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Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks. This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Spatial extent This subdataset covers the Rocky Mountains CONUS, i.e. CO ID MT UT WY For the remaining CONUS, see the related identifiers. Temporal extent The map is representative for ca. 2018. Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. t at 10m x 10m kt at 100m x 100m Mt at 1km x 1km Gt at 10km x 10km For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layers Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers): A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337. Further information For further information, please see the publication. A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Publication D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. In prep Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404. Acknowledgments We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

Humanity's role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the 'anthropocene', as humans are 'overwhelming the great forces of nature'. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed 'manufactured capital', 'technomass', 'human-made mass', 'in-use stocks' or 'socioeconomic material stocks', they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with 'real' (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called 'built structures') represent the overwhelming majority of all socioeconomic material stocks. This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Spatial extentThis dataset covers the whole CONUS. Due to upload constraints, detailed data were split into 7 regions and were uploaded into sub-repositories - see related identifiers. (This repository holds aggregated values for the whole CONUS) Great Plains Mid West North East Rocky Mountains South South West West Coast Temporal extentThe map is representative for ca. 2018. Data formatThe data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. t at 10m x 10m kt at 100m x 100m Mt at 1km x 1km Gt at 10km x 10km For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layersNote that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers): A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337. Further informationFor further information, please see the publication.A web-visualization of this dataset is available here.Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. PublicationD. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gómez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, and H. Haberl (2023): Unveiling patterns in human dominated landscapes through mapping the mass of US built structures. Nature Communications 14, 8014. https://doi.org/10.1038/s41467-023-43755-5 FundingThis research was primarly funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404. AcknowledgmentsWe thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC, and Wolfgang Wagner for granting access to preprocessed Sentinel-1 data.

Climate change is impacting virtually all marine life. Adaptation strategies will require a robust understanding of the risk to species and ecosystems and how those propagate to human societies. We develop a unified and spatially explicit index to comprehensively evaluate the climate risks to marine life. Under high emissions (SSP5-8.5), almost 90% of ~25,000 species are at high or critical risk, with species at risk across 85% of their native distributions. One-tenth of the ocean contains ecosystems where the aggregated climate risk, endemism, and extinction threat of their constituent species are high. Climate change poses the greatest risk for exploited species in low-income countries with high dependence on fisheries. Mitigating emissions (SSP1-2.6) reduces the risk for virtually all species (98.2%), enhances ecosystem stability, and disproportionally benefits food-insecure populations in low-income countries. Our climate risk assessment can help prioritize vulnerable species and ecosystems for climate-adapted marine conservation and fisheries management efforts.

Gonatus fabricii (Gonatidae)Time series; Year; n squids; Area; SourceLate XIXth C.; 1882; 1; Baffin Bay; BycatchLate XIXth C.; 1891; 1; ---; ---;1990s; 1900; 9; Nordic Seas; Northern fulmar's stomach contents1930s; 1930; 2; Davis Strait; Sleeper shark's stomach contents1930s; 1936; 5; Baffin Bay; Bycatch1970s; 1975; 5; ---; Bycatch1970s; 1977; 2; ---; Bycatch1970s; 1979; 2; ---; Bycatch2000s; 2004; 10; ---; Bycatch2010s; 2010; 10; Nordic Seas; Sperm whale's stomach contentsTodarodes sagittatus1840s; 1844; 4; Faroe Islands; Long-finned pilot whale's stomach contents1880s; 1881; 10; Iceland; Long-finned pilot whale's stomach contents1890s; 1897; 10; Faroe Islands; Long-finned pilot whale's stomach contentsContemporary 2016; 1; Ireland; BycatchContemporary; 2017; 1; Ireland; BycatchContemporary; 2018; 1; Ireland; BycatchContemporary; 2023; 4; Ireland; Bycatch Raw data obtained from stable isotope analysis of δ13C and δ15N in beaks of the squids Gonatus fabricii (Lichtenstein, 1818) and Todarodes sagittatus (Lamarck, 1798) (Cephalopoda: Oegopsida), and primary analyses of these data. Squids sampled in the Baffin Bay, Davis Strait and Nordic Seas (1882-2010) and Iceland, Faroe Islands and Ireland (1844-2023), respectively. The beaks either come from the squids caught as bycatch, or from natural history museums, from stomach contents of predators.

Between September 2016 and August 2017, we conducted year-long reciprocal transplantation experiments using the cold-water coral Desmophyllum dianthus along natural oceanographic horizontal and vertical gradients (vertically: 20 m to 300 m depth and horizontally: head to mouth of fjord) in Comau Fjord to study seasonal changes and the acclimatisation potential of its biochemical composition. Seasonal energy reserves (proteins, carbohydrates and lipids) and the C:N ratio of native and novel (cross-transplanted) corals were measured at six shallow (A-F, 20 m) and one deep station (Ed, 300 m) during autral summer (January), autumn (May) and winter (August).

Laboratory experiments were conducted in the climate chambers at GEOMAR Helmholtz Centre for Ocean Research Kiel in the time between March and November 2018. Experiments were designed to study the effect of long-term (1 month) exposure to low salinity in osmoconforming invertebrates. The study organisms (Asterias rubens, Mytilus edulis, Littorina littorea, Diadumene lineata, Strongylocentrotus droebachiensis and Psammechinus milliaris) were collected in Kiel Fjord, Eckernförder Bight or the Kattegat from spring to autumn 2018. Organisms were acclimated to climate chamber conditions for 1 week (under habitat salinity, 14˚C, constant aeration) and then subjected to salinity acclimation for 1-2 weeks until the final salinity treatment level was reached. Then different salinity treatments were maintained for 4 weeks. Water physiochemistry (temperature, salinity, pH, nitrite, nitrate, phosphate) was recorded frequently. Throughout the experiment survival was recorded. Before and after the experiment organism weight and, where feasible, size was measured. Weight data was also used to calculate tissue water content. This dataset comprises the physiological fitness parameters for each species at the respective salinity treatment. Given are data for survival, growth and tissue water content.

Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks. This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Spatial extent This subdataset covers the North East CONUS, i.e. CT DC DE MA MD ME NH NJ NY PA RI VA For the remaining CONUS, see the related identifiers. Temporal extent The map is representative for ca. 2018. Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. t at 10m x 10m kt at 100m x 100m Mt at 1km x 1km Gt at 10km x 10km For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layers Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers): A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337. Further information For further information, please see the publication. A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Publication D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. In prep Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404. Acknowledgments We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

Climate change has profound impacts on marine biodiversity and biodiversity changes in turn might affect the community sensitivity to impacts of abiotic changes. We used mesocosm experiments and Next Generation Sequencing to study the response of the natural Baltic and Mediterranean unicellular eukaryotic plankton communities (control and +6°C heat shock) to subsequent salinity changes (-5 psu, +5 psu). The impact on Operational Taxonomic Unit (OTU) richness, taxonomic and functional composition and rRNA:rDNA ratios were examined. Our results showed that heat shock leads to lower OTU richness (21% fewer OTUs in the Baltic and 14% fewer in the Mediterranean) and a shift in composition toward pico- and nanophytoplankton and heterotrophic related OTUs. Heat shock also leads to increased rRNA:rDNA ratios for pico- and micrograzers. Less than 18% of shared OTUs were found among the different salinities indicating the crucial role of salinity in shaping communities. The response of rRNA:rDNA ratios varied highly after salinity changes. In both experiments the diversity decrease brought about by heat shock influenced the sensitivity to salinity changes. The heat shock either decreased or increased the sensitivity of the remaining community, depending on whether it removed the more salinity-sensitive or the salinity-tolerant taxa.