Behavioural mapping of a pelagic seabird: combining multiple sensors and a hidden Markov model reveals the distribution of at-sea behaviour

Abstract

The use of miniature data loggers is rapidly increasing our understanding of the movements and habitat preferences of pelagic seabirds. However, objectively interpreting behavioural information from the large volumes of highly detailed data collected by such devices can be challenging. We combined three biologging technologies-global positioning system (GPS), saltwater immersion and time-depth recorders-to build a detailed picture of the at-sea behaviour of the Manx shearwater (Puffinus puffinus) during the breeding season. We used a hidden Markov model to explore discrete states within the combined GPS and immersion data, and found that behaviour could be organized into three principal activities representing (i) sustained direct flight, (ii) sitting on the sea surface, and (iii) foraging, comprising tortuous flight interspersed with periods of immersion. The additional logger data verified that the foraging activity corresponded well to the occurrence of diving. Applying this approach to a large tracking dataset revealed that birds from two different colonies foraged in local waters that were exclusive, but overlapped in one key area: the Irish Sea Front (ISF). We show that the allocation of time to each activity differed between colonies, with birds breeding furthest from the ISF spending the greatest proportion of time engaged in direct flight and the smallest proportion of time engaged in foraging activity. This type of analysis has considerable potential for application in future biologging studies and in other taxa.

Simultaneous multi-colony tracking of a pelagic seabird reveals cross-colony utilization of a shared foraging area

“…The aim of this study was to understand the spatial patterns of multi-colony resource use in a colonially breeding pelagic seabird, using an ethoinformatics approach (Dean et al 2012, Freeman et al 2013) to identify the foraging activity of known individuals remotely. Specifically, we aimed to (1) map the foraging distributions of birds tracked from several colonies across a species' core breeding range during the same period, (2) assess the extent to which birds from different colonies either segregated or foraged in the same locations at the same time and (3) assess the consistency of those patterns across years.…”

“…We studied the 400 g Manx shearwater Puffinus puffinus, a pelagic central-place forager, predominantly of small clupeids, which does not show interactions with fishing vessels or exploit discards. Manx shearwaters engage in long-distance foraging movements, over both inshore waters and open sea (Brooke 1990, Gray & Hamer 2001, Guilford et al 2008, Dean et al 2012, Freeman et al 2013), but observations of foraging distributions have so far been largely limited to at-sea counts of birds of unknown provenance (Stone et al 1994). Whilst this species is IUCN Least Concern, the majority of breeding is restricted to a small number of colonies around the UK and Ireland, with little known about colonies' patterns of at-sea resource use.…”

Within-pair similarity in migration route and female winter foraging effort predict pair breeding performance in a monogamous seabird

“…Briefly, each immersion data point collected during daylight between August and March was allocated to 1 of 3 categories: sustained flight (≥ 98% dry), sitting on the water surface (≥ 98% immersed) or foraging-related activity (hereafter foraging, > 2% dry and > 2% wet). The latter represents an alternation of short flight bouts (searching for prey) with short wet bouts (sitting or diving) indicative of foraging and was found to be strongly associated with intermediate speed and high tortuosity (area-restricted search) and diving behaviour in Manx shearwaters Puffinus puffinus (Dean et al 2012), seabirds of similar size and relatively similar diving behaviour to puffins (Shoji et al 2015. Energy budgets were estimated by combining these daily daytime budgets with nighttime budgets (resting or sleeping; see Section 1 of the Supplement at www.…”

Embracing Complexity and Complexity-Awareness in Marine Megafauna Conservation and Research

“…Other examples include the use of Stochastic Dynamic Programming (SDP) and state-dependent behavioral theory to investigate how disturbance affects pinniped pup recruitment (McHuron et al, 2017), a dynamic state model of blue whale migratory behavior and physiology to explore the effects of perturbations on reproductive success (Balaenoptera musculus) (Pirotta et al, 2018), and a study of tagged southern elephant seals (Mirounga leonina) that identifies intrinsic drivers of movement, to describe the migratory and foraging habitats (Rodríguez et al, 2017). State space models have also been used to characterize dynamic movement of sea turtles (Jonsen et al, 2007;Bailey et al, 2008), seabirds (Dean et al, 2013), other marine mammal species (Moore and Barlow, 2011), and sharks (Block et al, 2011).…”