Accessing BCO-DMO data
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BCO-DMO ERDDAP
Accessing BCO-DMO data |
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| Dataset Title: | [Catch revenue by community] - Total catches and estimated revenue by species for communities-at-sea based on landings reported on Vessel Trip Reports (Adaptations of fish and fishing communities to rapid climate change) 
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| Institution: | BCO-DMO (Dataset ID: bcodmo_dataset_765560) |
| Information: | Summary
| License
| ISO 19115
| Metadata
| Background
| Files
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Attributes {
s {
community {
String bcodmo_name "site";
String description "Community-at-sea";
String long_name "Community";
String units "unitless";
}
years {
Byte _FillValue 127;
String _Unsigned "false";
Byte actual_range 8, 18;
String bcodmo_name "site_descrip";
String description "Number of years the community was extant";
String long_name "Years";
String units "unitless";
}
sppcode {
String bcodmo_name "taxon";
String description "Code used to identify species";
String long_name "Sppcode";
String units "unitless";
}
sppname {
String bcodmo_name "taxon";
String description "Species common name";
String long_name "Sppname";
String units "unitless";
}
species {
String bcodmo_name "taxon";
String description "Genus species. Only given for the species included in this study.";
String long_name "Species";
String units "unitless";
}
lbs_caught {
Int32 _FillValue 2147483647;
Int32 actual_range 0, 185511325;
String bcodmo_name "unknown";
String description "Total number of pounds caught of this species";
String long_name "Lbs Caught";
String units "pounds (lbs)";
}
totalCatch {
Int32 _FillValue 2147483647;
Int32 actual_range 551213, 511919510;
String bcodmo_name "unknown";
String description "Total catch (in pounds) of all species for this community.";
String long_name "Total Catch";
String units "pounds (lbs)";
}
pCatch {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 0.88816;
String bcodmo_name "unknown";
String description "Proportion of totalCatch comprised of this species.";
String long_name "P Catch";
String units "unitless";
}
meanPrice {
Float32 _FillValue NaN;
Float32 actual_range 0.0838, 438.3695;
String bcodmo_name "unknown";
String description "Mean price per pound (inflation adjusted) based on estimated state-level prices.";
String long_name "Mean Price";
String units "US dollars per pound (USD/lb)";
}
rev {
Float64 _FillValue NaN;
Float64 actual_range 0.0, 1.479754241e+8;
String bcodmo_name "unknown";
String description "Estimated revenue associated with catch of this species for this community";
String long_name "Rev";
String units "US dollars (USD)";
}
totalRev {
Int32 _FillValue 2147483647;
Int32 actual_range 410261, 671450908;
String bcodmo_name "unknown";
String description "Total estimated revenue for this community.";
String long_name "Total Rev";
String units "US dollars (USD)";
}
pRev {
Float32 _FillValue NaN;
Float32 actual_range 0.0, 0.95256;
String bcodmo_name "unknown";
String description "Proportion of totalRev from this species for this community.";
String long_name "P Rev";
String units "unitless";
}
}
NC_GLOBAL {
String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
String acquisition_description
"The following methods are excerpted from Rogers et al. (in press):
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.\\u00a0";
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
"Catch revenue by community
PI: Lauren Rogers & Malin Pinsky
Version date: 24-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 "2019-04-24T14:57:40Z";
String date_modified "2019-05-21T20:07:32Z";
String defaultDataQuery "&time<now";
String doi "10.1575/1912/bco-dmo.765560.1";
String history
"2024-12-21T17:47:37Z (local files)
2024-12-21T17:47:37Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_765560.html";
String infoUrl "https://www.bco-dmo.org/dataset/765560";
String institution "BCO-DMO";
String keywords "bco, bco-dmo, biological, catch, caught, chemical, community, data, dataset, dmo, erddap, lbs, lbs_caught, management, mean, meanPrice, oceanography, office, pCatch, preliminary, pRev, price, rev, species, sppcode, sppname, total, totalCatch, totalRev, years";
String license "https://www.bco-dmo.org/dataset/765560/license";
String metadata_source "https://www.bco-dmo.org/api/dataset/765560";
String param_mapping "{'765560': {}}";
String parameter_source "https://www.bco-dmo.org/mapserver/dataset/765560/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)";
String standard_name_vocabulary "CF Standard Name Table v55";
String summary "Total catches and estimated revenue by species for communities-at-sea based on landings reported on Vessel Trip Reports (VTRs). Landings data were compiled from VTRs and summed over the available years of data for each community.";
String title "[Catch revenue by community] - Total catches and estimated revenue by species for communities-at-sea based on landings reported on Vessel Trip Reports (Adaptations of fish and fishing communities to rapid climate change)";
String version "1";
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.