LATTE - Linking Acoustic Tests and Tagging Using Statistical Estimation: Modeling the Behavior of Beaked Whales in Response to Mid-Frequency Active Sonar

Abstract

The goal of this project is to improve our ability to predict the behavioral response of beaked whales to mid-frequency active (MFA) sonar, by making better use of data already collected, or being collected as part of other projects. We aim to construct and fit mathematical models of the diving behavior of beaked whales, and their response to MFA sonar. These models will be parameterized by fitting them simultaneously to three sources of data: (1) short-term, high fidelity tagging studies on individual whales (some of which comes from animals exposed to acoustic stimuli); (2) medium-term satellite tagging studies of individual whales (some of which we hope will come from data collected during navy exercises); and (3) long-term passive acoustic monitoring from bottom-mounted hydrophones (much of which comes from data collected during navy exercises). All data will come from the Atlantic Undersea Test and Evaluation Center (AUTEC), Bahamas, and the surrounding area. Hence our models and predictions will be directly applicable to animals in that area, although we hope they will be of more general relevance. Outputs of the model are designed to be compatible with risk evaluation and mitigation tools and models developed under other ONR initiatives, such as Effects of Sound on the Marine Environment (ESME) and Population Consequences of Acoustic Disturbance (PCADS). Hence, the model will: (1) predict the behavioral responses of individual beaked whales to MFA sonar; (2) provide sufficient information to assess the level of "take" that is likely as a result of sonar operations; (3) provide sufficient information to allow the energetic costs of disturbance by MFA to be estimated; (4) provide a modeling framework within which information concerning behavioral responses of beaked whales can be interpreted.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2010
Accession Number
ADA541702

Entities

People

  • David Moretti
  • Ian L. Boyd
  • John Harwood
  • Len Thomas

Organizations

  • University of St Andrews

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Active Sonar
  • Algorithms
  • Animals
  • Data Analysis
  • Data Processing
  • Frequency
  • Information Science
  • Mammals
  • Marine Mammals
  • Navy
  • Odontocetes
  • Sonar
  • Statistical Estimation
  • Test And Evaluation
  • Undersea Warfare

Fields of Study

  • Environmental science

Readers

  • Computational Modeling and Simulation
  • Marine Mammal Biology

Technology Areas

  • Space