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Dataset Title:  Water quality data and Olympia oyster abundance counts from depth-specific
sampling collected by boat in Fidalgo Bay, WA, during July 2017
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_752902)
Range: time = 2017-07-11T16:41:00Z to 2017-07-15T00:21:00Z
Information:  Summary ? | License ? | Metadata | Background (external link) | Data Access Form | Files
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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.49979126e+9, 1.50007806e+9;
    String axis "T";
    String bcodmo_name "ISO_DateTime_UTC";
    String description "Date/Time (UTC) ISO formatted based on ISO 8601:2004(E) with format YYYY-mm-ddTHH:MM:SS[.xx]Z (year;month;day;hour;minute;second)";
    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";
  date_local {
    String bcodmo_name "date_local";
    String description "Calendar month/ day/ and year in US Pacific time; formatted as yyyy-mm-dd";
    String long_name "Date Local";
    String time_precision "1970-01-01";
    String units "unitless";
  time_local {
    String bcodmo_name "time_local";
    String description "Time in 24-hour US Pacific time; HH:MM";
    String long_name "Time Local";
    String units "unitless";
  tide_category {
    String bcodmo_name "tide";
    String description "Tidal direction based on NOAA tidal predictions";
    String long_name "Tide Category";
    String units "unitless";
  profile {
    Byte _FillValue 127;
    Byte actual_range 1, 44;
    String bcodmo_name "sample";
    String description "Each unique profile # represents four depth-specific samples";
    String long_name "Profile";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  depth_seafloor_m {
    Float32 _FillValue NaN;
    Float32 actual_range 2.5, 4.5;
    String bcodmo_name "depth_w";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "Depth from seasurface to seafloor at the time of sampling measured in meters";
    String long_name "Depth";
    String standard_name "depth";
    String units "meters";
  depth_sample_m {
    Float32 _FillValue NaN;
    Float32 actual_range 0.5, 4.0;
    String bcodmo_name "depth";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "Depth of the collected sample in meters below the seasurface";
    String long_name "Depth";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
    String standard_name "depth";
    String units "meters";
  depth_cat {
    Float64 _FillValue NaN;
    String bcodmo_name "depth";
    String description "Depth category of sample collection: (s): surface (0.5 m below seasurface); bottom (0.5 m above seafloor); and two mid-depth samples labeled midlower and midupper which evenly split the depth between surface and bottom samples.";
    String long_name "Depth";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
    String standard_name "depth";
    String units "unitless";
  current_velocity_m_s {
    Float32 _FillValue NaN;
    Float32 actual_range -0.61, 0.56;
    String bcodmo_name "curr_speed_abs";
    String description "Estimated current velocity given in meters per second from data collected with an ADCP. Negative values indicate current moving in the offshore direction and positive values indicate current moving inshore.";
    String long_name "Current Velocity M S";
    String units "meters per second";
  chla_ug_L {
    Float32 _FillValue NaN;
    Float32 actual_range 2.66, 45.34;
    String bcodmo_name "chlorophyll a";
    Float64 colorBarMaximum 30.0;
    Float64 colorBarMinimum 0.03;
    String colorBarScale "Log";
    String description "Concentration of chlorophyll-a measured from filtered whole water samples.";
    String long_name "Concentration Of Chlorophyll In Sea Water";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLHPP1/";
    String units "micrograms per liter seawater";
  temp_c {
    Float32 _FillValue NaN;
    Float32 actual_range 12.77, 19.13;
    String bcodmo_name "temperature";
    String description "Temperature measured with a Hydrolab instrument.";
    String long_name "Temp C";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Celcius";
  salinity {
    Float32 _FillValue NaN;
    Float32 actual_range 28.36, 31.89;
    String bcodmo_name "sal";
    Float64 colorBarMaximum 37.0;
    Float64 colorBarMinimum 32.0;
    String description "Salinity measured with a Hydrolab instrument.";
    String long_name "Sea Water Practical Salinity";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PSALST01/";
    String units "PSU";
  wind_anemometer_m_s {
    Float32 _FillValue NaN;
    Float32 actual_range 1.4, 7.7;
    String bcodmo_name "wind_speed";
    String description "Wind measured with a handheld anemometer.";
    String long_name "Wind Anemometer M S";
    String units "meters per second";
  oyster_larvae_100L {
    Int16 _FillValue 32767;
    Int16 actual_range 2, 317;
    String bcodmo_name "count";
    String description "Counted number of Olympia oyster larvae in the 100-Liter collected seawater";
    String long_name "Oyster Larvae 100 L";
    String units "larvae per 100-Liters seawater";
  oyster_larvae_m3 {
    Int16 _FillValue 32767;
    Int16 actual_range 20, 3170;
    String bcodmo_name "abundance";
    String description "Calculated number of Olympia oyster larvae";
    String long_name "Oyster Larvae M3";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "larvae per cubic meter";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"We measured larval abundance, chlorophyll-a, temperature, and salinity from
four depths at one location in Fidalgo Bay, WA, by boat each day from July 11
to July 14, 2017. Each day, we completed eleven sampling events. During each
sampling event, we collected samples from four depths in the water column:
surface (0.5 m below surface), bottom (0.5 m above seafloor), and two mid-
depth samples, which evenly split the depth between surface and bottom
samples. We planned each sampling event to begin at specific times relative to
the predicted low tide with the goal of collecting approximately equal numbers
of samples during ebb and flood tide.
To collect each larval sample, we used a modified bilge pump to filter
100-liters of water from our targeted depths through a 102-\\u00b5m mesh
plankton net to ensure retention of Olympia oyster larvae. Each sample was
stored on ice while in the field and then preserved in 70% ethanol. At the end
of filtering each 100-L sample, we collected 60-ml of bulk seawater from the
pump for measurement of chlorophyll-a. We filtered the 60-ml of seawater
through a glass microfiber filter (WhatmanTM GF/F). The foil-wrapped filters
were held on ice in the field and then stored them at -80\\u00b0C for later
extraction. We measured chlorophyll-a concentration from each filtered sample
by extracting the chlorophyll-a pigment using 90% acetone for 24 hours in the
dark at -20\\u00b0C and then reading fluorescence of each sample with a Turner
Trilogy Fluorometer (Parsons et al. 1984; Welschmeyer 1994). We also
programmed a Hach Environmental Company HydroLab DS5 water quality multiprobe
instrument to collect temperature and salinity measurements at the same times
and depths as our pump sampling. A Hach Hydras 3 Pocket instrument enabled us
to calibrate, program, and retrieve data from the HydroLab.
This dataset includes unprocessed data and simple data calculations
accomplished with R (Version 3.3.2).
We programmed a Nortek 1MHz Aquadopp acoustic Doppler current profiler (ADCP)
to record velocity measurements in 0.3 meter vertical bins every 60 seconds.
We then attached the ADCP instrument with sensors facing skyward to steel
cross-bar frame and deployed it on the seafloor in Fidalgo Bay\\u2019s main
channel for four days. We utilized Nortek AS software AquaPro version 1.27 to
program and retrieve current velocity data from the Aquadopp instrument. This
dataset includes these raw unprocessed data.";
    String awards_0_award_nid "684166";
    String awards_0_award_number "OCE-1538626";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1538626";
    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 "Michael E. Sieracki";
    String awards_0_program_manager_nid "50446";
    String cdm_data_type "Other";
    String comment 
"Water quality and oyster abundance 
   Fidalgo Bay, WA, during July 2017 
   S. Arellano, B. Olson, S. Yang (WWU) 
   version: 2019-01-14";
    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 "2019-01-16T16:43:09Z";
    String date_modified "2019-07-01T12:30:54Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.752902.1";
    String history 
"2023-01-30T20:19:00Z (local files)
2023-01-30T20:19:00Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_752902.das";
    String infoUrl "https://www.bco-dmo.org/dataset/752902";
    String institution "BCO-DMO";
    String instruments_0_acronym "Anemometer";
    String instruments_0_dataset_instrument_description "Used to measure wind speed.";
    String instruments_0_dataset_instrument_nid "752914";
    String instruments_0_description "An anemometer is a device for measuring the velocity or the pressure of the wind. It is commonly used to measure wind speed.  Aboard research vessels, it is often mounted with other meteorological instruments and sensors.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/101/";
    String instruments_0_instrument_name "Anemometer";
    String instruments_0_instrument_nid "481";
    String instruments_0_supplied_name "hand-held anemometer";
    String instruments_1_acronym "Fluorometer";
    String instruments_1_dataset_instrument_description "Used to measure fluorescence for the chlorophyll-a samples.";
    String instruments_1_dataset_instrument_nid "752913";
    String instruments_1_description "A fluorometer or fluorimeter is a device used to measure parameters of fluorescence: its intensity and wavelength distribution of emission spectrum after excitation by a certain spectrum of light. The instrument is designed to measure the amount of stimulated electromagnetic radiation produced by pulses of electromagnetic radiation emitted into a water sample or in situ.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/113/";
    String instruments_1_instrument_name "Fluorometer";
    String instruments_1_instrument_nid "484";
    String instruments_1_supplied_name "Turner Trilogy Fluorometer model number 7200-000";
    String instruments_2_acronym "Water Quality Multiprobe";
    String instruments_2_dataset_instrument_description "A Hach Environmental Company HydroLab DS5 water quality multiprobe is designed for in-situ measurements. We used the Hach Temperature and Hach Conductivity sensors to measure temperature (°C) and salinity. The Hach Temperature sensor is factory set and did not require re-calibration. We performed a two-point calibration on the Hach Conductivity sensor using YSI conductivity calibrator solution before each days use.";
    String instruments_2_dataset_instrument_nid "752912";
    String instruments_2_description "An instrument which measures multiple water quality parameters based on the sensor configuration.";
    String instruments_2_instrument_name "Water Quality Multiprobe";
    String instruments_2_instrument_nid "678";
    String instruments_2_supplied_name "HydroLab DS5 (Hach Environmental Co.)";
    String keywords "anemometer, bco, bco-dmo, biological, category, chemical, chemistry, chla_ug_L, chlorophyll, concentration, concentration_of_chlorophyll_in_sea_water, current, current_velocity_m_s, data, dataset, date, date_local, density, depth, depth_cat, depth_sample_m, depth_seafloor_m, dmo, earth, Earth Science > Oceans > Ocean Chemistry > Chlorophyll, Earth Science > Oceans > Salinity/Density > Salinity, erddap, iso, larvae, local, management, ocean, oceanography, oceans, office, oyster, oyster_larvae_100L, oyster_larvae_m3, practical, preliminary, profile, salinity, science, sea, sea_water_practical_salinity, seawater, temp_c, temperature, tide, tide_category, time, time_local, velocity, water, wind, wind_anemometer_m_s";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "https://www.bco-dmo.org/dataset/752902/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/752902";
    String param_mapping "{'752902': {'ISO_DateTime_UTC': 'master - time', 'depth_cat': 'master - depth'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/752902/parameters";
    String people_0_affiliation "Western Washington University";
    String people_0_affiliation_acronym "WWU";
    String people_0_person_name "Shawn M Arellano";
    String people_0_person_nid "684169";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Western Washington University";
    String people_1_affiliation_acronym "WWU";
    String people_1_person_name "Dr Brady  M. Olson";
    String people_1_person_nid "51528";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Western Washington University";
    String people_2_affiliation_acronym "WWU";
    String people_2_person_name "Dr Sylvia Yang";
    String people_2_person_nid "684172";
    String people_2_role "Co-Principal Investigator";
    String people_2_role_type "originator";
    String people_3_affiliation "Woods Hole Oceanographic Institution";
    String people_3_affiliation_acronym "WHOI BCO-DMO";
    String people_3_person_name "Nancy Copley";
    String people_3_person_nid "50396";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "Climate stressors on larvae";
    String projects_0_acronym "Climate stressors on larvae";
    String projects_0_description 
"In the face of climate change, future distribution of animals will depend not only on whether they adjust to new conditions in their current habitat, but also on whether a species can spread to suitable locations in a changing habitat landscape. In the ocean, where most species have tiny drifting larval stages, dispersal between habitats is impacted by more than just ocean currents alone; the swimming behavior of larvae, the flow environment the larvae encounter, and the length of time the larvae spend in the water column all interact to impact the distance and direction of larval dispersal. The effects of climate change, especially ocean acidification, are already evident in shellfish species along the Pacific coast, where hatchery managers have noticed shellfish cultures with 'lazy larvae syndrome.' Under conditions of increased acidification, these 'lazy larvae' simply stop swimming; yet, larval swimming behavior is rarely incorporated into studies of ocean acidification. Furthermore, how ocean warming interacts with the effects of acidification on larvae and their swimming behaviors remains unexplored; indeed, warming could reverse 'lazy larvae syndrome.' This project uses a combination of manipulative laboratory experiments, computer modeling, and a real case study to examine whether the impacts of ocean warming and acidification on individual larvae may affect the distribution and restoration of populations of native oysters in the Salish Sea. The project will tightly couple research with undergraduate education at Western Washington University, a primarily undergraduate university, by employing student researchers, incorporating materials into undergraduate courses, and pairing marine science student interns with art student interns to develop art projects aimed at communicating the effects of climate change to public audiences
As studies of the effects of climate stress in the marine environment progress, impacts on individual-level performance must be placed in a larger ecological context. While future climate-induced circulation changes certainly will affect larval dispersal, the effects of climate-change stressors on individual larval traits alone may have equally important impacts, significantly altering larval transport and, ultimately, species distribution. This study will experimentally examine the relationship between combined climate stressors (warming and acidification) on planktonic larval duration, morphology, and swimming behavior; create models to generate testable hypotheses about the effects of these factors on larval dispersal that can be applied across systems; and, finally, use a bio-physically coupled larval transport model to examine whether climate-impacted larvae may affect the distribution and restoration of populations of native oysters in the Salish Sea.";
    String projects_0_end_date "2018-08";
    String projects_0_geolocation "Coastal Pacific, USA";
    String projects_0_name "RUI: Will climate change cause 'lazy larvae'? Effects of climate stressors on larval behavior and dispersal";
    String projects_0_project_nid "684167";
    String projects_0_start_date "2015-09";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "This dataset reports water quality data and Olympia oyster abundance counts from depth-specific sampling collected by boat in Fidalgo Bay, WA, during July 2017. These data were published in the following Masters Thesis: McIntyre, Brooke A., \\Vertical Distribution of Olympia oyster (Ostrea lurida) larvae in Fidalgo Bay, WA\\ (2018). WWU Graduate School Collection. 694. https://cedar.wwu.edu/wwuet/694";
    String time_coverage_end "2017-07-15T00:21:00Z";
    String time_coverage_start "2017-07-11T16:41:00Z";
    String title "Water quality data and Olympia oyster abundance counts from depth-specific sampling collected by boat in Fidalgo Bay, WA, during July 2017";
    String version "1";
    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
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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