Passive Acoustic Monitoring
Passive acoustic monitoring is a central methodological approach of the PEASE Lab. Using Autonomous Recording Units (ARUs) and species identification software like BirdNET, we deploy networks of sound recorders across the landscape to study bird populations.
Across multiple projects, our group has deployed ARUs at hundreds of sites in the Midwest region, generating terabytes of acoustic data that we analyze using machine learning classifiers. Below are current or recent projects in our group:
ARU hardware and BirdNET optimization (Led by Shasta Corvus, MS 2024)
Shasta conducted a head-to-head comparison of four ARU models (AudioMoth, SM4, SMMicro, and SwiftOne) using BirdNET with standardized bird communities in southern Illinois. Shasta also systematically evaluated 18 combinations of BirdNET’s Overlap and Sensitivity settings and compared confidence-based threshold filtering strategies - work that directly informs best practices in a rapidly evolving research field.
Acoustic Indices in Human-modified Landscapes (Led by Rebecca Ducay, MS 2024)
Using computer-generated bird soundscapes and empirical PAM recordings from 220 sites across Illinois, Rebecca assessed how vehicular noise degrades nine acoustic indices commonly used to estimate biodiversity. Rebecca identified which indices are most resilient to road noise, with implications for acoustic ecologists recording in developed areas near roads.
Nightjar distribution and habitat use via ARUs (Led by Lainey Metz, MS 2023)
Lainey resampled 146 locations of the Illinois Breeding Bird Atlas with ARUs to map the current distribution of eastern whip-poor-will and chuck-will’s-widow - two declining nightjar species - at a regional scale. Lainey combined ARUs with BirdNET and Google Earth Engine to study how these species’ distribution has changed over 30 years and to identify key drivers of change in their distribution.
Forest management effects on nightjar abundance (Led by Lauren Benedict, MS 2026)
Lauren evaluated the effects of prescribed burning and midstory thinning on nightjar abundance at 28 paired sites using ARUs. Lauren used time-to-detection models to estimate abundance from the ARU recordings, building on our work on nightjars in the midwest.
Density Estimate of eastern whip-poor-will via ARUs (Led by Haley Holiman, current Ph.D. student)
Haley is leading research at the Hardwood Ecosystem Experience in southern Indiana to 1) study the impacts of forest management on the abundance of eastern whip-poor-wills and 2) develop methods and analytical approaches for general density estimation via ARUs. Read more here: Density Estimation with ARUs — PEASE Lab