This research provides a quantitative means of determining which communities in West Coast coastal counties are most connected to fishing, both commercial and recreational fishing, and allows for a quantitative approach to concepts like community “engagement” and “dependence” on fishing. The project employs a methodology that incorporates a diverse range of secondary data and proxy measures of human community attributes with the aim of considering multiple social and ecological community dimensions simultaneously. We analyze demographic, economic, geographic, meteorological, quality of life and fisheries-specific data for all coastal communities at the U.S. census-designated place (CDP) level in Washington, Oregon and California. A factor analysis approach to these data allows us to examine relative similarities among variables for a set of proposed indices of community vulnerability and connections to fishing, and allows us to compare communities relative to one another for each measure. Social vulnerability and fishing dependence composite scores are available for multiple years, and this is a multi-year project developed, carried out and updated each year in coordination with all NMFS fishery management regions. These community-level analyses are also conducted in concert with the analysis of primary fisheries participation data, collected via a coast-wide survey of West Coast fishery participants. We will implement the coast-wide survey at regular intervals, including in 2019, providing us with longitudinal data and potential time series analyses to be paired with our broader community indicators. These data represent generalized social vulnerability composite scores for each of 880 West Coast communities.

To enable fisheries managers to comply with National Standard 8 (NS8), NMFS social scientists around the nation are preparing fishing community profiles that present the features and characteristics of such communities. PIFSC has published or is developing four such profiles: one each for Hawaii, Guam, the Commonwealth of the Northern Mariana Islands, and American Samoa.

Data Contains info on Fishing Communities

The National Marine Fisheries Service (NMFS) conducted a survey of fisheries stakeholders on the Gulf and East Coasts of the United States seeking their views on ecosystem-based fisheries management (EBFM) of fisheries resources. The survey asked a series of attitude and opinion questions along with general environmental literacy and demographic questions to a sample of 7,850 fisheries stakeholders, stratified by region. Results indicate that respondents’ knowledge of the status of fisheries resources is qualitatively similar to NMFS ratings, though generally respondents were less than satisfied with current fisheries management. Results also suggest that, despite concerns over several specific measures, respondents generally see potential in an EBFM approach to management.

A compilation of ocean water quality (temperature, salinity, and dissolved oxygen) data at ¼ degree spatial resolution for the entire United States Exclusive Economic Zone. The dataset is derived from the ESRI Ecological Marine Unit (EMU) dataset, which was assembled from non-supervised statistical clustering of over 52 million points from NOAA’s World Ocean Atlas (2013) WoA database, an authoritative 57 year archive of global water column data. This derived dataset is divided into three separate point shapefiles, each representing either temperature (degrees Celsius), salinity (practical salinity units), or dissolved oxygen (mg/L). Values represent a climatological average. Each shapefile is formatted such that a single point location (i.e., unique associated latitude and longitude) contains a unique column entry for a given depth interval. Depth intervals are variable from 5 m near the surface to 100 m in the deeper regions (> 2000 m) for a total of 102 depth levels. All disclaimers provided by the original dataset authors apply to this derived dataset. For detail on these disclaimers, please refer to the following reference: Sayre, R., J. Dangermond, D. Wright, S. Breyer, K. Butler, K. Van Graafeiland, M.J. Costello, P. Harris, K. Goodin, M. Kavanaugh, N. Cressie, J. Guinotte, Z. Basher, P. Halpin, M. Monaco, P. Aniello, C. Frye, D. Stephens, P. Valentine, J. Smith, R. Smith, D.P. VanSistine, J. Cress, H. Warner, C. Brown, J. Steffenson, D. Cribbs, B. Van Esch, D. Hopkins, G. Noll, S. Kopp, and C. Convis. 2017. A New Map of Global Ecological Marine Units – An Environmental Stratification Approach. Washington, DC: American Association of Geographers. 36 pages.

Scientists at NOAA Northeast Fisheries Science Center (NEFSC) are using environmental DNA (eDNA) to identify fish communities and monitor ecosystems by collecting a water sample and analyzing the DNA found in it, identifying the species that left it behind without capturing a single animal. As animals swim, they shed scales, tissue, and waste, leaving traces of DNA in the water. A water sample is first collected from the ocean and filtered to concentrate DNA in it. NOAA scientists then make millions of copies of a target DNA region through polymerase chain reaction (PCR) to make enough genetic material for high throughput sequencing. The metabarcoding process described above for eDNA analysis allows scientists to look for many species in the same sample. The final step is like a matching game, in which the DNA sequences are compared with a reference library of known species to find a match. The eDNA method is particularly useful for detecting species that are not easily captured, including rare or migratory species. It can also help in areas that are difficult to sample because of challenging ocean conditions, sensitive habitats, or a rugged seafloor. An eDNA analysis provides a snapshot of the community of species at the time of sampling and over time. This can help us detect shifts in marine ecosystems. eDNA samples have been collected on NOAA Ecosystem Monitoring (EcoMon) surveys since 2019. These samples will help develop best eDNA practices using metabarcoding, an innovative way to determine what fish species live in what parts of the ocean without actually seeing any fish.

Scientists at NOAA's Northeast Fisheries Science Center (NEFSC) are using environmental DNA (eDNA) to identify fish communities and monitor ecosystems by collecting a water sample and analyzing the DNA found in it, identifying the species that left it behind without capturing a single animal. As animals swim, they shed scales, tissue, and waste, leaving traces of DNA in the water. A water sample is first collected from the ocean and filtered to concentrate DNA in it. NOAA scientists then make millions of copies of a target DNA region through polymerase chain reaction (PCR) to make enough genetic material for high throughput sequencing. The metabarcoding process described above for eDNA analysis allows scientists to look for many species in the same sample. The final step is like a matching game, in which the DNA sequences are compared with a reference library of known species to find a match. The eDNA method is particularly useful for detecting species that are not easily captured, including rare or migratory species. It can also help in areas that are difficult to sample because of challenging ocean conditions, sensitive habitats, or a rugged seafloor. An eDNA analysis provides a snapshot of the community of species at the time of sampling and over time. This can help us detect shifts in marine ecosystems. eDNA samples have been collected on NOAA Ecosystem Monitoring (EcoMon) surveys since 2019. These samples will help develop best eDNA practices using metabarcoding, an innovative way to determine what fish species live in what parts of the ocean without actually seeing any fish.

This data set contains sensitive biological resource data for finfish in Maine, New Hampshire. Vector points in this data set represent selected marine, estuarine, and diadromous species of commercial, recreational, ecological and/or conservation interest. Species-specific abundance, seasonality, status, life history, and source information are stored in associated data tables (described below) designed to be used in conjunction with this spatial data layer. This data set is a portion of the ESI data for Maine, New Hampshire. As a whole, the ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil, and include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. See also the FISH (Fish Polygons) and FISHL (Fish Lines) data layers for additional fish information.

This data set contains vector points and lines representing human-use resource data for Columbia River. In the data set, vector points represent aquaculture sites, boat ramps, coast guard stations, ferry sites, hatchery sites, locks and dams, marinas, recreational fishing sites, subsistence sites, and water intakes. Vector lines represent roads, bridges, and state borders. Location-specific type and source information are stored in relational data tables (described below) designed to be used in conjunction with this spatial data layer.This data set comprises a portion of the Environmental Sensitivity Index (ESI) data for Columbia River. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. See also the MGT (Management Area Polygons) data layer, part of the larger Columbia River ESI database, for additional human-use information.

This data set contains vector points and lines representing human-use resource data for Columbia River. In the data set, vector points represent aquaculture sites, boat ramps, coast guard stations, ferry sites, hatchery sites, locks and dams, marinas, recreational fishing sites, subsistence sites, and water intakes. Vector lines represent roads, bridges, and state borders. Location-specific type and source information are stored in relational data tables (described below) designed to be used in conjunction with this spatial data layer.This data set comprises a portion of the Environmental Sensitivity Index (ESI) data for Columbia River. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. See also the MGT (Management Area Polygons) data layer, part of the larger Columbia River ESI database, for additional human-use information.