Minke whales are difficult to study and little information exists regarding their responses to anthropogenic sound. This study pools data from behavioural response studies off California and Norway. Data are derived from four tagged animals, of which one from each location was exposed to naval sonar signals. Statistical analyses were conducted using Mahalanobis distance to compare overall changes in parameters summarising dive behaviour, avoidance behaviour, and potential energetic costs of disturbance.

Characterization of multivariate time series of behaviour data from animal-borne sensors is challenging. Biologists require methods to objectively quantify baseline behaviour, and then assess behaviour changes in response to environmental stimuli. Here, we apply hidden Markov models (HMMs) to characterize blue whale movement and diving behaviour, identifying latent states corresponding to three main underlying behaviour states: shallow feeding, travelling, and deep feeding.

For marine animals, acoustic communication is critical for many life functions, yet individual calling behavior is poorly understood for most large whale species. Until recently, identifying the calling individual in a group of socializing baleen whales, through either passive acoustic monitoring or acoustic tagging methods, has been challenging because of inadequate spatial resolution in localization, and ambiguities in sound measurements recorded from animal-borne tags.

We used field experiments to measure potential changes in behavior from noise exposure to blue whales off southern California from 2010-2014. High-resolution movement and acoustic data were obtained from DTAGs (n=43) while surface positions and behavioral observations were made through dedicated visual focal follows. Controlled exposure experiments were used to obtain direct measurements of behavior before, during, and after simulated and actual military mid-frequency active sonar (MFAS), pseudorandom noise (PRN), and no noise controls.

Rorqual whales exhibit an extreme lunge filter-feeding strategy characterized by acceleration to high speed and engulfment of a large volume of prey-laden water [ 1–4 ]. Although tagging studies have quantified the kinematics of lunge feeding, the timing of engulfment relative to body acceleration has been modeled conflictingly because it could never be directly measured [ 5–7 ]. The temporal coordination of these processes has a major impact on the hydrodynamics and energetics of this high-cost feeding strategy [ 5–9 ].

Background: As biologging technology has advanced to study whale behavior, various tag attachment methods have been developed. Suction cup attachments were developed for short-term (<24 h) studies using high-resolution archival tags, and implantable or dart attachments were developed for long-term (months) studies using coarseresolution satellite tags. The purpose of this study was to test various tag attachment configurations to increase the deployment duration of archival tags while minimizing potential physical impacts to the whale.

The introduction of animal-borne, multisensor tags has opened up many opportunities for ecological research, making previously inaccessible species and behaviors observable. The advancement of tag technology and the increasingly widespread use of bio-logging tags are leading to large volumes of sometimes extremely detailed data. With the increasing quantity and duration of tag deployments, a set of tools needs to be developed to aid in facilitating and standardizing the analysis of movement sensor data.

Behavioral 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.

Early studies that categorized odontocete pulsed sounds had few means of discriminating signals used for biosonar-based foraging from those used for communication. This capability to identify the function of sounds is important for understanding and interpreting behavior; it is also essential for monitoring and mitigating potential disturbance from human activities. Archival tags were placed on free-ranging Grampus griseus to quantify and discriminate between pulsed sounds used for echolocation-based foraging and those used for communication.