Cooperative Fish and Wildlife Research Units Program: Vermont
Education, Research and Technical Assistance for Managing Our Natural Resources

Vermont Project

Advancing Adaptive Management in the Riverside East Solar Energy Zone

August 2016 - January 2020


Participating Agencies

  • Bureau of Land Management

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. This study is a pilot study for monitoring the Riverside East Solar Energy Zone (SEZ), a vast area in southern California that may be developed for solar production. The Bureau of Land Management is charged with monitoring wildlife in this area to minimize the effects of the solar production on wildlife species.

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. The pilot approach involves the following key elements:

1. Twenty cell phones are deployed in strategic locations in the SEZ as “prototype” data collection units.
2. Each phone is associated with a unique Google account.
3. The phones collect audio recordings based on a schedule that is input into Google Calendar, and push the recordings to the cloud on a daily basis.
4. Recordings are analyzed for target species.

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 the U.S Bureau of Land Management’s Riverside East Solar Energy Zone, this study developed 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) 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

This project is a collaboration of BLM, the Vermont Cooperative Fish and Wildlife Research Unit, and the UVM IGERT SMART program. The primary products are two open-source R packages (AMModels and AMMonitor), coupled with a SQLite database that stores not just recording results but also the full suite of information required to effectively run a wildlife monitoring program.

Research Publications Publication Date
Balantic, C., and T. M. Donovan. 2019. Temporally-adaptive acoustic sampling to maximize detection across a suite of focal wildlife species. Ecology and Evolution 9(18)10582-10600. DOI: 10.1002/ece3.5579 | Abstract | Download | Publisher Website August 2019
Balantic, C., and T. M. Donovan. 2019. Statistical learning mitigation of false positive detections in automated acoustic wildlife monitoring. Bioacoustics 29(3):296-321. DOI: 10.1080/09524622.2019.1605309 | Abstract | Download | Publisher Website May 2019
Balantic, C., and T. M. Donovan. 2019. Dynamic wildlife occupancy models using automated acoustic monitoring data. Ecological Applications 29(3):e01854. | Abstract | Download | Publisher Website April 2019
Balantic, C., and T. Donovan. 2020. AMMonitor: Remote monitoring of biodiversity in an adaptive framework with R. Methods in Ecology and Evolution 11:869-877. DOI: 10.1111/2041-210X.13397 | Abstract | Download | Publisher Website April 2020
Theses and Dissertations Publication Date
Balantic, C. 2018. Tools for landscape-scale automated acoustic monitoring to characterize wildlife occurrence dynamics. PhD Dissertation. University of Vermont, Burlington, VT USA. February 2018