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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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A hierarchical occupancy model adapted from Royle & Dorazio (2008) and Rota et al. (2011) for use in R. References: Royle, J.A. and Dorazio, R.M., 2008. Hierarchical modeling and inference in ecology: the analysis of data from populations, metapopulations and communities. Academic Press. doi:10.1016/B978-0-12-374097-7.50001-5 J. Andrew Royle, Robert M. Dorazio, Rota, C. T., Fletcher Jr, R. J., Dorazio, R. M. and Betts, M. G. (2009), Occupancy estimation and the closure assumption. Journal of Applied Ecology, 46: 1173-1181. doi:10.1111/j.1365-2664.2009.01734.x
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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 Raven study site locations in the Great Basin, derived from survey locations 2007 - 2016 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) Hierarchical Occupancy Model Code for R and Accompanying Files Raven study site locations in the Great Basin, derived from survey locations 2007 - 2016 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) Hierarchical Occupancy Model Code for R and Accompanying Files 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)