Accelerometers Identify New Behaviors and Show Little Difference in the Activity Budgets of Lactating Northern Fur Seals (Callorhinus ursinus) between Breeding Islands and Foraging Habitats in the Eastern Bering Sea

Abstract

We tagged 82 lactating northern fur seals (Callorhinus ursinus) with tri-axial accelerometers and magnetometers on two eastern Bering Sea islands (Bogoslof and St. Paul) with contrasting population trajectories. Using depth data, accelerometer data and spectral analysis we classified time spent diving (30%), resting (~7%), shaking and grooming their pelage (9%), swimming in the prone position (~10%) and two types of previously undocumented rolling behavior (29%), with the remaining time (~15%) unspecified. The reason for the extensive rolling behavior is not known. We ground-truthed the accelerometry signals for shaking and grooming and rolling behaviors—and identified the acceleration signal for porpoising—by filming tagged northern fur seals in captivity. Speeds from GPS interpolated data indicated that animals traveled fastest while in the prone position, suggesting that this behavior is indicative of destination-based swimming. Very little difference was found in the percentages of time spent in the categorical behaviors with respect to breeding islands (Bogoslof or St. Paul Island), forager type (cathemeral or nocturnal), and the region where the animals foraged (primarily on-shelf <200m, or off-shelf > 200m). The lack of significant differences between islands, regions and forager type may indicate that behaviors summarized over a trip are somewhat hardwired even though foraging trip length and when and where animals dive are known to vary with island, forager type and region.

Assessing the utility and limitations of accelerometers and machine learning approaches in classifying behaviour during lactation in a phocid seal

“…In addition, a higher sampling rate may be able to highlight minute differences in postural dynamics that may improve in the identification of contextual interactions in grey seals. Nevertheless, the resolution of behaviour identified in the current study is comparable to other previous efforts to classify behaviour in various other vertebrates, such as [13,23,40,59,66].…”

“…With the advancement of animal-borne data loggers, researchers have been able to extend the application of behavioural studies to species that have typically been very difficult to observe in the wild, such as marine mammals. More specifically, triaxial accelerometers have been used to infer behaviour remotely in pinnipeds when they are unobservable during trips to and from feeding aggregations [10][11][12][13][14] and other at-sea activities [15,16]. Often, these accelerometry deployments focus on building coarse-scale activity budgets for resolving energetics associated with foraging and diving or towards more finescale event detection, such as head-striking behaviour, to infer the rate of prey consumption relative to energy expenditure at sea [17][18][19][20].…”

Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours

“…To date, quantifying pinniped behaviour using accelerometers has focussed on identifying foraging and travelling behavioural states [24]. Less attention has been paid to other potentially important behaviour states, such as grooming, reproductive and resting behaviours, despite these being major components of their behavioural repertoire and possible indicators of important underlying indicators such as condition [25, 26]. As yet, no studies have sought to quantify the terrestrial behaviours displayed by pinnipeds using accelerometers.…”

Coping styles in capital breeders modulate behavioural trade-offs in time allocation: assessing fine-scale activity budgets in lactating grey seals (Halichoerus grypus) using accelerometry and heart rate variability

“…In herding prey species, higher conspecific density can mean that certain individuals are able to spend less time Alert for predators, devoting a greater proportion of their time activity towards foraging or maternal care (Burger and Gochfeld 1994;Hunter and Skinner 1998). These trade-offs are also applicable where individuals must strike a balance between the energetic cost of foraging as a function of distance and search time and the acquisition of energy upon locating sufficient resources in the environment (Hill et al 2003;Lagarde et al 2008;Therrien et al 2008;Watanabe et al 2012;Patrick et al 2014;Battaile et al 2015;Flack et al 2016;Costelloe and Rubenstein 2018). These examples principally illustrate the relationships between conserving or acquiring energy and expending energy to ensure survival.…”