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Dataset Title:  Changes in groundfish fishing communities in the northeast US from 1997-2014
as captured in the vessel trip report (VTR) data collected by the National
Oceanic and Atmospheric Administration National Marine Fisheries Service (NOAA-
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Institution:  BCO-DMO   (Dataset ID: bcodmo_dataset_752624)
Information:  Summary ? | License ? | ISO 19115 | Metadata | Background (external link) | Data Access Form | Files
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Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
 s {
  community {
    String bcodmo_name "sample";
    String description "Fishing community based on port, gear, and vessel size (greater than or smaller than 65'), categorized using Communities-At-Sea methodology. In this case all communities use trawls targeting groundfish. (See Young et al. 2018 for additional details.)";
    String long_name "Community";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P02/current/ACYC/";
    String units "unitless";
  size {
    String bcodmo_name "sample_descrip";
    String description "Vessel length: large (>=65') or small (";
    String long_name "Size";
    String units "unitless";
  km_shift {
    Float64 _FillValue NaN;
    Float64 actual_range -2.262169129, 21.55583837;
    String bcodmo_name "unknown";
    String description "Annual latitudinal shift in km (degrees * 110.57)";
    String long_name "Km Shift";
    String units "kilometers (km)";
  still_extant {
    Byte _FillValue 127;
    Byte actual_range 0, 1;
    String bcodmo_name "sample_descrip";
    String description "Whether the community is still in the data set at the end (2014). 0 = no, 1 = yes.";
    String long_name "Still Extant";
    String units "unitless";
  last_year {
    Int16 _FillValue 32767;
    Int16 actual_range 2002, 2014;
    String bcodmo_name "year";
    String description "Year the community disappeared from the dataset";
    String long_name "Last Year";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/YEARXXXX/";
    String units "unitless";
  sw_mean {
    Float64 _FillValue NaN;
    Float64 actual_range 0.353700997, 2.51848005;
    String bcodmo_name "unknown";
    String description "Mean Shannon-Wiener diversity index of catch composition across all years";
    String long_name "Sw Mean";
    String units "unitless";
  species_percent {
    String bcodmo_name "unknown";
    String description "Percentages of species contributing to 90% in catch across all years";
    String long_name "Species Percent";
    String units "unitless";
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"We used the Communities-at-Sea framework developed by St. Martin and
colleagues (St Martin and Hall-Arber, 2008\\u00a0 (Human Ecology Review); St
Martin and Hall-Arber, 2008 (Marine Policy); St Martin and Olson, 2017) to
define fishing communities. This approach aggregates peer groups of vessels
into community-based fleets, each defined as a unique combination of port,
gear, and vessel size. We used two categories of vessel size: vessels that are
longer than or equal to 65\\u2019, and those that are shorter. Each trip in the
vessel trip report (VTR) trawl data was grouped into a community fleet by the
port of landing and vessel size, as long as at least one of the following two
conditions were met: (1) the vessel landed at least 50% of its trips that year
at that port, or (2) the vessel reported that landing port as its home port or
principal port on its permit. Trips that landed at a port that did not meet
either category were not included in this analysis. To ensure confidentiality
of harvester information, we only analyzed community fleets that included at
least three vessels in a given year.
For each community fleet in a given year (a \\\"fleet-year\\\"), we estimated an
annual geographic effort-weighted centroid of fishing activity using a
bootstrapping approach. We first used DePiper's (2014) model to calculate a
90% confidence interval for each individual fishing trip. Then, using that
confidence interval as a radius around the reported trip location and assuming
a uniform distribution, we chose a random point within that area to represent
the trip location. For each fleet-year, we used all estimated trip locations
to calculate a weighted geographic centroid. Each centroid was weighted by
crew size multiplied by trip length to represent a measure of labor time and
investment (St Martin and Olson, 2017). We repeated this process 1000 times to
generate a distribution of centroids for each community-year.\\u00a0See script:
Communities-at-Sea table, output: centroids table).
We used an inverse weighted regression analysis to assess if and to what
degree the annual fishing center for each community fleet shifted
significantly over time. For each community fleet, we fit a linear regression
of latitude against year for each set of bootstrap-replicated centroids
described above. We weighted each centroid by the inverse variance of the trip
latitudes used to calculate that centroid. This approach weights a centroid
with tightly clustered trips more heavily than one with more dispersed trips.
We used the mean effect strength from those 1000 regressions as the rate of
change in latitude for each fleet. To ensure sufficient data for analysis over
time, we restricted this analysis to only community fleets with at least seven
years of trip data. See script: [build_delta_gf.R](\\\\\"http://datadocs.bco-
(input: Communites-at-Sea table, output: delta_gf table).
We summarized the delta_gf table in a results table. See script:
(input: delta_gf table, output: results_table.csv (this dataset; use \\\"Get
Data\\\" button). Also see\\u00a0Table S1 of Young et al. (2018).
In order to assess the effect of factors correlated with changes in fishing
latitude, we fit a series of multiple linear regressions between the rate of
latitudinal change and five explanatory variables:  
 (1) Vessel size,  
 (2) Species diversity of catch,  
 (3) Change in composition of catch species,\\u00a0  
 (4) Change in depth of fishing location. In order to estimate change in
depth of fishing location for each community, we first found the nearest depth
recording for each trip using a U.S. coastal relief model (NOAA National
Centers for Environmental Information, U.S. Coastal Relief Model, n.d.), and
calculated an effort-weighted average depth for all the trips in a community
year. As above, we then regressed depth against year and used the resulting
slope as the covariate.  
 (5) Port latitude. Species diversity of catch was correlated with port
latitude (fleets from more northern ports had greater catch diversity), so we
also included latitude of port as a covariate so that we could assess the
separate effects of catch diversity and port latitude.
We evaluated models with all possible combinations of main effects as well as
three interactions: vessel size and port latitude, vessel size and catch
species diversity, and catch species diversity and change in catch species
composition. We calculated the corrected Akaike Information Criterion (AICc)
for each model and the Relative Variable Importance (RVI) for each variable
and interaction included in the model. See script:
(input: delta_gf table).
In order to assess factors mediating changes in community size, we fit a
series of regressions to assess the effect on rate of change in community size
(change in number of unique permits over time; linear) and community
disappearance (fewer than 3 permits by 2014; logistic) of three predictor
variables: (1) vessel size, (2) species diversity of catch, and (3) port
latitude. We evaluated models with all possible combinations of main effects
as well as interactions, and calculated AICc and RVI as described above. We
used the number of unique fishing permits in a community as a proxy for
community fleet size. See script:
(input: delta_gf table).
Additional details and references can be found in Young et al. (2018).";
    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 
   Results of inverse weighted regressions analysis 
   assessing community fleet shift over time 
  PI: Malin Pinsky (Rutgers) 
  Co-PI: Talia Young (Rutgers) 
  Version date: 09-Jan-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 "2019-01-09T18:28:44Z";
    String date_modified "2019-03-15T19:57:53Z";
    String defaultDataQuery "&time<now";
    String doi "10.1575/1912/bco-dmo.752624.1";
    String history 
"2024-04-20T09:49:14Z (local files)
2024-04-20T09:49:14Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_752624.das";
    String infoUrl "https://www.bco-dmo.org/dataset/752624";
    String institution "BCO-DMO";
    String keywords "bco, bco-dmo, biological, chemical, community, data, dataset, dmo, erddap, extant, km_shift, last_year, management, mean, oceanography, office, percent, preliminary, shift, size, species, species_percent, still, still_extant, sw_mean, year";
    String license "https://www.bco-dmo.org/dataset/752624/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/752624";
    String param_mapping "{'752624': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/752624/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 "Talia Young";
    String people_1_person_nid "752628";
    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 "Shannon Rauch";
    String people_2_person_nid "51498";
    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)";
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "This dataset describes changes in groundfish fishing communities in the northeast US from 1997-2014 as captured in the vessel trip report (VTR) data collected by the National Oceanic and Atmospheric Administration National Marine Fisheries Service Northeast Fisheries Science Center (NOAA-NMFS-NEFSC).";
    String title "Changes in groundfish fishing communities in the northeast US from 1997-2014 as captured in the vessel trip report (VTR) data collected by the National Oceanic and Atmospheric Administration National Marine Fisheries Service (NOAA-NMFS-NEFSC)";
    String version "1";
    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
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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