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Dataset Title:  Growth rates and equivalent spherical diameters of Heterosigma akashiwo after
temperature transition
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_783500)
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 {
  Culture {
    String bcodmo_name "sample";
    String description "culture identifier";
    String long_name "Culture";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  time2 {
    String bcodmo_name "flag";
    String description "either growth rate immediately after temperature change (t1) or growth rate after 1 week to acclimate (tf)";
    String long_name "Time";
    String units "unitless";
  }
  Temperature {
    Byte _FillValue 127;
    Byte actual_range 6, 31;
    String bcodmo_name "temperature";
    String description "temperature treatment";
    String long_name "Temperature";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Celsius";
  }
  GrowthRate {
    Float32 _FillValue NaN;
    Float32 actual_range -0.16, 0.73;
    String bcodmo_name "growth";
    String description "divison-based growth rate";
    String long_name "Growth Rate";
    String units "per day";
  }
  BioRate {
    Float32 _FillValue NaN;
    Float32 actual_range -0.05, 0.93;
    String bcodmo_name "growth";
    String description "biovolume-based growth rate";
    String long_name "Bio Rate";
    String units "per day";
  }
  MeanESD_t {
    Float32 _FillValue NaN;
    Float32 actual_range 10.0, 18.0;
    String bcodmo_name "diameter";
    String description "Mean Equivalent Spherical Diameter for a culture during the period of growth rate calculation";
    String long_name "Mean ESD T";
    String units "micrometers";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Experimental setup
 
H. akashiwo (CCMP 3374) was isolated from Narragansett Bay June 10, 2010 when
in situ water temperature was 21.2\\u00b0C. After isolation the culture was
maintained at 15\\u00b0C. All experiments were conducted with cells that were
grown in autoclaved, 0.2 \\u00b5m sterile-filtered seawater (30-31 ppt) amended
with F/2 media without silica (Guillard 1975). All cultures were maintained
under a light intensity of 150 \\u00b5mol photons m-2 s-1 and a 12:12 h light:
dark cycle. Preliminary experiments to determine the characteristics of this
strains\\u2019 growth patterns were used to maintain cultures in exponential
phase by transferring cultures as needed every 4 to 10 days (depending on
growth temperature), resulting in cell densities of 500-24,000 cell
mL-1.\\u00a0 To avoid convolution of thermal response and effect of
unconditioned media just after transfer culture transfers were restricted from
1 day prior and 1 day post change in temperature (Grabski and Tukaj 2008 ).
 
Temperature treatments
 
Temperature was manipulated in the experiment through a series of sequential
temperature shifts. Beginning with a culture which had remained at 15 \\u00b0C
since collection, every four days a triplicate set of the most extreme,
current temperature treatments were split with one fraction retained at its
current treatment and one fraction shifted a temperature step outward (i.e.
further towards the temperature extremes).\\u00a0 Only the cultures growing at
the highest and lowest temperatures were split and transferred to new
temperatures, but all other cultures were continually maintained in
exponential growth phase. Cultures maintained at 15 \\u00b0C throughout the
duration of the experiment served as an acclimated control, and as a reference
for the temporal consistency in acclimated rates. Including all temperature
steps and extrema, the nine treatments included: 6, 8, 10, 12, 18, 22, 25, 28,
31, and the control at 15\\u00b0C. It took 20 days to complete the individual
temperature shifts at 4-day intervals to reach the most extreme temperature
treatments.
 
Dedicated incubators were used for control (15\\u00b0C, Model 2015 Low
Temperature Incubator, VWR Scientific); 4, 6 (I-41LLVL, Percival Scientific);
8, 18, 22 (I-36LLVL, Percival Scientific); and 10 \\u00b0C (Environ Air, Holman
Engineering). Twenty-five, 28, and 31 \\u00b0C treatments were accomplished
with clear 10 L baths controlled by a coupled aquarium heater and thermostat
(Fluval, Tru Temp), housed in an illuminated incubator. Light intensity was
consistently controlled across temperature treatments.
 
Population and growth rate measurements
 
To quantify changes in cell size, population growth rate, and volumetric
growth rate, the abundance and size (Equivalent Spherical Diameter, ESD)
distribution of each culture were measured with a 100 \\u00b5m aperture on a
Beckman Coulter Multisizer 3 (Beckman Coulter, Brea California; Kim and
Menden-Deuer 2013). The default bin size was the instrument standard of 0.2
\\u00b5m. Each treatment set was measured daily for 15 days following the
initial transfer to the target temperature. Experiments were terminated after
15 days because the objective of these experiments was to establish the
response to relatively short-term temperature fluctuations.
 
Statistical analysis
 
Mean and standard deviation of size distribution and cell counts as measured
by the Coulter Counter were estimated from the raw data by a gaussian
distribution, fit with maximum likelihood estimation to the frequency of
particles greater than 8 \\u00b5m. Measurements that immediately followed
culture inoculation and those from the growth were omitted. Cell volumes were
estimated with equivalent spherical diameter (ESD) and spherical shape
approximation. Cell biomass was calculated using ESD measured with the coulter
counter and calculated cell volume with the relationship of pg C cell-1=0.288x
volume0.811 (Menden-Deuer and Lessard 2000). Total biomass was approximate
using a culture and time specific mean ESD and cell count.
 
Specific growth rate was calculated by linear regression of natural log
transformed abundance. Production rates were calculated by linear regression
of natural log transformed total estimated biomass for each culture. Specific
growth rates were used to fit a thermal reaction norm modified from Norberg
(2004), where a, b, and z are shape parameters, w the thermal niche width, T
\\u00a0a given temperature, and k(T) the specific growth rate at that
temperature.
 
k(T) = a * e^bT [1-(T-z)/w/2)^2]
 
To quantify the effect of time (acclimation) on growth rate the initial growth
rates, measured in the first three days (\\u00b50; \\u0394 time<3 days), as well
as final rates (\\u00b5f, 7< \\u0394 time<15 days) were calculated for each
temperature treatment.
 
These experiments were conducted at the University of Rhode Island, Graduate
School of Oceanography in the laboratory of Dr. Susanne Menden-Deuer";
    String awards_0_award_nid "739231";
    String awards_0_award_number "OCE-1736635";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1736635";
    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 
"Growth rates and ESD 
   Growth rates and equivalent spherical diameters of Heterosigma akashiwo after temperature transition 
   PI: S. Menden-Deuer (URI) 
   version date: 2020-01-06 
 	replaces version 2019-12-04";
    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 "2019-12-04T19:30:11Z";
    String date_modified "2020-01-09T14:54:34Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.783500.2";
    String history 
"2020-10-28T20:14:59Z (local files)
2020-10-28T20:14:59Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_783500.das";
    String infoUrl "https://www.bco-dmo.org/dataset/783500";
    String institution "BCO-DMO";
    String instruments_0_dataset_instrument_description "Used to count cells.";
    String instruments_0_dataset_instrument_nid "783507";
    String instruments_0_description 
"An apparatus for counting and sizing particles suspended in electrolytes. It is used for cells, bacteria, prokaryotic cells and virus particles. A typical Coulter counter has one or more microchannels that separate two chambers containing electrolyte solutions.

from https://en.wikipedia.org/wiki/Coulter_counter";
    String instruments_0_instrument_name "Coulter Counter";
    String instruments_0_instrument_nid "668847";
    String instruments_0_supplied_name "Beckman Coulter Multisizer III Counter";
    String keywords "bco, bco-dmo, bio, biological, BioRate, chemical, culture, data, dataset, dmo, erddap, esd, growth, GrowthRate, management, mean, MeanESD_t, oceanography, office, preliminary, rate, temperature, time, time2";
    String license "https://www.bco-dmo.org/dataset/783500/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/783500";
    String param_mapping "{'783500': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/783500/parameters";
    String people_0_affiliation "University of Rhode Island";
    String people_0_affiliation_acronym "URI-GSO";
    String people_0_person_name "Susanne Menden-Deuer";
    String people_0_person_nid "739234";
    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 "Planktonic Herbivore Temp Dependence";
    String projects_0_acronym "Planktonic Herbivore Temp Dependence";
    String projects_0_description 
"NSF Award Abstract:
Plankton, single-celled organisms that inhabit the world's oceans are responsible for the generation of oxygen, cycling energy and matter between the atmosphere and the deep ocean and are the basis for virtually all seafood harvested. These life-giving functions critically depend on the relative rates at which plankton grow and get eaten. How temperature influences those rates is essential to understand plankton responses to environmental changes and ocean dynamics. It is well established that plankton grow faster when temperatures are higher however, whether feeding has a similar temperature dependence is unknown. That means oceanographers are missing key data required to build global predictive models. This project will fill essential knowledge gaps and measure physiological rates of singled celled zooplankton across temperature gradients representing the global ocean, from polar to tropical regions and throughout the seasonal cycle. Researchers will combine laboratory experiments with specimens taken from the coastal ocean (Narragansett Bay), which is exemplary in its strong seasonal temperature variations. These data will provide a clear picture of the production capacity and activity of plankton in a global and dynamic ocean. The project supports an early career scientist, as well as graduate and undergraduate students. Scientists will continue communicating their research to the public through large-scale outreach events, education at the high-school level, and engagement through online and other media. Moreover, researchers will continue collaborating with the Metcalf Institute for Marine & Environmental Reporting to support their Annual Science Immersion Workshop for Journalists and their ongoing work to disseminate research findings through web-based seminars.
Grazing is the single largest loss factor of marine primary production and thus affects a key transfer rate between global organic and inorganic matter pools. Remarkably, data for herbivorous protist growth and grazing rates at temperatures representative of the vast polar regions and during winter and spring periods are extremely sparse. By combining laboratory experiments with ground truthing fieldwork, this project alleviates a central knowledge gap in oceanography and delivers the empirical measurements necessary to derive algorithms to incorporate temperature dependence of heterotrophic protist growth and grazing rates into biogeochemical models. The extraordinary seasonal temperature fluctuations in a temperate coastal estuary (Narragansett Bay) are exploited to measure rates of heterotrophic protists isolated from different temperatures and seasons and to quantify the temperature and acclimation responses of these ecotypes. This project delivers data urgently needed to solve the conundrum of whether herbivorous growth and predation is depressed at low temperatures, implying low trophic transfer rates and high carbon export, or if predation proceeds at rates comparable to temperate systems with primary production largely lost to predation. Large temperature gradients in the global ocean mean that cross-biome and biogeochemical models are particularly sensitive to assumptions about the temperature dependence in modeled rate processes. Establishment of the dependence of heterotrophic plankton physiological rates (growth and grazing) to gradients of temperature, mimicking realistic conditions experienced by plankton in a changing ocean, is a key step towards integrating much needed biological information in biogeochemical modeling efforts. This project makes a significant contribution to linking ecological research with ecosystem models by providing empirically rooted algorithms of the temperature dependence of protistan herbivory and growth rates, key processes in the transformation of organic matter in global biogeochemical cycles and tools critically missing in ecosystem models.";
    String projects_0_end_date "2020-08";
    String projects_0_geolocation "Narragansett Bay";
    String projects_0_name "Quantifying Temperature Dependence In Growth & Grazing Rates of Planktonic Herbivores";
    String projects_0_project_nid "739232";
    String projects_0_start_date "2017-09";
    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 "Quantifying phytoplankton growth as a function of environmental conditions such as temperature is critical for understanding and predicting production in the ocean. Typically, thermal response is described as the steady state growth rates under static conditions. However, here, with a clonal culture of Heterosigma akashiwo, temperature was manipulated in the laboratory with the goal of describing how growth rates may change over time through the acclimation process. Growth rates and equivalent spherical diameter of Heterosigma akashiwo after temperature transition are reported.";
    String title "Growth rates and equivalent spherical diameters of Heterosigma akashiwo after temperature transition";
    String version "2";
    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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