Animal-Borne Metrics Enable Acoustic Detection of Blue Whale Migration

William K Oestreich,James A Fahlbusch,David E Cade,John Calambokidis,Tetyana Margolina,John Joseph,Ari S Friedlaender,Megan F Mckenna,Alison K Stimpert,Brandon L Southall,Jeremy A Goldbogen,John P Ryan

doi:10.1016/j.cub.2020.08.105

Abstract

Linking individual and population scales is fundamental to many concepts in ecology [1], including migration [2, 3]. This behavior is a critical [4] yet increasingly threatened [5] part of the life history of diverse organisms. Research on migratory behavior is constrained by observational scale [2], limiting ecological understanding and precise management of migratory populations in expansive, inaccessible marine ecosystems [6]. This knowledge gap is magnified for dispersed oceanic predators such as endangered blue whales (Balaenoptera musculus). As capital breeders, blue whales migrate vast distances annually between foraging and breeding grounds, and their population fitness depends on synchrony of migration with phenology of prey populations [7, 8]. Despite previous studies of individual-level blue whale vocal behavior via bio-logging [9, 10] and population-level acoustic presence via passive acoustic monitoring [11], detection of the life history transition from foraging to migration remains challenging. Here, we integrate direct high-resolution measures of individual behavior and continuous broad-scale acoustic monitoring of regional song production (Figure1A) to identify an acoustic signature of the transition from foraging to migration in the Northeast Pacific population. We find that foraging blue whales sing primarily at night, whereas migratory whales sing primarily during the day. The ability to acoustically detect population-level transitions in behavior provides a tool to more comprehensively study the life history, fitness, and plasticity of population behavior in a dispersed, capital breeding population. Real-time detection of this behavioral signal can also inform dynamic management efforts [12] to mitigate anthropogenic threats to this endangered population [13, 14]).

Highlights

Population-Level Vocal Behavior We analyzed acoustic data collected from a hydrophone in the central CCLME [15] (Figure 1B) to quantify population-level vocal behavior
Acoustic Signature of Migration The difference in diel patterns of song production between foraging and migrating individual blue whales (Figures 3 and 4), plus the population-level inversion of this diel pattern preceding the cessation of acoustic detection (Figure 2), indicates a previously unrecognized acoustic signature of the populationlevel behavioral transition from foraging to southward migration
Using high-resolution individual-level behavioral data, we demonstrate that the strong diel patterning in vocal behavior during late summer and early fall is driven by a separation between foraging and singing behaviors (Figure 3)

Summary

Introduction

Population-Level Vocal Behavior We analyzed acoustic data collected from a hydrophone in the central CCLME [15] (Figure 1B) to quantify population-level vocal behavior. This instrument samples a large area (Figure 2A) of blue whale foraging habitat [16, 17] in and around Monterey Bay, California. By ‘‘population-level,’’ we mean that we characterized vocally active individuals (singing males) collectively, in the context of population-level migration. Two linked metrics of blue whale song production were used to quantify regional population-level vocal behavior (see STAR Methods for details): ‘‘cal

Results

Discussion

Conclusion