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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.
Abstract: Freshwater mussels are among the most imperiled faunal groups globally, and relevant ecological information is urgently needed to guide their management and conservation in the face of global change. We explored the influence of species traits, host fishes, and habitat at three spatial scales (micro-, reach-, and catchment-scale) on the detection and occupancy of 14 species of freshwater mussels in the Tar River basin, North Carolina. Detection probability for all species was 0.42 (95% CI, 0.36 –0.47) with no species- or site-specific detection effects identified. Mean occupancy probability among species ranged from 0.04 (95% CI, 0.01 – 0.16) for Alasmidonta undulata, an undescribed Lampsilis sp., and...
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Climate change is expected to alter the distributions and community composition of stream fishes in the Great Lakes region in the 21st century, in part as a result of altered hydrological systems (stream temperature, streamflow, and habitat). Resource managers need information and tools to understand where fish species and stream habitats are expected to change under future conditions. Fish sample collections and environmental variables from multiple sources across the United States Great Lakes Basin were integrated and used to develop empirical models to predict fish species occurrence under present-day climate conditions. Random Forests models were used to predict the probability of occurrence of 13 lotic fish...
Categories: Data, Project; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service, Shapefile; Tags: 2011, 2011, 2012, 2012, 2013, All tags...
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Climate change is expected to alter the distributions and community composition of stream fishes in the Great Lakes region in the 21st century, in part as a result of altered hydrological systems (stream temperature, streamflow, and habitat). Resource managers need information and tools to understand where fish species and stream habitats are expected to change under future conditions. Fish sample collections and environmental variables from multiple sources across the United States Great Lakes Basin were integrated and used to develop empirical models to predict fish species occurrence under present-day climate conditions. Random Forests models were used to predict the probability of occurrence of 13 lotic fish...
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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 Regional decision support tool for identifying vulnerabilities of riverine habitat and fishes to climate change Report: Regional decision support tool for identifying vulnerabilities of riverine habitat and fishes to climate change 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) Regional decision support tool for identifying vulnerabilities of riverine habitat and fishes to climate change Report: Regional decision support tool for identifying vulnerabilities of riverine habitat and fishes to climate change