ALBERT

Description: Raw data acquired by two thermosalinographs (SBE21, SeaBird GmbH) on board RV Polarstern were processed to yield a calibrated and validated data set of temperature, conductivity and salinity during expedition PS122/4. Both sensors were equipped with a more accurate external temperature sensor (SBE38, Sea-Bird GmbH). Data were downloaded from the DAVIS SHIP data base (https://dship.awi.de) with a resolution of 1 sec. The raw hex data were converted to temperature and conductivity while a sensor drift correction was applied using calibration coefficients from before and after the expedition. Salinity was calculated according to the instructions from the Practical Salinity Scale PSS-78, using the obtained (internal) temperature and conductivity data and a pressure of 11 dbar which represents the water depth of the inlet of the TSG system on Polarstern. Processed data are provided as 10min means of salinity and water temperature aligned with position data taken from master track of the respective cruise. A speed filter was not applied to the PS122 dataset because of the slow drift speed. This slow movement may lead to an overestimation of the mixed-layer temperature; in particular, small heat fluxes from the ship may raise the temperature in adjacent water in the lee, i.e. during times of drift in the direction opposite to the TSG inlet. The effect is expected to be small, but can potentially be higher than the accuracy of the temperature measurement. Further details and evaluation of the data is outlined in the data processing report found at the EPIC repository under URL (https://hdl.handle.net/10013/epic.7fffb528-06bd-48ae-8489-cea0444c4eab).

Description: Raw data acquired by two thermosalinographs (SBE21, SeaBird GmbH) on board RV Polarstern were processed to yield a calibrated and validated data set of temperature, conductivity and salinity during expedition PS122/2. Both sensors were equipped with a more accurate external temperature sensor (SBE38, Sea-Bird GmbH). Data were downloaded from the DAVIS SHIP data base (https://dship.awi.de) with a resolution of 1 sec. The raw hex data were converted to temperature and conductivity while a sensor drift correction was applied using calibration coefficients from before and after the expedition. Salinity was calculated according to the instructions from the Practical Salinity Scale PSS-78, using the obtained (internal) temperature and conductivity data and a pressure of 11 dbar which represents the water depth of the inlet of the TSG system on Polarstern. Processed data are provided as 10min means of salinity and water temperature aligned with position data taken from master track of the respective cruise. A speed filter was not applied to the PS122 dataset because of the slow drift speed. This slow movement may lead to an overestimation of the mixed-layer temperature; in particular, small heat fluxes from the ship may raise the temperature in adjacent water in the lee, i.e. during times of drift in the direction opposite to the TSG inlet. The effect is expected to be small, but can potentially be higher than the accuracy of the temperature measurement. Further details and evaluation of the data is outlined in the data processing report found at the EPIC repository under URL (https://hdl.handle.net/10013/epic.7fffb528-06bd-48ae-8489-cea0444c4eab).

Description: Raw data acquired by two thermosalinographs (SBE21, SeaBird GmbH) on board RV Polarstern were processed to yield a calibrated and validated data set of temperature, conductivity and salinity during expedition PS122/1. Both sensors were equipped with a more accurate external temperature sensor (SBE38, Sea-Bird GmbH). Data were downloaded from the DAVIS SHIP data base (https://dship.awi.de) with a resolution of 1 sec. The raw hex data were converted to temperature and conductivity while a sensor drift correction was applied using calibration coefficients from before and after the expedition. Salinity was calculated according to the instructions from the Practical Salinity Scale PSS-78, using the obtained (internal) temperature and conductivity data and a pressure of 11 dbar which represents the water depth of the inlet of the TSG system on Polarstern. Processed data are provided as 10min means of salinity and water temperature aligned with position data taken from master track of the respective cruise. A speed filter was not applied to the PS122 dataset because of the slow drift speed. This slow movement may lead to an overestimation of the mixed-layer temperature; in particular, small heat fluxes from the ship may raise the temperature in adjacent water in the lee, i.e. during times of drift in the direction opposite to the TSG inlet. The effect is expected to be small, but can potentially be higher than the accuracy of the temperature measurement. Further details and evaluation of the data is outlined in the data processing report found at the EPIC repository under URL (https://hdl.handle.net/10013/epic.7fffb528-06bd-48ae-8489-cea0444c4eab).

Description: Raw data acquired by two thermosalinographs (SBE21, SeaBird GmbH) on board RV Polarstern were processed to yield a calibrated and validated data set of temperature, conductivity and salinity during expedition PS122/5. Both sensors were equipped with a more accurate external temperature sensor (SBE38, Sea-Bird GmbH). Data were downloaded from the DAVIS SHIP data base (https://dship.awi.de) with a resolution of 1 sec. The raw hex data were converted to temperature and conductivity while a sensor drift correction was applied using calibration coefficients from before and after the expedition. Salinity was calculated according to the instructions from the Practical Salinity Scale PSS-78, using the obtained (internal) temperature and conductivity data and a pressure of 11 dbar which represents the water depth of the inlet of the TSG system on Polarstern. Processed data are provided as 10min means of salinity and water temperature aligned with position data taken from master track of the respective cruise. A speed filter was not applied to the PS122 dataset because of the slow drift speed. This slow movement may lead to an overestimation of the mixed-layer temperature; in particular, small heat fluxes from the ship may raise the temperature in adjacent water in the lee, i.e. during times of drift in the direction opposite to the TSG inlet. The effect is expected to be small, but can potentially be higher than the accuracy of the temperature measurement. Further details and evaluation of the data is outlined in the data processing report found at the EPIC repository under URL (https://hdl.handle.net/10013/epic.7fffb528-06bd-48ae-8489-cea0444c4eab).

Description: Raw data acquired by two thermosalinographs (SBE21, SeaBird GmbH) on board RV Polarstern were processed to yield a calibrated and validated data set of temperature, conductivity and salinity during expedition PS122/3. Both sensors were equipped with a more accurate external temperature sensor (SBE38, Sea-Bird GmbH). Data were downloaded from the DAVIS SHIP data base (https://dship.awi.de) with a resolution of 1 sec. The raw hex data were converted to temperature and conductivity while a sensor drift correction was applied using calibration coefficients from before and after the expedition. Salinity was calculated according to the instructions from the Practical Salinity Scale PSS-78, using the obtained (internal) temperature and conductivity data and a pressure of 11 dbar which represents the water depth of the inlet of the TSG system on Polarstern. Processed data are provided as 10min means of salinity and water temperature aligned with position data taken from master track of the respective cruise. A speed filter was not applied to the PS122 dataset because of the slow drift speed. This slow movement may lead to an overestimation of the mixed-layer temperature; in particular, small heat fluxes from the ship may raise the temperature in adjacent water in the lee, i.e. during times of drift in the direction opposite to the TSG inlet. The effect is expected to be small, but can potentially be higher than the accuracy of the temperature measurement. Further details and evaluation of the data is outlined in the data processing report found at the EPIC repository under URL (https://hdl.handle.net/10013/epic.7fffb528-06bd-48ae-8489-cea0444c4eab).

Description: The deep-sea is the largest habitat on Earth, but its biodiversity and ecosystem dynamics are still underexplored. Deep-sea sponge grounds (syn. aggregations, gardens) are sponge-dominated ecosystems that are found throughout the world´s oceans. They are considered vulnerable marine ecosystems (VMEs) and warrant protection against human intervention. Deep-sea sponge grounds are considered hotspots of diversity and function in the deep ocean. While a significant body of information has been accrued on shallow-water sponges, our understanding of deep-sea sponges and their associated microbiomes at the beginning of this PhD thesis was still very limited. This PhD thesis therefore aims to provide a first comprehensive overview on the diversity, evolution, biogeography, and ecology of deep-sea sponge microbiomes. The overarching aim was to assess whether the concepts obtained in shallow-water sponge microbiology would also hold in the deep-sea. In addition, novel themes such as biogeochemistry, physical oceanography, and trait-based approaches were integrated and further expand the existing theoretical framework in sponge microbiology. Sampling was conducted during 20 deep-sea expeditions, largely to sponge grounds of the North Atlantic in the context of the EU project “SponGES: Deep-sea sponge grounds ecosystems of the North Atlantic - an integrated approach towards their preservation and sustainable exploitation”. In total 1077 sponge-associated microbiomes were sampled along with 355 seawater microbiomes and 114 sediment microbiomes from 52 sponge ground locations. Microbial diversity was assessed by 16S rRNA gene amplicon sequencing and host taxonomy was determined by a combination of taxonomic and molecular markers. To this end, a state-of-the-art high-throughput 16S amplicon pipeline was established and corresponding metadata workflows were developed. The resulting data were analysed by six specific case studies (of which all were published) and one overarching meta-analysis (manuscript in preparation). The microbial community composition of deep-sea sponges was explored across different scales, from the ecosystem- and biogeography-level, to individual sponge species and to the microbial taxon (Amplicon Single nucleotide Variant; ASV) level. By exploring sponge microbiomes on different levels of integration and by using a nested sampling design, I was able to identify overarching factors, that drive microbiome composition in a statistically proven manner. The main identified environmental drivers of microbial community variability were temperature, salinity, nutrients/oxygen, and depth. It is noteworthy, that these parameters were identified from a total set of 24 environmental parameters. Furthermore, sponge phylogeny, taxonomy, and morphology were found to be related with the microbial community composition. Interestingly, microbial diversity can be predicted based on sponge morphology, which offers exciting opportunities for future studies in respect to imaging or trait-based approaches. My conclusions on the microbiome composition of deep-sea sponge microbiomes are that each deep-sea sponge harbours an individual set of microbes and a large pool of hidden diversity. Furthermore, deep-sea sponge microbiomes are globally not well connected and rather display heterogeneity on local scales. Interestingly, a deep-sea specific sponge microbiome was discovered. Overall, the results of my thesis suggest a strong nestedness of deep-sea sponge microbiomes within their ecological context. In the context of this PhD thesis, I established a baseline of deep-sea sponge-associated microbiomes, discovered a large extent of novel diversity and described patterns of specificity, stability and variability. I further identified the environmental and host-related drivers of sponge microbiome composition. From a methodological point, I have designed and developed a software tool (termed SVAmpEx) that allows the archiving and user-friendly accessibility of deep-sea sponge microbiome baseline data. Since microbiome composition is directly related to sponge health, reference baselines are valuable to monitor the integrity and resilience of deep-sea sponges. The collective information gathered in this PhD thesis provides the scientific basis to improve conservation and management strategies of the vulnerable deep-sea sponge ground ecosystems in the long run.

Description: The ²³⁴Th-²³⁸U radioactive pair has been extensively used to evaluate the efficiency with which photosyntetically fixed carbon is exported from the surface ocean by means of the biological pump since the 90's. The seminal work of Buesseler et al. (1992) proposed that particulate organic carbon (POC) flux can be indirectly calculated from ²³⁴Th distributions if the ratio of POC to ²³⁴Th measured on sinking particles (POC:²³⁴Th) at the desired export depth is known. Since then, a huge amount of ²³⁴Th depth profiles have been collected using a variety of sampling instruments and strategies that have changed along years. This is a global oceanic compilation of ²³⁴Th measurements, that collects results from innumerable researchers and laboratories over a period exceeding 50 years. The present compilation is made of a total 223 datasets: 214 from studies published either in articles in referred journals, PhD thesis or repositories, and 9 unpublished datasets. Including measurements from JGOFS, VERTIGO and GEOTRACES programs, with sampling from approximately 5000 locations spanning all the oceans. The compilation includes total ²³⁴Th profiles, dissolved and particulate ²³⁴Th concentrations, and POC:²³⁴Th ratios (both from pumps and sediment traps) for two sizes classes (1-53 μm and 〈 53 μm) when available. Appropriate metadata have been included, including geographic location, date, and sample depth, among others. When available, we also include water temperature, salinity, ²³⁸U data and particulate organic nitrogen data. Data sources and methods information (including ²³⁸U and ²³⁴Th) are also detailed along with valuable information for future data analysis such as bloom stage and steady/non-steady state conditions at the sampling moment. This undertaking is a treasure of data to understand and quantify how oceanic carbon cycle functions and how it will change in future. The compilation can be downloaded in three different ways: 1) A single merged file including all the individual excel files. This option can be accessed under "Other version: More than 50 years of Th-234 data: a comprehensive global oceanic compilation (single xlsx file)". 2) A summary table that includes details from cruise, sampling dates, techniques applied, authors and DOI of the compiled ²³⁴Th data, among others, each line corresponds to a specific dataset. The table can be accessed by clicking ""View dataset as HTML" and downloaded in "Download dataset as tab-delimited text". 3) Individual Excel files for each dataset can be manually chosen from the summary table, corresponding to the complete ²³⁴Th dataset and metadata from a specific publication or program. This option is available by clicking "View dataset as HTML". Furthermore, all files referred to can be downloaded in one go as ZIP or TAR.

Description: A gas inlet was installed at the bow of RV Poseidon. Atmospheric gas concentrations of CH4 and CO2 were measured continuously with a Picarro G2301-f analyzer.

Description: This study estimated the short-term decomposition effects of the invasive jellyfish Rhopilema nomadica on nutrient dynamics at the sediment-water interface in the Eastern Mediterranean Sea using core incubations. The degradation of R. nomadica has led to increased oxygen demand and acidification of overlying water as well as high rates of dissolved organic nitrogen and phosphate production.