Context-dependent variability in the predicted daily energetic costs of disturbance for blue whales

Abstract

Assessing the long-term consequences of sub-lethal anthropogenic disturbance on wildlife populations requires integrating data on fine-scale individual behavior and physiology into spatially and temporally broader, population-level inference. A typical behavioral response to disturbance is the cessation of foraging, which can be translated into a common metric of energetic cost. However, this necessitates detailed empirical information on baseline movements, activity budgets, feeding rates and energy intake, as well as the probability of an individual responding to the disturbance-inducing stressor within different exposure contexts. Here, we integrated data from blue whales (Balaenoptera musculus) experimentally exposed to military active sonar signals with fine-scale measurements of baseline behavior over multiple days or weeks obtained from accelerometry loggers, telemetry tracking and prey sampling. Specifically, we developed daily simulations of movement, feeding behavior and exposure to localized sonar events of increasing duration and intensity and predicted the effects of this disturbance source on the daily energy intake of an individual. Activity budgets and movements were highly variable in space and time and among individuals, resulting in large variability in predicted energetic intake and costs. In half of our simulations, an individual’s energy intake was unaffected by the simulated source. However, some individuals lost their entire daily energy intake under brief or weak exposure scenarios. Given this large variation, population-level models will have to assess the consequences of the entire distribution of energetic costs, rather than only consider single summary statistics. The shape of the exposure-response functions also strongly influenced predictions, reinforcing the need for contextually explicit experiments and improved mechanistic understanding of the processes driving behavioral and physiological responses to disturbance. This study presents a robust approach for integrating different types of empirical information to assess the effects of disturbance at spatio-temporal and ecological scales that are relevant to management and conservation.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2021
Source ID
10.1093/conphys/coaa137

Entities

People

  • Ari S. Friedlaender
  • Brandon L. Southall
  • Cormac Booth
  • Daniel P. Costa
  • David E. Cade
  • Elliott L. Hazen
  • Enrico Pirotta
  • James A. Fahlbusch
  • Jeremy A. Goldbogen
  • John Calambokidis
  • John Harwood
  • Leslie New

Organizations

  • National Oceanic and Atmospheric Administration
  • National Science Foundation
  • Office of Naval Research
  • Stanford University
  • University College Cork
  • University of California
  • University of St Andrews
  • Washington State University

Tags

Fields of Study

  • Environmental science
  • Psychology

Readers

  • Computational Modeling and Simulation
  • Exercise and Sports Science.
  • Marine Mammal Biology

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Space