AMMonitor: Remote monitoring of biodiversity in an adaptive framework. Version 2.0.0

Amid climate change and rapidly shifting land uses, effective methods
for monitoring natural resources are critical to support
scientifically-informed resource management decisions. The
practice of using Autonomous Monitoring Units (AMUs) to monitor wildlife
species has grown immensely in the past decade, with monitoring projects
across species from birds, to bats, amphibians, insects, terrestrial
mammals, and marine mammals.
AMMonitor is an open source R package dedicated to collecting,
storing, and analyzing AMU information in a way that 1) is
cost-effective, 2) can efficiently process and store information, and 3)
can take advantage of the vast and growing community of R analytics. We
created AMMonitor for the Bureau of Land Management to monitor high
priority wildlife across the southern California Solar Energy Zone
(SEZ).
In broad terms, the AMMonitor approach starts with ecological
hypotheses or natural resource management objectives (Figure 1).
Data are collected with Autonomous Monitoring Units (AMUs) to
test hypotheses or to evaluate the state of a resource with respect to a
management objective. Acoustic recordings and photos are collected and
delivered to the cloud. Raw and processed data are stored in a SQLite
database. The data can be analyzed with a wide variety of analytical
methods, often models of abundance or occupancy pattern. These analyses
can be stored, and resulting outputs can be compared with research and
monitoring objectives to track progress toward management goals. The
final results are assessed with respect to hypotheses or objectives.