Code to examine habitat selection and survival of greater sage-grouse in Nevada and northeastern California

This repository centralizes R code used to examine habitat selection and survival of greater sage-grouse (Centrocercus urophasianus) during multiple seasons and life stages, utilizing data collected from a wide range of study sites in Nevada and California, USA.

The R script "selection_model.R" provides code for a GLMM framework with regularization within a Bayesian modeling environment to evaluate the effects of landscape predictors on habitat selection during each season and life stage. As noted in the script, the input data can be adjusted to run the model for each season (spring, summer, winter) and life stage (nest, early and late brood-rearing). The R script "nest_survival_model.R" provides code for a Bayesian logistic exposure model framework that estimates impacts of landscape covariates on daily nest survival of sage-grouse. The R script "brood_survival_model.R" provides code for a Bayesian logistic exposure model framework (Shaffer 2004) that estimates impacts of landscape covariates on brood survival of sage-grouse, with covariates included separately for early and late brood-rearing periods. The R script "corridor_iSSA.R" provides code for an integrated step-selection model (Avgar et al. 2016) that estimates the effects of landscape covariates on movement of sage-grouse between nesting and brood-rearing habitat.

All selection and survival models are implemented using NIMBLE (version 1.0.1; de Valpine et al. 2017, 2022) in R (version 4.1.3) and were fit in a hierarchical modeling framework subject to Bayesian lasso regulation. The corridor model is implemented using the package inla (Rue et al. 2009). All models were fit within a Bayesian framework and model outputs will differ slightly from published outputs due to the random nature of MCMC algorithms (selection and survival models) or approximate Bayesian inference (corridor model).