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Dataset Title:  OTU molecular abundances for coral Symbiodinium, Belize Mesoamerican Barrier
Reef System (MBRS), 2014-2015
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_734674)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Files | Make a graph
 
Variable ?   Optional
Constraint #1 ?
Optional
Constraint #2 ?
   Minimum ?
 
   Maximum ?
 
 species (unitless) ?          "Pseudodiploria_str..."    "Siderastrea_siderea"
 species_code (unitless) ?          "pstr"    "ssid"
 Sample (unitless) ?          "p161pm"    "s60de"
 site (unitless) ?          "Belize City 1"    "Sapodilla 3"
 thermal_type (unitless) ?          "1-Low"    "3-High"
 lat_location (Latitude, unitless) ?          "1-Belize City"    "4-Sapodilla"
 illumina_run (unitless) ?          1    2
 otu_diversity (OTU's) ?          2    8
 C1_I (unitless) ?          1    170259
 B1_I (unitless) ?          0    123302
 C1_II (unitless) ?          0    107514
 C1_III (unitless) ?          0    29764
 D1a (unitless) ?          0    37100
 B1_II (unitless) ?          0    12000
 G3 (unitless) ?          0    7517
 A4a (unitless) ?          0    3692
 B_BG (unitless) ?          0    2758
 C3 (unitless) ?          0    1143
 
Server-side Functions ?
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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  species {
    String bcodmo_name "species";
    String description "taxonomic species name";
    String long_name "Species";
    String units "unitless";
  }
  species_code {
    String bcodmo_name "taxon_code";
    String description "species code";
    String long_name "Species Code";
    String units "unitless";
  }
  Sample {
    String bcodmo_name "sample";
    String description "coral sample identifier";
    String long_name "Sample";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  site {
    String bcodmo_name "site";
    String description "site identifier: nearby city and the thermally distinct regime code: 1=low; 2=moderate; 3=high";
    String long_name "Site";
    String units "unitless";
  }
  thermal_type {
    String bcodmo_name "treatment";
    String description "thermal regime code: 1=lowTP; 2=modTP; 3=highTP. These 3 categories are based on low; moderate; and high temperature parameters (see Baumann et al 2016 for details)";
    String long_name "Thermal Type";
    String units "unitless";
  }
  lat_location {
    String bcodmo_name "region";
    String description "site location and code number";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "unitless";
  }
  illumina_run {
    Byte _FillValue 127;
    Byte actual_range 1, 2;
    String bcodmo_name "replicate";
    String description "Illumina run number";
    String long_name "Illumina Run";
    String units "unitless";
  }
  otu_diversity {
    Byte _FillValue 127;
    Byte actual_range 2, 8;
    String bcodmo_name "count";
    String description "total number of operational taxonomic units (OTU) in sample";
    String long_name "Otu Diversity";
    String units "OTU's";
  }
  C1_I {
    Int32 _FillValue 2147483647;
    Int32 actual_range 1, 170259;
    String bcodmo_name "relative_abund";
    String description "relative abundance of OTU C1.I";
    String long_name "C1 I";
    String units "unitless";
  }
  B1_I {
    Int32 _FillValue 2147483647;
    Int32 actual_range 0, 123302;
    String bcodmo_name "relative_abund";
    String description "relative abundance of OTU B1.I";
    String long_name "B1 I";
    String units "unitless";
  }
  C1_II {
    Int32 _FillValue 2147483647;
    Int32 actual_range 0, 107514;
    String bcodmo_name "relative_abund";
    String description "relative abundance of OTU C1.II";
    String long_name "C1 II";
    String units "unitless";
  }
  C1_III {
    Int16 _FillValue 32767;
    Int16 actual_range 0, 29764;
    String bcodmo_name "relative_abund";
    String description "relative abundance of OTU C1.III";
    String long_name "C1 III";
    String units "unitless";
  }
  D1a {
    Int32 _FillValue 2147483647;
    Int32 actual_range 0, 37100;
    String bcodmo_name "relative_abund";
    String description "relative abundance of OTU D1a";
    String long_name "D1a";
    String units "unitless";
  }
  B1_II {
    Int16 _FillValue 32767;
    Int16 actual_range 0, 12000;
    String bcodmo_name "relative_abund";
    String description "relative abundance of OTU B1.II";
    String long_name "B1 II";
    String units "unitless";
  }
  G3 {
    Int16 _FillValue 32767;
    Int16 actual_range 0, 7517;
    String bcodmo_name "relative_abund";
    String description "relative abundance of OTU G3";
    String long_name "G3";
    String units "unitless";
  }
  A4a {
    Int16 _FillValue 32767;
    Int16 actual_range 0, 3692;
    String bcodmo_name "relative_abund";
    String description "relative abundance of OTU A4a";
    String long_name "A4a";
    String units "unitless";
  }
  B_BG {
    Int16 _FillValue 32767;
    Int16 actual_range 0, 2758;
    String bcodmo_name "relative_abund";
    String description "relative abundance of OTU B.BG";
    String long_name "B BG";
    String units "unitless";
  }
  C3 {
    Int16 _FillValue 32767;
    Int16 actual_range 0, 1143;
    String bcodmo_name "relative_abund";
    String description "relative abundance of OTU C3";
    String long_name "C3";
    String units "unitless";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"From Baumann et al (2017):
 
DNA Extraction
 
Coral holobiont (coral, algae, and microbiome) DNA was isolated from each
sample following a modified phenol-chloroform [86,87,88] method described in
detail by Davies et al. [87]. Briefly, DNA was isolated by immersing the
tissue in digest buffer (100 mM NaCL, 10 mM Tris-Cl pH 8.0, 25 mM EDTA pH 9.0,
0.5% SDS, 0.1 mg ml-1 Proteinase K, and 1 \\u00b5g ml-1 RNaseA) for 1 h at 42
\\u00b0C followed by a standard phenol-chloroform extraction. Extracted DNA was
confirmed on an agarose gel and quantified using a Nanodrop 2000
Spectrophotometer (Thermo Scientific).
 
PCR Amplification and Metabarcoding
 
The ITS-2 region (350 bp) was targeted and amplified in each sample using
custom primers that incorporated Symbiodinium specific ITS-2-dino-forward and
its2rev2-reverse regions [65, 73, 89]. Each primer was constructed with a
universal linker, which allowed for the downstream incorporation of Illumina
specific adapters and barcodes during the second PCR as well as four
degenerative bases whose function was to increase the complexity of library
composition. The forward primer was 5'-GTCTCGTCGGCTCGG + AGATGTGTATAAGAGACAG+
NNNN + CCTCCGCTTACTTATATGCTT-3', where the underlined bases are the
5'-universal linker, italicized bases indicate spacer sequences, Ns denote
degenerative bases, and the bold bases are the ITS-2-dino. The reverse primer
was 5'-TCGTCGGCAGCGTCA + AGATGTGTATAAGAGACAG + NNNN + GTGAATTGCAGAACTCGTG-3'.
 
Each 20 \\u00b5L PCR reaction contained 5-100 ng DNA template, 12.4 \\u00b5L
Milli-Q H2O, 0.2 \\u00b5M dNTPs, 1 \\u00b5M forward and 1 \\u00b5M reverse
primers, 1\\u00d7 Extaq buffer, and 0.5 U (units) Extaqpolymerase (Takara
Biotechnology). PCR cycles were run for all samples using the following PCR
profile: 95 \\u00b0C for 5 min, 95 \\u00b0C for 40 s, 59 \\u00b0C for 2 min, 72
\\u00b0C for 1 min per cycle and a final elongation step of 72 \\u00b0C for 7
min. The optimal number of PCR cycles for each sample was determined from
visualization of a faint band on a 2% agarose gel (usually between 22 and 28
cycles) as per Quigley et al. [65]. PCR products were cleaned using GeneJET
PCR purification kits (Fermentas Life Sciences), and then a second PCR
reaction was performed to incorporate custom barcode-primer sequences [65]
modified for Illumina Miseq as in Klepac et al. [90]. Custom barcode primer
sequences included 5'-Illumina adaptor + 6 bp barcode sequence + one of two
universal linkers-3' (e.g., 5'-CAAGCAGAAGACGGCATACGAGAT + GTATAG +
GTCTCGTGGGCTCGG-3', or 5'-AATGATACGGCGACCACCGAGATCTACAC + AGTCAA +
TCGTCGGCAGCGTC-3'). Following barcoding, PCR samples were visualized on a 2%
agarose gel and pooled based on band intensity (to ensure equal contributions
of each sample in the pool). The resulting pool was run on a 1% SYBR Green
(Invitrogen) stained gel for 60 min at 90 V and 120 mA. The target band was
excised, soaked in 30 \\u00b5L of Milli-Q water overnight at 4 \\u00b0C, and the
supernatant was submitted for sequencing to the University of North Carolina
at Chapel Hill High Throughput Sequencing Facility across two lanes of
Illumina MiSeq (one 2 \\u00d7 250, one 2 \\u00d7 300). The two lanes produced
similar mapping efficiencies (73 and 73%, respectively; Table S3).
 
Bioinformatic Pipeline
 
The bioinformatic pipeline used here builds upon previous work by Quigley et
al. [65] and Green et al. [73]. Raw sequences were renamed to retain sample
information, and then all forward (R1) and reverse (R2) sequences were
concatenated into two files, which were processed using CD-HIT-OTU [91]. CD-
HIT-OTU clusters concatenated reads into identical groups at 100% similarity
for identification of operational taxonomic units (OTUs). Each sample was then
mapped back to the resulting reference OTUs, and an abundance count for each
sample across all OTUs was produced. A BLASTn search of each reference OTU was
then run against the GenBank (NCBI) nucleotide reference collection using the
representative sequence from each OTU to identify which Symbiodinium lineage
was represented by each OTU (Table S2).
 
The phylogeny of representative sequences of each distinct Symbiodinium OTU
was constructed using the PhyML tool [92, 93] within Geneious version 10.0.5
([http://geneious.com](\\\\\"http://geneious.com\\\\\")) [94]. PhyML was run using
the GTR+I model (chosen based on delta AIC values produced from jModelTest
[92, 95]) to determine the maximum likelihood tree. The TreeDyn tool in
Phylogeny.fr was used to view the tree (Fig. 2) [96, 97, 98]. The reference
sequences included in the phylogeny were accessed from GenBank (Table S6).
 
 These data are reported in:  
 Baumann, J.H., Davies, S.W., Aichelman, H.E. and Castillo, K. D. (2017)
Coral Symbiodinium Community Composition Across the Belize Mesoamerican
Barrier Reef System is Influenced by Host Species and Thermal Variability.
Microb Ecol.
[https://doi.org/10.1007/s00248-017-1096-6](\\\\\"https://doi.org/10.1007/s00248-017-1096-6\\\\\").
 
Methodology References:
 
65\\. Quigley KM, Davies SW, Kenkel CD, Willis BL, Matz MV, Bay LK (2014) Deep-
sequencing method for quantifying background abundances of Symbiodinium types:
exploring the rare Symbiodinium biosphere in reef-building corals. PLoS One
9:e94297
 
73\\. Green EA, Davies SW, Matz MV, Medina M (2014) Quantifying cryptic
Symbiodinium diversity within Orbicella faveolata and Orbicella franksi at the
Flower Garden Banks, Gulf of Mexico. PeerJ 2:e386
 
86\\. Aronson RB, Precht WF, Toscano MA, Koltes KH (2002) The 1998 bleaching
event and its aftermath on a coral reef in Belize. Marine Biology xxx
 
87\\. Davies SW, Rahman M, Meyer E, Green EA, Buschiazzo E, Medina M, Matz MV
(2013) Novel polymorphic microsatellite markers for population genetics of the
endangered Caribbean star coral, Montastraea faveolata. Mar Biodivers
43:167-172
 
88\\. Chomczynski P, Sacchi N (2006) The single-step method of RNA isolation by
acid guanidinium thiocyanate-phenol-chloroform extraction: twenty-something
years on. Nat Protoc 1:581-585
 
89\\. Stat M, Loh WKH, Hoegh-Guldberg O, Carter DA (2009) Stability of coral-
endosymbiont associations during and after a thermal stress event in the
southern Great Barrier Reef. Coral Reefs 28:709-713
 
90\\. Klepac CN, Beal J, Kenkel CD, Sproles A, Polinski JM, Williams MA, Matz
MV, Voss JD (2015) Seasonal stability of coral-Symbiodinium associations in
the subtropical coral habitat of St. Lucie Reef, Florida. Mar Ecol Prog Ser
532:137-151
 
91\\. Li W, Fu L, Niu B, Wu S, Wooley J (2012) Ultrafast clustering algorithms
for metagenomic sequence analysis. Briefings in bioinformatics: bbs035
 
92\\. Guindon S, Gascuel O (2003) A simple, fast, and accurate algorithm to
estimate large phylogenies by maximum likelihood. Syst Biol 52:696-704
 
93\\. Guindon S, Dufayard J-F, Lefort V, Anisimova M, Hordijk W, Gascuel O
(2010) New algorithms and methods to estimate maximum-likelihood phylogenies:
assessing the performance of PhyML 3.0. Syst Biol 59:307-321
 
94\\. Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, Buxton
S, Cooper A, Markowitz S, Duran C (2012) Geneious Basic: an integrated and
extendable desktop software platform for the organization and analysis of
sequence data. Bioinformatics 28:1647-1649
 
95\\. Darriba D, Taboada GL, Doallo R, Posada D (2012) jModelTest 2: more
models, new heuristics and parallel computing. Nat Methods 9:772-772
 
96\\. Dereeper A, Guignon V, Blanc G, Audic S, Buffet S, Chevenet F, Dufayard
J-F, Guindon S, Lefort V, Lescot M (2008) Phylogeny.fr: robust phylogenetic
analysis for the non-specialist. Nucleic Acids Res. 36:W465-W469
 
97\\. Dereeper A, Audic S, Claverie J-M, Blanc G (2010) BLAST-EXPLORER helps
you building datasets for phylogenetic analysis. BMC Evol. Biol. 10:8
 
98\\. Chevenet F, Brun C, Ba\\u00f1uls A-L, Jacq B, Christen R (2006) TreeDyn:
towards dynamic graphics and annotations for analyses of trees. BMC
bioinformatics 7:439";
    String awards_0_award_nid "635862";
    String awards_0_award_number "OCE-1459522";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1459522";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "Michael E. Sieracki";
    String awards_0_program_manager_nid "50446";
    String cdm_data_type "Other";
    String comment 
"OTU molecular data for coral Symbiodinium abundances 
     Belize Mesoamerican Barrier Reef System (MBRS), 2014-2015 
   PI's: K. Castillo, J. Baumann 
   version: 2018-04-16 
   Published in Baumann et al, Microb Ecol (2017). https://doi.org/10.1007/s00248-017-1096-6";
    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 "2018-04-30T17:16:00Z";
    String date_modified "2019-12-11T13:30:36Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.734674.1";
    String history 
"2020-11-29T19:25:52Z (local files)
2020-11-29T19:25:52Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_734674.html";
    String infoUrl "https://www.bco-dmo.org/dataset/734674";
    String institution "BCO-DMO";
    String instruments_0_acronym "Automated Sequencer";
    String instruments_0_dataset_instrument_nid "734685";
    String instruments_0_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_0_instrument_name "Automated DNA Sequencer";
    String instruments_0_instrument_nid "649";
    String instruments_0_supplied_name "Illumina Mi-seq";
    String instruments_1_acronym "Spectrophotometer";
    String instruments_1_dataset_instrument_description "Used to confirm presence of extracted DNA on agarose gel.";
    String instruments_1_dataset_instrument_nid "734687";
    String instruments_1_description "An instrument used to measure the relative absorption of electromagnetic radiation of different wavelengths in the near infra-red, visible and ultraviolet wavebands by samples.";
    String instruments_1_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L05/current/LAB20/";
    String instruments_1_instrument_name "Spectrophotometer";
    String instruments_1_instrument_nid "707";
    String instruments_1_supplied_name "Nanodrop 2000 Spectrophotometer (Thermo Scientific)";
    String instruments_2_acronym "Thermal Cycler";
    String instruments_2_dataset_instrument_nid "734684";
    String instruments_2_description 
"General term for a laboratory apparatus commonly used for performing polymerase chain reaction (PCR). The device has a thermal block with holes where tubes with the PCR reaction mixtures can be inserted. The cycler then raises and lowers the temperature of the block in discrete, pre-programmed steps.

(adapted from http://serc.carleton.edu/microbelife/research_methods/genomics/pcr.html)";
    String instruments_2_instrument_name "PCR Thermal Cycler";
    String instruments_2_instrument_nid "471582";
    String keywords "a4a, B1_I, B1_II, B_BG, bco, bco-dmo, biological, C1_I, C1_II, C1_III, chemical, code, d1a, data, dataset, diversity, dmo, erddap, iii, illumina, illumina_run, lat_location, latitude, management, oceanography, office, otu, otu_diversity, preliminary, run, sample, site, species, species_code, thermal, thermal_type, type";
    String license "https://www.bco-dmo.org/dataset/734674/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/734674";
    String param_mapping "{'734674': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/734674/parameters";
    String people_0_affiliation "University of North Carolina at Chapel Hill";
    String people_0_affiliation_acronym "UNC-Chapel Hill";
    String people_0_person_name "Karl D. Castillo";
    String people_0_person_nid "51711";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "University of North Carolina at Chapel Hill";
    String people_1_affiliation_acronym "UNC-Chapel Hill";
    String people_1_person_name "Justin Baumann";
    String people_1_person_nid "733684";
    String people_1_role "Student";
    String people_1_role_type "related";
    String people_2_affiliation "University of North Carolina at Chapel Hill";
    String people_2_affiliation_acronym "UNC-Chapel Hill";
    String people_2_person_name "Justin Baumann";
    String people_2_person_nid "733684";
    String people_2_role "Contact";
    String people_2_role_type "related";
    String people_3_affiliation "Woods Hole Oceanographic Institution";
    String people_3_affiliation_acronym "WHOI BCO-DMO";
    String people_3_person_name "Nancy Copley";
    String people_3_person_nid "50396";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "Thermal History and Coral Growth";
    String projects_0_acronym "Thermal History and Coral Growth";
    String projects_0_description 
"Description from NSF award abstract:
Rising global ocean surface temperatures have reduced coral growth rates, thereby negatively impacting the health of coral reef ecosystems worldwide. Recent studies on tropical reef building corals reveal that corals' growth in response to ocean warming may be influenced by their previous seawater temperature exposure - their thermal history. Although these recent findings highlight significant variability in coral growth in response to climate change, uncertainty remains as to the spatial scale at which corals' thermal history influences how they have responded to ocean warming and how they will likely respond to predicted future increases in ocean temperature. This study investigates the influence of thermal history on coral growth in response to recent and predicted seawater temperatures increases across four ecologically relevant spatial scales ranging from reef ecosystems, to reef communities, to reef populations, to an individual coral colony. By understanding how corals have responded in the past across a range of ecological scales, the Principal Investigator will be able to improve the ability to predict their susceptibility and resilience, which could then be applied to coral reef conservation in the face of climate change. This research project will broaden the participation of undergraduates from underrepresented groups and educate public radio listeners using minority voices and narratives. The scientist will leverage current and new partnerships to recruit and train minority undergraduates, thus allowing them to engage high school students near field sites in Florida, Belize, and Panama. Through peer advising, undergraduates will document this research on a digital news site for dissemination to the public. The voice of the undergraduates and scientist will ground the production of a public radio feature exploring the topic of acclimatization and resilience - a capacity for stress tolerance within coral reef ecosystems. This project will provide a postdoctoral researcher and several graduate students with opportunities for field and laboratory research training, teaching and mentoring, and professional development. The results will allow policy makers from Florida, the Mesoamerican Barrier Reef System countries, and several Central American countries to benefit from Caribbean-scale inferences that incorporate corals' physiological abilities, thereby improving coral reef management for the region.
Coral reefs are at significant risk due to a variety of local and global scale anthropogenic stressors. Although various stressors contribute to the observed decline in coral reef health, recent studies highlight rising seawater temperatures due to increasing atmospheric carbon dioxide concentration as one of the most significant stressors influencing coral growth rates. However, there is increasing recognition of problems of scale since a coral's growth response to an environmental stressor may be conditional on the scale of description. This research will investigate the following research questions: (1) How has seawater temperature on reef ecosystems (Florida Keys Reef Tract, USA; Belize Barrier Reef System, Belize; and Bocas Del Toro Reef Complex, Panama), reef communities (inshore and offshore reefs), reef populations (individual reefs), and near reef colonies (individual colonies), varied in the past? (2) How has seawater temperature influenced rates of coral growth and how does the seawater temperature-coral growth relationship vary across these four ecological spatial scales? (3) Does the seawater temperature-coral growth relationship forecast rates of coral growth under predicted end-of-century ocean warming at the four ecological spatial scales? Long term sea surface temperature records and small-scale high-resolution in situ seawater temperature measurements will be compared with growth chronologies for the reef building corals Siderastrea siderea and Orbicella faveolata, two keystone species ubiquitously distributed throughout the Caribbean Sea. Nutrients and irradiance will be quantified via satellite-derived observations, in situ measurements, and established colorimetric protocols. Field and laboratory experiments will be combined to examine seawater temperature-coral growth relationships under recent and predicted end-of-century ocean warming at four ecologically relevant spatial scales. The findings of this study will help us bridge the temperature-coral growth response gap across ecologically relevant spatial scales and thus improve our understanding of how corals have responded to recent warming. This will lead to more meaningful predictions about future coral growth response to climate change.";
    String projects_0_end_date "2018-02";
    String projects_0_geolocation "Western Caribbean";
    String projects_0_name "Investigating the influence of thermal history on coral growth response to recent and predicted end-of-century ocean warming across a cascade of ecological scales";
    String projects_0_project_nid "635863";
    String projects_0_project_website "http://www.unc.edu/~kdcastil/research.html";
    String projects_0_start_date "2015-03";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "This dataset contains relative abundance (counts?) of operational taxonomic units (OTUs) from Sumbiodinium samples collected from three coral species (S. siderea, S. radians, and P. strigosa) at nine sites across four latitudes along the Belize MBRS in 2014 and 2015. These sites were previously characterized into three thermally distinct regimes (lowTP, modTP, highTP) and exhibited variations in coral species diversity and richness.";
    String title "OTU molecular abundances for coral Symbiodinium, Belize Mesoamerican Barrier Reef System (MBRS), 2014-2015";
    String version "1";
    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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