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미국
NCCOS spatial modeling of threatened Caribbean corals: presence-only modeling for threatened Orbicella species from the nearshore to the mesophotic from 2007-01-01 to 2018-12-31 (NCEI Accession 0241110)
This dataset is a compilation of modeled spatial distributions of Threatened corals, Orbicella annularis (lobed star coral) and Orbicella faveolata (mountainous star coral)/Orbicella franksi (boulder star coral). in the shallow and upper mesophotic waters (0 – 60 meters depth) on the eastern Puerto Rico shelf (encompassing St. Thomas and St. John, U.S. Virgin Islands and the partial waters around Culebra and Vieques, Puerto Rico). Models for O. faveolata/O. franksi were combined into one model. All three Orbicella spp. area listed as Threatened under the U.S. Endangered Species Act. The raster datasets contain predicted probability of occurrence and prediction uncertainty for O. annularis and O. faveolata/O. franksi at four different model extents: • 0 – 60 meter depth model: Orbicella spp. models encompassing the entire modeling region, both shallow and mesophotic depths from 0 to 60 meters (capped at 60 meters for Orbicella spp. known depth range in this region) • 0 – 60 meter depth south shore only model: Orbicella spp. models of the south shore only removal of the mesophotic depths of the north shore, north of St. Thomas and St. John, USVI • Shallow only model (0 – 30 meter depth): Orbicella spp. models of the shallow waters only, removal of the mesophotic depths • Mesophotic only model (30 – 60 meter depth: Models of O. faveolata/O. franksi at mesophotic depths only remove of the shallow depths Models were conducted using a presence/background sample model, which involves the use of presence-only data. Maximum entropy modeling was used specifically, initiated through the Java software program, MaxEnt.
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연관 데이터
NCCOS spatial modeling of threatened Caribbean corals: process-based models for Acropora palmata (elkhorn coral) distributions in the U.S. Virgin Islands (NCEI Accession 0220087)
공공데이터포털
This dataset is a compilation of modeled current and future density distributions of threatened elkhorn corals Acropora palmata in the shallow water (bottom depth ≥ -20 m) off St. Thomas, St. John and St. Croix, U.S. Virgin Islands. The raster data sets contain predicted distributions of species density and the prediction uncertainty in 2013, 2014, 2015, 2035 and 2055 estimated using process-based random forest (RF) and dynamic range models (DRM). These predictions were generated to inform Caribbean A. palmata restoration plans in the U.S. Virgin Islands.
NCCOS assessment: Predicting deep-sea coral habitats within the Papahānaumokuākea Marine National Monument, Hawaii (NCEI Accession 0244006)
공공데이터포털
This dataset contains geospatial data from spatial predictive models that were developed for 22 deep-sea coral and sponge (DSCS) taxa within the Papahānaumokuākea Marine National Monument (PMNM) from depths of 100-3,500 m. It includes raster datasets at 360 x 360 m spatial resolution depicting the predicted probability of occurrence for each of these taxa and a raster dataset at 360 x 360 m spatial resolution depicting the predicted taxonomic richness. These predictions provide a baseline for the potential distribution of these vulnerable and ecologically significant communities in the northwestern Hawaiian Islands (NWHI), and will support management planning, permitting, exploration and sanctuary designation efforts by the Monument. The data collection also includes raster datasets at 360 x 360 m spatial resolution depicting each of the 44 spatial environmental predictor variables considered for fitting the models.
NCCOS Assessment: Southeastern U.S. Predictive Modeling of Deep-Sea Corals and Hardbottom Habitats, 2016-10-01 to 2021-09-30 (NCEI Accession 0282806)
공공데이터포털
This data collection contains geospatial data from models predicting the spatial distributions of deep-sea corals (DSCs) and hardbottom habitats offshore of the southeastern U.S. It includes a database (.csv text file) containing records of occurrence (presence-absence) for DSCs with associated measures of sampling effort and bottom type from 20 datasets comprised of data from visual field surveys conducted with underwater vehicles. It also includes raster datasets at 100 x 100 m spatial resolution depicting the median and coefficient variation of the predicted occurrence (occupancy probability) for 24 taxa of DSCs (23 genera, 1 family) and hardbottom habitats. Additional raster datasets depict the median and coefficient of variation of the predicted genus richness for the 23 genera of DSCs. The data collection also includes raster datasets at 100 x 100 m spatial resolution depicting each of the 62 spatial environmental predictors considered for fitting the models. For more information, see Poti et al. (2022). The project to compile this model took place between 2016 and 2021, however the model input data range from 2001-2018 and the model output covers the same timeframe.
NCCOS Assessment: U.S. West Coast Cross-Shelf Habitat Suitability Modeling of Deep-Sea Corals and Sponges, 2016-10-01 to 2020-09-30 (NCEI Accession 0276883)
공공데이터포털
This data collection contains geospatial data from models predicting the spatial distributions of deep-sea corals and sponges offshore of the continental U.S. West Coast to 1200 m depth. It includes raster datasets at 200 x 200 m spatial resolution depicting the mean of the predicted relative habitat suitability, the coefficient of variation of the predicted relative habitat suitability, the classified mean relative habitat suitability, and the ‘robust high’ habitat suitability prediction for each of 31 taxa of deep-sea corals and 15 taxa of sponges and raster datasets at 200 x 200 m spatial resolution depicting the number of taxa of deep-sea corals associated with hard substrate that have ‘high’ habitat suitability or ‘robust high’ habitat suitability at each grid cell. The data collection also includes raster datasets at 200 x 200 m spatial resolution depicting each of the 66 spatial environmental predictor variables considered for fitting the models.
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