Tools for landscape-scale automated acoustic monitoring to characterize wildlife occurrence dynamics

In a world confronting climate change and rapidly shifting land uses, effective methods for monitoring natural resources are critical to support scientifically-informed management decisions. By taking audio recordings of the environment, scientists can acquire presence-absence data to characterize populations of sound-producing wildlife over time and across vast spatial scales. Remote acoustic monitoring presents new challenges, however: monitoring programs are often constrained in the total time they can record, automated detection algorithms typically produce a prohibitive number of detection mistakes, and there is no streamlined framework for moving from raw acoustic data to models of wildlife occurrence dynamics. In partnership with a proof-of-concept field study in the U.S Bureau of Land Management’s Riverside East Solar Energy Zone in southern California, this dissertation introduces a new R software package, AMMonitor, alongside a novel body of work: 1) temporally-adaptive acoustic sampling to maximize the detection probabilities of target species despite recording constraints, 2) values-driven statistical learning tools for template-based automated detection of target species, and 3) methods supporting the construction of dynamic species occurrence models from automated acoustic detection data. Unifying these methods with streamlined data management, the AMMonitor software package supports the tracking of species occurrence, colonization, and extinction patterns through time, introducing the potential to perform adaptive management at landscape scales