Skip to main content
Advanced Search

Filters: Tags: habitat alteration (X) > Types: Downloadable (X)

17 results (114ms)   

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
Map of cumulative 38-day nest survival predicted from a Bayesian hierarchical shared frailty model of sage-grouse nest fates. The midpoint of coefficient conditional posterior distributions of 38-day nest survival were used for prediction at each 30 meter pixel across the landscape.
We assessed the impacts of co-occurring invasive plant species on fire regimes and postfire native communities in the Mojave Desert, western USA by analyzing the distribution and co-occurrence patterns of three invasive annual grasses known to alter fuel conditions and community structure: Red Brome (Bromus rubens), Cheatgrass (Bromus tectorum), and Mediterranean grass (Schismus spp.: Schismus arabicus and Schismus barbatus), and an invasive forb, red stemmed filaree (Erodium cicutarium) which can dominate postfire sites. We developed species distribution models (SDMs) for each of the four taxa and analyzed field plot data to assess the relationship between invasives and fire frequency, years postfire, and the impacts...
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
Ranked index of model-projected nest site selection integrated with nesting productivity (i.e., nest survival), demonstrating the spatial distribution of adaptive vs. maladaptive habitat selection at each 30 m pixel. Hierarchical models of nest selection and survival were fit to landscape covariates within a Bayesian modeling framework in Nevada and California from 2009 through 2017 to develop spatially explicit information about nest site selection and survival consequences across the landscape. Habitat was separated into 16 classes ranking from high (1) to low (16). Habitat ranked highest where the top nest selection and survival classes intersected (adaptive selection), whereas the lowest rank occurred where...
This model was constructed to model the risk of invasion by exotic plant species. Roads may directly influence exotic plant dispersal via disturbance during road construction or via alterations in soil regimes. For example, in Californian serpentine soil ecosystems, exotic plant species can be found up to 1km from the nearest road and Russian thistle (Salsola kali), an exotic forb growing along roads, is wind-dispersed over distances greater than 4km. Roads may also indirectly facilitate the dispersal of exotic grasses, such as crested wheatgrass (Agropyron cristatum), via human seeding along road verges or in burned areas near roads as a management strategy to curb the establishment of less desirable exotic grass...
thumbnail
Map of nesting habitat selection scores predicted from a resource selection function (RSF) developed from sage-grouse nest locations. Nest site selection was modeled using a generalized linear mixed model of used and random locations in a Bayesian modeling environment, and the midpoint of coefficient conditional posterior distributions were used for prediction. Continuous values were reclassified and ranked using a percent isopleth approach with respect to observed nest locations.
thumbnail
Data are abundance and body size (length) of juvenile salmon, forage fish, and other species captured with a lampara net in eelgrass and nearby unvegetated habitat on the Skagit River Delta monthly, April-September, 2008-2010, as well as vegetation status, water depth, temperature, salinity, and clarity for each fish netting event.
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 Exotic Plant Invasion Risk in the Western United States Data collected in 2008-2010 to evaluate juvenile salmon and forage fish use of eelgrass on the Skagit River Delta, Washington State, USA 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) Invasive Plant Cover in the Mojave Desert, 2009 - 2013 (ver. 2.0, April 2021) Greater Sage-grouse Nest Survival, Nevada and California 2019 Greater Sage-grouse Nest Selection, Nevada and California 2019 Greater Sage-grouse Nest Site Source-Sink, Nevada and California 2019 Species distribution model of the invasive annual grass Bromus rubens (red brome) in the Mojave Desert Species distribution model of the invasive annual forb Erodium cicutarium (red-stemmed filaree) in the Mojave Desert Species distribution model of the invasive annual grass Schismus spp (Mediterranean split grass) in the Mojave Desert Species distribution model of the invasive annual grass Bromus tectorum (cheatgrass) in the Mojave Desert Data collected in 2008-2010 to evaluate juvenile salmon and forage fish use of eelgrass on the Skagit River Delta, Washington State, USA Invasive Plant Cover in the Mojave Desert, 2009 - 2013 (ver. 2.0, April 2021) Species distribution model of the invasive annual grass Bromus rubens (red brome) in the Mojave Desert Species distribution model of the invasive annual forb Erodium cicutarium (red-stemmed filaree) in the Mojave Desert Species distribution model of the invasive annual grass Schismus spp (Mediterranean split grass) in the Mojave Desert Species distribution model of the invasive annual grass Bromus tectorum (cheatgrass) in the Mojave Desert Greater Sage-grouse Nest Selection, Nevada and California 2019 Greater Sage-grouse Nest Site Source-Sink, Nevada and California 2019 Greater Sage-grouse Nest Survival, Nevada and California 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) 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) Exotic Plant Invasion Risk in the Western United States