This raster data set is a part of Ecotrust's project entitled: Establishing a Baseline and Assessing Initial Spatial and Economic Change in the California North Central Coast Commercial Fisheries. This project is a component of the California North Central Coast Marine Protected Area Baseline Monitoring Project that is designed to characterize the ecological and socioeconomic conditions and changes within the North Central Coast Region since MPA implementation. The North Central Coast study region extends from the north at Alder Creek to the south at Pigeon Point. This data set consists of data collected in the summer of 2011 from fisheries mapping interviews conducted with commercial fishermen who had California halibut—hook and line landings in the California North Central Coast in 2010. During interviews fishermen were asked to map their fishing grounds for 2010 and determine the relative importance of each fishing ground by allocating 100 pennies across their fishing grounds for this fishery. The spatial data from these interviews were then combined through an aggregation process where the weighted fishing grounds each fisherman gave were further weighted by their ex-vessel revenue from the California halibut—hook and line fishery in 2010. This created spatial data sets for each port for this fishery. For regional or all port data sets, port level data was aggregated by weighting each port by the port’s total ex-vessel revenue in 2010 for the fishery based on California Department of Fish and Wildlife commercial landings data. This data set represents the spatial extent and relative value of California halibut—hook and line commercial fishing grounds for the North Central Coast Region in the year 2010.

The goal of this research is to estimate and visualize the global impact that humans are having on the oceans' ecosystems.

The Magnuson-Stevens Fishery Conservation and Management Act requires the description and identification of essential fish habitat (EFH) for species included in federal fishery management plans (FMPs). NMFS Alaska Region provides a collection with EFH maps, supporting data, and supplemental habitat datasets.

Alaska EFH maps are developed by species’ life history stages within the spatial extent of the fishery management units of the FMPs. The data included varies by FMP, representing EFH based on species survey distribution maps developed through analysis of species distributions from fishery independent surveys and fishery observer data (Scallop FMP), cumulative frequency distribution model maps developed through analysis of species survey data and environmental covariates (Salmon FMP), and species distribution model maps developed through analysis of species data from fishery independent surveys or fishery observer data and environmental covariates (Groundfish, Crab, and Arctic FMPs). The EFH mapping data available in this collection include GIS files (geodatabases, shapefiles, raster files) that are organized by FMP with supporting documents. The EFH maps and supporting data are updated with the completion of an EFH 5-year Review.

One of the challenges for understanding EFH in Alaska’s nearshore is the extensive and complex coastline (~55,000 km) with a diversity of estuarine and marine habitats. Supplemental datasets in this collection are Alaska ShoreZone, Shore Station, and the Nearshore Fish Atlas of Alaska (NFAA).

ShoreZone is an aerial imaging, coastal habitat classification and mapping system used to inventory alongshore and across-shore geomorphological and biological attributes of the shoreline. The georeferenced, oblique, low altitude aerial imagery is acquired during the lowest tides of the year and then used to classify habitat attributes into a searchable database. Alaska ShoreZone data in this collection include videos, video still images, photos, GIS files (geodatabase and shapefiles), data dictionary, protocol, and flight plans.

Shore Station is a compilation of data collected during low tide surveys from hundreds of intertidal sites throughout coastal Alaska. Survey data include observed species, species assemblages, geomorphic features (e.g., sediment, substrate, bedform), beach length, slope, specific elevation profiles. Shore Station data in the collection include photos, GIS files (geodatabase and shapefiles), and supporting documentation.

The NFAA is a centralized, relational database of nearshore fish surveys, providing data on the distribution, relative abundance, and habitat use of nearshore fishes in Alaska. This dataset includes numerous nearshore surveys collected by various agencies and organizations over the past several decades with different objectives and gear types (e.g., beach seines, purse seines, and trawls). NFAA data in the collection include photos, GIS files (geodatabase and shapefiles), and supporting documentation.

Communities-at-sea are peer-groups of vessels which share a gear type and are associated with a particular port (e.g., vessels from New Bedford, MA that use gillnets). For vessels using trawl gear, small and large trawlers are considered separate communities according to vessel length (<> 65 feet). We used Vessel Trip Report (VTR) data for commercial fishing trips from 1996 to 2014, as reported by vessel captains, to determine the at-sea \servicesheds\ or customary fishing grounds of communities. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=The following methods are excerpted from Rogers et al. (in press):
A trip was classified as belonging to a community if it shared the community's gear type and landing port, and the vessel either declared that port as its principal port or landed in that port at least 50% of its trips that year.

Once aggregated into communities, trips were then weighted by a variable (\u201cfisherdays\u201d) indicating labor time expended on each trip: trip length (in days) multiplied by the number of crew on board. Fisherdays indicate how important an area at sea is to a community in terms of how much time they invest in that location.

Given reported trip locations and fisherdays, we then created raster maps using a kernel density method. The resultant maps distribute fisherdays using different size kernels depending upon the fishery/gear-type/length. Nearshore fishing was processed using a smaller kernel (7.5 - 10 km) than offshore fishing (10 - 15 km). We used the area defined by a 90% volume contour (i.e., an area which encompasses 90% of fisherdays) to define the customary fishing grounds or servicesheds for a community.

To compare the relative historical importance of particular species to a community-at-sea, 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). 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.

We assessed a community's exposure to risk based on their historical dependence on species and spatial fishing patterns. A community was more exposed to risk if the species from which it historically earned the most revenue were projected to lose habitat in the locations where the community has traditionally fished. Specifically, risk exposure scores for communities were calculated as:

where S\u209b,\ua700 is the mean projected change in habitat suitability for species s across the serviceshed of community c, and pRev\u209b,\ua700 is the proportion of historical revenues from fishing that the community has derived from species s. Positive risk exposure scores indicated expanding opportunities for communities based on their historical fishing revenue portfolios and projected changes to species habitat at sea, while negative values indicated shrinking opportunities and increased exposure to negative impacts of climate change.

The R file, "Servicesheds.rData" (see\u00a0"Supplemental Documents" below) is\u00a0a spatial polygon dataframe (SPDF) giving 90% volume contours of fisher-days at sea for 98 communities-at-sea. The polygons outline the at-sea "servicesheds" or customary fishing grounds of communities. We use "serviceshed"\u00a0to describe the area from which a community has historically received ecosystem services, specifically fish in this case. The file is intended to be read by the program "R",\u00a0with data stored in the SPDF object "Servicesheds". awards_0_award_nid=559955 awards_0_award_number=OCE-1426891 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1426891 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=Michael E. Sieracki awards_0_program_manager_nid=50446 cdm_data_type=Other comment=Attributes of communities-at-sea, including the size of servicesheds and climate change risk exposure scores PIs: Lauren Rogers & Malin Pinsky Version date: 22-April-2019 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.765477.1 Easternmost_Easting=-69.253 geospatial_lat_max=43.927 geospatial_lat_min=34.718 geospatial_lat_units=degrees_north geospatial_lon_max=-69.253 geospatial_lon_min=-76.693 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/765477 institution=BCO-DMO metadata_source=https://www.bco-dmo.org/api/dataset/765477 Northernmost_Northing=43.927 param_mapping={'765477': {'PortLon': 'flag - longitude', 'PortLat': 'flag - latitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/765477/parameters people_0_affiliation=Rutgers University people_0_person_name=Malin Pinsky people_0_person_nid=554708 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Stanford University people_1_person_name=Lauren Rogers people_1_person_nid=765425 people_1_role=Principal Investigator people_1_role_type=originator people_2_affiliation=Stanford University people_2_person_name=Robert Griffin people_2_person_nid=768380 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Rutgers University people_3_person_name=Kevin St. Martin people_3_person_nid=559961 people_3_role=Co-Principal Investigator people_3_role_type=originator people_4_affiliation=Princeton University people_4_person_name=Emma Fuller people_4_person_nid=748888 people_4_role=Scientist people_4_role_type=originator people_5_affiliation=Rutgers University people_5_person_name=Talia Young people_5_person_nid=752628 people_5_role=Scientist people_5_role_type=originator people_6_affiliation=National Oceanic and Atmospheric Administration - Alaska Fisheries Science Center people_6_affiliation_acronym=NOAA-AFSC people_6_person_name=Lauren Rogers people_6_person_nid=765425 people_6_role=Contact people_6_role_type=related people_7_affiliation=Woods Hole Oceanographic Institution people_7_affiliation_acronym=WHOI BCO-DMO people_7_person_name=Shannon Rauch people_7_person_nid=51498 people_7_role=BCO-DMO Data Manager people_7_role_type=related project=CC Fishery Adaptations projects_0_acronym=CC Fishery Adaptations 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

Reason for SelectionHardbottom provides an anchor for important seafloor habitats such as deep-sea corals, plants, and sponges. Hardbottom is also sometimes associated with chemosynthetic communities that form around cold seeps or hydrothermal vents. In these unique ecosystems, micro-organisms that convert chemicals into energy form the base of complex food webs (Love et al. 2013). Hardbottom and associated species provide important habitat structure for many fish and invertebrates (NOAA 2018). Hardbottom areas serve as fish nursery, spawning, and foraging grounds, supporting commercially valuable fisheries like snapper and grouper (NCDEQ 2016).According to Dunn and Halpin (2009), “hardbottom habitats support high levels of biodiversity and are frequently used as a surrogate for it in marine spatial planning.” Artificial reefs arealso known to provide additional habitat that is quickly colonized to provide a suite of ecosystem services commonly associated with naturally occurring hardbottom (Wu et al. 2019). We did not include active oil and gas structures as human-created hardbottom. Although they provide habitat, because of their temporary nature, risk of contamination, and contributions to climate change, they do not have the same level of conservation value as other artificial structures.Input DataSoutheast Blueprint 2024 extentSoutheast Blueprint 2024 subregionsCoral & hardbottomusSEABED Gulf of America sediments, accessed 12-14-2023; download the data; view and read more about the data on the National Oceanic and Atmospheric Administration (NOAA) Gulf of Mexico Atlas (select Physical --> Marine geology --> 1. Dominant bottom types and habitats)Bureau of Ocean Energy Management (BOEM) Gulf of America, seismic water bottom anomalies, accessed 12-20-2023The Nature Conservancy’s (TNC)South Atlantic Bight Marine Assessment(SABMA); chapter 3 ofthe final reportprovides more detail on the seafloor habitats analysisNOAA deep-sea coral and sponge locations, accessed 12-20-2023 on theNOAA Deep-Sea Coral & Sponge Map PortalFlorida coral and hardbottom habitats, accessed 12-19-2023Shipwrecks & artificial reefsNOAA wrecks and obstructions layer, accessed 12-12-2023 on theMarine CadastreLouisiana Department of Wildlife and Fisheries (LDWF) Artificial Reefs: Inshore Artificial Reefs, Nearshore Artificial Reefs, Offshore and Deepwater Artificial Reefs (Google Earth/KML files), accessed 12-19-2023Texas Parks and Wildlife Department (TPWD) Artificial Reefs, accessed 12-19-2023; download the data fromThe Artificial Reefs Interactive Mapping Application(direct download from interactive mapping application)Mississippi Department of Marine Resources (MDMR) Artificial Reef Bureau: Inshore Reefs, Offshore Reefs, Rigs to Reef (lat/long coordinates), accessed 12-19-2023Alabama Department of Conservation and Natural Resources (ADCNR) Artificial Reefs: Master Alabama Public Reefs v2023 (.xls), accessed 12-19-2023Florida Fish and Wildlife Conservation Commission (FWC):Artificial Reefs in Florida(.xlsx), accessed 12-19-2023Defining inland extent & split with AtlanticMarine Ecoregions Level III from the Commission for Environmental Cooperation North American Environmental Atlas, accessed 12-8-20212023NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024National Oceanic and Atmospheric Administration (NOAA)Characterizing Spatial Distributions of Deep-sea Corals and Hardbottom Habitats in the U.S. Southeast Atlantic;read the final report; data shared prior to official release on 2-4-2022 by Matt Poti with the NOAA National Centers for Coastal Ocean Science (NCCOS) (matthew.poti@noaa.gov)Predictive Modeling and Mapping of Hardbottom Seafloor Habitats off the Southeast U.S: unpublished NOAA data anddraft final report entitled Assessment of Benthic Habitats for Fisheries Managementprovided on 1-28-2021 by Matt Poti with NOAA NCCOS (matthew.poti@noaa.gov)Mapping StepsNote: Most of the mapping steps were accomplished using the graphical modeler in QGIS 3.34. Individual models were created to combine data sources and assign ranked values. These models were combined in a single model to assemble all the data sources and create a summary raster.Create a seamless vector layer to constrain the extent of the Atlantic coral and hardbottom indicator to marine and estuarine areas <1 m in elevation. This defines how far inland it extends.Merge together all coastal relief model rasters (.nc format) using the create virtual raster tool in QGIS.Save the merged raster to .tif format and import it into ArcPro.Reclassify the NOAA coastal relief model data to assign a value of 1 to areas from deep marine to 1 m elevation. Assign all other areas (land) a value of 0.Convert the raster produced above to vector using the raster to polygon tool.Clip to the 2024 Blueprint subregions using the pairwise clip tool.Hand-edit to remove terrestrial polygons (one large terrestrial polygon and the Delmarva peninsula).Dissolve the resulting data layer to produce a seamless polygon defining marine and estuarine areas <1 m in elevation.Hand-edit to select all but the main marine polygon and delete.Define the extent of the Gulf version of this indicator to separate it from the Atlantic. This split reflects the extent of the different datasets available to represent coral and hardbottom habitat in the Atlantic and Gulf, rather than a meaningful ecological transition.Use the select tool to select the Florida Keys class from the Level III marine ecoregions (“NAME_L3 = "Florida Keys"“).Buffer the “Florida Keys” Level III marine ecoregion by 2 km to extend it far enough inland to intersect the inland edge of the <1 m elevation layer.Reclassify the two NOAA Atlantic hardbottom suitability datasets to give all non-NoData pixels a value of 0. Combine the reclassified hardbottom suitability datasets to define the total extent of these data. Convert the raster extent to vector and dissolve to create a polygon representing the extent of both NOAA hardbottom datasets.Union the buffered ecoregion with the combined NOAA extent polygon created above. Add a field and use it to dissolve the unioned polygons into one polygon. This leaves some holes inside the polygon, so use the eliminate polygon part tool to fill in those holes, then convert the polygon to a line.Hand-edit to extract the resulting line between the Gulf and Atlantic.Hand-edit to use this line to split the <1 m elevation layer created earlier in the mapping steps to create the separation between the Gulf and Atlantic extent.From the BOEM seismic water bottom anomaly data, extract the following shapefiles: anomaly_confirmed_relic_patchreefs.shp, anomaly_Cretaceous.shp, anomaly_relic_patchreefs.shp, seep_anomaly_confirmed_buried_carbonate.shp, seep_anomaly_confirmed_carbonate.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_positives.shp, seep_anomaly_positives_confirmed_gas.shp, seep_anomaly_positives_confirmed_oil.shp, seep_anomaly_positives_possible_oil.shp, seep_anomaly_confirmed_corals.shp, seep_anomaly_confirmed_hydrate.shp.To create a class of confirmed BOEM features, merge anomaly_confirmed_relic_patchreefs.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_confirmed_corals.shp, and seep_anomaly_confirmed_hydrate.shp and assign a value of 6.To create a class of predicted BOEM features, merge the remaining extracted shapefiles and assign a value of 3.From usSEABED sediments data, use the field “gom_domnc” to extract polygons: rock (dominant and subdominant) receives a value of 2 and gravel (dominant and subdominant) receives a value of 1.From the wrecks database, extract locations having “high” and “medium” confidence (positionQuality = “high” and positionQuality = “medium”). Buffer these locations by 150 m and assign a value of 4. The buffer distance used here, and later for coral locations, follows guidance from the Army Corps of Engineers for setbacks around artificial reefs and fish havens (Riley et al. 2021).Merge artificial reef point locations from FL, AL, MS and TX. Buffer these locations by 150 m. Merge this file with the three LA artificial reef polygons and assign a value of 5.From the NOAA deep-sea coral and sponge point locations, select all points. Buffer the point locations by 150 m and assign a value of 7.From the FWC coral and hardbottom dataset polygon locations, fix geometries, reproject to EPSG=5070, then assign coral reefs a value of 7, hardbottom a value of 6, hardbottom with seagrass a value of 6, and probable hardbottom a value of 3. Hand-edit to remove an erroneous hardbottom polygon off of Matagorda Island, TX, resulting from a mistake by Sheridan and Caldwell (2002) when they digitized a DOI sediment map. This error is documented on page 6 of the Gulf of Mexico Fishery Management Council’s5-Year Review of the Final Generic Amendment Number 3.From the TNC SABMA data, fix geometries and reproject to EPSG=5070, then select all polygons with TEXT_DESC = "01. mapped hard bottom area" and assign a value of 6.Union all of the above vector datasets together—except the vector for class 6 that combines the SABMA and FL data—and assign final indicator values. Class 6 had to be handled separately due to some unexpected GIS processing issues. For overlapping polygons, this value will represent the maximum value at a given location.Clip the unioned polygon dataset to the buffered marine subregions.Convert both the unioned polygon dataset and the separate vector layer for class 6 using GDAL “rasterize”.Fill NoData cells in both rasters with zeroes and, using Extract by Mask, mask the resulting raster with the Gulf indicator extent. Adding zero values helps users better understand the extent of this indicator and to make this indicator layer perform better in online tools.Use the raster calculator to evaluate the maximum value among