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Waterbase - Biology, 2024

Waterbase serves as the EEA’s central database for managing and disseminating data regarding the status and quality of Europe's rivers, lakes, groundwater bodies, transitional, coastal, and marine waters. It also includes information on the quantity of Europe’s water resources and the emissions from point and diffuse sources of pollution into surface waters. Specifically, Waterbase - Biology focuses on biology data from rivers, lakes, transitional and coastal waters collected annually through the Water Information System for Europe (WISE) – State of Environment (SoE) reporting framework. The data are expected to be collected within monitoring programs defined under the Water Framework Directive (WFD) and used in the classification of the ecological status or potential of rivers, lakes, transitional and coastal water bodies. These datasets provide harmonised, quality-assured biological monitoring data reported by EEA member and cooperating countries, as Ecological Quality Ratios (EQRs) from all surface water categories (rivers, lakes, transitional and coastal waters).

GTS Bulletin: WOSN01 ESNZ - Warnings (details are described in the abstract)

The WOSN01 TTAAii Data Designators decode as: T1 (W): Warnings T1T2 (WO): Other A1A2 (SN): Sweden (Remarks from Volume-C: NilReason)

GTS Bulletin: FXDL44 EDDM - Forecast (details are described in the abstract)

The FXDL44 TTAAii Data Designators decode as: T1 (F): Forecast T1T2 (FX): Miscellaneous A1A2 (DL): Germany (The bulletin collects reports from stations: EDDM;MUNICH INT ;) (Remarks from Volume-C: BALLONING BULLETIN (IN GERMAN))

A spatially explicit Global Reef Island Database (GRID) that captures distribution, diversity and relative vulnerability of the world's low-lying reef islands

Low-lying coral reef islands harbour a distinct, yet highly threatened biological and cultural diversity that is increasingly exposed to climate change impacts. The combination of low elevation, small size, sensitivity to changes in boundary conditions (sea level, waves and currents, locally generated sediment supply) and at some locations high population densities, is why low-lying reef islands (LRIs) are considered among the most vulnerable environments on Earth to climate change. To date, their global distribution and influence of climatic, oceanographic, and geologic setting are only poorly documented or restricted to smaller scales. Here, I present the first detailed global analysis of LRIs utilising freely available global datasets to produce a global reef island database (GRID) and associated intrinsic and extrinsic characteristics that can be used within a coastal vulnerability index (CVI). All datasets used to create the GRID were released between 30 November 2015 and 3 August 2023, while the current version of the GRID database was completed in November 2024. When developing the GRID, LRIs are defined as landmasses <30 km² located on or within 1 km of coral reef and with an elevation of <16 m. Development of the GRID required: 1) the creation of a global shoreline vector file containing the geographic distribution of LRIs and 2) the development of a comprehensive global database of LRIs including eight intrinsic and ten extrinsic variables extracted from global datasets. Intrinsic variables include: 1) human populations, 2) island area, 3) island perimeter, 4) mean elevation, 5) island circularity/shape, 6) underlying reef type, 7) geographic isolation and 8) distance to the nearest neighbouring reef island. Extrinsic variables include: 1) mean water depth, 2) standard deviation of mean water depth, 3) mean annual significant wave height, 4) mean annual wave period, 5) mean spring tidal range, 6) relative tidal range, 7) wave-tide regime, 8) relative wave exposure, 9) relative tropical storm exposure and 10) year-2100 projected median sea level rise rate. The GRID was initially derived from version 2.1 of the UNEP-WCMC Global Island Database, a global shoreline vector file based on geometry data from Open Street Map® (OSM) and released in November 2015. The initial vector file was projected using the Mollweide projection, an equal-area pseudo cylindrical map projection chosen for its accurate derivation of area, especially in regions close to the equator, where most LRIs are located. The final GRID contains 34,404 individual LRIs distributed throughout tropical regions of the world's oceans, amassing a total land area of nearly 11,000 km² with approximately 60,740 km of shoreline and housing around 2.6 million people. While intrinsic variables are typically spatially homogenous, LRIs are generally highly spatially clustered throughout the GRID with respect to extrinsic variables. The spatial distribution of LRIs within the GRID was validated using: 1) published data and 2) quantitative accuracy assessments using satellite imagery. Spatial distributions of LRIs captured in the GRID are extremely consistent with those published in the literature (r² = 0.96) and those derived from independent analysis of satellite imagery (r² = 0.94). Finally, the GRID was used to develop an island vulnerability index (IVI) for each LRI on a scale of 0-1 with 0 representing no vulnerability and 1 representing maximum vulnerability. The GRID database is provided as a tab-delimited text file as well as ESRI shapefiles (points and polygons in WGS84 and Mollweide projection) and a comma-separated value file.

Entwicklung eines intelligenten Nachhaltigkeitsrechners von Infrastrukturprojekten auf Basis digitaler, georeferenzierter BIM-Modelle, Teilvorhaben: albert.ing GmbH

Grasslands for biodiversity: supporting the protection of the biodiversity-rich grasslands and related management practices in the Alps and Carpathians

Semi-natural grasslands are among the most species-rich habitats in Europe but have sharply declined in spatial extent and biodiversity in recent decades. Within Europe, the grasslands of the Alps and the Carpathians harbour extraordinary plant diversity but their biodiversity varies significantly due to local environmental conditions and management intensities. Thus, there is general agreement that, in order to prevent further grassland biodiversity loss, the protection, enhancement and potential expansion of species-rich grasslands is necessary. Knowledge of the areas suitable for protection, enhancement and potential expansion comes largely from vegetation samples and experimental studies. However, these are unaffordable and unfeasible for systematic evaluation of biodiversity patterns over large areas. Further, existing monitoring programs generally lack information on grassland management regimes and a historical perspective, both of which can strongly influence current biodiversity. Fortunately, the availability of earth observational data over large areas now allows extrapolation of field measurements over time and space with acceptable accuracy. Combining these data with biodiversity datasets and an understanding of the socioeconomic context offers powerful opportunities for reaching conservation targets. The aims of the proposed project are to (1) identify diversity-rich grasslands and their distribution in the Alps and Carpathians; (2) identify diversity-supporting grassland management practices and their change and persistence; (3) identify the areas suitable for expanding the grassland protection network; and (4) propose new protection areas and their management across Alps and Carpathians. By addressing these aims we will cooperate with stakeholders to (i) identify effective methods for extrapolation of vegetation samples across the mountain ranges; (ii) identify the grassland management drivers and legacy effects on grassland diversity; (iii) identify constraints and motivations for biodiversity-supporting management practices (iv) provide scientific background for expanding the protection area network in the Alps and Carpathians. The proposed research provides a great opportunity to strengthen the cooperation, data and knowledge exchange between the researchers and stakeholders across the two largest mountain ranges in Europe: the Alps and the Carpathians.

GTS Bulletin: FCSN32 ESSV - Forecast (details are described in the abstract)

The FCSN32 TTAAii Data Designators decode as: T1 (F): Forecast T1T2 (FC): Aerodrome (VT < 12 hours) A1A2 (SN): Sweden (Remarks from Volume-C: NilReason)

GTS Bulletin: PWEE70 EDZW - Pictorial information (Binary coded) (details are described in the abstract)

The PWEE70 TTAAii Data Designators decode as: T1 (P): Pictorial information (Binary coded) T1T2 (PW): Wind A1 (E): 0° - 90°W tropical belt A2 (E): 24 hours forecast T1ii (P70): 700 hPa (Remarks from Volume-C: WAFS W+T H+24 FL 100 (ICAO A))

Master tracks in different resolutions of ELISABETH MANN BORGESE cruise EMB383, Rostock - Rostock, 2025-07-19 - 2025-07-20

Raw data acquired by position sensors on board RV Elisabeth Mann Borgese during expedition EMB383 were processed to receive a validated master track which can be used as reference of further expedition data. During EMB383 data from the motion reference unit exail Phins Gen.3, the Trimble SPS356 GPS receiver, the Furuno GP-170 and the Furuno GP-150 GPS receiver were used to calculate the mastertrack. Data were downloaded from DAVIS SHIP data base (https://dship.bsh.de) with a resolution of 1 sec. Processing and evaluation of the data is outlined in the data processing report. Processed data are provided as a master track with 1 sec resolution derived from the position sensors' data selected by priority and a generalized track with a reduced set of the most significant positions of the master track.

Edaphobase

Edaphobase is a GBIF-D project that collects information from literature, museum collections and research data about the distribution and ecology of soil organisms (earthworms, potworms, nematodes, springtails, proturans, diplurans, moss/beetle mites, gamasina mites, centipedes, millipedes, woodlice, soil fungi and soil prokaryotes). After March 2023, some data previously included in the overall Edaphobase-dataset have now been marked as their own specific datasets. If you are referencing the Edaphobase-dataset before this date, it will include all datasets uploaded to GBIF from the GBIF publisher 'Edaphobase'.

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