Filters: Tags: decision support (X) > Date Range: {"choice":"month"} (X)
6 results (38ms)|
Filters
Date Types (for Date Range)
Types Contacts
Categories Tag Types
|
These tables serve as input data for hierarchical models investigating interactions between raven density and Greater Sage-grouse nest success. Observations were recorded over an 11 year time period, spanning from 2009 through 2019. The model is run in JAGS via R, the code is publicly available via the U.S. Geological Survey's GitLab (O'Neil et al. 2023). We recommend not making any changes or edits to the tables unless the user is experienced with hierarchical modeling. References: O'Neil, S.T., Coates, P.S., Webster, S.C., Brussee, B.E., Dettenmaier, S.J., Tull, J.C., Jackson, P.J., Casazza, M.L., and Espinosa, S.P., 2023, Code for a hierarchical model of raven densities linked with sage-grouse nest survival...
As global temperatures continue to rise, the frequency and severity of droughts in North America are expected to increase, leading to a wide range of social and ecological impacts. Identifying these impacts and the consequences for ecosystems and human communities are essential for effective drought management. Equally important is to improve the capacity of nature and people to prepare for and cope with drought by identifying management strategies that benefit both. An interdisciplinary working group within the Science for Nature and People Partnership (SNAPP) was established by the U.S. Geological Survey, The Wildlife Conservation Society, and The Nature Conservancy to synthesize our current understanding of...
Categories: Project;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: 2015,
CASC,
Completed,
Decision support,
Drought,
A Groundwater Nitrate Decision Support Tool (GW-NDST) for wells in Wisconsin was developed to assist resource managers with assessing how legacy and possible future nitrate leaching rates, combined with groundwater lag times and potential denitrification, influence nitrate concentrations in wells (Juckem et al. 2024). Running and using the GW-NDST software involves downloading the software code (version 1.1.0) from the code repository (https://doi.org/10.5066/P13ETB4Q), downloading GIS data for the machine learning support models (child data release "GIS files required to run the Groundwater Nitrate Decision Support Tool for Wisconsin"), downloading the parameter uncertainty file (child data release "Parameter ensemble...
Rasters representing median raven density estimates, calculated from approximately 28,000 raven point count surveys conducted between 2009 and 2019. Estimates were the result of a Bayesian hierarchical distance sampling model, using environmental covariates on detection and abundance.
We combined approximately 28,000 raven point count surveys with data from more than 900 sage-grouse nests between 2009 and 2019 within the Great Basin, USA. We modeled variation in raven density using a Bayesian hierarchical distance sampling approach with environmental covariates on detection and abundance. Concurrently, we modeled sage-grouse nest survival using a hierarchical frailty model as a function of raven density as well as other environmental covariates that influence risk of failure. Raven density commonly exceeded more than 0.5 ravens per square kilometer and increased at low relative elevations with prevalent anthropogenic development and/or agriculture. Reduced sage-grouse nest survival was strongly...
These rasters are the result of calculating the difference in Greater Sage-grouse nest survival after a simulated reduction of raven density to 0.1 ravens per square kilometer. The difference in nest survival represents spatial variation in potential to improve nest survival by reducing raven impacts. The extent of each individual raster is the extent of the field site at which sage-grouse nest observations were recorded.
|
|
