The emergence of continental to global scale biodiversity data has led to growing understanding of patterns in species distributions, and the determinants of these distributions, at large spatial scales. However, identifying the specific mechanisms, including demographic processes, and determining species distributions remains difficult, as large-scale data are typically restricted to observations of only species presence. New remote automated approaches for collecting data, such as automated recording units (ARUs), provide a promising avenue towards direct measurement of demographic processes, such as reproduction, that cannot feasibly be measured at scale by traditional survey methods. In this study, we analyze data collected by ARUs from 452 survey points across an approximately 1500 km study region to compare patterns in adult and juvenile distributions in the Great Horned Owl (Bubo virginianus). We specifically examine whether habitat associated with successful reproduction is the same as that associated with adult presence. We postulated that congruence between these two distributions would suggest that all areas of the species’ range contribute equally to maintenance of the population, whereas significant differences would suggest more specificity in the species’ requirements for successful reproduction. We filtered adult and juvenile calls of the species for manual review using automated classification and constructed single season occupancy models to compare land cover and vegetation covariates which significantly predicted presence of each life stage. We found that habitat use by adults was significantly predicted by increasing amounts of forest cover, reduced forest basal area, and lower elevations whereas juvenile presence was significantly predicted only by decreasing amounts of forest cover, a pattern opposite that of adults. These results show that presence of adult Great Horned Owls is not a sufficient proxy for locations at which reproduction occurs, and also demonstrate a highly scalable workflow that could be used for similar analyses in other sound-producing species.

Owl surveys:

Nighttime autonomous acoustic recordings were collected from 452 survey locations across 1500 km of the eastern United States. Two Convolutional Neural Networks were developed to classify the adult song and juvenile begging call of the Great Horned Owl (Bubo virginianus). These classifiers were run on the recordings and the highest scoring ten five-second clips occurring on ten separate days at each survey location were extracted. These clips were manually reviewed by a human listener to ensure they contained the relevant owl sounds. Presence/absence was translated into 1/0 detection histories to be used in occupancy models.

Covariates:

GPS coordinates were collected at each survey location (these are not provided to protect landowner identity). National Land Cover Database information was extracted for the amount of forest and agricultural land cover within a 1750 m radius of each survey location for use as occupancy covariates. Tree basal area and < 10 cm DBH stem density were estimated for each survey location for use as occupancy covariates.  The elevation at each survey location was extracted from the GMTED 2010 for use as occupancy covariates. Latitude was used as a detection covariate. All covariates were centered and scaled before deposit.