Strategies for weighting exposure in the development of acoustic criteria for marine mammals

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TitleStrategies for weighting exposure in the development of acoustic criteria for marine mammals
Publication TypeJournal Article
Year of Publication2005
AuthorsMiller, J. H., A. E. Bowles, R. L. Gentry, W. T. Ellison, J. J. Finneran, C. R. Greene, Jr., D. Kastak, D. R. Ketten, P. L. Tyack, P. E. Nachtigall, J. W. Richardson, and J. A. Thomas
JournalThe Journal of the Acoustical Society of America
Volume118
Issue3
Pagination2019
Date Published09/2005
Call NumberDRK8298
Keywordsambient noise, marine mammals, model noise exposure, modeling, noise, noise exposure
Abstract

The Noise Exposure Criteria Group has been developing noise exposure criteria for marine mammals. Although the primary focus of the effort is development of criteria to prevent injury, the Group has also emphasized the development of exposure metrics that can be used to predict injury with accuracy and precision. Noise exposure metrics for humans have proven to be more effective when they account for psychophysical properties of the auditory system, particularly loudness perception. Usually noise is filtered using the A-weighting function, an idealized curve based on the human 40-phon equal loudness function. However, there are no empirical studies to show whether a comparable procedure for animals will improve predictions. The Noise Exposure Criteria Group panel has proposed to weight noise data by functions that admit sound throughout the frequency range of hearing in five marine mammal groupings—low frequency cetaceans (mysticetes), mid-frequency cetaceans, high-frequency cetaceans, pinnipeds in air, and pinnipeds in water. The algorithm for the functions depends only on the upper and lower frequency limits of hearing and does not differentially weight frequencies based on sensitivity within the range. This procedure is considered conservative. However, if the human case may be taken as a model, it is not likely to produce precise predictions. Empirical data are essential to finding better estimators of exposure.