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Dataset Title:  Relative Bycatch:Target Catch Probability Product (daily), EcoCast Project Subscribe RSS
Institution:  NOAA NMFS SWFSC ERD   (Dataset ID: ecocast)
Information:  Summary ? | License ? | FGDC | ISO 19115 | Metadata | Background (external link) | Make a graph
Dimensions ? Start ? Stride ? Stop ?  Size ?    Spacing ?
 time (Centered Time, UTC) ?      46    1 day (even)
  < slider >
 latitude (degrees_north) ?      71    -0.2487562 (even)
  < slider >
 longitude (degrees_east) ?      65    0.2487562 (even)
  < slider >
Grid Variables (which always also download all of the dimension variables)
 ecocast (EcoCast Relative Bycatch-Target Catch Probability Product, 1) ?

File type: (more info)

(Documentation / Bypass this form) ?
(Please be patient. It may take a while to get the data.)


The Dataset Attribute Structure (.das) for this Dataset

Attributes {
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.5258672e+9, 1.5297552e+9;
    String axis "T";
    String calendar "gregorian";
    String coverage_content_type "coordinate";
    String ioos_category "Time";
    String long_name "Centered Time";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 actual_range 29.652364600000002, 47.0653;
    String axis "Y";
    String comment "Latitude values are the centers of the grid cells";
    String coverage_content_type "coordinate";
    String ioos_category "Location";
    String long_name "Latitude";
    String point_spacing "even";
    String standard_name "latitude";
    String units "degrees_north";
    Float64 valid_max 48.0;
    Float64 valid_min 29.0;
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 actual_range -131.5914, -115.67100192;
    String axis "X";
    String comment "Longitude values are the centers of the grid cells";
    String coverage_content_type "coordinate";
    String ioos_category "Location";
    String long_name "Longitude";
    String point_spacing "even";
    String standard_name "longitude";
    String units "degrees_east";
    Float64 valid_max -115.0;
    Float64 valid_min -132.0;
  ecocast {
    Float64 _FillValue -9999.0;
    String cell_methods "time: mean (interval: 1.0 day)";
    String colorBarMaximum "1.0";
    String colorBarMinimum "-1.0";
    String colorBarPalette "RedWhiteBlue";
    String comment "The relative likelihood of catching target species vs bycatch species given the current species weightings. The values range from -1 to 1, indicating high likelihoods of catching bycatch species and target species, respectively.";
    String coverage_content_type "modelResult";
    String ioos_category "Ecology";
    String long_name "EcoCast Relative Bycatch-Target Catch Probability Product";
    String units "1";
    Float64 valid_max 1.0;
    Float64 valid_min -1.0;
    String acknowledgment "We thank the scientific teams and all those who supported animal tagging efforts in addition to the SWFSC fisheries observer program that collected bycatch data aboard drift gillnet vessels. We are grateful to the numerous captains and crews who provided ship time and logistical support, and NOAA regional managers including Heidi Taylor and Tina Fahy that provided feedback and support along the way. We also thank Lucie Hazen at Stanford’s Center for Ocean Solutions for logistical and meeting support towards achieving the NASA project goals. This project also was a brain-child of the late Dave Foley whose career was dedicated to incorporate oceanographic data into fisheries management.";
    String cdm_data_type "Grid";
    String comment "Match up the time from this dataset to the time in the EcoCast Inputs dataset ( the obtain species weightings and enviromental data dates used to generate the EcoCast Maps";
    String contributor_name "Elliott L. Hazen, Dana K. Briscoe, Heather Welch, Steven J. Bograd, Dale Robinson, Tomo Eguchi, Heidi Dewar, Suzy Kohin, Daniel P. Costa, Scott R. Benson (NOAA Southwest Fisheries Science Center / University of California Santa Cruz), Rebecca Lewison (San Diego State University), Helen Bailey (University of Maryland Center for Environmental Science), Sara M. Maxwell (Old Dominion University), Larry B. Crowder (Stanford University)";
    String contributor_role "Co-PIs";
    String Conventions "CF-1.6, COARDS, ACDD-1.3";
    String creator_email " ;";
    String creator_name "NOAA NMFS SWFSC ERD";
    String creator_url "";
    Float64 Easternmost_Easting -115.67100192;
    Float64 geospatial_lat_max 47.0653;
    Float64 geospatial_lat_min 29.652364600000002;
    Float64 geospatial_lat_resolution 0.24875621999999997;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -115.67100192;
    Float64 geospatial_lon_min -131.5914;
    Float64 geospatial_lon_resolution 0.24875621999999997;
    String geospatial_lon_units "degrees_east";
    String history 
    String infoUrl "";
    String institution "NOAA NMFS SWFSC ERD";
    String keywords "blue shark, California, California sea lion, Dermochelys coricea, dynamic ecosystem management, Earth Science > Biological Classification > Animals/Vertebrates > Fish > Ray-Finned Fishes, Earth Science > Biological Classification > Animals/Vertebrates > Fish > Sharks/Rays/Chimaeras, Earth Science > Biological Classification > Animals/Vertebrates > Mammals > Carnivores > Seals/Sea Lions/Walruses, Earth Science > Biological Classification > Animals/Vertebrates > Reptiles > Turtles, Earth Science > Human Dimensions > Environmental Governance/Management > Environmental Regulations, Earth Science > Human Dimensions > Sustainability > Environmental Sustainability, Earth Science > Oceans > Marine Environment Monitoring, Ecocast, leatherback sea turtle, NOAA, ocean, Prionace glauca, Southwest fisheries, SWFSC, Zalophus californianus";
    String keywords_vocabulary "NASA Global Change Master Directory (GCMD) Keywords, Version 7.0.0";
    String license "These data are available for use without restriction.  Please acknowledge the use of these data by citing the following publications: Hazen et al. Sustaining pelagic fisheries: An Eco-Informatic solution to fisheries bycatch. In prep.  Welch et al. Practical considerations for operationalizing dynamic management tools. In prep.). The data may be used and redistributed for free but are not intended or legal use, since they may contain inaccuracies. Neither the data contributor, ERD, NOAA, nor the United States Government, nor any of their employees or contractors, makes any warranty, express or implied, including warranties of merchantability and fitness for a particular purpose, or assumes any legal liability for the accuracy, completeness, or usefulness, of this information.";
    String nameing_authority "gov.noaa.pfeg.coastwatch";
    Float64 Northernmost_Northing 47.0653;
    String project "EcoCast";
    String publisher_email "";
    String publisher_name "NOAA NMFS SWFSC ERD";
    String publisher_url "";
    String references ",";
    String source_data "CoastWatch West Coast ERDDAP (ncdcOwDly_LonPM180, jplG1SST, jplMURSST41), CMEMS (SEALEVEL_GLO_SLA_MAP_L4_NRT_OBSERVATIONS_008_026), AVISO+ (msla)";
    String sourceUrl "";
    Float64 Southernmost_Northing 29.652364600000002;
    String summary "The Relative Bycatch:Target Catch Probability Product is produced using a data-driven, multi-species predictive habitat modelling framework. First, boosted regression tree models were fit to determine the habitat preferences of the target species, broadbill swordfish (Xiphias gladius), and three bycatch-sensitive species that interact with the California drift gillnet fishery (leatherback sea turtle (Dermochelys coricea), blue shark (Prionace glauca), California sea lion (Zalophus californianus)). Then, individual species weightings were set to reflect the level of bycatch and management concern for each species. Prediction layers for each species were then combined into a single surface by multiplying the layer by the species weighting, summing the layers, and then re-calculating the range of values in the final predictive surface from -1 (low catch & high bycatch probabilities) to 1 (high catch & low bycatch probabilities).";
    String time_coverage_end "2018-06-23T12:00:00Z";
    String time_coverage_resolution "PD1";
    String time_coverage_start "2018-05-09T12:00:00Z";
    String title "Relative Bycatch:Target Catch Probability Product (daily), EcoCast Project";
    Float64 Westernmost_Easting -131.5914;


Using griddap to Request Data and Graphs from Gridded Datasets

griddap lets you request a data subset, graph, or map from a gridded dataset (for example, sea surface temperature data from a satellite), via a specially formed URL. griddap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its projection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

griddap request URLs must be in the form{?query}
For example,[(2002-06-01T09:00:00Z)][(-89.99):1000:(89.99)][(-179.99):1000:(180.0)]
Thus, the query is often a data variable name (e.g., analysed_sst), followed by [(start):stride:(stop)] (or a shorter variation of that) for each of the variable's dimensions (for example, [time][latitude][longitude]).

For details, see the griddap Documentation.

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