Accessing BCO-DMO data
log in    
Brought to you by BCO-DMO    

ERDDAP > tabledap > Make A Graph ?

Dataset Title:  Experimental grazing rates of sand dollar larvae (Dendraster excentricus) on
algae (Dunaliella tertiolecta) under different ocean acidification conditions,
July 2017
Subscribe RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_753017)
Information:  Summary ? | License ? | Metadata | Background (external link) | Data Access Form | Files
Graph Type:  ?
X Axis: 
Y Axis: 
Constraints ? Optional
Constraint #1 ?
Constraint #2 ?
Server-side Functions ?
 distinct() ?
? ("Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.")
Graph Settings
Marker Type:   Size: 
Color Bar:   Continuity:   Scale: 
   Minimum:   Maximum:   N Sections: 
Y Axis Minimum:   Maximum:   
(Please be patient. It may take a while to get the data.)
Then set the File Type: (File Type information)
or view the URL:
(Documentation / Bypass this form ? )
    [The graph you specified. Please be patient.]


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 {
  temp_c {
    Byte _FillValue 127;
    Byte actual_range 12, 17;
    String bcodmo_name "temperature";
    String description "Temperature treatment";
    String long_name "Temp C";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Celcius";
  pH {
    String bcodmo_name "pH";
    String description "pCO2 treatment: low =  400ppm; medium =  800ppm; high = 1500ppm";
    String long_name "pH";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PHXXZZXX/";
    String units "unitless";
  jar_type {
    String bcodmo_name "treatment";
    String description "Type of treatment jar; a jar containing larval and algae or a control jar containing just algae";
    String long_name "Jar Type";
    String units "unitless";
  replicate {
    Byte _FillValue 127;
    Byte actual_range 1, 4;
    String bcodmo_name "replicate";
    String description "Replicate number of each treatment combination including temperature; pH; and jar type";
    String long_name "Replicate";
    String units "unitless";
  count {
    String bcodmo_name "count";
    String description "The initial or final algae count to calculate algal cell concentration for the experiment";
    String long_name "Count";
    String units "unitless";
  cells {
    Int16 _FillValue 32767;
    Int16 actual_range 149, 876;
    String bcodmo_name "count";
    String description "Algae cell count using a Sedgewick-Rafter counting chamber";
    String long_name "Cells";
    String units "algae cells";
  squares {
    Byte _FillValue 127;
    Byte actual_range 60, 100;
    String bcodmo_name "num_reps";
    String description "Number of squares counted within the Sedgewick-Rafter";
    String long_name "Squares";
    String units "squares";
  cell_concentration {
    Float32 _FillValue NaN;
    Float32 actual_range 2483.33, 10566.67;
    String bcodmo_name "cell_concentration";
    String description "Calculated concentration of algae cells within treatment jar from Sedgewick-Rafter counts. Cell concentration = (cells/squares)*1000";
    String long_name "Cell Concentration";
    String units "algae cells per milliliter (#/mL)";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Spawning and fertilization
We collected adult sand dollars (D. excentricus) from Semiahmoo Bay, WA, on
July 7, 2017 and maintained them in 14\\u00b0C continuous flowing seawater at
the Shannon Point Marine Center. On July 12, 2017 we induced twelve
individuals to spawn by injecting 1-mL of 0.5-M KCl into the coelom following
methods outlined by Strathmann (1987).\\u00a0 We then collected and mixed
concentrated gametes of four males and four females for fertilization. We
added five drops of sperm to 500-mL of filtered seawater and 5-mL of eggs. We
placed the fertilized eggs in 12\\u00b0C incubator and bubbled them with
ambient pCO2 condition for 12-hrs before dividing the embryos into pCO2
treatment conditions before gastrulation. We then counted and transferred the
larvae into jars with 1.5 L of nanopore filtered seawater at densities of 1-2
individuals mL-1.
Grazing experiment
To assess the interactive effects of temperature and pCO2 on Dunaliella
excentricus feeding behavior, our experimental design had six treatments with
four experimental jars (replicates) in each. The treatments combined three
levels of CO2: 400 ppmv (ambient atmospheric level), 800 ppmv (moderate
atmoshpheric level) and 1,500 ppmv (high atmospheric level), and two
temperatures: 12\\u00b0C (ambient temperature) and 17\\u00b0C (high
temperature). We fed Dunaliella tertiolecta at approximately 6,000 cells ml-1
to six-arm stage larvae to evaluate feeding rates at each treatment condition.
For each replicate, a corresponding 150-mL control bottle containing only D.
tertiolecta was also prepared. Feeding rate was estimated as ingestion rate by
measuring the algal concentration (cells ml-1) at the beginning (T0) and after
24 hours (Tf) in control bottles and experimental jars using a Sedgewick
Rafter Chamber (Stumpp et al., 2011). Ingestion rate (cells ind-1 hr-1) was
calculated as I = (Clearance rate) x (time-average algae concentration).
This dataset includes unprocessed data and simple data calculations
accomplished with Excel.";
    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 
"Dendraster Grazing Rates - OA Expt 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 "2019-01-16T21:21:35Z";
    String date_modified "2019-09-25T20:00:41Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.753017.1";
    String history 
"2021-12-03T05:12:02Z (local files)
2021-12-03T05:12:02Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_753017.das";
    String infoUrl "https://www.bco-dmo.org/dataset/753017";
    String institution "BCO-DMO";
    String instruments_0_dataset_instrument_description "Used to count cells.";
    String instruments_0_dataset_instrument_nid "753035";
    String instruments_0_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_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB05/";
    String instruments_0_instrument_name "Microscope-Optical";
    String instruments_0_instrument_nid "708";
    String keywords "bco, bco-dmo, biological, cell, cell_concentration, cells, chemical, concentration, count, data, dataset, dmo, erddap, jar, jar_type, management, oceanography, office, preliminary, replicate, squares, temp_c, temperature, type";
    String license "https://www.bco-dmo.org/dataset/753017/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/753017";
    String param_mapping "{'753017': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/753017/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 "Experimental grazing rates of sand dollar larvae (Dendraster excentricus) on algae (Dunaliella tertiolecta) under different ocean acidification conditions, July 2017.";
    String title "Experimental grazing rates of sand dollar larvae (Dendraster excentricus) on algae (Dunaliella tertiolecta) under different ocean acidification conditions, 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.

ERDDAP, Version 2.02
Disclaimers | Privacy Policy | Contact