ERDDAP > info > bcodmo_dataset_752624

Grid DAP Data	Sub- set	Table DAP Data	Make A Graph	W M S	Source Data Files	Acces- sible	Title	Sum- mary	ISO, Metadata	Back- ground Info	RSS	E mail	Institution	Dataset ID
		data	graph		files	public	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)		I   M	background			BCO-DMO	bcodmo_dataset_752624

The Dataset's Variables and Attributes

Row Type	Variable Name	Attribute Name	Data Type	Value
attribute	NC_GLOBAL	access_formats	String	.htmlTable,.csv,.json,.mat,.nc,.tsv
attribute	NC_GLOBAL	acquisition_description	String	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: [bootstrap_centroid.R](\\"http://datadocs.bco- dmo.org/docs/CC_Fishery_Adaptations/data_docs/752624/1/bootstrap_centroid.R\\")\u00a0(input: 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- dmo.org/docs/CC_Fishery_Adaptations/data_docs/752624/1/build_delta_gf.R\\") (input: Communites-at-Sea table, output: delta_gf table). We summarized the delta_gf table in a results table. See script: [build_results_table.R](\\"http://datadocs.bco- dmo.org/docs/CC_Fishery_Adaptations/data_docs/752624/1/build_results_table.R\\") (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: [lat_shift_model.R](\\"http://datadocs.bco- dmo.org/docs/CC_Fishery_Adaptations/data_docs/752624/1/lat_shift_model.R\\") (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: [community_decline_model.R](\\"http://datadocs.bco- dmo.org/docs/CC_Fishery_Adaptations/data_docs/752624/1/community_decline_model.R\\") (input: delta_gf table). Additional details and references can be found in Young et al. (2018).
attribute	NC_GLOBAL	awards_0_award_nid	String	559955
attribute	NC_GLOBAL	awards_0_award_number	String	OCE-1426891
attribute	NC_GLOBAL	awards_0_data_url	String	http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1426891
attribute	NC_GLOBAL	awards_0_funder_name	String	NSF Division of Ocean Sciences
attribute	NC_GLOBAL	awards_0_funding_acronym	String	NSF OCE
attribute	NC_GLOBAL	awards_0_funding_source_nid	String	355
attribute	NC_GLOBAL	awards_0_program_manager	String	Michael E. Sieracki
attribute	NC_GLOBAL	awards_0_program_manager_nid	String	50446
attribute	NC_GLOBAL	cdm_data_type	String	Other
attribute	NC_GLOBAL	comment	String	results_table.csv: 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
attribute	NC_GLOBAL	Conventions	String	COARDS, CF-1.6, ACDD-1.3
attribute	NC_GLOBAL	creator_email	String	info at bco-dmo.org
attribute	NC_GLOBAL	creator_name	String	BCO-DMO
attribute	NC_GLOBAL	creator_type	String	institution
attribute	NC_GLOBAL	creator_url	String	https://www.bco-dmo.org/
attribute	NC_GLOBAL	data_source	String	extract_data_as_tsv version 2.3 19 Dec 2019
attribute	NC_GLOBAL	date_created	String	2019-01-09T18:28:44Z
attribute	NC_GLOBAL	date_modified	String	2019-03-15T19:57:53Z
attribute	NC_GLOBAL	defaultDataQuery	String	&time<now
attribute	NC_GLOBAL	doi	String	10.1575/1912/bco-dmo.752624.1
attribute	NC_GLOBAL	infoUrl	String	https://www.bco-dmo.org/dataset/752624
attribute	NC_GLOBAL	institution	String	BCO-DMO
attribute	NC_GLOBAL	keywords	String	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
attribute	NC_GLOBAL	license	String	https://www.bco-dmo.org/dataset/752624/license
attribute	NC_GLOBAL	metadata_source	String	https://www.bco-dmo.org/api/dataset/752624
attribute	NC_GLOBAL	param_mapping	String	{'752624': {}}
attribute	NC_GLOBAL	parameter_source	String	https://www.bco-dmo.org/mapserver/dataset/752624/parameters
attribute	NC_GLOBAL	people_0_affiliation	String	Rutgers University
attribute	NC_GLOBAL	people_0_person_name	String	Malin Pinsky
attribute	NC_GLOBAL	people_0_person_nid	String	554708
attribute	NC_GLOBAL	people_0_role	String	Principal Investigator
attribute	NC_GLOBAL	people_0_role_type	String	originator
attribute	NC_GLOBAL	people_1_affiliation	String	Rutgers University
attribute	NC_GLOBAL	people_1_person_name	String	Talia Young
attribute	NC_GLOBAL	people_1_person_nid	String	752628
attribute	NC_GLOBAL	people_1_role	String	Co-Principal Investigator
attribute	NC_GLOBAL	people_1_role_type	String	originator
attribute	NC_GLOBAL	people_2_affiliation	String	Woods Hole Oceanographic Institution
attribute	NC_GLOBAL	people_2_affiliation_acronym	String	WHOI BCO-DMO
attribute	NC_GLOBAL	people_2_person_name	String	Shannon Rauch
attribute	NC_GLOBAL	people_2_person_nid	String	51498
attribute	NC_GLOBAL	people_2_role	String	BCO-DMO Data Manager
attribute	NC_GLOBAL	people_2_role_type	String	related
attribute	NC_GLOBAL	project	String	CC Fishery Adaptations
attribute	NC_GLOBAL	projects_0_acronym	String	CC Fishery Adaptations
attribute	NC_GLOBAL	projects_0_description	String	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.
attribute	NC_GLOBAL	projects_0_end_date	String	2018-08
attribute	NC_GLOBAL	projects_0_geolocation	String	Northeast US Continental Shelf Large Marine Ecosystem
attribute	NC_GLOBAL	projects_0_name	String	Adaptations of fish and fishing communities to rapid climate change
attribute	NC_GLOBAL	projects_0_project_nid	String	559948
attribute	NC_GLOBAL	projects_0_start_date	String	2014-09
attribute	NC_GLOBAL	publisher_name	String	Biological and Chemical Oceanographic Data Management Office (BCO-DMO)
attribute	NC_GLOBAL	publisher_type	String	institution
attribute	NC_GLOBAL	sourceUrl	String	(local files)
attribute	NC_GLOBAL	standard_name_vocabulary	String	CF Standard Name Table v55
attribute	NC_GLOBAL	summary	String	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).
attribute	NC_GLOBAL	title	String	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)
attribute	NC_GLOBAL	version	String	1
attribute	NC_GLOBAL	xml_source	String	osprey2erddap.update_xml() v1.3
variable	community		String
attribute	community	bcodmo_name	String	sample
attribute	community	description	String	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.)
attribute	community	long_name	String	Community
attribute	community	nerc_identifier	String	https://vocab.nerc.ac.uk/collection/P02/current/ACYC/
attribute	community	units	String	unitless
variable	size		String
attribute	size	bcodmo_name	String	sample_descrip
attribute	size	description	String	Vessel length: large (>=65') or small (
attribute	size	long_name	String	Size
attribute	size	units	String	unitless
variable	km_shift		double
attribute	km_shift	_FillValue	double	NaN
attribute	km_shift	actual_range	double	-2.262169129, 21.55583837
attribute	km_shift	bcodmo_name	String	unknown
attribute	km_shift	description	String	Annual latitudinal shift in km (degrees * 110.57)
attribute	km_shift	long_name	String	Km Shift
attribute	km_shift	units	String	kilometers (km)
variable	still_extant		byte
attribute	still_extant	_FillValue	byte	127
attribute	still_extant	actual_range	byte	0, 1
attribute	still_extant	bcodmo_name	String	sample_descrip
attribute	still_extant	description	String	Whether the community is still in the data set at the end (2014). 0 = no, 1 = yes.
attribute	still_extant	long_name	String	Still Extant
attribute	still_extant	units	String	unitless
variable	last_year		short
attribute	last_year	_FillValue	short	32767
attribute	last_year	actual_range	short	2002, 2014
attribute	last_year	bcodmo_name	String	year
attribute	last_year	description	String	Year the community disappeared from the dataset
attribute	last_year	long_name	String	Last Year
attribute	last_year	nerc_identifier	String	https://vocab.nerc.ac.uk/collection/P01/current/YEARXXXX/
attribute	last_year	units	String	unitless
variable	sw_mean		double
attribute	sw_mean	_FillValue	double	NaN
attribute	sw_mean	actual_range	double	0.353700997, 2.51848005
attribute	sw_mean	bcodmo_name	String	unknown
attribute	sw_mean	description	String	Mean Shannon-Wiener diversity index of catch composition across all years
attribute	sw_mean	long_name	String	Sw Mean
attribute	sw_mean	units	String	unitless
variable	species_percent		String
attribute	species_percent	bcodmo_name	String	unknown
attribute	species_percent	description	String	Percentages of species contributing to 90% in catch across all years
attribute	species_percent	long_name	String	Species Percent
attribute	species_percent	units	String	unitless

The information in the table above is also available in other file formats (.csv, .htmlTable, .itx, .json, .jsonlCSV1, .jsonlCSV, .jsonlKVP, .mat, .nc, .nccsv, .tsv, .xhtml) via a RESTful web service.