Accessing BCO-DMO data
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| Dataset Title: | [Growth rates - evolution expt] - Daily growth rates of 8 populations of Chaetoceros simplex grown at 31C with control population at 25C, in regular L1 medium (884 µm NO3-) (Dimensions: Collaborative Research: Genetic, functional and phylogenetic diversity determines marine phytoplankton community responses to changing temperature and nutrients) 
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| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_778869) |
| Information: | Summary
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| ISO 19115
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Attributes {
s {
Evol_strain {
String bcodmo_name "sample";
String description "evolved population identifier; L1 signifies strains raised in 'regular' medium at 884 micromoles nitrate; 5 signifies medium with reduced nitrate at 5 micromoles; last number is replicate";
String long_name "Evol Strain";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
String units "unitless";
}
period {
Byte _FillValue 127;
String _Unsigned "false";
Byte actual_range 1, 84;
String bcodmo_name "unknown";
String description "Culture transfer for which the rate is calculated";
String long_name "Period";
String units "unitless";
}
Temperature {
Byte _FillValue 127;
String _Unsigned "false";
Byte actual_range 31, 31;
String bcodmo_name "temperature";
String description "Culture maintenance temperature";
String long_name "Temperature";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
String units "Celsius degrees";
}
Nitrate_Concentration {
String bcodmo_name "treatment";
String description "Culture media nitrate concentration; L1 signifies 'regular' medium at 884 micromoles nitrate; 5 signifies reduced nitrate at 5 micromoles";
String long_name "Nitrate Concentration";
String units "Micromolar";
}
Replicate {
Byte _FillValue 127;
String _Unsigned "false";
Byte actual_range 1, 4;
String bcodmo_name "replicate";
String description "Replicate number";
String long_name "Replicate";
String units "unitless";
}
Growth_rate {
Float32 _FillValue NaN;
Float32 actual_range -0.2, 3.0;
String bcodmo_name "growth";
String description "Growth rate calculated from biomass";
String long_name "Growth Rate";
String units "day-1";
}
}
NC_GLOBAL {
String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
String acquisition_description
"Chaetoceros simplex cultures, were obtained from population strain CCMP 200
(National Center for Marine Algae and Microbiota, NCMA).
Evolution experiment:
Eight populations were grown at 31 \\u00b0C, and one was maintained as a
control at 25 \\u00b0C in regular L1 medium (884 \\u03bcm NO3\\u2212).
At 31 \\u00b0C, four populations remained in regular L1 medium (884 \\u03bcm
NO3\\u2212), while the other four received nitrogen\\u2010reduced L1 medium (5
\\u03bcm NO3\\u2212);
Populations were maintained in 50 mL polycarbonate culture flasks, at 100
\\u03bcmol quanta m\\u22122 s\\u22121 cool white fluorescent light on a 14/10 h
day/night cycle. We gently inverted and randomly repositioned flasks daily.
Every three days c. 10^6 cells (never < 6 \\u00d7 10^5 cells) from each
population were transferred to fresh media. We monitored populations by
measuring in vivo optical density daily (436 nm wavelength absorbance) using a
Shimadzu UV\\u20102401PC spectrophotometer before and after each transfer
Growth rate calculations:
When more than two biomass observations (optical density or fluorescence,
depending on the experiment) within the exponential growth phase were
available, we calculated population growth rates (day\\u22121), as the slope of
the linear regression of ln(biomass) vs. time (days). Alternatively, when
biomass measurements were made every 2\\u20133 days, we calculated growth rate
as
(lnB2-LnB1)/(t2-t1)
where B is biomass and t is time (days) and the number of generations within a
particular time range, \\u0394t, as
(u/ln2)^\\u0394t
where u is growth rate.\\u00a0
Calculations were made with R, and scripts can be downloaded from:
[https://github.com/MariaArangurenGassis/PhytoEvolutionPaper2019](\\\\\"https://github.com/MariaArangurenGassis/PhytoEvolutionPaper2019\\\\\").
More details in Aranguren-Gassis et al. 2019, Ecology Letters.";
String awards_0_award_nid "712786";
String awards_0_award_number "OCE-1638958";
String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1638958";
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 awards_1_award_nid "712792";
String awards_1_award_number "OCE-1638804";
String awards_1_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1638804";
String awards_1_funder_name "NSF Division of Ocean Sciences";
String awards_1_funding_acronym "NSF OCE";
String awards_1_funding_source_nid "355";
String awards_1_program_manager "Michael E. Sieracki";
String awards_1_program_manager_nid "50446";
String awards_2_award_nid "712795";
String awards_2_award_number "OCE-1638834";
String awards_2_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1638834";
String awards_2_funder_name "NSF Division of Ocean Sciences";
String awards_2_funding_acronym "NSF OCE";
String awards_2_funding_source_nid "355";
String awards_2_program_manager "Michael E. Sieracki";
String awards_2_program_manager_nid "50446";
String cdm_data_type "Other";
String comment
"Growth Rates from Evolution Experiment
Daily growth rates of 8 populations of Chaetoceros simplex grown at 31C
with control population at 25C, in regular L1 medium (884 um NO3-)
P.I.'s: M. Aranguren-Gassis (U. Vigo), E. Litchman (MSU), C. Klausmeier (MSU)
version date: 2019-10-07";
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 "2019-10-09T16:42:38Z";
String date_modified "2019-10-30T17:29:31Z";
String defaultDataQuery "&time<now";
String doi "10.1575/1912/bco-dmo.778869.1";
String history
"2024-11-23T17:04:01Z (local files)
2024-11-23T17:04:01Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_778869.html";
String infoUrl "https://www.bco-dmo.org/dataset/778869";
String institution "BCO-DMO";
String instruments_0_acronym "UV Spectrophotometer-Shimadzu";
String instruments_0_dataset_instrument_nid "778878";
String instruments_0_description "The Shimadzu UV Spectrophotometer is manufactured by Shimadzu Scientific Instruments (ssi.shimadzu.com). Shimadzu manufacturers several models of spectrophotometer; refer to dataset for make/model information.";
String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB20/";
String instruments_0_instrument_name "UV Spectrophotometer-Shimadzu";
String instruments_0_instrument_nid "595";
String instruments_0_supplied_name "Shimadzu UV‐2401PC spectrophotometer";
String keywords "bco, bco-dmo, biological, chemical, concentration, data, dataset, dmo, erddap, evol, Evol_strain, growth, Growth_rate, management, n02, nitrate, Nitrate_Concentration, no3, oceanography, office, period, preliminary, rate, replicate, strain, temperature";
String license "https://www.bco-dmo.org/dataset/778869/license";
String metadata_source "https://www.bco-dmo.org/api/dataset/778869";
String param_mapping "{'778869': {}}";
String parameter_source "https://www.bco-dmo.org/mapserver/dataset/778869/parameters";
String people_0_affiliation "Michigan State University";
String people_0_affiliation_acronym "MSU";
String people_0_person_name "Elena Litchman";
String people_0_person_nid "543190";
String people_0_role "Principal Investigator";
String people_0_role_type "originator";
String people_1_affiliation "Michigan State University";
String people_1_affiliation_acronym "MSU";
String people_1_person_name "Christopher Klausmeier";
String people_1_person_nid "543192";
String people_1_role "Co-Principal Investigator";
String people_1_role_type "originator";
String people_2_affiliation "Michigan State University";
String people_2_affiliation_acronym "MSU";
String people_2_person_name "Colin T. Kremer";
String people_2_person_nid "779889";
String people_2_role "Scientist";
String people_2_role_type "originator";
String people_3_affiliation "Universidad de Vigo";
String people_3_person_name "Maria Aranguren-Gassis";
String people_3_person_nid "778758";
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 "Phytoplankton Community Responses";
String projects_0_acronym "Phytoplankton Community Responses";
String projects_0_description
"NSF Award Abstract:
Photosynthetic marine microbes, phytoplankton, contribute half of global primary production, form the base of most aquatic food webs and are major players in global biogeochemical cycles. Understanding their community composition is important because it affects higher trophic levels, the cycling of energy and elements and is sensitive to global environmental change. This project will investigate how phytoplankton communities respond to two major global change stressors in aquatic systems: warming and changes in nutrient availability. The researchers will work in two marine systems with a long history of environmental monitoring, the temperate Narragansett Bay estuary in Rhode Island and a subtropical North Atlantic site near Bermuda. They will use field sampling and laboratory experiments with multiple species and varieties of phytoplankton to assess the diversity in their responses to different temperatures under high and low nutrient concentrations. If the diversity of responses is high within species, then that species may have a better chance to adapt to rising temperatures and persist in the future. Some species may already be able to grow at high temperatures; consequently, they may become more abundant as the ocean warms. The researchers will incorporate this response information in mathematical models to predict how phytoplankton assemblages would reorganize under future climate scenarios. Graduate students and postdoctoral associates will be trained in diverse scientific approaches and techniques such as shipboard sampling, laboratory experiments, genomic analyses and mathematical modeling. The results of the project will be incorporated into K-12 teaching, including an advanced placement environmental science class for underrepresented minorities in Los Angeles, data exercises for rural schools in Michigan and disseminated to the public through an environmental journalism institute based in Rhode Island.
Predicting how ecological communities will respond to a changing environment requires knowledge of genetic, phylogenetic and functional diversity within and across species. This project will investigate how the interaction of phylogenetic, genetic and functional diversity in thermal traits within and across a broad range of species determines the responses of marine phytoplankton communities to rising temperature and changing nutrient regimes. High genetic and functional diversity within a species may allow evolutionary adaptation of that species to warming. If the phylogenetic and functional diversity is higher across species, species sorting and ecological community reorganization is likely. Different marine sites may have a different balance of genetic and functional diversity within and across species and, thus, different contribution of evolutionary and ecological responses to changing climate. The research will be conducted at two long-term time series sites in the Atlantic Ocean, the Narragansett Bay Long-Term Plankton Time Series and the Bermuda Atlantic Time Series (BATS) station. The goal is to assess intra- and inter-specific genetic and functional diversity in thermal responses at contrasting nutrient concentrations for a representative range of species in communities at the two sites in different seasons, and use this information to parameterize eco-evolutionary models embedded into biogeochemical ocean models to predict responses of phytoplankton communities to projected rising temperatures under realistic nutrient conditions. Model predictions will be informed by and tested with field data, including the long-term data series available for both sites and in community temperature manipulation experiments. This project will provide novel information on existing intraspecific genetic and functional thermal diversity for many ecologically and biogeochemically important phytoplankton species, estimate generation of new genetic and functional diversity in evolution experiments, and develop and parameterize novel eco-evolutionary models interfaced with ocean biogeochemical models to predict future phytoplankton community structure. The project will also characterize the interaction of two major global change stressors, warming and changing nutrient concentrations, as they affect phytoplankton diversity at functional, genetic, and phylogenetic levels. In addition, the project will develop novel modeling methodology that will be broadly applicable to understanding how other types of complex ecological communities may adapt to a rapidly warming world.";
String projects_0_end_date "2020-09";
String projects_0_geolocation "Narragansett Bay, RI and Bermuda, Bermuda Atlantic Time-series Study (BATS)";
String projects_0_name "Dimensions: Collaborative Research: Genetic, functional and phylogenetic diversity determines marine phytoplankton community responses to changing temperature and nutrients";
String projects_0_project_nid "712787";
String projects_0_start_date "2016-10";
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 subsetVariables "Temperature";
String summary "Daily growth rates of 8 populations of Chaetoceros simplex grown at 31C and control population at 25C, in regular L1 medium (884 \\u03bcm NO3\\u2212) or nitrogen\\u2010reduced L1 medium (5 \\u03bcm NO3\\u2212).";
String title "[Growth rates - evolution expt] - Daily growth rates of 8 populations of Chaetoceros simplex grown at 31C with control population at 25C, in regular L1 medium (884 µm NO3-) (Dimensions: Collaborative Research: Genetic, functional and phylogenetic diversity determines marine phytoplankton community responses to changing temperature and nutrients)";
String version "1";
String xml_source "osprey2erddap.update_xml() v1.3";
}
}
Data Access Protocol (DAP) and its
selection constraints.
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.