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We developed a new method to detect and locate North Atlantic right whale (NARW) calls using underwater sound. This passive acoustic monitoring technique aids in protecting this endangered species from human threats.

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Area of Science:

  • Marine Biology
  • Acoustics
  • Conservation Technology

Background:

  • North Atlantic right whales (NARW) are critically endangered, facing threats from ship strikes and fishing gear.
  • Passive acoustic monitoring (PAM) is crucial for tracking marine mammals and implementing conservation strategies.
  • Effective detection and localization of NARW vocalizations are vital for mitigating human-caused risks.

Purpose of the Study:

  • To present a robust processing routine for detecting and localizing North Atlantic right whale (NARW) calls.
  • To demonstrate the efficacy of a three-step passive acoustic monitoring analysis as a mitigation tool for NARW conservation.
  • To enhance the information provided by acoustic surveys for effective management.

Main Methods:

  • Utilized a sonobuoy grid to passively record NARW calls (upcalls and moans) in the southern Gulf of St. Lawrence.
  • Employed a deep learning model for initial NARW call detection, adapted for the sonobuoy dataset.
  • Applied spectrogram correlation for call association and time difference of arrival (TDOA) estimation across sonobuoys.
  • Performed nonlinear Bayesian inversion for call localization, including uncertainty estimates.

Main Results:

  • Successfully detected and localized North Atlantic right whale (NARW) upcalls using the developed three-step analysis.
  • The method proved effective for calls within and near the sonobuoy grid.
  • The analysis provided realistic uncertainty estimates for call source positions.
  • Acoustic data enhanced information compared to traditional visual surveys.

Conclusions:

  • The presented processing routine is an effective tool for detecting and localizing North Atlantic right whale (NARW) vocalizations.
  • This passive acoustic monitoring approach significantly supports conservation and management efforts for endangered whales.
  • The method offers improved spatial and temporal information for mitigating human impacts on NARW populations.