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Dataset Title:  Fish species survey from the Bahamas from 2009-2012.   RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_700226)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Subset | 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 2009, 2011;
    String description "Year of observation";
    String ioos_category "Time";
    String long_name "Year";
    String units "unitless";
  }
  family {
    String description "Family of fish";
    String ioos_category "Taxonomy";
    String long_name "Family";
    String units "unitless";
  }
  site {
    String description "Name of study site (reef)";
    String ioos_category "Unknown";
    String long_name "Site";
    String units "unitless";
  }
  site_treatment {
    String description "Lionfish treatment of site (reef): Low-lionfish-density reef or High-lionfish-density reef";
    String ioos_category "Unknown";
    String long_name "Site Treatment";
    String units "unitless";
  }
  time2 {
    Byte _FillValue 127;
    Byte actual_range 0, 7;
    String description "Time step of observation (0 -7 where 0 = baseline)";
    String ioos_category "Time";
    String long_name "Time";
    String units "unitless";
  }
  site_pair {
    String description "Site pairs";
    String ioos_category "Unknown";
    String long_name "Site Pair";
    String units "unitless";
  }
  sample_type {
    String description "Sampling method used: PLOT = square 10 x 10 m plots; STRIP = 2 x 25 m line transects";
    String ioos_category "Unknown";
    String long_name "Sample Type";
    String units "unitless";
  }
  subsample_ID {
    String description "ID of sampling area per study site";
    String ioos_category "Identifier";
    String long_name "Subsample ID";
    String units "unitless";
  }
  species {
    String description "Species of fish: species codes are first two letters of genus and species (see species key)";
    String ioos_category "Taxonomy";
    String long_name "Species";
    String units "unitless";
  }
  size_1 {
    Byte _FillValue 127;
    Byte actual_range 0, 4;
    String description "Number of individuals counted of each size (1 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 1";
    String units "count";
  }
  size_2 {
    Byte _FillValue 127;
    Byte actual_range 0, 90;
    String description "Number of individuals counted of each size (2 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 2";
    String units "count";
  }
  size_3 {
    Byte _FillValue 127;
    Byte actual_range 0, 84;
    String description "Number of individuals counted of each size (3 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 3";
    String units "count";
  }
  size_4 {
    Byte _FillValue 127;
    Byte actual_range 0, 51;
    String description "Number of individuals counted of each size (4 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 4";
    String units "count";
  }
  size_5 {
    Byte _FillValue 127;
    Byte actual_range 0, 44;
    String description "Number of individuals counted of each size (5 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 5";
    String units "count";
  }
  size_6 {
    Byte _FillValue 127;
    Byte actual_range 0, 81;
    String description "Number of individuals counted of each size (6 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 6";
    String units "count";
  }
  size_15 {
    Byte _FillValue 127;
    Byte actual_range 0, 75;
    String description "Number of individuals counted of each size (15 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 15";
    String units "count";
  }
  size_20 {
    Byte _FillValue 127;
    Byte actual_range 0, 50;
    String description "Number of individuals counted of each size (20 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 20";
    String units "count";
  }
  size_25 {
    Byte _FillValue 127;
    Byte actual_range 0, 20;
    String description "Number of individuals counted of each size (25 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 25";
    String units "count";
  }
  size_30 {
    Byte _FillValue 127;
    Byte actual_range 0, 6;
    String description "Number of individuals counted of each size (30 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 30";
    String units "count";
  }
  size_35 {
    Byte _FillValue 127;
    Byte actual_range 0, 4;
    String description "Number of individuals counted of each size (35 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 35";
    String units "count";
  }
  size_40 {
    Byte _FillValue 127;
    Byte actual_range 0, 2;
    String description "Number of individuals counted of each size (40 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 40";
    String units "count";
  }
  size_45 {
    Byte _FillValue 127;
    Byte actual_range 0, 2;
    String description "Number of individuals counted of each size (45 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 45";
    String units "count";
  }
  size_50 {
    Byte _FillValue 127;
    Byte actual_range 0, 1;
    String description "Number of individuals counted of each size (50 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 50";
    String units "count";
  }
  size_100 {
    Byte _FillValue 127;
    Byte actual_range 0, 1;
    String description "Number of individuals counted of each size (100 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 100";
    String units "count";
  }
  size_150 {
    Byte _FillValue 127;
    Byte actual_range 0, 0;
    String description "Number of individuals counted of each size (150 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 150";
    String units "count";
  }
  size_200 {
    Byte _FillValue 127;
    Byte actual_range 0, 0;
    String description "Number of individuals counted of each size (200 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 200";
    String units "count";
  }
  size_250 {
    Byte _FillValue 127;
    Byte actual_range 0, 0;
    String description "Number of individuals counted of each size (250 cm total body length)";
    String ioos_category "Unknown";
    String long_name "Size 250";
    String units "count";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"Methods from Kindinger and Albins (2016) \\\"Consumptive and non-consumptive
effects of an invasive marine predator on native\\u00a0coral-
reef\\u00a0herbivores\\\"\\u00a0doi:\\u00a0[10.1007/s10530-016-1268-1](\\\\\"https://link.springer.com/article/10.1007%2Fs10530-016-1268-1\\\\\")
 
Visual surveys of reef fishes were conducted by a pair of SCUBA divers
throughout (seafloor to surface) two permanent square plots (10 9 10 m) and
four permanent strip transects (2 9 25 m), for a total area of 400 m2 per reef
(see Albins 2015 for detailed description). We positioned square plots to
include areas of the reef with the highest apparent relief, and strip
transects were placed randomly across the remaining hard substrate, with the
intent of including all important high-relief habitat features. Divers
conducted censuses of each sampling unit whereby each fish was identified to
the species-level and total length (TL) was visually estimated to the nearest
cm. Paired reefs (low- and high-lionfish-densities) were surveyed within 24 h
by the same set of observers, and all reefs were surveyed by the author (M.
Albins). Every 3\\u20135 months thereafter, we resurveyed the fish community at
all experimental reefs.
 
We quantified CEs of invasive lionfish on native herbivorous fish populations
throughout the 2-year experiment by comparing the change in density and
biomass of small and large herbivorous fishes between lionfish-density
treatments. Small fish were B 10 cm TL, which encompasses the majority of prey
fish sizes reported in invasive lionfish gut-content studies for the size
range of lionfish (2\\u201335 cm TL) observed on our experimental reefs (Morris
and Akins 2009 ; Mun\\u02dcoz et al. 2011 ). Responses of fish [ 10 cm TL were
consistent, regardless of whether individuals were binned into medium
(11\\u201320 cm TL) and large ([ 20 cm TL) size classes, so hereafter we refer
to all fish[ 10 cm TL as large . To determine the relative response of
different sub guilds of herbivorous fishes, we also calculated the change in
small and large fish density and biomass by fish family: (1) parrotfishes
(Labridae); (2) surgeonfishes (Acanthuridae); (3) angelfishes (Pomacanthidae);
and (4) damselfishes (Pomacentridae). We used published length-weight
conversions to calculate fish biomass; parameters of closely related species
were used when conversions were not available (Online Resource 1). We
calculated changes in fish density and biomass at every survey interval by
subtracting the baseline value (prior to initial lionfish manipulation) for
each sub-sample (plots and transects) from the corresponding value of each
subsequent survey.
 
To test for an effect of invasive lionfish through time on changes in density
and/or biomass of each group of native fishes (described above), we fitted
linear mixed effects models (LMMs) with lionfish density treatment and time as
categorical fixed effects, and sub -sample nested within reef as random
effects (Pinheiro and Bates 2000 ; Bolker et al. 2009 ; Zuur et al. 2009 ).
Time was a categorical variable because we had no a priori reason to assume
any linear relationships with response variables. Full models included
weighted terms allowing variances to differ among reefs and AR1 covariance
structures to account for temporal autocorrelation (Zuur et al. 2009 ). We
fitted full and reduced models (with vs. without weighted terms and/or AR1
structures) using restricted maximum likelihood (REML) and compared full and
reduced models using Akaike\\u2019s Information Criterion (AIC) and likelihood
ratio tests (LRTs, Online Resource 2). Visual examination of residuals of the
best-fit models indicated that the assumptions of normality, homogeneity, and
independence were all met.
 
To assess the significance of fixed effects, we refit each model using maximum
likelihood estimation (ML) and applied LRTs (Zuur et al. 2009 ). Fixed effects
that were not significant were sequentially dropped from models. The resulting
best-fit models in terms of variance structure, temporal correlation, and
fixed effects were refit using REML in order to estimate the fixed-effects
parameters and associated effect sizes. If LRTs indicated the lionfish 9 time
interaction was significant, we made simultaneous inferences about the
marginal effects of the lionfish treatment at each survey period, and adjusted
the associated p values to maintain an approximately 5 % family-wise error
rate (Hothorn et al. 2008 ). Regardless of whether the lionfish 9 time
interaction was significant, we estimated expected values and standard error
of the means (SEMs) for all response variables from low- and high-lionfish-
density treatments during each survey period. We also fit LMMs to compare the
baseline levels of each response variable between lionfish-density treatments
using a similar procedure to the one outlined above, but with density and
biomass of each group of small and large fishes (described above) as the
response (rather than the change in these variables). Additionally, we fit
LMMs to assess whether small (B 10 cm) and large ([ 10 cm) native
mesopredators (Online Resource 1) that are potentially ecologically-similar to
invasive lionfish differed between the reefs assigned to each lionfish density
treatment at the baseline survey (mesopredator density and biomass) and at
each subsequent survey period (change in mesopredator density and biomass).";
    String awards_0_award_nid "561016";
    String awards_0_award_number "OCE-1233027";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1233027";
    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 David  L. Garrison";
    String awards_0_program_manager_nid "50534";
    String cdm_data_type "Other";
    String comment 
"Fish Survey 
  M. Hixon and T. Kindinger 
  Version 16 May 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 2.2d  13 Jun 2019";
    String date_created "2017-05-18T16:12:37Z";
    String date_modified "2019-03-28T18:34:53Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/bco-dmo.700226.1";
    String history 
"2019-06-25T09:32:48Z (local files)
2019-06-25T09:32:48Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_700226.das";
    String infoUrl "https://www.bco-dmo.org/dataset/700226";
    String institution "BCO-DMO";
    String keywords "bco, bco-dmo, biological, chemical, data, dataset, dmo, erddap, family, identifier, management, oceanography, office, pair, preliminary, sample, sample_type, site, site_pair, site_treatment, size, size_1, size_100, size_15, size_150, size_2, size_20, size_200, size_25, size_250, size_3, size_30, size_35, size_4, size_40, size_45, size_5, size_50, size_6, species, subsample, subsample_ID, taxonomy, time, time2, treatment, type, year";
    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/700226";
    String param_mapping "{'700226': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/700226/parameters";
    String people_0_affiliation "University of Hawaii";
    String people_0_person_name "Dr Mark Hixon";
    String people_0_person_nid "51647";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Auburn University";
    String people_1_person_name "Mark  A Albins";
    String people_1_person_nid "51666";
    String people_1_role "Scientist";
    String people_1_role_type "originator";
    String people_2_affiliation "Oregon State University";
    String people_2_affiliation_acronym "OSU";
    String people_2_person_name "Tye  L Kindinger";
    String people_2_person_nid "51707";
    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 "Mechanisms and Consequences of Fish Biodiversity Loss on Atlantic Coral Reefs Caused by Invasive Pacific Lionfish";
    String projects_0_acronym "BiodiversityLossEffects_lionfish";
    String projects_0_description 
"The Pacific red lionfish (Pterois volitans), a popular aquarium fish, was introduced to the Atlantic Ocean in the vicinity of Florida in the late 20th century. Voraciously consuming small native coral-reef fishes, including the juveniles of fisheries and ecologically important species, the invader has undergone a population explosion that now ranges from the U.S. southeastern seaboard to the Gulf of Mexico and across the greater Caribbean region. The PI's past research determined that invasive lionfish (1) have escaped their natural enemies in the Pacific (lionfish are much less abundant in their native range); (2) are not yet controlled by Atlantic predators, competitors, or parasites; (3) have strong negative effects on populations of native Atlantic fishes; and (4) locally reduce the diversity (number of species) of native fishes. The lionfish invasion has been recognized as one of the major conservation threats worldwide.
The Bahamas support the highest abundances of invasive lionfish globally. This system thus provides an unprecedented opportunity to understand the direct and indirect effects of a major invader on a diverse community, as well as the underlying causative mechanisms. The PI will focus on five related questions: (1) How does long-term predation by lionfish alter the structure of native reef-fish communities? (2) How does lionfish predation destabilize native prey population dynamics, possibly causing local extinctions? (3) Is there a lionfish-herbivore-seaweed trophic cascade on invaded reefs? (4) How do lionfish modify cleaning mutualisms on invaded reefs? (5) Are lionfish reaching densities where natural population limits are evident?�";
    String projects_0_end_date "2016-07";
    String projects_0_geolocation "Three Bahamian sites: 24.8318, -076.3299;  23.8562, -076.2250; 23.7727, -076.1071; Caribbean Netherlands: 12.1599, -068.2820";
    String projects_0_name "Mechanisms and Consequences of Fish Biodiversity Loss on Atlantic Coral Reefs Caused by Invasive Pacific Lionfish";
    String projects_0_project_nid "561017";
    String projects_0_project_website "http://hixon.science.oregonstate.edu/content/highlight-lionfish-invasion";
    String projects_0_start_date "2012-08";
    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 subsetVariables "size_150, size_200, size_250";
    String summary "Fish species survey from the Bahamas from 2009-2012.";
    String title "Fish species survey from the Bahamas from 2009-2012.";
    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
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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