Estimating population size for California spotted owls and barred owls across the Sierra Nevada ecosystem with bioacoustics

Kelly, Kevin G.; Wood, Connor M.; McGinn, Kate; Kramer, H. Anu; Sawyer, Sarah C.; Whitmore, Sheila; Reid, Dana; Kahl, Stefan; Reiss, Aimee; Eiseman, Jonathan; Berigan, William; Keane, John J.; Shaklee, Paula; Gallagher, Lief; Munton, Thomas E.; Klinck, Holger; Gutiérrez, R.J.; Peery, M. Zachariah

doi:10.1016/j.ecolind.2023.110851

Cited by 5 publications

(1 citation statement)

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“…To account for all vocalization types in this analysis, we used the highest‐scoring spotted owl vocalization from each 3‐second clip (either specific or general). Previous analyses suggested that any spotted owl vocalization type (either specific or general) with a BirdNET‐Sierra confidence score ≥0.989 provided a true positive detection rate of 0.813 (e.g., 81 of 100 BirdNET‐Sierra detections that met or exceeded that threshold were true positives; Kelly et al 2023). BirdNET confidence scores are influenced by the acoustic characteristics of the recording system, background noise conditions, and sample rate, so a given threshold will produce different accuracies if applied to different recording settings (Wood et al 2019 a , Kahl et al 2021).…”

Section: Methodsmentioning

confidence: 99%

Using bioacoustics to enhance the efficiency of spotted owl surveys and facilitate forest restoration

Kramer,

Kelly,

Whitmore

et al. 2023

J Wildl Manag

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The California spotted owl (Strix occidentalis occidentalis) is an older‐forest associated species that resides at the center of forest management planning in the Sierra Nevada and Southern California, USA, which are experiencing increasingly large and severe wildfires and drought‐related tree mortality. We leveraged advances in passive acoustic survey technologies to develop an acoustically assisted survey design that could increase the efficiency and effectiveness of project‐level surveys for spotted owls, allowing surveys to be completed in a single year instead of in multiple years. We deployed an array of autonomous recording units (ARUs) across a landscape and identified spotted owl vocalizations in the resulting audio using BirdNET. We then evaluated spatio‐temporal patterns in spotted owl vocalizations near occupied territories and the ability of a crew naïve to the location of occupied territories to locate spotted owls based on patterns of acoustic detections. After only 3 weeks of acoustic surveys, ≥1 ARU within 750 m of all 17 occupied territories obtained spotted owl detections across ≥2 nights. When active surveys using broadcast calling were conducted near ARUs with spotted owl detections by surveyors naïve to territory occupancy status and locations, surveyors located owls in 93% to 100% of occupied territories with ≤3 surveys. To further improve the efficiency of spotted owl surveys, we developed a statistical model to identify and prioritize areas across the Sierra Nevada for different survey methods (active only, acoustically assisted, no surveys) based on the expected probability of occupancy predicted from remotely sensed measurements of tree height and historical occupancy. Depending on managers' tolerance for false negatives, this model could help identify large areas that might not benefit from surveys based on low expected occupancy probabilities and areas where acoustically assisted surveys might enhance survey effectiveness and efficiency. Collectively, these findings can help managers streamline the survey process and thus increase the pace of forest restoration while minimizing potential near‐term adverse effects on California spotted owls.

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Section: Methodsmentioning

confidence: 99%