DOI: 10.5061/dryad.5hqbzkhcz
title: Evaluating the predictors of habitat use and successful reproduction in a model bird species using a large scale automated acoustic array

description: 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.
description: <strong>Owl surveys:</strong>
 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 (<em>Bubo
 virginianus</em>). 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.
 <strong>Covariates:</strong> 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 &lt; 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.
description: # Evaluating the predictors of habitat use and successful reproduction in
 a model bird species using a large scale automated acoustic array
 [https://doi.org/10.5061/dryad.5hqbzkhcz](https://doi.org/10.5061/dryad.5hqbzkhcz) ## Description of the data and file structure Data are provided as a single CSV file **owl_data.csv** with columns * **site_number** (1-452 denoting unique survey locations), * **survey_number** (1-10 denoting the survey number in a sequence of 10), * **song_detections** (1 or 0 indicating presence or absence of Great Horned Owl song), * **beg_detections** (1 or 0 indicating the presence or absence of Great Horned Owl begging calls), * **f_cover_1750m** (the amount of forest within a 1750 m radius of the survey location, centered and scaled), * **f_cover_250m** (the amount of forest within a 250 m radius of the survey location, centered and scaled), * **ag_cover_1750m** (the amount of agricultural land cover within a 1750 m radius of the survey location, centered and scaled), * **basal_area** (the estimated tree basal area at the survey location, centered and scaled), * **stem_den** (the estimated sapling and shrub density at the survey location, centered and scaled), * **elevation** (the elevation at the survey location, centered and scaled), * and **latitude** (the latitude of the survey location, centered and scaled). ## Code/Software In addition to the data, we provide the code for reproducing analyses in **owl_analyses.R**.