Joo, W. 2009. Environmental acoustics as an ecological variable to understand the dynamics of ecosystems. Dissertation, Michigan State University, East Lansing, Michigan, USA.

Citable PDF link: https://lter.kbs.msu.edu/pub/2190

Although acoustic variables play a key role in understanding the ecology and behavior of vocal organisms, little work has been done to investigate whether acoustic signals can serve as an ecological variable to assess the current state of ecosystems. Our research was guided by two overarching questions. The first question is can environmental acoustics be used as ecological attributes that reflect ecosystem structure and processes? The second question is can environmental acoustics provide a key means to measure and monitor the biodiversity and distribution of vocal species? The study first developed analytical methods to understand acoustic properties including: (1) development and refinement of an Acoustic Habitat Quality Index using the distribution of acoustic power across different frequency spectrum bands; and (2) measurement and analysis of vocalizing species diversity using multiple methods of recording acoustic signals. The second part of the study investigated a new approach to surveying avian species using acoustic recordings. This analysis revealed that automated acoustic recordings facilitated simultaneous breeding bird surveys at multiple locations with minimal variability and high accuracy of bird community measures. Third, the study characterized the urban-rural variability using environmental sounds based on quantification of environmental acoustic properties across a gradient of ecosystems and landscapes. Finally, the study illustrated that using wireless sensor networks as a new sampling tool in ecology and environmental science provides tremendous opportunities to measure and monitor complex ecological variables at relevant spatial and temporal scales. The integration of acoustic research with the multi-science communities and advances in wireless sensor networks will potentially enable and enhance our understanding of ecological change and our ability to forecast changes in complex, interconnected ecosystems at scales ranging from the ecosystem to global level.

Associated Treatment Areas:

T7 T5 T2 T1

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