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Dataset Title:  [ABLE] - Autonomous Behaving Lagrangian Explorer (ABLE) output for 5 taxonomic
groups examined from the R/V Cape Horn in the upwelling region of the west
coast of California, USA in August 2015 (Collaborative Research: Field test of
larval behavior on transport and connectivity in an upwelling regime)
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_681042)
Range: longitude = -123.08404 to -122.96394°E, latitude = 38.218773 to 38.309513°N, time = 2015-08-19T16:17:02Z to 2015-08-21T19:08:00Z
Information:  Summary ? | License ? | FGDC | ISO 19115 | Metadata | Background (external link) | Subset | 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 {
  taxon {
    String bcodmo_name "taxon";
    String description "Taxonomic group examined using ABLE";
    String long_name "Taxon";
    String units "unitless";
  }
  cumulative_secs {
    Int32 _FillValue 2147483647;
    Int32 actual_range 493316222, 493499280;
    String bcodmo_name "unknown";
    String description "Cumulative seconds";
    String long_name "Cumulative Secs";
    String units "seconds";
  }
  date_utc {
    String bcodmo_name "date_utc";
    String description "Date (UTC) formatted as yyyy-mm-dd";
    String long_name "Date Utc";
    String time_precision "1970-01-01";
    String units "unitless";
  }
  time_utc {
    String bcodmo_name "time_utc";
    String description "Time (UTC) formatted as HH:MM:SS";
    String long_name "Time Utc";
    String units "unitless";
  }
  Z {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 19.65;
    String bcodmo_name "unknown";
    String description "Current depth";
    String long_name "Z";
    String units "meters (m)";
  }
  Z_flag {
    Byte _FillValue 127;
    String _Unsigned "false";
    Byte actual_range 0, 5;
    String bcodmo_name "unknown";
    Float64 colorBarMaximum 150.0;
    Float64 colorBarMinimum 0.0;
    String description "Quality flag for current depth: 0 – no QC, 1 – good, 2 – unreliable, 3 – bad, 4 – changed, 5 – no data.";
    String long_name "Z Flag";
    String units "unitless";
  }
  target_Z {
    Float32 _FillValue NaN;
    Float32 actual_range 0.25, 125.0;
    String bcodmo_name "unknown";
    String description "Target depth in meters";
    String long_name "Target Z";
    String units "meters (m)";
  }
  Z_bot {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 127.89;
    String bcodmo_name "unknown";
    String description "Assumed bottom depth";
    String long_name "Z Bot";
    String units "meters (m)";
  }
  temp {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 17.44;
    String bcodmo_name "temperature";
    String description "Temperature";
    String long_name "Temperature";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Centigrade (C)";
  }
  temp_flag {
    Byte _FillValue 127;
    String _Unsigned "false";
    Byte actual_range 0, 0;
    String bcodmo_name "unknown";
    Float64 colorBarMaximum 150.0;
    Float64 colorBarMinimum 0.0;
    String description "Quality flag for temperature: 0 – no QC, 1 – good, 2 – unreliable, 3 – bad, 4 – changed, 5 – no data";
    String long_name "Temp Flag";
    String units "unitless";
  }
  PAR {
    Int32 _FillValue 2147483647;
    Int32 actual_range -18088, 121003;
    String bcodmo_name "PAR";
    Float64 colorBarMaximum 70.0;
    Float64 colorBarMinimum 0.0;
    String description "Photosynthetically active radiation (PAR)";
    String long_name "Downwelling Photosynthetic Photon Radiance In Sea Water";
    String units "moles per square meter per second (mol m-2 s-1)";
  }
  PAR_flag {
    Byte _FillValue 127;
    String _Unsigned "false";
    Byte actual_range 0, 5;
    String bcodmo_name "unknown";
    Float64 colorBarMaximum 150.0;
    Float64 colorBarMinimum 0.0;
    String description "Quality flag for PAR: 0 – no QC, 1 – good, 2 – unreliable, 3 – bad, 4 – changed, 5 – no data";
    String long_name "PAR Flag";
    String units "unitless";
  }
  sal {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 39.59;
    String bcodmo_name "sal";
    String description "Salinity";
    String long_name "Sal";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PSALST01/";
    String units "practical salinity scale (PSU)";
  }
  sal_flag {
    Byte _FillValue 127;
    String _Unsigned "false";
    Byte actual_range 0, 0;
    String bcodmo_name "unknown";
    Float64 colorBarMaximum 150.0;
    Float64 colorBarMinimum 0.0;
    String description "Quality flag for salinity: 0 – no QC, 1 – good, 2 – unreliable, 3 – bad, 4 – changed, 5 – no data";
    String long_name "Sal Flag";
    String units "unitless";
  }
  ang_vel_X {
    Int16 _FillValue 32767;
    Int16 actual_range -8154, 9122;
    String bcodmo_name "unknown";
    String description "Angular velocity in x direction";
    String long_name "Ang Vel X";
    String units "unitless (raw counts from the gyro)";
  }
  ang_vel_Y {
    Int16 _FillValue 32767;
    Int16 actual_range -7849, 9319;
    String bcodmo_name "unknown";
    String description "Angular velocity in y direction";
    String long_name "Ang Vel Y";
    String units "unitless (raw counts from the gyro)";
  }
  ang_vel_Z {
    Int16 _FillValue 32767;
    Int16 actual_range -6182, 15328;
    String bcodmo_name "unknown";
    String description "Angular velocity in z direction";
    String long_name "Ang Vel Z";
    String units "unitless (raw counts from the gyro)";
  }
  pump_down {
    Byte _FillValue 127;
    String _Unsigned "false";
    Byte actual_range 0, 125;
    String bcodmo_name "unknown";
    String description "Number of pump strokes in a downward direction";
    String long_name "Pump Down";
    String units "unitless";
  }
  pump_up {
    Byte _FillValue 127;
    String _Unsigned "false";
    Byte actual_range 0, 125;
    String bcodmo_name "unknown";
    String description "Number of pump strokes in an upward direction";
    String long_name "Pump Up";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 38.218773, 38.309512;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude";
    String ioos_category "Location";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -123.08404, -122.963942;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude";
    String ioos_category "Location";
    String long_name "Longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String standard_name "longitude";
    String units "degrees_east";
  }
  DOP {
    Float32 _FillValue NaN;
    Float32 actual_range 0.9, 5.2;
    String bcodmo_name "Dissolved Organic Phosphorus";
    String description "Dilution of precision for GPS quality";
    String long_name "DOP";
    String units "unitless";
  }
  batt_V {
    Float32 _FillValue NaN;
    Float32 actual_range 8.49, 8.79;
    String bcodmo_name "unknown";
    String description "Battery voltage";
    String long_name "Batt V";
    String units "volts";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.440001022e+9, 1.44018408e+9;
    String axis "T";
    String bcodmo_name "ISO_DateTime_UTC";
    String description "Date and time (UTC) formatted ISO 8601 standard: yyyy-mm-ddTHH:MM:SS.xxZ (where T indicates the start of the time string and Z indicates UTC)";
    String ioos_category "Time";
    String long_name "ISO Date Time UTC";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DTUT8601/";
    String source_name "ISO_DateTime_UTC";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String time_precision "1970-01-01T00:00:00Z";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt";
    String acquisition_description 
"Sampling:  
 ABLE 6218 (aka Velella) \\u2013 constant depth of 3m  
 ABLE 6155 (aka Cyprid) \\u2013 diel vertical migration 3-15m, 2.0 cm/s, up
3:00/down 13:00  
 ABLE 6157 (aka Pteropod) \\u2013 diel vertical migration 3-15m, 2.0 cm/s, up
3:00/down 13:00  
 ABLE 5996 (aka Pluteus) \\u2013 constant depth 15m  
 ABLE 6031 (aka Melibe) \\u2013 constant depth 15m
 
We simulated documented behaviors using the Autonomous Behaving Lagrangian
Explorer (ABLE). ABLE, designed by Tom Wolcott,\\u00a0is a biomimetic robotic
drifter that senses in situ environmental stimuli (e.g., variations in PAR,
pressure, salinity, or temperature). It\\u00a0can be programmed to maintain
depth or vertically migrate in response to in-situ variables, like the larvae
under study. It can reveal quasi-Lagrangian transport of vertically migrating
plankters that swim between water parcels at different depths. ABLE weighs 3
kg and is 36 cm tall, topped by a 15 cm antenna mast. It necessarily
integrates water motions at and below its own scale. Consequently, it cannot
mimic transport of individual plankters, nor diffusive processes at scales
smaller than its own. ABLE best simulates the transport of the centroid of a
cloud of plankters that is large relative to its own dimensions.
 
ABLE dynamically calculates its target depth from measurements of its
immediate microenvironment and a behavioral model for the organism being
simulated. It moves toward the new target depth at a biologically realistic
velocity, permitting it to show transport consequences of adaptive behaviors
in response to actual (not average) conditions and actual (not modeled) water
movements. Because behavioral patterns are under the experimenter\\u2019s
control, ABLE can reveal effects of either known or hypothetical behavior
patterns. ABLE has no structures outside the parcel of water in which it is
embedded, hence no extraneous drag that would cause drift errors. Use of ABLE
(unlike modeling) requires no a priori characterization of the system before
the first data can be collected; immediately upon deployment it begins
yielding information on how water and organisms in the system move.
 
Although ABLE has no extraneous drag, hence no drift errors, while embedded in
the tracked water parcel, it must periodically leave that parcel and make
excursions to the surface to obtain and transmit GPS fixes. A drift error is
created by velocity differences (relative to the target parcel) at other
depths multiplied by the time ABLE spends transiting each during a pop-up,
which cannot be simply estimated in heterogeneous systems. A rule of thumb
analogous to that for suspended-drogue drifters would be that ABLE must spend
<1/40 of the time making excursions to the surface. As target (operating)
depth increases, transit time to the surface increases, and hence allowable
fix frequency decreases.
 
To facilitate tracking, it has an ultrasonic beacon that provides bearings and
telemeters depth during operation at depth; when at the surface it obtains
fixes from its GPS receiver and transmits the fix data by VHF radio (short
range) and satellite modem (global range). The GPS fix obtained at each
surface interval is logged in ABLE\\u2019s data memory, even if it is not
received by the Globalstar satellite system. To facilitate recovery at the sea
surface, it transmits updated fixes continuously by VHF and periodically via
satellite while blinking high-brightness LED beacons for visual fixes. We also
command ABLE to surface for recovery by decoding ultrasonic signals while
rejecting noise from surf and biota. It senses the bottom and swims up a
programmed distance above the substrate.
 
When deployed, it uses measurements of in-situ variables (depth, T, S, PAR,
time of day, vertical speed relative to water). It subtly adjusts buoyancy (by
< 1g) to \\\"swim\\\" toward that target depth, maintaining a rate realistic for
the organism being simulated (0 to >10 cm/s). It periodically pops to the
surface to obtain a GPS fix and transmit it by VHF, ultrasonic pinger and
satellite (or cell phone) modem. Along its entire trajectory, it logs in-situ
measurements; the suite of variables and frequency of logging are user-
selectable. On the bench, ABLE communicates by wireless Bluetooth with a host
computer or smart phone and presents a menu for downloading logged data,
testing and calibrating sensors, altering data logging parameters, or even
rewriting the entire program. Endurance during deployments is about 2 wk with
7 NiMH \\\"D\\\"\\u00a0cells, depending on frequency of excursions to the surface
and pumping of ballast to hoist antennas.
 
ABLEs were deployed by boat at pre-defined locations and retrieved a pre-
defined number of days later.\\u00a0";
    String awards_0_award_nid "568290";
    String awards_0_award_number "OCE-1334448";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1334448";
    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 "David L. Garrison";
    String awards_0_program_manager_nid "50534";
    String awards_1_award_nid "568297";
    String awards_1_award_number "OCE-1334553";
    String awards_1_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1334553";
    String awards_1_funder_name "NSF Division of Ocean Sciences";
    String awards_1_funding_acronym "NSF OCE";
    String awards_1_funding_source_nid "355";
    String awards_1_program_manager "David L. Garrison";
    String awards_1_program_manager_nid "50534";
    String cdm_data_type "Other";
    String comment 
"Autonomous Behaving Lagrangian Explorer (ABLE) output for 5 taxonomic groups 
 PI: Steven G. Morgan (Bodega Marine Laboratory) 
 Co-PI: John L. Largier (Bodega Marine Laboratory), Thomas G. Wolcott & Donna Wolcott (North Carolina State University) 
 Version: 15 February 2017";
    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 2.3  19 Dec 2019";
    String date_created "2017-02-07T19:06:46Z";
    String date_modified "2019-08-01T19:32:45Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.681042.1";
    Float64 Easternmost_Easting -122.963942;
    Float64 geospatial_lat_max 38.309512;
    Float64 geospatial_lat_min 38.218773;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -122.963942;
    Float64 geospatial_lon_min -123.08404;
    String geospatial_lon_units "degrees_east";
    String history 
"2024-11-08T05:54:28Z (local files)
2024-11-08T05:54:28Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_681042.das";
    String infoUrl "https://www.bco-dmo.org/dataset/681042";
    String institution "BCO-DMO";
    String instruments_0_acronym "ABLE";
    String instruments_0_dataset_instrument_nid "682398";
    String instruments_0_description "The Autonomous Behaving Lagrangian Explorer (ABLE), designed by Tom Wolcott, is a biomimetic robotic drifter that senses in situ environmental stimuli (e.g., variations in PAR, pressure, salinity, or temperature) and can be programmed to respond to these cues with vertical migration behavior like that of the planktonic organism of interest.";
    String instruments_0_instrument_name "Autonomous Behaving Lagrangian Explorer";
    String instruments_0_instrument_nid "682336";
    String instruments_0_supplied_name "ABLE";
    String keywords "active, altimetry, ang, ang_vel_X, ang_vel_Y, ang_vel_Z, available, batt, batt_V, bco, bco-dmo, biological, bot, chemical, cumulative, cumulative_secs, data, dataset, date, date_utc, dmo, dop, down, downwelling, downwelling_photosynthetic_photon_radiance_in_sea_water, earth, Earth Science > Oceans > Ocean Optics > Photosynthetically Active Radiation, Earth Science > Oceans > Ocean Optics > Radiance, erddap, flag, iso, laboratory, latitude, longitude, management, ocean, oceanography, oceans, office, optics, par, PAR_flag, photon, photosynthetic, photosynthetically, preliminary, pump, pump_down, pump_up, radiance, radiation, sal, sal_flag, satellite, science, sea, seawater, secs, target, target_Z, taxon, temp_flag, temperature, time, time_utc, v, vel, water, Z_bot, Z_flag";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "https://www.bco-dmo.org/dataset/681042/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/681042";
    Float64 Northernmost_Northing 38.309512;
    String param_mapping "{'681042': {'lat': 'master - latitude', 'lon': 'master - longitude', 'ISO_DateTime_UTC': 'flag - time'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/681042/parameters";
    String people_0_affiliation "University of California-Davis";
    String people_0_affiliation_acronym "UC Davis-BML";
    String people_0_person_name "Steven Morgan";
    String people_0_person_nid "506186";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "University of California-Davis";
    String people_1_affiliation_acronym "UC Davis-BML";
    String people_1_person_name "John L. Largier";
    String people_1_person_nid "568294";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "North Carolina State University";
    String people_2_affiliation_acronym "NCSU";
    String people_2_person_name "Donna Wolcott";
    String people_2_person_nid "568301";
    String people_2_role "Co-Principal Investigator";
    String people_2_role_type "originator";
    String people_3_affiliation "North Carolina State University";
    String people_3_affiliation_acronym "NCSU";
    String people_3_person_name "Thomas G. Wolcott";
    String people_3_person_nid "568299";
    String people_3_role "Co-Principal Investigator";
    String people_3_role_type "originator";
    String people_4_affiliation "University of California-Davis";
    String people_4_affiliation_acronym "UC Davis-BML";
    String people_4_person_name "Steven Morgan";
    String people_4_person_nid "506186";
    String people_4_role "Contact";
    String people_4_role_type "related";
    String people_5_affiliation "Woods Hole Oceanographic Institution";
    String people_5_affiliation_acronym "WHOI BCO-DMO";
    String people_5_person_name "Shannon Rauch";
    String people_5_person_nid "51498";
    String people_5_role "BCO-DMO Data Manager";
    String people_5_role_type "related";
    String project "ABLE";
    String projects_0_acronym "ABLE";
    String projects_0_description 
"Description from NSF award abstract:
The majority of larvae of coastal marine species are planktonic and generally weak swimmers. Thus, they are thought to be dispersed widely by coastal currents. However, there is accumulating evidence that their behavior can strongly influence their transport: some remain within estuaries, while others make true migrations between adult and larval habitats, even out to the edge of the continental shelf and back. Rates and directions of larval transport are thought to be determined largely by the timing, duration, and amplitude of vertical migrations and the mean depth that larvae occupy in stratified flows. The PIs propose to provide one of the first direct tests of how behavior affects across-shelf and alongshore transport using biomimetic drifters. The study will be conducted in a region of persistent upwelling, where strong currents are widely believed to overwhelm larval swimming and limit recruitment to adult populations.
Knowledge of underlying mechanisms regulating larval transport is central to understanding ecology and evolution in the sea and anticipating the impacts of climate change on marine populations and communities. The proposed research will provide the first experimental field-test of how larval behavior affects the rates, directions and distances of transport and population connectivity in an upwelling regime. The PIs will test three hypotheses:
1. Residence below the wind-driven surface layer and vertical migrations below that layer keep larvae closer to shore compared to residence in the surface layer or larvae without depth preferences and vertical migration.
2. Residence at depth enhances northward transport near shore, and vertical migration leads to decreased alongshore mean displacement but increased variance for a group.
3. Depth preferences and vertical migrations have pronounced effects on retention and transport of plankton in upwelling regions.
The study will compare direct measurements from mimetic drifters with observed and modeled cross-shelf larval distributions, and with modeled alongshore transport. Results will be broadly applicable to upwelling regimes along the western margins of continents, and the approach can be applied to non-upwelling systems throughout the world.";
    String projects_0_end_date "2017-08";
    String projects_0_geolocation "Upwelling region, West coast of USA, Northern California";
    String projects_0_name "Collaborative Research: Field test of larval behavior on transport and connectivity in an upwelling regime";
    String projects_0_project_nid "568291";
    String projects_0_start_date "2013-09";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 38.218773;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String subsetVariables "temp_flag,sal_flag";
    String summary "Autonomous Behaving Lagrangian Explorer (ABLE) output for 5 taxonomic groups (Cyprid, Melibe, Pluteus, Pteropod, and Velella) from the R/V Cape Horn in the upwelling region of the west coast of California, USA in August 2015.";
    String time_coverage_end "2015-08-21T19:08:00Z";
    String time_coverage_start "2015-08-19T16:17:02Z";
    String title "[ABLE] - Autonomous Behaving Lagrangian Explorer (ABLE) output for 5 taxonomic groups examined from the R/V Cape Horn in the upwelling region of the west coast of California, USA in August 2015 (Collaborative Research: Field test of larval behavior on transport and connectivity in an upwelling regime)";
    String version "1";
    Float64 Westernmost_Easting -123.08404;
    String xml_source "osprey2erddap.update_xml() v1.3";
  }
}

 

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
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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