Analytical approaches for evaluating passive acoustic monitoring data: A case study of avian vocalizations

• Published in: Ecology and Evolution
• Main Authors: Symes, Laurel B., Kittelberger, Kyle D., Stone, Sophia M., Holmes, Richard T., Jones, Jessica S., Castaneda Ruvalcaba, Itzel P., Webster, Michael S., Ayres, Matthew P.
• Other Authors: Division of Environmental Biology, Dartmouth College, College of Agriculture and Life Sciences, Cornell University
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2022
• Subjects: Avian Studies
• Online Access: http://dx.doi.org/10.1002/ece3.8797; https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.8797; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ece3.8797

Abstract The interface between field biology and technology is energizing the collection of vast quantities of environmental data. Passive acoustic monitoring, the use of unattended recording devices to capture environmental sound, is an example where technological advances have facilitated an influx of data that routinely exceeds the capacity for analysis. Computational advances, particularly the integration of machine learning approaches, will support data extraction efforts. However, the analysis and interpretation of these data will require parallel growth in conceptual and technical approaches for data analysis. Here, we use a large hand‐annotated dataset to showcase analysis approaches that will become increasingly useful as datasets grow and data extraction can be partially automated. We propose and demonstrate seven technical approaches for analyzing bioacoustic data. These include the following: (1) generating species lists and descriptions of vocal variation, (2) assessing how abiotic factors (e.g., rain and wind) impact vocalization rates, (3) testing for differences in community vocalization activity across sites and habitat types, (4) quantifying the phenology of vocal activity, (5) testing for spatiotemporal correlations in vocalizations within species, (6) among species, and (7) using rarefaction analysis to quantify diversity and optimize bioacoustic sampling. To demonstrate these approaches, we sampled in 2016 and 2018 and used hand annotations of 129,866 bird vocalizations from two forests in New Hampshire, USA, including sites in the Hubbard Brook Experiment Forest where bioacoustic data could be integrated with more than 50 years of observer‐based avian studies. Acoustic monitoring revealed differences in community patterns in vocalization activity between forests of different ages, as well as between nearby similar watersheds. Of numerous environmental variables that were evaluated, background noise was most clearly related to vocalization rates. The songbird community included one cluster ...