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Dataset Title:  Data from freely drifting kelp plants tagged with drifters in the Santa
Barbara Channel between November of 2015 and December of 2017
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_739111)
Range: longitude = -120.44976 to -119.500015°E, latitude = 34.16299 to 34.47243°N, depth = -589.51 to 7.2m, time = 2015-11-30T17:10:00Z to 2017-10-13T21:50:00Z
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | 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:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  id {
    Int32 _FillValue 2147483647;
    Int32 actual_range 1, 20000049;
    String description "drifter id";
    String ioos_category "Identifier";
    String long_name "Id";
    String units "unitless";
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 34.162992, 34.472431;
    String axis "Y";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -120.449764, -119.500015;
    String axis "X";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  matime {
    Float64 _FillValue NaN;
    Float64 actual_range 736298.7153, 736981.9097;
    String description "Matlab datenum data type";
    String ioos_category "Unknown";
    String long_name "Matime";
    String units "unitless";
  u {
    Float32 _FillValue NaN;
    Float32 actual_range -76.05, 286.03;
    String description "u velocity";
    String ioos_category "Unknown";
    String long_name "U";
    String units "centimeters per second (cm/s)";
  v {
    Float32 _FillValue NaN;
    Float32 actual_range -33.9, 86.93;
    String description "v velocity";
    String ioos_category "Unknown";
    String long_name "V";
    String units "centimeters per second (cm/s)";
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range -589.51, 7.2;
    String axis "Z";
    String description "water depth interpolated from bathymetry data to each drifter position (negative values are�beneath the sea surface)";
    String ioos_category "Location";
    String long_name "Z";
    String positive "down";
    String standard_name "depth";
    String units "m";
  date {
    String description "date in format yyyy-mm-dd";
    String ioos_category "Time";
    String long_name "Date";
    String units "unitlesss";
  time2 {
    String description "time in format HH:MM";
    String ioos_category "Time";
    String long_name "Time";
    String units "unitless";
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.4489034e+9, 1.5079314e+9;
    String axis "T";
    String description "timestamp (UTC) in standard ISO 8601:2004(E) format YYYY-mm-ddTHH:MMZ";
    String ioos_category "Time";
    String long_name "ISO Date Time UTC";
    String source_name "ISO_DateTime_UTC";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt";
    String acquisition_description 
"GPS positions were sampled every 10 minutes. Velocities were computed as
centered differences in position (first differences at endpoints).
The instruments were deployed by finding kelp plants near the edges of the
three study forests that had been detached from the bottom and were freely
drifting, and then attaching gps buoys to the plants with 1/4\\u201d
polypropylene line. The line was generally attached to part of the kelp plant
nearest the surface. The instruments were then left to record their position
every 10 minutes. The buoys were retrieved when they reached the shoreline or
when it appeared they were leaving the Santa Barbara Channel and would thus
become unrecoverable.
More information about Microstar drifters (manufactured by Pacific Gyre Corp.)
can be found in Ohlmann et al., 2005.";
    String awards_0_award_nid "542227";
    String awards_0_award_number "OCE-1458845";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward?AWD_ID=1458845";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "Dr David  L. Garrison";
    String awards_0_program_manager_nid "50534";
    String cdm_data_type "Other";
    String comment 
"Kelp drifter data 
  PI: Carter Ohlmann 
  data version 1: 2018-08-03";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_type "institution";
    String creator_url "https://www.bco-dmo.org/";
    String data_source "extract_data_as_tsv version 2.2d  13 Jun 2019";
    String date_created "2018-06-21T19:21:19Z";
    String date_modified "2018-08-03T21:15:12Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/bco-dmo.739111.1";
    Float64 Easternmost_Easting -119.500015;
    Float64 geospatial_lat_max 34.472431;
    Float64 geospatial_lat_min 34.162992;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -119.500015;
    Float64 geospatial_lon_min -120.449764;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 7.2;
    Float64 geospatial_vertical_min -589.51;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2019-12-07T22:19:10Z (local files)
2019-12-07T22:19:10Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_739111.das";
    String infoUrl "https://www.bco-dmo.org/dataset/739111";
    String institution "BCO-DMO";
    String instruments_0_acronym "unknown";
    String instruments_0_dataset_instrument_description "More information about Microstar drifters (manufactured by Pacific Gyre Corp.) can be found in Ohlmann et al., 2005.";
    String instruments_0_dataset_instrument_nid "743171";
    String instruments_0_description "No relevant match in BCO-DMO instrument vocabulary.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/999/";
    String instruments_0_instrument_name "unknown";
    String instruments_0_instrument_nid "575";
    String instruments_0_supplied_name "Microstar drifter";
    String keywords "bco, bco-dmo, biological, chemical, data, dataset, date, dmo, erddap, identifier, iso, latitude, longitude, management, matime, oceanography, office, preliminary, time, time2, u, v";
    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.";
    String metadata_source "https://www.bco-dmo.org/api/dataset/739111";
    Float64 Northernmost_Northing 34.472431;
    String param_mapping "{'739111': {'lat': 'master - latitude', 'z': 'master - depth', 'lon': 'master - longitude', 'ISO_DateTime_UTC': 'master - time'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/739111/parameters";
    String people_0_affiliation "University of California-Santa Barbara";
    String people_0_affiliation_acronym "UCSB-ERI";
    String people_0_person_name "Dr Carter Ohlmann";
    String people_0_person_nid "542222";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Woods Hole Oceanographic Institution";
    String people_1_affiliation_acronym "WHOI BCO-DMO";
    String people_1_person_name "Amber York";
    String people_1_person_nid "643627";
    String people_1_role "BCO-DMO Data Manager";
    String people_1_role_type "related";
    String project "Linking nearshore kelp forest dynamics to sandy beach ecosystems";
    String projects_0_acronym "Linking Kelp to Beaches";
    String projects_0_description 
"This project is affiliated with the Santa Barbara Coastal LTER project.
Description from NSF award abstract:
Primary producers, such as plants and algae, form the basis of most food webs and their productivity and fate fundamentally shape ecosystems.�Often, however, food and other resources are delivered to a food web from an outside source, providing a subsidy to the recipient ecosystem. Understanding these types of trophic connections and exchanges between ecosystems is necessary for predicting how food webs may respond to change, whether environmental or anthropogenic. Despite their potential importance, quantitative evaluations of cross-ecosystem material fluxes, variation of these fluxes in time and space, and ecological responses of recipient communities are lacking, particularly for marine ecosystems. By investigating links between a source ecosystem, kelp forests, and a recipient ecosystem, sandy beaches, this project will expand and transform our understanding of cross-ecosystem fluxes in the coastal ocean. Nearshore kelp forests are highly productive marine ecosystems characterized by large seasonal and interannual variations in net primary production (NPP). More than 90% of kelp forest NPP is exported to adjacent ecosystems including the intertidal zone. Lacking attached plants and algae, sandy beach ecosystems near kelp forests depend heavily on imported drift kelp (wrack) to support complex and diverse food webs. Although sandy beaches are a dominant shoreline type along all U.S. coasts, provide habitat and prey for wildlife, including endangered species, and are highly valued by society as recreational and cultural resources that drive vibrant coastal economies, they receive little ecological study compared to other shoreline types. This lack of knowledge hinders the conservation and management of beaches as ecosystems. Perched on the narrow rim between land and sea, beaches are highly vulnerable to climate change, particularly sea level rise, and will be impacted by changes in climate, as will kelp forests. This project integrates biological and physical approaches to achieve an understanding of the fate and transport of exported kelp, and how variability in this resource subsidy shapes the community structure and function of recipient beach ecosystems. Graduate and undergraduate students will be integral members of the research team, receiving scientific training and mentoring in coastal marine ecology and in public outreach and education. The training and participation of local residents and coastal managers in regular shoreline surveys for beached kelp plants will provide an essential research component of the study and enhance public awareness of scientific research, coastal ecology and the role of links between kelp forest and beach ecosystems. The results of this project will provide new insights into the dynamics of connectivity between coastal marine ecosystems that can be applied to their conservation and management.
The project seeks to understand trophic connectivity between a donor ecosystem, kelp forests, and a recipient ecosystem, sandy beaches, with two primary goals:
1) an evaluation of how variation in kelp wrack input affects patterns and processes in beach ecosystems and
2) a quantitative understanding of trophic connectivity through physical transport and input of drift kelp biomass from kelp forests to sandy beaches.
The project will begin with two years of intensive work at a well-studied kelp forest in the Santa Barbara Channel, Mohawk Reef, and along 10 km of adjacent coastline, where the research team will measure intertidal community structure over time in response to variability in kelp inputs. To assess effects of variation in wrack input on ecosystem function, they will also measure kelp consumption and secondary production rates of intertidal consumers on adjacent beaches. They will directly observe fate and transport of kelp using complimentary approaches: 1) tracking kelp plants tagged at Mohawk Reef using drifters with GPS; and 2) tagging large numbers of kelp plants (2000) with \"drift cards\" at Mohawk Reef for recovery by the project team and trained volunteer beachcombers. Ending distributions of recovered drift cards and drifter tracks along the shoreline will then be computed. These data will be used to inform and validate a kelp forest-to-beach kelp transport model based on numerical simulations of coastal surface currents from the Regional Oceanic Modeling System (ROMS). Using predicted kelp beaching rates from this model run regionally, the investigators will then sample community structure and wrack biomass at a larger set of beaches spanning 100 km of the southern California shoreline to test the generality of research findings. This combination of fate and transport observations, beach community surveys and process measurements, and modeling will allow the investigators to characterize temporal variability in kelp subsidy inputs and the consequences of this variability for community structure and function of recipient beach ecosystems.";
    String projects_0_end_date "2019-03";
    String projects_0_geolocation "Santa Barbara Channel, California, USA 34 N, 119 W";
    String projects_0_name "Linking nearshore kelp forest dynamics to sandy beach ecosystems";
    String projects_0_project_nid "542223";
    String projects_0_start_date "2015-04";
    String publisher_name "Amber York";
    String publisher_role "BCO-DMO Data Manager(s)";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 34.162992;
    String standard_name_vocabulary "CF Standard Name Table v29";
    String summary "Kelp plants were tagged monthly with drifters in the Santa Barbara Channel between November of 2015 and December of 2017.  This dataset contains GPS positions of freely drifting kelp plants (nominally) every 10 minutes.  Tagged kelp plants begin at one of three kelp forests (Mohawk, Hope Ranch, or Isla Vista) off the Santa Barbara coast.";
    String time_coverage_end "2017-10-13T21:50:00Z";
    String time_coverage_start "2015-11-30T17:10:00Z";
    String title "Data from freely drifting kelp plants tagged with drifters in the Santa Barbara Channel between November of 2015 and December of 2017";
    String version "1";
    Float64 Westernmost_Easting -120.449764;
    String xml_source "osprey2erddap.update_xml() v1.5-beta";


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.

Tabledap request URLs must be in the form
For example,
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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