Alfred Wegener Institute for Polar and Marine Research
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Diverse plasmid systems and their ecology across human gut metagenomes revealed by PlasX and MobMess
Get PDFPlasmids alter microbial evolution and lifestyles by mobilizing genes that often confer fitness in changing environments across clades. Yet our ecological and evolutionary understanding of naturally occurring plasmids is far from complete. Here we developed a machine-learning model, PlasX, which identified 68,350 non-redundant plasmids across human gut metagenomes and organized them into 1,169 evolutionarily cohesive ‘plasmid systems’ using our sequence containment-aware network-partitioning algorithm, MobMess. Individual plasmids were often country specific, yet most plasmid systems spanned across geographically distinct human populations. Cargo genes in plasmid systems included well-known determinants of fitness, such as antibiotic resistance, but also many others including enzymes involved in the biosynthesis of essential nutrients and modification of transfer RNAs, revealing a wide repertoire of likely fitness determinants in complex environments. Our study introduces computational tools to recognize and organize plasmids, and uncovers the ecological and evolutionary patterns of diverse plasmids in naturally occurring habitats through plasmid systems
Multiannual patterns of genetic structure and mating type ratios highlight the complex bloom dynamics of a marine planktonic diatom
Get PDFUnderstanding the genetic structure of populations and the processes responsible for its spatial and temporal dynamics is vital for assessing species’ adaptability and survival in changing environments. We investigate the genetic fingerprinting of blooming populations of the marine diatom Pseudo-nitzschia multistriata in the Gulf of Naples (Mediterranean Sea) from 2008 to 2020. Strains were genotyped using microsatellite fingerprinting and natural samples were also analysed with Microsatellite Pool-seq Barcoding based on Illumina sequencing of microsatellite loci. Both approaches revealed a clonal expansion event in 2013 and a more stable genetic structure during 2017–2020 compared to previous years. The identification of a mating type (MT) determination gene allowed to assign MT to strains isolated over the years. MTs were generally at equilibrium with two notable exceptions, including the clonal bloom of 2013. The populations exhibited linkage equilibrium in most blooms, indicating that sexual reproduction leads to genetic homogenization. Our findings show that P. multistriata blooms exhibit a dynamic genetic and demographic composition over time, most probably determined by deeper-layer cell inocula. Occasional clonal expansions and MT imbalances can potentially affect the persistence and ecological success of planktonic diatoms
Microplastic hotspots mapped across the Southern Ocean reveal areas of potential ecological impact
Get PDFMarine microplastic is pervasive, polluting the remotest ecosystems including the Southern Ocean. Since this region is already undergoing climatic changes, the additional stress of microplastic pollution on the ecosystem should not be considered in isolation. We identify potential hotspot areas of ecological impact from a spatial overlap analysis of multiple data sets to understand where marine biota are likely to interact with local microplastic emissions (from ship traffic and human populations associated with scientific research and tourism). Then we account for cumulative effects by identifying which areas with potential elevated microplastic-biota interaction are already subject to climate change related stresses (ocean warming and acidification). Our analysis indicates that biologically productive coastal areas in proximity to populated facilities are where microplastics pose most risk to the ecosystem, and that the northern Antarctic Peninsula is likely to be the main risk hotspot. This study is the first to map the threat of microplastics to the Southern Ocean ecosystem in a multi-stressor context, locating where microplastic monitoring programmes and mitigation measures may be considered a matter of urgency
Using Deep Learning to Advance Global Monitoring of Retrogressive Thaw Slumps at High Spatio-Temporal Resolution
Get PDFWRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework
Get PDFData assimilation is a common technique employed to estimate the state and its associated uncertainties in numerical models. Ensemble-based methods are a prevalent choice, although they can be computationally expensive due to the required ensemble integrations. In this study, we enhance the capabilities of the Weather Research and Forecasting-Advanced Research WRF (WRF-ARW) model by coupling it with the Parallel Data Assimilation Framework (PDAF) in a fully online mode. Through minimal modifications to the WRF-ARW model code, we have developed an efficient data assimilation system. This system leverages parallelization and in-memory data transfers between the model and data assimilation processes, greatly reducing the need for file I/O and model restarts during assimilation. We detail the necessary program modifications in this study. One advantage of the resulting assimilation system is a clear separation of concerns between data assimilation method development and model application resulting from PDAF's model-Agnostic structure. To evaluate the assimilation system, we conduct a twin experiment simulating an idealized tropical cyclone. Cycled data assimilation experiments focus on the impact of temperature profiles. The assimilation not only significantly enhances temperature field accuracy but also improves the initial U and V fields. The assimilation process introduces only minimal overhead in runtime when compared to the model without data assimilation and exhibits excellent parallel performance. Consequently, the online WRF-PDAF system emerges as an efficient framework for implementing high-resolution mesoscale forecasting and reanalysis
SMOS-derived Antarctic thin sea ice thickness: data description and validation in the Weddell Sea
Get PDFAbstract. Accurate satellite measurements of the thickness of Antarctic sea ice are urgently needed but pose a particular challenge. The Antarctic data presented here were produced using a method to derive the sea ice thickness from 1.4 GHz brightness temperatures previously developed for the Arctic, with only modified auxiliary data. The ability to observe the thickness of thin sea ice using this method is limited to cold conditions, meaning it is only reasonable during the freezing period, typically March to October. The Soil Moisture and Ocean Salinity (SMOS) level-3 sea ice thickness product contains estimates of the sea ice thickness and its uncertainty up to a thickness of about 1 m. The sea ice thickness is provided as a daily average on a polar stereographic projection grid with a sample resolution of 12.5 km, while the SMOS brightness temperature data used have a footprint size of about 35–40 km in diameter. Data from SMOS have been available since 2010, and the mission's operation has been extended to continue until at least the end of 2025. Here we compare two versions of the SMOS Antarctic sea ice thickness product which are based on different level-1 input data (v3.2 based on SMOS L1C v620 and v3.3 based on SMOS L1C 724). A validation is performed to generate a first baseline reference for future improvements of the retrieval algorithm and synergies with other sensors. Sea ice thickness measurements to validate the SMOS product are particularly rare in Antarctica, especially during the winter season and for the valid range of thicknesses. From the available validation measurements, we selected datasets from the Weddell Sea that have varying degrees of representativeness: Helicopter-based EM Bird (HEM), Surface and Under-Ice Trawl (SUIT), and stationary Upward-Looking Sonars (ULS). While the helicopter can measure hundreds of kilometres, SUIT's use is limited to distances of a few kilometres and thus only captures a small fraction of an SMOS footprint. Compared to SMOS, the ULS are point measurements and multi-year time series are necessary to enable a statistically representative comparison. Only four of the ULS moorings have a temporal overlap with SMOS in the year 2010. Based on selected averaged HEM flights and monthly ULS climatologies, we find a small mean difference (bias) of less than 10 cm and a root mean square deviation of about 20 cm with a correlation coefficient R > 0.9 for the valid sea ice thickness range between 0 and about 1 m. The SMOS sea ice thickness showed an underestimate of about 40 cm with respect to the less representative SUIT validation data in the marginal ice zone. Compared with sea ice thickness outside the valid range, we find that SMOS strongly underestimates the real values, which underlines the need for combination with other sensors such as altimeters. In summary, the overall validity of the SMOS sea ice thickness for thin sea ice up to a thickness of about 1 m has been demonstrated through validation with multiple datasets. To ensure the quality of the SMOS product, an independent regional sea ice extent index was used for control. We found that the new version, v3.3, is slightly improved in terms of completeness, indicating fewer missing data. However, it is worth noting that the general characteristics of both datasets are very similar, also with the same limitations. Archived data are available in the PANGAEA repository at https://doi.org/10.1594/PANGAEA.934732 (Tian-Kunze and Kaleschke, 2021) and operationally at https://doi.org/10.57780/sm1-5ebe10b
(European Space Agency, 2023).
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Seasonal patterns of microbial diversity across the world oceans
Get PDFUnderstanding the patterns of marine microbial diversity (Bacteria + Archaea) is essential, as variations in their alpha- and beta-diversities can affect ecological processes. Investigations of microbial diversity from global oceanographic expeditions and basin-wide transects show positive correlations between microbial diversity and either temperature or productivity, but these studies rarely captured seasonality, especially in polar regions. Here, using multiannual alpha-diversity data from eight time series in the northern and southern hemispheres, we show that marine microbial community richness and evenness generally correlate more strongly with daylength than with temperature or chlorophyll a (a proxy for photosynthetic biomass). This pattern is observable across time series found in the northern and southern hemispheres regardless of collection method, DNA extraction protocols, targeted 16S rRNA hypervariable region, sequencing technology, or bioinformatics pipeline
Assessing the benefits of approximately exact step sizes for Picard and Newton solver in simulating ice flow (FEniCS-full-Stokes v.1.3.2)
Get PDFSolving the momentum balance is the computationally expensive part of simulating the evolution of ice sheets. The momentum balance is described by the nonlinear full-Stokes equations, which are solved iteratively. We use the Picard iteration and Newton's method combined with Armijo step sizes and approximately exact step sizes, respectively, to solve these equations. The Picard iteration uses either no step size control or the approximately exact step sizes. We compare the variants of Newton's method and the Picard iteration in benchmark experiments, called ISMIP-HOM experiments A, B, E1, and E2. The ISMIP-HOM experiments consist of a more realistic domain and are designed to test the quality of ice models. For an even more realistic test case, we simulate the experiments E1 and E2 with a time-dependent surface. We obtain that approximately exact step sizes greatly reduce the necessary number of iterations for the Picard iteration and Newton's method with nearly no increase in the computation time for each iteration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Get PDFAbstract. Data assimilation (DA) in marine and freshwater systems combines numerical models and observations to deliver the best possible characterization of a waterbody's physical and biogeochemical state. DA underpins the widely used 3D ocean state reanalyses and forecasts produced operationally by, e.g., the Copernicus Marine Service. The use of DA in natural waters is an active field of research, but testing new developments in realistic setting can be challenging as operational DA systems are demanding in terms of computational resources and technical skill. There is a need for test beds that are sufficiently realistic but also efficient to run and easy to operate. Here, we present the Ensemble and Assimilation Tool (EAT), a flexible and extensible software package that enables data assimilation of physical and biogeochemical variables in a one-dimensional water column. EAT builds on established open-source components for hydrodynamics (GOTM), biogeochemistry (FABM), and data assimilation (PDAF). It is easy to install and operate and is flexible through support for user-written plugins. EAT is well suited to explore and advance the state of the art in DA in natural waters thanks to its support for (1) strongly and weakly coupled data assimilation, (2) observations describing any prognostic and diagnostic element of the physical–biogeochemical model, and (3) the estimation of biogeochemical parameters. Its range of capabilities is demonstrated with three applications: ensemble-based coupled physical–biogeochemical assimilation, the use of variational methods (3D-Var) to assimilate sea surface chlorophyll, and the estimation of biogeochemical parameters