Abstract
Abstract 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. 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 384 kHz. 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. 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 1 km with continuous recording. AudioMoth is more energy efficient than currently available passive acoustic monitoring 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.
Highlights
Emerging technologies for remote monitoring and species identification bring the promise of more affordable and versatile methods of sampling which are predicted to drive future conservation efforts (Pimm et al, 2015)
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
Summary
Emerging technologies for remote monitoring and species identification bring the promise of more affordable and versatile methods of sampling which are predicted to drive future conservation efforts (Pimm et al, 2015). The technical know-how and infrastructure needed to implement these devices in large-scale environmental monitoring often requires a total investment beyond the budgets assigned to conservation projects (James, Green, & Paine, 1999). This cost issue is being addressed with an increasingly free availability of online data sources such as satellite images (Kalyvas, Kokkos, & Tzouramanis, 2017). Such databases cannot capture cryptic biodiversity and exploitation. Events which are hidden by tree cover, or those that are too fine-scale for image resolution, remain unaccounted for without ground-level monitoring (Peres, Barlow, & Laurance, 2006)
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