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

Dataset DOI: [10.5061/dryad.djh9w0wbb](https://doi.org/10.5061/dryad.djh9w0wbb)

## Description of the data and file structure

We estimated Sooty Grouse population trends using hierarchical models that account for imperfect detection when estimating abundance or occupancy dynamics. We compared trend estimates (and 95% CIs), measured as lambda, the finite rate of increase per unit time (in this case per year) from the following modeling frameworks: 1) The Dail-Madsen model with exponential growth (EGM) in JAGS framework, 2) Binomial N-mixture model with Poisson linear regression (PLR) in ubms and JAGS frameworks, 3) Occupancy trend model with logistic regression (OTM) in ubms and JAGS frameworks.

### Files and variables

**Files and variables**

**File: abund_trend_PLR_ubms.R**

**Description:** Abundance trend model using..., ,