Using autonomous monitoring systems to inform wildlife and natural resource management

Autonomous monitoring systems such as acoustic recording units, camera traps, and remote sensing methods (e.g., LiDAR) can provide massive amounts of data to inform biodiversity conservation, yet analyzing these data presents novel computational complexities. In my work, I develop quantitative approaches to leverage these complex data to inform a variety of wildlife and natural resource management objectives.

During my PhD, I developed hierarchical Bayesian models that leveraged acoustic recording data to understand relationships between anthropogenic noise and biological sounds in avian soundscapes (Doser et al. 2020, Landscape Ecology) and the effects of a shelterwood logging on avian soundscapes in northern Michigan (Doser et al. 2020 Ecological Indicators). Additionally, I integrated automated acoustic data, machine learning algorithms, and point count survey data in a hierarchical modeling framework to estimate bird abundance (Doser et al. 2021 MEE), which can serve as a cost-effective approach to understand spatial patterns in abundance of acoustically active species.

With colleagues at the University of Montana and the National Park Service, I used automated acoustic data to inform management of Clark’s Nutcracker in relation to declining Whitebark pine trees in the western US (Kovalenko et al. 2024 Ecology and Evolution). I am actively involved in multiple projects that leverage acoustic and/or remote sensing data to assess spatio-temporal drivers of insects, birds, and forest communities across the world.