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| Dataset Title: | [Ehux physiology under thermal variation] - Intracellular elemental quotas under low and high temperatures for E. huxleyi in constant and fluctuating thermal environments (How does intensity and frequency of environmental variability affect phytoplankton growth?) 
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| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_782901) |
| Information: | Summary
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Attributes {
s {
Temperature {
String bcodmo_name "temperature";
String description "treatment temperature";
String long_name "Temperature";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
String units "degrees Celsius";
}
variation {
String bcodmo_name "treatment";
String description "temperature variation treatment description";
String long_name "Variation";
String units "unitless";
}
POC {
Float32 _FillValue NaN;
Float32 actual_range 5.3068, 22.4954;
String bcodmo_name "POC";
String description "concentration of Particulate Organic Carbon";
String long_name "Particulate Organic Carbon";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CORGCAP1/";
String units "picograms/cell";
}
PIC {
Float32 _FillValue NaN;
Float32 actual_range 1.5017, 6.1264;
String bcodmo_name "PIC";
String description "concentration of Particulate Inorganic Carbon";
String long_name "Particulate Inorganic Carbon";
String units "picograms/cell";
}
PON {
Float32 _FillValue NaN;
Float32 actual_range 1.1631, 4.8458;
String bcodmo_name "PON";
String description "concentration of Particulate Organic Nitrogen";
String long_name "PON";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/MDMAP013/";
String units "picograms/cell";
}
TPC {
Float32 _FillValue NaN;
Float32 actual_range 9.3102, 21.7491;
String bcodmo_name "TPC";
String description "concentration of Total Particulate Carbon";
String long_name "TPC";
String units "picograms/cell";
}
POP {
Float32 _FillValue NaN;
Float32 actual_range 0.0467, 0.0855;
String bcodmo_name "POP";
String description "concentration of Particulate Organic Phosphorous";
String long_name "POP";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/OPHSVLPT/";
String units "picograms/cell";
}
Chla {
Float32 _FillValue NaN;
Float32 actual_range 0.1088, 0.2669;
String bcodmo_name "chlorophyll a";
Float64 colorBarMaximum 30.0;
Float64 colorBarMinimum 0.03;
String colorBarScale "Log";
String description "concentration of Chlorophyll a";
String long_name "Concentration Of Chlorophyll In Sea Water";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/CPHLHPP1/";
String units "picograms/cell";
}
carbon_fix_rate {
Float32 _FillValue NaN;
Float32 actual_range 0.5151, 0.8688;
String bcodmo_name "C_photosyn";
String description "carbon fixation rate using a 14C incubation technique";
String long_name "Carbon Fix Rate";
String units "10 -7 umol Carbon cell-1 hr-1";
}
photosyn_rate {
Float32 _FillValue NaN;
Float32 actual_range 0.316, 0.5926;
String bcodmo_name "C_photosyn";
String description "photosynthetic rate using a 14C incubation technique";
String long_name "Photosyn Rate";
String units "10 -7 umol Carbon cell-1 hr-1";
}
calcification_rate {
Float32 _FillValue NaN;
Float32 actual_range 0.0704, 0.3775;
String bcodmo_name "calcification";
String description "calcification rate using a 14C incubation technique";
String long_name "Calcification Rate";
String units "10 -7 umol Carbon cell-1 hr-1";
}
Chla_POC {
Float32 _FillValue NaN;
Float32 actual_range 11.189, 21.0844;
String bcodmo_name "unknown";
Float64 colorBarMaximum 30.0;
Float64 colorBarMinimum 0.03;
String colorBarScale "Log";
String description "Chla to POC ratio";
String long_name "Concentration Of Chlorophyll In Sea Water";
String units "milligrams/gram";
}
calcif_photosyn {
Float32 _FillValue NaN;
Float32 actual_range 0.1188, 0.8421;
String bcodmo_name "unknown";
String description "calcificationto photosynthesis ratio";
String long_name "Calcif Photosyn";
String units "unitless";
}
PIC_POC {
Float32 _FillValue NaN;
Float32 actual_range 0.0897, 0.7544;
String bcodmo_name "unknown";
String description "PIC to POC ratio (mol/mol)";
String long_name "PIC POC";
String units "unitless";
}
POC_POP {
Float32 _FillValue NaN;
Float32 actual_range 155.893, 218.7171;
String bcodmo_name "unknown";
String description "POC to POP ratio (mol/mol)";
String long_name "POC POP";
String units "unitless";
}
PON_POP {
Float32 _FillValue NaN;
Float32 actual_range 17.1259, 42.3091;
String bcodmo_name "unknown";
String description "PON to POP ratio (mol/mol)";
String long_name "PON POP";
String units "unitless";
}
TPC_PON {
Float32 _FillValue NaN;
Float32 actual_range 4.7712, 9.3391;
String bcodmo_name "unknown";
String description "TPC to PON ratio (mol/mol)";
String long_name "TPC PON";
String units "unitless";
}
}
NC_GLOBAL {
String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
String acquisition_description
"Chlorophyll a, total particulate carbon (TPC), particulate organic carbon
(POC),\\u00a0 particulate organic nitrogen (PON), and particulate organic
carbon (POP) were filtered onto GF/F filters and analyzed following the
methodology used in Fu et al., 2007. Particulate inorganic carbon was defined
as the difference between TPC and POC after POC filters had been subjected to
concentrated HCl fumes for 24 hours to remove all inorganic carbon.
Calcification, photosynthesis, and carbon fixation rates were all measured
following the procedures outlined in Feng et al., 2008.
All data was processed using either R (v 3.4.4) or Microsoft Excel 2016.\\u00a0";
String awards_0_award_nid "668546";
String awards_0_award_number "OCE-1538525";
String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1538525";
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
"Intracellular elemental quotas under low and high temperatures for E. huxleyi in constant and fluctuating thermal environments
PI: D. Hutchins (USC)
version date: 2019-11-26";
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 "2019-11-26T14:53:42Z";
String date_modified "2020-04-30T12:58:37Z";
String defaultDataQuery "&time<now";
String doi "10.26008/1912/bco-dmo.782901.1";
String history
"2024-11-08T06:16:33Z (local files)
2024-11-08T06:16:33Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_782901.html";
String infoUrl "https://www.bco-dmo.org/dataset/782901";
String institution "BCO-DMO";
String instruments_0_acronym "Turner Fluorometer 10-AU";
String instruments_0_dataset_instrument_nid "782906";
String instruments_0_description "The Turner Designs 10-AU Field Fluorometer is used to measure Chlorophyll fluorescence. The 10AU Fluorometer can be set up for continuous-flow monitoring or discrete sample analyses. A variety of compounds can be measured using application-specific optical filters available from the manufacturer. (read more from Turner Designs, turnerdesigns.com, Sunnyvale, CA, USA)";
String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0393/";
String instruments_0_instrument_name "Turner Designs Fluorometer 10-AU";
String instruments_0_instrument_nid "464";
String instruments_1_acronym "LSC";
String instruments_1_dataset_instrument_description "Used to process radioactive assays.";
String instruments_1_dataset_instrument_nid "782908";
String instruments_1_description "Liquid scintillation counting is an analytical technique which is defined by the incorporation of the radiolabeled analyte into uniform distribution with a liquid chemical medium capable of converting the kinetic energy of nuclear emissions into light energy. Although the liquid scintillation counter is a sophisticated laboratory counting system used the quantify the activity of particulate emitting (ß and a) radioactive samples, it can also detect the auger electrons emitted from 51Cr and 125I samples.";
String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB21/";
String instruments_1_instrument_name "Liquid Scintillation Counter";
String instruments_1_instrument_nid "624";
String instruments_1_supplied_name "Perkin Elmer (CA) Liquid Scintillation Counter";
String instruments_2_dataset_instrument_description "Used to measure organic/inorganic carbon and nitrogen.";
String instruments_2_dataset_instrument_nid "782907";
String instruments_2_description "Instruments that quantify carbon, nitrogen and sometimes other elements by combusting the sample at very high temperature and assaying the resulting gaseous oxides. Usually used for samples including organic material.";
String instruments_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB01/";
String instruments_2_instrument_name "Elemental Analyzer";
String instruments_2_instrument_nid "546339";
String instruments_2_supplied_name "440 elemental analyzer (Costech Inc., CA)";
String keywords "bco, bco-dmo, biological, calcif, calcif_photosyn, calcification, calcification_rate, carbon, carbon_fix_rate, chemical, chemistry, Chla, Chla_POC, chlorophyll, chlorophyll-a, concentration, concentration_of_chlorophyll_in_sea_water, data, dataset, dmo, earth, Earth Science > Oceans > Ocean Chemistry > Chlorophyll, erddap, fix, inorganic, management, ocean, oceanography, oceans, office, organic, particulate, photosyn, photosyn_rate, pic, PIC_POC, poc, POC_POP, pon, PON_POP, pop, preliminary, rate, science, sea, seawater, temperature, tpc, TPC_PON, variation, water";
String keywords_vocabulary "GCMD Science Keywords";
String license "https://www.bco-dmo.org/dataset/782901/license";
String metadata_source "https://www.bco-dmo.org/api/dataset/782901";
String param_mapping "{'782901': {}}";
String parameter_source "https://www.bco-dmo.org/mapserver/dataset/782901/parameters";
String people_0_affiliation "University of Southern California";
String people_0_affiliation_acronym "USC";
String people_0_person_name "David A. Hutchins";
String people_0_person_nid "51048";
String people_0_role "Principal Investigator";
String people_0_role_type "originator";
String people_1_affiliation "Woods Hole Oceanographic Institution";
String people_1_affiliation_acronym "WHOI BCO-DMO";
String people_1_person_name "Nancy Copley";
String people_1_person_nid "50396";
String people_1_role "BCO-DMO Data Manager";
String people_1_role_type "related";
String project "Environmental variability and phytoplankton growth";
String projects_0_acronym "Environmental variability and phytoplankton growth";
String projects_0_description
"NSF Award Abstract:
Microscopic plants called phytoplankton are key members of global oceanic ecosystems, since their photosynthesis supports the majority of the marine food chain and produces about as much oxygen as land plants. Because of this, oceanographers have often carried out experiments examining how factors such as temperature and carbon dioxide levels may affect phytoplankton growth. Most previous experiments have used constant levels of temperature and carbon dioxide, but it is clear from looking at measurements from real ocean ecosystems that these two factors often vary greatly over timescales of days to weeks. Using field and laboratory experiments along with computer modeling, this project will test how the growth of several major groups of phytoplankton differs under constant conditions of temperature and carbon dioxide, compared to conditions in which these factors fluctuate in intensity and frequency. This research will give marine scientists a better picture of how phytoplankton may respond to a varying natural environment today and in the future, and therefore help us to understand how ocean food webs function to support critical living resources such as fisheries. The project will train graduate and undergraduate students and a postdoctoral researcher, and the lead scientists will be involved in an ocean science education program for largely minority high school students from a downtown Los Angeles school district.
The goal of this project is to use laboratory culture and natural community experiments to understand how realistically fluctuating temperature and pCO2 conditions may affect globally important phytoplankton groups in ways that differ from the artificial constant exposures used in previous work. Culture experiments will test how the intensity and frequency of short-term thermal and carbonate fluctuations affects the growth responses of diazotrophic and picoplanktonic cyanobacteria, coccolithophores, and diatoms under both current and projected future environmental conditions. These lab results will be supported and extended by parallel experiments using mixed natural assemblages from the California upwelling regime, allowing us to test these same questions using phytoplankton communities that experience large seasonal shifts between highly dynamic thermal and carbonate system conditions during the spring upwelling season, and relatively much more static conditions during fall stratification events. These results will be synthesized using a new generation of numerical models that employ novel approaches to incorporating realistic environmental variations to allow more accurate predictions of phytoplankton responses to a dynamic environment in today's marine ecosystems, and in the future changing ocean.";
String projects_0_end_date "2018-11";
String projects_0_geolocation "laboratory experiment";
String projects_0_name "How does intensity and frequency of environmental variability affect phytoplankton growth?";
String projects_0_project_nid "668547";
String projects_0_start_date "2015-12";
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 "Intracellular elemental quotas under low and high temperatures for E. huxleyi in constant and fluctuating thermal environments. This dataset includes the growth rates under low and high temperatures for E. huxleyi in constant and fluctuating thermal environments. Global warming will be combined with predicted increases in thermal variability in the future surface ocean, but how temperature dynamics will affect phytoplankton biology and biogeochemistry is largely unknown. Here, we examine the responses of the globally important marine coccolithophore Emiliania huxleyi to thermal variations at two frequencies (1 d and 2 d) at low (18.5 \\u00b0C) and high (25.5 \\u00b0C) mean temperatures. Elevated temperature and thermal variation decreased growth, calcification and physiological rates, both individually and interactively. The 1 d thermal variation frequencies were less inhibitory than 2 d variations under high temperatures, indicating that high-frequency thermal fluctuations may reduce heat-induced mortality and mitigate some impacts of extreme high-temperature events. Cellular elemental composition and calcification was significantly affected by both thermal variation treatments relative to each other and to the constant temperature controls. The negative effects of thermal variation on E. huxleyi growth rate and physiology are especially pronounced at high temperatures. These responses of the key marine calcifier E. huxleyi to warmer, more variable temperature regimes have potentially large implications for ocean productivity and marine biogeochemical cycles under a future changing climate.";
String title "[Ehux physiology under thermal variation] - Intracellular elemental quotas under low and high temperatures for E. huxleyi in constant and fluctuating thermal environments (How does intensity and frequency of environmental variability affect phytoplankton growth?)";
String version "1";
String xml_source "osprey2erddap.update_xml() v1.5";
}
}
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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
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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
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