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| griddap | Subset | tabledap | Make A Graph | wms | files | Accessible | Title | Summary | FGDC | ISO 19115 | Info | Background Info | RSS | Institution | Dataset ID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_773466 | https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_773466.graph | https://erddap.bco-dmo.org/erddap/files/bcodmo_dataset_773466/ | public | [Global Bleaching and Environmental Data] - Bleaching and environmental data for global coral reef sites from 1980-2020 (Identifying coral reef 'bright spots' from the global 2015-2017 thermal-stress event) | The coral bleaching data were composed of the Reef Check dataset (reefcheck.org), collected by a mixture of professional scientists (56%) and trained and certi\\ufb01ed citizen-scientists (44%) using a standardized transect protocol. The dataset includes counts of the number of coral colonies showing bleaching (i.e., the percent of reef corals that were recorded as bleached), which was classi\\ufb01ed as site-wide bleaching. We also examined the prevalence of coral bleaching per coral ecoregion (as de\\ufb01ned by Veron et al. 2015). We used the global Coral Reef Temperature Anomaly Database (CoRTAD Version 6) from the National Oceanic and Atmospheric Administration (www.nodc.noaa.gov/sog/cortad/) to predict coral bleaching prevalence and intensity across reefs worldwide. All CoRTAD variables were weekly data provided on a grid cell basis, of ~4km resolution, from 1982 to 2017(Sully 2019).\n\ncdm_data_type = Other\nVARIABLES:\nID (unitless)\nlatitude (degrees_north)\nlongitude (degrees_east)\nOcean (unitless)\nRealm (unitless)\nEcoregion (unitless)\nCountry_Name (unitless)\nState_Island_Province (unitless)\nCity_Town (unitless)\nCity_Town_2 (unitless)\nCity_Town_3 (unitless)\nDate (unitless)\nDate2 (unitless)\ndepth (m)\nAverage_Bleaching (percent)\nClimSST (Clim SST, Degrees Celsius)\nTemperature_Kelvin (Kelvin)\nTemperature_Mean (Degree Celsius)\n... (30 more variables)\n | https://erddap.bco-dmo.org/erddap/metadata/fgdc/xml/bcodmo_dataset_773466_fgdc.xml | https://erddap.bco-dmo.org/erddap/info/bcodmo_dataset_773466/index.htmlTable | https://www.bco-dmo.org/dataset/773466
| https://erddap.bco-dmo.org/erddap/rss/bcodmo_dataset_773466.rss | https://erddap.bco-dmo.org/erddap/subscriptions/add.html?datasetID=bcodmo_dataset_773466&showErrors=false&email= | BCO-DMO | bcodmo_dataset_773466 | ||||
| log in | [Ocean Conditions at Seabird Colony Sites] - Seasonally and spatially averaged stratification mixed layer temperature and chlorophyll-a around Northern Hemisphere seabird colonies from 1993 to 2019 (Stratification impacts on seabirds project) (Global analysis of stratification impacts on seabirds through food resources ) | This dataset provides seasonal averages of mixed layer temperature, water column stratification, and mixed layer chlorophyll-a content for coastal marine regions around the world using the Marine Ecoregions of the World (MEOW) framework. The MEOW framework spatially partitions coastal marine areas into a hierarchical, nested structure based on biogeographic and environmental characteristics. Seven ecosystems are represented in the dataset: the Arctic, Cold Temperate Northeast Pacific, Cold Temperate Northwest Pacific, Warm Temperate Northeast Pacific, Hawaii, Northern European Seas, and Cold Temperate Northwest Atlantic, which are roughly equivalent to Large Marine Ecosystems. Environmental data was extracted for the domain of these seven ecosystems using the GLORYS12V1 global ocean reanalysis, which resolves monthly ocean conditions at 1/12-degree, daily resolution across 50 vertical depth levels. This state-of-the-art reanalysis assimilates historical data from multiple sources, including satellite observations and buoy data, and covers the period from 1993 to 2019. We extracted temperature, mixed layer depth, and salinity for the upper 200 m of the water column, which were used to calculate coastal stratification. Stratification was quantified using the Potential Energy Anomaly (PEA), a robust metric that measures the energy required to thoroughly mix the water column and reflects seasonal stratification patterns. All environmental variables were spatially averaged over corresponding ecoregions and temporally averaged over the pre-breeding and breeding seasons of seabird colonies within the ecoregion. By offering detailed classifications and environmental characterizations, this dataset serves as a valuable resource for analyzing regional oceanographic patterns and understanding how physical processes shape seabird populations and marine ecosystems.\n\ncdm_data_type = Other\nVARIABLES:\nidnum (exp_id)\nyear (years)\nspp (unitless)\nScientificName_accepted (unitless)\nAphiaID (unitless)\nLSID (unitless)\nsite (unitless)\nsppsite (unitless)\n... (27 more variables)\n | BCO-DMO | bcodmo_dataset_985768_v1 |