DOI: 10.5061/dryad.x95x69pkr
title: Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data

description: Occupancy modeling is used to evaluate avian distributions and habitat
 associations, yet it typically requires extensive survey
 effort because a minimum of three repeat samples are required for
 accurate parameter estimation. Autonomous recording units (ARUs) can
 reduce the need for surveyors on site, yet ARUs utility
 were limited by hardware costs and the time required to manually
 annotate recordings. Software that identifies bird vocalizations may
 reduce expert time needed, if classification is sufficiently accurate. We
 assessed the performance of BirdNET – an automated classifier capable of
 identifying vocalizations from &gt;900 North American and European
 bird species – by comparing automated to manual annotations of recordings
 of 13 breeding bird species collected in northwestern California. We
 compared the parameter estimates of occupancy models evaluating habitat
 associations supplied with manually annotated data (9 min recording
 segments) to output from models supplied with BirdNET detections. We used
 three sets of BirdNET output to evaluate the duration of automatic
 annotation needed to approach manually annotated model parameter
 estimates: 9-min, 87-min, and 87-min of high-confidence detections. We
 incorporated 100 3-sec manually validated BirdNET detections per species
 to estimate true and false positive rates within an occupancy model.
 BirdNET correctly identified 90% and 65% of the bird species a human
 detected when data were restricted to detections exceeding a low or high
 confidence score threshold, respectively. Occupancy estimates, including
 habitat associations, were similar regardless of method. Precision
 (proportion of true positives to all detections) was &gt;0.70 for 9 of
 13 species, and a low of 0.29. However, processing of longer recordings
 was needed to rival manually annotated data. We conclude that
 BirdNET is suitable for annotating multispecies recordings for occupancy
 modeling when extended recording durations are used. Together, ARUs and
 BirdNET may benefit monitoring and, ultimately, conservation of bird
 populations by greatly increasing monitoring opportunities. 
  
description: We collected acoustic recordings from 44 locations within
 Carnegie State Vehicular Recreation Area in California, USA (37.6263°N,
 121.5536°W). The recordings were collected using Wildlife Acoustics
 SongMeter 4 recording units - see text for more details about sampling
 duration. Of the 44 recording locations we only analyzed 34 in this paper
 because the remainder contained too much wind noise to be usable. In this
 dataset we include manual annotations (an expert observed listened to
 9-min of a days long recording from each site), automated annotations (of
 &gt;260 min of recordings from each site) generated by processing
 recordings with the BirdNET classifier software
 (https://github.com/kahst/BirdNET), and output of 4 occupancy models. We
 have also included R code for running analyses using the data the we
 provided to reproduce the results reported in our manuscript. Recording
 files were not uploaded due to the sheer space required to store such a
 dataset.
description: The usage details for files are included in the comments of R
 code files included in this repository. Data files are supplied inside a
 zipped folder (Data.zip) that contains a subfolders with files needed for
 reproducing results in the text. All files included in the repository with
 the .R suffix are R scripts for running occupancy models and generating
 data summaries. See README file for more details.