This National Science Foundation (NSF) Division of Environmental Biology Project Grant for $146,066 awarded to Furman University will leverage machine learning techniques to better understand biodiversity patterns measured through passive acoustic sampling. The key activities and deliverables include:
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Advancing the analysis of acoustic data from diverse ecosystems to address three lines of inquiry: i) how noise affects bird vocalizations and communications, ii) quantification of acoustic indices as proxies for biodiversity, and iii) improving occupancy modeling for conservation applications using passive acoustic sampling.
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Training the next generation of students and researchers in machine learning techniques for biodiversity research through curriculum advancement and workshops.
The project will contribute to advancements in the application of passive acoustic sampling and machine learning to address fundamental biological questions. This award is jointly funded under the CFDA 47.074 Biological Sciences program, which aims to promote progress in the biological sciences and enhance understanding of major problems facing the nation.
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