Skip to main content
Advanced Search

Filters: Tags: occupancy model (X) > partyWithName: U.S. Geological Survey - ScienceBase (X)

12 results (12ms)   

View Results as: JSON ATOM CSV
thumbnail
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.
thumbnail
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.
thumbnail
This data shows the quantification cycle at which fluorescence signals crossed a threshold fluorescence for samples analyzed as part of controlled experiments to determine whether contaminating DNA is present in samples under varying experimental conditions.
thumbnail
Expanding human enterprise across remote environments impacts many wildlife species, including sage-grouse (Centrocercus urophasianus), an indicator species whose decline is at the center of national conservation strategies and land use policies. Anthropogenic resources provide subsidies for generalist predators, potentially leading to cascading effects on sensitive prey species at lower trophic levels. In semi-arid western ecosystems, common ravens (Corvus corax) are expanding in distribution and abundance, and may be negatively affecting sage-grouse reproductive success at broad spatial scales. Ravens are a common predator of sage-grouse nests, and potentially prey on chicks as well. This research aimed to address...
thumbnail
These data represent the results of trapping efforts for native northwestern pond turtles (Actinemys marmorata) and non-native red-eared sliders (Trachemys scripta elegans) in wetlands and irrigation canals in agricultural regions of the Sacramento Valley in 2018 and the Sacramento-San Joaquin River Delta in 2019. In addition to detection data for these two turtle species, the dataset includes habitat data from each trapped site and data on the capture rate of fish, frogs, and tadpoles at each site. These data support the following publication: Fulton, A.M., Rose, J.P. and Halstead, B.J., 2022. Rural turtles: estimating the occupancy of northwestern pond turtles and non-native red-eared sliders in agricultural...
thumbnail
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
thumbnail
Extant population monitoring and habitat assessment data sets of benthic species were used as inputs for occupancy models focused on Sicklefin and Sturgeon chub with the goals of describing temporal, spatial, and environmental factors associated with occupancy patterns of each chub species, assessing co-occurrence of the two species, and determining relationships between co-occurrence and environmental factors. We also used three-species occupancy models to assess co-occurrence of these chubs with other primarily benthic species. This data set is comprised of the outputs of these models.
thumbnail
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.
thumbnail
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.
thumbnail
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 Data from: Broad-scale occurrence of a subsidized avian predator: reducing impacts of ravens on sage-grouse and other sensitive prey 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 Quantitative polymerase chain reaction detection data for controlled DNA contamination experiments Data to Fit an Occupancy Model to Trapping Data for the Northwestern Pond Turtle and Red-Eared Slider in the Sacramento Valley (2018) and Sacramento-San Joaquin River Delta (2019) Occupancy model coefficients and observed co-occurrence simulations for sicklefin chub, sturgeon chub, and associated fishes in the Missouri River Quantitative polymerase chain reaction detection data for controlled DNA contamination experiments Data to Fit an Occupancy Model to Trapping Data for the Northwestern Pond Turtle and Red-Eared Slider in the Sacramento Valley (2018) and Sacramento-San Joaquin River Delta (2019) 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) Data from: Broad-scale occurrence of a subsidized avian predator: reducing impacts of ravens on sage-grouse and other sensitive prey 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) Occupancy model coefficients and observed co-occurrence simulations for sicklefin chub, sturgeon chub, and associated fishes in the Missouri River