Description: The MPA Inventory is a comprehensive catalog that provides detailed information for existing marine protected areas in the United States. The inventory provides geospatial boundary information (in polygon format) and classification attributes that seek to define the conservation objectives, protection level, governance and related management criteria for all sites in the database. The comprehensive inventory of federal, state and territorial MPA sites provides governments and stakeholders with access to information to make better decisions about the current and future use of place-based conservation. The information also will be used to inform the development of the national system of marine protected areas as required by Executive Order 13158.
Copyright Text: NOAA Marine Protected Areas Center in joint effort with the US Department of the Interior
Description: Project area for the insular shelf south of St. Thomas and St. John. The main objective of this project was to produce new, highly detailed habitat maps within this project area. The project was based on high resolution bathymetry, use of ground validation data, modelling the probability of occurrence of individual substrate and cover types, combining model layers into a classified benthic map, and a spatial accuracy assessment using independent field data. This work was conducted in close partnership with U.S. Geological Survey, NOAA’s Office of Marine and Aircraft Operations NOAA ship Nancy Foster, University of North Carolina at Wilmington Undersea Vehicles Program, NOAA’s Office of Coast Survey, National Park Service, Caribbean Fishery Management Council, U.S. Virgin Islands, Department of Planning and Natural Resources, Puerto Rico’s Department of Natural and Environmental Resources, University of the Virgin Islands, University of Puerto Rico, and Solmar Hydro.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: Ground Validation (GV) data are the basis for finding relationships between the observed substrate and cover types and the values in the environmental predictor datasets. Ultimately, the GV data from 1005 sites were used to train and optimize mathematical models and to predict habitats throughout the region. Locations of the GV sites were selected deliberately to include the full range of habitats, depths, and environmental settings found in the region.Accuracy Assessment (AA) data were used to independently evaluate the performance of the predictive models and the accuracy of the composite habitat map. Underwater HD videos were collected at 348 sites from January 30 to February 13, 2017 . The locations of these sites were randomly assigned within six habitat strata based on a preliminary draft of the habitat mapand had a minimum distance of 300 m between them to ensure independence. The purpose of this stratification was to have at least 30 sites randomly distributed in each habitat class. Additional sites were randomly assigned in habitats that were more common (i.e., covered larger amounts of area) on the Insular Shelf
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: Ground Validation (GV) data are the basis for finding relationships between the observed substrate and cover types and the values in the environmental predictor datasets. Ultimately, the GV data from 1005 sites were used to train and optimize mathematical models and to predict habitats throughout the region. Locations of the GV sites were selected deliberately to include the full range of habitats, depths, and environmental settings found in the region.Accuracy Assessment (AA) data were used to independently evaluate the performance of the predictive models and the accuracy of the composite habitat map. Underwater HD videos were collected at 348 sites from January 30 to February 13, 2017 . The locations of these sites were randomly assigned within six habitat strata based on a preliminary draft of the habitat mapand had a minimum distance of 300 m between them to ensure independence. The purpose of this stratification was to have at least 30 sites randomly distributed in each habitat class. Additional sites were randomly assigned in habitats that were more common (i.e., covered larger amounts of area) on the Insular Shelf)
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: Full dataset Citation: Taylor, C.; Ebert, E.; Kracker, L.; Battista, T.; Costa, B. (2016). NCCOS Caribbean Fishery Acoustic Assessment: Fish Density Data Collection from NOAA Ship Nancy Foster, from 2008-03-26 to 2014-05-31 (NCEI Accession 0156395). Version 1.1. NOAA National Centers for Environmental Information
Copyright Text: Taylor, C.; Ebert, E.; Kracker, L.; Battista, T.; Costa, B. (2016). NCCOS Caribbean Fishery Acoustic Assessment: Fish Density Data Collection from NOAA Ship Nancy Foster, from 2008-03-26 to 2014-05-31 (NCEI Accession 0156395). Version 1.1. NOAA National Centers for Environmental Information
Description: Dataset shows the interpolated densities of all fish detected using fishery echosounders during NOAA cruises NF09-01, NF10-03, and NF11-01. Original data are available as points representing 100 square meters along tracklines. Interpolation was point-to-raster, selecting maximum values in a 200x200m grid cell from original sampling points that were sampled along survey tracklines.
Copyright Text: Full dataset Citation: Taylor, C.; Ebert, E.; Kracker, L.; Battista, T.; Costa, B. (2016). NCCOS Caribbean Fishery Acoustic Assessment: Fish Density Data Collection from NOAA Ship Nancy Foster, from 2008-03-26 to 2014-05-31 (NCEI Accession 0156395). Version 1.1. NOAA National Centers for Environmental Information
Description: Dataset shows the interpolated densities of large fish (greater than about 29cm) detected using fishery echosounders during NOAA cruises NF09-01, NF10-03, and NF11-01. Original data are available as points representing 100 square meters along tracklines. Interpolation was point-to-raster, selecting maximum values in a 200x200m grid cell from original sampling points that were sampled along survey tracklines.
Full dataset Citation: Taylor, C.; Ebert, E.; Kracker, L.; Battista, T.; Costa, B. (2016). NCCOS Caribbean Fishery Acoustic Assessment: Fish Density Data Collection from NOAA Ship Nancy Foster, from 2008-03-26 to 2014-05-31 (NCEI Accession 0156395). Version 1.1. NOAA National Centers for Environmental Information
Copyright Text: Full dataset Citation: Taylor, C.; Ebert, E.; Kracker, L.; Battista, T.; Costa, B. (2016). NCCOS Caribbean Fishery Acoustic Assessment: Fish Density Data Collection from NOAA Ship Nancy Foster, from 2008-03-26 to 2014-05-31 (NCEI Accession 0156395). Version 1.1. NOAA National Centers for Environmental Information
Description: Dataset shows the interpolated densities of medium-sized fish (about 11cm-29cm) detected using fishery echosounders during NOAA cruises NF09-01, NF10-03, and NF11-01. Original data are available as points representing 100 square meters along tracklines. Interpolation was point-to-raster, selecting maximum values in a 200x200m grid cell from original sampling points that were sampled along survey tracklines.
Full dataset Citation: Taylor, C.; Ebert, E.; Kracker, L.; Battista, T.; Costa, B. (2016). NCCOS Caribbean Fishery Acoustic Assessment: Fish Density Data Collection from NOAA Ship Nancy Foster, from 2008-03-26 to 2014-05-31 (NCEI Accession 0156395). Version 1.1. NOAA National Centers for Environmental Information
Copyright Text: Full dataset Citation: Taylor, C.; Ebert, E.; Kracker, L.; Battista, T.; Costa, B. (2016). NCCOS Caribbean Fishery Acoustic Assessment: Fish Density Data Collection from NOAA Ship Nancy Foster, from 2008-03-26 to 2014-05-31 (NCEI Accession 0156395). Version 1.1. NOAA National Centers for Environmental Information
Description: Dataset shows the interpolated densities of small-sized fish (<11cm) detected using fishery echosounders during NOAA cruises NF09-01, NF10-03, and NF11-01. Original data are available as points representing 100 square meters along tracklines. Interpolation was point-to-raster, selecting maximum values in a 200x200m grid cell from original sampling points that were sampled along survey tracklines.
Full dataset Citation: Taylor, C.; Ebert, E.; Kracker, L.; Battista, T.; Costa, B. (2016). NCCOS Caribbean Fishery Acoustic Assessment: Fish Density Data Collection from NOAA Ship Nancy Foster, from 2008-03-26 to 2014-05-31 (NCEI Accession 0156395). Version 1.1. NOAA National Centers for Environmental Information
Copyright Text: Full dataset Citation: Taylor, C.; Ebert, E.; Kracker, L.; Battista, T.; Costa, B. (2016). NCCOS Caribbean Fishery Acoustic Assessment: Fish Density Data Collection from NOAA Ship Nancy Foster, from 2008-03-26 to 2014-05-31 (NCEI Accession 0156395). Version 1.1. NOAA National Centers for Environmental Information
Description: A composite habitat map depicting substrate and cover types that commonly co-occur in the Insular Shelf. Habitat classes include: Rhodoliths with Macroalgae, Bare Sand, Rhodoliths with Macroalgae and Bare Sand, Pavement Colonized with Live Coral, Coral Reef Colonized with Live Coral. The overall thematic accuracy for this map is 85.8% (corrected for proportional bias). These habitats were also translated into the Coastal and Marine Ecological Classification Standard (CMECS) scheme.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: This composite habitat map depicts substrate and cover types that commonly co-occur in the Insular Shelf (Habitat classes include: Rhodoliths with Macroalgae, Bare Sand, Rhodoliths with Macroalgae and Bare Sand, Pavement Colonized with Live Coral, Coral Reef Colonized with Live Coral). The overall thematic accuracy for this map is 85.8% (corrected for proportional bias).
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: Spatial predictions denoting the mean probability of occurrence and coefficient of variation or CV for four substrate types and two cover types. Mean and CV were calculated by creating 100 separate models, and then calculating the average and standard deviation of probability of occurrence in each pixel across the models. Substrate types include: Bare Sand, Coral Reef, Pavement, Rhodolith. Cover types include: Live Hard Coral and Live Soft Coral
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: Spatial predictions denoting the mean probability of occurrence and the coefficient of variation (or CV) for two biological cover types. Mean and CV were calculated by creating 100 separate models, and then calculating the average and standard deviation of probability of occurrence in each pixel across the models. Cover types include Live Hard Coral and Live Soft Coral
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Name: Live Hard Coral - Mean Probability of Occurrence
Display Field:
Type: Raster Layer
Geometry Type: null
Description: Spatial predictions denoting the mean probability (%) of occurrence for Live Hard Coral. Average probabilities were calculated by creating 100 separate models, and then averaging the predicted probabilities in each pixel across the models.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: This layer depicts the precision of the predicted probability of occurrence value for Live Hard Coral. Precision is denoted as the coefficient of variation (CV), which is unitless. CV is the ratio of the standard deviation to the mean for each pixel. It was calculated by creating 100 separate models, and then calculating the mean and standard deviation of probability of occurrence in each pixel.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Name: Live Soft Coral - Mean Probability of Occurrence
Display Field:
Type: Raster Layer
Geometry Type: null
Description: Spatial predictions denoting the mean probability (%) of occurrence for Live Soft Coral. Average probabilities were calculated by creating 100 separate models, and then averaging the predicted probabilities in each pixel across the models.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: This layer depicts the precision of the predicted probability of occurrence value for Live Soft Coral. Precision is denoted as the coefficient of variation (CV), which is unitless. CV is the ratio of the standard deviation to the mean for each pixel. It was calculated by creating 100 separate models, and then calculating the mean and standard deviation of probability of occurrence in each pixel.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: Spatial predictions denoting the mean probability of occurrence and the coefficient of variation (or CV) for four substrate types. Mean and CV were calculated by creating 100 separate models, and then calculating the average and standard deviation of probability of occurrence in each pixel across the models. Substrate types include: Bare Sand, Coral Reef, Pavement, Rhodolith.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: Spatial predictions denoting the mean probability (%) of occurrence for Coral Reef. Average probabilities were calculated by creating 100 separate models, and then averaging the predicted probabilities in each pixel across the models.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: This layer depicts the precision of the predicted probability of occurrence value for Coral Reef. Precision is denoted as the coefficient of variation (CV), which is unitless. CV is the ratio of the standard deviation to the mean for each pixel. It was calculated by creating 100 separate models, and then calculating the mean and standard deviation of probability of occurrence in each pixel.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: SSpatial predictions denoting the mean probability (%) of occurrence for Pavement. Average probabilities were calculated by creating 100 separate models, and then averaging the predicted probabilities in each pixel across the models.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: This layer depicts the precision of the predicted probability of occurrence value for Pavement. Precision is denoted as the coefficient of variation (CV), which is unitless. CV is the ratio of the standard deviation to the mean for each pixel. It was calculated by creating 100 separate models, and then calculating the mean and standard deviation of probability of occurrence in each pixel.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: Spatial predictions denoting the mean probability (%) of occurrence for Rhodoliths. Average probabilities were calculated by creating 100 separate models, and then averaging the predicted probabilities in each pixel across the models.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: This layer depicts the precision of the predicted probability of occurrence value for Rhodoliths. Precision is denoted as the coefficient of variation (CV), which is unitless. CV is the ratio of the standard deviation to the mean for each pixel. It was calculated by creating 100 separate models, and then calculating the mean and standard deviation of probability of occurrence in each pixel.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: Spatial predictions denoting the mean probability (%) of occurrence for Sand. Average probabilities were calculated by creating 100 separate models, and then averaging the predicted probabilities in each pixel across the models.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: This layer depicts the precision of the predicted probability of occurrence value for Sand. Precision is denoted as the coefficient of variation (CV), which is unitless. CV is the ratio of the standard deviation to the mean for each pixel. It was calculated by creating 100 separate models, and then calculating the mean and standard deviation of probability of occurrence in each pixel.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert
Description: Depth (in meters).Seafloor mapping was conducted over an eight year period (2003-2011) using high-resolution SoNAR systems called multibeam echosounders (MBES) and phase differencing bathymetric sonars (PDBS). These surveys were funded mainly by NOAA's Coral Reef Conservation Program, and led by NOAA’s National Centers for Coastal Ocean Science (NCCOS) and the U.S. Geological Survey (USGS), in collaboration with several other regional partners.
Copyright Text: Costa, B., L. Kracker, T. Battista, W. Sautter, A. Mabrouk, K. Edwards, C. Taylor, and E. Ebert