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Dataset Title:  Bag Seine Catch Data in Bays along the Texas Coast from 1982 to 2016 Subscribe RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_773137)
Range: longitude = 934311.0 to 974440.0°E, latitude = 260100.0 to 300320.0°N
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 {
  major_area {
    Byte _FillValue 127;
    Byte actual_range 1, 8;
    String bcodmo_name "Site_ID";
    String description "ID number for bays";
    String long_name "Major Area";
    String units "unitless";
  }
  Year {
    Int16 _FillValue 32767;
    Int16 actual_range 1982, 2016;
    String bcodmo_name "year";
    String description "4-digit year";
    String long_name "Year";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/YEARXXXX/";
    String units "unitless";
  }
  Month {
    Byte _FillValue 127;
    Byte actual_range 1, 12;
    String bcodmo_name "month";
    String description "1- or 2-digit month";
    String long_name "Month";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/MNTHXXXX/";
    String units "unitless";
  }
  station_id {
    String bcodmo_name "station";
    String description "Station ID number (from the bag seine data file)";
    String long_name "Station Id";
    String units "unitless";
  }
  catch {
    Int32 _FillValue 2147483647;
    Int32 actual_range 1, 107309;
    String bcodmo_name "count";
    String description "Number of individuals caught";
    String long_name "Catch";
    String units "unitless";
  }
  species_code {
    Int16 _FillValue 32767;
    Int16 actual_range 2, 9876;
    String bcodmo_name "taxon_code";
    String description "Species code";
    String long_name "Species Code";
    String units "unitless";
  }
  Species_common_name {
    String bcodmo_name "common_name";
    String description "Common name of species";
    String long_name "Species Common Name";
    String units "unitless";
  }
  Species_latin_name {
    String bcodmo_name "species";
    String description "Latin name of species";
    String long_name "Species Latin Name";
    String units "unitless";
  }
  Diss_Oxygen {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 32.1;
    String bcodmo_name "dissolved Oxygen";
    String description "Dissolved oxygen";
    String long_name "Diss Oxygen";
    String units "parts per million (PPM)";
  }
  Salinity {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 92.5;
    String bcodmo_name "sal";
    Float64 colorBarMaximum 37.0;
    Float64 colorBarMinimum 32.0;
    String description "Salinity";
    String long_name "Sea Water Practical Salinity";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/PSALST01/";
    String units "PPT";
  }
  Temperature {
    Float32 _FillValue NaN;
    Float32 actual_range 1.4, 39.9;
    String bcodmo_name "temperature";
    String description "Temperature";
    String long_name "Temperature";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
    String units "degrees Celsius";
  }
  Turbidity {
    Int16 _FillValue 32767;
    Int16 actual_range 0, 962;
    String bcodmo_name "turbidity";
    String description "Turbidity";
    String long_name "Turbidity";
    Int16 missing_value 999;
    String units "NTU";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 260100.0, 300320.0;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude North. Formatted as degree-minutes-seconds without hyphens or separators. This is described in the manual and provided in this format to be consistent with other TPWD data.";
    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 934311.0, 974440.0;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude West. Formatted as degree-minutes-seconds without hyphens or separators. This is described in the manual and provided in this format to be consistent with other TPWD data. When converting to decimal degrees, add a minus sign to indicate the west direction).";
    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";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
    String acquisition_description 
"The data were sampled in Sabine Lake, Galveston Bay, Matagorda Bay, San
Antonio Bay, Aransas Bay, Corpus Christi Bay, the upper Laguna Madre, and the
lower Laguna Madre from January 1982 to December 2016 (except in Sabine Lake,
where sampling begun in January 1986). The surveys were conducted bi-weekly
using bag seines (18.3 m long and 1.8 m deep with 19 mm stretched nylon mesh
in wings and 13 mm stretched mesh in the bag), which were deployed along the
shoreline. Bag seines were deployed multiple times during the first and second
halves of the month in every bay system. The location of each sample was
determined by randomly selecting one station from a predefined sampling
universe and, once in the field, selecting a section of available shoreline
within that station. At the selected sampling location, the bag seine was
extended 12.2 m perpendicularly to the shoreline, then pulled parallel to the
shoreline over 15.5 m. The offshore end was then retrieved to shore while
keeping the onshore end stationary and maintaining the full extent (12.2 m) of
the bag seine using a limit line. Organisms greater than 5 mm in total length
were identified to the lowest taxonomic level.\\u00a0 Further details of
sampling protocols are described in the [Marine Resource Monitoring Operations
Manual](\\\\\"http://datadocs.bco-dmo.org/docs/Texas_Coastal_Fish/data_docs/CF-
Mar-Res-Mon-Ops-Manual-2015.pdf\\\\\")\\u00a0(PDF).\\u00a0
 
Note:\\u00a0All station_id numbers\\u00a0are included at least once in this
dataset (e.g.\\u00a0some organisms were observed at every station/sampling
event).";
    String awards_0_award_nid "704688";
    String awards_0_award_number "OCE-1656923";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1656923";
    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 
"Bag Seine Catch Data 
  PI: Masami Fujiwara (Texas A&M) 
  Co-PI: Fernando Martinez-Andrade (Texas Parks & Wildlife Dept) 
  Version date: 20-August-2019 
  Note: All station_id numbers are included at least once in this dataset  
       (e.g. some organisms were observed at every station/sampling event).";
    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 "2019-07-15T20:02:44Z";
    String date_modified "2019-09-23T19:42:38Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.773137.1";
    Float64 Easternmost_Easting 974440.0;
    Float64 geospatial_lat_max 300320.0;
    Float64 geospatial_lat_min 260100.0;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 974440.0;
    Float64 geospatial_lon_min 934311.0;
    String geospatial_lon_units "degrees_east";
    String history 
"2020-09-18T13:42:52Z (local files)
2020-09-18T13:42:52Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_773137.das";
    String infoUrl "https://www.bco-dmo.org/dataset/773137";
    String institution "BCO-DMO";
    String instruments_0_dataset_instrument_description "Data were collected using a bag seine: 18.3 m long and 1.8 m deep with 19 mm stretched nylon mesh in wings and 13 mm stretched mesh in the bag.";
    String instruments_0_dataset_instrument_nid "773261";
    String instruments_0_description 
"A seine net is a very long net, with or without a bag in the centre, which is set either from the shore or from a boat for surrounding a certain area and is operated with two (long) ropes fixed to its ends (for hauling and herding the fish).

Seine nets are operated both in inland and in marine waters. The surrounded and catching area depends on the length of the seine and of the hauling lines.

(definition from: fao.org)";
    String instruments_0_instrument_name "Seine Net";
    String instruments_0_instrument_nid "716403";
    String instruments_0_supplied_name "Bag seine";
    String keywords "area, bco, bco-dmo, biological, catch, chemical, code, common, data, dataset, density, diss, Diss_Oxygen, dmo, earth, Earth Science > Oceans > Salinity/Density > Salinity, erddap, latin, latitude, longitude, major, major_area, management, month, name, O2, ocean, oceanography, oceans, office, oxygen, practical, preliminary, salinity, science, sea, sea_water_practical_salinity, seawater, species, species_code, Species_common_name, Species_latin_name, station, station_id, temperature, turbidity, water, year";
    String keywords_vocabulary "GCMD Science Keywords";
    String license "https://www.bco-dmo.org/dataset/773137/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/773137";
    Float64 Northernmost_Northing 300320.0;
    String param_mapping "{'773137': {'Latitude': 'master - latitude', 'Longitude': 'master - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/773137/parameters";
    String people_0_affiliation "Texas A&M University";
    String people_0_affiliation_acronym "TAMU";
    String people_0_person_name "Masami Fujiwara";
    String people_0_person_nid "704691";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Texas Parks and Wildlife Department";
    String people_1_person_name "Fernando Martinez-Andrade";
    String people_1_person_nid "773142";
    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 "Shannon Rauch";
    String people_2_person_nid "51498";
    String people_2_role "BCO-DMO Data Manager";
    String people_2_role_type "related";
    String project "Texas Coastal Fish";
    String projects_0_acronym "Texas Coastal Fish";
    String projects_0_description 
"NSF Award Abstract:
Understanding how changes in environmental conditions affect biota in the oceans is critically important for maintaining biodiversity and sustainable fisheries and projecting potential responses to future climate scenarios. The aims of this project are to determine how the distribution of fish and invertebrates has changed over time along the Texas coast and to assess the extent to which these changes are attributable to changes in local environmental conditions, such as sea surface temperature, coastal sea level, salinity, turbidity, and river discharge rate. Studies of biological systems in the Gulf of Mexico are lacking compared to coastal research in the Atlantic and Pacific oceans. Addressing these regional knowledge gaps is crucial because the Gulf of Mexico supports a wide diversity of temperate and tropical species that are ecologically and economically important. Poleward shifts in species distributions associated with increasing sea surface temperature have been observed along the Atlantic and Pacific coasts. In contrast, the northern edge of the Gulf of Mexico is bound by land that places biogeographic constraints on the potential responses of coastal organisms to changing environmental conditions. This project will use advanced statistical methods to analyze long-term species composition data for the northwestern Gulf of Mexico and characterize past relationships of species composition and local environmental conditions. These findings will help guide the development of predictive models to assess potential biological responses to projected environmental conditions. Research results will be shared with local and state resource agencies responsible for managing coastal fisheries. As an integral part of this project, a three-level (faculty-graduate-undergraduate) mentoring system will be established to promote diversity in science through undergraduate and graduate training. Undergraduate students will be recruited through the Texas A&M University Chapter of the Society for Advancement of Chicano and Native Americans in Science (SACNAS), for which the principal investigator is currently a faculty advisor. Both graduate and undergraduate students will work as a team on the project and develop quantitative data analysis and other general scientific skills. Finally, the research program will be used as a case study for establishing mentoring systems for promoting diversity in science.
The availability of long-term species composition data provides a unique opportunity to substantially improve knowledge toward understanding the effects of climate change on marine organisms in a low latitude system. This project will examine species composition data for eight bays distributed over approximately 650 km of the Texas coast; comprehensive data of this type are uncommon elsewhere. The biological data have been collected over 35-40 years as part of a long-term monitoring program and includes information on more than 1000 species of fish and invertebrates. This unique dataset will be analyzed using modern statistical approaches, including occupancy data analysis, co-integration method, and state-space vector autoregressive modeling. These methods overcome common difficulties in statistical analyses, including datasets having multi-collinearity among independent variables and those involving non-stationarity. Based on the results of the statistical analyses, models enabling the prediction of species composition under projected local environmental conditions will be developed. As part of this project, undergraduate and graduate students will acquire expertise in contemporary analytical methods, research findings will be broadly shared with both the academic and resource management communities, and computational code will be made publically available. This project will provide better understanding of the effects of environmental conditions on fish and invertebrate distribution and will provide valuable information for improved fishery management and conservation efforts under changing environmental conditions.";
    String projects_0_end_date "2020-05";
    String projects_0_geolocation "coastal bays, Texas";
    String projects_0_name "Effects of physical environmental conditions on the species distribution and composition of marine fish and invertebrates along the Texas coast";
    String projects_0_project_nid "704689";
    String projects_0_start_date "2017-06";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 260100.0;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "Data on vertebrates and invertebrates caught by bag seine in Sabine Lake, Galveston Bay, Matagorda Bay, San Antonio Bay, Aransas Bay, Corpus Christi Bay, Upper Laguna Madre, and Lower Laguna Madre. Data were collected monthly from 1982 to 2016 (except in Sabine Lake sampling begun in 1986). Environmental data include oxygen, salinity, temperature, and turbidity.";
    String title "Bag Seine Catch Data in Bays along the Texas Coast from 1982 to 2016";
    String version "1";
    Float64 Westernmost_Easting 934311.0;
    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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