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Dataset Title:  ChemTax based chl-a of algal groups from R/V Atlantic Explorer cruises AE1102,
AE1118, AE1206, AE1219 in the Sargasso Sea, Bermuda Atlantic Time-Series
Station (BATS) from 2011-2012 (Trophic BATS project)
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_3885)
Range: longitude = -65.7996 to -63.4806°E, latitude = 29.5474 to 33.5007°N, depth = 1.0 to 200.0m, time = 2011-02-24T15:10:00Z to 2012-07-30T10:35:00Z
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";
  }
  date_gmt {
    String bcodmo_name "date_gmt";
    String description "Date of sample collection (GMT) in mmddYYYY format.";
    String long_name "Date Gmt";
    String units "unitless";
  }
  cast {
    Byte _FillValue 127;
    Byte actual_range 1, 39;
    String bcodmo_name "cast";
    String description "CTD cast number.";
    String long_name "Cast";
    String units "dimensionless";
  }
  time_gmt {
    String bcodmo_name "time_gmt";
    String description "Time of sample collection (GMT); 24-hour clock.";
    String long_name "Time Gmt";
    String units "HHMM";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 29.5474, 33.5007;
    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 -65.7996, -63.4806;
    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";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.2985602e+9, 1.3436445e+9;
    String axis "T";
    String bcodmo_name "ISO_DateTime_UTC";
    String description "Date/Time (UTC) formatted to ISO 8601 standard in YYYY-mm-ddTHH:MM:SS.ssZ format. T indicates start of time string; Z indicates UTC.";
    String ioos_category "Time";
    String long_name "ISO Date Time UTC";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DTUT8601/";
    String source_name "ISO_DateTime_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";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 1.0, 200.0;
    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";
  }
  sample {
    String bcodmo_name "sample";
    String description "Sample identification number.";
    String long_name "Sample";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "dimensionless";
  }
  size_fraction {
    String bcodmo_name "unknown";
    String description "Size fraction; whole = whole water (not pre-screened).";
    String long_name "Size Fraction";
    String units "micrometers";
  }
  cyanobacteria {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 6.547;
    String bcodmo_name "chlorophyll a";
    String description "Contribution by cyanobacteria to total community composition measured in ug/L of Chlorophyll-a.";
    String long_name "Cyanobacteria";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLHPP1/";
    String units "micrograms of Chl-a per liter";
  }
  prasinophytes {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 2.612;
    String bcodmo_name "chlorophyll a";
    String description "Contribution by prasinophytes to total community composition measured in ug/L of Chlorophyll-a.";
    String long_name "Prasinophytes";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLHPP1/";
    String units "micrograms of Chl-a per liter";
  }
  cryptophytes {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 0.111;
    String bcodmo_name "chlorophyll a";
    String description "Contribution by cryptophytes to total community composition measured in ug/L of Chlorophyll-a.";
    String long_name "Cryptophytes";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLHPP1/";
    String units "micrograms of Chl-a per liter";
  }
  diatoms {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 0.073;
    String bcodmo_name "chlorophyll a";
    String description "Contribution by diatoms to total community composition measured in ug/L of Chlorophyll-a.";
    String long_name "Diatoms";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLHPP1/";
    String units "micrograms of Chl-a per liter";
  }
  pelagophytes {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 0.407;
    String bcodmo_name "chlorophyll a";
    String description "Contribution by pelagophytes to total community composition measured in ug/L of Chlorophyll-a.";
    String long_name "Pelagophytes";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLHPP1/";
    String units "micrograms of Chl-a per liter";
  }
  haptophytes {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 2.844;
    String bcodmo_name "chlorophyll a";
    String description "Contribution by haptophytes to total community composition measured in ug/L of Chlorophyll-a.";
    String long_name "Haptophytes";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLHPP1/";
    String units "micrograms of Chl-a per liter";
  }
  dinoflagellates {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 0.432;
    String bcodmo_name "chlorophyll a";
    Float64 colorBarMaximum 150.0;
    Float64 colorBarMinimum 0.0;
    String description "Contribution by dinoflagellates to total community composition measured in ug/L of Chlorophyll-a.";
    String long_name "Dinoflagellates";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLHPP1/";
    String units "micrograms of Chl-a per liter";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt";
    String acquisition_description 
"Study Site and CTD Casts  
 Data were collected on four cruises in the Sargasso Sea on board the R/V
Atlantic Explorer. On each cruise, sampling was conducted at three stations:
the center and edge of a mesoscale eddy and at one station outside of the
eddy. Eddies were identified using satellite-derived sea level anomaly (SLA)
data provided by Dr. Dennis McGillicuddy and Dr. Valery Kosnyrev of the Woods
Hole Oceanographic Institution. Target eddies (one per cruise) were initially
identified on the day of departure; the ship's position within the eddy (at
the center or the edge, as appropriate) was confirmed by daily checks of SLA
data.
 
At each station, high resolution CTD casts to ~2000 m were performed at noon
to measure core physical, chemical and biological parameters of the water
column. In addition to the core CTD casts, pre-dawn \\\"Productivity\\\" CTD casts
were performed to collect water for measurements of size-fractionated biomass
(as chl a) and size-fractionated primary productivity. Samples were obtained
using the 24 bottle Niskin rosette from 3-4 depths (20 m, 40-50 m, deep
fluorescence maximum (~80 m), and 100 m). Ten-liter polycarbonate collection
bottles (covered with black tape) were pre-rinsed with sample water and were
filled by draining the Niskin bottles through opaque tubing. All samples were
pre-filtered through a 200 um Nitex screen. Further handling of the samples
was done in the dark or under red light.
 
The 200 um pre-screened water from pre-dawn productivity casts was used for
measurements of size-fractionated biomass (as chl a) and biomarker
photopigments by HPLC and for measurements of size-fractionated primary
productivity.\\u00a0 HPLC pigments were also used for taxonomic identification
of total and size-fractionated phytoplankton groups using ChemTax analyses.
Samples for microscopy were also taken from productivity casts as verification
of the ChemTax results using methods described above for core CTD casts.\\u00a0
 
Total phytoplankton biomass was measured directly by filtering triplicate
aliquots of 1 to 2 liters of pre-screened water onto GF/F filters. This gave
total chl a in the size fraction 0.7 to 200 um. The biomass of three size
classes of phytoplankton was quantified by differential filtration: the
picophytoplankton (0.7-2 um), the nanophytoplankton (2-20 um) and the
microphytoplankton (20-200 um) as follows. Triplicate aliquots of 1 to 2
liters of pre-screened water were filtered through a 2 um Nuclepore filter
then onto a GF/F filter (= picophytoplankton, 0.7-2 um). Triplicate aliquots
of pre-screened water were also filtered through a 20 um Nitex mesh then onto
a GF/F filter (= 0.7-20 um). Biomass of the nanophytoplankton size class was
determined by subtracting the picophytoplankton biomass from the 0.7-20 um
biomass. Microphytoplankton biomass was determined by subtracting the 0.7-20
um biomass from the total chl a value. Filters were folded and placed in 1.5
ml cryotubes and frozen at -80\\u00b0 C until later analysis at the University
of South Carolina (USC) using the methods below.\\u00a0
 
Primary Prodcutivity Measurements  
 For size-fractionated primary productivity measurements, 200 um pre-screened
water collected from discrete depths were dispensed into Nalgene polycarbonate
incubation bottles (7-8 clear bottles plus 1-2 dark bottles per depth;
800-1200 ml each). Bottles were spiked with 14C-labeled sodium bicarbonate
(PerkinElmer Health Sciences Inc.) to a final activity 0.04-0.08 uCi ml-1 per
bottle. An additional bottle per depth was used as a particulate blank (T0)
(Barber et al., 1996). The T0 bottles were immediately filtered onto a GF/F,
acidified with 500 ul 0.5 N HCl and left open to fume for 24 hours (Barber et
al., 1996). Samples for total counts (Tc; 100 ul) were collected from one
bottle per depth and combined with 200 ul of phenylethylamine (PEA) and 5 ml
of scintillation cocktail (EcoLume, MPBiomedicals, Solon, Ohio). All bottles
were incubated in situ at the depth of collection. Incubations were started
before sunrise (usually between 05:00 and 06:00 h) and were terminated 24 h
later. The productivity array was tracked using a Telonics, Inc. transponder
platform subscribed to the ARGOS satellite tracking system.
 
Total phytoplankton primary productivity was measured directly by filtering
triplicate incubation bottles onto GF/F filters. This gave total primary
productivity in the size fraction 0.7 to 200 um. Dark bottle productivity was
also measured directly by filtering dark bottles directly onto GF/F filters (=
dark productivity; 0.7-200 um). Size-fractionated rates of primary
productivity of the picophytoplankton, nanophytoplankton and
microphytoplankton were made by differential filtration. Two 1 liter bottles
were filtered through a 20 um Nitex mesh then onto a 2 um Nuclepore filter (=
nanophytoplankton, 2-20 um). Two or three 1 liter bottles were filtered
through a 20 um Nitex mesh then onto a GF/F filter (= 0.7-20 um).\\u00a0
Filters were treated with 500 ul of 0.5 N HCl and left under a fumehood for 24
hours, then combined with 10 ml scintillation cocktail.\\u00a0 Radioactivity
was determined in disintegrations per minute (DPM) by the shipboard liquid
scintillation analyzer (Packard Tri-Carb 2000CA).\\u00a0
 
Rates of primary productivity (PP) were calculated in units of mg C m-3 d-1
using the methods of Barber et al. (1996) with the addition of dark bottles:
 
\\u00a0\\u00a0\\u00a0 PP = (DPM24 \\u2013 DPM0 \\u2013 DPMd)/(1.05)(25200 mg C
m-2)(DPMtot * time)-1\\u00a0\\u00a0\\u00a0 \\u00a0\\u00a0\\u00a0
 
where DPM24 = activity on filter after 24 hour incubation; DPM0 = activity of
(depth-specific) T0 particulate blank; DPMd = average of (depth-specific) dark
bottles; DPMtot = total activity DPM of isotope added multiplied by volume of
water filtered (DPM ml-1); 1.05 = constant that accounts for preferential
uptake of the lighter isotope 12C over 14C; 25,200 = concentration (in mg m-2)
of inorganic carbon in seawater.
 
The rate of primary productivity for the picophytoplankton size class was
determined by subtracting the nanophytoplankton productivity from the 0.7-20
um productivity. Primary productivity for the microphytoplankton size class
was determined by subtracting the 0.7-20 um productivity from the total
primary productivity, 0.7-200 um. Total and size-fractionated rates of primary
productivity were integrated to 100 meters using trapezoidal integration (mg C
m-2 d-1).
 
Microscopy  
 Samples preserved in Lugol\\u2019s were settled overnight in a 100 ml
sedimentation chamber and enumerated at 400x using a Nikon TS-100 Eclipse
inverted microscope. A minimum of 400 cells per sample were counted to give a
confidence interval of \\u00b110 % (Guillard, 1973). Phytoplankton taxa were
identified to the lowest taxonomic level possible, that is, in most cases, to
genus.
 
HPLC and ChemTax  
 Samples for HPLC analysis were lyophilized for 24 h at -50\\u00b0 C, placed
in 90% acetone (0.45-0.55 ml), and extracted at -20\\u00b0 C for 24 h.\\u00a0
Filtered extracts (350 \\u00b5l) were injected into a Shimadzu HPLC equipped
with a monomeric (Rainin Microsorb-MV, 0.46 x 10 cm, 3 \\u00b5m) and a
polymeric (Vydac 201TP54, 0.46 x 25 cm, 5 um) reverse-phase C18 column in
series. A nonlinear binary gradient consisting of the solvents 80% methanol:
20% 0.50 M ammonium acetate and 80% methanol: 20% acetone was used for pigment
separations (Pinckney et al. 1996). Absorption spectra and chromatograms (440
\\u00b1 4 nm) were acquired using a Shimadzu SPD-M10av photodiode array
detector. Pigment peaks were identified by comparison of retention times and
absorption spectra with pure standards (DHI, Denmark). The synthetic
carotenoid \\u00df-apo-8'-carotenal (Sigma) was used as an internal standard.
Contributions of individual algal groups to total community composition and to
each size class was determined by chemical taxonomy using the ChemTax matrix
(Mackey et al., 1996).";
    String awards_0_award_nid "54642";
    String awards_0_award_number "OCE-1030345";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1030345";
    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 
"ChemTax-based Chl-a in Algal Classes 
  (Reported as ug/L of chlorohyll a in each class) 
 Project: Trophic BATS 
 PI: Tammi L. Richardson (U. of S. Carolina) 
 Co-PIs: Rob Condon (DISL) & Susanna Neuer (Arizona State U.) 
 Version: 17 June 2013 
  
 Note: 'nd' = 'not determined'.";
    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 "2013-03-11T16:00:14Z";
    String date_modified "2019-11-01T15:34:31Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.3885.1";
    Float64 Easternmost_Easting -63.4806;
    Float64 geospatial_lat_max 33.5007;
    Float64 geospatial_lat_min 29.5474;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -63.4806;
    Float64 geospatial_lon_min -65.7996;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 200.0;
    Float64 geospatial_vertical_min 1.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2020-08-09T17:00:09Z (local files)
2020-08-09T17:00:09Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_3885.das";
    String infoUrl "https://www.bco-dmo.org/dataset/3885";
    String institution "BCO-DMO";
    String instruments_0_acronym "Niskin bottle";
    String instruments_0_dataset_instrument_description "Samples were obtained using the 24 bottle Niskin rosette from 3-4 depths.";
    String instruments_0_dataset_instrument_nid "6102";
    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 "CTD SBE 9";
    String instruments_1_dataset_instrument_description "CTD casts were perfomed using a Sea-Bird Electronics SBE-09 plus (24 bottle Niskin rosette).";
    String instruments_1_dataset_instrument_nid "6101";
    String instruments_1_description "The Sea-Bird SBE 9 is a type of CTD instrument package.  The SBE 9 is the Underwater Unit and is most often combined with the SBE 11 Deck Unit (for real-time readout using conductive wire) when deployed from a research vessel. The combination of the SBE 9 and SBE 11 is called a SBE 911.  The SBE 9 uses Sea-Bird's standard modular temperature and conductivity sensors (SBE 3 and SBE 4). The SBE 9 CTD can be configured with auxiliary sensors to measure other parameters including dissolved oxygen, pH, turbidity, fluorometer, altimeter, etc.). Note that in most cases, it is more accurate to specify SBE 911 than SBE 9 since it is likely a SBE 11 deck unit was used.  more information from Sea-Bird Electronics";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/130/";
    String instruments_1_instrument_name "CTD Sea-Bird 9";
    String instruments_1_instrument_nid "488";
    String instruments_1_supplied_name "CTD Sea-Bird 9";
    String instruments_2_acronym "HPLC";
    String instruments_2_dataset_instrument_description "HPLC analysis was performed using a Shimadzu HPLC equipped with a  monomeric (Rainin Microsorb-MV, 0.46 x 10 cm, 3 µm) and a polymeric  (Vydac 201TP54, 0.46 x 25 cm, 5 um) reverse-phase C18 column in series.  Absorption spectra and chromatograms (440 ± 4 nm) were acquired using a  Shimadzu SPD-M10av photodiode array detector.";
    String instruments_2_dataset_instrument_nid "6103";
    String instruments_2_description "A High-performance liquid chromatograph (HPLC) is a type of liquid chromatography used to separate compounds that are dissolved in solution. HPLC instruments consist of a reservoir of the mobile phase, a pump, an injector, a separation column, and a detector. Compounds are separated by high pressure pumping of the sample mixture onto a column packed with microspheres coated with the stationary phase. The different components in the mixture pass through the column at different rates due to differences in their partitioning behavior between the mobile liquid phase and the stationary phase.";
    String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB11/";
    String instruments_2_instrument_name "High Performance Liquid Chromatograph";
    String instruments_2_instrument_nid "506";
    String instruments_2_supplied_name "High Performance Liquid Chromatograph";
    String instruments_3_acronym "Inverted Microscope";
    String instruments_3_dataset_instrument_description "Samples preserved in Lugol’s were settled overnight in a 100 ml  sedimentation chamber and enumerated at 400x using a Nikon TS-100  Eclipse inverted microscope";
    String instruments_3_dataset_instrument_nid "6222";
    String instruments_3_description 
"An inverted microscope is a microscope with its light source and condenser on the top, above the stage pointing down, while the objectives and turret are below the stage pointing up. It was invented in 1850 by J. Lawrence Smith, a faculty member of Tulane University (then named the Medical College of Louisiana).

Inverted microscopes are useful for observing living cells or organisms at the bottom of a large container (e.g. a tissue culture flask) under more natural conditions than on a glass slide, as is the case with a conventional microscope. Inverted microscopes are also used in micromanipulation applications where space above the specimen is required for manipulator mechanisms and the microtools they hold, and in metallurgical applications where polished samples can be placed on top of the stage and viewed from underneath using reflecting objectives.

The stage on an inverted microscope is usually fixed, and focus is adjusted by moving the objective lens along a vertical axis to bring it closer to or further from the specimen. The focus mechanism typically has a dual concentric knob for coarse and fine adjustment. Depending on the size of the microscope, four to six objective lenses of different magnifications may be fitted to a rotating turret known as a nosepiece. These microscopes may also be fitted with accessories for fitting still and video cameras, fluorescence illumination, confocal scanning and many other applications.";
    String instruments_3_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB05/";
    String instruments_3_instrument_name "Inverted Microscope";
    String instruments_3_instrument_nid "675";
    String instruments_3_supplied_name "Inverted Microscope";
    String keywords "bco, bco-dmo, biological, cast, chemical, cruise, cruise_id, cryptophytes, cyanobacteria, data, dataset, date, date_gmt, depth, diatoms, dinoflagellates, dmo, erddap, fraction, haptophytes, iso, latitude, longitude, management, oceanography, office, pelagophytes, prasinophytes, preliminary, sample, size, size_fraction, time, time_gmt";
    String license "https://www.bco-dmo.org/dataset/3885/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/3885";
    Float64 Northernmost_Northing 33.5007;
    String param_mapping "{'3885': {'lat': 'master - latitude', 'depth': 'flag - depth', 'lon': 'master - longitude', 'ISO_DateTime_UTC': 'master - time'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/3885/parameters";
    String people_0_affiliation "University of South Carolina";
    String people_0_person_name "Tammi Richardson";
    String people_0_person_nid "50838";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Dauphin Island Sea Lab";
    String people_1_affiliation_acronym "DISL";
    String people_1_person_name "Robert Condon";
    String people_1_person_nid "51335";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    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 "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 "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 29.5474;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "ChemTax based chl-a of algal groups are reported from four cruises in the Sargasso Sea during 2011 and 2012.";
    String time_coverage_end "2012-07-30T10:35:00Z";
    String time_coverage_start "2011-02-24T15:10:00Z";
    String title "ChemTax based chl-a of algal groups from R/V Atlantic Explorer cruises AE1102, AE1118, AE1206, AE1219 in the Sargasso Sea, Bermuda Atlantic Time-Series Station (BATS) from 2011-2012 (Trophic BATS project)";
    String version "1";
    Float64 Westernmost_Easting -65.7996;
    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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