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Dataset Title:  Series 3A: Multiple stressor experiments on T. pseudonana (CCMP1014) -
Chlorophyll, particulate organic carbon and particulate organic nitrogen.
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_771594)
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 {
  Phase {
    String bcodmo_name "sample_descrip";
    String description "Indicates whether the sample was collected during the acclimation phase or the experiment phase of the experiment.";
    String long_name "Phase";
    String units "unitless";
  }
  CO2 {
    Int16 _FillValue 32767;
    Int16 actual_range 410, 1000;
    String bcodmo_name "pCO2";
    String description "concentration of CO2 in the CO2-Air mix that was bubbled through the samples over the course of the experiment";
    String long_name "CO2";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PCO2C101/";
    String units "parts per million (ppm)";
  }
  Temp {
    Byte _FillValue 127;
    Byte actual_range 15, 30;
    String bcodmo_name "temperature";
    String description "temperature at which the samples were incubated";
    String long_name "Temperature";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Celsius";
  }
  Day {
    String bcodmo_name "days";
    String description "timepoint (day) of sampling. D0 = day 0; D1 = day 1; etc.";
    String long_name "Day";
    String units "unitless";
  }
  Light {
    Int16 _FillValue 32767;
    Int16 actual_range 60, 800;
    String bcodmo_name "irradiance";
    String description "light intensity";
    String long_name "Light";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/VSRW/";
    String units "micromol photons/meter^2/second (umol photons · m-2 · s-1)";
  }
  Replicate {
    Byte _FillValue 127;
    Byte actual_range 1, 3;
    String bcodmo_name "replicate";
    String description "replication within a treatment";
    String long_name "Replicate";
    String units "unitless";
  }
  Reference_Label {
    String bcodmo_name "sample";
    String description "Reference label (used internally for verifying sample identity)";
    String long_name "Reference Label";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  C_ug {
    Float32 _FillValue NaN;
    Float32 actual_range 1.1, 3252.7;
    String bcodmo_name "POC";
    String description "Particulate organic carbon concentration in sample";
    String long_name "C Ug";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CORGCAP1/";
    String units "micrograms/milliliter";
  }
  N_ug {
    Float32 _FillValue NaN;
    Float32 actual_range -1.8, 327.8;
    String bcodmo_name "PON";
    String description "Particulate organic nitrogen concentration in sample";
    String long_name "N Ug";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/MDMAP013/";
    String units "micrograms/milliliter";
  }
  Flags {
    String bcodmo_name "flag";
    String description "Indicates if C or N levels were below detection limits (BDL) of the instrument";
    String long_name "Flags";
    String units "unitless";
  }
  C_detect_lim {
    Float32 _FillValue NaN;
    Float32 actual_range 0.9, 7.1;
    String bcodmo_name "unknown";
    String description "Instrument detection limit for particulate organic carbon concentration";
    String long_name "C Detect Lim";
    String units "micrograms/milliliter";
  }
  N_detect_lim {
    Float32 _FillValue NaN;
    Float32 actual_range 0.4, 4.24;
    String bcodmo_name "unknown";
    String description "Instrument detection limit for particulate organic carbon concentration";
    String long_name "N Detect Lim";
    String units "micrograms/milliliter";
  }
  Volume_filtered {
    Float32 _FillValue NaN;
    Float32 actual_range 2.25, 40.0;
    String bcodmo_name "vol_filt";
    String description "Volume of the sample that was filtered";
    String long_name "Volume Filtered";
    String units "milliliters";
  }
  Chl_a_pg_L {
    Float32 _FillValue NaN;
    Float32 actual_range 4.72, 1843.72;
    String bcodmo_name "chlorophyll a";
    Float64 colorBarMaximum 30.0;
    Float64 colorBarMinimum 0.03;
    String colorBarScale "Log";
    String description "Chlorophyll concentration";
    String long_name "Concentration Of Chlorophyll In Sea Water";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLHPP1/";
    String units "picograms/liter";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Three CO2 concentrations were tested: 410 ppm, 750 ppm, and 1000 ppm
respectively. For each CO2 concentration, four temperatures were tested: 15
degrees-C, 20 degrees-C, 25 degrees-C, and 30 degrees-C. Within each
temperature, three light levels were tested: a sub-optimum light (SOL)
intensity of 60 umol photons \\u00b7 m-2 \\u00b7 s-1, an optimum light (OL)
intensity of 400 umol photons \\u00b7 m-2 \\u00b7 s-1 and an extreme light (EL)
intensity of 800 umol photons \\u00b7 m-2 \\u00b7 s-1. All lights were set at a
12 h day: 12 h dark cycle. For logistical reasons, experiments were partially
conducted in series, with all light treatments at two temperatures (either 15
degrees-C and 25 degrees-C or 20 degrees-C and 30 degrees-C) running
simultaneously. This was repeated for each CO2 concentration.
 
Experiments were conducted in Multicultivator MC-1000 OD units (Photon Systems
Instruments, Drasov, Czech Republic). Each unit consists of eight 85 ml test-
tubes immersed in a thermostated water bath, each independently illuminated by
an array of cool white LEDs set at specific intensity and timing. A 0.2um
filtered CO2-air mix (Praxair Distribution Inc.) was bubbled through sterile
artificial seawater, and the humidified gas mix was supplied to each tube via
gentle sparging through a 2um stainless steel diffuser. Flow rates were
gradually increased over the course of the incubation to compensate for the
DIC uptake of actively growing cells, and ranged from <0.04 Liters per minute
(LPM) at the start of the incubations to 0.08 LPM in each tube after 2 days.
For each CO2 and temperature level, replication was achieved by incubating
three tubes at sub-optimum light intensities, two tubes at optimum light
intensity, and three tubes at extreme light intensities. Each experiment was
split into two phases: An acclimation phase spanning 4 days, was used to
acclimate cultures to their new environment. Pre-acclimated, exponentially-
growing cultures were then inoculated into fresh media and incubated through a
3-day experimental phase during which assessments of growth, photophysiology,
and nutrient cycling were carried out daily. All sampling started 5 hours into
the daily light cycle to minimize the effects of diurnal cycles.
 
Experiments were conducted with artificial seawater (ASW) prepared using
previously described methods (Kester et. al 1967), and enriched with nitrate
(NO3), phosphate (PO4), silicic acid (Si[OH]4), at levels ensuring that the
cultures would remain nutrient-replete over the course of the experiment.
Trace metals and vitamins were added as in f/2 (Guillard 1975). The expected
DIC concentration and pH of the growth media was determined for the different
pCO2 and temperatures using the CO2SYS calculator (Pierrot et al. 2006), with
constants from Mehrbach et al. (1973, refit by Dickson & Millero 1987), and
inputs of temperature, salinity, total alkalinity (2376.5 umol \\u00b7 kg-1),
pCO2, phosphate, and silicic acid. DIC levels in ASW at the start of each
phase of the experiments were manipulated by the addition of NaHCO3, and was
then maintained by bubbling a CO2-Air mix through the cultures over the course
of the experiments. The pH of the growth media was measured
spectrophometrically using the m-cresol purple method (Dickson 1993), and
adjusted using 0.1N HCl or 0.1M NaOH. The media was distributed into 75 ml
aliquots and each aliquot was inoculated with 5 ml of the T. pseudonana CCMP
1014 (TP1014) stock culture at the start of the experiments.
 
Organic Carbon and Nitrogen concentrations:  
 Samples were filtered onto pre-combusted GF/F filters, dried at 60 degrees
C, and stored at room temperature until analyses of particulate organic carbon
(POC), and particulate organic nitrogen (PON). Between 3 and > 10 mL were
filtered, with larger filtration volumes used on the final day of the
experiment. Samples were analyzed using an elemental analyzer (CEC 44OHA;
Control Equipment). Samples where C or N concentrations were below instrument
detection limits were flagged.
 
Chlorophyll:  
 Daily subsamples from each treatment were filtered onto 0.45 \\u00b5m
polycarbonate filters and stored at -20 degrees C. Filters were placed in 90%
acetone (v/v) overnight at -20 degrees C, and the extracted chlorophyll was
measured fluorometrically on a Turner 700 fluorometer (Strickland 1972).
Chlorophyll-a liquid standards in 90% acetone (Turner Designs Inc.), and
adjustable solid secondary standards (Turner Designs Inc. P/N 8000-952) were
used for calibrations, and to calculate the chlorophyll content of the samples
(Column O)";
    String awards_0_award_nid "654346";
    String awards_0_award_number "OCE-1538602";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1538602";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "Michael E. Sieracki";
    String awards_0_program_manager_nid "50446";
    String cdm_data_type "Other";
    String comment 
"Series 3A-4: Multiple stressor experiments on T. pseudonana (CCMP1014): chlorophyll, particulate organic carbon (POC) and particulate organic nitrogen (PON) 
   PI: U. Passow, N. D'Souza  (UCSB), E. Laws (LSU) 
   version date: 2019-06-17";
    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 dataset_current_state "Final and no updates";
    String date_created "2019-06-20T18:36:54Z";
    String date_modified "2020-06-29T13:05:31Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.26008/1912/bco-dmo.771594.1";
    String history 
"2022-09-28T19:29:33Z (local files)
2022-09-28T19:29:33Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_771594.das";
    String infoUrl "https://www.bco-dmo.org/dataset/771594";
    String institution "BCO-DMO";
    String instruments_0_acronym "Fluorometer";
    String instruments_0_dataset_instrument_description "Used for fluorometric analyses of extracted chlorophyll.";
    String instruments_0_dataset_instrument_nid "771604";
    String instruments_0_description "A fluorometer or fluorimeter is a device used to measure parameters of fluorescence: its intensity and wavelength distribution of emission spectrum after excitation by a certain spectrum of light. The instrument is designed to measure the amount of stimulated electromagnetic radiation produced by pulses of electromagnetic radiation emitted into a water sample or in situ.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/113/";
    String instruments_0_instrument_name "Fluorometer";
    String instruments_0_instrument_nid "484";
    String instruments_0_supplied_name "Turner 700 fluorometer";
    String instruments_1_acronym "CHN_EA";
    String instruments_1_dataset_instrument_description "Used for analysis of total organic carbon content.";
    String instruments_1_dataset_instrument_nid "771606";
    String instruments_1_description "A CHN Elemental Analyzer is used for the determination of carbon, hydrogen, and  nitrogen content in organic and other types of materials, including  solids, liquids, volatile, and viscous samples.";
    String instruments_1_instrument_name "CHN Elemental Analyzer";
    String instruments_1_instrument_nid "625";
    String instruments_1_supplied_name "Elemental analyzer (CEC 44OHA; Control Equipment)";
    String instruments_2_dataset_instrument_description "Used for incubation of TP1014 cultures.";
    String instruments_2_dataset_instrument_nid "771602";
    String instruments_2_description "An instrument used for the purpose of culturing small cells such as algae or bacteria. May provide temperature and light control and bubbled gas introduction.";
    String instruments_2_instrument_name "Cell Cultivator";
    String instruments_2_instrument_nid "714540";
    String instruments_2_supplied_name "Multicultivator MC-1000 OD (Qubit Systems)";
    String keywords "bco, bco-dmo, biological, C_detect_lim, C_ug, carbon, carbon dioxide, chemical, chemistry, Chl_a_pg_L, chlorophyll, co2, concentration, concentration_of_chlorophyll_in_sea_water, data, dataset, day, detect, dioxide, dmo, earth, Earth Science > Oceans > Ocean Chemistry > Chlorophyll, erddap, filtered, flags, label, light, lim, management, N_detect_lim, N_ug, ocean, oceanography, oceans, office, phase, preliminary, reference, Reference_Label, replicate, science, sea, seawater, Temp, temperature, volume, Volume_filtered, water";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "https://www.bco-dmo.org/dataset/771594/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/771594";
    String param_mapping "{'771594': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/771594/parameters";
    String people_0_affiliation "University of California-Santa Barbara";
    String people_0_affiliation_acronym "UCSB-MSI";
    String people_0_person_name "Uta Passow";
    String people_0_person_nid "51317";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Louisiana State University";
    String people_1_affiliation_acronym "LSU-SC&E";
    String people_1_person_name "Dr Edward Laws";
    String people_1_person_nid "50767";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "University of California-Santa Barbara";
    String people_2_affiliation_acronym "UCSB-MSI";
    String people_2_person_name "Nigel D'Souza";
    String people_2_person_nid "748936";
    String people_2_role "Scientist";
    String people_2_role_type "originator";
    String people_3_affiliation "University of California-Santa Barbara";
    String people_3_affiliation_acronym "UCSB-MSI";
    String people_3_person_name "Nigel D'Souza";
    String people_3_person_nid "748936";
    String people_3_role "Contact";
    String people_3_role_type "related";
    String people_4_affiliation "Woods Hole Oceanographic Institution";
    String people_4_affiliation_acronym "WHOI BCO-DMO";
    String people_4_person_name "Nancy Copley";
    String people_4_person_nid "50396";
    String people_4_role "BCO-DMO Data Manager";
    String people_4_role_type "related";
    String project "Stressors on Marine Phytoplankton";
    String projects_0_acronym "Stressors on Marine Phytoplankton";
    String projects_0_description 
"The overarching goal of this project is to develop a framework for understanding the response of phytoplankton to multiple environmental stresses. Marine phytoplankton, which are tiny algae, produce as much oxygen as terrestrial plants and provide food, directly or indirectly, to all marine animals. Their productivity is thus important both for global elemental cycles of oxygen and carbon, as well as for the productivity of the ocean. Globally the productivity of marine phytoplankton appears to be changing, but while we have some understanding of the response of phytoplankton to shifts in one environmental parameter at a time, like temperature, there is very little knowledge of their response to simultaneous changes in several parameters. Increased atmospheric carbon dioxide concentrations result in both ocean acidification and increased surface water temperatures. The latter in turn leads to greater ocean stratification and associated changes in light exposure and nutrient availability for the plankton. Recently it has become apparent that the response of phytoplankton to simultaneous changes in these growth parameters is not additive. For example, the effect of ocean acidification may be severe at one temperature-light combination and negligible at another. The researchers of this project will carry out experiments that will provide a theoretical understanding of the relevant interactions so that the impact of climate change on marine phytoplankton can be predicted in an informed way. This project will engage high schools students through training of a teacher and the development of a teaching unit. Undergraduate and graduate students will work directly on the research. A cartoon journalist will create a cartoon story on the research results to translate the findings to a broader general public audience.
Each phytoplankton species has the capability to acclimatize to changes in temperature, light, pCO2, and nutrient availability - at least within a finite range. However, the response of phytoplankton to multiple simultaneous stressors is frequently complex, because the effects on physiological responses are interactive. To date, no datasets exist for even a single species that could fully test the assumptions and implications of existing models of phytoplankton acclimation to multiple environmental stressors. The investigators will combine modeling analysis with laboratory experiments to investigate the combined influences of changes in pCO2, temperature, light, and nitrate availability on phytoplankton growth using cultures of open ocean and coastal diatom strains (Thalassiosira pseudonana) and an open ocean cyanobacteria species (Synechococcus sp.). The planned experiments represent ideal case studies of the complex and interactive effects of environmental conditions on organisms, and results will provide the basis for predictive modeling of the response of phytoplankton taxa to multiple environmental stresses.";
    String projects_0_end_date "2018-09";
    String projects_0_name "Collaborative Research: Effects of multiple stressors on Marine Phytoplankton";
    String projects_0_project_nid "654347";
    String projects_0_start_date "2015-10";
    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 "The experiments were designed to test the combined effects of CO2, temperatures, and light on growth and photophysiology of the diatom T. pseudonana CCMP1014 in a multifactorial design. This dataset contains measurements of extracted chlorophyll, particulate organic carbon (POC), and particulate organic nitrogen (PON) made over the course of the experiments.";
    String title "Series 3A: Multiple stressor experiments on T. pseudonana (CCMP1014) - Chlorophyll, particulate organic carbon and particulate organic nitrogen.";
    String version "1";
    String xml_source "osprey2erddap.update_xml() v1.5";
  }
}

 

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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