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Dataset Title:  Edge-lists for all US west coast port-group participation networks and for the
entire coast from 2009-2010 for US California Current Large Marine
Ecosystem (CCLME)
Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_748875)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Files
Variable ?   Optional
Constraint #1 ?
Constraint #2 ?
   Minimum ?
   Maximum ?
 V1 (unitless) ?              
 V2 (unitless) ?              
 weight (unitless) ?          0.193969109819506    1.02145750783526E7
 port_group (unitless) ?              
Server-side Functions ?
 distinct() ?
? (" ")

File type: (more info)

(Documentation / Bypass this form ? )
(Please be patient. It may take a while to get the data.)


The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  V1 {
    String description "node 1, a metier code";
    String ioos_category "Unknown";
    String long_name "V1";
    String units "unitless";
  V2 {
    String description "node 2, a metier code";
    String ioos_category "Unknown";
    String long_name "V2";
    String units "unitless";
  weight {
    Float64 _FillValue NaN;
    Float64 actual_range 0.193969109819506, 1.02145750783526e+7;
    String description "The fisheries connectivity between the two fisheries (see paper for definition details)";
    String ioos_category "Unknown";
    String long_name "Weight";
    String units "unitless";
  port_group {
    String description "The port group code for the network described with CCLME = to the entire US west coast";
    String ioos_category "Unknown";
    String long_name "Port Group";
    String units "unitless";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"In order to quantify and explore fisheries connectivity in the US California
Current Large Marine Ecosystem (CCLME), we first synthesized fisheries
landings ticket data for the entire region, from which we defined fisheries
and subsequently fisheries connectivity. We analyzed fisheries connectivity
using network theoretic metrics applied at the port-group level (i.e. clusters
of geographically proximate ports), and relating them to the social
vulnerability framework (Adger, 2006), with a focus on sensitivity to change
and adaptive capacity. The port-group spatial scale was chosen so as to best
represent fisheries connectivity in terms of coastal fishing communities.
However, we also calculated fisheries connectivity at larger spatial scales,
specifically at the scale of the whole CCLME. All our calculations were
performed for a short period (2009\\u20132010); 2\\u2009years without El Nino or
La Nina conditions, and without major management changes) and in the
discussion, we mention the importance of collecting longer time-series data,
from which changes in fisheries connectivity could be observed.
Port-groups were defined as:  
 NPS (Bellingham Bay, Port Townsend, Port Angeles, Anacortes, Sequim, La
Conner, Neah Bay, Friday Harbor, Blaine, other north Puget Sound ports)  
  SPS (Seattle, Olympia, Everett, Shelton, Tacoma)  
  CWA (Westport, La Push, Willapa Bay, Grays Harbor, other Washington coastal
  CLW (Ilwaco/Chinook, other Columbia River ports)  
  CLO (Astoria, Cannon Beach, Seaside-Gearhart)  
  TLA (Tillamook/Garibaldi, Pacific City, Netarts Bay, Nehalem Bay)  
  NPA (Newport, Depoe Bay, Waldport, Siletz Bay)  
  CBA (Winchester Bay, Charleston (Coos Bay), Bandon, Florence)  
  BRA (Brookings, Port Orford, Gold Beach, Crescent City, other Del Norte
county ports)  
  ERA (Trinidad, Eureka, Fields Landing, other Humboldt county ports)  
  BGA (Fort Bragg, Albion, Point Arena, other Mendocino county ports)  
  BDA (Bodga Bay, Bolinas, Point Reyes, Tomales Bay, other Sonoma and Marin
county ports)  
  SFA (Princeton/Half Moon Bay, San Francisco, Berkley, Richmond, Oakland,
Sausalito, Alameda, other SF Bay and San Mateo county ports)  
  MNA (Santa Cruz, Moss Landing, Moneterey, other Santa Crus and Monterey
county ports)  
  MRA (Morro Bay, Avila, other San Luis Obispo county ports)  
  SBA Santa Barbara, Port Hueneme, Oxnard, Ventura, other Santa Barbara
Ventura county ports)  
  LAA (Long Beach, San Pedro, Dana Point, Terminal Island, Newport Beach,
Wilmington, other LA and Orange county ports)  
  SDA (Oceanside, San Diego, other San Diego county ports)";
    String awards_0_award_nid "559952";
    String awards_0_award_number "OCE-1426746";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1426746";
    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 "Dr Michael E. Sieracki";
    String awards_0_program_manager_nid "50446";
    String awards_1_award_nid "748879";
    String awards_1_award_number "GEO-1211972";
    String awards_1_funder_name "National Science Foundation";
    String awards_1_funding_acronym "NSF";
    String awards_1_funding_source_nid "350";
    String awards_1_program_manager "Sarah L. Ruth";
    String awards_1_program_manager_nid "748878";
    String cdm_data_type "Other";
    String comment 
"Participation networks (all) 
  PI: Emma Fuller 
  Data version 1: 2018-10-26";
    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 2.2d  13 Jun 2019";
    String date_created "2018-10-26T15:40:40Z";
    String date_modified "2019-04-08T16:12:55Z";
    String defaultDataQuery "&time";
    String doi "10.1575/1912/bco-dmo.748875.1";
    String history 
"2019-06-25T10:26:57Z (local files)
2019-06-25T10:26:57Z https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_748875.html";
    String infoUrl "https://www.bco-dmo.org/dataset/748875";
    String institution "BCO-DMO";
    String keywords "bco, bco-dmo, biological, chemical, data, dataset, dmo, erddap, group, management, oceanography, office, port, port_group, preliminary, weight";
    String license 
"The data may be used and redistributed for free but is not intended
for legal use, since it may contain inaccuracies. Neither the data
Contributor, ERD, NOAA, nor the United States Government, nor any
of their employees or contractors, makes any warranty, express or
implied, including warranties of merchantability and fitness for a
particular purpose, or assumes any legal liability for the accuracy,
completeness, or usefulness, of this information.";
    String metadata_source "https://www.bco-dmo.org/api/dataset/748875";
    String param_mapping "{'748875': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/748875/parameters";
    String people_0_affiliation "Princeton University";
    String people_0_person_name "Emma Fuller";
    String people_0_person_nid "748888";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "Princeton University";
    String people_1_person_name "Emma Fuller";
    String people_1_person_nid "748888";
    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 "Adaptations of fish and fishing communities to rapid climate change";
    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 "Amber York";
    String publisher_role "BCO-DMO Data Manager(s)";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v29";
    String summary "The dataset includes edge-lists for all US west coast port-group participation networks and for the entire coast from 2009-2010 for US California Current Large Marine Ecosystem  (CCLME).";
    String title "Edge-lists for all US west coast port-group participation networks and for the entire coast from 2009-2010 for US California Current Large Marine Ecosystem  (CCLME)";
    String version "1";
    String xml_source "osprey2erddap.update_xml() v1.5-beta";


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
For example,
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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