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Environmental predictors included position on the shelf, sea floor habitat, depth, slope, aspect, slope of slope, and oceanographic productivity were used to predict areas with relatively high groundfish biodiversity.\n\nBinary logistic regression trees were used to associate species observations with environmental covariates and predict categorical results (Hotspot/Low classes). Mapped values indicate the probability of a raster cell belonging in the hotspot class.", "mapName": "Groundfish biodiversity maps", "description": "This mapping service comprises maps of predicted groundfish biodiversity hotspot probabilities off the Pacific Coast of Oregon and Washington. Predicted hotspot probabilities are given for four biodiversity indices: 1) relative abundance, 2) relative biomass, and 3) species number for all groundfishes, and 4) relative abundance for only nearshore groundfishes. 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Environmental predictors included position on the shelf, sea floor habitat, depth, slope, aspect, slope of slope, and oceanographic productivity were used to predict areas with relatively high groundfish biodiversity.\n\nBinary logistic regression trees were used to associate species observations with environmental covariates and predict categorical results (Hotspot/Low classes). Mapped values indicate the probability of a raster cell belonging in the hotspot class.", "Subject": "This mapping service comprises maps of predicted groundfish biodiversity hotspot probabilities off the Pacific Coast of Oregon and Washington. 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