Abundance, survival, and annual rate of change of Cuvier's beaked whales (Ziphius cavirostris) on a Navy sonar range

Abstract

Bayesian mark‐recapture estimates of survival, abundance, and trend are reported for Cuvier's beaked whales (Ziphius cavirostris) using a Navy training range off southern California. The deep‐diving beaked whale family is exceptionally vulnerable to mid‐frequency active sonar (MFAS), which has been implicated in mass strandings and altered foraging behavior. Extremely low sighting probabilities impede studies of population‐level impacts of MFAS on beaked whales. The San Nicolas Basin hosts a Navy training range subject to frequent MFAS use and attracts high densities of Z. cavirostris. An 11‐year (2007–2018) photo‐identification program leveraged automated acoustic detection and location capabilities on the range's 1,800‐km2 hydrophone array to enhance capture probability. Estimated population parameters for Z. cavirostris using the range included mean (90% credibility intervals) apparent annual survival of 0.950 (0.899–0.986), annual number of individuals as 121 (71–219), and annual rate of change of −0.8% (−5.6%–4.1%). Simulations show the probability of detecting abundance changes is currently low, but can be greatly improved through continued monitoring and increased effort. Complementary data collection on habitat use and demographic rates in San Nicolas and surrounding basins is also essential to relating direct effects of MFAS use to changes in vital rates and broader population outcomes.

Document Details

Document Type
Pub Defense Publication
Publication Date
Sep 22, 2020
Source ID
10.1111/mms.12747

Entities

People

  • David J. Moretti
  • Erin A Falcone
  • Erin Keene
  • Gregory S. Schorr
  • Jay Barlow
  • Jeffrey E. Moore
  • Katherine Alexandra Curtis

Organizations

  • National Marine Fisheries Service
  • Naval Undersea Warfare Center
  • Office of Naval Research

Tags

Fields of Study

  • Environmental science

Readers

  • Marine Mammal Biology
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference