Data and code from: Abundance and occupancy trends of sooty grouse in western Oregon: Determining best modeling practices by comparing observed and simulated data

We estimated sooty grouse population trends using hierarchical models that
account for imperfect detection when estimating abundance or occupancy and
their dynamics. We used the survey point along a route as the sampling
unit. We included route random effects to account for the non-independence
of points along a route. We compared population trend estimates of
abundance and occupancy and 95% credible intervals (CIs) from the
following modeling frameworks: 1) Binomial N-mixture model with
Poisson linear regression (PLR): We ran this model in both JAGS and the
'ubms' package frameworks. 2) Occupancy trend model
with logistic regression (OTM): We ran this model in both JAGS and the
'ubms' package frameworks. 3) The Dail-Madsen model
with exponential growth (EGM): We ran this model in the JAGS framework.
Finally, we assessed model fit to select one abundance and one occupancy
trend model to use in simulation tests to determine which model provides
the most accurate trend estimates for sooty grouse. All statistical code
is in the R programming language.