Prey-mediated behavioral responses of feeding blue whales in controlled sound exposure experiments

To the best of our knowledge, one or more authors of this paper were federal employees when contributing to this work. This is the publisher’s final pdf. The published article is copyrighted by the Ecological Society of America and can be found at: http://esajournals.onlinelibrary.wiley.com/hub/journal/10.1002/%28ISSN%291939-5582/Data associated with this paper have been deposited in the Stanford Digital Repository: https://purl.stanford.edu/ty541py2242Behavioral response studies provide significant insights into the nature, magnitude, and consequences of changes in animal behavior in response to some external stimulus. Controlled exposure experiments (CEEs) to study behavioral response have faced challenges in quantifying the importance of and interaction among individual variability, exposure conditions, and environmental covariates. To investigate these complex parameters relative to blue whale behavior and how it may change as a function of certain sounds, we deployed multi-sensor acoustic tags and conducted CEEs using simulated mid-frequency active sonar (MFAS) and pseudo-random noise (PRN) stimuli, while collecting synoptic, quantitative prey measures. In contrast to previous approaches that lacked such prey data, our integrated approach explained substantially more variance in blue whale dive behavioral responses to mid-frequency sounds (r² = 0.725 vs. 0.14 previously). Results demonstrate that deep-feeding whales respond more clearly and strongly to CEEs than those in other behavioral states, but this was only evident with the increased explanatory power provided by incorporating prey density and distribution as contextual covariates. Including contextual variables increases the ability to characterize behavioral variability and empirically strengthens previous findings that deep-feeding blue whales respond significantly to mid-frequency sound exposure. However, our results are only based on a single behavioral state with a limited sample size, and this analytical framework should be applied broadly across behavioral states. The increased capability to describe and account for individual response variability by including environmental variables, such as prey, that drive foraging behavior underscores the importance of integrating these and other relevant contextual parameters in experimental designs. Our results suggest the need to measure and account for the ecological dynamics of predator–prey interactions when studying the effects of anthropogenic disturbance in feeding animals