BCO-DMO ERDDAP
Accessing BCO-DMO data

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Dataset Title:	[Algal Habitat Coverage] - Percent cover of algal habitat types on a by- transect and/or subsample basis recorded during emergent and rapid emergent surveys conducted in the subtidal zone of northern California, Sonoma and Mendocino counties, from 1999 to 2023 (Collaborative Research: The effects of marine heatwaves on reproduction, larval transport and recruitment in sea urchin metapopulations)
Institution:	BCO-DMO (Dataset ID: bcodmo_dataset_929546_v1)
Range:	longitude = -123.829 to -123.0715°E, latitude = 38.31536 to 39.42859°N
Information:	Summary \| License \| FGDC \| ISO 19115 \| Metadata \| Background \| Data Access Form \| Files

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Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

Web page authors can embed a graph of the latest data in a web page using HTML <img> tags.
Anyone can use ERDDAPs Slide Sorter to build a personal web page that displays graphs with the latest data (or other images or HTML content), each in its own, draggable slide.

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Survey_Num {
    String long_name "Survey_num";
    String units "unitless";
  }
  DFW_short_code {
    String long_name "Dfw_short_code";
    String units "unitless";
  }
  SiteName {
    String long_name "Sitename";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float32 actual_range 38.31536, 39.42859;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float32 actual_range -123.829, -123.0715;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  SurveyType {
    String long_name "Surveytype";
    String units "unitless";
  }
  SurveyDate {
    String long_name "Surveydate";
    String units "unitless";
  }
  Year {
    Int32 actual_range 1999, 2016;
    String long_name "Year";
    String units "unitless";
  }
  Month {
    Int32 actual_range 1, 12;
    String long_name "Month";
    String units "unitless";
  }
  Day {
    Int32 actual_range 1, 31;
    String long_name "Day";
    String units "unitless";
  }
  Timezone {
    String long_name "Timezone";
    String units "unitless";
  }
  Subsample {
    String long_name "Subsample";
    String units "unitless";
  }
  HabitatID {
    String long_name "Habitatid";
    String units "unitless";
  }
  Habitat {
    String long_name "Habitat";
    String units "unitless";
  }
  PercentCover {
    Float32 actual_range 0.0, 140.0;
    String long_name "Percentcover";
    String units "unitless (percent)";
  }
  HabitatDefinition {
    String long_name "Habitatdefinition";
    String units "unitless";
  }
  Comments {
    String long_name "Comments";
    String units "unitless";
  }
  ProtectionStatus {
    String long_name "Protectionstatus";
    String units "unitless";
  }
  depth_f {
    Float32 actual_range 5.0, 67.5;
    String long_name "Averagedepth";
    String units "feet";
  }
  MinimumDepth {
    Int32 actual_range 2, 63;
    String long_name "Minimumdepth";
    String units "feet";
  }
  MaximumDepth {
    Int32 actual_range 6, 80;
    String long_name "Maximumdepth";
    String units "feet";
  }
 }
  NC_GLOBAL {
    String cdm_data_type "Other";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_url "https://www.bco-dmo.org/";
    String doi "10.26008/1912/bco-dmo.929546.1";
    Float64 Easternmost_Easting -123.0715;
    Float64 geospatial_lat_max 39.42859;
    Float64 geospatial_lat_min 38.31536;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -123.0715;
    Float64 geospatial_lon_min -123.829;
    String geospatial_lon_units "degrees_east";
    String history 
"2025-01-15T19:43:16Z (local files)
2025-01-15T19:43:16Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_929546_v1.das";
    String infoUrl "https://www.bco-dmo.org/dataset/929546";
    String institution "BCO-DMO";
    String license 
"The data may be used and redistributed for free but is not intended
for legal use, since it 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.";
    Float64 Northernmost_Northing 39.42859;
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 38.31536;
    String summary 
"The Kelp Forest Monitoring data record span surveys across 24 years from 1999 through 2023 at 20 locations on the Sonoma-Mendocino Coast, Northern California. Years without data, inclusive: 2002, 2020, 2021. These surveys are ongoing and are conducted by the California Department of Fish and Wildlife dive team with participation from dive program partners at UC Davis, UC Santa Cruz, Cal Poly Humboldt, Sonoma State and other dive programs and volunteers. Not all sites were surveyed in all years. Surveys prior to 2003 were not conducted by the same teams or with the same methods except that all surveys were done using Scuba along 30 x 2m transects randomly placed in the subtidal zone in rocky habitats dominated by bull kelp, Nereocystis luetkeana, forests. These randomly placed band transects surveys were stratified by depth (A=0-15, B=16-30, C=31-45, D=46-60 ft) as we know sea urchin and abalone populations differ by depth.

Data collected for algal habitat coverage from 17 years of those surveys (1999-2016) include quantification by percent coverage of six substrate/algal types: bare rock, canopy, encrusting, foliose, subcanopy, and turf. These data provide a baseline of algal coverage in the kelp forest before, during and after the major marine heatwave of 2014-2016 in northern California, Sonoma and Mendocino counties.

Algal composition in the subtidal has changed dramatically from the time before 2014 to post marine heatwave and sea urchin population explosion. The substrate is now dominated by encrusting algae including crustose coralline algae, Ralfsia, Hildenbrandia, and other fleshy crusts.";
    String title "[Algal Habitat Coverage] - Percent cover of algal habitat types on a by-transect and/or subsample basis recorded during emergent and rapid emergent surveys conducted in the subtidal zone of northern California, Sonoma and Mendocino counties, from 1999 to 2023 (Collaborative Research: The effects of marine heatwaves on reproduction, larval transport and recruitment in sea urchin metapopulations)";
    Float64 Westernmost_Easting -123.829;
  }
}

Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP (external link)

Data Access Protocol (DAP) (external link)

and its selection 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.

(easy) You can get data by using the dataset's Data Access Form or Subset form. They make the URL for you.
(easy) You can make a graph or map by using the dataset's Make A Graph form. It makes the URL for you.
(not hard) You can bypass the forms and get the data or make a graph or map by generating the URL by hand or with a computer program or script.

Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.

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