Passive acoustic monitoring (PAM) is a key technology for studying marine mammal populations. PAM typically generates large volumes of data that contain signals from multiple overlapping sources. To extract meaningful information from these data, automated tools are required that can cope with multiple sources, missed detections, and false alarms. This paper presents the Multiple-Animal Model-Based Acoustic Tracking (MAMBAT) framework, which integrates model-based localization with Bayesian multi-target tracking to automatically track multiple sound sources using acoustic data from wide baseline arrays. MAMBAT leverages a “Track-before-Localize” strategy followed by a “Localize-then-Track” strategy that does not require detection, classification, or association steps. The framework’s effectiveness is demonstrated through application to real-world datasets that contain multiple sperm whales from two ocean basins. MAMBAT advances our ability to monitor marine mammal distribution, abundance, and behavior, with potential to provide valuable information for conservation and management efforts.

Diving behaviour of short-finned pilot whales is often described by two states; deep foraging and shallow, non-foraging dives. However, this simple classification system ignores much of the variation that occurs during subsurface periods. We used multi-state hidden Markov models (HMM) to characterize states of diving behaviour and the transitions between states in short-finned pilot whales. We used three parameters (number of buzzes, maximum dive depth and duration) measured in 259 dives by digital acoustic recording tags (DTAGs) deployed on 20 individual whales off Cape Hatteras, North Carolina, USA. The HMM identified a four-state model as the best descriptor of diving behaviour. The state-dependent distributions for the diving parameters showed variation between states, indicative of different diving behaviours. Transition probabilities were considerably higher for state persistence than state switching, indicating that dive types occurred in bouts. Our results indicate that subsurface behaviour in short-finned pilot whales is more complex than a simple dichotomy of deep and shallow diving states, and labelling all subsurface behaviour as deep dives or shallow dives discounts a significant amount of important variation. We discuss potential drivers of these patterns, including variation in foraging success, prey availability and selection, bathymetry, physiological constraints and socially mediated behaviour.