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

Vermont Project


Adaptive Management with AMMonitor

August 2020 - August 2025


Personnel

Participating Agencies

  • USGS

Adaptive management (AM) is a cyclical process that improves natural resource management and generates knowledge that can inform future decisions. Autonomous monitoring units (AMUs) such as camera traps or recording devices provide opportunities to collect large volumes of species’ occurrence data over long periods of time with relatively low cost. However, the data management requirements of an AMU-based monitoring effort can be immense: processing audio/image data to generate detections can be complicated, and integrating streams of AMU data into species’ distribution models or other analyses may appear unattainable.

To operationalize AM, the USGS Vermont Cooperative Fish and Wildlife Research Unit created the open-source R package AMMonitor, which simplifies the process of moving from remotely collected data to species’ status, trend analysis, and reports.

The objectives of this project are to 1) Strengthen and expand the AMMonitor community, and 2) Develop the photographic monitoring capacity of AMMonitor. The Vermont Cooperative Fish and Wildlife Research Unit is working with USGS ScienceBase to facilitate and document monitoring files as a permanent data repository. Further, we are working with USGS Cloud Hosting to develop CNN models to identify species within monitoring files (images, audio) in an automated fashion. The results of this work should streamline data analysis and ultimately permit adaptive management of natural resources in an expedient manner.

Research Publications Publication Date
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
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. 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