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Dataset Title:  Growth rates under low and high temperatures for Emiliania huxleyi in constant
and fluctuating thermal environments
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_782888)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Files
Variable ?   Optional
Constraint #1 ?
Constraint #2 ?
   Minimum ?
   Maximum ?
 Growth_Rate (per day) ?          -0.5085    0.942
 Temperature (degrees Celsius) ?          "16"    "mean_25.5"
 variation (unitless) ?          "constant"    "two_day"
Server-side Functions ?
 distinct() ?
? ("Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.Hover here to see a list of options. Click on an option to select it.")

File type: (more info)

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Growth_Rate {
    Float32 _FillValue NaN;
    Float32 actual_range -0.5085, 0.942;
    String bcodmo_name "growth";
    String description "E. huxleyi growth rate by cell counts";
    String long_name "Growth Rate";
    String units "per day";
  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";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Specific growth rates were calculated using change in fluorescence over time
(verified using cell count data) and the equation
\\u03bc=ln[N(T2)/N(T1)]/(T2-T1). 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 
"Growth rates 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 "2019-11-26T14:29:50Z";
    String date_modified "2020-04-30T12:58:58Z";
    String defaultDataQuery "&time<now";
    String doi "10.26008/1912/bco-dmo.782888.1";
    String history 
"2022-08-10T22:31:18Z (local files)
2022-08-10T22:31:18Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_782888.html";
    String infoUrl "https://www.bco-dmo.org/dataset/782888";
    String institution "BCO-DMO";
    String instruments_0_acronym "Turner Fluorometer 10-AU";
    String instruments_0_dataset_instrument_nid "782893";
    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_dataset_instrument_description "Used to count cell samples.";
    String instruments_1_dataset_instrument_nid "782920";
    String instruments_1_description "Instruments that generate enlarged images of samples using the phenomena of fluorescence and phosphorescence instead of, or in addition to, reflection and absorption of visible light. Includes conventional and inverted instruments.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB06/";
    String instruments_1_instrument_name "Microscope-Fluorescence";
    String instruments_1_instrument_nid "695";
    String instruments_1_supplied_name "Olympus BX51 microscope";
    String keywords "bco, bco-dmo, biological, chemical, data, dataset, dmo, erddap, growth, Growth_Rate, management, oceanography, office, preliminary, rate, temperature, variation";
    String license "https://www.bco-dmo.org/dataset/782888/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/782888";
    String param_mapping "{'782888': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/782888/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 "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 "Growth rates under low and high temperatures for Emiliania huxleyi in constant and fluctuating thermal environments";
    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
For example,
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.

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