Vermont Project: Advancing Adaptive Management in the Riverside East Solar Energy Zone

Vermont Project

Advancing Adaptive Management in the Riverside East Solar Energy Zone

August 2016 - January 2020

Personnel

Participating Agencies

Bureau of Land Management

Cathleen Balantic setting up a cell phone monitoring station in the Riverside East Solar Energy Zone.

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

Research Publications	Publication Date
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. 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. 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	1. Acoustic recordings of the environment can produce species presence–absence data for characterizing populations of sound‐producing wildlife over multiple spatial scales. If a species is present at a site but does not vocalize during a scheduled audio recording survey, researchers may incorrectly conclude that the species is absent (“false negative”). The risk of false negatives is compounded when audio devices have sampling constraints, do not record continuously, and must be manually scheduled to operate at pre‐selected times of day, particularly when research programs target multiple species with acoustic availability that varies across temporal conditions.<br><br>2. We developed a temporally adaptive acoustic sampling algorithm to maximize detection probabilities for a suite of focal species amid sampling constraints. The algorithm combines user‐supplied species vocalization models with site‐specific weather forecasts to set an optimized sampling schedule for the following day. To test our algorithm, we simulated hourly vocalization probabilities for a suite of focal species in a hypothetical monitoring area for the year 2016. We conducted a factorial experiment that sampled from the 2016 acoustic environment to com ‐pare the probability of acoustic detection by a fixed (stationary) schedule versus a temporally adaptive optimized schedule under several sampling efforts and monitoring durations.<br><br>3. We found that over the course of a study season, the probability of acoustically capturing a focal species (given presence) at least once via automated acoustic monitoring was greater (and acoustic capture occurred earlier in the season) when using the temporally adaptive optimized schedule as compared to a fixed schedule.<br><br>4. The advantages of a temporally adaptive optimized acoustic sampling schedule are magnified when a study duration is short, sampling effort is low, and/or species acoustic availability is minimal. This methodology presents the opportunity to maximize acoustic monitoring sampling efforts amid constraints.
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

Presentations

Presentations	Presentation Date
Balantic, C., T. M. Donovan, J. Katz, and M. Massar. Machine learning mitigation of false positives in automated acoustic wildlife monitoring with the R package AMMonitor. April 15-17, 2018. 74th Annual Northeast Fish and Wildlife Conference, Burlington, Vermont.	April 2018
Balantic, C., T. M. Donovan, J. Katz, and M. Massar. Temporally-adaptive acoustic sampling to maximize detection across a suite of focal wildlife species with the R package AMMonitor. April 15-17, 2018. 74th Annual Northeast Fish and Wildlife Conference, Burlington, Vermont.	April 2018
Balantic, C., T. M. Donovan, J. Katz, and M. Massar. Temporally-adaptive acoustic sampling to maximize detection across a suite of focal wildlife species with the R package AMMonitoR. Northeast Regional Environmental Acoustics Symposium, University of New Hampshire. March 20, 2018. Durham, NH.	March 2018
Balantic, C. and T. M. Donovan. 2019. Landscape-scale monitoring using smartphones and the R package AMMonitor. April 14-16, 2019. 75th Annual Northeast Fish and Wildlife Conference, Groton, Connecticut.	April 2019

Theses and Dissertations

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

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

Personnel

Participating Agencies

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