Abstract
Abstract Automated monitoring approaches offer an avenue to unlocking large‐scale insight into how ecosystems respond to human pressures. However, since data collection and data analyses are often treated independently, there are currently no open‐source examples of end‐to‐end, real‐time ecological monitoring networks. Here, we present the complete implementation of an autonomous acoustic monitoring network deployed in the tropical rainforests of Borneo. Real‐time audio is uploaded remotely from the field, indexed by a central database, and delivered via an API to a public‐facing website. We provide the open‐source code and design of our monitoring devices, the central web2py database, and the ReactJS website. Furthermore, we demonstrate an extension of this infrastructure to deliver real‐time analyses of the eco‐acoustic data. By detailing a fully functional, open source, and extensively tested design, our work will accelerate the rate at which fully autonomous monitoring networks mature from technological curiosities, and towards genuinely impactful tools in ecology.
Highlights
Natural ecosystems around the world are undergoing rapid changes due to increasing human pressures[1,2]
The SAFE Acoustics monitoring network can be broadly split into three key components: (i) an array of real-time acoustic monitoring devices deployed in the field (Fig. 1a); (ii) a web server that indexes the incoming audio and provides a standardised machine-readable interface to the data (Fig. 1b), and; (iii) a website which allows the public to browse and listen to the latest audio from the network
To expose the audio collected from our acoustic monitoring network in a user-friendly interface we developed a public-facing website
Summary
Natural ecosystems around the world are undergoing rapid changes due to increasing human pressures[1,2]. 3. We provide the open-source code and design of our monitoring devices, the central web2py database and the ReactJS website. 4. By detailing a fully functional, open-source, and extensively tested design, our work will accelerate the rate at which fully autonomous monitoring networks mature from technological curiosities, and towards genuinely impactful tools in ecology.
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