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Dataset Title:  Zooplankton community abundances from meter net tows on multiple cruises on RV/
Savannah in the South Atlantic Bight, Mid-Continental Shelf from 2015-2017
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_692753)
Range: longitude = 80.1061 to 80.6478°E, latitude = 29.9558 to 31.5675°N, time = 2015-08-04T08:00:00Z to (now?)
Information:  Summary ? | License ? | 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 {
  cruise_id {
    String bcodmo_name "cruise_id";
    String description "cruise identifier";
    String long_name "Cruise Id";
    String units "unitless";
  date {
    String bcodmo_name "date";
    String description "local date";
    String long_name "Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String source_name "date";
    String time_precision "1970-01-01";
    String units "unitless";
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 29.9558, 31.5675;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "latitude; north is positive";
    String ioos_category "Location";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String source_name "lat_decdeg";
    String standard_name "latitude";
    String units "degrees_north";
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range 80.1061, 80.6478;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "longitude; east is positive";
    String ioos_category "Location";
    String long_name "Longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String source_name "lon_decdeg";
    String standard_name "longitude";
    String units "degrees_east";
  cast {
    String bcodmo_name "cast";
    String description "cast number";
    String long_name "Cast";
    String units "unitless";
  station_depth_water {
    Byte _FillValue 127;
    Byte actual_range 25, 45;
    String bcodmo_name "depth_w";
    String description "sampling depth";
    String long_name "Station Depth Water";
    String units "meters";
  mesh {
    Int16 _FillValue 32767;
    Int16 actual_range 202, 202;
    String bcodmo_name "net_mesh";
    String description "mesh opening size";
    String long_name "Mesh";
    String units "microns";
  replicate {
    Byte _FillValue 127;
    Byte actual_range 1, 2;
    String bcodmo_name "tow";
    String description "replicate tow number; two tows were taken at each station";
    String long_name "Replicate";
    String units "unitless";
  time_tow {
    String bcodmo_name "time";
    String description "local time of the replicate tow";
    String long_name "Time Tow";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AHMSAA01/";
    String units "unitless";
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.4386752e+9, NaN;
    String axis "T";
    String bcodmo_name "ISO_DateTime_UTC";
    String description "UTC date and time of tow in ISO format: yyyy-mm-ddTHH:MM:SSZ";
    String ioos_category "Time";
    String long_name "ISO Date Time UTC";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DTUT8601/";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  vol_filt {
    Float32 _FillValue NaN;
    Float32 actual_range 0.49, 215.78;
    String bcodmo_name "vol_filt";
    String description "volume of water filtered by net for a replicate tow";
    String long_name "Vol Filt";
    String units "cubic meters (m^3)";
  splits {
    Byte _FillValue 127;
    Byte actual_range 0, 2;
    String bcodmo_name "unknown";
    String description "number of times the sample was split; fraction of sample = 1/n^2 so splits=2 means 1/4 of sample was processed. Except for splits = 0, this means the entire sample was used for the taxonomic count.";
    String long_name "Splits";
    String units "splits";
  taxon_1 {
    String bcodmo_name "taxon";
    String description "taxon 1: holoplankton or meroplankton";
    String long_name "Taxon 1";
    String units "unitless";
  taxon_2 {
    String bcodmo_name "taxon";
    String description "most specific taxonomic group identified";
    String long_name "Taxon 2";
    String units "unitless";
  dilution_factor {
    Int16 _FillValue 32767;
    Int16 actual_range 0, 400;
    String bcodmo_name "unknown";
    String description "sample dilution factor";
    String long_name "Dilution Factor";
    String units "unitless";
  count_aliquot_1 {
    Int16 _FillValue 32767;
    Int16 actual_range 0, 542;
    String bcodmo_name "count";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String description "first aliquot raw count from replicate sample split";
    String long_name "Count Aliquot 1";
    String units "number of individuals per sample";
  count_aliquot_2 {
    Int16 _FillValue 32767;
    Int16 actual_range 0, 642;
    String bcodmo_name "count";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String description "second aliquot raw count from replicate sample split";
    String long_name "Count Aliquot 2";
    String units "number of individuals per sample";
  count_mean {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 592.0;
    String bcodmo_name "count";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String description "mean raw count";
    String long_name "Count Mean";
    String units "number of individuals per sample";
  count_sample {
    Int32 _FillValue 2147483647;
    Int32 actual_range 0, 118400;
    String bcodmo_name "count";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String description "calculated average abundance of individuals per sample";
    String long_name "Count Sample";
    String units "number of individuals per sample";
  abundance_m3 {
    Float64 _FillValue NaN;
    Float64 actual_range 0.0, 45698.32;
    String bcodmo_name "abundance";
    String description "estimated abundance";
    String long_name "Abundance M3";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "individuals per cubic meter of water";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt";
    String acquisition_description 
"Zooplankton was collected from 31\\u00b0N to 29\\u00b0N aboard the R/V Savannah
at the 25m and 40m isobaths using a 1M (mouth opening) 5M length 200 \\u00b5m
mesh cone plankton net equipped with a filtering (202 \\u00b5m mesh) cod-
end.\\u00a0 The mouth of the net was mounted in a swivel towing frame that
allowed the opening of the net to track the water current. A mechanical flow
meter (General Oceanics Part# 2030RC) was mounted in the center of the net.
[Flowmeter measurements for 2015 cruises (pdf)](\\\\\"http://dmoserv3.bco-
The net was deployed from a gently drifting ship and the entire water column
was sampled by lowering the net to near the bottom and retrieving it (oblique
tow).\\u00a0 Net speed was maintained at approximately 15 meters per minute.
Once retrieved the contents of the net were collected on a 200 \\u00b5m sieve
and preserved in 60% non-denatured ethanol.
Samples were split twice to create four subsamples using a Folsom plankton
One of the subsamples was diluted to a known volume and stirred gently.
Dilutions were adjusted depending on the density of zooplankton so that
sufficient numbers of species were present in each sample to obtain a reliable
estimate of the major taxonomic groups in each sample. Replicate aliquots from
the subsample were counted under a dissecting microscope using a Bogorov
counting chamber. The volume of sample counted was adjusted based on the
density of zooplankton present but was generally 5 ml. A Stempel pipette was
used to dispense the counted volume.
In the case where the identity was uncertain, representative examples of the
unidentified zooplankton were identified by DNA barcoding.\\u00a0 DNA
extractions were performed using the DNeasy Blood & Tissue Kit (Qiagen)
according to manufacturer\\u2019s protocol except that samples were macerated
and homogenized with Kimble/Kontes Cordless Motor, the proteinase K incubation
was extended to ~24 hours (overnight) and the volume of elution buffer AE
varied depending on sample size (60ul for most). For DNA quantitation and
quality testing, one (1) ul of DNA was used to measure the 260/280 ratio using
the Thermo Scientific\\u2122\\u00a0 NanoDrop Lite Spectrophotometer.\\u00a0 The
presence of high molecular weight DNA in each extract was visually confirmed
by agarose (1%) gel electrophoresis.\\u00a0 Good quality DNA extractions,
inferred by 260/280 ratio and electrophoresis, were selected for amplification
by Polymerase Chain Reaction (PCR) of a fragment of the mitochondrial
cytochrome oxidase subunit I (mtCOI) gene.\\u00a0 A 708 base-pair mtCOI gene
fragment was amplified using the consensus primer pairs LCO-1490
(5\\u2019-GGTCAACAAATCATAAAGATATTGG-3\\u2019) and HCO-2198
(5\\u2019-TAAACTTCAGGGTGACCAAAAAATCA-3\\u2019) (Folmer et al. 1994).\\u00a0
Amplification was facilitated using a Bio-Rad T100Thermocycler with GoTaq Hot
Start Green Master Mix. Cycling conditions were: 10 min at 95 \\u00b0C; 35
cycles of 30 sec at 95 \\u00b0C, 1 min at 47 \\u00b0C and 1 min at 72 \\u00b0C;
and 10 min at 72 \\u00b0C. PCR product quality and quantity was assessed by 2%
agarose gel electrophoresis and subsequently sequenced by Eurofins Genomics.
DNA sequences obtained were compared to a reference database library using the
Basic Local Alignment Search Tool (BLAST) (Altschul et al. 1990) available at
the National Center for Biotechnology Information (NCBI) and Bold Systems
Identification engine for CO1 gene.\\u00a0";
    String awards_0_award_nid "641282";
    String awards_0_award_number "OCE-1459293";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1459293";
    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 awards_1_award_nid "641288";
    String awards_1_award_number "OCE-1459510";
    String awards_1_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1459510";
    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 "Michael E. Sieracki";
    String awards_1_program_manager_nid "50446";
    String cdm_data_type "Other";
    String comment 
"Zooplankton abundance 
    PI: M. Frischer (SkIO) 
    version 3: 2020-04-08";
    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 "2017-04-28T14:28:51Z";
    String date_modified "2020-04-09T17:31:40Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.26008/1912/bco-dmo.692753.3";
    Float64 Easternmost_Easting 80.6478;
    Float64 geospatial_lat_max 31.5675;
    Float64 geospatial_lat_min 29.9558;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 80.6478;
    Float64 geospatial_lon_min 80.1061;
    String geospatial_lon_units "degrees_east";
    String history 
"2024-07-15T04:52:42Z (local files)
2024-07-15T04:52:42Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_692753.das";
    String infoUrl "https://www.bco-dmo.org/dataset/692753";
    String institution "BCO-DMO";
    String instruments_0_acronym "Meter Net";
    String instruments_0_dataset_instrument_description "1M (mouth opening) 5M length 200 µm mesh cone plankton net equipped with a filtering (202 µm mesh) cod-end.  The mouth of the net was mounted in a swivel towing frame that allowed the opening of the net to track the water current.  A mechanical flow meter (General Oceanics Part# 2030RC) was mounted in the center of the net.";
    String instruments_0_dataset_instrument_nid "692766";
    String instruments_0_description "A meter net is a plankton net with a one meter diameter opening and a mesh size of .333 mm, towed horizontally, obliquely or vertically, also known as a Ring Net.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/22/";
    String instruments_0_instrument_name "Meter Net";
    String instruments_0_instrument_nid "433";
    String instruments_0_supplied_name "meter net";
    String instruments_1_acronym "Automated Sequencer";
    String instruments_1_dataset_instrument_nid "794068";
    String instruments_1_description "General term for a laboratory instrument used for deciphering the order of bases in a strand of DNA. Sanger sequencers detect fluorescence from different dyes that are used to identify the A, C, G, and T extension reactions. Contemporary or Pyrosequencer methods are based on detecting the activity of DNA polymerase (a DNA synthesizing enzyme) with another chemoluminescent enzyme. Essentially, the method allows sequencing of a single strand of DNA by synthesizing the complementary strand along it, one base pair at a time, and detecting which base was actually added at each step.";
    String instruments_1_instrument_name "Automated DNA Sequencer";
    String instruments_1_instrument_nid "649";
    String instruments_2_acronym "Flow Meter";
    String instruments_2_dataset_instrument_description "Used to measure volume of water passing through the plankton net.";
    String instruments_2_dataset_instrument_nid "794162";
    String instruments_2_description "General term for a sensor that quantifies the rate at which fluids (e.g. water or air) pass through sensor packages, instruments, or sampling devices. A flow meter may be mechanical, optical, electromagnetic, etc.";
    String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/388/";
    String instruments_2_instrument_name "Flow Meter";
    String instruments_2_instrument_nid "650";
    String instruments_2_supplied_name "mechanical flow meter (General Oceanics Part# 2030RC)";
    String instruments_3_acronym "Spectrophotometer";
    String instruments_3_dataset_instrument_description "Used for DNA quantitation and quality testing.";
    String instruments_3_dataset_instrument_nid "794065";
    String instruments_3_description "An instrument used to measure the relative absorption of electromagnetic radiation of different wavelengths in the near infra-red, visible and ultraviolet wavebands by samples.";
    String instruments_3_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB20/";
    String instruments_3_instrument_name "Spectrophotometer";
    String instruments_3_instrument_nid "707";
    String instruments_3_supplied_name "Thermo Scientific™  NanoDrop Lite Spectrophotometer";
    String instruments_4_acronym "Thermal Cycler";
    String instruments_4_dataset_instrument_nid "794067";
    String instruments_4_description 
"General term for a laboratory apparatus commonly used for performing polymerase chain reaction (PCR). The device has a thermal block with holes where tubes with the PCR reaction mixtures can be inserted. The cycler then raises and lowers the temperature of the block in discrete, pre-programmed steps.

(adapted from http://serc.carleton.edu/microbelife/research_methods/genomics/pcr.html)";
    String instruments_4_instrument_name "PCR Thermal Cycler";
    String instruments_4_instrument_nid "471582";
    String instruments_4_supplied_name "Bio-Rad T100Thermocycler";
    String instruments_5_acronym "Folsom Splitter";
    String instruments_5_dataset_instrument_description "Used to split plankton samples into equivalent subsamples.";
    String instruments_5_dataset_instrument_nid "692767";
    String instruments_5_description "A Folsom Plankton Splitter is used for sub-sampling of plankton and ichthyoplankton samples.";
    String instruments_5_instrument_name "Folsom Plankton Splitter";
    String instruments_5_instrument_nid "540984";
    String keywords "abundance, abundance_m3, aliquot, bco, bco-dmo, biological, cast, chemical, count, count_aliquot_1, count_aliquot_2, count_mean, count_sample, cruise, cruise_id, data, dataset, date, depth, dilution, dilution_factor, dmo, erddap, factor, filt, iso, ISO_DateTime_UTC, latitude, longitude, management, mean, mesh, oceanography, office, preliminary, replicate, sample, splits, station, station_depth_water, taxon, taxon_1, taxon_2, time, time_tow, tow, vol, vol_filt, water";
    String license "https://www.bco-dmo.org/dataset/692753/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/692753";
    Float64 Northernmost_Northing 31.5675;
    String param_mapping "{'692753': {'lat_decdeg': 'flag - latitude', 'lon_decdeg': 'flag - longitude', 'ISO_DateTime_UTC': 'flag - time'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/692753/parameters";
    String people_0_affiliation "Skidaway Institute of Oceanography";
    String people_0_affiliation_acronym "SkIO";
    String people_0_person_name "Marc E. Frischer";
    String people_0_person_nid "51144";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Hampton University";
    String people_1_person_name "Dr Deidre M. Gibson";
    String people_1_person_nid "641291";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Woods Hole Oceanographic Institution";
    String people_2_affiliation_acronym "WHOI BCO-DMO";
    String people_2_person_name "Nancy Copley";
    String people_2_person_nid "50396";
    String people_2_role "BCO-DMO Data Manager";
    String people_2_role_type "related";
    String project "Doliolid Diet";
    String projects_0_acronym "Doliolid Diet";
    String projects_0_description 
"Project description from NSF award abstract:
Gelatinous (soft-bodied) zooplankton can play a crucial role in food webs and in cycling of materials in the world's oceans, and it has been suggested that they may become even more important in the future. However, because they are so difficult to study, gelatinous species remain poorly understood. This is especially true for smaller filter feeding gelatinous animals such as pelagic tunicates (salps, larvaceans, and doliolids). For example, it remains unclear what and how much these abundant filter feeders eat in nature and who eats them. This project will address this large and significant knowledge gap by using a combination of new and traditional methods to investigate the diet of the gelatinous pelagic tunicate Dolioletti gegenbauri, a species common on productive continental shelves such as the South Atlantic Bight. This project will also help train the next generation of ocean scientists to be competent in classical biology, modern molecular biology, and ecosystem modeling. Training will also focus on increasing representation of African Americans in the future science, technology, engineering, and math (STEM) workforce.
This study will provide the first quantitative estimates of the in situ diet of a key continental shelf gelatinous zooplankton species, the doliolid Dolioletta gegenbauri. Large blooms of doliolids have the potential to control the trophic structure of shelf pelagic ecosystems by shunting primary production to the microbial food web and by limiting copepod production via the consumption of their eggs. The long-term objective is to understand the ecological role and significance of doliolids in continental shelf pelagic ecosystems, specifically the underlying processes that lead to their high level of spatial and temporal patchiness. The basic questions to be addressed here include: What do doliolids eat, in situ, at different life stages? Are early life stages of larger metazoans important components of their diets? Do doliolids act as trophic cascade agents promoting primary production and phytoplankton diversity? Because of methodological challenges, there have not yet been definitive studies addressing these fundamental questions. In this project, the investigators will conduct field-based studies that will combine state-of-the art molecular techniques with more traditional methods in zooplankton ecology to answer questions about trophic interactions. Monthly oceanographic expeditions in the South Atlantic Bight will allow the research team to study wild doliolids at different time points in their life cycle and under different plankton bloom conditions. Application of recently developed molecular diagnostic assays will enable the quantitative description of the diversity and quantity of prey consumed, unbiased by experimental manipulation. Additional experimental and theoretical modeling will allow the investigators to link these data with larger ecological significance and scale.";
    String projects_0_end_date "2018-02";
    String projects_0_geolocation "South Atlantic Bight";
    String projects_0_name "The cryptic diet of the globally significant pelagic tunicate Dolioletta gegenbauri (Uljanin, 1884.)";
    String projects_0_project_nid "641283";
    String projects_0_start_date "2015-03";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 29.9558;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String subsetVariables "mesh";
    String summary "Zooplankton community abundances from meter net tows taken on multiple cruises RV/Savannah cruises in the South Atlantic Bight, Mid-Continental Shelf from August 2015 to December 2017.";
    String time_coverage_start "2015-08-04T08:00:00Z";
    String title "Zooplankton community abundances from meter net tows on multiple cruises on RV/Savannah in the South Atlantic Bight, Mid-Continental Shelf from 2015-2017";
    String version "3";
    Float64 Westernmost_Easting 80.1061;
    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
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