1 PeerJ 2013 Vol: 1():. DOI: 10.7717/peerj.103

Real-time bioacoustics monitoring and automated species identification

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.

Mentions
Figures
Figure 1: Workflow of data acquisition, processing, and management. Figure 2: The ARBIMON-acoustic web-based tools for creating, testing, and applying the species-specific identification models. Figure 3: Vocal activity in Sabana Seca.Daily (A–C) and monthly (D–F) vocal activity of three species from Sabana Seca, Puerto Rico. The number in parenthesis is the number of recordings where the species was detected by the model. The detection frequency was calculated as the number of recordings with a positive detection divided by the total number of recordings during the time period. Figure 4: Vocal activity in La Selva.Daily vocal activity of six species from La Selva Biological Station, Costa Rica. The number in parenthesis is the number of recordings where the species was detected by the model. The detection frequency was calculated as the number of recordings with a positive detection divided by the total number of recordings during the time period.
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References
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    • . . . In contrast, automated digital recording systems can monitor animal populations 24 h a day, every day of the year, in stations across a variety of habitats simultaneously, and all recordings can be permanently stored (Acevedo & Villanueva-Rivera, 2006; Brandes, 2008; Lammers et al., 2008; Sueur et al., 2008; Acevedo et al., 2009; Hoeke et al., 2009; Tricas & Boyle, 2009) . . .
    • . . . In addition, a soundscape index, an integrated measure of the acoustic environment, can be calculated and measured across time to estimate changes in biodiversity or other factors affecting the acoustic environment (Sueur et al., 2008; Pijanowski et al., 2011) . . .
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    • . . . The great tinamou (Tinamus major) and the chestnut-mandibled toucan (Ramphastos swainsonii) had peaks of activity at dawn and another at dusk, as is expected for most bird species (Terborgh et al., 1990) . . .
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    • . . . Automated data collection systems can collect an overwhelming amount of data, creating problems with data management and analysis (Villanueva-Rivera & Pijanowski, 2012) . . .
  42. CL Walters; R Freeman; A Collen; C Dietz; M Brock Fenton; G Jones; MK Obrist; SJ Puechmaille; T Sattler; BM Siemers; S Parsons; KE Jones A continental-scale tool for acoustic identification of European bats Journal of Applied Ecology 49, 1064-1074 (2012) .
    • . . . To help solve these problems, researchers have developed algorithms to automate species identification of vocalizations of bats (Herr, Klomp & Atkinson, 1997; Walters et al., 2012; Parsons & Jones, 2000), whales (Murray, Mercado & Roitblat, 1998; Brandes, 2008; Marques et al., 2012; Mellinger & Clark, 2000; Moore et al., 2006), dolphins (Oswald, Barlow & Norris, 2003), insects (Chesmore, 2004; Chesmore & Ohya, 2004), and birds and amphibians (Anderson, Dave & Margoliash, 1996; Kogan & Margoliash, 1998; Acevedo & Villanueva-Rivera, 2006; Hilje & Aide, 2012; Ospina et al., 2013) . . .
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