[1]¿p#1 Sooty Grouse (Dendragapus fuliginosus) is a large game bird that occupies montane forests in the Pacific Northwest, USA. These forests have been disturbed by human activities, which has been documented to have positive and negative impacts on populations. The North American Breeding Bird Survey (BBS) indicated population declines for this species across their range (-1.2 % per year [95% CI = -3.0 – 0.25 % per year], 1966 – 2022). However, Sooty Grouse is inadequately represented along BBS routes due to little overlap with habitat, survey timing, low population density, and low detectability. We developed a monitoring protocol specialized for Sooty Grouse to better evaluate population trends for this species. We surveyed Sooty Grouse from 2011 – 2024 along 119 10- to 20-km survey routes across western Oregon. We estimated abundance and occupancy trends utilizing hierarchical models that simultaneously address the observation and ecological processes of monitoring wildlife populations. We did this in five common modeling frameworks, including exponential growth, Poisson linear regression, and logistic regression using JAGS, ubms, and unmarked (program R). Trend estimates varied across approaches, Poisson linear regression models displayed the most precise trend estimates, indicating that Sooty Grouse populations declined 2.9 % (95% CI = 1.4 – 4.5 %) annually over the span of our study. Given differences among frameworks, we simulated data to test which provided the most accurate trend estimate. Occupancy models did not perform well estimating trends on simulated data, whereas abundance models yielded robust results, particularly when the dataset did not contain missing data. Detection probability also varied across models, with occupancy models producing higher estimates (mean = 0.84) than abundance models (mean = 0.46). Our results confirm Sooty Grouse trends have declined in the recent past and warrant a more detailed assessment to determine what factors are driving this pattern.

Many bird species are monitored using auditory point count surveys during the breeding season. Advances in passive acoustic technology have enabled the use of autonomous recording units (ARUs) alongside point-count surveys, improving survey methodologies. However, automated song/call identification and manual review of recordings are required to assess accuracy of data collected from ARUs. We evaluated the feasibility and accuracy of PNW-Cnet, an AI-based detection application, for identifying the hooting song of male Sooty Grouse (Dendragapus fuliginosus). From 2020–2023, we deployed ARUs at 149 locations in western Oregon, USA near known hooting males. We used PNW-Cnet to identify hoots and the accuracy of the detector was calculated by manually verifying 10,000 detections. Accuracy was near perfect relative to false detections. Once hoots were identified, we used generalized additive models with random effects to examine seasonal (across the breeding season) and daily (relative to time since sunrise) hooting patterns. Model results indicated hooting rates peaked in late-April, providing guidance on the optimal timing of point count surveys based on the estimated number of hoots that would be heard per survey. Daily patterns revealed a rapid increase in hooting 30 minutes before sunrise, then leveling at a relatively constant hooting rate up to at least six hours after sunrise (latest time our ARUs were recording). Thus, our results suggest males continue to vocalize throughout the entire morning allowing effective surveys to be conducted beyond the early morning. By integrating ARUs and an AI-based detection application, we gained detailed information about hooting patterns that will allow improvements to future data collection, increasing survey efficiency, and ultimately leading to a more efficient population monitoring.