studies on seafloor mapping have presented different modelling methods for the automatic classification of seafloor sediments. However, most of these studies have applied these models to seafloor data with appropriate numbers of ground-truth samples and without consideration of the imbalances in the ground-truth datasets. In this study, we aim to address these issues by conducting class-specific predictions using ensemble modelling to map seafloor sediment distributions with minimal ground-truth data combined with hydroacoustic datasets. The resulting class-specific maps were then assembled into a sediment classification map, in which the most probable class was assigned to the appropriate location. Our approach was able to predict sediment classes without bias to the class with more ground-truth data and produced reliable seafloor sediment distributions maps that can be used for seafloor monitoring. The methods presented can also be used for other underwater exploration studies with minimal ground-truth data. Sediment shifts of a heterogenous seafloor in the Sylt Outer Reef, German North Sea were also assessed to understand the sediment dynamics in the marine conservation area during two different short timescales: 2016–2018 (17 months) and 2018–2019 (4 months). The analyses of the sediment shifts showed that the western area of the Sylt Outer Reef experienced sediment fluctuations but the morphology of the bedform features was relatively stable. The results provided information on the seafloor dynamics, which can assist in the management of the marine conservation area.

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: Various species of Alexandrium can produce a number of bioactive compounds, e.g., paralytic shellfish toxins (PSTs), spirolides, gymnodimines, goniodomins, and also uncharacterised bioactive extracellular compounds (BECs). The latter metabolites are released into the environment and affect a large range of organisms (from protists to fishes and mammalian cell lines). These compounds mediate allelochemical interactions, have anti-grazing and anti-parasitic activities, and have a potentially strong structuring role for the dynamic of Alexandrium blooms. In many studies evaluating the effects of Alexandrium on marine organisms, only the classical toxins were reported and the involvement of BECs was not considered. A lack of information on the presence/absence of BECs in experimental strains is likely the cause of contrasting results in the literature that render impossible a distinction between PSTs and BECs effects. We review the knowledge on Alexandrium BEC, (i.e., producing species, target cells, physiological effects, detection methods and molecular candidates). Overall, we highlight the need to identify the nature of Alexandrium BECs and urge further research on the chemical interactions according to their ecological importance in the planktonic chemical warfare and due to their potential collateral damage to a wide range of organisms.

Description: We provide an overview of the results devoted to the analysis of the dynamics and economics of shallow lakes, spanning the period from 1999 until now. A shallow lake serves as a typical representative of an ecological system subject to (possibly irreversible) regime shifts. The dynamics of a shallow lake are described by a non-linear model with multiple steady states and multiple domains of attraction and is thus suitable to model the evolution of an ecosystem featuring both resilience within a domain of stability and an abrupt regime shift outside of it. Beyond this, the shallow lake model can also be viewed as a metaphor for many other ecological problems. Due to the broad applicability of this model, there is substantial interest in the management of shallow lakes and both their optimal regulation and competitive usage.

Description: Massive fouling by the invasive ascidian Ciona intestinalis in Prince Edward Island (PEI, Canada) has been causing devastating losses to the local blue mussel farms. In order to gain first insights into so far unexplored factors that may contribute to the invasiveness of C. intestinalis in PEI, we undertook comparative microbiome and metabolome studies on specific tissues from C. intestinalis populations collected in invaded (PEI) and native regions (Helgoland and Kiel, Germany). Microbial community analyses and untargeted metabolomics revealed clear location- and tissue-specific patterns showing that biogeography and the sampled tissue shape the microbiome and metabolome of C. intestinalis. Moreover, we observed higher microbial and chemical diversity in C. intestinalis from PEI than in the native populations. Bacterial OTUs specific to C. intestinalis from PEI included Cyanobacteria (e.g., Leptolyngbya sp.) and Rhodobacteraceae (e.g., Roseobacter sp.), while populations from native sampling sites showed higher abundances of e.g., Firmicutes (Helgoland) and Epsilonproteobacteria (Kiel). Altogether 121 abundant metabolites were putatively annotated in the global ascidian metabolome, of which 18 were only detected in the invasive PEI population (e.g., polyketides and terpenoids), while six (e.g., sphingolipids) or none were exclusive to the native specimens from Helgoland and Kiel, respectively. Some identified bacteria and metabolites reportedly possess bioactive properties (e.g., antifouling and antibiotic) that may contribute to the overall fitness of C. intestinalis. Hence, this first study provides a basis for future studies on factors underlying the global invasiveness of Ciona species

Description: Jellyfish outbreaks are conspicuous natural events in marine ecosystems that have a substantial impact on the structure and dynamics of marine ecosystems and different economic sectors of human activities. Understanding the life cycle strategies of jellyfish species is therefore critical to mitigate the impacts these organisms may have. In this context, the present study investigated the effect of different temperature and salinity regimes on the rearing success of the jellyfish Aurelia solida in microcosm experiments on two different life stages: polyps and ephyrae. Polyps showed high survival rates across the different conditions (except at 28◦C/20 psu) and reproduced asexually in all combinations, with the highest budding activity at 20◦C and 30 psu. Strobilation occurred mainly at 16◦C and 35 psu. Although ephyra survival was highest at low salinities (20 psu) and lower temperatures (10 and 15◦C), the highest growth rates were reached at intermediate temperatures (20◦C). The comparison to other Aurelia species underlines the differences between even closely related species. Given the high tolerance capacity that A. solida presented in the experiments, the species has the potential to cope well under current climate change scenarios and possibly adapt successfully to other regions and ecosystems.

Description: In large areas of the Clarion Clipperton Fracture Zone (northeast Pacific), exploration of deep-sea polymetallic nodules as a potential source of high-technology metals is ongoing. Deep-sea mining may have a severe impact on the benthic communities. Here, we investigated meiofauna communities in the abyss at the scale of a prospective mining operation area. Random forest regressions were computed to spatially predict continuous layers of environmental variables as well as the distribution of meiofauna abundance across the area. Significant models could be computed for 26 sediment and polymetallic nodule parameters. Meiofauna abundance, taxon richness and diversity were also modelled, as well as abundance of the taxon Nematoda. Spatial correlation is high if the predictions of meiofauna are either based on bathymetry and backscatter or include sediment and nodule variables; Pearson’s correlation coefficient varies between 0.42 and 0.91. Comparison of differences in meiofauna abundance between different years shows that spatial patterns do change, with an elevated abundance of meiofauna in the eastern part of the study area in 2013. On the spatial scale of a potential mining operation, distribution models prove to be a useful tool to gain insight into both temporal variability and the influence of potential environmental drivers on meiofauna distribution.

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: Machine learning spatial modeling is used for mapping the distribution of deep-sea polymetallic nodules (PMN). However, the presence and influence of spatial autocorrelation (SAC) have not been extensively studied. SAC can provide information regarding the variable selection before modeling, and it results in erroneous validation performance when ignored. ML models are also problematic when applied in areas far away from the initial training locations, especially if the (new) area to be predicted covers another feature space. Here, we study the spatial distribution of PMN in a geomorphologically heterogeneous area of the Peru Basin, where SAC of PMN exists. The local Moran’s I analysis showed that there are areas with a significantly higher or lower number of PMN, associated with different backscatter values, aspect orientation, and seafloor geomorphological characteristics. A quantile regression forests (QRF) model is used using three cross-validation (CV) techniques (random-, spatial-, and cluster-blocking). We used the recently proposed “Area of Applicability” method to quantify the geographical areas where feature space extrapolation occurs. The results show that QRF predicts well in morphologically similar areas, with spatial block cross-validation being the least unbiased method. Conversely, random-CV overestimates the prediction performance. Under new conditions, the model transferability is reduced even on local scales, highlighting the need for spatial model-based dissimilarity analysis and transferability assessment in new areas.