Accessing BCO-DMO data
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| Dataset Title: | [Attributes of communities-at-sea] - Attributes of communities-at-sea, including the size of servicesheds and climate change risk exposure scores, determined from Vessel Trip Report (VTR) data for commercial fishing trips from 1996 to 2014 (Adaptations of fish and fishing communities to rapid climate change) 
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| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_765477) |
| Information: | Summary
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Attributes {
s {
Community {
String bcodmo_name "site";
String description "Community-at-sea name, indicating port city and gear/vessel type";
String long_name "Community";
String units "unitless";
}
Port {
String bcodmo_name "site";
String description "Port city";
String long_name "Port";
String units "unitless";
}
State {
String bcodmo_name "site";
String description "State";
String long_name "State";
String units "unitless";
}
Gear {
String bcodmo_name "instrument";
String description "Type of fishing gear/vessel. Large Trawl indicates vessels longer than 65 feet using trawl gear. Small Trawl are vessels less than 65 feet using trawl gear.";
String long_name "Gear";
String units "unitless";
}
longitude {
String _CoordinateAxisType "Lon";
Float64 _FillValue NaN;
Float64 actual_range -76.693, -69.253;
String axis "X";
String bcodmo_name "longitude";
String description "Longitude of port city.";
String ioos_category "Location";
String long_name "Port Lon";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LONX/";
String standard_name "longitude";
String units "degrees_east";
}
latitude {
String _CoordinateAxisType "Lat";
Float64 _FillValue NaN;
Float64 actual_range 34.718, 43.927;
String axis "Y";
String bcodmo_name "latitude";
String description "Latitude of port city.";
String ioos_category "Location";
String long_name "Port Lat";
String nerc_identifier "https://vocab.nerc.ac.uk/collection/P09/current/LATX/";
String standard_name "latitude";
String units "degrees_north";
}
Fleetsize {
Byte _FillValue 127;
String _Unsigned "false";
Byte actual_range 3, 82;
String bcodmo_name "unknown";
String description "Average number of vessels in the community.";
String long_name "Fleetsize";
String units "number of vessels";
}
Trips {
Int32 _FillValue 2147483647;
Int32 actual_range 198, 50794;
String bcodmo_name "unknown";
String description "Total number of trips taken over the duration of the study.";
String long_name "Trips";
String units "number of trips";
}
Years {
Byte _FillValue 127;
String _Unsigned "false";
Byte actual_range 8, 18;
String bcodmo_name "unknown";
String description "Number of years the community was extant.";
String long_name "Years";
String units "number of years";
}
ShedArea {
Int32 _FillValue 2147483647;
Int32 actual_range 973, 62952;
String bcodmo_name "site_descrip";
String description "Area of the serviceshed, defined as the 90% volume contour of fisher days at sea.";
String long_name "Shed Area";
String units "square kilometers (sq km)";
}
RiskExposure {
Float32 _FillValue NaN;
Float32 actual_range -0.241, 0.112;
String bcodmo_name "unknown";
String description "Calculated risk exposure score based on projected changes in habitat suitability for harvested species. Negative values indicate increased exposure to risk.";
String long_name "Risk Exposure";
String units "unitless";
}
}
NC_GLOBAL {
String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson";
String acquisition_description
"The following methods are excerpted from Rogers et al. (in press):
A trip was classified as belonging to a community if it shared the
community's gear type and landing port, and the vessel either declared that
port as its principal port or landed in that port at least 50% of its trips
that year.
Once aggregated into communities, trips were then weighted by a variable
(\\u201cfisherdays\\u201d) indicating labor time expended on each trip: trip
length (in days) multiplied by the number of crew on board. Fisherdays
indicate how important an area at sea is to a community in terms of how much
time they invest in that location.
Given reported trip locations and fisherdays, we then created raster maps
using a kernel density method. The resultant maps distribute fisherdays using
different size kernels depending upon the fishery/gear-type/length. Nearshore
fishing was processed using a smaller kernel (7.5 - 10 km) than offshore
fishing (10 - 15 km). We used the area defined by a 90% volume contour (i.e.,
an area which encompasses 90% of fisherdays) to define the customary fishing
grounds or servicesheds for a community.
To compare the relative historical importance of particular species to a
community-at-sea, landings data were compiled from vessel trip reports and
summed over the available years of data for each community. Price information
was extracted from NOAA Fisheries, Fisheries Statistics Division
([https://www.st.nmfs.noaa.gov/st1/commercial/landings/annual_landings.html](\\\\\"https://www.st.nmfs.noaa.gov/st1/commercial/landings/annual_landings.html\\\\\")).
We used the average price per lb by species, adjusted for inflation (real 2014
prices in US$), over the period for which we had community-level data. State-
level prices were used when available, and otherwise regional prices were
used.
We assessed a community's exposure to risk based on their historical
dependence on species and spatial fishing patterns. A community was more
exposed to risk if the species from which it historically earned the most
revenue were projected to lose habitat in the locations where the community
has traditionally fished. Specifically, risk exposure scores for communities
were calculated as:
[](\\\\\"https://datadocs.bco-
dmo.org/docs/CC_Fishery_Adaptations/data_docs/765477/communityAttributesRisk_Formula.png\\\\\")
where S\\u209b,\\ua700 is the mean projected change in habitat suitability for
species s across the serviceshed of community c, and pRev\\u209b,\\ua700 is the
proportion of historical revenues from fishing that the community has derived
from species s. Positive risk exposure scores indicated expanding
opportunities for communities based on their historical fishing revenue
portfolios and projected changes to species habitat at sea, while negative
values indicated shrinking opportunities and increased exposure to negative
impacts of climate change.
The R file, \\\"Servicesheds.rData\\\" (see\\u00a0\\\"Supplemental Documents\\\" below)
is\\u00a0a spatial polygon dataframe (SPDF) giving 90% volume contours of
fisher-days at sea for 98 communities-at-sea. The polygons outline the at-sea
\\\"servicesheds\\\" or customary fishing grounds of communities. We use
\\\"serviceshed\\\"\\u00a0to describe the area from which a community has
historically received ecosystem services, specifically fish in this case. The
file is intended to be read by the program \\\"R\\\",\\u00a0with data stored in the
SPDF object \\\"Servicesheds\\\".";
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
"Attributes of communities-at-sea,
including the size of servicesheds and climate change risk exposure scores
PIs: Lauren Rogers & Malin Pinsky
Version date: 22-April-2019";
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 "2019-04-22T19:43:43Z";
String date_modified "2019-05-21T20:06:35Z";
String defaultDataQuery "&time<now";
String doi "10.1575/1912/bco-dmo.765477.1";
Float64 Easternmost_Easting -69.253;
Float64 geospatial_lat_max 43.927;
Float64 geospatial_lat_min 34.718;
String geospatial_lat_units "degrees_north";
Float64 geospatial_lon_max -69.253;
Float64 geospatial_lon_min -76.693;
String geospatial_lon_units "degrees_east";
String history
"2024-11-23T16:54:40Z (local files)
2024-11-23T16:54:40Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_765477.html";
String infoUrl "https://www.bco-dmo.org/dataset/765477";
String institution "BCO-DMO";
String keywords "area, bco, bco-dmo, biological, chemical, community, data, dataset, dmo, erddap, exposure, fleetsize, gear, management, oceanography, office, port, PortLat, PortLon, preliminary, risk, RiskExposure, shed, ShedArea, state, trips, years";
String license "https://www.bco-dmo.org/dataset/765477/license";
String metadata_source "https://www.bco-dmo.org/api/dataset/765477";
Float64 Northernmost_Northing 43.927;
String param_mapping "{'765477': {'PortLon': 'flag - longitude', 'PortLat': 'flag - latitude'}}";
String parameter_source "https://www.bco-dmo.org/mapserver/dataset/765477/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 "Stanford University";
String people_1_person_name "Lauren Rogers";
String people_1_person_nid "765425";
String people_1_role "Principal Investigator";
String people_1_role_type "originator";
String people_2_affiliation "Stanford University";
String people_2_person_name "Robert Griffin";
String people_2_person_nid "768380";
String people_2_role "Co-Principal Investigator";
String people_2_role_type "originator";
String people_3_affiliation "Rutgers University";
String people_3_person_name "Kevin St. Martin";
String people_3_person_nid "559961";
String people_3_role "Co-Principal Investigator";
String people_3_role_type "originator";
String people_4_affiliation "Princeton University";
String people_4_person_name "Emma Fuller";
String people_4_person_nid "748888";
String people_4_role "Scientist";
String people_4_role_type "originator";
String people_5_affiliation "Rutgers University";
String people_5_person_name "Talia Young";
String people_5_person_nid "752628";
String people_5_role "Scientist";
String people_5_role_type "originator";
String people_6_affiliation "National Oceanic and Atmospheric Administration - Alaska Fisheries Science Center";
String people_6_affiliation_acronym "NOAA-AFSC";
String people_6_person_name "Lauren Rogers";
String people_6_person_nid "765425";
String people_6_role "Contact";
String people_6_role_type "related";
String people_7_affiliation "Woods Hole Oceanographic Institution";
String people_7_affiliation_acronym "WHOI BCO-DMO";
String people_7_person_name "Shannon Rauch";
String people_7_person_nid "51498";
String people_7_role "BCO-DMO Data Manager";
String people_7_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 34.718;
String standard_name_vocabulary "CF Standard Name Table v55";
String summary "Communities-at-sea are peer-groups of vessels which share a gear type and are associated with a particular port (e.g., vessels from New Bedford, MA that use gillnets). For vessels using trawl gear, small and large trawlers are considered separate communities according to vessel length (<> 65 feet). We used Vessel Trip Report (VTR) data for commercial fishing trips from 1996 to 2014, as reported by vessel captains, to determine the at-sea \\servicesheds\\ or customary fishing grounds of communities.";
String title "[Attributes of communities-at-sea] - Attributes of communities-at-sea, including the size of servicesheds and climate change risk exposure scores, determined from Vessel Trip Report (VTR) data for commercial fishing trips from 1996 to 2014 (Adaptations of fish and fishing communities to rapid climate change)";
String version "1";
Float64 Westernmost_Easting -76.693;
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.