| Summary: | White-headed Woodpeckers ( Dryobates albolarvatus ) are strongly associated with late-successional dry forest types. Callback surveys along transects are typically used to understand their status and trends in response to forest management. However, this survey method has proven to be logistically challenging because of the number of spatial and temporal replicate surveys needed to accurately interpret surveys that yield no detections. Passive acoustic recording units (ARUs) effectively detect certain avian species and may offer a more efficient and effective survey method, but few studies have focused on detecting White-headed Woodpeckers. Our objectives were to: (1) compare detection probabilities of White-headed Woodpeckers between callback surveys and ARUs, and (2) estimate the number of surveys needed to infer White-headed Woodpeckers’ absence under different levels of certainty. We surveyed for White-headed Woodpeckers from 5 May to 15 July 2021 by conducting callback surveys along six transects, with 10 survey stations along each, and deploying ARUs at 25 survey stations across three watersheds in the Wallowa-Whitman National Forest, Oregon, USA. We developed a classifier for White-headed Woodpeckers to detect their two-, three-, and four-note calls in our ARU data. Using single-season occupancy models and Akaike Information Criterion corrected for small sample sizes, the best fit model indicated that the odds of detecting White-headed Woodpeckers were 1.28 times higher approximately every 10 days into the breeding season and 4.41 times lower when using callback surveys compared to using ARUs. The cumulative detection probability for ARUs ranged from 0.95 to 0.99 after being deployed for 5 and 8 days, respectively. The cumulative detection probability was only 0.15–0.38 after 1 and 3 replicate callback survey(s) at a survey station, respectively. Our study demonstrates that managers can gather more accurate data related to the presence/absence of White-headed Woodpeckers to inform forest management decisions when using a passive acoustic monitoring design.
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