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Predictions of raven occurrence in the absence of anthropogenic environmental effects. Raven point counts were related to landscape covariates using Bayesian hierarchical occupancy models and the means of the posterior distributions for relevant effects were used to generate the predictions.
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Predictions of raven occurrence in the absence of natural environmental effects. Raven point counts were related to landscape covariates using Bayesian hierarchical occupancy models and the means of the posterior distributions for relevant effects were used to generate the predictions.
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Contamination to aquatic resources from co-produced water (brine) associated with energy development has been documented in the northeastern portion of the Williston Basin; an area mantled by glacial drift. The presence and magnitude of brine contamination can be determined using the contamination index (CI) value from water samples. Recently, the U.S. Geological Survey published a section (~2.59 km2) level risk assessment of brine contamination to aquatic resources for Sheridan County, Montana, using oilfield and hydrogeological parameters. Our goal was to improve the Sheridan County assessment (SCA) and evaluate the use of this new Williston Basin assessment (WBA) across 31 counties mantled by glacial drift in...
Categories: Data; Types: ArcGIS REST Map Service, ArcGIS Service Definition, Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: Bakken Formatioin, Bakken Formation, Benson, North Dakota, Blaine, Montana, Bottineau, North Dakota, All tags...
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Predictions of an anthropogenic influence on raven occurrence index intersected with sage-grouse concentration areas. The anthropogenic influence index indicates where resource subsidies are contributing the most to raven occurrence.
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Raven point counts were related to landscape covariates using Bayesian hierarchical occupancy models and the mean of the predicted posterior distribution for raven occurrence was used to visualize results.
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An index of anthropogenic influences on raven populations. Raven point counts were related to landscape covariates using Bayesian hierarchical occupancy models and the means of the posterior distributions for relevant effects were used to generate the predictions.
    map background search result map search result map Williston Basin Assessment Web Map (2014) Predicted probability of raven occurrence across the Great Basin, USA, 2007 – 2016 (Fig. 3) Predictions of raven occurrence in the absence of natural environmental effects in the Great Basin, 2007-2016 (Fig. 4A) Predictions of raven occurrence in the absence of anthropogenic environmental effects in the Great Basin, 2007-2016 (Fig. 4B) Anthropogenic influence index for raven populations in the Great Basin, 2007-2016 (Fig. 4C) Prediction of raven occurrence intersected with high impact areas for sage-grouse populations in the Great Basin, 2007-2016 (Fig. 5A) Anthropogenic influence on raven occurrence index within sage-grouse concentration areas in the Great Basin, 2007-2016 (Fig. 5B) Williston Basin Assessment Web Map (2014) Prediction of raven occurrence intersected with high impact areas for sage-grouse populations in the Great Basin, 2007-2016 (Fig. 5A) Anthropogenic influence on raven occurrence index within sage-grouse concentration areas in the Great Basin, 2007-2016 (Fig. 5B) Predicted probability of raven occurrence across the Great Basin, USA, 2007 – 2016 (Fig. 3) Predictions of raven occurrence in the absence of natural environmental effects in the Great Basin, 2007-2016 (Fig. 4A) Predictions of raven occurrence in the absence of anthropogenic environmental effects in the Great Basin, 2007-2016 (Fig. 4B) Anthropogenic influence index for raven populations in the Great Basin, 2007-2016 (Fig. 4C)