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Dataset Title:	Location, abundance, and size of various octocoral species in St. John, USVI from 2014 to 2015.
Institution:	BCO-DMO   (Dataset ID: bcodmo_dataset_735137)
Range:	longitude = -64.729935 to -64.71882°E, latitude = 18.309183 to 18.316717°N
Information:	Summary | License | ISO 19115 | Metadata | Background | Subset | Data Access Form | Files

 

Graph Type:  lines linesAndMarkers markers sticks vectors X Axis:  latitude longitude transect_m height Y Axis:  latitude longitude transect_m height Color:  latitude longitude transect_m height   Constraints Optional Constraint #1 Optional Constraint #2 site latitude longitude date transect_pos field_code species transect_side transect_m height     != =~ <= >= = < >     != =~ <= >= = < >     site latitude longitude date transect_pos field_code species transect_side transect_m height     != =~ <= >= = < >     != =~ <= >= = < >     site latitude longitude date transect_pos field_code species transect_side transect_m height     != =~ <= >= = < >     != =~ <= >= = < >     site latitude longitude date transect_pos field_code species transect_side transect_m height     != =~ <= >= = < >     != =~ <= >= = < >     site latitude longitude date transect_pos field_code species transect_side transect_m height     != =~ <= >= = < >     != =~ <= >= = < >       Server-side Functions  distinct() orderBy orderByClosest orderByCount orderByLimit orderByMax orderByMin orderByMinMax orderByMean (" site latitude longitude date transect_pos field_code species transect_side transect_m height site latitude longitude date transect_pos field_code species transect_side transect_m height site latitude longitude date transect_pos field_code species transect_side transect_m height site latitude longitude date transect_pos field_code species transect_side transect_m height ")   Graph Settings Marker Type:  None Plus X Dot Square Filled Square Circle Filled Circle Up Triangle Filled Up Triangle  Size:  3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Color:  Color Bar:  BlackBlueWhite BlackGreenWhite BlackRedWhite BlackWhite BlueWhiteRed BlueWideWhiteRed LightRainbow Ocean OceanDepth Rainbow Rainbow2 Rainfall ReverseRainbow RedWhiteBlue RedWhiteBlue2 RedWideWhiteBlue Spectrum Topography TopographyDepth WhiteBlueBlack WhiteGreenBlack WhiteRedBlack WhiteBlack YellowRed KT_algae KT_amp KT_balance KT_curl KT_deep KT_delta KT_dense KT_gray KT_haline KT_ice KT_matter KT_oxy KT_phase KT_solar KT_speed KT_tempo KT_thermal KT_turbid  Continuity:  Continuous Discrete  Scale:  Linear Log    Minimum:   Maximum:   N Sections:  2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 Draw land mask:  under over Y Axis Minimum:   Maximum:    Ascending Descending Linear Log   Redraw the Graph (Please be patient. It may take a while to get the data.)   Optional: Then set the File Type: .asc .csv .csvp .csv0 .dataTable .das .dds .dods .esriCsv .fgdc .geoJson .graph .help .html .htmlTable .iso19115 .itx .json .jsonlCSV1 .jsonlCSV .jsonlKVP .mat .nc .ncHeader .ncCF .ncCFHeader .ncCFMA .ncCFMAHeader .nccsv .nccsvMetadata .ncoJson .odvTxt .subset .tsv .tsvp .tsv0 .wav .xhtml .kml .smallPdf .pdf .largePdf .smallPng .png .largePng .transparentPng (File Type information) and or view the URL: (Documentation / Bypass this form )

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• Anyone can use ERDDAPs Slide Sorter to build a personal web page that displays graphs with the latest data (or other images or HTML content), each in its own, draggable slide.
   

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  site {
    String bcodmo_name "site";
    String description "Study site";
    String long_name "Site";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 18.309183, 18.316717;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude";
    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 -64.729933, -64.718817;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude";
    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";
  }
  date {
    String bcodmo_name "date";
    String description "Date";
    String long_name "Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String source_name "date";
    String time_precision "1970-01-01";
    String units "unitless";
  }
  transect_pos {
    String bcodmo_name "transect";
    String description "Transect position; referring to the position within a 50x10m sampling area divided by 6 transects (0m; 10m; 20m; 30m; 40m; 50m) or random.";
    String long_name "Transect Pos";
    String units "unitless";
  }
  field_code {
    String bcodmo_name "sample";
    String description "Field code for octocoral species";
    String long_name "Field Code";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  species {
    String bcodmo_name "species";
    String description "Octocoral species";
    String long_name "Species";
    String units "unitless";
  }
  transect_side {
    String bcodmo_name "transect";
    String description "L= left side of transect line; R= right side of the transect line (orientation towards the nearest coast)";
    String long_name "Transect Side";
    String units "unitless";
  }
  transect_m {
    Byte _FillValue 127;
    Byte actual_range 0, 10;
    String bcodmo_name "transect";
    String description "Distance in meters along the transect line starting opposite of the shore side";
    String long_name "Transect M";
    String units "meters";
  }
  height {
    Byte _FillValue 127;
    Byte actual_range 0, 118;
    String bcodmo_name "height";
    String description "Height in cm of cotcoroal colonies measured from the base to the farthest tips";
    String long_name "Height";
    String units "centimeters";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"Methodology from Tsounis, G., Edmunds, P.J., Bramanti, L. et al. Mar Biol
(2018) 165: 29.
[https://doi.org/10.1007/s00227-018-3286-2](\\\\\"https://doi.org/10.1007/s00227-018-3286-2\\\\\")
 
Surveys were conducted at East Cabritte and Europa Bay (Fig. 1) in July and
August 2014, and March 2015. The sites represent contrasting exposure regimes,
suggested by the exposure of East Cabritte to prevailing winds and swells, and
the shelter of Europa Bay in the lee of Cabritte Horn. Measurements of
physical environmental conditions (described below) were used to quantify
these differences.
 
At each site, a 50 \\u00d7 10 m study area was haphazardly established, within
which aspects of the biota and the physical environment were measured. The
long axes of the areas were parallel to the shore and ranged from 7.5- to
9.0-m depth at East Cabritte, and from 5.6- to 8.0-m depth at Europa Bay, and
the short axes were perpendicular to the shore and crossed a depth gradient of
5.6\\u20137.2 m at Europa Bay, and 7.5\\u20139.0 m at East Cabritte. The depth
ranges of the study sites differed, because the reef at Europa did not extend
into deeper water, while the reef at East Cabritte markedly steepened above 7
m. Five, 10-m transects were equally spaced along, and perpendicular to, the
long axis of each study area. Octocoral community structure was compared
between sites using octocoral diversity, size, and density in both
multivariate and univariate statistics frameworks.
 
Physical environmental condition  
 Environmental conditions at each site were characterized in the summer of
2014 and the winter of 2014\\u20132015 through measurements of water motion,
benthic rugosity, sedimentation, and light intensity. Water motion was
characterized using two methods, first, using the wave climate recorded by an
NOAA buoy moored 7.8 km from the study site (CariCOOS Data Buoy C at Mooring
VI-105), and second, through direct measurements of integrated water motion
using clod cards (Doty 1971).
 
Hourly wave direction (degrees relative to north) from March 15th 2011, 17:00
h to March 2nd 2015, 21:00 h was obtained from the NOAA buoy VI-105
([https://www.caricoos.org/drupal/virgin_islands](\\\\\"https://www.caricoos.org/drupal/virgin_islands\\\\\")),
with measurements averaged by hour from a sampling frequency of 2 Hz in 17 min
bursts. To obtain hourly averages, a varying number of records were averaged
depending on the coincidence of the 17-min sampling bursts with the 60-min
averaging period. Using hourly averages, the proportion of time (i.e.,
percentages based on number of hours) when waves directly impacted each site
was calculated based on the direction from which the waves originated. The two
sites were impacted by waves originating from dissimilar, but partially
overlapping directions, because the sites differed in orientation and location
along the shore relative to the southerly projection of Cabritte Horn (Fig.
1). Europa Bay is exposed to waves from 135\\u00b0 to 250\\u00b0, and East
Cabritte to waves from 60\\u00b0 to 135\\u00b0. To capture these effects, the
number of hours describing mean wave directions corresponding to each of these
directional bins was quantified, without considering wave refraction around
Cabritte Horn. Wave height was not evaluated using data from this buoy, as its
distance from our study sites made estimates of wave height unreliable.
 
Integrated water motion was measured in situ using clod cards (Doty 1971) that
were prepared in a single batch for each deployment, dried to a constant
weight at 50 \\u00b0C, and weighed prior to use. Clod cards had similar initial
weights [128 \\u00b1 2 g (mean \\u00b1 SE, n = 78)], and were deployed in July
and August 2014, and March 2015, and assigned to each site in a paired design
(two clods per site). Clods were secured for 24\\u201348 h to posts ~ 30 cm
above the benthos at 9-m depth adjacent to, but outside of, the octocoral
canopy. Following deployment, clods were dried to a constant weight at 50
\\u00b0C, and integrated water motion was evaluated from the dissolution of
plaster in units of g day\\u22121.
 
Sedimentation was measured with sediment traps in two deployments for 8 and 9
days in 2014 (to begin a new measurement when a storm at the end of the first
deployment saturated the traps), and in a single deployment for 12 day in
2015. Both sites were monitored simultaneously. The traps consisted of PVC
tubes (20 \\u00d7 5 cm ID) that were deployed 60 cm above the benthos (Edmunds
and Gray 2014). Traps were capped in situ, returned to the lab, and filtered
through pre-weighed filters (Whatman #113). Filters and sediment were rinsed
with freshwater to remove salt, dried to a constant weight at 50 \\u00b0C, and
weighed (\\u00b1 1 mg). Sedimentation was normalized by catchment area of the
traps, and time (mg cm\\u22122 day\\u22121).
 
In situ light intensity was measured using two integrating submersible light
meters (JFE-Advantech Compact-LW) fitted with a cosine-corrected collector
sensitive to photosynthetically active radiation (PAR, 400\\u2013700 nm) and a
wiper blade that cleaned the collector prior to each measurement. The meters
were deployed in a paired design at the two sites for 8 days in 2014 (August
10\\u201315th and August 18\\u201319th) and 8 days in 2015 (March 3\\u201313th).
Each meter was attached to a post at 9-m depth adjacent to the octocoral
community, but ~ 5 m from the nearest octocoral colony to avoid shading. Light
intensity was recorded at 0.033 Hz, and data were used to generate two
dependent variables, one recording the maximum daily intensity (\\u03bcmol
m\\u22122 s\\u22121) and the other recording the intensity integrated over each
24-h period (units of mol m\\u22122 day\\u22121).
 
Benthic rugosity was determined along the five transects at each study plot
using a light chain (10-mm links) which was laid along each transect to
conform to the reef surface. Rugosity was calculated as the quotient of the
linear distance and the conformed length of the chain (Luckhurst and Luckhurst
1978).
 
The hypothesis that the sites differed in environmental parameters was tested
with univariate ANOVA using R (R Development Core Team 2008). Sediment traps
and clods cards were not deployed at both sites in synchronous deployments due
to logistical constraints, and these data were compared between sites and
times using a two way, Model I ANOVA. Light intensity differs among days, and,
therefore, was compared between sites using a within-subject design in the aov
function in R, accounting for variation over time by considering deployment
day as a blocking factor. Substratum rugosity was compared between sites using
one-way ANOVA. In all cases, the ANOVA assumptions of normality and
homoscedasticity were tested through graphical analyses of residuals.
 
Octocoral community structure
 
Species richness:  
 Octocoral species richness was compared between sites based on 50 quadrats
(1 \\u00d7 1 m) that were sequentially placed along the five, 10 m transects
that crossed the short axis of the study plots, and censused for octocoral
presence. Surveys began in July and August 2014, and were concluded in
February and March 2015 (i.e., two field trips were required). Octocoral
diversity was determined using Pielou\\u2019s Evenness Index (J\\u00b4) (Pielou
1966), and the Shannon\\u2013Wiener Diversity index, H\\u00b4 (Shannon 1948).
This study considered adult octocorals, and excluded recruits (i.e., colonies
\\u2264 5 cm tall [HR Lasker, unpublished data]) from the surveys. However
colonies \\u2264 5 cm were censused if it was obvious that they had been larger
adults that were reduced in size by predators. Octocorals were identified to
the lowest taxonomic-level possible, as determined through voucher samples
that were microscopically inspected for sclerites (after Bayer 1961).
Preliminary sampling revealed 10 genera and 35 species at the two sites, but a
small number (< 1.6%, n = 1290 colonies) could not be identified to species
and were scored by genus (mostly Eunicea and Pseudoplexaura). Initial work
indicated 39 nominal species (Edmunds and Lasker 2016), though subsequent
analysis refined the species count to 35 (this study). We do, however,
highlight the fact that the distinctions between Pseudoplexaura wagenaari and
P. flagellosa, those between Plexaurella dichotoma and P. fusifera and and
those between Eunicea laxispica, Eunicea mammosa and Eunicea succinea are
difficult to make, especially in the field. For this study, we opted to
distinguish between these species in our analyses, based on the best
information available (spicule analysis), but acknowledge that this might not
always be feasible in future studies, where pooling these pairs will
facilitate consistent long-term data series analyses using multiple observers.
Rarefaction curves (sensu Coleman et al. 1982) were used to evaluate the
efficacy of the sampling regime (i.e., number of 1 m2 quadrats) in quantifying
octocoral species abundance. At each site, the number of species as a function
of sample size (number of quadrats) was analyzed using the specaccum option in
the vegan package (version 2.3.2) for R [R Development Core Team 2008 (Oksanen
et al. 2015)], and species abundance was evaluated by the asymptote of the
curves against sample size.
 
Colony abundance:  
 To compare community structure of octocoral colonies between sites, we
randomly subsampled 32 of the 50 quadrats (each 1 \\u00d7 1 m) along the
transects (described above) to remove the biases associated with uniform
sampling (Sokal and Rohlf 1995). Densities (colonies m\\u22122) by species were
log(x) transformed and used to compute Bray\\u2013Curtis dissimilarity indices
after applying a dummy value (+ 1) to account for paired observations of zero
(Clarke et al. 2006). Dissimilarity indices were compared between sites using
a one factor PERMANOVA with 999 permutations. Dissimilarity indices were
produced using the vegdist function, and PERMANOVA was performed using the
ADONIS function, both in the vegan package (version 2.3.2) for R [R
Development Core Team 2008 (Oksanen et al. 2015)]. A similarity percentage
analysis (SIMPER, Clarke 1993) was performed using the simper function in the
vegan package (version 2.3.2) for R, and used to assess the contribution of
individual species to the total dissimilarity between sites. Spatial variation
in multivariate community structure was visualized using ordination plots
generated by non-metric dimensional scaling (NMDS) that were based on
Bray\\u2013Curtis dissimilarities (using the Vegan package in R).
 
Colony size:  
 The colony size\\u2013frequency distributions of the three most common
octocorals that could be identified in the field (Antillogorgia americana,
Eunicea flexuosa, and Gorgonia ventalina) were compared between sites. Colony
heights were surveyed using 1-m-wide belt transects placed along the five
transects dividing the study plots. Colonies were measured as encountered
within these survey areas, with the objective of measuring 75\\u2013100
colonies of each species for each size class at each site. When too few
colonies were found to meet the target sample size, additional non-overlapping
belt transects were censused within the study plot to reach the target number
of colonies. To test for differences in colony sizes for the three species
between sites, one-way PERMANOVA with 999 permutations were performed
(Anderson 2001) using the Adonis function in the vegan package (version 2.3.2)
for the R software [R Development Core Team 2008 (Oksanen et al. 2015)]. Two-
sample Kolmogorov\\u2013Smirnov tests using the R software were performed to
compare the complete size\\u2013frequency distributions for each species
between sites.
 
Community structure resolved by genus versus by species:  
 To evaluate the effect of taxonomic resolution on the differences in
community structure detected between sites, multivariate analyses were
conducted with genus- and species resolution, and the contribution of each
genus or species (respectively) to total dissimilarity between sites was
resolved using SIMPER.";
    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 cdm_data_type "Other";
    String comment 
"Octocoral Transects 
  P. Edmunds, PI 
  Version 5 September 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 "2018-05-04T19:21:40Z";
    String date_modified "2019-03-26T19:13:20Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.735137.1";
    Float64 Easternmost_Easting -64.718817;
    Float64 geospatial_lat_max 18.316717;
    Float64 geospatial_lat_min 18.309183;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -64.718817;
    Float64 geospatial_lon_min -64.729933;
    String geospatial_lon_units "degrees_east";
    String history 
"2024-04-18T23:21:04Z (local files)
2024-04-18T23:21:04Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_735137.das";
    String infoUrl "https://www.bco-dmo.org/dataset/735137";
    String institution "BCO-DMO";
    String keywords "bco, bco-dmo, biological, chemical, code, data, dataset, date, dmo, erddap, field, field_code, height, latitude, longitude, management, oceanography, office, pos, preliminary, side, site, species, time, transect, transect_m, transect_pos, transect_side";
    String license "https://www.bco-dmo.org/dataset/735137/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/735137";
    Float64 Northernmost_Northing 18.316717;
    String param_mapping "{'735137': {'lat': 'master - latitude', 'lon': 'master - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/735137/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 "California State University Northridge";
    String people_2_affiliation_acronym "CSU-Northridge";
    String people_2_person_name "Dr Georgios Tsounis";
    String people_2_person_nid "565353";
    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 "St. John LTREB,VI Octocorals";
    String projects_0_acronym "St. John LTREB";
    String projects_0_description 
"Long Term Research in Environmental Biology (LTREB) in US Virgin Islands:
From the NSF award abstract:
In an era of growing human pressures on natural resources, there is a critical need to understand how major ecosystems will respond, the extent to which resource management can lessen the implications of these responses, and the likely state of these ecosystems in the future. Time-series analyses of community structure provide a vital tool in meeting these needs and promise a profound understanding of community change. This study focuses on coral reef ecosystems; an existing time-series analysis of the coral community structure on the reefs of St. John, US Virgin Islands, will be expanded to 27 years of continuous data in annual increments. Expansion of the core time-series data will be used to address five questions: (1) To what extent is the ecology at a small spatial scale (1-2 km) representative of regional scale events (10's of km)? (2) What are the effects of declining coral cover in modifying the genetic population structure of the coral host and its algal symbionts? (3) What are the roles of pre- versus post-settlement events in determining the population dynamics of small corals? (4) What role do physical forcing agents (other than temperature) play in driving the population dynamics of juvenile corals? and (5) How are populations of other, non-coral invertebrates responding to decadal-scale declines in coral cover? Ecological methods identical to those used over the last two decades will be supplemented by molecular genetic tools to understand the extent to which declining coral cover is affecting the genetic diversity of the corals remaining. An information management program will be implemented to create broad access by the scientific community to the entire data set.
The importance of this study lies in the extreme longevity of the data describing coral reefs in a unique ecological context, and the immense potential that these data possess for understanding both the patterns of comprehensive community change (i.e., involving corals, other invertebrates, and genetic diversity), and the processes driving them. Importantly, as this project is closely integrated with resource management within the VI National Park, as well as larger efforts to study coral reefs in the US through the NSF Moorea Coral Reef LTER, it has a strong potential to have scientific and management implications that extend further than the location of the study.
The following publications and data resulted from this project:
2015    Edmunds PJ, Tsounis G, Lasker HR (2015) Differential distribution of octocorals and scleractinians around St. John and St. Thomas, US Virgin Islands. Hydrobiologia. doi: 10.1007/s10750-015-2555-zoctocoral - sp. abundance and distributionDownload complete data for this publication (Excel file)
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 recruitmentDownload complete data for this publication (Excel file)
2014    Edmunds PJ. Landscape-scale variation in coral reef community structure in the United States Virgin Islands. Marine Ecology Progress Series 509: 137–152. DOI 10.3354/meps10891.
Data at MCR-VINP.
Download complete data for this publication (Excel file)
2014    Edmunds PJ, Nozawa Y, Villanueva RD.  Refuges modulate coral recruitment in the Caribbean and Pacific.  Journal of Experimental Marine Biology and Ecology 454: 78-84. DOI: 10.1016/j.jembe.2014.02.00
Data at MCR-VINP.Download complete data for this publication (Excel file)
2014    Edmunds PJ, Gray SC.  The effects of storms, heavy rain, and sedimentation on the shallow coral reefs of St. John, US Virgin Islands.  Hydrobiologia 734(1):143-148.
Data at MCR-VINP.Download complete data for this publication (Excel file)
2014    Levitan, D, Edmunds PJ, Levitan K. What makes a species common? No evidence of density-dependent recruitment or mortality of the sea urchin Diadema antillarum after the 1983-1984 mass mortality.  Oecologia. DOI 10.1007/s00442-013-2871-9.
Data at MCR-VINP.Download complete data for this publication (Excel file)
2014    Lenz EA, Brown D, Didden C, Arnold A, Edmunds PJ.  The distribution of hermit crabs and their gastropod shells on shallow reefs in St. John, US Virgin Islands.  Bulletin of Marine Science 90(2):681-692. https://dx.doi.org/10.5343/bms.2013.1049
Data at MCR-VINP.Download complete data for this publication (Excel file)
2013    Edmunds PJ.  Decadal-scale changes in the community structure of coral reefs in St. John, US Virgin Islands.  Marine Ecology Progress Series 489: 107-123.
Data at MCR-VINP.Download complete data for this publication (zipped Excel files)
2013    Brown D, Edmunds PJ.  Long-term changes in the population dynamics of the Caribbean hydrocoral Millepora spp.  J. Exp Mar Biol Ecol 441: 62-70. doi: 10.1016/j.jembe.2013.01.013Millepora colony sizeMillepora cover - temps - storms 1992-2008Millepora cover 1992-2008seawater temperature USVI 1992-2008storms USVI 1992-2008Download complete data for this publication (Excel file)
2012    Brown D, Edmunds PJ. The hermit crab Calcinus tibicen lives commensally on Millepora spp. in St. John, United States Virgin Islands.  Coral Reefs 32: 127-135. doi: 10.1007/s00338-012-0948-2crab abundance and coral sizecrab displacement behaviorcrab nocturnal surveyscrab predator avoidanceDownload complete data for this publication (Excel file)
2011    Green DH, Edmunds PJ.  Spatio-temporal variability of coral recruitment on shallow reefs in St. John, US Virgin Islands.  Journal of Experimenal Marine Biology and Ecology 397: 220-229.
Data at MCR-VINP.Download complete data for this publication (Excel file)
2011    Colvard NB, Edmunds PJ. (2011) Decadal-scale changes in invertebrate abundances on a Caribbean coral reef.  Journal of Experimental Marine Biology and Ecology. 397(2): 153-160. doi: 10.1016/j.jembe.2010.11.015benthic invert codesinverts - Tektite and Yawzi Ptinverts - pooledDownload complete data for this publication (Excel file)";
    String projects_0_end_date "2014-04";
    String projects_0_geolocation "St. John, U.S. Virgin Islands; California State University Northridge";
    String projects_0_name "LTREB Long-term coral reef community dynamics in St. John, USVI: 1987-2019";
    String projects_0_project_nid "2272";
    String projects_0_project_website "http://coralreefs.csun.edu/";
    String projects_0_start_date "2009-05";
    String projects_1_acronym "VI Octocorals";
    String projects_1_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_1_end_date "2016-08";
    String projects_1_geolocation "St. John, US Virgin Islands:  18.3185, 64.7242";
    String projects_1_name "Ecology and functional biology of octocoral communities";
    String projects_1_project_nid "562086";
    String projects_1_project_website "http://coralreefs.csun.edu/";
    String projects_1_start_date "2013-09";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 18.309183;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String subsetVariables "transect_side";
    String summary "Location, abundance, and size of various octocoral species in St. John, USVI from 2014 to 2015.";
    String title "Location, abundance, and size of various octocoral species in St. John, USVI from 2014 to 2015.";
    String version "1";
    Float64 Westernmost_Easting -64.729933;
    String xml_source "osprey2erddap.update_xml() v1.3";
  }
}

 

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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 Data Access Protocol (DAP) and its selection constraints.

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.

• (easy) You can get data by using the dataset's Data Access Form or Subset form. They make the URL for you.
• (easy) You can make a graph or map by using the dataset's Make A Graph form. It makes the URL for you.
• (not hard) You can bypass the forms and get the data or make a graph or map by generating the URL by hand or with a computer program or script.

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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