Data from: AudioMoth: evaluation of a smart open acoustic device for monitoring biodiversity and the environment

1. The cost, usability and power efficiency of available wildlife
monitoring equipment currently inhibits full ground-level coverage of many
natural systems. Developments over the last decade in technology, open
science, and the sharing economy promise to bring global access to more
versatile and more affordable monitoring tools, to improve coverage for
conservation researchers and managers. 2. Here we describe the development
and proof-of-concept of a low-cost, small-sized and low-energy acoustic
detector: 'AudioMoth'. The device is open-source and
programmable, with diverse applications for recording animal calls or
human activity at sample rates of up to 384kHz. We briefly outline two
ongoing real-world case studies of large-scale, long-term monitoring for
biodiversity and exploitation of natural resources. These studies
demonstrate the potential for AudioMoth to enable a substantial shift away
from passive continuous recording by individual devices, towards smart
detection by networks of devices flooding large and inaccessible
ecosystems. 3. The case studies demonstrate one of the smart capabilities
of AudioMoth, to trigger event logging on the basis of classification
algorithms that identify specific acoustic events. An algorithm to trigger
recordings of the New Forest cicada (Cicadetta montana) demonstrates the
potential for AudioMoth to vastly improve the spatial and temporal
coverage of surveys for the presence of cryptic animals. An algorithm for
logging gunshot events has potential to identify a shotgun blast in
tropical rainforest at distances of up to 500 m, extending to 1km with
continuous recording. 4. AudioMoth is more energy efficient than currently
available passive acoustic monitoring (PAM) devices, giving it
considerably greater portability and longevity in the field with smaller
batteries. At a build cost of ~US$43 per unit, AudioMoth has potential for
varied applications in large-scale, long-term acoustic surveys. With
continuing developments in smart, energy-efficient algorithms and
diminishing component costs, we are approaching the milestone of local
communities being able to afford to remotely monitor their own natural
resources.