Variation in foraging activity influences area-restricted search behaviour by bottlenose dolphins

Area-restricted search (ARS) behaviour is commonly used to characterise spatio-temporal variation in foraging activity of predators, but evidence of the drivers underlying this behaviour in marine systems is sparse. Advances in underwater sound recording techniques and automated processing of acoustic data now provide opportunities to investigate these questions where species use different vocalisations when encountering prey. Here, we used passive acoustics to investigate drivers of ARS behaviour in a population of dolphins, to determine if residency in key foraging areas increased following encounters with prey. Analyses were based on two independent proxies of foraging: echolocation buzzes (widely used as foraging proxies), and bray calls (vocalizations linked to salmon predation attempts). Echolocation buzzes were extracted from echolocation data loggers and bray calls from broadband recordings by a convolutional neural network (CNN). We found a strong positive relationship between the duration of encounters and the frequency of both foraging proxies, supporting the theory that bottlenose dolphins engage in ARS behaviour in response to higher prey encounter rates. This study provides empirical evidence for one driver of ARS behaviour and demonstrates the potential for applying passive acoustic monitoring in combination with deep learning-based techniques to investigate the behaviour of vocal animals. 

Methods
Echolocation data loggers (CPODs; Chelonia Ltd) and broadband acoustic recorders (SoundTrap ST300HF; Ocean Instruments, NZ) were deployed in 2018 to characterise both the occurrence and the foraging activity of bottlenose dolphins and investigate the drivers of the area-restricted search behaviour. We used two independent proxies for foraging: 1) echolocation buzzes, identified by modelling echolocation inter-click intervals (Pirota et al., 2014); and 2) bray calls, automatically detected using deep learning techniques, building upon the methodology of Bergler et al. (2019).