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Dataset Title:  Collections of fish and invertebrates settled in artificial seagrass landscapes Subscribe RSS
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_784927)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Subset | Files | Make a graph
 
Variable ?   Optional
Constraint #1 ?
Optional
Constraint #2 ?
   Minimum ?
   or a List of Values ?
   Maximum ?
 
 Sample_ID (unitless) ?          "10%-0.1_6/14/2018"    "Nat9_6/21/2018"
 Landscape_type (unitless) ?          "ASU"    "Natural"
 Percent_cover_treatment (percentage (%)) ?          10.0    60.0
 Fragmentation_treatment (unitless) ?          0.1    0.59
 Site (unitless) ?          "10%-0.1"    "Nat9"
 Pick_up_date (unitless) ?          "06-07-18"    "08-17-18"
 Pick_up_time (unitless) ?          "10:01"    "NaN"
 Gear (unitless) ?      
   - +  ?
 latitude (degrees_north) ?          34.70033333    34.7067
  < slider >
 longitude (degrees_east) ?          -76.602848    -76.588016
  < slider >
 Processed_by (unitless) ?      
   - +  ?
 Taxa (unitless) ?          "Abudefduf saxatilis"    "Unidentified shrimp"
 Size (millimeter (mm)) ?          1.0    224.0
 Larvae_settler (unitless) ?          "N"    "Y"
 N (unitless) ?          0    48
 Notes (unitless) ?          "Caranx?"    "got away; 90-100 mm"
 time (Pick Up Date Time UTC ISO, UTC) ?          2018-06-07T14:01:00Z    
  < slider >
 
Server-side Functions ?
 distinct() ?
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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  Sample_ID {
    String bcodmo_name "sample";
    String description "Unique sample identifier:  site + date";
    String long_name "Sample ID";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  }
  Landscape_type {
    String bcodmo_name "sample_type";
    String description "Was the SMURF placed in a natural seagrass bed (natural) or one made of artifical seagrass units (ASU)";
    String long_name "Landscape Type";
    String units "unitless";
  }
  Percent_cover_treatment {
    Float32 _FillValue NaN;
    Float32 actual_range 10.0, 60.0;
    String bcodmo_name "cover_pcent";
    Float64 colorBarMaximum 100.0;
    Float64 colorBarMinimum 0.0;
    String description "Percent cover treatment- area of the landscape covered in artificial seagrass";
    String long_name "Percent Cover Treatment";
    String units "percentage (%)";
  }
  Fragmentation_treatment {
    Float32 _FillValue NaN;
    Float32 actual_range 0.1, 0.59;
    String bcodmo_name "treatment";
    String description "Fragmentation treatment -  percolation coefficient from random landscape generation\"";
    String long_name "Fragmentation Treatment";
    String units "unitless";
  }
  Site {
    String bcodmo_name "site";
    String description "Site name";
    String long_name "Site";
    String units "unitless";
  }
  Pick_up_date {
    String bcodmo_name "date";
    String description "Date retrieved";
    String long_name "Pick Up Date";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/ADATAA01/";
    String units "unitless";
  }
  Pick_up_time {
    String bcodmo_name "time";
    String description "Time retrieved on 24-hour clock in local time (EST)";
    String long_name "Pick Up Time";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/AHMSAA01/";
    String units "unitless";
  }
  Gear {
    String bcodmo_name "sampling_method";
    String description "Collection gear";
    String long_name "Gear";
    String units "unitless";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 _FillValue NaN;
    Float64 actual_range 34.70033333, 34.7067;
    String axis "Y";
    String bcodmo_name "latitude";
    Float64 colorBarMaximum 90.0;
    Float64 colorBarMinimum -90.0;
    String description "Latitude (South is negative)";
    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 -76.602848, -76.588016;
    String axis "X";
    String bcodmo_name "longitude";
    Float64 colorBarMaximum 180.0;
    Float64 colorBarMinimum -180.0;
    String description "Longitude (West is negative)";
    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";
  }
  Processed_by {
    String bcodmo_name "investigator";
    String description "Intials of person who processed the sample";
    String long_name "Processed By";
    String units "unitless";
  }
  Taxa {
    String bcodmo_name "taxon";
    String description "Taxa name";
    String long_name "Taxa";
    String units "unitless";
  }
  Size {
    Float32 _FillValue NaN;
    Float32 actual_range 1.0, 224.0;
    String bcodmo_name "length";
    String description "Standard length (fish), carapace length (shrimp), or carapace width (crabs)";
    String long_name "Size";
    String units "millimeter (mm)";
  }
  Larvae_settler {
    String bcodmo_name "flag";
    String description "Was the organism in the larval stage or recently settled?  Y = yes, N = no";
    String long_name "Larvae Settler";
    String units "unitless";
  }
  N {
    Byte _FillValue 127;
    Byte actual_range 0, 48;
    String bcodmo_name "count";
    String description "Count, number of individuals";
    String long_name "N";
    String units "unitless";
  }
  Notes {
    String bcodmo_name "comment";
    String description "Notes";
    String long_name "Notes";
    String units "unitless";
  }
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 1.52838006e+9, NaN;
    String axis "T";
    String bcodmo_name "ISO_DateTime_UTC";
    String description "Pick up date and time in UTC and ISO formatted. The column shows up as nd when there was no local time defined for that specific row.";
    String ioos_category "Time";
    String long_name "Pick Up Date Time UTC ISO";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/DTUT8601/";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson,.odvTxt";
    String acquisition_description 
"We measured recruitment of fishes and crabs to artificial and natural seagrass
beds in Back Sound, North Carolina from June to August 2018.\\u00a0 We sampled
26 artificial seagrass landscapes defined by 18m x 13m landscape extents (234
m2). Within each landscape, we varied habitat amount and fragmentation
independently in a crossed design. We created a custom algorithm to generate
random landscapes using the randomHabitat function in the secr package in R
(Efford 2018, R Core Team 2018). Landscapes were generated along two
orthogonal axes of habitat cover (10-60%) and fragmentation using a random
modified clusters method (percolation probability = 0.10-0.59 which determines
patch number; higher percolation coefficients correspond to less
patchiness)(Saura and Martinez-Millan 2000). Landscapes were constrained to
fall within 2% of the area input parameter, while holding patch number similar
within fragmentation levels (1, 2-3, 4-5, 6-7, or 8-10 patches for each
level). Using this approach, we constructed landscapes in which seagrass area
and number of patches were uncorrelated (R = -0.02), allowing us to
independently assess the effects of seagrass area and habitat configuration on
fish communities across experimental study sites. Artificial seagrass units
(ASUs) were used to create artificial seagrass landscapes.\\u00a0 ASUs were
made by tying green plastic ribbon to polyethylene mesh cut into 1.2m x 0.86m
rectangles, with ~450, 15 cm tall shoots (with 2 blades each) attached each
unit. ASUs were deployed by affixing ASUs in designated configurations to bare
sediment using metal lawn staples. These artificial landscapes were deployed
between May 23 to May 31, 2018.\\u00a0 We compared the artificial seagrass
landscapes to natural landscapes in the same area.\\u00a0 We sampled 18
haphazardly selected natural seagrass patches that were at least 100 m away
from the artificial seagrass landscapes.
 
We used Standardized Monitoring Units for Reef Fishes (SMURFs, Ammann 2004) to
measure recruitment of fishes and invertebrates into each landscape.\\u00a0
SMURFs used in this study were 60 x 15 x 20 cm in size with an outer shell
made from 5 cm x 7.6 cm polyethylene mesh.\\u00a0 Each SMURF was filled with
crumpled 2.5 x 2.5 cm polyethylene mesh and plastic mesh produce bags
([https://www.amazon.com/dp/B01LWL1YFT/ref=cm_sw_r_cp_api_i_J9TzCbZZ5GKR6](\\\\\"https://www.amazon.com/dp/B01LWL1YFT/ref=cm_sw_r_cp_api_i_J9TzCbZZ5GKR6\\\\\")).
For deployment, SMURFs were deployed attached to the bottom at one end, but
floated vertically in the water column.\\u00a0 SMURFs were deployed for ~24hrs,
including overnight, and collected before noon on the following day (pick-up
times are included in the data).\\u00a0 Upon retrieval, the SMURF was enclosed
in a custom made BINKE net (Anderson and Carr 1998) made out of 1.5 mm square
nylon netting.\\u00a0 The SMURF was brought to the boat within the enclosed
BINKE net and placed into a large plastic tub.\\u00a0 It was shaken vigorously
while being rinsed with buckets of seawater and then the rinse water in the
tub was visually inspected for organisms which we collected, placed in a
resealable plastic bag, and quickly froze in an ice and seawater slurry. This
process was repeated until three successive rinses yielded no new
organisms.\\u00a0 The water in the tub was passed through a 1 mm mesh sieve to
ensure no organisms remained in the tub. All organisms were returned to the
lab for processing. Each landscape was sampled on 9 different dates between
June and August, representing the major recruitment period for most fishes in
this system.";
    String awards_0_award_nid "714025";
    String awards_0_award_number "OCE-1635950";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1635950";
    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 "Michael E. Sieracki";
    String awards_0_program_manager_nid "50446";
    String awards_1_award_nid "714031";
    String awards_1_award_number "OCE-1661683";
    String awards_1_data_url "https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661683";
    String awards_1_funder_name "NSF Division of Ocean Sciences";
    String awards_1_funding_acronym "NSF OCE";
    String awards_1_funding_source_nid "355";
    String awards_1_program_manager "Michael E. Sieracki";
    String awards_1_program_manager_nid "50446";
    String cdm_data_type "Other";
    String comment 
"Smurf settlement 
  PI: Lauren Yeager  
  Data Version 1: 2019-12-23";
    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 "2019-12-20T21:38:04Z";
    String date_modified "2020-02-24T16:59:41Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.784927.1";
    Float64 Easternmost_Easting -76.588016;
    Float64 geospatial_lat_max 34.7067;
    Float64 geospatial_lat_min 34.70033333;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -76.588016;
    Float64 geospatial_lon_min -76.602848;
    String geospatial_lon_units "degrees_east";
    String history 
"2021-10-21T12:36:09Z (local files)
2021-10-21T12:36:09Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_784927.html";
    String infoUrl "https://www.bco-dmo.org/dataset/784927";
    String institution "BCO-DMO";
    String keywords "bco, bco-dmo, biological, chemical, cover, data, dataset, date, dmo, erddap, fragmentation, Fragmentation_treatment, gear, iso, landscape, Landscape_type, larvae, Larvae_settler, latitude, longitude, management, notes, oceanography, office, percent, Percent_cover_treatment, pick, Pick_up_date, Pick_up_time, PickUp_DateTime_UTC_ISO, preliminary, processed, Processed_by, sample, Sample_ID, settler, site, size, taxa, time, treatment, type";
    String license "https://www.bco-dmo.org/dataset/784927/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/784927";
    Float64 Northernmost_Northing 34.7067;
    String param_mapping "{'784927': {'Latitude': 'flag - latitude', 'PickUp_DateTime_UTC_ISO': 'flag - time', 'Longitude': 'flag - longitude'}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/784927/parameters";
    String people_0_affiliation "University of Texas - Marine Science Institute";
    String people_0_affiliation_acronym "UTMSI";
    String people_0_person_name "Lauren A. Yeager";
    String people_0_person_nid "714030";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "University of North Carolina at Chapel Hill";
    String people_1_affiliation_acronym "UNC-Chapel Hill-IMS";
    String people_1_person_name "Dr F. Joel Fodrie";
    String people_1_person_nid "559341";
    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 "Karen Soenen";
    String people_2_person_nid "748773";
    String people_2_role "BCO-DMO Data Manager";
    String people_2_role_type "related";
    String project "Habitat Fragmentation";
    String projects_0_acronym "Habitat Fragmentation";
    String projects_0_description 
"Amount and quality of habitat is thought to be of fundamental importance to maintaining coastal marine ecosystems. This research will use large-scale field experiments to help understand how and why fish populations respond to fragmentation of seagrass habitats. The question is complex because increased fragmentation in seagrass beds decreases the amount and also the configuration of the habitat (one patch splits into many, patches become further apart, the amount of edge increases, etc). Previous work by the investigators in natural seagrass meadows provided evidence that fragmentation interacts with amount of habitat to influence the community dynamics of fishes in coastal marine landscapes. Specifically, fragmentation had no effect when the habitat was large, but had a negative effect when habitat was smaller. In this study, the investigators will build artificial seagrass habitat to use in a series of manipulative field experiments at an ambitious scale. The results will provide new, more specific information about how coastal fish community dynamics are affected by changes in overall amount and fragmentation of seagrass habitat, in concert with factors such as disturbance, larval dispersal, and wave energy. The project will support two early-career investigators, inform habitat conservation strategies for coastal management, and provide training opportunities for graduate and undergraduate students. The investigators plan to target students from underrepresented groups for the research opportunities.
Building on previous research in seagrass environments, this research will conduct a series of field experiments approach at novel, yet relevant scales, to test how habitat area and fragmentation affect fish diversity and productivity. Specifically, 15 by 15-m seagrass beds will be created using artificial seagrass units (ASUs) that control for within-patch-level (~1-10 m2) factors such as shoot density and length. The investigators will employ ASUs to manipulate total habitat area and the degree of fragmentation within seagrass beds in a temperate estuary in North Carolina. In year one, response of the fishes that colonize these landscapes will be measured as abundance, biomass, community structure, as well as taxonomic and functional diversity. Targeted ASU removals will then follow to determine species-specific responses to habitat disturbance. In year two, the landscape array and sampling regime will be doubled, and half of the landscapes will be seeded with post-larval fish of low dispersal ability to test whether pre- or post-recruitment processes drive landscape-scale patterns. In year three, the role of wave exposure (a natural driver of seagrass fragmentation) in mediating fish community response to landscape configuration will be tested by deploying ASU meadows across low and high energy environments.";
    String projects_0_end_date "2019-08";
    String projects_0_geolocation "North Carolina";
    String projects_0_name "Collaborative Research: Habitat fragmentation effects on fish diversity at landscape scales: experimental tests of multiple mechanisms";
    String projects_0_project_nid "714026";
    String projects_0_start_date "2016-09";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 34.70033333;
    String standard_name_vocabulary "CF Standard Name Table v55";
    String subsetVariables "Gear,Processed_by";
    String summary "Collections of fish and invertebrates settles in artificial seagrass landscapes collected between June and August 2018";
    String time_coverage_start "2018-06-07T14:01:00Z";
    String title "Collections of fish and invertebrates settled in artificial seagrass landscapes";
    String version "1";
    Float64 Westernmost_Easting -76.602848;
    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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