Navigating uncertain waters: a critical review of inferring foraging behaviour from location and dive data in pinnipeds

Abstract

In the last thirty years, the emergence and progression of biologging technology has led to great advances in marine predator ecology. Large databases of location and dive observations from biologging devices have been compiled for an increasing number of diving predator species (such as pinnipeds, sea turtles, seabirds and cetaceans), enabling complex questions about animal activity budgets and habitat use to be addressed. Central to answering these questions is our ability to correctly identify and quantify the frequency of essential behaviours, such as foraging. Despite technological advances that have increased the quality and resolution of location and dive data, accurately interpreting behaviour from such data remains a challenge, and analytical methods are only beginning to unlock the full potential of existing datasets. This review evaluates both traditional and emerging methods and presents a starting platform of options for future studies of marine predator foraging ecology, particularly from location and two-dimensional (time-depth) dive data. We outline the different devices and data types available, discuss the limitations and advantages of commonly-used analytical techniques, and highlight key areas for future research. We focus our review on pinnipeds - one of the most studied taxa of marine predators - but offer insights that will be applicable to other air-breathing marine predator tracking studies. We highlight that traditionally-used methods for inferring foraging from location and dive data, such as first-passage time and dive shape analysis, have important caveats and limitations depending on the nature of the data and the research question. We suggest that more holistic statistical techniques, such as state-space models, which can synthesise multiple track, dive and environmental metrics whilst simultaneously accounting for measurement error, offer more robust alternatives. Finally, we identify a need for more research to elucidate the role of physical oceanography, device effects, study animal selection, and developmental stages in predator behaviour and data interpretation.Electronic supplementary materialThe online version of this article (doi:10.1186/s40462-016-0090-9) contains supplementary material, which is available to authorized users.

Scaling marine fish movement behavior from individuals to populations

“…() reveal that the directed horizontal movements in multiple Antarctic pinniped species are associated with longer dive durations, whereas an inverted relationship is noted in blue whales ( Balaenoptera musculus ) with perceived shallow foraging behaviors being characterized by shallow dives and short horizontal movements (DeRuiter et al., ). Future studies may find similar observation models a powerful tool for investigating the dependences of horizontal and vertical movement rates (Carter, Bennett, Embling, Hosegood, & Russell, ).…”

Sex‐related differences in the postmolt distribution of Weddell seals (Leptonychotes weddellii) in the southern Weddell Sea

“…The study of the at‐sea distribution of marine predators has widened with the development of animal‐attached tags (see Carter et al . for a critical review in pinnipeds). Satellite relay data loggers report information collected by on‐board sensors, via the Argos satellite system, enabling us to collect data from previously inaccessible environments (Boehme et al .…”

Long‐term sound and movement recording tags to study natural behavior and reaction to ship noise of seals

“…Breath‐hold resting has also been observed in fur seals ( Arctocephalinae ) (Jeanniard‐du‐Dot, Trites, Arnould, Speakman, & Guinet, ) and asymmetrical dive profiles (so‐called drift dives) performed by elephant seals are assumed to represent resting behavior (Crocker, Boeuf, & Costa, ; Watanabe, Baranov, & Miyazaki, ). For harbor and gray seals, U‐shaped dives are typically associated with foraging behavior (Russell et al, ; Thompson et al, ) and a standard 2D dive profile analysis may have categorized the resting dives found here (e.g., in Figure ) as U‐shaped foraging dives due to lack of data on fine‐scale movements (Carter et al, ). This could lead to overestimation of the number of foraging dives.…”

“…New state‐space models (SSM), like hidden Markov models (HMM), have the potential of integrating 3D movements and environmental parameters, increasing the power to distinguish behaviors. However, the accuracy of such models to quantify various states of behavior is highly influenced by data resolution (Carter et al, ). Higher‐sampling‐rate sensors, and in particular accelerometers, have proven to be useful for interpreting fine‐scale dive behaviors such as prey capture events (Gallon et al, ; Heerah, Hindell, Guinet, & Charrassin, ; Volpov et al, ).…”