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Dataset Title:  Mortality of L. pertusa specimens exposed to different DO levels collected on
R/V Ronald Brown in Florida from October to November 2010 (Lophelia OA project)
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_659064)
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 {
  DO_treatment {
    String bcodmo_name "treatment";
    String description "Level of dissolved oxygen treatment; High, ambient, or low";
    String long_name "DO Treatment";
    String units "unitless";
  }
  tank {
    String bcodmo_name "tank";
    String description "Tank where specimen was located";
    String long_name "Tank";
    String units "unitless";
  }
  individual {
    Byte _FillValue 127;
    Byte actual_range 1, 16;
    String bcodmo_name "individual";
    String description "Individual ID number";
    String long_name "Individual";
    String units "unitless";
  }
  temp_mean {
    Float32 _FillValue NaN;
    Float32 actual_range 8.5, 8.79;
    String bcodmo_name "temperature";
    String description "Average temperature";
    String long_name "Temp Mean";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "celsius";
  }
  DO_mean {
    Float32 _FillValue NaN;
    Float32 actual_range 1.57, 5.32;
    String bcodmo_name "O2_ml_L";
    String description "Average dissolved oxygen concentration";
    String long_name "DO Mean";
    String units "milliliters of oxygen per liter of seawater (mL/L -1)";
  }
  percent_survivorship {
    Byte _FillValue 127;
    Byte actual_range 0, 100;
    String bcodmo_name "unknown";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String description "Percent of specimens that survived experiment";
    String long_name "Percent Survivorship";
    String units "percent";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"All methods are fully described in:
 
Lunden et al. 2014 Frontiers in Marine Science \\u201cAcute survivorship of the
deep-sea coral Lophelia pertusa from the Gulf of Mexico under acidification,
warming, and deoxygenation\\u201d
 
From the Paper:
 
Forty-one nubbins of L. pertusa used in the experiments were collected in
November 2010 on the NOAA Ship Ronald H. Brown with ROV Jason II as part of
the \\u201cLophelia II\\u201d project jointly sponsored by the Bureau of Ocean
Energy Management and the NOAA Office of Ocean Exploration and Research in the
Gulf of Mexico (GoM). Permits for the collection of corals were obtained from
the U.S. Department of the Interior prior to any collection activities.
Spatially discrete coral branches were collected with the ROV and placed in
temperature-insulated bioboxes (volume = 20 l) at depth. Upon return to the
surface, corals were kept alive in 20 l aquaria in the ship\\u2019s constant-
temperature room. Partial water changes were made regularly while at sea. Upon
return to port, corals were immediately transported overnight to the
laboratory on wet ice.
 
In the laboratory, corals were maintained in one of two 570 liter
recirculating aquaria systems at temperature 8 degrees celsius and salinity 35
ppt (Lunden et al., 2014). Regular partial water changes (15\\u201320%) were
performed with seawater made using Instant Ocean\\u00a0sea salt. Submersible
power heads were placed in each holding tank to ensure water movement and
turbulence sufficient to cause swaying of coral polyps. Corals were fed three
times weekly using a combination of MarineSnow\\u00a0PlanktonDiet (Two Little
Fishies, Miami Gardens, FL) and freshly hatched Artemia nauplii.
 
Survivorship was assessed by daily observations of polyp tissue presence and
behavior. Final survivorship counts were taken 3 to 4 days following the end
of each treatment after transfer to the maintenance tank. Survivorship is
reported as percent cumulative mortality.
 
Net calcification was measured using the buoyant weight technique (Davies,
1989). Coral nubbins were buoyantly weighed at the start and end of each
experimental period (days eight and fifteen) using a Denver Instruments SI-64
analytical balance (d = 0.1mg, Fisher Scientific, Waltham, MA). A weighing
chamber was constructed using 1/2\\u201d plexiglass to prevent disturbances
from air movement during weighing. Each coral nubbin was transported
individually from its respective aquarium to the weighing chamber in a four-
liter Pyrex\\u00a0beaker and suspended from the balance. The buoyant weight was
recorded after the coral nubbin stabilized, typically 2 min. Each coral nubbin
was weighed three times to determine measurement precision (2\\u20133 mg).
Seawater density was determined in each aquarium by buoyantly weighing a 2.5
cm^2 aluminum block with known density (2.7 g/cm^\\u22123). Coral weight in air
(i.e., dry weight) was calculated by the following equation:\\u00a0
 
Wa = Ww / (1\\u2212 (Dw/SD))\\u00a0
 
Where  
 Wa = coral weight in air (dry weight)\\u00a0  
 Ww = coral weight in water (buoyant weight)\\u00a0  
 Dw = density of seawater\\u00a0  
 SD = coral skeletal density (= 2.82 g/cm^\\u22123, Lunden et al.,
2013).\\u00a0
 
Coral growth rate is reported as percent growth per day (%/d\\u22121), which
was calculated by the equation:
 
Gt = 100 \\u00d7 (Mt2 \\u2212 Mt1)/(Mt1(T2 \\u2212T1))
 
Where  
 Gt = growth rate as %/d^\\u22121\\u00a0  
 Mt2 = mass (mg, dry weight) at time 2 (end of experimental period, day
15)\\u00a0  
 Mt1= mass (mg, dry weight) at time 1 (start of experimental period, day 8)  
 T2 = time 2 (end of experimental period, day 15)\\u00a0  
 T1= time 1 (start of experimental period, day 8)";
    String awards_0_award_nid "54992";
    String awards_0_award_number "OCE-1220478";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward?AWD_ID=1220478";
    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 cdm_data_type "Other";
    String comment 
"DO data from Lophelia pertusa experiments 
  Erik Cordes, PI 
  Version 16 September 2016";
    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 "2016-09-19T21:32:53Z";
    String date_modified "2019-05-13T14:28:30Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.659064.1";
    String history 
"2024-03-29T13:23:02Z (local files)
2024-03-29T13:23:02Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_659064.das";
    String infoUrl "https://www.bco-dmo.org/dataset/659064";
    String institution "BCO-DMO";
    String instruments_0_acronym "Water Temp Sensor";
    String instruments_0_dataset_instrument_description "Indicates water temperature";
    String instruments_0_dataset_instrument_nid "659589";
    String instruments_0_description "General term for an instrument that measures the temperature of the water with which it is in contact (thermometer).";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/134/";
    String instruments_0_instrument_name "Water Temperature Sensor";
    String instruments_0_instrument_nid "647";
    String instruments_0_supplied_name "Temperature sensor";
    String instruments_1_acronym "Aquarium";
    String instruments_1_dataset_instrument_description "20 L aquaria were used on the ship and 570 L recirculating aquaria systems were used in the lab";
    String instruments_1_dataset_instrument_nid "659587";
    String instruments_1_description "Aquarium - a vivarium consisting of at least one transparent side in which water-dwelling plants or animals are kept";
    String instruments_1_instrument_name "Aquarium";
    String instruments_1_instrument_nid "711";
    String instruments_1_supplied_name "Aquarium";
    String instruments_2_acronym "Scale";
    String instruments_2_dataset_instrument_description "Used for buoyant weights; d = 0.1mg, Fisher Scientific";
    String instruments_2_dataset_instrument_nid "659588";
    String instruments_2_description "An instrument used to measure weight or mass.";
    String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB13/";
    String instruments_2_instrument_name "Scale";
    String instruments_2_instrument_nid "714";
    String instruments_2_supplied_name "Denver Instruments SI-64 Analytical Balance";
    String keywords "bco, bco-dmo, biological, chemical, data, dataset, dmo, DO_mean, DO_treatment, erddap, individual, management, mean, oceanography, office, percent, percent_survivorship, preliminary, survivorship, tank, temp_mean, temperature, treatment";
    String license "https://www.bco-dmo.org/dataset/659064/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/659064";
    String param_mapping "{'659064': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/659064/parameters";
    String people_0_affiliation "Temple University";
    String people_0_affiliation_acronym "Temple";
    String people_0_person_name "Erik E Cordes";
    String people_0_person_nid "51539";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Temple University";
    String people_1_affiliation_acronym "Temple";
    String people_1_person_name "Erik E Cordes";
    String people_1_person_nid "51539";
    String people_1_role "Contact";
    String people_1_role_type "related";
    String people_2_affiliation "Lock Haven University";
    String people_2_affiliation_acronym "LHU";
    String people_2_person_name "Dr Jay Lunden";
    String people_2_person_nid "659079";
    String people_2_role "Contact";
    String people_2_role_type "related";
    String people_3_affiliation "Woods Hole Oceanographic Institution";
    String people_3_affiliation_acronym "WHOI BCO-DMO";
    String people_3_person_name "Hannah Ake";
    String people_3_person_nid "650173";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "Lophelia OA";
    String projects_0_acronym "Lophelia OA";
    String projects_0_description 
"The Gulf of Mexico deep water ecosystems are threatened by the persistent threat of ocean acidification. Deep-water corals will be among the first to feel the effects of this process, in particular the deep-water scleractinians that form their skeleton from aragonite. The continued shoaling of the aragonite saturation horizon (the depth below which aragonite is undersaturated) will place many of the known, and as yet undiscovered, deep-water corals at risk in the very near future. The most common deep-water framework-forming scleractinian in the world's oceans is Lophelia pertusa. This coral is most abundant in the North Atlantic, where aragonite saturation states are relatively high, but it also creates extensive reef structures between 300 and 600 m depth in the Gulf of Mexico where aragonite saturation states were previously unknown. Preliminary data indicate that pH at this depth range is between 7.85 and 8.03, and the aragonite saturation state is typically between 1.28 and 1.69. These are the first measurements of aragonite saturation state for the deep Gulf of Mexico, and are among the lowest Aragonite saturation state yet recorded for framework-forming corals in any body of water, at any depth.
This project will examine the effects of ocean acidification on L. pertusa, combining laboratory experiments, rigorous oceanographic measurements, the latest genome and transcriptome sequencing platforms, and quantitative PCR and enzyme assays to examine changes in coral gene expression and enzyme activity related to differences in carbonate chemistry. Short-term and long-term laboratory experiments will be performed at Aragonite saturation state of 1.45 and 0.75 and the organismal (e.g., survivorship and calcification rate) and genetic (e.g., transcript abundance) responses of the coral will be monitored. Genomic DNA and RNA will be extracted, total mRNA purified, and comprehensive and quantitative profiles of the transcriptome generated using a combination of 454 and Illumina sequencing technologies. Key genes in the calcification pathways as well as other differentially expressed genes will be targeted for specific qPCR assays to verify the Illumina sequencing results. On a research cruise, L. pertusa will be sampled (preserved at depth) along a natural gradient in carbonate chemistry, and included in the Illumina sequencing and qPCR assays. Water samples will be obtained by submersible-deployed niskin bottles adjacent to the coral collections as well as CTD casts of the water column overlying the sites. Water samples will be analyzed for pH, alkalinity, nitrates and soluble reactive phosphorus. These will be used in combination with historical data in a model to hindcast Aragonite saturation state.
This project will provide new physiological and genetic data on an ecologically-significant and anthropogenically-threatened deepwater coral in the Gulf of Mexico. An experimental system, already developed by the PIs, offers controlled conditions to test the effect of Aragonite saturation state on calcification rates in scleractinians and, subsequently, to identify candidate genes and pathways involved in the response to reduced pH and Aragonite saturation state. Both long-term and population sampling experiments will provide additional transcriptomic data and specifically investigate the expression of the candidate genes. These results will contribute to our understanding of the means by which scleractinians may acclimate and acclimatize to low pH, alkalinity, and Aragonite saturation state. Furthermore, the investigators will continue a time series of oceanographic measurements of the carbonate system in the Gulf of Mexico, which will allow the inclusion of this significant body of water in models of past and future ocean acidification scenarios.";
    String projects_0_end_date "2015-08";
    String projects_0_geolocation "Northern Gulf of Mexico";
    String projects_0_name "Physiological and genetic responses of the deep-water coral, Lophelia pertusa, to ongoing ocean acidification in the Gulf of Mexico";
    String projects_0_project_nid "2224";
    String projects_0_start_date "2012-09";
    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 
"Mortality data for Lophelia pertusa exposed to dissolved oxygen experiments.
Specimens used in this experiment were collected on RB-10-07: NOAA Ship Ronald
H. Brown, from October-November 2010.";
    String title "Mortality of L. pertusa specimens exposed to different DO levels collected on R/V Ronald Brown in Florida from October to November 2010 (Lophelia OA project)";
    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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