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Dataset Title:  Kd averages calculated by month from studies conducted in St. John, US Virgin
Islands from 2014-2017.
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_739882)
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 {
  Year {
    Int16 _FillValue 32767;
    Int16 actual_range 2014, 2017;
    String bcodmo_name "year";
    String description "Year of sampling";
    String long_name "Year";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/YEARXXXX/";
    String units "unitless";
  }
  Month {
    String bcodmo_name "unknown";
    String description "Month of sampling";
    String long_name "Month";
    String units "unitless";
  }
  Study_Number {
    Byte _FillValue 127;
    Byte actual_range 1, 15;
    String bcodmo_name "sample";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String description "Study citation as in legend in paper; DOI: 10.1007/s00338-018-1721-y";
    String long_name "Study Number";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  Kd {
    Float32 _FillValue NaN;
    Float32 actual_range 0.06, 1.8;
    String bcodmo_name "PAR_photons";
    String description "Kd values published in studies";
    String long_name "KD";
    String units "umol photons per square meter per second";
  }
  Kd_Mean {
    Float32 _FillValue NaN;
    Float32 actual_range 0.097, 0.164;
    String bcodmo_name "PAR_photons";
    String description "Kd mean from values published in studies";
    String long_name "Kd Mean";
    String units "umol photons per square meter per second";
  }
  Kd_SE {
    Float32 _FillValue NaN;
    Float32 actual_range 0.001, 0.005;
    String bcodmo_name "PAR_photons";
    String description "Kd standard error from values published in studies";
    String long_name "Kd SE";
    String units "umol photons per square meter per second";
  }
  Note {
    String bcodmo_name "comment";
    String description "Two values tied by vertical bar to show high and low";
    String long_name "Note";
    String units "unitless";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Light was measured as the radiant energy between 400 and 700 nm wavelength
(i.e., PAR, \\u03bcmol quanta m-2 s-1) as Photosynthetic Photon Flux Density
(PPFD). In situ light was measured using two logging meters fitted with a
cosine-corrected PAR sensor and wiper (Compact LW, JFE Advantech Co., Ltd,
Japan), that were deployed at ~ 19.1-m depth (height of the sensor) in Great
Lameshur Bay (18\\u00b0 18 \\u0301 37.04N, 63\\u00b0 43 \\u0301 23.17W).
 
These instruments recorded downwelling PAR, and were deployed six times from
2014and 2017, from August to March and from March to August. The meters were
operated in burst mode, during which they would wake up, clean the sensor with
a wiper, and record a burst of multiple records before returning to sleep. The
Compact LW meter is designed for oceanographic applications to 200 m depths,
is fitted with a photodiode sensor, and has a stated accuracy of \\u00b1 4%
(over 0\\u20132000 \\u03bcmol photons m2 s-1) and resolution of 0.1 \\u03bcmol
photons m2 s-1. Both meters were purchased new for this study, and were
deployed individually and sequentially between field samplings with
comparisons between consecutive deployments used to screen for calibration
drift. One sensor was used for a combined duration of 16 months during, and
the other sensor was used for 4 months, returned to the manufacturer for
servicing (May 2016), and then used again for 3 months. In between
deployments, sensors were inspected for abrasions that would affect
calibration, and were carefully cleaned with vinegar.
 
Different configurations of the meter were employed to prolong battery life.
In the first and second deployments (starting 21 August 2014 and 19 March
2015, respectively), a burst of 10 measurements was recorded at 0.033 Hz
(i.e., every 30 s) every 1.5 h; the instrument failed during the third
deployment (starting August 2015); in the fourth and fifth deployments
(starting 16 March 2016 and 29 July 2016, respectively) a burst of 10
measurements was recorded at 0.033 Hz every 1.0 h; and in the sixth
deployment(starting 23 February 2017) a burst of 30 measurements was recorded
at 0.100 Hz (i.e.,every 10 s) every 2.0 h. The timing of bursts was not
standardized to local time and, therefore, the number and timing of bursts
bracketing noon (which were used to calculate transmission, described below)
differed among deployments. The sampling frequency within each burst was
sufficient to alleviate the bias resulting from wave-induced light flecking
(Zheng et al. 2002). As a result of varying power demands of each sampling
configuration, the meter did not always record for the full duration of each
deployment.\\u00a0
 
Surface light was recorded with two cosine-corrected PAR sensor (S-LIA-M003,
OnsetComputer Corporation) attached to loggers (Micro Station Data Logger
H21-002, OnsetComputer Corporation) recording at 0.0033 Hz (i.e., every 5
minutes). The sensors were calibrated by the manufacturers, and were mounted ~
4-m above sea level adjacent to Great Lameshur Bay (18\\u00b0 19 \\u0301 6.61N,
64\\u00b0 43 \\u0301 27.73W), and ~ 0.875 km from the sensor recording in situ
light. The paired surface sensors were used to ensure data integrity should
one sensor fail, and the paired deployments provided a means to detect
erroneous records due to sensor drift or failure. The surface sensors were
downloaded, reprogrammed, and cleaned in July of each year, and have been
deployed for 11 y(from 2007). Here, surface light data for 2014-2017 are
presented to provide temporal concordance with the submerged sensor.";
    String awards_0_award_nid "562085";
    String awards_0_award_number "OCE-1332915";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1332915";
    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 "562593";
    String awards_1_award_number "DEB-1350146";
    String awards_1_data_url "http://www.nsf.gov/awardsearch/showAward?AWD_ID=1350146";
    String awards_1_funder_name "NSF Division of Environmental Biology";
    String awards_1_funding_acronym "NSF DEB";
    String awards_1_funding_source_nid "550432";
    String awards_1_program_manager "Betsy Von Holle";
    String awards_1_program_manager_nid "701685";
    String cdm_data_type "Other";
    String comment 
"Kd PAR Values 
  P. Edmunds, PI 
  Data associated with Fig. 2 of: 
  Edmunds, Coral Reefs (2018) Long-term variation in light intensity on a coral reef. 
  Version 13 July 2018";
    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 "2018-07-13T18:05:50Z";
    String date_modified "2018-08-22T18:23:32Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.744503";
    String history 
"2022-10-03T04:04:42Z (local files)
2022-10-03T04:04:42Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_739882.das";
    String infoUrl "https://www.bco-dmo.org/dataset/739882";
    String institution "BCO-DMO";
    String instruments_0_acronym "LI-COR Biospherical PAR";
    String instruments_0_dataset_instrument_description "Used to determine PAR";
    String instruments_0_dataset_instrument_nid "742233";
    String instruments_0_description "The LI-COR Biospherical PAR Sensor is used to measure Photosynthetically Available Radiation (PAR) in the water column.  This instrument designation is used when specific make and model are not known.";
    String instruments_0_instrument_external_identifier "https://vocab.nerc.ac.uk/collection/L22/current/TOOL0074/";
    String instruments_0_instrument_name "LI-COR Biospherical PAR Sensor";
    String instruments_0_instrument_nid "480";
    String instruments_0_supplied_name "PAR Sensor";
    String keywords "bco, bco-dmo, biological, chemical, data, dataset, dmo, erddap, Kd_Mean, Kd_SE, management, mean, month, note, number, oceanography, office, preliminary, study, Study_Number, year";
    String license "https://www.bco-dmo.org/dataset/739882/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/739882";
    String param_mapping "{'739882': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/739882/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 "Woods Hole Oceanographic Institution";
    String people_1_affiliation_acronym "WHOI BCO-DMO";
    String people_1_person_name "Hannah Ake";
    String people_1_person_nid "650173";
    String people_1_role "BCO-DMO Data Manager";
    String people_1_role_type "related";
    String project "VI Octocorals,RUI-LTREB";
    String projects_0_acronym "VI Octocorals";
    String projects_0_description 
"The recent past has not been good for coral reefs, and journals have been filled with examples of declining coral cover, crashing fish populations, rising cover of macroalgae, and a future potentially filled with slime. However, reefs are more than the corals and fishes for which they are known best, and their biodiversity is affected strongly by other groups of organisms. The non-coral fauna of reefs is being neglected in the rush to evaluate the loss of corals and fishes, and this project will add on to an on-going long term ecological study by studying soft corals. This project will be focused on the ecology of soft corals on reefs in St. John, USVI to understand the Past, Present and the Future community structure of soft corals in a changing world. For the Past, the principal investigators will complete a retrospective analysis of octocoral abundance in St. John between 1992 and the present, as well as Caribbean-wide since the 1960's. For the Present, they will: (i) evaluate spatio-temporal changes between soft corals and corals, (ii) test for the role of competition with macroalgae and between soft corals and corals as processes driving the rising abundance of soft corals, and (iii) explore the role of soft corals as \"animal forests\" in modifying physical conditions beneath their canopy, thereby modulating recruitment dynamics. For the Future the project will conduct demographic analyses on key soft corals to evaluate annual variation in population processes and project populations into a future impacted by global climate change.
This project was funded to provide and independent \"overlay\" to the ongoing LTREB award (DEB-1350146, co-funded by OCE, PI Edmunds) focused on the long-term dynamics of coral reefs in St. John.
Note: This project is closely associated with the project \"RAPID: Resilience of Caribbean octocorals following Hurricanes Irma and Maria\". See: https://www.bco-dmo.org/project/749653.
The following publications and data resulted from this project:
2017 Tsounis, G., and P. J. Edmunds. Three decades of coral reef community dynamics in St. John, USVI: a contrast of scleractinians and octocorals. Ecosphere 8(1):e01646. DOI: 10.1002/ecs2.1646Rainfall and temperature dataCoral and macroalgae abundance and distributionDescriptions of hurricanes affecting St. John
2016 Gambrel, B. and Lasker, H.R. Marine Ecology Progress Series 546: 85–95, DOI: 10.3354/meps11670Colony to colony interactionsEunicea flexuosa interactionsGorgonia ventalina asymmetryNearest neighbor surveys
2015 Lenz EA, Bramanti L, Lasker HR, Edmunds PJ. Long-term variation of octocoral populations in St. John, US Virgin Islands. Coral Reefs DOI 10.1007/s00338-015-1315-xoctocoral survey - densitiesoctocoral counts - photoquadrats vs. insitu surveyoctocoral literature reviewDownload complete data for this publication (Excel file)
2015 Privitera-Johnson, K., et al., Density-associated recruitment in octocoral communities in St. John, US Virgin Islands, J.Exp. Mar. Biol. Ecol. DOI: 10.1016/j.jembe.2015.08.006octocoral density dependenceDownload complete data for this publication (Excel file)
Other datasets related to this project:octocoral transects - adult colony height";
    String projects_0_end_date "2016-08";
    String projects_0_geolocation "St. John, US Virgin Islands:  18.3185, 64.7242";
    String projects_0_name "Ecology and functional biology of octocoral communities";
    String projects_0_project_nid "562086";
    String projects_0_project_website "http://coralreefs.csun.edu/";
    String projects_0_start_date "2013-09";
    String projects_1_acronym "RUI-LTREB";
    String projects_1_description 
"Describing how ecosystems like coral reefs are changing is at the forefront of efforts to evaluate the biological consequences of global climate change and ocean acidification. Coral reefs have become the poster child of these efforts. Amid concern that they could become ecologically extinct within a century, describing what has been lost, what is left, and what is at risk, is of paramount importance. This project exploits an unrivalled legacy of information beginning in 1987 to evaluate the form in which reefs will persist, and the extent to which they will be able to resist further onslaughts of environmental challenges. This long-term project continues a 27-year study of Caribbean coral reefs. The diverse data collected will allow the investigators to determine the roles of local and global disturbances in reef degradation. The data will also reveal the structure and function of reefs in a future with more human disturbances, when corals may no longer dominate tropical reefs.
The broad societal impacts of this project include advancing understanding of an ecosystem that has long been held emblematic of the beauty, diversity, and delicacy of the biological world. Proposed research will expose new generations of undergraduate and graduate students to natural history and the quantitative assessment of the ways in which our planet is changing. This training will lead to a more profound understanding of contemporary ecology at the same time that it promotes excellence in STEM careers and supports technology infrastructure in the United States. Partnerships will be established between universities and high schools to bring university faculty and students in contact with k-12 educators and their students, allow teachers to carry out research in inspiring coral reef locations, and motivate children to pursue STEM careers. Open access to decades of legacy data will stimulate further research and teaching.";
    String projects_1_end_date "2019-04";
    String projects_1_geolocation "USVI";
    String projects_1_name "RUI-LTREB Renewal: Three decades of coral reef community dynamics in St. John, USVI: 2014-2019";
    String projects_1_project_nid "734983";
    String projects_1_project_website "http://coralreefs.csun.edu/";
    String projects_1_start_date "2014-05";
    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 "Diffuse attenuation coefficient (Kd) for light in reef water of Great Lameshur Bay. These data describe how light is attenuated with depth in seawater in Lameshur Bay, with the coefficient calculated using standard procedures constrain (as described in the ms) by using surface light instead of immediately sub surface";
    String title "Kd averages calculated by month from studies conducted in St. John, US Virgin Islands from 2014-2017.";
    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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