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 gunshotevents 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