Accessing BCO-DMO data
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| Dataset Title: | [Observed and modeled presence 1968-2014] - Trawl survey data and species distribution model predictions for presence, absence and abundance (Adaptations of fish and fishing communities to rapid climate change) 
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| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_753142) |
| Information: | Summary
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Attributes {
s {
spp {
String bcodmo_name "species";
String description "species scientific name";
String long_name "SPP";
String units "unitless";
}
haulid {
String bcodmo_name "haul identification";
String description "trawl haulid created from CRUISE6 (196803), STATION with max of 3 digits (8), and STRATUM (1010) from NEFSC survey data";
String long_name "Haulid";
String units "unitless";
}
latitude {
String _CoordinateAxisType "Lat";
Float64 _FillValue NaN;
Float64 actual_range 35.25, 44.5;
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 source_name "lat_25";
String standard_name "latitude";
String units "degrees_north";
}
longitude {
String _CoordinateAxisType "Lon";
Float64 _FillValue NaN;
Float64 actual_range -75.75, -65.75;
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 source_name "lon_25";
String standard_name "longitude";
String units "degrees_east";
}
btemp {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 20.7;
String bcodmo_name "temperature";
String description "in situ bottom temperature from trawl";
String long_name "Btemp";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
String units "degrees Celsius";
}
stemp {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 20.7;
String bcodmo_name "temperature";
String description "in situ surface temperature from trawl";
String long_name "Stemp";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/TEMPP901/";
String units "degrees Celsius";
}
depth {
String _CoordinateAxisType "Height";
String _CoordinateZisPositive "down";
Float64 _FillValue NaN;
Float64 actual_range 13.0, 485.0;
String axis "Z";
String bcodmo_name "depth";
Float64 colorBarMaximum 8000.0;
Float64 colorBarMinimum -8000.0;
String colorBarPalette "TopographyDepth";
String description "trawl depth";
String ioos_category "Location";
String long_name "Depth";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/DEPH/";
String positive "down";
String standard_name "depth";
String units "m";
}
sed_grain {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 255.0;
String bcodmo_name "sediment grain size";
String description "relative sediment grain size, a proxy for habitat type";
String long_name "Sed Grain";
String units "unitless";
}
mean_wtcpue {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 153.459;
String bcodmo_name "biomass";
String description "annual mean biomass (kg) per tow for the species for all hauls in region";
String long_name "Mean Wtcpue";
String units "kilograms per tow (kg/tow)";
}
wtcpue {
Float64 _FillValue NaN;
Float64 actual_range 0.0, 14519.493;
String bcodmo_name "biomass";
String description "biomass per tow in given haul";
String long_name "Wtcpue";
String units "kilograms per tow (kg/tow)";
}
preds1 {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 0.989;
String bcodmo_name "unknown";
String description "predicted probability of occurrence (0-1)";
String long_name "Preds1";
String units "dimensionless";
}
preds1_upr {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 0.993;
String bcodmo_name "unknown";
String description "upper bound of predicted probability of occurrence (fit + 2SE)";
String long_name "Preds1 Upr";
String units "dimensionless";
}
preds1_lwr {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 0.983;
String bcodmo_name "unknown";
String description "lower bound of predicted probability of occurrence (fit - 2SE)";
String long_name "Preds1 Lwr";
String units "dimensionless";
}
preds {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 757.69;
String bcodmo_name "biomass";
String description "predicted biomass";
String long_name "Preds";
String units "kilograms (kg)";
}
year {
Int16 _FillValue 32767;
Int16 actual_range 1968, 2014;
String bcodmo_name "year";
String description "year in format yyyy";
String long_name "Year";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/YEARXXXX/";
String units "unitless";
}
pres2 {
String bcodmo_name "unknown";
String description "observed presence (TRUE) or absence (FALSE)";
String long_name "Pres2";
String units "unitless";
}
}
NC_GLOBAL {
String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
String acquisition_description
"NMFS Trawl Survey data was used to fit species distribution models and the
resulting modeled predictions for presence/absence and abundance.
We analyzed the influence of environmental characteristics on the spatial
distribution of our eight focal species using data collected by the Northeast
Fisheries Science Center (NEFSC) spring (March-May), and fall (September-
November) bottom trawl surveys along the Northeast US Shelf (65-75\\u00b0W
longitude, and 35-45\\u00b0N latitude) for the period 1968-2014. Sea surface
temperature, sea bottom temperature, and depth were sampled concurrently with
trawl samples. We used sediment grain size as a measure of substrate type
using existing data layers from the Nature Conservancy. Surveys were trimmed
to strata that were sampled in at least 43 of the 46 years. Data from the fall
and spring surveys 1968-2014 were combined in order to fit species
distribution models (SDMs) using a generalized additive model (GAM) fit
separately for each species. Presence or absence of species x in haul location
i in year y was modeled using a logistic model with a binomial error
distribution and a logit link function. The probability of species occurrence
in each haul was modeled as an additive function of the five environmental
variables and regional species biomass: haul-specific observations of sea
surface temperature, sea bottom temperature, depth, sediment grain size, and
average region-wide biomass (kg/tow) of species x in year y in the season in
which the haul was conducted. Penalized regression splines were fitted using
the \\u201cgam\\u201d function in the mgcv package in R.
In addition to examining changes in overall range size, we also predicted
historical and projected biomass using a delta-lognormal GAM that combines the
predictions of the presence/absence model with that for biomass when present.
Range size and species overlap wer calculated using the R-file
species_overlap_BCO.R available in the \"Supplemental Documents\" section on
this page.";
String awards_0_award_nid "559955";
String awards_0_award_number "OCE-1426891";
String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1426891";
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 cdm_data_type "Other";
String comment
"Observed and modeled presence 1968-2014
PI: Malin Pinsky
data version 1: 2019-03-05";
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 "2019-01-17T17:11:25Z";
String date_modified "2019-11-08T18:56:21Z";
String defaultDataQuery "&time<now";
String doi "10.1575/1912/bco-dmo.753142.1";
Float64 Easternmost_Easting -65.75;
Float64 geospatial_lat_max 44.5;
Float64 geospatial_lat_min 35.25;
String geospatial_lat_units "degrees_north";
Float64 geospatial_lon_max -65.75;
Float64 geospatial_lon_min -75.75;
String geospatial_lon_units "degrees_east";
Float64 geospatial_vertical_max 485.0;
Float64 geospatial_vertical_min 13.0;
String geospatial_vertical_positive "down";
String geospatial_vertical_units "m";
String history
"2024-12-21T16:21:46Z (local files)
2024-12-21T16:21:46Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_753142.html";
String infoUrl "https://www.bco-dmo.org/dataset/753142";
String institution "BCO-DMO";
String keywords "bco, bco-dmo, biological, btemp, chemical, data, dataset, depth, dmo, erddap, grain, haulid, latitude, longitude, lwr, management, mean, mean_wtcpue, oceanography, office, preds, preds1, preds1_lwr, preds1_upr, preliminary, pres2, sed, sed_grain, spp, stemp, upr, wtcpue, year";
String license "https://www.bco-dmo.org/dataset/753142/license";
String metadata_source "https://www.bco-dmo.org/api/dataset/753142";
Float64 Northernmost_Northing 44.5;
String param_mapping "{'753142': {'depth': 'flag - depth', 'lat_25': 'flag - latitude', 'lon_25': 'flag - longitude'}}";
String parameter_source "https://www.bco-dmo.org/mapserver/dataset/753142/parameters";
String people_0_affiliation "Rutgers University";
String people_0_person_name "Malin Pinsky";
String people_0_person_nid "554708";
String people_0_role "Principal Investigator";
String people_0_role_type "originator";
String people_1_affiliation "Rutgers University";
String people_1_person_name "Rebecca Selden";
String people_1_person_nid "753186";
String people_1_role "Contact";
String people_1_role_type "related";
String people_2_affiliation "Woods Hole Oceanographic Institution";
String people_2_affiliation_acronym "WHOI BCO-DMO";
String people_2_person_name "Amber York";
String people_2_person_nid "643627";
String people_2_role "BCO-DMO Data Manager";
String people_2_role_type "related";
String project "CC Fishery Adaptations";
String projects_0_acronym "CC Fishery Adaptations";
String projects_0_description
"Description from NSF award abstract:
Climate change presents a profound challenge to the sustainability of coastal systems. Most research has overlooked the important coupling between human responses to climate effects and the cumulative impacts of these responses on ecosystems. Fisheries are a prime example of this feedback: climate changes cause shifts in species distributions and abundances, and fisheries adapt to these shifts. However, changes in the location and intensity of fishing also have major ecosystem impacts. This project's goal is to understand how climate and fishing interact to affect the long-term sustainability of marine populations and the ecosystem services they support. In addition, the project will explore how to design fisheries management and other institutions that are robust to climate-driven shifts in species distributions. The project focuses on fisheries for summer flounder and hake on the northeast U.S. continental shelf, which target some of the most rapidly shifting species in North America. By focusing on factors affecting the adaptation of fish, fisheries, fishing communities, and management institutions to the impacts of climate change, this project will have direct application to coastal sustainability. The project involves close collaboration with the National Oceanic and Atmospheric Administration, and researchers will conduct regular presentations for and maintain frequent dialogue with the Mid-Atlantic and New England Fisheries Management Councils in charge of the summer flounder and hake fisheries. To enhance undergraduate education, project participants will design a new online laboratory investigation to explore the impacts of climate change on fisheries, complete with visualization tools that allow students to explore inquiry-driven problems and that highlight the benefits of teaching with authentic data. This project is supported as part of the National Science Foundation's Coastal Science, Engineering, and Education for Sustainability program - Coastal SEES.
The project will address three questions:
1) How do the interacting impacts of fishing and climate change affect the persistence, abundance, and distribution of marine fishes?
2) How do fishers and fishing communities adapt to species range shifts and related changes in abundance? and
3) Which institutions create incentives that sustain or maximize the value of natural capital and comprehensive social wealth in the face of rapid climate change?
An interdisciplinary team of scientists will use dynamic range and statistical models with four decades of geo-referenced data on fisheries catch and fish biogeography to determine how fish populations are affected by the cumulative impacts of fishing, climate, and changing species interactions. The group will then use comprehensive information on changes in fisher behavior to understand how fishers respond to changes in species distribution and abundance. Interviews will explore the social, regulatory, and economic factors that shape these strategies. Finally, a bioeconomic model for summer flounder and hake fisheries will examine how spatial distribution of regulatory authority, social feedbacks within human communities, and uncertainty affect society's ability to maintain natural and social capital.";
String projects_0_end_date "2018-08";
String projects_0_geolocation "Northeast US Continental Shelf Large Marine Ecosystem";
String projects_0_name "Adaptations of fish and fishing communities to rapid climate change";
String projects_0_project_nid "559948";
String projects_0_start_date "2014-09";
String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
String publisher_type "institution";
String sourceUrl "(local files)";
Float64 Southernmost_Northing 35.25;
String standard_name_vocabulary "CF Standard Name Table v55";
String summary "NMFS Trawl Survey data used to fit species distribution models and the resulting modeled predictions for presence/absence and abundance.";
String title "[Observed and modeled presence 1968-2014] - Trawl survey data and species distribution model predictions for presence, absence and abundance (Adaptations of fish and fishing communities to rapid climate change)";
String version "1";
Float64 Westernmost_Easting -75.75;
String xml_source "osprey2erddap.update_xml() v1.3";
}
}
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.
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.