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Dataset Title:  Olympia oyster larvae length measurements 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_753098)
Range: time = 2017-07-11T16:41:00Z to 2017-07-15T00:21:00Z
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 {
  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 "Date Time UTC";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DTUT8601/";
    String source_name "date_time_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 "Local date 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";
  }
  profile {
    Byte _FillValue 127;
    Byte actual_range 1, 44;
    String bcodmo_name "sample";
    String description "Each unique profile # represents four depth-specific samples: bottom; midlower; midupper;  surface";
    String long_name "Profile";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  sample {
    Byte _FillValue 127;
    Byte actual_range 1, 52;
    String bcodmo_name "sample";
    String description "Water column collection sample identifier";
    String long_name "Sample";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  tide {
    String bcodmo_name "tide";
    String description "Tidal direction based on NOAA tidal predictions: ebb; flood; and low";
    String long_name "Tide";
    String units "unitless";
  }
  depth_seafloor_m {
    Float32 _FillValue NaN;
    Float32 actual_range 2.0, 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_des {
    Float64 _FillValue NaN;
    String bcodmo_name "depth";
    String description "Depth category of sample collection: surface = 0.5 m below sea surface; bottom = 0.5 m above seafloor; 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";
  }
  larvae_length_um {
    Float32 _FillValue NaN;
    Float32 actual_range 180.0, 304.157;
    String bcodmo_name "length";
    String description "Length of Olympia oyster larva measured perpendicular to the hinge";
    String long_name "Larvae Length Um";
    String units "micrometers (um )";
  }
 }
  NC_GLOBAL {
    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.
 
We used an Olympus Optical Company SZ-ST stereoscope fit with polarized lens
filters to hand sort Olympia oyster larvae from each sample. First, we
narrowed down all the potential local species of bivalve larvae that might be
in our samples based on reproductive season (Loosanoff et al. 1966, personal
communication Julie Barber). We then distinguished Olympia oyster larvae from
these other species by comparing morphological features relative to size based
on identification keys (Loosanoff et al. 1966, Shanks 2001) and reference
Olympia oyster larvae that we reared in the laboratory. Reference larvae were
fixed and photographed under an Olympus CH-2 microscope to aid
identification.We measured larva shell lengths perpendicular to the hinge of
each larva digitally using a stereomicroscope equipped with a camera and
ImageJ software (Leica MC170 HD and Leica Application Suite, Leica, Wetzlar,
Germany).";
    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 
"oyster larvae length measurements 
   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 "2019-01-17T14:46:28Z";
    String date_modified "2019-09-25T20:04:23Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.753098.1";
    String history 
"2020-11-25T13:07:31Z (local files)
2020-11-25T13:07:31Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_753098.das";
    String infoUrl "https://www.bco-dmo.org/dataset/753098";
    String institution "BCO-DMO";
    String instruments_0_acronym "camera";
    String instruments_0_dataset_instrument_nid "753109";
    String instruments_0_description "All types of photographic equipment including stills, video, film and digital systems.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/311/";
    String instruments_0_instrument_name "Camera";
    String instruments_0_instrument_nid "520";
    String instruments_0_supplied_name "Leica MC170 HD camera";
    String instruments_1_dataset_instrument_nid "753108";
    String instruments_1_description "Instruments that generate enlarged images of samples using the phenomena of reflection and absorption of visible light. Includes conventional and inverted instruments. Also called a \"light microscope\".";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB05/";
    String instruments_1_instrument_name "Microscope-Optical";
    String instruments_1_instrument_nid "708";
    String instruments_1_supplied_name "an Olympus Optical Company SZ-ST stereoscopeand a Leica stereomicroscope";
    String keywords "bco, bco-dmo, biological, chemical, data, dataset, date, date_local, depth, depth_des, depth_sample_m, depth_seafloor_m, dmo, erddap, larvae, larvae_length_um, length, local, management, oceanography, office, preliminary, profile, sample, tide, time, time_local";
    String license "https://www.bco-dmo.org/dataset/753098/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/753098";
    String param_mapping "{'753098': {'date_time_UTC': 'master - time', 'depth_des': 'master - depth'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/753098/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 includes Olympia oyster larvae length measurements from depth-specific sampling collected by boat in Fidalgo Bay, WA, during July 2017.";
    String time_coverage_end "2017-07-15T00:21:00Z";
    String time_coverage_start "2017-07-11T16:41:00Z";
    String title "Olympia oyster larvae length measurements 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
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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