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Dataset Title:  Octocoral sampling effort (density by pooled taxa and site) from surveys
conducted in St. John, US Virgin Islands from 2014 to 2017
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_789188)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Files | Make a graph
 
Variable ?   Optional
Constraint #1 ?
Optional
Constraint #2 ?
   Minimum ?
 
   Maximum ?
 
 Year (unitless) ?          2014    2017
 Taxon (unitless) ?          "All Taxa"    "Gorgonia"
 Site (unitless) ?          "Cabritte Horn"    "West Little Lameshur"
 Number_of_quadrats (unitless) ?          1    40
 Density (number per quadrat) ?          0    8
 
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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Year {
    Int16 _FillValue 32767;
    Int16 actual_range 2014, 2017;
    String bcodmo_name "year";
    String description "Year (2014-2017)";
    String long_name "Year";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/YEARXXXX/";
    String units "unitless";
  }
  Taxon {
    String bcodmo_name "taxon";
    String description "Taxon (All Taxa, Eunicea, Gorgonia, Antillogorgia)";
    String long_name "Taxon";
    String units "unitless";
  }
  Site {
    String bcodmo_name "site";
    String description "Site (Cabritte Horn, Europa Bay, West Tektite, East Tektite, White)";
    String long_name "Site";
    String units "unitless";
  }
  Number_of_quadrats {
    Byte _FillValue 127;
    Byte actual_range 1, 40;
    String bcodmo_name "site_descrip";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String description "Quadrat number (1-40)";
    String long_name "Number Of Quadrats";
    String units "unitless";
  }
  Density {
    Byte _FillValue 127;
    Byte actual_range 0, 8;
    String bcodmo_name "density";
    String description "Density (colonies per 0.5 x 0.5 m quadrat)";
    String long_name "Density";
    String units "number per quadrat";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"The following methodology applies to this dataset in addition to other
datasets published in Edmunds and Lasker (2019).
 
Sampling and analytical procedures:
 
Surveys were completed at six sites on shallow (7\\u20139-m depth) fringing
reefs on the south shore of St. John, between Cabritte Horn and White Point.
In 1992, these sites were randomly selected on hard substrata along 4.5 km of
shore between these headlands, and they have been censused annually to
present. Each site consists of a permanently marked transect that has been
40-m long since 2000. The present project began in 2014 with the objective of
augmenting a long-standing analysis of benthic community structure (which
emphasized scleractinians with new analyses focused on octocorals. As part of
this effort, arborescent octocorals were surveyed in situ with genus
resolution, using 40 quadrats (0.5 \\u00d7 0.5 m) placed at random, non-
overlapping positions along the same transect (and re-randomized annually)
located at each of the six sites. Surveys were completed over four weeks
beginning on ~ 20th July of each of 2014\\u20132017, and were conducted by
counting and measuring the height of octocorals attached by holdfasts within
each quadrat.
 
Height was determined (\\u00b1 1 cm) using a flexible tape measure stretched
from the holdfast to the colony apex. Abundances were analyzed separately for
adults (> 5-cm tall), and recruits (\\u2264 5-cm tall), with this size cut-off
based on the maximal height to which the recruits of most octocoral species
are likely to grow in one year. While the benthos was inspected for all small
octocorals, sampling efficiency probably was low for recruits consisting of
only a few polyps (i.e., < 1-cm tall). Analyses testing for the effects of
density dependence (DD) and self-thinning (ST) were first, completed for
octocorals pooled among taxa, and second, for the three most common genera of
octocorals. Evidence of DD recruitment also was sought from analyses of per
capita recruitment by site, with these values obtained by dividing the density
of recruits by mean density of adults.
 
For more information about statistical analyses performed using these data see
Edmunds and Lasker (2019).";
    String awards_0_award_nid "752507";
    String awards_0_award_number "OCE-1756678";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1756678";
    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 
"Edmunds and Lasker MEPS 2019 Fig S1: Sampling effort 
  PI: Peter J. Edmunds 
  Data Version 1: 2020-02-04";
    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 "2020-02-04T19:07:47Z";
    String date_modified "2020-02-14T21:12:19Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.789188.1";
    String history 
"2020-12-04T03:36:16Z (local files)
2020-12-04T03:36:16Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_789188.html";
    String infoUrl "https://www.bco-dmo.org/dataset/789188";
    String institution "BCO-DMO";
    String instruments_0_dataset_instrument_nid "789194";
    String instruments_0_description "A tape measure or measuring tape is a flexible ruler. It consists of a ribbon of cloth, plastic, fibre glass, or metal strip with linear-measurement markings. It is a common measuring tool.";
    String instruments_0_instrument_name "Measuring Tape";
    String instruments_0_instrument_nid "645010";
    String keywords "bco, bco-dmo, biological, chemical, data, dataset, density, dmo, erddap, management, number, Number_of_quadrats, oceanography, office, preliminary, quadrats, site, taxon, year";
    String license "https://www.bco-dmo.org/dataset/789188/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/789188";
    String param_mapping "{'789188': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/789188/parameters";
    String people_0_affiliation "California State University Northridge";
    String people_0_affiliation_acronym "CSU-Northridge";
    String people_0_person_name "Peter J. Edmunds";
    String people_0_person_nid "51536";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "State University of New York at Buffalo";
    String people_1_affiliation_acronym "SUNY Buffalo";
    String people_1_person_name "Howard Lasker";
    String people_1_person_nid "562092";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Woods Hole Oceanographic Institution";
    String people_2_affiliation_acronym "WHOI BCO-DMO";
    String people_2_person_name "Amber York";
    String people_2_person_nid "643627";
    String people_2_role "BCO-DMO Data Manager";
    String people_2_role_type "related";
    String project "Octocoral Community Dynamics";
    String projects_0_acronym "Octocoral Community Dynamics";
    String projects_0_description 
"NSF abstract:
Coral reefs are exposed to a diversity of natural and anthropogenic disturbances, and the consequences for ecosystem degradation have been widely publicized. However, the reported changes have been biased towards fishes and stony corals, and for Caribbean reefs, the most notable example of this bias are octocorals (\"soft corals\"). Although they are abundant and dominate many Caribbean reefs, they are rarely included in studies due to the difficulty of both identifying them and in quantifying their abundances. In some places there is compelling evidence that soft corals have increased in abundance, even while stony corals have become less common. This suggests that soft corals are more resilient than stony corals to the wide diversity of disturbances that have been impacting coral corals. The best coral reefs on which to study these changes are those that have been studied for decades and can provide a decadal context to more recent events, and in this regard the reefs of St. John, US Virgin Islands are unique. Stony corals on the reefs have been studied since 1987, and the soft corals from 2014. This provides unrivalled platform to evaluate patterns of octocoral abundance and recruitment; identify the patterns of change that are occurring on these reefs, and identify the processes responsible for the resilience of octocoral populations. The project will extend soft coral monitoring from 4 years to 8 years, and within this framework will examine the roles of baby corals, and their response to seafloor roughness, seawater flow, and seaweed, in determining the success of soft corals. The work will also assess whether the destructive effects of Hurricanes Irma and Maria have modified the pattern of change. In concert with these efforts the project will be closely integrated with local high schools at which the investigators will host marine biology clubs and provide independent study opportunities for their students and teachers. Unique training opportunities will be provided to undergraduate and graduate students, as well as a postdoctoral researcher, all of whom will study and work in St. John, and the investigators will train coral reef researchers to identify the species of soft corals through a hands-on workshop to be conducted in the Florida Keys.
Understanding how changing environmental conditions will affect the community structure of major biomes is the ecological objective defining the 21st century. The holistic effects of these conditions on coral reefs will be studied on shallow reefs within the Virgin Islands National Park in St. John, US Virgin Islands, which is the site of one of the longest-running, long-term studies of coral reef community dynamics in the region. With NSF-LTREB support, the investigators have been studying long-term changes in stony coral communities in this location since 1987, and in 2014 NSF-OCE support was used to build an octocoral \"overlay\" to this decadal perspective. The present project extends from this unique history, which has been punctuated by the effects of Hurricanes Irma and Maria, to place octocoral synecology in a decadal context, and the investigators exploit a rich suite of legacy data to better understand the present and immediate future of Caribbean coral reefs. This four-year project will advance on two concurrent fronts: first, to extend time-series analyses of octocoral communities from four to eight years to characterize the pattern and pace of change in community structure, and second, to conduct a program of hypothesis-driven experiments focused on octocoral settlement that will uncover the mechanisms allowing octocorals to more effectively colonize substrata than scleractinian corals on present day reefs. Specifically, the investigators will conduct mensurative and manipulative experiments addressing four hypotheses focusing on the roles of: (1) habitat complexity in distinguishing between octocoral and scleractinian recruitment niches, (2) the recruitment niche in mediating post-settlement success, (3) competition in algal turf and macroalgae in determining the success of octocoral and scleractian recruits, and (4) role of octocoral canopies in modulating the flux of particles and larvae to the seafloor beneath. The results of this study will be integrated to evaluate the factors driving higher ecological resilience of octocorals versus scleractinians on present-day Caribbean reefs.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.";
    String projects_0_end_date "2022-03";
    String projects_0_geolocation "St. John, US Virgin Islands";
    String projects_0_name "Collaborative Research: Pattern and process in the abundance and recruitment of Caribbean octocorals";
    String projects_0_project_nid "752508";
    String projects_0_start_date "2018-04";
    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 "Octocoral sampling effort (density by pooled taxa and site) from surveys conducted in St. John, US Virgin Islands from 2014 to 2017. These data were used in Edmunds and Lasker (2019) Figure S1.";
    String title "Octocoral sampling effort (density by pooled taxa and site) from surveys conducted in St. John, US Virgin Islands from 2014 to 2017";
    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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