Vermont Project: Adaptive Management with AMMonitor

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

Research Publications	Publication Date
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	Audio sampling of the environment can provide long-term, landscape-scale presence-absence data to model populations of sound-producing wildlife. Automated detection systems allow researchers to avoid manually searching through large volumes of recordings, but often produce unacceptable false positive rates. We developed methods that allow researchers to improve template-based automated detection using a suite of statistical learning algorithms when false positive rates are problematic. To test our method, we acquired 668 hours of recordings in the Sonoran Desert, California USA between March 2016 and May 2017, and created spectrogram cross-correlation templates for three target avian species. We trained and tested five classification algorithms and four performance-weighted ensemble classifier methods on target signals and false alarms from March 2016, and then selected high-performing ensemble classifiers from the train/test phase to predict the class of new detections thereafter. For three target species, our ensemble classifiers were able to identify 98%, 81%, and 100% of false alarms compared with the baseline template detection system, and comparative positive predictive values improved from 6% to 69%, 87% to 95%, and 2% to 77%. We show that statistical learning approaches can be implemented to mitigate false detections acquired via template-based automated detection in automated acoustic wildlife monitoring.
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	Automated acoustic monitoring of wildlife has been used to characterize populations of sound‐producing species across large spatial scales. However, false negatives and false positives produced by automated detection systems can compromise the utility of these data for researchers and land managers, particularly for research programs endeavoring to describe colonization and extinction dynamics that inform land use decision‐making. To investigate the suitability of automated acoustic monitoring for dynamic occurrence models, we simulated underlying occurrence dynamics, calling patterns, and the automated acoustic detection process for a hypothetical species under a range of scenarios. We investigated an automated species detection aggregation method that considered a suite of options for creating encounter histories. From these encounter histories, we generated parameter estimates and computed bias for occurrence, colonization, and extinction rates using a dynamic occupancy modeling framework that accounts for false positives via small amounts of manual confirmation. We were able to achieve relatively unbiased estimates for all three state parameters under all scenarios, even when the automated detection system was simulated to be poor, given particular encounter history aggregation choices. However, some encounter history aggregation choices resulted in unreliable estimates; we provide caveats for avoiding these scenarios. Given specific choices during the detection aggregation process, automated acoustic monitoring data may provide an effective means for tracking species occurrence, colonization, and extinction patterns through time, with the potential to inform adaptive management at multiple spatial scales.
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	1. Ecological research and management programs are increasingly using autonomous monitoring units (AMUs) to collect large volumes of acoustic and/or photo data to address pressing management objectives or research goals. The data management requirements of an AMU-based monitoring effort are often overwhelming, with a considerable amount of processing to translate raw data into models and analyses that have research and management utility. <br><br>2. We created the r package AMMonitor to simplify the process of moving from remotely collected data to analysis and results, using a comprehensive SQLite database for data management that tracks all components of a remote monitoring program. This framework enables the tracking of analyses and research/ management objectives through time. <br><br>3. We illustrate the AMMonitor approach with the example of evaluating an occurrence-based management objective for a target species. First, we provide an overview of the database and data management approach. Next, we illustrate a few available workflows: temporally adaptive sampling, automated detection of species sounds from acoustic recordings and aggregation of automated detections into an encounter history for use in an occupancy analysis, the outcome of which can be analysed with respect to the motivating management objective.<br><br> 4. Without a comprehensive framework for efficiently moving from raw remote monitoring data collection to results and analysis, monitoring programs are limited in their capacity to systematically characterize ecological processes and inform management decisions through time. AMMonitor provides an option for such a framework. Code, comprehensive documentation and step-by-step examples are available online at https://code.usgs.gov/vtcfwru/AMMonitor

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

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