BCO-DMO ERDDAP
Accessing BCO-DMO data
log in    
Brought to you by BCO-DMO    

ERDDAP > tabledap > Make A Graph ?

Dataset Title:  Diel, daily, and spatial variation of coral reef seawater microbial
communities from US Virgin Islands, 2017
Subscribe RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_775229)
Range: longitude = -70.6731 to -64.70403°E, latitude = 18.30204 to 41.5265°N
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Subset | Data Access Form | Files
 
Graph Type:  ?
X Axis: 
Y Axis: 
Color: 
-1+1
 
Constraints ? Optional
Constraint #1 ?
Optional
Constraint #2 ?
       
       
       
       
       
 
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.")
 
Graph Settings
Marker Type:   Size: 
Color: 
Color Bar:   Continuity:   Scale: 
   Minimum:   Maximum:   N Sections: 
Draw land mask: 
Y Axis Minimum:   Maximum:   
 
(Please be patient. It may take a while to get the data.)
 
Optional:
Then set the File Type: (File Type information)
and
or view the URL:
(Documentation / Bypass this form ? )
    Click on the map to specify a new center point. ?
Zoom: 
[The graph you specified. Please be patient.]

 

Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

The Dataset Attribute Structure (.das) for this Dataset

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 "NCBI BioProject accession number";
    String long_name "NCBI Bio Project Accession Number";
    String units "unitless";
  }
  NCBI_BioSample_accession_number {
    String bcodmo_name "accession number";
    String description "NCBI BioSample accession number";
    String long_name "NCBI Bio Sample Accession Number";
    String units "unitless";
  }
  Sample_type {
    String bcodmo_name "sample_type";
    String description "Sample type";
    String long_name "Sample Type";
    String units "unitless";
  }
  Coral_Colony_or_sand {
    String bcodmo_name "sample";
    String description "Coral Colony or sand identifier";
    String long_name "Coral Colony Or Sand";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  Collection_time {
    String bcodmo_name "time";
    String description "Collection time (day or night) and day relative to the start of the study";
    String long_name "Collection Time";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AHMSAA01/";
    String units "unitless";
  }
  Collection_Date {
    String bcodmo_name "date";
    String description "Collection Date; fomatted as Mon-yyyy";
    String long_name "Collection Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String units "unitless";
  }
  Collection_location {
    String bcodmo_name "site";
    String description "Collection location";
    String long_name "Collection Location";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 18.30204, 41.5265;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "latitude; north is positive";
    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 -70.6731, -64.70403;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "longitude; east is postive";
    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";
  }
  Prochlorococcus_cells_mL {
    Int32 _FillValue 2147483647;
    Int32 actual_range 11250, 47419;
    String bcodmo_name "cell_concentration";
    String description "concentration of Prochlorococcus";
    String long_name "Prochlorococcus Cells M L";
    String units "cell/milliliter";
  }
  Synechococcus_cells_mL {
    Int32 _FillValue 2147483647;
    Int32 actual_range 27721, 79875;
    String bcodmo_name "cell_concentration";
    String description "concentration of Synechococcus";
    String long_name "Synechococcus Cells M L";
    String units "cell/milliliter";
  }
  Picoeukaryotes_cells_mL {
    Int16 _FillValue 32767;
    Int16 actual_range 440, 4331;
    String bcodmo_name "cell_concentration";
    String description "concentration of Picoeukaryotes";
    String long_name "Picoeukaryotes Cells M L";
    String units "cell/milliliter";
  }
  Unpigmented_cells_cells_mL {
    Int32 _FillValue 2147483647;
    Int32 actual_range 397448, 802850;
    String bcodmo_name "cell_concentration";
    String description "concentration of unpigmented cells";
    String long_name "Unpigmented Cells Cells M L";
    String units "cell/milliliter";
  }
  Phosphate_uM {
    Float32 _FillValue NaN;
    Float32 actual_range 0.13, 1.465;
    String bcodmo_name "PO4";
    String description "concentration of Phosphate_uM";
    String long_name "Mass Concentration Of Phosphate In Sea Water";
    String units "micromoles";
  }
  Silicate_uM {
    Float32 _FillValue NaN;
    Float32 actual_range 0.3, 13.7;
    String bcodmo_name "SiOH_4";
    String description "concentration of Silicate_uM";
    String long_name "Mass Concentration Of Silicate In Sea Water";
    String units "micromoles";
  }
  Nitrate_uM {
    Float32 _FillValue NaN;
    Float32 actual_range -0.001, 0.4004;
    String bcodmo_name "NO3";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "concentration of Nitrate_uM";
    String long_name "Mole Concentration Of Nitrate In Sea Water";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/NTRAIGGS/";
    String units "micromoles";
  }
  Nitrite_uM {
    Float32 _FillValue NaN;
    Float32 actual_range -0.02, 0.08;
    String bcodmo_name "NO2";
    Float64 colorBarMaximum 1.0;
    Float64 colorBarMinimum 0.0;
    String description "concentration of Nitrite_uM";
    String long_name "Mole Concentration Of Nitrite In Sea Water";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/NTRIAAZX/";
    String units "micromoles";
  }
  Ammonium_uM {
    Float32 _FillValue NaN;
    Float32 actual_range 0.13, 2.23;
    String bcodmo_name "Ammonium";
    Float64 colorBarMaximum 5.0;
    Float64 colorBarMinimum 0.0;
    String description "concentration of Ammonium_uM";
    String long_name "Mole Concentration Of Ammonium In Sea Water";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AMONAAZX/";
    String units "micromoles";
  }
  Temperature_F {
    Float32 _FillValue NaN;
    Float32 actual_range 85.014, 86.641;
    String bcodmo_name "temperature";
    String description "Temperature";
    String long_name "Temperature F";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Fahrenheit";
  }
  Depth_Feet {
    Float64 _FillValue NaN;
    Float64 actual_range 23.0, 28.0;
    String bcodmo_name "depth";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "Depth";
    String long_name "Depth";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
    String standard_name "depth";
    String units "feet";
  }
  Relative_light_levels {
    Int16 _FillValue 32767;
    Int16 actual_range 0, 864;
    String bcodmo_name "unknown";
    String description "Relative_light_levels";
    String long_name "Relative Light Levels";
    String units "lumens/foot^2";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Sample collection:  
 Five Porites astreoides colonies and a sand patch were selected and marked
with flagging tape by divers on Ram Head reef (18\\u00ba18\\u201907.3\\u201d N,
64\\u00ba42\\u201914.5\\u201d W; 8 m depth in sand) in St. John, U. S. Virgin
Islands. Colonies of various sizes (3 \\u2013 16 inches in diameter) from a
range of heights above the seafloor (1 \\u2013 27 cm) were selected and these
colonies were labeled A through E. Additionally, colonies were evenly
distributed across the reef in order to minimize location effects (range of
3.6 to 14 meters between each colony). All colonies were located directly next
to sand patches based on colony size constraints and the space needed for
deployment of the custom made Coral Ecosphere Sampling Devices (CESD). Six
CESD made out of aluminum strut material were deployed adjacent to each
sampling location with sand screws. The last CESD was placed in a wide sand
patch with no corals or benthic organisms located in its vicinity and this
sampling location was used as a \\u2018no-coral\\u2019 control. Divers
positioned the CESD so that a 60 ml syringe with an attached filter holder
could be placed 5 cm away from the middle of the colony. Light and temperature
loggers (8K HOBO/PAR loggers; Onset, Wareham, MA) were zip-tied to the end of
each CESD and programmed to collect temperature and relative light intensity
measurements every 5 minutes over the course of the three-day study. An hour
after CESD deployment, scuba divers collected the first set of samples (Day 1,
3:00 pm). Filter holders were pre-loaded with 0.22 \\u00b5m pore size
Supor\\u00ae filters (Pall Corporation, Ann Arbor, MI, USA) and were contained
within sterile Whirl-pack\\u00ae bags prior to sampling.\\u00a0 Divers also
descended with acid-washed polyethylene nutrient bottles (30 ml volume) to
collect seawater samples for unfiltered inorganic nutrient analysis and flow
cytometry. At depth, seawater samples (60 ml) collected for amplicon-based
microbial community analysis were conducted at 2 different stationary
locations relative to the CESD device (with the exception of collections
completed at the sand-patch location). Reef-depth samples were collected first
at the top of the CESD (2 m from the colony) in order to minimize stirring
close to the coral ecosphere sampling area. To collect the sample, a diver
attached a piece of acid-cleaned Masterflex silicone tubing to connect the end
of the filter holder to the mouth of the syringe and then used reverse
filtration to pull seawater through the filter. The filter-holder was then
placed in an individual Whirl-pack\\u00ae bag and sealed. After collection of
microbial biomass with the syringe, a nutrient sample was collected. After
collection of the reef-depth sample, a diver attached the filter holder to the
syringe, slowly descended closer to the coral colony, but behind the CESD to
maintain sufficient distance from the sampling area and then placed the
syringe into the syringe holder located on the horizontal arm of the CESD. As
before, the diver first collected the coral ecosphere sample (5 cm from the
colony) onto the filter followed by a nutrient sample in the same location.
Replicate samples collected for DNA analysis were collected from both seawater
environments surrounding each colony on the first dive, but were not collected
on the following dives due to time constraints. Surface seawater samples (< 1
m) were collected using 60 mL syringes at each time point from the dive boat.
 
This sampling scheme was repeated at approximately 3 am and 3 pm for the next
three days, totaling up to 6 sampling time points. Divers sampled each colony
and collected samples in the same order (reef-depth followed by coral
ecosphere) during all time points. After collection, samples were placed in a
cooler equipped with blue-ice packs for the transit from the reef to the lab
and then samples were processed immediately. Over the course of sampling, 85
seawater samples were collected.
 
After the last time point, coral tissue was collected from each colony (close
to the area where the coral ecosphere seawater was sampled) using a hammer and
chisel and the CESD were removed. Sand was also collected in the location
where the sand control CESD device was deployed.
 
Sample processing:  
 In the laboratory, sterile syringes were used to remove residual seawater
trapped within filter holders and then filters were placed into cryovials,
flash-frozen in a dry shipper charged with liquid nitrogen, and then
transferred into a\\u00a0 -20 C freezer.
 
Seawater collected for flow cytometric analysis was subsampled from unfiltered
nutrient samples and preserved with paraformaldehyde (Electron Microscopy
Sciences, Allentown, PA) to a final concentration of 1% (by volume). Nutrient,
DNA, and flow cytometry samples were shipped frozen back to Woods Hole
Oceanographic Institution and ultimately stored at -80 C prior to analysis.
The coral tissue and sand samples were stored in a second dry shipper and
ultimately at -80 C until they were processed.\\u00a0
 
Macronutrient analysis and flow cytometry:  
 Frozen and unfiltered nutrient samples were analyzed with a continuous
segmented flow-system using previously described methods (as in Apprill and
Rappe 2011). The concentrations of NO2- + NO3-, NO2-, PO43-, NH4+, and
silicate were measured in all of the samples. Nitrate concentrations were
obtained by subtracting the nitrite concentration from the nitrite + nitrate
measurements for each sample.
 
Samples collected for flow cytometry were analyzed using colinear analysis
(laser excitation wavelength of 488 nm, UV) on an Altra flow cytometer
(Beckman Coulter, Pasadena, CA.). Unstained subsamples were used to enumerate
the abundances of picocyanobacteria (Prochlorococcus, Synechococcus) and
picoeukaryotes. Stained (Hoechst stain, 1 \\u00b5g ml-1 final concentration)
subsamples were analyzed to estimate the abundance of unpigmented cells (an
estimate of heterotrophic bacterial abundance) (Marie et al. 1997). FlowJo (v.
6.4.7) software was used to estimate the abundance of each cell type. The
abundance of total cells was calculated by adding the cell counts obtained for
each of the respective picoplankton classes together for each sample.
 
DNA extraction, amplification, pooling, and sequencing:  
 DNA was extracted from filters using a sucrose-lysis extraction method and
Qiagen spin-columns (Santoro et al. 2010) Control extractions were also
completed with unused filters (control filters without biomass) in order to
account for contamination from the filters or extraction reagents. Lastly,
diluted DNA from a synthetic staggered mock community (BEI Resources,
Manassas, VA, USA) was used to account for amplification and sequencing errors
in downstream microbial community analysis. Coral tissue was removed from the
skeleton using air-brushing with autoclaved 1% phosphate-buffered-saline (PBS)
solution (Apprill et al. 2016; Weber et al. 2017). The coral tissue slurry was
pelleted using a centrifuge and the PBS supernatant was discarded. DNA was
extracted from each pellet (300 mg of tissue) using a modified version of the
DNeasy DNA extraction kit protocol (Qiagen, Germantown, MD). The lysis buffer
in the kit was added to each tube followed by approximately 300 mg of garnet
beads (from a MOBIO DNA extraction kit) and 300 mg of Lysing B matrix beads
(MP Biomedicals, Solon, OH). The tubes were subjected to a bead-beating step
for 15 minutes so that the beads could break up the coral tissue (Weber et al.
2017). After bead-beating, 20 \\u00b5l of proteinase-k was added to each tube
and the samples were incubated with gentle agitation for 10 minutes at 56
\\u00b0C. After these modifications, the DNeasy protocol (Qiagen) was followed
to complete extractions.
 
Extracts were amplified with barcoded primers targeting the V4 hypervariable
region of the bacterial and archaeal small subunit ribosomal RNA gene (Kozich
et al. 2013). The forward primer: 5\\u2019 TATGGTAATTGTGTGYCAGCMGCCGCGGTAA
3\\u2019 (Parada et al. 2016) and reverse primer: 3\\u2019
AGTCAGTCAGCCGGACTACNVGGGTWTCTAAT 5\\u2019 (Apprill et al. 2015) were used,
along with the barcodes, to amplify and tag each sample prior to pooling. We
used forward and reverse primers with degeneracies in order to eliminate
amplification biases against Crenarchaeota/ Thaumarchaeota (Parada et al.
2016)\\u00a0 and SAR 11 (Apprill et al. 2015). Triplicate Polymerase Chain
Reactions (25 l volume) were run with 2 l of DNA template from each sample
using the same barcodes in order to minimize the formation of chimeras during
amplification. The reaction conditions included: a 2-minute hot start at 95
\\u00b0C followed by 36 cycles of 95 \\u00b0C for 20 seconds, 55 \\u00b0C for 15
seconds, and 72 \\u00b0C for 5 minutes. The final extension step was 72 \\u00b0C
for 10 minutes. Triplicate barcoded amplicons were pooled and screened using
gel electrophoresis to assess the quality and the relative concentration of
amplicons. Amplicons were purified using the MinElute Gel Extraction Kit
(Qiagen) and pooled to form the sequencing library. The library was sequenced
(paired-end 2x250 bp) at the Georgia Genomics and Bioinformatics Core with a
Miseq (Illumina, San Diego, CA) sequencer and raw sequence reads are available
at the NCBI Sequence Read Archive under BioProject # PRJNA550343.
 
Microbial community analyses:  
 Raw sequences were quality-filtered and grouped into amplicon sequence
variants (ASVs) using DADA2 (Callahan et al. 2016). Reads were filtered,
trimmed, dereplicated and error rates were calculated using the program\\u2019s
parametric error model. The DADA2 algorithm was used to infer the number of
different ASVs (8357 distinct ASVs), paired reads were merged, an ASV table
was constructed, and chimeras were removed (1% of all ASVs). Taxonomy was
assigned to each ASV using the Silva v.132 reference database (Quast et al.
2013). Mock communities were used to assess the performance of the program as
well as sequencing error rates. DADA2 inferred 15, 17, and 17 strains within
the mock community (compared to the 20 expected stains present at different
concentrations within the staggered community) and 13, 14, and 14 of the
strains were exact matches to the expected sequences from the mock community
reference file. Sequence recovery is slightly lower than expected, but is
comparable to normal performance of DADA2 on this staggered mock community
(Callahan et al. 2016).
 
The R packages Phyloseq (McMurdie and Holmes 2013), Vegan (Oksanen et al.
2017), DESeq2 (Love et al. 2014), and ggplot2 (Wickham 2016) were used for
downstream analysis of the microbial community. Sequences were not subsampled,
but samples with less than 1000 reads (2 samples) were removed. In addition,
ASVs identifying as chloroplasts were removed.\\u00a0 Sequences representing
ASVs that identified as \\u201cNA\\u201d at the Phylum level were checked using
the SINA aligner and classifier (v.1.2.11) (Pruesse et al. 2012) and then
removed if not identified as bacteria or archaea at 70% similarity. The
average number of reads across all seawater samples used in microbial
community analyses was 58,398 (\\u00b1 32,184 standard deviation) with a range
of 11,502 \\u2013 206,689 reads. The average number of reads in coral tissue
samples was 38,096 (\\u00b123,854) with a range of 11,538 \\u2013 59,437 reads.
DNA extraction control communities were initially inspected and then removed
because they fell out as outliers compared to the highly similar seawater
microbial communities. Taxonomic bar plots, metrics of alpha diversity
(observed richness of ASVs), and boxplots of alpha diversity were made and
calculated using Phyloseq. Alpha diversity was also calculated for samples
after Prochlorococcus and Synechococcus ASVs were removed in order to
understand how much their dynamics influenced observed richness. Constrained
analysis of principal coordinates (CAP) based on Bray \\u2013 Curtis
dissimilarity was completed (using \\u2018capscale\\u2019 in Vegan) and variance
partitioning was used to identify which of the measured environmental
parameters significantly (p<0.01) contributed to shifts in the microbial
community composition over time. Permutational Multivariate Analysis of
Variance using distance matrices (PERMANOVA/Adonis) tests identified
categorical factors that significantly (p<0.05) contributed to a similarity
between the microbial communities. DESeq2 was used to identify differentially
abundant ASVs between day and night as well as reef-associated (reef-depth and
coral ecosphere) compared to surface microbial communities (using the
\\u201clocal\\u201d fitType parameter to estimate gene dispersion). Lastly, the
Rhythmicity Analysis Incorporating Non-parametric methods (RAIN)\\u00a0 R
package was used to identify ASVs that experienced rhythmic change in relative
abundance over a period of 24 hours (Thaben and Westermark 2014). This
analysis was completed separately for reef-depth and coral ecosphere seawater
and the input ASV matrix was center log-ratio transformed and detrended
following previous methods (Hu et al. 2018). Only ASVs with significant
p-values (p<0.05) after adaptive Benjamini-Hochberg correction were reported
to control for false recovery rates (Benjamini and Hochberg 2000).
 
Statistical analyses:  
 A Principal Coordinates Analysis (PCA) was completed to summarize changes in
picoplankton abundances, inorganic nutrient concentrations, and relative light
and temperature information collected from the HOBO loggers and reduce the
dimensionality of this data. Separate PCAs were also generated using samples
collected during either day or night to observe trends specific to these
times. Kruskal-Wallis rank sums tests were used to test for significant
differences (p<0.05) in alpha diversity between the different sample
groupings. Pairwise post-hoc Dunn\\u2019s tests with Bonferonni corrections
were used to identify which groups were significantly different from each
other. These tests were also used to test for significant differences in
picoplankton cell abundance overtime, between day and night samples, and
between coral ecosphere and reef-depth samples.";
    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 
"Diel, daily, and spatial variation of coral reef seawater microbial communities, US Virgin Islands, 2017 
   PI: A. Apprill (WHOI) 
   version date: 2019-08-12";
    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 "2019-08-14T13:23:09Z";
    String date_modified "2019-08-19T12:17:28Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.775229.1";
    Float64 Easternmost_Easting -64.70403;
    Float64 geospatial_lat_max 41.5265;
    Float64 geospatial_lat_min 18.30204;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -64.70403;
    Float64 geospatial_lon_min -70.6731;
    String geospatial_lon_units "degrees_east";
    String history 
"2022-08-13T03:25:49Z (local files)
2022-08-13T03:25:49Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_775229.das";
    String infoUrl "https://www.bco-dmo.org/dataset/775229";
    String institution "BCO-DMO";
    String instruments_0_acronym "Nutrient Autoanalyzer";
    String instruments_0_dataset_instrument_description "Used to analyze nutrient samples.";
    String instruments_0_dataset_instrument_nid "775252";
    String instruments_0_description "Nutrient Autoanalyzer is a generic term used when specific type, make and model were not specified.  In general, a Nutrient Autoanalyzer is an automated flow-thru system for doing nutrient analysis (nitrate, ammonium, orthophosphate, and silicate) on seawater samples.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB04/";
    String instruments_0_instrument_name "Nutrient Autoanalyzer";
    String instruments_0_instrument_nid "558";
    String instruments_0_supplied_name "A continuous segmented flow-system";
    String instruments_1_acronym "Automated Sequencer";
    String instruments_1_dataset_instrument_description "Used to obtain genetic data.";
    String instruments_1_dataset_instrument_nid "775254";
    String instruments_1_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_1_instrument_name "Automated DNA Sequencer";
    String instruments_1_instrument_nid "649";
    String instruments_1_supplied_name "Miseq (Illumina, San Diego, CA) sequencer";
    String instruments_2_acronym "Flow Cytometer";
    String instruments_2_dataset_instrument_description "Used for measuring cell concentrations. Samples collected for flow cytometry were analyzed using colinear analysis (laser excitation wavelength of 488 nm, UV) on an Altra flow cytometer (Beckman Coulter, Pasadena, CA.).";
    String instruments_2_dataset_instrument_nid "775253";
    String instruments_2_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_2_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB37/";
    String instruments_2_instrument_name "Flow Cytometer";
    String instruments_2_instrument_nid "660";
    String instruments_3_dataset_instrument_description "Measured temperature and relative light level.";
    String instruments_3_dataset_instrument_nid "775251";
    String instruments_3_description "Records temperature data over a period of time.";
    String instruments_3_instrument_name "Temperature Logger";
    String instruments_3_instrument_nid "639396";
    String instruments_3_supplied_name "Light temperature loggers (8K HOBO/PAR loggers; Onset, Wareham, MA)";
    String keywords "accession, ammonia, ammonium, Ammonium_uM, bco, bco-dmo, bio, biological, cells, chemical, chemistry, collection, Collection_Date, Collection_location, Collection_time, colony, concentration, coral, Coral_Colony_or_sand, data, dataset, date, depth, Depth_Feet, 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, erddap, latitude, levels, light, longitude, management, mass, mass_concentration_of_phosphate_in_sea_water, mass_concentration_of_silicate_in_sea_water, mole, mole_concentration_of_ammonium_in_sea_water, mole_concentration_of_nitrate_in_sea_water, mole_concentration_of_nitrite_in_sea_water, n02, ncbi, NCBI_BioProject_accession_number, NCBI_BioSample_accession_number, nh4, nitrate, Nitrate_uM, nitrite, Nitrite_uM, no3, number, ocean, oceanography, oceans, office, phosphate, Phosphate_uM, picoeukaryotes, Picoeukaryotes_cells_mL, po4, preliminary, prochlorococcus, Prochlorococcus_cells_mL, project, relative, Relative_light_levels, sample, Sample_ID, Sample_type, sand, science, sea, seawater, silicate, Silicate_uM, synechococcus, Synechococcus_cells_mL, temperature, Temperature_F, time, type, unpigmented, Unpigmented_cells_cells_mL, water";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "https://www.bco-dmo.org/dataset/775229/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/775229";
    Float64 Northernmost_Northing 41.5265;
    String param_mapping "{'775229': {'Depth_Feet': 'master - depth', 'lat': 'master - latitude', 'lon': 'master - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/775229/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 "Laura Weber";
    String people_1_person_nid "662109";
    String people_1_role "Contact";
    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 "Nancy Copley";
    String people_2_person_nid "50396";
    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 18.30204;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String subsetVariables "NCBI_BioProject_accession_number";
    String summary "Bacterial and archaeal diversity and composition, microbial cell abundances, inorganic nutrient concentrations, and physicochemical conditions were determined and measured in coral reef seawater over a three-day, diel time series on one reef in St. John, U.S. Virgin Islands.";
    String title "Diel, daily, and spatial variation of coral reef seawater microbial communities from US Virgin Islands, 2017";
    String version "1";
    Float64 Westernmost_Easting -70.6731;
    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.


 
ERDDAP, Version 2.02
Disclaimers | Privacy Policy | Contact