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Dataset Title:  Annual health surveys of the sea fan Gorgonia ventalina in La Parguera, Puerto
Rico from 2006-2010
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_3720)
Range: longitude = -67.0489 to -66.9848°E, latitude = 17.8897 to 17.9349°N, depth = 3.0 to 25.0m
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Subset | Data Access Form | Files
 
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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 {
  site {
    String bcodmo_name "site";
    String description "Name of the reef where sampling occurred.";
    String long_name "Site";
    String units "text";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 17.8897, 17.9349;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude of the site. North = 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 -67.0489, -66.9848;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude of the site. West = negative.";
    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";
  }
  year {
    Int16 _FillValue 32767;
    Int16 actual_range 2006, 2010;
    String bcodmo_name "year";
    String description "4-digit year of the survey. in YYYY format";
    String long_name "Year";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/YEARXXXX/";
    String units "unitless";
  }
  month {
    String bcodmo_name "month_local";
    String description "2-digit month of the survey.";
    String long_name "Month";
    String units "mm (01 to 12)";
  }
  depth {
    String _CoordinateAxisType "Height";
    String _CoordinateZisPositive "down";
    Float64 _FillValue NaN;
    Float64 actual_range 3.0, 25.0;
    String axis "Z";
    String bcodmo_name "depth";
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "Depth of the survey.";
    String ioos_category "Location";
    String long_name "Depth";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
    String positive "down";
    String standard_name "depth";
    String units "m";
  }
  healthy_avg {
    Float32 _FillValue NaN;
    Float32 actual_range 18.0, 74.66;
    String bcodmo_name "unknown";
    String description "Average percentage of sea fans per depth which were healthy.";
    String long_name "Healthy Avg";
    String units "%";
  }
  bleached_avg {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 14.03;
    String bcodmo_name "unknown";
    String description "Average percentage of sea fans per depth which were bleached.";
    String long_name "Bleached Avg";
    String units "%";
  }
  lg_purple_spts_avg {
    Float32 _FillValue NaN;
    Float32 actual_range 3.67, 49.58;
    String bcodmo_name "unknown";
    String description "Average percentage of sea fans per depth which had Large Purple Spots.";
    String long_name "Lg Purple Spts Avg";
    String units "%";
  }
  MFPS_avg {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 47.75;
    String bcodmo_name "unknown";
    String description "Average percentage of sea fans per depth which had multi-focal purple spots (MFPS).";
    String long_name "MFPS Avg";
    String units "%";
  }
  predation_avg {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 13.33;
    String bcodmo_name "unknown";
    String description "Average percentage of sea fans per depth which showed signs of predation.";
    String long_name "Predation Avg";
    String units "%";
  }
  other_avg {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 18.94;
    String bcodmo_name "unknown";
    String description "Average percentage of sea fans per depth which had other abnormalities. See Acquisition Description.";
    String long_name "Other Avg";
    String units "%";
  }
  healthy_avg_sd {
    Float32 _FillValue NaN;
    Float32 actual_range 5.44, 31.21;
    String bcodmo_name "unknown";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Standard deviation of healthy_avg.";
    String long_name "Healthy Avg Sd";
    String units "%";
  }
  bleached_avg_sd {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 10.97;
    String bcodmo_name "unknown";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Standard deviation of bleached_avg.";
    String long_name "Bleached Avg Sd";
    String units "%";
  }
  lg_purple_spts_avg_sd {
    Float32 _FillValue NaN;
    Float32 actual_range 2.22, 38.01;
    String bcodmo_name "unknown";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Standard deviation of lg_purple_spts_avg.";
    String long_name "Lg Purple Spts Avg Sd";
    String units "%";
  }
  MFPS_avg_sd {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 37.5;
    String bcodmo_name "unknown";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Standard deviation of MFPS_avg.";
    String long_name "MFPS Avg Sd";
    String units "%";
  }
  predation_avg_sd {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 16.67;
    String bcodmo_name "unknown";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Standard deviation of predation_avg.";
    String long_name "Predation Avg Sd";
    String units "%";
  }
  other_avg_sd {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 21.43;
    String bcodmo_name "unknown";
    Float64 colorBarMaximum 50.0;
    Float64 colorBarMinimum 0.0;
    String description "Standard deviation of other_avg.";
    String long_name "Other Avg Sd";
    String units "%";
  }
  healthy {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 100.0;
    String bcodmo_name "unknown";
    String description "Percent healthy per transect.";
    String long_name "Healthy";
    String units "%";
  }
  bleached {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 25.0;
    String bcodmo_name "unknown";
    String description "Percent bleached per transect.";
    String long_name "Bleached";
    String units "%";
  }
  lg_purple_spts {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 100.0;
    String bcodmo_name "unknown";
    String description "Percent large purple spots (i.e. Aspergillosis-like) per transect.";
    String long_name "Lg Purple Spts";
    String units "%";
  }
  MFPS {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 75.0;
    String bcodmo_name "unknown";
    String description "Percent multi-focal purple spots (MFPS) per transect.";
    String long_name "MFPS";
    String units "%";
  }
  predation {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 33.33;
    String bcodmo_name "unknown";
    String description "Percent sea fans with signs of predation per transect.";
    String long_name "Predation";
    String units "%";
  }
  other {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 42.86;
    String bcodmo_name "unknown";
    String description "Percent sea fans with other abnormalities per transect. See Acquisition Description.";
    String long_name "Other";
    String units "%";
  }
  MFPS_log10 {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 1.88;
    String bcodmo_name "unknown";
    String description "log 10 transformed percent MFPS per transect.";
    String long_name "MFPS Log10";
    String units "%";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Sampling and Analytical Methodology:  
 Sixteen 20 square-meter band transects were randomly laid (4 transects per
depth) at two reefs in La Parguera, PR, Media Luna (3 to 18 m depths) and Buoy
(18 to 25 m depths). Surveys occurred annually in September or October from
2006 to 2010 at Media Luna and 2007 to 2010 at Buoy. Along each transect all
apparently healthy G. ventalina colonies and colonies with lesions were
enumerated. Any purple or abnormally colored area on the sea fans was checked
carefully. Lesions were also recorded and described as follows: large
irregular purple spots (aspergillosis-like; full description Work and Aeby
2006), MFPS, predation, bleaching, and other abnormalities. Other
abnormalities included growth anomalies, cyanobacteria, red band disease etc.
Signs of predation by snails (flamingo tongue) and by fireworms were noted,.
Prevalence (the total number of each abnormality divided by the total number
of individuals) of each type of abnormality was calculated per transect,
depth, and year.";
    String awards_0_award_nid "55012";
    String awards_0_award_number "OCE-0849776";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0849776";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "David L. Garrison";
    String awards_0_program_manager_nid "50534";
    String cdm_data_type "Other";
    String comment 
"Annual health surveys of Gorgonia ventalina in La Parguera, PR 
 PI: Drew Harvell (Cornell University) 
 Co-PI: Laura Mydlarz (University of Texas Arlington) 
 Version: 14 Sept 2012";
    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 "2012-09-14T14:59:42Z";
    String date_modified "2019-03-01T21:37:19Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.3720.1";
    Float64 Easternmost_Easting -66.9848;
    Float64 geospatial_lat_max 17.9349;
    Float64 geospatial_lat_min 17.8897;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -66.9848;
    Float64 geospatial_lon_min -67.0489;
    String geospatial_lon_units "degrees_east";
    Float64 geospatial_vertical_max 25.0;
    Float64 geospatial_vertical_min 3.0;
    String geospatial_vertical_positive "down";
    String geospatial_vertical_units "m";
    String history 
"2024-03-29T07:39:55Z (local files)
2024-03-29T07:39:55Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_3720.das";
    String infoUrl "https://www.bco-dmo.org/dataset/3720";
    String institution "BCO-DMO";
    String keywords "average, bco, bco-dmo, biological, bleached, bleached_avg, bleached_avg_sd, chemical, data, dataset, depth, dmo, erddap, healthy, healthy_avg, healthy_avg_sd, latitude, lg_purple_spts, lg_purple_spts_avg, lg_purple_spts_avg_sd, log10, longitude, management, mfps, MFPS_avg, MFPS_avg_sd, MFPS_log10, month, oceanography, office, other_avg, other_avg_sd, predation, predation_avg, predation_avg_sd, preliminary, purple, site, spts, year";
    String license "https://www.bco-dmo.org/dataset/3720/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/3720";
    Float64 Northernmost_Northing 17.9349;
    String param_mapping "{'3720': {'lat': 'master - latitude', 'depth': 'flag - depth', 'lon': 'master - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/3720/parameters";
    String people_0_affiliation "Cornell University";
    String people_0_affiliation_acronym "Cornell";
    String people_0_person_name "Drew Harvell";
    String people_0_person_nid "51556";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "University of Texas at Arlington";
    String people_1_affiliation_acronym "UT Arlington";
    String people_1_person_name "Dr Laura Mydlarz";
    String people_1_person_nid "51558";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Cornell University";
    String people_2_affiliation_acronym "Cornell";
    String people_2_person_name "Dr Colleen Burge";
    String people_2_person_nid "51557";
    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 "Shannon Rauch";
    String people_3_person_nid "51498";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "Climate_CoralDisease";
    String projects_0_acronym "Climate_CoralDisease";
    String projects_0_description 
"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
Coral reef ecosystems are highly endangered by recent increases in temperature and by projected increases in ocean acidification. Although temperature has been identified as a driver of some coral disease outbreaks, nothing is known about direct effects of acidification on host immunity and pathogen virulence, or the potential for synergism with temperature. Natural coral populations often suffer from simultaneous infection by multiple pathogens that can also influence host immune responses, but co-infection dynamics have not been investigated in invertebrate systems lacking classical adaptive immunity. Changing climate will very likely influence the outcome of single and co-infection.
This project will investigate the influence of environmental stress on co-infection dynamics of the sea fan coral, Gorgonia ventalina, with a fungal pathogen, Aspergillus sydowii and a protist parasite, SPX. The goal is to identify the mechanisms through which multiple infections, temperature and acidification modify host resistance, leading to changes in within- and among-colony rates of disease spread.
The objectives of this project are to:
(1) Identify incidence and co-infection frequency of Aspergillus sydowii and SPX. Detailed field surveys of the two diseases will test the hypothesis that co-infection is significant, provide valuable information about drivers of aspergillosis, and will help to characterize an emerging new sea fan disease.
(2) Investigate how co-infection influences sea fan susceptibility, resistance, and within host disease dynamics.  Through manipulative lab inoculation experiments we will test the hypothesis that single infections increase susceptibility to a second pathogen.
(3) Examine the effects of temperature increase and ocean acidification on pathogen virulence, on underlying host resistance, and on the dynamics of single and co-infections.
The hypotheses that acidification will increase pathogen virulence and host susceptibility will be tested in a temperature and pH controlled experimental system. This system will also allow the potential synergistic effects of temperature and acidification on host immunity and co-infection dynamics to be explored. The primary intellectual merit of the proposed work will be a greater understanding of how changing climate mediates co-infection and immunity in a non-model invertebrate. While fungal pathogens are primarily opportunistic, labyrinthulid protozoans are recognized as primary pathogens in shellfish. Even in shellfish, little is known about co-infections involving labyrinthulids, and these protists are entirely unstudied in corals.
Publications associated with this project:
Burge CA, Douglas N, Conti-Jerpe I, Weil E, Roberts S, Friedman CS & CD Harvell. (May 2012) Friend or foe: the association of Labyrinthulomycetes with the Caribbean sea fan, Gorgonia ventalina. Dis Aquat Org. 101:1-12. doi: 10.3354/dao02487
Burge CA, Mouchka, ME, Harvell, CD & S Roberts. (In review) Immune response of the Caribbean sea fan, Gorgonia ventalina exposed to an Aplanochytrium parasite as revealed by transcriptome sequencing.";
    String projects_0_end_date "2012-07";
    String projects_0_geolocation "Florida Keys & Puerto Rico";
    String projects_0_name "Influence of Temperature and Acidification on the Dynamics of Coral Co-Infection and Resistance";
    String projects_0_project_nid "2232";
    String projects_0_start_date "2009-08";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 17.8897;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String subsetVariables "month";
    String summary "Health surveys of the sea fan Gorgonia ventalina were carried out from 2006 to 2010 at two coral reefs in La Parguera, Puerto Rico. The proportions of healthy and abnormal G. ventalina colonies along survey transects are reported.";
    String title "Annual health surveys of the sea fan Gorgonia ventalina in La Parguera, Puerto Rico from 2006-2010";
    String version "1";
    Float64 Westernmost_Easting -67.0489;
    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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