Abundance and occupancy trends of sooty grouse in western Oregon: determining best modeling practices by comparing observed and simulated data

Sooty grouse Dendragapus fuliginosus are large game birds that occupy montane forests in the Pacific Northwest, USA. These forests have been altered by human activities, which have been shown to have both positive and negative impacts on local populations. The North American Breeding Bird Survey (BBS) indicated imprecise range‐wide population declines for this species (1966–2022). However, sooty grouse are inadequately represented along BBS routes, given minimal overlap with their habitat and breeding period, low population densities, and low detectability. We developed a targeted monitoring protocol to better evaluate sooty grouse population trends. We surveyed sooty grouse from 2011–2025 along 116, 10‐ to 20‐km routes across western Oregon. We estimated abundance and occupancy trends utilizing hierarchical models that simultaneously addressed the observation and ecological processes of monitoring populations using five common modeling frameworks, including exponential growth, Poisson linear regression, and logistic regression. Trend estimates all indicated declines but varied in magnitude and precision across analytical approaches. Given analysis framework differences, we simulated data to test the accuracy of the best‐fitting abundance and occupancy model. The occupancy model adequately fit trends but did not perform well estimating trends on simulated data. The best‐fitting abundance model yielded reliable trend estimates based on simulation tests and showed that sooty grouse populations declined 3.6% (95% CI = 0.8–6.4%) annually over the span of our study. Detection varied across models, with occupancy models producing higher estimates (mean = 0.48) than abundance models (mean = 0.19). Our study found declining population trends warranting a more detailed assessment to determine mechanisms driving this pattern. Our results also point to the importance of carefully assessing model fit and the incorporation of study design with judicious use of random effects for analyzing point‐level data, as these choices can lead to variable results, interpretations, and management recommendations.