Accessing BCO-DMO data
Brought to you by BCO-DMO
|
|
BCO-DMO ERDDAP
Accessing BCO-DMO data |
log in
Brought to you by BCO-DMO |
| Dataset Title: | [Tidal study of seawater microbial communities] - Flow cytometry and nutrient analyses data from a tidal study over 48 hours of mangrove, seagrass, and seawater from the US Virgin Islands in July of 2017 (Signature exometabolomes of Caribbean corals and influences on reef picoplankton) 
|
| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_783679) |
| Information: | Summary
| License
| FGDC
| ISO 19115
| Metadata
| Background
| Subset
| Files
| Make a graph
|
Attributes {
s {
Sample_ID {
String bcodmo_name "sample";
String description "Sample identifier";
String long_name "Sample ID";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
String units "unitless";
}
NCBI_BioProject_accession_number {
String bcodmo_name "accession number";
String description "BioProject accession number for the NCBI Sequence read archive";
String long_name "NCBI Bio Project Accession Number";
String units "unitless";
}
NCBI_BioSample_accession_number {
String bcodmo_name "accession number";
String description "BioSample accession number for the NCBI Sequence read archive";
String long_name "NCBI Bio Sample Accession Number";
String units "unitless";
}
Sample_type {
String bcodmo_name "sample_descrip";
String description "Sample type as reported to NCBI for sequence upload";
String long_name "Sample Type";
String units "unitless";
}
Sequencing_Strategy {
String bcodmo_name "brief_desc";
String description "Describes what type of sequencing library preparation we did";
String long_name "Sequencing Strategy";
String units "unitless";
}
Sequencing_Instrument_model {
String bcodmo_name "brief_desc";
String description "This is the model type of sequencer used for the dataset uploaded to NCBI";
String long_name "Sequencing Instrument Model";
String units "unitless";
}
Sequencing_strategy {
String bcodmo_name "brief_desc";
String description "This is a more detailed description of how we sequenced. This is included in the methods in more depth.";
String long_name "Sequencing Strategy";
String units "unitless";
}
Site_Name {
String bcodmo_name "site";
String description "This is the local name of the site where samples were collected";
String long_name "Site Name";
String units "unitless";
}
Biome {
String bcodmo_name "site_descrip";
String description "This is a qualitative assessment of the type of ecosystem";
String long_name "Biome";
String units "unitless";
}
latitude {
String _CoordinateAxisType "Lat";
Float64 _FillValue NaN;
Float64 actual_range 18.30964, 18.32065;
String axis "Y";
String bcodmo_name "latitude";
Float64 colorBarMaximum 90.0;
Float64 colorBarMinimum -90.0;
String description "Latitude";
String ioos_category "Location";
String long_name "Latitude";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
String standard_name "latitude";
String units "degrees_north";
}
longitude {
String _CoordinateAxisType "Lon";
Float64 _FillValue NaN;
Float64 actual_range -64.76453, -64.72223;
String axis "X";
String bcodmo_name "longitude";
Float64 colorBarMaximum 180.0;
Float64 colorBarMinimum -180.0;
String description "Longitude";
String ioos_category "Location";
String long_name "Longitude";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
String standard_name "longitude";
String units "degrees_east";
}
Collection_Date {
String bcodmo_name "date_local";
String description "Local date when samples were collected (UTC-4)";
String long_name "Collection Date";
String time_precision "1970-01-01";
String units "unitless";
}
Collection_Time {
String bcodmo_name "time_local";
String description "Local time when samples were collected (UTC-4)";
String long_name "Collection Time";
String units "unitless";
}
time {
String _CoordinateAxisType "Time";
Float64 actual_range 1.5007389e+9, 1.50089298e+9;
String axis "T";
String bcodmo_name "ISO_DateTime_UTC";
String description "ISO Datetime (UTC) when samples were collected in ISO 8601:2004(E) format yyyy-mm-ddTHH:MM:SSZ";
String ioos_category "Time";
String long_name "ISO Date Time UTC";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DTUT8601/";
String source_name "ISO_DateTime_UTC";
String standard_name "time";
String time_origin "01-JAN-1970 00:00:00";
String time_precision "1970-01-01T00:00:00Z";
String units "seconds since 1970-01-01T00:00:00Z";
}
Tide {
String bcodmo_name "tide";
String description "Qualitative description of the tide level during the sample timepoint";
String long_name "Tide";
String units "unitless";
}
Tide_Height {
Float32 _FillValue NaN;
Float32 actual_range -0.042, 0.401;
String bcodmo_name "height";
Float64 colorBarMaximum 2.0;
Float64 colorBarMinimum -2.0;
String description "Tide height corresponding to verified mean low low water (MLLW), as taken from the NOAA station 9751381 in Lameshur Bay, USVI.";
String long_name "Sea Surface Height";
String units "meters (m)";
}
Time_elapsed_between_tide_timepoint_and_collection {
Byte _FillValue 127;
String _Unsigned "false";
Byte actual_range 0, 58;
String bcodmo_name "time_elapsed";
Float64 colorBarMaximum 2.0;
Float64 colorBarMinimum -2.0;
String description "Time that elapsed between the designated collection timepoint and when the sample was actually collected during the time series.";
String long_name "Sea Surface Height";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ELTMZZZZ/";
String units "minutes";
}
depth {
String _CoordinateAxisType "Height";
String _CoordinateZisPositive "down";
Float64 _FillValue NaN;
Float64 actual_range 0.3, 0.3;
String axis "Z";
String bcodmo_name "depth";
String description "Collection depth of all samples";
String ioos_category "Location";
String long_name "Collection Depth";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
String positive "down";
String standard_name "depth";
String units "m";
}
Site_Depth {
Float32 _FillValue NaN;
Float32 actual_range 0.2, 12.2;
String bcodmo_name "depth";
String description "Approximate depth of the collection site. Generally taken from the CTD cast.";
String long_name "Site Depth";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
String units "meters (m)";
}
Temperature {
Float32 _FillValue NaN;
Float32 actual_range 27.84, 34.4;
String bcodmo_name "temperature";
String description "Temperature of seawater as taken from the CastAway CTD (SonTek)";
String long_name "Temperature";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
String units "degrees Celsius (ºC)";
}
Salinity {
Float32 _FillValue NaN;
Float32 actual_range 34.52, 37.38;
String bcodmo_name "sal";
Float64 colorBarMaximum 37.0;
Float64 colorBarMinimum 32.0;
String description "Salinity of seawater as taken from the CastAway CTD (SonTek)";
String long_name "Sea Water Practical Salinity";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PSALST01/";
String units "Practical Salinity Units (PSU)";
}
Prochlorococcus {
Int32 _FillValue 2147483647;
Int32 actual_range 0, 197086;
String bcodmo_name "cell_concentration";
String description "Prochlorococcus cell counts as determined by flow cytometry";
String long_name "Prochlorococcus";
String units "cells per milliliter (cells/ml)";
}
Synechococcus {
Int32 _FillValue 2147483647;
Int32 actual_range 1547, 95963;
String bcodmo_name "cell_concentration";
String description "Synechococcus cell counts as determined by flow cytometry";
String long_name "Synechococcus";
String units "cells per milliliter (cells/ml)";
}
Picoeukaryotes {
Int16 _FillValue 32767;
Int16 actual_range 844, 6441;
String bcodmo_name "cell_concentration";
String description "Picoeukaryote abundances as determined by flow cytometry";
String long_name "Picoeukaryotes";
String units "cells per milliliter (cells/ml)";
}
Unpigmented_cells {
Int32 _FillValue 2147483647;
Int32 actual_range 366483, 4500000;
String bcodmo_name "cell_concentration";
String description "unpigmented cells as determined by flow cytometry of Hoescht stained cells.";
String long_name "Unpigmented Cells";
String units "cells per milliliter (cells/ml)";
}
Phosphate {
Float32 _FillValue NaN;
Float32 actual_range 0.157, 0.553;
String bcodmo_name "PO4";
String description "Phosphate concentrations in seawater";
String long_name "Mass Concentration Of Phosphate In Sea Water";
String units "micromolar (µM)";
}
Silicate {
Float32 _FillValue NaN;
Float32 actual_range 1.7, 9.1;
String bcodmo_name "SiOH_4";
String description "Silicate concentrations in seawater";
String long_name "Mass Concentration Of Silicate In Sea Water";
String units "micromolar (µM)";
}
Nitrate_Nitrite {
Float32 _FillValue NaN;
Float32 actual_range -0.01, 1.17;
String bcodmo_name "NO3_NO2";
Float64 colorBarMaximum 50.0;
Float64 colorBarMinimum 0.0;
String description "Nitrate and Nitrite concentrations in seawater";
String long_name "Mole Concentration Of Nitrate In Sea Water";
String units "micromolar (µM)";
}
Nitrite {
Float32 _FillValue NaN;
Float32 actual_range 0.01, 0.26;
String bcodmo_name "NO2";
Float64 colorBarMaximum 1.0;
Float64 colorBarMinimum 0.0;
String description "Nitrite concentrations in seawater";
String long_name "Mole Concentration Of Nitrite In Sea Water";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/NTRIAAZX/";
String units "micromolar (µM)";
}
Ammonium {
Float32 _FillValue NaN;
Float32 actual_range 0.02, 2.94;
String bcodmo_name "Ammonium";
Float64 colorBarMaximum 5.0;
Float64 colorBarMinimum 0.0;
String description "Ammonium concentrations in seawater";
String long_name "Mole Concentration Of Ammonium In Sea Water";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AMONAAZX/";
String units "micromolar (µM)";
}
}
NC_GLOBAL {
String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt";
String acquisition_description
"Materials and Methods
Sampling. Sampling occurred from July 22-24, 2017 and coincided with the
spring tides. A new moon occurred on July 23 at 5:45 (EST). While St. John
tidal cycles are typically semidiurnal, during spring tide the tide cycle is
diurnal. Sampling time points coincided with the low, flood, high, and ebb
tides over a 48 hour (hr) window, resulting in 8 total sampling time points.
Samples were collected \\u00b11 hr from the designated time point, placed on
ice, and processed within two hours of collection. Samples from nine locations
across Lameshur Bay and Fish Bay on the south shore of St. John, U.S. Virgin
Islands represented coral reef, seagrass bed and mangrove biomes. The Lameshur
Bay mangrove location included two distinct sampling sites: an inland area
that was only submerged and sampled during high tide and an outlet area that
was always submerged and sampled at each time point. The majority of sampling
sites were within the boundaries of the Virgin Islands National Park, which is
largely undeveloped except for a small research station. The Fish Bay
mangrove, Fish Bay seagrass, and Ditliff reef sites are outside the boundary
of the park, and the land surrounding Fish Bay is inhabited.
At all sites, a CTD (Castaway, SonTek, San Diego, CA, USA) was deployed from
the surface to the bottom depth in reef and seagrass seawater, and single
point measurements were collected from mangrove seawater, to capture the
temperature and salinity at each timepoint over the course of the 48 hr
sampling window. Only temperature and salinity at the surface of the cast was
used for analysis. Samples for inorganic nutrients were collected by filling
30ml of surface seawater into acid-washed and seawater-rinsed vials (HDPE,
Nalgene), followed by freezing to 20C. Seawater (875\\u00b5l) was transferred
to a 2ml cryovial (Corning) for analysis of microbial abundances and fixed to
a final concentration of 1% paraformaldehyde (Electron Microscopy Sciences),
allowed to fix for 20 min at 4C, then flash-frozen in an LN2 dry shipper. To
capture seawater microbial communities, acid-washed 4L bottles (LDPE, Nalgene,
ThermoFisher Scientific, Waltham, MA, USA) were rinsed three times with
seawater prior to collection of 3L of surface seawater. The specific 4L bottle
used for a site at the first timepoint remained consistent across all sampling
timepoints, and the bottles were rinsed with freshwater between uses.
Following collection, 1L of seawater was pumped using a Masterflex peristaltic
pump (Cole-Palmer, Vernon Hills, IL, USA) through Masterflex silicone tubing
(L/S, platinum-cured, #96410-24 size, Cole-Parmer) to rinse the tubing. The
remaining 2L of seawater was filtered through a 0.22\\u00b5m Supor filter
(25mm; Pall, Ann Arbor, MI, USA). For the mangrove and seagrass sites, 2L
could not always be filtered completely and therefore 0.3 - 2L and 1.2 - 2L of
water was filtered through the membrane, respectively. For the coral reef
sites, 1.5 - 2L passed through the filter membrane. All filters were placed
into 2ml cryovials (Corning, Corning, NY, USA) and flash-frozen in a liquid
nitrogen dry shipper until returned to Woods Hole, MA and placed at 80C.
Flow cytometry and Nutrient analyses. Samples for microbial abundance were
analyzed at the University of Hawaii with an EPICS Altra flow cytometry
(Beckman Coulter Life Sciences, Inc, Indianapolis, IN) as described in Furby
et al (Furby et al. 2014), with some modifications. Briefly, to obtain
concentrations of cyanobacteria (Prochlorococcus and Synechococcus) and
eukaryotic phytoplankton (picoeukaryotes), an unstained aliquot was run on the
instrument and excited by visible wavelengths. To enumerate unpigmented cells,
which is a proxy for heterotrophic bacteria and archaea (Marie et al. 1997),
an aliquot was stained with a Hoechst DNA stain and run on the flow cytometer
with excitation at 488nm. The number of cells per ml was estimated following
analysis of fluorescence spectra using FlowJo software (v 6.4.7, Tree Star,
Inc., Ashland, OR, USA).
Samples for nutrient analysis were analyzed at Oregon State University using
methods described in Furby et al. (Furby et al. 2014) to measure dissolved
concentrations (\\u00b5M) of phosphate, ammonium, nitrite, nitrite + nitrate,
and silicate.
DNA Extraction, PCR amplification, and Sequencing. DNA was extracted from the
filters using a sucrose-EDTA lysis method similar to Santoro et al. (Santoro
et al. 2010) that combines lysis with filter column purification. Briefly, the
25mm filter was subjected to physical and chemical lysis using 0.1mm glass
beads (Lysing Matrix B, MP Biomedicals, Irvine, CA, USA), sucrose-EDTA lysis
buffer (0.75M Sucrose, 20mM EDTA, 400mM NaCl, 50 mM Tris) and 10% sodium
dodecyl sulfate (Teknova, Hollister, CA, USA), followed by a proteinase-K
digestion (20 mg/ml Promega, Madison, WI, USA). Lysate was then purified using
the DNeasy Blood and Tissue Kit (Qiagen, Germantown, MD, USA) spin column
filters. Purified DNA was fluorometrically quantified using a high sensitivity
dsDNA assay on a Qubit 2.0 fluorometer (ThermoFisher Scientific).
Sample DNA was diluted 1:100 in UV-sterilized PCR-grade H2O and 1\\u00b5l was
used in a PCR reaction. Barcoded primers recommended by the Earth Microbiome
Project, 515FY and 806RB, were used to amplify the V4 region of the SSU rRNA
gene in bacteria and archaea (Apprill et al. 2015, Parada et al. 2016).
Triplicate 25\\u00b5l reactions contained 1.25 units of GoTaq DNA Polymerase
(Promega, Madison, WI, USA), 0.2\\u00b5M forward and reverse primers, 0.2mM
deoxynucleoside triphosphate (dNTP) mix (Promega), 2.5mM MgCl2, 5\\u00b5l GoTaq
5X colorless flexi buffer (Promega), and nuclease-free water. The reactions
were run on a Bio-Rad Thermocycler (Hercules, CA, USA) using the following
criteria: denaturation at 95C for 2 min; 28C cycles of 95C for 20 s, 55C for
15 s, and 72C for 5 min; and extension at 72C for 10 min. Successful
amplification was verified by running 5\\u00b5l of product on a 1% agarose-TBE
gel stained with SYBR Safe gel stain (Invitrogen, Carlsbad, CA, USA).
Triplicate PCR products were pooled and purified using the MinElute PCR
purification kit (Qiagen). Concentration of purified products was quantified
using the high sensitivity dsDNA assay on the Qubit 2.0 fluorometer
(ThermoFisher Scientific). Barcoded PCR products were diluted to equal
concentrations and pooled for sequencing. Samples were shipped to the Georgia
Genomics and Bioinformatics Core at the University of Georgia for sequencing
on an Illumina MiSeq using paired-end 250bp sequencing.
Data analysis. All sequence processing and data analysis was performed in R
Studio (v 1.1.463) running R (v 3.4.0, 2017-04-21). Sequence reads were
inspected for quality, filtered, trimmed, and dereplicated in the DADA2 R
package (v.1.10.0) (Callahan et al. 2016). Specific filtering parameters used
included the following: truncLen = c(240, 200), maxN = 0, maxEE = c(2,2),
rm.phix = TRUE, and compress = TRUE. DADA2 was then used to generate amplicon
sequence variants (ASVs) and remove chimeras. Taxonomy was assigned in DADA2
using the SILVA SSU rRNA database down to the species level where applicable
(v.132, (Quast et al. 2012)). ASV counts in each sample were transformed to
relative abundance for further data analysis.
To understand the variability in microbial communities over time at all sites,
Bray-Curtis dissimilarity was calculated between each sample in the R package
vegan (v2.5.4) (Oksanen et al. 2019) and illustrated using non-metric
multidimensional scaling (NMDS) in the R package, ggplot2 (v3.2.1) (Wickham
2016). Environmental vectors that significantly associated (cutoff p<0.01)
with the ordination were produced using the function envfit() in the vegan R
package. Pairwise dissimilarity was plotted to represent the range of
dissimilarity in microbial communities over 48 hrs at each site. A higher
average dissimilarity would suggest that the site experiences more variable
microbial communities than a site with a lower average dissimilarity. A
Kruskal-Wallis test was used to examine if there is a significant difference
between sites (significance level p<0.05). To determine which pairs of sites
had significantly different dissimilarities, a pairwise Wilcoxon Rank Sum test
was used with a Benjamini-Hochberg correction for multiple testing and a
cutoff of 0.05.
Differential abundance (DA) and of ASVs in relation to the tide was evaluated
at mangrove sites using the corncob R package (v0.1.0) (Martin et al. 2019).
The following analyses were conducted on a subset of the data representing
mangrove communities. All ASV relative abundances were modeled in corncob
using a logit-link for mean and dispersion. DA was modeled as a linear
function of sea level (a continuous covariate that is representative of the
tide cycle) while controlling for differential variance and the effect of site
and day or night on DA. Controlling for the effect of day or night was
imperative because over the 48 hr period low and flood tide only occurred
during the day and high and low tide only occurred during dusk and night,
respectively. The parametric Wald test was used to test the hypotheses that
the relative abundance or variance of a given ASV changed significantly with
respect to sea level and the Benjamini-Hochberg false discovery rate (FDR)
correction was applied to account for multiple comparisons, with the cutoff at
0.05.";
String awards_0_award_nid "746195";
String awards_0_award_number "OCE-1736288";
String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1736288";
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 "Daniel Thornhill";
String awards_0_program_manager_nid "722161";
String cdm_data_type "Other";
String comment
"Tidal study of seawater microbial communities
PI: Amy Apprill
Data Version 1: 2019-12-09";
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 "2019-12-06T16:55:52Z";
String date_modified "2019-12-12T20:24:25Z";
String defaultDataQuery "&time<now";
String doi "10.1575/1912/bco-dmo.783679.1";
Float64 Easternmost_Easting -64.72223;
Float64 geospatial_lat_max 18.32065;
Float64 geospatial_lat_min 18.30964;
String geospatial_lat_units "degrees_north";
Float64 geospatial_lon_max -64.72223;
Float64 geospatial_lon_min -64.76453;
String geospatial_lon_units "degrees_east";
Float64 geospatial_vertical_max 0.3;
Float64 geospatial_vertical_min 0.3;
String geospatial_vertical_positive "down";
String geospatial_vertical_units "m";
String history
"2024-11-13T01:16:48Z (local files)
2024-11-13T01:16:48Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_783679.html";
String infoUrl "https://www.bco-dmo.org/dataset/783679";
String institution "BCO-DMO";
String instruments_0_acronym "CTD";
String instruments_0_dataset_instrument_nid "783835";
String instruments_0_description "The Conductivity, Temperature, Depth (CTD) unit is an integrated instrument package designed to measure the conductivity, temperature, and pressure (depth) of the water column. The instrument is lowered via cable through the water column and permits scientists observe the physical properties in real time via a conducting cable connecting the CTD to a deck unit and computer on the ship. The CTD is often configured with additional optional sensors including fluorometers, transmissometers and/or radiometers. It is often combined with a Rosette of water sampling bottles (e.g. Niskin, GO-FLO) for collecting discrete water samples during the cast. This instrument designation is used when specific make and model are not known.";
String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/130/";
String instruments_0_instrument_name "CTD profiler";
String instruments_0_instrument_nid "417";
String instruments_0_supplied_name "CTD (Castaway, SonTek, San Diego, CA, USA)";
String instruments_1_acronym "Fluorometer";
String instruments_1_dataset_instrument_nid "783845";
String instruments_1_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_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/113/";
String instruments_1_instrument_name "Fluorometer";
String instruments_1_instrument_nid "484";
String instruments_1_supplied_name "Qubit 2.0 fluorometer (ThermoFisher Scientific)";
String instruments_2_acronym "Automated Sequencer";
String instruments_2_dataset_instrument_description "Samples were shipped to the Georgia Genomics and Bioinformatics Core at the University of Georgia for sequencing on an Illumina MiSeq using paired-end 250bp sequencing";
String instruments_2_dataset_instrument_nid "783846";
String instruments_2_description "General term for a laboratory instrument used for deciphering the order of bases in a strand of DNA. Sanger sequencers detect fluorescence from different dyes that are used to identify the A, C, G, and T extension reactions. Contemporary or Pyrosequencer methods are based on detecting the activity of DNA polymerase (a DNA synthesizing enzyme) with another chemoluminescent enzyme. Essentially, the method allows sequencing of a single strand of DNA by synthesizing the complementary strand along it, one base pair at a time, and detecting which base was actually added at each step.";
String instruments_2_instrument_name "Automated DNA Sequencer";
String instruments_2_instrument_nid "649";
String instruments_2_supplied_name "Illumina MiSeq";
String instruments_3_acronym "Flow Cytometer";
String instruments_3_dataset_instrument_nid "783836";
String instruments_3_description
"Flow cytometers (FC or FCM) are automated instruments that quantitate properties of single cells, one cell at a time. They can measure cell size, cell granularity, the amounts of cell components such as total DNA, newly synthesized DNA, gene expression as the amount messenger RNA for a particular gene, amounts of specific surface receptors, amounts of intracellular proteins, or transient signalling events in living cells.
(from: http://www.bio.umass.edu/micro/immunology/facs542/facswhat.htm)";
String instruments_3_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB37/";
String instruments_3_instrument_name "Flow Cytometer";
String instruments_3_instrument_nid "660";
String instruments_3_supplied_name "EPICS Altra flow cytometry (Beckman Coulter Life Sciences, Inc, Indianapolis, IN)";
String keywords "accession, ammonia, ammonium, bco, bco-dmo, bio, biological, biome, cells, chemical, chemistry, collection, Collection_Date, Collection_Depth, Collection_Time, concentration, data, dataset, date, density, depth, dmo, earth, Earth Science > Oceans > Ocean Chemistry > Ammonia, Earth Science > Oceans > Ocean Chemistry > Nitrate, Earth Science > Oceans > Ocean Chemistry > Phosphate, Earth Science > Oceans > Ocean Chemistry > Silicate, Earth Science > Oceans > Salinity/Density > Salinity, Earth Science > Oceans > Sea Surface Topography > Sea Surface Height, erddap, height, instrument, iso, latitude, longitude, management, mass, mass_concentration_of_phosphate_in_sea_water, mass_concentration_of_silicate_in_sea_water, model, mole, mole_concentration_of_ammonium_in_sea_water, mole_concentration_of_nitrate_in_sea_water, mole_concentration_of_nitrite_in_sea_water, n02, name, ncbi, NCBI_BioProject_accession_number, NCBI_BioSample_accession_number, nh4, nitrate, Nitrate_Nitrite, nitrite, no3, number, ocean, oceanography, oceans, office, phosphate, picoeukaryotes, po4, practical, preliminary, prochlorococcus, project, salinity, sample, Sample_ID, Sample_type, science, sea, sea_surface_height, sea_water_practical_salinity, seawater, sequencing, Sequencing_Instrument_model, Sequencing_Strategy, Sequencing_strategy, silicate, site, Site_Depth, Site_Name, strategy, surface, synechococcus, temperature, tide, Tide_Height, time, Time_elapsed_between_tide_timepoint_and_collection, topography, type, unpigmented, Unpigmented_cells, water";
String keywords_vocabulary "GCMD Science Keywords";
String license "https://www.bco-dmo.org/dataset/783679/license";
String metadata_source "https://www.bco-dmo.org/api/dataset/783679";
Float64 Northernmost_Northing 18.32065;
String param_mapping "{'783679': {'Latitude': 'master - latitude', 'Collection_Depth': 'master - depth', 'Longitude': 'master - longitude', 'ISO_DateTime_UTC': 'master - time'}}";
String parameter_source "https://www.bco-dmo.org/mapserver/dataset/783679/parameters";
String people_0_affiliation "Woods Hole Oceanographic Institution";
String people_0_affiliation_acronym "WHOI";
String people_0_person_name "Amy Apprill";
String people_0_person_nid "553489";
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";
String people_1_person_name "Cynthia Becker";
String people_1_person_nid "784315";
String people_1_role "Student";
String people_1_role_type "related";
String people_2_affiliation "Woods Hole Oceanographic Institution";
String people_2_affiliation_acronym "WHOI BCO-DMO";
String people_2_person_name "Amber York";
String people_2_person_nid "643627";
String people_2_role "BCO-DMO Data Manager";
String people_2_role_type "related";
String project "Coral Exometabolomes";
String projects_0_acronym "Coral Exometabolomes";
String projects_0_description
"NSF abstract:
Coral reefs are some of the most diverse and productive ecosystems in the ocean. Globally, reefs have declined in stony (reef-building) coral abundance due to environmental variations, and in the Caribbean this decline has coincided with an increase in octocoral (soft coral) abundance. This phase shift occurring on Caribbean reefs may be impacting the interactions between the sea floor and water column and particularly between corals and picoplankton. Picoplankton are the microorganisms in the water column that utilize organic matter released from corals to support their growth. These coral-picoplankton interactions are relatively unstudied, but could have major implications for reef ecology and coral health. This project will take place in the U.S. territory of the Virgin Islands (USVI) and will produce the first detailed knowledge about the chemical diversity and composition of organic matter released from diverse stony coral and octocoral species. This project will advance our understanding of coral reef microbial ecology by allowing us to understand how different coral metabolites impact picoplankton growth and dynamics over time. The results from this project will be made publically accessible in a freely available online magazine, and USVI minority middle and high school students will be exposed to a lesson about chemical-biological interactions on coral reefs through established summer camps. This project will also contribute to the training of USVI minority undergraduates as well as a graduate student.
Coral exometabolomes, which are the sum of metabolic products of the coral together with its microbiome, are thought to structure picoplankton communities in a species-specific manner. However, a detailed understanding of coral exometabolomes, and their influences on reef picoplankton, has not yet been obtained. This project will utilize controlled aquaria-based experiments with stony corals and octocorals, foundational species of Caribbean reef ecosystems, to examine how the exometabolomes of diverse coral species differentially influence the reef picoplankton community. Specifically, this project will capitalize on recent developments in mass spectrometry-based metabolomics to define the signature exometabolomes of ecologically important and diverse stony corals and octocorals. Secondly, this project will determine how the exometabolomes of these corals vary with factors linked to coral taxonomy as well as the coral-associated microbiome (Symbiodinium algae, bacteria and archaea). With this new understanding of coral exometabolomes, the project will then apply a stable isotope probe labeling approach to the coral exometabolome and will examine if and how (through changes in growth and activity) the seawater picoplankton community incorporates coral exometabolomes from different coral species over time. This project will advance our ability to evaluate the role that coral exometabolomes play in contributing to benthic-picoplankton interactions on changing Caribbean reefs.";
String projects_0_end_date "2020-09";
String projects_0_geolocation "U.S. Virgin Islands";
String projects_0_name "Signature exometabolomes of Caribbean corals and influences on reef picoplankton";
String projects_0_project_nid "746196";
String projects_0_start_date "2017-10";
String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
String publisher_type "institution";
String sourceUrl "(local files)";
Float64 Southernmost_Northing 18.30964;
String standard_name_vocabulary "CF Standard Name Table v55";
String subsetVariables "NCBI_BioProject_accession_number,Sample_type,Sequencing_Strategy,Sequencing_Instrument_model";
String summary "Data from a tidal study over 48 hours of mangrove, seagrass, and seawater from the US Virgin Islands in 2017. These data include tidal height, depth, temperature, salinity, Prochlorococcus counts, Synechococcus counts, Picoeukaryote abundances, nutrient concentrations at accession numbers for sequences at The National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA).";
String time_coverage_end "2017-07-24T10:43:00Z";
String time_coverage_start "2017-07-22T15:55:00Z";
String title "[Tidal study of seawater microbial communities] - Flow cytometry and nutrient analyses data from a tidal study over 48 hours of mangrove, seagrass, and seawater from the US Virgin Islands in July of 2017 (Signature exometabolomes of Caribbean corals and influences on reef picoplankton)";
String version "1";
Float64 Westernmost_Easting -64.76453;
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.