Agriculture Reference
In-Depth Information
2011b). In addition, ecology journals have dedicated issues to soundscape ecol-
ogy and ecological acoustics (e.g., see
Landscape Ecology
[2011] 26;
Ecological
Informatics
[2013] 21).
Summary
There is rising interest in using sound as an ecological attribute that can be moni-
tored and analyzed to provide information about ecological phenomena. Acoustic
sensors can further advance ecological science by allowing researchers to capture
observations in locations and times that are not easily accessible or feasible, and at
time scales that were previously impractical to accommodate. Acoustic sensors will
provide new knowledge about organisms and further our understanding of human
activities that cause environmental disturbance. The commercialization of program-
mable acoustic sensor platforms that can be deployed for months with little mainte-
nance will revolutionize how we listen to and interpret our environment. Although
there are still constraints to developing a real-time acoustic sensor network system
(e.g., power consumption and wireless communication distances), progress in sen-
sor system development will enable biologists to measure and observe complex
ecological attributes at detailed spatial and temporal scales, and potentially to fore-
cast changes in ecosystems at regional scales (NRC 2001, Porter et al. 2005, Joo
2009, Joo et al. 2011).
The AAOS has been tested in an operations framework at KBS LTER and now
has been expanded to other locations. This web-enabled system has been developed
(
http://www.real.msu.edu
) to accommodate a large number of sensor observations
(Kasten et al. 2012) and includes >1,000,000 recordings in 20 soundscape projects
ranging in location from Alaska to Australia. The infrastructure developed for this
soundscape application will readily fit into a scalable cyber-infrastructure schema
such as cloud computing for large-scale acoustic observation networks. New appli-
cations using commercially available automated acoustic sensors coupled with
digital libraries, remote access systems, and pattern recognition technologies have
enabled rapid advances in the large-scale observation and interpretation of sound-
scapes and their attributes.
References
Acevedo, M. A., C. J. Corrada-Bravo, H. Corrada-Bravo, L. J. Villamnueva-Rivera, and T.
M. Aide. 2009. Automated classification of bird and amphibian calls using machine
learning: a comparison of methods. Ecological Informatics 4:206-214.
Aide, T. M., C. Corrada-Bravo, M. Campos-Cerqueira, C. Milan, G. Vega, and R. Alvarez.
2013. Real-time bioacoustics monitoring and automated species identification.
PeerJ
1:e103. doi:10.7717/peerj.103
Anderson, S. E., A. S. Dave, and D. Margoliash. 1996. Template-based automatic recogni-
tion of birdsong syllables from continuous recordings. Journal of the Acoustical Society
of America 100:1209-1219.
