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Dataset Title:  Abundance and biomass of protists from epifluorescence counts and bulk biomass
from extracted chl-a from samples from R/V Atlantic Explorer cruises AE1102,
AE1118, AE1206, AE1219 in the Sargasso Sea, Bermuda Atlantic Time-Series
Station in 2011-12
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_4019)
Range: longitude = -65.75 to -63.48°E, latitude = 30.05 to 33.5°N, depth = 20.0 to 110.0m
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
 
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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  cruise_id {
    String bcodmo_name "cruise_id";
    String description "Official cruise identifier e.g. AE1102 = R/V Atlantic Explorer cruise number 1102.";
    String long_name "Cruise Id";
    String units "dimensionless";
  }
  cast {
    Byte _FillValue 127;
    Byte actual_range 2, 38;
    String bcodmo_name "cast";
    String description "Cast number.";
    String long_name "Cast";
    String units "dimensionless";
  }
  station {
    Byte _FillValue 127;
    Byte actual_range 1, 6;
    String bcodmo_name "station";
    String description "Station number.";
    String long_name "Station";
    String units "dimensionless";
  }
  location_description {
    String bcodmo_name "site_descrip";
    String description "Description of sampling location.";
    String long_name "Location Description";
    String units "dimensionless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 30.05, 33.5;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude. Positive values = North.";
    String ioos_category "Location";
    String long_name "Latitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 _FillValue NaN;
    Float64 actual_range -65.75, -63.48;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude. Positive values = East.";
    String ioos_category "Location";
    String long_name "Longitude";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
    String standard_name "longitude";
    String units "degrees_east";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 20.0, 110.0;
    String axis "Z";
    String bcodmo_name "depth";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "Sample depth.";
    String ioos_category "Location";
    String long_name "Depth";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  taxon {
    String bcodmo_name "taxon";
    String description 
"Name of the taxonomic group. Codes:
H_dinos = Heterotrophic dinoflagellates
H_nano = Heterotrophic nanoflagellates
Mixo_dino = Mixotrophic dinoflagellates
Nano_Photo_Eukaryotes = Nano Phototrophic Eukaryotes 
Pico_Photo_Eukaryotes = Pico Phototrophic Eukaryotes
Photo_Eukaryotes = Phototrophic Eukaryotes";
    String long_name "Taxon";
    String units "dimensionless";
  }
  total_biomass_per_taxon {
    Float32 _FillValue NaN;
    Float32 actual_range 3.5, 10213.0;
    String bcodmo_name "biomass_C";
    String description "Total biomass (pg C/mL) at the particular cast and depth for the taxonomic group.";
    String long_name "Total Biomass Per Taxon";
    String units "picograms C per milliliter";
  }
  experiment_num {
    Byte _FillValue 127;
    Byte actual_range 1, 19;
    String bcodmo_name "unknown";
    String description "Experiment number.";
    String long_name "Experiment Num";
    String units "dimensionless";
  }
  date {
    String bcodmo_name "date";
    String description "2-digit month, 2-digit day, and 4-digit year of sampling. Reported in UTC. Format: mmddYYYY";
    String long_name "Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String units "unitless";
  }
  season_year {
    String bcodmo_name "unknown";
    String description "Sampling season and year.";
    String long_name "Season Year";
    String units "text";
  }
  length {
    Float32 _FillValue NaN;
    Float32 actual_range 1.0, 7.5;
    String bcodmo_name "length";
    String description "Length/diameter (in um).";
    String long_name "Length";
    String units "micrometers";
  }
  width {
    Float32 _FillValue NaN;
    Float32 actual_range 1.0, 16.929;
    String bcodmo_name "unknown";
    String description "Width/height (in um).";
    String long_name "Width";
    String units "micrometers";
  }
  shape {
    String bcodmo_name "unknown";
    String description "Description of the 3D shape.";
    String long_name "Shape";
    String units "dimensionless";
  }
  abundance {
    Float32 _FillValue NaN;
    Float32 actual_range 7.0, 7263.7;
    String bcodmo_name "abundance";
    String description "Abundance (cells/mL).";
    String long_name "Abundance";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P03/current/B070/";
    String units "cells per milliliter";
  }
  abund_lower_95pcnt_CI {
    Float32 _FillValue NaN;
    Float32 actual_range 15.5, 7433.1;
    String bcodmo_name "unknown";
    String description "Upper 95% confidence interval for abundance.";
    String long_name "Abund Lower 95pcnt CI";
    String units "cells per milliliter";
  }
  abund_upper_95pcnt_CI {
    Float32 _FillValue NaN;
    Float32 actual_range 4.1, 7099.0;
    String bcodmo_name "unknown";
    String description "Lower 95% confidence interval for abundance.";
    String long_name "Abund Upper 95pcnt CI";
    String units "cells per milliliter";
  }
  cells_counted {
    Int16 _FillValue 32767;
    Int16 actual_range 13, 1935;
    String bcodmo_name "count";
    String description "Number of cells counted.";
    String long_name "Cells Counted";
    String units "dimensionless";
  }
  biovolume {
    Float32 _FillValue NaN;
    Float32 actual_range 4.5, 55553.0;
    String bcodmo_name "unknown";
    String description "Biovolume (um^3/mL).";
    String long_name "Biovolume";
    String units "cubic micrometers per milliliter";
  }
  dino_biomass {
    Float32 _FillValue NaN;
    Float32 actual_range 220.8, 1597.6;
    String bcodmo_name "unknown";
    String description "Dinoflagellate biomass (pg C/cell).";
    String long_name "Dino Biomass";
    String units "picograms C per cell";
  }
  diatoms_biomass {
    Float32 _FillValue NaN;
    Float32 actual_range 80.8, 112.0;
    String bcodmo_name "unknown";
    String description "Diatom biomass (pg C/cell).";
    String long_name "Diatoms Biomass";
    String units "picograms C per cell";
  }
  protists_biomass {
    Float32 _FillValue NaN;
    Float32 actual_range 0.9, 6162.2;
    String bcodmo_name "unknown";
    String description "Protist biomass (pg C/cell).";
    String long_name "Protists Biomass";
    String units "picograms C per cell";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Water Column Sampling:  
 Water column sampling was performed on four cruises during the spring and
the summer of 2011 and 2012 at the Bermuda Atlantic Time-series Study station
(31\\u201940\\u00b0N 64\\u201910\\u00b0W, BATS) and in the mesoscale eddies found
in the surrounding area of the Sargasso Sea. For each cruise, two stations
were sampled, usually in the center of a mesoscale eddy and at BATS. The edge
of the eddy was sampled two times, as well. To be able to get a better
reproducibility of data, each experiment was replicated.
 
For each experiment, seawater samples were collected pre-dawn (on deck
2:30-4:00, local time) at four different depths within the euphotic zone (20m,
50m, 80m and the Deep Chlorophyll Maximum, DCM). Twenty-one 10L Niskin bottles
were attached to a rosette with conductivity, temperature, depth sensors
(CTD), and an in vivo fluorometer. This sensor allowed for recording in real
time of chlorophyll fluorescence and the DCM for each station. The water that
was collected from the 10L Niskin bottles was sampled for abundance and
biomass of the plankton community.
 
Bulk measurements:  
 Chlorophyll-a was extracted from seawater (250 ml and 400 ml depending on
the dilution), with 90% acetone and measured after 24hrs at 4 degrees C in the
dark onboard the ship using a TD 700 Laboratory Fluorometer using the non-
acidification technique (Welschmeyer 1994). These data were used as a proxy
for the phytoplankton biomass in the water column and to calculate the bulk
growth and grazing rates of the phytoplankton community.
 
Microscopy Analyses:  
 To determine cell abundance and the biomass of the protist community (other
than ciliates), epifluorescence miscoscopy was used. Ciliate abundance and
biomass was determined using bright-field inverted microscopy (Amacher et al.
2009; Neuer and Cowles 1994). Epiflourescence microscopy: 25-50ml of seawater
from each depth was filtered onto black membrane filters with 0.2 um pore
size. Each sample was fixed first with 0.1 ml of 50% of cold glutaraldehyde,
stored for 24 hours at 4 degrees C, and then filtered after addition of 0.2 ml
of 1% 4', 6-diamino-2-phenylindole (DAPI). Slides were stored frozen at -20
degrees C onboard ship until transport back to the laboratory at ASU, and
stored at -40 degrees C until analysis. The organisms were counted using a
ZEISS Axioplan Epifluorescence Microscope equipped with a 100x Plan-NEOFLUAR
100x/1.30 oil, objective lens. Pico, nano and micro plankton were identified
and separated in categories based on their approximate geometric shape, size,
and on their fluorescence under blue and UV light excitation as described in
[Table 1](\\\\\"https://datadocs.bco-
dmo.org/docs/richardson_bats/data_docs/neuer/Table1_methodology_neuer.pdf\\\\\")
(Amacher et al. 2009, Hansen et al. in prep). Organisms were counted in one to
several stripes across the slide. Abundance was then calculated based on
number of counted cells, fraction of slide area counted and sample volume. The
95% confidence interval of each organismal count was determined as a function
of total cells counted in a given category, according to Lund et al. (1958).
The following equations were applied, where x stands for the number of cells
counted on each slide:  
 LL = x + 1.42 - 1.960 (sqrt(x + 0.5))\\u00a0 [Lower limit]  
 UL = x + 2.42 - 1.960 (sqrt(x + 1.5))\\u00a0 [Upper limit]
 
Biomass calculations were done for each category of organism counted.
Biovolume for each group was determined based on size and shape of the
organism by approximating the closest geometric shape (Hillebrand et al. 1999)
and then converted into units of carbon based on the carbon to volume ratio
(Menden-Deuer and Lessard 2000).
 
Flow cytometry analyses:  
 Collection and fixation of flow cytometry samples was carried out according
to established methods of the BATS program
([http://www.bios.edu/research/projects/bats/](\\\\\"http://www.bios.edu/research/projects/bats/\\\\\"))
and analyzed by the group of Co-PI Dr. Mike Lomas.
 
Refer to the original [dataset legend](\\\\\"https://datadocs.bco-
dmo.org/docs/richardson_bats/data_docs/neuer/Biomass_Neuer_Legend_011315.pdf\\\\\")
(PDF) for more information.";
    String awards_0_award_nid "55163";
    String awards_0_award_number "OCE-1030476";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward?AWD_ID=1030476&HistoricalAwards=false";
    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 
"Abundance and biomass of protists 
  based on epifluorescence counts, and 
  bulk biomass based on extracted chl-a measurements. 
 PI: Susanne Neuer (Arizona State U.) 
 Version: 13 January 2015 
 See Documentation for full taxon names.";
    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 "2013-08-26T14:25:18Z";
    String date_modified "2019-08-05T19:54:12Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.4019.1";
    Float64 Easternmost_Easting -63.48;
    Float64 geospatial_lat_max 33.5;
    Float64 geospatial_lat_min 30.05;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -63.48;
    Float64 geospatial_lon_min -65.75;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 110.0;
    Float64 geospatial_vertical_min 20.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2020-08-14T16:41:49Z (local files)
2020-08-14T16:41:49Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_4019.das";
    String infoUrl "https://www.bco-dmo.org/dataset/4019";
    String institution "BCO-DMO";
    String instruments_0_acronym "Niskin bottle";
    String instruments_0_dataset_instrument_description "Samples were collected using 10-Liter Niskin bottles attached to a CTD rosette.";
    String instruments_0_dataset_instrument_nid "6244";
    String instruments_0_description "A Niskin bottle (a next generation water sampler based on the Nansen bottle) is a cylindrical, non-metallic water collection device with stoppers at both ends.  The bottles can be attached individually on a hydrowire or deployed in 12, 24 or 36 bottle Rosette systems mounted on a frame and combined with a CTD.  Niskin bottles are used to collect discrete water samples for a range of measurements including pigments, nutrients, plankton, etc.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0412/";
    String instruments_0_instrument_name "Niskin bottle";
    String instruments_0_instrument_nid "413";
    String instruments_0_supplied_name "Niskin bottle";
    String instruments_1_acronym "Fluorescence Microscope";
    String instruments_1_dataset_instrument_description "The organisms were counted using a  ZEISS Axioplan Epifluorescence  Microscope equipped with a 100x Plan-NEOFLUAR 100x/1.30 oil, objective  lens";
    String instruments_1_dataset_instrument_nid "6245";
    String instruments_1_description "A Fluorescence (or Epifluorescence) Microscope Image Analysis System uses semi-automated color image analysis to determine cell abundance, dimensions and biovolumes from an Epifluorescence Microscope. An Epifluorescence Microscope (conventional and inverted) includes a camera system that generates enlarged images of prepared samples.  The microscope uses excitation ultraviolet light and the phenomena of fluorescence and phosphorescence instead of, or in addition to, reflection and absorption of visible light.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB06/";
    String instruments_1_instrument_name "Fluorescence Microscope Image Analysis System";
    String instruments_1_instrument_nid "508";
    String instruments_1_supplied_name "Fluorescence Microscope Image Analysis System";
    String instruments_2_acronym "TD-700";
    String instruments_2_dataset_instrument_description "Chlorophyll a was extracted from seawater (250 ml and 400 ml depending on the dilution), with 90% acetone and measured after 24hrs at 4 degrees C in the dark onboard ship using a TD 700 Laboratory Fluorometer using the non-acidification technique (Welschmeyer 1994).";
    String instruments_2_dataset_instrument_nid "545858";
    String instruments_2_description "The TD-700 Laboratory Fluorometer is a benchtop fluorometer designed to detect fluorescence over the UV to red range. The instrument can measure concentrations of a variety of compounds, including chlorophyll-a and fluorescent dyes, and is thus suitable for a range of applications, including chlorophyll, water quality monitoring and fluorescent tracer studies. Data can be output as concentrations or raw fluorescence measurements.";
    String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0510/";
    String instruments_2_instrument_name "Turner Designs 700 Laboratory Fluorometer";
    String instruments_2_instrument_nid "694";
    String instruments_2_supplied_name "TD 700 Laboratory Fluorometer";
    String keywords "95pcnt, abund, abund_lower_95pcnt_CI, abund_upper_95pcnt_CI, abundance, bco, bco-dmo, biological, biomass, biovolume, cast, cells, cells_counted, chemical, counted, cruise, cruise_id, data, dataset, date, depth, description, diatoms, diatoms_biomass, dino, dino_biomass, dmo, erddap, experiment, experiment_num, latitude, length, location_description, longitude, lower, management, num, oceanography, office, per, preliminary, protists, protists_biomass, season, season_year, shape, station, taxon, total, total_biomass_per_taxon, upper, width, year";
    String license "https://www.bco-dmo.org/dataset/4019/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/4019";
    Float64 Northernmost_Northing 33.5;
    String param_mapping "{'4019': {'lat': 'master - latitude', 'depth': 'flag - depth', 'lon': 'master - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/4019/parameters";
    String people_0_affiliation "Arizona State University";
    String people_0_affiliation_acronym "ASU";
    String people_0_person_name "Susanne Neuer";
    String people_0_person_nid "51336";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Arizona State University";
    String people_1_affiliation_acronym "ASU";
    String people_1_person_name "Francesca De Martini";
    String people_1_person_nid "51719";
    String people_1_role "Student";
    String people_1_role_type "related";
    String people_2_affiliation "Arizona State University";
    String people_2_affiliation_acronym "ASU";
    String people_2_person_name "Susanne Neuer";
    String people_2_person_nid "51336";
    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 "Shannon Rauch";
    String people_3_person_nid "51498";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "Trophic BATS";
    String projects_0_acronym "Trophic BATS";
    String projects_0_description 
"Fluxes of particulate carbon from the surface ocean are greatly influenced by the size, taxonomic composition and trophic interactions of the resident planktonic community. Large and/or heavily-ballasted phytoplankton such as diatoms and coccolithophores are key contributors to carbon export due to their high sinking rates and direct routes of export through large zooplankton. The potential contributions of small, unballasted phytoplankton, through aggregation and/or trophic re-packaging, have been recognized more recently. This recognition comes as direct observations in the field show unexpected trends. In the Sargasso Sea, for example, shallow carbon export has increased in the last decade but the corresponding shift in phytoplankton community composition during this time has not been towards larger cells like diatoms. Instead, the abundance of the picoplanktonic cyanobacterium, Synechococccus, has increased significantly. The trophic pathways that link the increased abundance of Synechococcus to carbon export have not been characterized. These observations helped to frame the overarching research question, \"How do plankton size, community composition and trophic interactions modify carbon export from the euphotic zone\". Since small phytoplankton are responsible for the majority of primary production in oligotrophic subtropical gyres, the trophic interactions that include them must be characterized in order to achieve a mechanistic understanding of the function of the biological pump in the oligotrophic regions of the ocean.
This requires a complete characterization of the major organisms and their rates of production and consumption. Accordingly, the research objectives are: 1) to characterize (qualitatively and quantitatively) trophic interactions between major plankton groups in the euphotic zone and rates of, and contributors to, carbon export and 2) to develop a constrained food web model, based on these data, that will allow us to better understand current and predict near-future patterns in export production in the Sargasso Sea.
The investigators will use a combination of field-based process studies and food web modeling to quantify rates of carbon exchange between key components of the ecosystem at the Bermuda Atlantic Time-series Study (BATS) site. Measurements will include a novel DNA-based approach to characterizing and quantifying planktonic contributors to carbon export. The well-documented seasonal variability at BATS and the occurrence of mesoscale eddies will be used as a natural laboratory in which to study ecosystems of different structure. This study is unique in that it aims to characterize multiple food web interactions and carbon export simultaneously and over similar time and space scales. A key strength of the proposed research is also the tight connection and feedback between the data collection and modeling components.
Characterizing the complex interactions between the biological community and export production is critical for predicting changes in phytoplankton species dominance, trophic relationships and export production that might occur under scenarios of climate-related changes in ocean circulation and mixing. The results from this research may also contribute to understanding of the biological mechanisms that drive current regional to basin scale variability in carbon export in oligotrophic gyres.";
    String projects_0_end_date "2014-09";
    String projects_0_geolocation "Sargasso Sea, BATS site";
    String projects_0_name "Plankton Community Composition and Trophic Interactions as Modifiers of Carbon Export in the Sargasso Sea";
    String projects_0_project_nid "2150";
    String projects_0_start_date "2010-10";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 30.05;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "Abundance and biomass of protists from epifluorescence counts and bulk biomass from extracted chl-a from samples from R/V Atlantic Explorer cruises AE1102, AE1118, AE1206, AE1219 in the Sargasso Sea, Bermuda Atlantic Time-Series Station in 2011-12.";
    String title "Abundance and biomass of protists from epifluorescence counts and bulk biomass from extracted chl-a from samples from R/V Atlantic Explorer cruises AE1102, AE1118, AE1206, AE1219 in the Sargasso Sea, Bermuda Atlantic Time-Series Station in 2011-12";
    String version "1";
    Float64 Westernmost_Easting -65.75;
    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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