Integrating immersion withGPSdata improves behavioural classification for wandering albatrosses and shows scavenging behind fishing vessels mirrors natural foraging

Abstract

Advances in biologging techniques and the availability of high‐resolution fisheries data have improved our ability to understand the interactions between seabirds and fisheries and to evaluate mortality risk due to bycatch. However, it remains unclear whether movement patterns and behaviour differ between birds foraging naturally or scavenging behind vessels and whether this could be diagnostic of fisheries interactions. We deployed novel loggers that record the GPS position of birds at sea and scan the surroundings to detect radar transmissions from vessels and immersion (activity) loggers on wandering albatrosses Diomedea exulans from South Georgia. We matched these data to remotely sensed fishing vessel positions and used a combination of hidden Markov and random forest models to investigate whether it was possible to detect a characteristic signature from the seabird tracking and activity data that would indicate fine‐scale vessel overlap and interactions. Including immersion data in our hidden Markov models allowed two distinct foraging behaviours to be identified, both indicative of Area Restricted Search (ARS) but with or without landing behaviour (likely prey capture attempts) that would not be detectable with location data alone. Birds approached vessels during all behavioural states, and there was no clear pattern associated with this type of scavenging behaviour. The random forest models had very low sensitivity, partly because foraging events at vessels occurred very rarely, and did not contain any diagnostic movement or activity pattern that was distinct from natural behaviours away from vessels. Thus, we were unable to predict accurately whether foraging bouts occurred in the vicinity of a fishing vessel, or naturally, based on behaviour alone. Our method provides a coherent and generalizable framework to segment trips using auxiliary biologging (immersion) data and to refine the classification of foraging strategies of seabirds. These results nevertheless underline the value of using radar detectors that detect vessel proximity or remotely sensed vessel locations for a better understanding of seabird–fishery interactions.

Animal behaviour on the move: the use of auxiliary information and semi-supervision to improve behavioural inferences from Hidden Markov Models applied to GPS tracking datasets

“…In our study, dives only lasted 1.4 ± 0.6 s seconds and were infrequent and dispersed (just 1.2 ± 1.3 dives per GPS position, and only 22% of dives were recorded within the same or in adjacent GPS positions), suggesting that foraging may be obscured by resting and travelling if dive-specific auxiliary data is not available. Similar observations have been made in the attempt to distinguish mating behaviour in GPS-tracked deer [ 53 ] or in the differentiation of natural and non-natural foraging in seabirds [ 3 ]. In these cases, the addition of more complexe auxiliary sensors (such as cameras, TDRs, and accelerometers etc.)…”

“…may be needed to truly identify these particular behaviours. Auxiliary devices have been used in combination with GPS data to identify foraging behaviours in many seabirds and seals, which may otherwise be impossible [ 3 , 32 , 54 , 55 ].…”

“…Here, wet-dry loggers generated the largest number of positions with known behaviours alone, primarily because tropicbirds spend the majority of time resting on water [ 28 ]. In seabird species that spend more time on the wing, wet-dry sensors may detect fewer resting positions, but can still be used to identify potential prey capture attempts within foraging [ 3 , 23 ]. TDR loggers, on the other hand, gave accurate measures of foraging attempts but could not detect when the bird was resting or travelling, and recorded fewer overall dives than accelerometers, possibly because of missed shallow dives [ 56 ] or the capture of flying fish in air [ 30 ].…”

“…Inferring behaviour from animal movements is crucial to understand relationships between species and their environments [ 1 , 2 ] or potential human-wildlife conflicts [ 3 – 5 ]. Over the last three decades, advances in biologging technology through the creation of smaller, cheaper and more sophisticated and accurate sensors, have facilitated rapid developments in the field of movement ecology, allowing for the study of movement in a wide array of species and environments (e.g.…”

“…Model performance can be improved by incorporating additional information on what an animal is doing from auxiliary sensors. For example, wet-dry sensors (WD) can distinguish when an animal is immersed in salt water [ 3 , 23 ], Time Depth Recorders (TDR) can be used to detect dives below a specific threshold [ 31 ] and high frequency tri-axial accelerometers can provide unprecedented information on fine-scale movements resulting in inferences that go as far as separating individual prey-capture attempts [ 17 , 32 – 34 ]. Data acquired from these sensors can be incorporated into behavioural models, allowing for more accurate classification.…”