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Dataset Title:  Bleaching, disease, and mortality on A. cervicornis individuals in Elliot Key,
Florida during 2014 and 2015 (EMUCoReS project)
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_709880)
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 {
  month_sampled {
    String bcodmo_name "month";
    String description "Month sampled: September 14; March 15; October 15";
    String long_name "Month Sampled";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/MNTHXXXX/";
    String units "unitless";
  }
  genet {
    String bcodmo_name "sample";
    String description "Asexual clone identifier; A through X";
    String long_name "Genet";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  condition {
    String bcodmo_name "sample_descrip";
    String description "Apparent health of coral; healthy or bleached";
    String long_name "Condition";
    String units "unitless";
  }
  sample_number {
    Byte _FillValue 127;
    Byte actual_range 1, 80;
    String bcodmo_name "sample";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String description "DNA tube number; 1-80";
    String long_name "Sample Number";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  A3_S_fitti {
    Float64 _FillValue NaN;
    Float64 actual_range 99.30868614, 100.0;
    String bcodmo_name "cover_pcent";
    String description "Percent of reads from S. fitti";
    String long_name "A3 S Fitti";
    String units "percent";
  }
  B2_S_psygmophilium {
    Float64 _FillValue NaN;
    Float64 actual_range 0.0, 0.691313859;
    String bcodmo_name "cover_pcent";
    String description "Percent of reads from S. psygmophilium";
    String long_name "B2 S Psygmophilium";
    String units "percent";
  }
  D1a_S_trenchii {
    Float64 _FillValue NaN;
    Float64 actual_range 0.0, 0.087270914;
    String bcodmo_name "cover_pcent";
    String description "Percent of reads from S. trenchii";
    String long_name "D1a S Trenchii";
    String units "percent";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"At indicated dates, individual corals (ramets) were visually assessed for
bleaching, disease, and mortality. Corals were determined to be diseased
by\\u00a0White\\u00a0band (white) when there was a band of white tissue
traversing the branches progressing from the bottom to the top of the colony.
 
Corals were identified as diseased with Rapid Tissue Loss (RTL) when gently
fanning by hand resulted in the dissociation of tissue from the coral's
skeleton. Mortality was noted as\\u00a0 no (no mortality observed), partial
(the minority of the colony was no longer covered with live tissue), mostly
dead (the majority of the colony was no longer covered with live tissue), or
dead (no observable tissue)
 
Bleaching was noted for those corals paling or entirely lacking in
pigmentation.\\u00a0 A reference color card (Seibeck et al., 2006) was used as
a pigmentation reference. The normal level of pigmentation was C3 or darker
(not bleached). Colors lighter than C3 were considered bleached.";
    String awards_0_award_nid "637742";
    String awards_0_award_number "OCE-1503483";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1503483";
    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 awards_1_award_nid "637749";
    String awards_1_award_number "OCE-1503430";
    String awards_1_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1503430";
    String awards_1_funder_name "NSF Division of Ocean Sciences";
    String awards_1_funding_acronym "NSF OCE";
    String awards_1_funding_source_nid "355";
    String awards_1_program_manager "David L. Garrison";
    String awards_1_program_manager_nid "50534";
    String cdm_data_type "Other";
    String comment 
"Visual Monitoring of A. cervicornis 
  M. Rodriguez-Lanetty, D. Lirman, and L. Richardson, PIs 
  Version 26 July 2017";
    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 "2017-07-26T17:39:35Z";
    String date_modified "2019-03-26T16:30:28Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.709880.1";
    String history 
"2024-03-29T14:40:36Z (local files)
2024-03-29T14:40:36Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_709880.das";
    String infoUrl "https://www.bco-dmo.org/dataset/709880";
    String institution "BCO-DMO";
    String keywords "A3_S_fitti, B2_S_psygmophilium, bco, bco-dmo, biological, chemical, condition, d1a, D1a_S_trenchii, data, dataset, dmo, erddap, fitti, genet, management, month, month_sampled, number, oceanography, office, preliminary, psygmophilium, sample, sample_number, sampled, trenchii";
    String license "https://www.bco-dmo.org/dataset/709880/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/709880";
    String param_mapping "{'709880': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/709880/parameters";
    String people_0_affiliation "Florida International University";
    String people_0_affiliation_acronym "FIU";
    String people_0_person_name "Dr Mauricio Rodriguez-Lanetty";
    String people_0_person_nid "516423";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "University of Miami Rosenstiel School of Marine and Atmospheric Science";
    String people_1_affiliation_acronym "UM-RSMAS";
    String people_1_person_name "Dr Diego Lirman";
    String people_1_person_nid "637751";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Florida International University";
    String people_2_affiliation_acronym "FIU";
    String people_2_person_name "Dr Laurie Richardson";
    String people_2_person_nid "637746";
    String people_2_role "Co-Principal Investigator";
    String people_2_role_type "originator";
    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 "EMUCoReS";
    String projects_0_acronym "EMUCoReS";
    String projects_0_description 
"Description from NSF award abstract:
Coral reefs are among the most biologically diverse and economically important ecosystems on the planet. However, coral reefs are in a state of global decline due to effects of climate change, disease outbreaks, and other stressors. Mass coral bleaching events, a breakdown of the association between corals and their symbiotic algae, are predicted to become more frequent and severe in response to climate change, and it is expected that subsequent disease outbreaks will become more common. Beginning in August 2014, nearly all coral species in the Florida Reef Tract have undergone severe bleaching, in some cases followed by coral mortality and/or disease outbreaks. This widespread, thermal-induced event presents a unique time-sensitive opportunity to explore the mechanisms underpinning the patterns of coral bleaching, disease, and recovery. The mechanisms linking patterns of bleaching, disease, mortality, and recovery remain relatively unexplored. This research will explore the influences that genotype combinations of host polyps, their algal symbionts, and associated bacterial have on bleaching/disease likelihood and recovery/mortality predisposition of coral specimens. By providing a mechanistic understanding of the processes that underlie coral bleaching and subsequent recovery this research will contribute to measures in support of preserving this invaluable natural resource. The study will further involve students from diverse backgrounds as well as provide project internship opportunities for high school students. A web based radio blog will disseminate project results and other relevant developments to the broad audiences
Mass coral bleaching events are predicted to become more frequent and severe in response to climate change, and it is expected that subsequent disease outbreaks will become more common. The lack of a baseline genetic datasets for coral holobionts prior to previous natural bleaching events has hindered our understanding of recovery patterns and physiological tolerance to thermal stress, also known as coral bleaching. An extensive pre-thermal stress baseline of genotypic identity of coral hosts, Symbiodinium, and associated bacterial community offers a unique opportunity to analyze changes associated with current bleaching event along the Florida coastline and to document holobiont compositions most and least resistant/resilient to bleaching and disease. Repeated sampling of the same coral colonies will allow the investigators to compare holobiont composition before, during and after bleaching of both healthy and diseased individuals. This bleaching event is a time-sensitive natural experiment to examine the dynamics of microbes (Symbiodinium and bacteria) associated with affected colonies, including their potential influence on disease susceptibility and resistance of reef corals. This effort would constitute the first time that high throughput sequencing of coral, Symbiodinium endosymbiont, and the coral-associated bacterial community genotypes are together used to explain patterns of disease, recovery, and mortality following natural bleaching. This study will likely change the way investigators study emerging wasting diseases of keystone species that define marine benthic communities.";
    String projects_0_end_date "2016-11";
    String projects_0_geolocation "Florida Reef Tract (24.868358, -80.643495)";
    String projects_0_name "RAPID: A hyper-thermal anomaly in the Florida Reef Tract: An opportunity to explore the mechanisms underpinning patterns of coral bleaching and disease";
    String projects_0_project_nid "637743";
    String projects_0_start_date "2014-12";
    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 "Bleaching, disease, and mortality on A. cervicornis individuals in Elliot Key, Florida during 2014 and 2015 (EMUCoReS project)";
    String title "Bleaching, disease, and mortality on A. cervicornis individuals in Elliot Key, Florida during 2014 and 2015 (EMUCoReS project)";
    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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