The ocean soundscape is a complex superposition of sound from natural and anthropogenic sources. Recent advances in acoustic remote sensing and marine bioacoustics have highlighted how animals use their soundscape and how the background sound levels are influenced by human activities. In this paper, developments in computational ocean acoustics, remote sensing, and oceanographic modeling are combined to generate modelled sound fields at multiple scales in time and space. Source mechanisms include surface shipping, surface wind, and wave fields. A basin scale model is presented and applied to the United States Atlantic Outer Continental Shelf (OCS). For model-data comparison at a single hydrophone location, the model is run for a single receiver position. Environmental and source model uncertainty is included in the site-specific modeling of the soundscape. An inversion of the local sediment type is made for a set of sites in the OCS. After performing this inversion, the qualitative comparison of the modelled sound pressure level (SPL) time series and observed SPL is excellent. The quantitative differences in the mean root mean square error between the model and data is less than 3 dB for most sites and frequencies above 90 Hz.
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http://dx.doi.org/10.1121/10.0026476 | DOI Listing |
J Acoust Soc Am
January 2025
Center for Acoustics Research and Education, University of New Hampshire, Durham, New Hampshire 03823, USA.
Fishes and aquatic invertebrates utilize acoustic particle motion for hearing, and some additionally detect sound pressure. Yet, few underwater soundscapes studies report particle motion, which is often assumed to scale predictably with pressure in offshore habitats. This relationship does not always exist for low frequencies or near reflective boundaries.
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Department of Chemistry and Biochemistry, Université de Moncton, Moncton, NB E1A 3E9, Canada.
Tick-borne pathogens are growing in importance for human and veterinary research worldwide. We developed, optimized, and validated a reliable quantitative PCR (qPCR; real-time PCR) assay to assess Borrelia burgdorferi infection by targeting two B. burgdorferi genes, and .
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Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico.
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Laboratório de Carcinologia; Departamento de Invertebrados; Museu Nacional/Universidade Federal do Rio de Janeiro; Quinta da Boa Vista; Horto Botânico; 20940-040; Rio de Janeiro; RJ; Brazil.
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