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Dataset Title:  Data describing every diseased coral record from surveys in the Caribbean
during 2012 (Contagious coral diseases project)
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_658275)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | 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 {
  temp_level {
    String description "Temperature stress level.";
    String ioos_category "Unknown";
    String long_name "Temp Level";
    String units "unitless";
  location {
    String description "Location where coral was located.";
    String ioos_category "Location";
    String long_name "Location";
    String units "unitless";
  reef {
    String description "Reef where coral was located.";
    String ioos_category "Unknown";
    String long_name "Reef";
    String units "unitless";
  site_number {
    Byte _FillValue 127;
    Byte actual_range 1, 25;
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String description "Site ID number.";
    String ioos_category "Statistics";
    String long_name "Site Number";
    String units "unitless";
  depth_max {
    Float64 colorBarMaximum 8000.0;
    Float64 colorBarMinimum -8000.0;
    String colorBarPalette "TopographyDepth";
    String description "Maximum depth of site.";
    String ioos_category "Location";
    String long_name "Depth";
    String standard_name "depth";
    String units "meters";
  meter {
    String description "Meter along 10 meter transect.";
    String ioos_category "Unknown";
    String long_name "Meter";
    String units "unitless";
  genus {
    String description "Genus of coral sampled.";
    String ioos_category "Taxonomy";
    String long_name "Genus";
    String units "unitless";
  species {
    String description "Species of coral sampled.";
    String ioos_category "Taxonomy";
    String long_name "Species";
    String units "unitless";
  UIN {
    Int16 _FillValue 32767;
    Int16 actual_range 1, 6599;
    String description "Individual coral ID number.";
    String ioos_category "Unknown";
    String long_name "UIN";
    String units "unitless";
  sign_1 {
    String description "First disease sign identified.";
    String ioos_category "Unknown";
    String long_name "Sign 1";
    String units "unitless";
  sign_2 {
    String description "Second disease sign identified.";
    String ioos_category "Unknown";
    String long_name "Sign 2";
    String units "unitless";
  sign_3 {
    String description "Third disease sign identified.";
    String ioos_category "Unknown";
    String long_name "Sign 3";
    String units "unitless";
  sign_4 {
    String description "Fourth disease sign identified.";
    String ioos_category "Unknown";
    String long_name "Sign 4";
    String units "unitless";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"[Adapted from: Randall et al. 2014 Ecology 95(7) 1981-1994]\\u00a0\\u00a0 \\u00a0
To assess the prevalence of coral diseases at each location, a survey area
(1-10 km^2 depending on the region\\u2019s geographic features) of hard-bottom
habitat was visually defined using [Google
Earth](\\\\\"http://earth.google.com/\\\\\"). The survey area was divided into 100
by 100 meter cells (using Google Earth Path 1.4.4). Within each location,
twenty-five 100 by 100 meter cells were randomly selected as sites. These
sites were defined as the primary sampling units. A single 10 by 10
meterquadrat was haphazardly placed within each site, for field-data
collection. To maintain consistency across locations and to minimize potential
effects of coral-assemblage differences, three criteria had to be met for a
site to be surveyed: (1) the depth averaged between 5 and 10 meters, (2) the
substrate was hard bottom, and (3) corals were present. If any one of these
criteria was not met at a given site, it was rejected and the next randomly
generated site was selected. In total, twenty-five, 10 by 10 meter quadrats
were sampled at each location, for a total of 50 quadrats across two frequent-
anomaly locations and 50 quadrats across two reference locations, for a total
survey area of 10,000 m^2.\\u00a0
All four locations were surveyed between 2 July and 1 September 2012. At each
site, divers surveyed each 100 m^2\\u00a0quadrat by systematically laying ten
contiguous 1 x 10 m belt transects onto the reef substrate. Each coral colony
with a disease sign was identified in situ and the species and disease signs
were recorded. Four disease signs were identified: (1) white sign was defined
as a bright, white band or patch of recent mortality adjacent to healthy-
appearing tissue (i.e., the tissue bordered a well-defined edge of exposed
skeleton not yet colonized by algae or other biofouling organisms) (sensu
Bythell et al. 2004) , (2) dark spot was defined as tissue with purple, brown
or black lesions, forming spots of irregular shapes (sensu Goreau et al.
1998), (3) black band was defined as a black band over the coral tissue
exposing white skeleton with different stages of biofouling (sensu Richardson
2004), and (4) yellow sign was defined as a yellow discoloration of tissue
forming a band or blotches (sensu Santavy et al. 1999). White signs and black
bands were associated with recent tissue loss; yellow signs and dark spots
were usually, but not always, associated with recent tissue loss. Notably,
very few yellow bands were observed that followed the classical description
(Reeves 1994). Instead, most coral colonies presented a patchy, non-uniform
yellowing of the tissue; therefore the condition was termed \\u2018yellow
sign.\\u2019 Additionally, any area of recently exposed white skeleton, which
was not clearly caused by predation or a competitive interaction, was recorded
as a white sign, including white plagues, white bands and white pox. The
white-sign diseases were not differentiated because of similar- or identical-
appearing signs, unknown etiologies for several diseases, and the possibility
that the diseases were caused by the same pathogens (Bythell et al. 2004,
Ainsworth et al. 2007). Coral colonies were occasionally recorded with two or
more signs of disease, when those signs appeared to be spatially independent.
Disease mapping:
Four 100 m^2 quadrats per location were mapped in their entirety, for a total
of eight 100 m^2 quadrats per temperature-stress level. Approximately 50
digital images were captured from each video-transect file using Free Video to
JPG Converter v. 5.0.58 build 324. The digital images were stitched together
using Adobe Photoshop\\u00a0CS5 v. 12.0, and ten 1\\u2013m by 10\\u2013m image
mosaics were created for each 10 m by 10 m site. Each photo-mosaic was printed
and, with the aid of the digital images and videos, the following data were
measured and recorded for every coral colony within each site: (1) species,
(2) spatial coordinates, (3) maximum diameter, (4) perpendicular diameter, (5)
an estimate of percent partial mortality (0, <5, 25, 50, 75, or >95 %), and
(6) \\u2018health\\u2019 status. Corals were identified as either healthy, or as
having white signs, dark spots, black bands, yellow signs, or unknown signs of
disease (as described above). Bleached or pale colonies also were recorded,
and when multiple disease signs were present on an individual colony, both
signs were recorded. Data from each site that was mapped are found in
individual excel files in the folder called Disease mapping site data
    String awards_0_award_nid "562562";
    String awards_0_award_number "OCE-1219804";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1219804";
    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 "Dr Michael E. Sieracki";
    String awards_0_program_manager_nid "50446";
    String cdm_data_type "Other";
    String comment 
"All Raw Mapping Data 
  R. van Woesik, PI 
  Version 2 September 2016";
    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 2.2d  13 Jun 2019";
    String date_created "2016-09-06T20:16:14Z";
    String date_modified "2019-05-13T20:42:07Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/bco-dmo.658275.1";
    String history 
"2019-06-25T01:40:51Z (local files)
2019-06-25T01:40:51Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_658275.das";
    String infoUrl "https://www.bco-dmo.org/dataset/658275";
    String institution "BCO-DMO";
    String keywords "bco, bco-dmo, biological, chemical, data, dataset, depth, depth_max, dmo, erddap, genus, level, management, meter, number, oceanography, office, preliminary, reef, sign, sign_1, sign_2, sign_3, sign_4, site, site_number, species, statistics, taxonomy, temp_level, temperature, uin";
    String license 
"The data may be used and redistributed for free but is not intended
for legal use, since it may contain inaccuracies. Neither the data
Contributor, ERD, NOAA, nor the United States Government, nor any
of their employees or contractors, makes any warranty, express or
implied, including warranties of merchantability and fitness for a
particular purpose, or assumes any legal liability for the accuracy,
completeness, or usefulness, of this information.";
    String metadata_source "https://www.bco-dmo.org/api/dataset/658275";
    String param_mapping "{'658275': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/658275/parameters";
    String people_0_affiliation "Florida Institute of Technology";
    String people_0_affiliation_acronym "FIT";
    String people_0_person_name "Dr Robert van Woesik";
    String people_0_person_nid "562565";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Florida Institute of Technology";
    String people_1_affiliation_acronym "FIT";
    String people_1_person_name "Dr Carly J. Randall";
    String people_1_person_nid "657875";
    String people_1_role "Contact";
    String people_1_role_type "related";
    String people_2_affiliation "Florida Institute of Technology";
    String people_2_affiliation_acronym "FIT";
    String people_2_person_name "Dr Robert van Woesik";
    String people_2_person_nid "562565";
    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 "Hannah Ake";
    String people_3_person_nid "650173";
    String people_3_role "BCO-DMO Data Manager";
    String people_3_role_type "related";
    String project "Are coral diseases contagious?";
    String projects_0_acronym "Contagious coral diseases?";
    String projects_0_description 
"Diseases are one of the greatest threats to corals in the Caribbean. Yet, very little is known about marine diseases in general and coral diseases in particular.� Although some pathogens have been acknowledged, identifying coral pathogens has proven difficult and evasive. Presently, coral diseases are assumed to be both infectious and contagious, suggesting that infection is caused by pathogens being passed from colony to colony through a vector. However, few studies have tested this assumption. Spatial epidemiology, or disease mapping, can provide insight into whether diseases cluster and follow a contagious-disease model. In this study we will take a two tiered approach. First, we will use a hierarchical sampling design to test whether coral diseases follow a contagious-disease model over two spatial scales in the Caribbean. We will also undertake this study in locations with and without a recent history of frequent thermal stress to test the alternate hypothesis that coral diseases are not infectious and contagious but are instead the result of compromised coral hosts that have undergone thermal stress. Second, we will undertake transmission experiments to examine whether coral diseases are indeed transmissible.
The research will take place in the Caribbean, at four locations: (1) Mahahual, Mexico (latitude� 18\"42’N, longitude� 87\"42’W) and (2) Tuxpan, Mexico (latitude� 21\"01’N, longitude� 97\"11'W), (3) Bocas del Toro, Panama (latitude� 9\"12’N, longitude� 82\"09’W) and (4) St. John, United States Virgin Islands (USVI) (latitude� 18\"18’N, longitude� 64\"45’W).
Intellectual merit
There is a certain urgency to identify coral diseases, predict their prevalence, and determine whether they are infectious and contagious or non-communicable. By understanding the etiology of coral diseases, we can determine whether human intervention will help reduce their prevalence. Without understanding these processes, we will merely continue to measure disease, continue to look for pathogens that may not exist, and watch coral populations continue to deteriorate. Although microbes play a role in disease infection, many coral diseases might not be transmissible. Therefore, we may need to incorporate environmental threshold parameters, which may be more likely the underlying mechanisms driving coral-disease dynamics. The results will have important implications for modeling diseases and predicting contemporary and future coral disease outbreaks. �
Broader Impact
The underlying assumption of most disease models is contagion, which is the transmission of pathogens from infected to susceptible hosts. This study will examine this basic assumption. If it turns out that coral diseases are a consequence of a two-step process, and the corals that are tolerant to temperature stress are also resistant to diseases, then making predictions based on temperature trends will be transformational, especially in rapidly warming, yet heterogeneous, oceans. The study will train students in the field of spatial epidemiology of coral diseases.";
    String projects_0_end_date "2016-05";
    String projects_0_geolocation "Caribbean";
    String projects_0_name "Are coral diseases contagious?";
    String projects_0_project_nid "562563";
    String projects_0_start_date "2012-06";
    String publisher_name "Hannah Ake";
    String publisher_role "BCO-DMO Data Manager(s)";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v29";
    String summary "Data describing every diseased coral record from surveys in the Caribbean during 2012 (Contagious coral diseases project)";
    String title "Data describing every diseased coral record from surveys in the Caribbean during 2012 (Contagious coral diseases project)";
    String version "1";
    String xml_source "osprey2erddap.update_xml() v1.5-beta";


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
For example,
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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