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Dataset Title:  Distribution of E. lori settlers on sponge habitat from sampling in South
Water Caye, Belize in 2015.
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_728451)
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 {
  Date {
    String description "Date of observation; YYYY/MM/DD";
    String ioos_category "Time";
    String long_name "Date";
    String source_name "Date";
    String units "unitless";
  Sp_ID {
    Byte _FillValue 127;
    Byte actual_range 1, 120;
    String description "Tag number from 1 - 120 use to identify sponges";
    String ioos_category "Identifier";
    String long_name "Sp ID";
    String units "unitless";
  Sp_depth {
    Float64 _FillValue NaN;
    Float64 actual_range 40.0, 60.0;
    String description "Depth at base of the sponge in feet";
    String ioos_category "Location";
    String long_name "Sp Depth";
    String units "feet";
  Sp_depth_m {
    Float32 _FillValue NaN;
    Float32 actual_range 12.2, 18.3;
    String description "Depth at base of the sponge in meters";
    String ioos_category "Location";
    String long_name "Sp Depth M";
    String units "meters";
  Sp_species {
    String description "Sponge species: Aplysina fistularis (Y); or Agelas conifera (B)";
    String ioos_category "Taxonomy";
    String long_name "Sp Species";
    String units "unitless";
  Sp_size {
    Byte _FillValue 127;
    Byte actual_range 13, 81;
    String description "Maximum tube length of sponge";
    String ioos_category "Unknown";
    String long_name "Sp Size";
    String units "centimeters";
  Sp_tubes {
    Byte _FillValue 127;
    Byte actual_range 1, 12;
    String description "Number of sponge tubes greater than 10 centimeters";
    String ioos_category "Unknown";
    String long_name "Sp Tubes";
    String units "count";
  Res_pres {
    Byte _FillValue 127;
    Byte actual_range 0, 1;
    String description "Residents presence (1) or absence (0)";
    String ioos_category "Unknown";
    String long_name "Res Pres";
    String units "unitless";
  Res_n {
    Byte _FillValue 127;
    Byte actual_range 0, 4;
    String description "Number of residents observed on each sponge";
    String ioos_category "Statistics";
    String long_name "Res N";
    String units "count";
  Settler_pres {
    Byte _FillValue 127;
    Byte actual_range 0, 1;
    String description "Settler presence (1) or absence (0)";
    String ioos_category "Unknown";
    String long_name "Settler Pres";
    String units "unitless";
  Settler_n {
    Byte _FillValue 127;
    Byte actual_range 0, 6;
    String description "Number of settlers observed on each sponge";
    String ioos_category "Statistics";
    String long_name "Settler N";
    String units "count";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Divers surveyed each of the 120 tagged sponges for the presence or absence of
E. lori settlers to test the hypothesis that habitat and/or social variables
are related to the natural settler distribution (8 \\u2013 18 mm SL; Table 1).
We constructed a set of generalized linear models (distribution= binomial;
link = logit) in R 3.2.3 (R Core Team 2015) to investigate the relationship
between the presence or absence of an E. lori settler on a sponge (1 or 0) and
all habitat and social variables (as defined above). Each variable was treated
as an alternative hypothesis for the factors that predict the distribution of
    String awards_0_award_nid "651264";
    String awards_0_award_number "OCE-1459546";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1459546";
    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 
"Distribution of E. lori settlers 
  P. Buston and J. Majoris, PIs 
  Version 28 Feb 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 2.2d  13 Jun 2019";
    String date_created "2018-02-27T19:46:41Z";
    String date_modified "2019-03-19T17:37:28Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/bco-dmo.728451.1";
    String history 
"2019-06-25T02:09:53Z (local files)
2019-06-25T02:09:53Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_728451.das";
    String infoUrl "https://www.bco-dmo.org/dataset/728451";
    String institution "BCO-DMO";
    String keywords "bco, bco-dmo, biological, chemical, data, dataset, date, depth, dmo, erddap, identifier, management, oceanography, office, preliminary, pres, res, Res_n, Res_pres, settler, Settler_n, Settler_pres, size, Sp_depth, Sp_depth_m, Sp_ID, Sp_size, Sp_species, Sp_tubes, species, statistics, taxonomy, time, tubes";
    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/728451";
    String param_mapping "{'728451': {'Sp_depth': 'master - depth'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/728451/parameters";
    String people_0_affiliation "Boston University";
    String people_0_affiliation_acronym "BU";
    String people_0_person_name "Dr Peter Buston";
    String people_0_person_nid "544437";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Boston University";
    String people_1_affiliation_acronym "BU";
    String people_1_person_name "Dr John Majoris";
    String people_1_person_nid "728439";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Boston University";
    String people_2_affiliation_acronym "BU";
    String people_2_person_name "Dr John Majoris";
    String people_2_person_nid "728439";
    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 "Collaborative Research: The Role of Larval Orientation Behavior in Determining Population Connectivity";
    String projects_0_acronym "Elacatinus Dispersal II";
    String projects_0_description 
"Description from NSF award abstract:
Understanding how far young fish move away from their parents is a major goal of marine ecology because this dispersal can make connections between distinct populations and thus influence population size and dynamics. Understanding the drivers of population dynamics is, in turn, essential for effective fisheries management. Marine ecologists have used two different approaches to understand how fish populations are connected: genetic methods that measure connectivity and oceanographic models that predict connectivity. There is, however, a mismatch between the predictions of oceanographic models and the observations of genetic methods. It is thought that this mismatch is caused by the behavior of the young, or larval, fish. The objective of this research is to study the orientation capabilities of larval fish in the wild throughout development and under a variety of environmental conditions to see if the gap between observations and predictions of population connectivity can be resolved. The project will have broader impacts in three key areas: integration of research and teaching by training young scientists at multiple levels; broadening participation of undergraduates from underrepresented groups; and wide dissemination of results through development of a website with information and resources in English and Spanish.
The overall objective of the research is to investigate the role of larval orientation behavior throughout ontogeny in determining population connectivity. This will be done using the neon goby, Elacatinus lori, as a model system in Belize. The choice of study system is motivated by the fact that direct genetic methods have already been used to describe the complete dispersal kernel for this species, and these observations indicate that dispersal is less extensive than predicted by a high-resolution biophysical model; E. lori can be reared in the lab from hatching to settlement providing a reliable source of larvae of all ages for proposed experiments; and a new, proven behavioral observation platform, the Drifting In Situ Chamber (DISC), allows measurements of larval orientation behavior in open water. The project has three specific objectives: to understand ontogenetic changes in larval orientation capabilities by correlating larval orientation behavior with developmental sensory anatomy; to analyze variation in the precision of larval orientation in different environmental contexts through ontogeny; and to test alternative hypotheses for the goal of larval orientation behavior, i.e., to determine where larvae are heading as they develop.";
    String projects_0_end_date "2018-04";
    String projects_0_geolocation "Belizean Barrier Reef System";
    String projects_0_name "Collaborative Research: The Role of Larval Orientation Behavior in Determining Population Connectivity";
    String projects_0_project_nid "651265";
    String projects_0_start_date "2015-05";
    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 "Distribution of E. lori settlers on sponge habitat from sampling in South Water Caye, Belize in 2015.";
    String title "Distribution of E. lori settlers on sponge habitat from sampling in South Water Caye, Belize in 2015.";
    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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