Abstract
Traditionally, animal species diversity and abundance is assessed using a variety of methods that are generally costly, limited in space and time, and most importantly, they rarely include a permanent record. Given the urgency of climate change and the loss of habitat, it is vital that we use new technologies to improve and expand global biodiversity monitoring to thousands of sites around the world. In this article, we describe the acoustical component of the Automated Remote Biodiversity Monitoring Network (ARBIMON), a novel combination of hardware and software for automating data acquisition, data management, and species identification based on audio recordings. The major components of the cyberinfrastructure include: a solar powered remote monitoring station that sends 1-min recordings every 10 min to a base station, which relays the recordings in real-time to the project server, where the recordings are processed and uploaded to the project website (arbimon.net). Along with a module for viewing, listening, and annotating recordings, the website includes a species identification interface to help users create machine learning algorithms to automate species identification. To demonstrate the system we present data on the vocal activity patterns of birds, frogs, insects, and mammals from Puerto Rico and Costa Rica.
Highlights
Ecologists, conservation biologists, and park and resource managers are expected to make decisions to mitigate or manage the threats of climate change and the high rates of species loss
Model creation – We describe the sequence of a song as a Hidden Markov Model (HMM)
The station was established in March 2008, and for this study we present the results of species-specific identification models of the endemic frog species, E. juanariveroi, an exotic frog species Rana gryllo, and an unidentified insect
Summary
Ecologists, conservation biologists, and park and resource managers are expected to make decisions to mitigate or manage the threats of climate change and the high rates of species loss. They rarely have the information needed to make informed decisions because our understanding of most biological systems is based on very limited spatial and temporal coverage. (Porter et al, 2005) From both a conceptual and management perspective there is an urgent challenge to increase biological data collection over large areas and through time
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