The influence of preceding dive cycles on the foraging decisions of Antarctic fur seals

Iwata, Tadahisa; Sakamoto, Kentaro Q.; Edwards, Ewan W. J.; Staniland, Iain J.; Trathan, Phil; Goto, Yusuke; Sato, Katsufumi; Naito, Yoshiro; Takahashi, Akinori

doi:10.1098/rsbl.2015.0227

Cited by 16 publications

(13 citation statements)

References 16 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When high prey encounter rate is met, elephant seals adjust their diving behaviour by increasing both their descent and ascent angle (Figs 1 and 2), likely to minimize their transit time (Figs 5 and 6), and increase both their horizontal and vertical sinuosity during the bottom phase of their dive zigzagging within the prey patch layer (Figs 1 and 2). The negative relationship between the horizontal transit rate and putative feeding activity has been observed for numerous marine predators (SES [19] see also Figure A in S3 Appendix, northern elephant seals ( Mirounga angustirostris ) [79], wandering albatross ( Diomedea exulan ), antarctic fur seals ( Arctocephalus gazella ) [80]). A number of these variables that impacted on the PEE rate at bottom (models 1a and 1b) affected oppositely the horizontal speed measured at surface with GPS location (model 2).…”

Section: Discussionmentioning

confidence: 94%

“…Without that information we could not estimate the volume of water prospected by SES and compare it to the number of PEE in order to get an indication of prey density independent from SES behaviour (in PEE/m 3 instead of PEE per unit of SES bottom time). Additional studies may use 3D dive reconstructions [13,80,91–93] to determine if the effect of the vertical extent of the bottom on the foraging success would be related to the presence/absence of small scale schooling-prey patches (leading to a small vertical extent of the bottom) or conversely, due to changes at a larger scale in the vertical aggregation of prey layers within the water column. We believe that the findings of this study are likely to be generalized to other air breathing divers foraging on small prey items such as mesopelagic fishes or crustaceans, but differences are likely to be found for diving predators foraging on large prey items.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

How Elephant Seals (Mirounga leonina) Adjust Their Fine Scale Horizontal Movement and Diving Behaviour in Relation to Prey Encounter Rate

et al. 2016

View full text Add to dashboard Cite

Understanding the diving behaviour of diving predators in relation to concomitant prey distribution could have major practical applications in conservation biology by allowing the assessment of how changes in fine scale prey distribution impact foraging efficiency and ultimately population dynamics. The southern elephant seal (Mirounga leonina, hereafter SES), the largest phocid, is a major predator of the southern ocean feeding on myctophids and cephalopods. Because of its large size it can carry bio-loggers with minimal disturbance. Moreover, it has great diving abilities and a wide foraging habitat. Thus, the SES is a well suited model species to study predator diving behaviour and the distribution of ecologically important prey species in the Southern Ocean. In this study, we examined how SESs adjust their diving behaviour and horizontal movements in response to fine scale prey encounter densities using high resolution accelerometers, magnetometers, pressure sensors and GPS loggers. When high prey encounter rates were encountered, animals responded by (1) diving and returning to the surface with steeper angles, reducing the duration of transit dive phases (thus improving dive efficiency), and (2) exhibiting more horizontally and vertically sinuous bottom phases. In these cases, the distance travelled horizontally at the surface was reduced. This behaviour is likely to counteract horizontal displacement from water currents, as they try to remain within favourable prey patches. The prey encounter rate at the bottom of dives decreased with increasing diving depth, suggesting a combined effect of decreased accessibility and prey density with increasing depth. Prey encounter rate also decreased when the bottom phases of dives were spread across larger vertical extents of the water column. This result suggests that the vertical aggregation of prey can regulate prey density, and as a consequence impact the foraging success of SESs. To our knowledge, this is one of only a handful of studies showing how the vertical distributions and structure of prey fields influence the prey encounter rates of a diving predator.

show abstract

Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

How Elephant Seals (Mirounga leonina) Adjust Their Fine Scale Horizontal Movement and Diving Behaviour in Relation to Prey Encounter Rate

et al. 2016

View full text Add to dashboard Cite

show abstract

“…; Iwata et al . ; Williams et al . ), but such data have not been examined in the context of hierarchical movements because the analytical framework was not available to quantify the spatial scale of such 3D search behaviour (c.f.…”

Section: Introductionmentioning

confidence: 99%

“…Fine-scale 3D dive paths and kinematics of diving animals have been reconstructed for several species to characterize foraging behaviour (e.g. Wilson & Wilson 1988;Davis et al 1999;Johnson & Tyack 2003;Aoki et al 2012;Goldbogen et al 2015;Iwata et al 2015;Williams et al 2015), but such data have not been examined in the context of hierarchical movements because the analytical framework was not available to quantify the spatial scale of such 3D search behaviour (c.f. Kotliar & Wiens 1990).…”

Section: Introductionmentioning

confidence: 99%

Searching for prey in a three‐dimensional environment: hierarchical movements enhance foraging success in northern elephant seals

et al. 2016

View full text Add to dashboard Cite

Summary Foraging theory predicts that predators adjust their movements according to the spatial distribution of prey. Since prey is often patchily distributed, area‐restricted search (ARS) behaviour, characterized by sinuous search paths of predators with increased turning frequency, should be effective in foraging. However, it remains unclear whether ARS behaviour actually enhances foraging success in free‐ranging animals, especially in marine animals that forage in a three‐dimensional (3D) environment. Here, we reconstructed 3D dive paths of a highly pelagic marine predator, the northern elephant seal (n = 3), with multisensor data loggers that recorded depth, tri‐axis acceleration, tri‐axis magnetism and swim speed. We identified spatial scales of volume‐restricted search (VRS, termed for 3D ARS) behaviour using spherical first‐passage time analysis on 3D dive paths, accompanied with quantifying feeding rates in VRS by using mandible accelerometers that recorded feeding events. Seals exhibited VRS behaviour at two spatial scales (radius of spheres): small‐VRS (8–10 m) and large‐VRS (17–19 m). Most feeding events occurred in VRS zones (78 and 86% for small and large‐VRS, respectively), although VRS accounted for a small proportion of bottom phase of dives in distance travelled. This suggests a strong link between VRS behaviour and foraging success. There was a hierarchical structure to the VRS; most small‐VRS (95%) were nested within large‐VRS (i.e. nested VRS). Importantly, nested VRS had significantly higher feeding rates than non‐nested VRS, because nested VRS contained small‐ and large‐VRS with higher and lower feeding rates, respectively. These results suggest that seals forage on mesopelagic prey in a hierarchical patch system where high‐density patches at small scales are nested within low‐density patches at larger scales. We demonstrated that seals employed scale‐dependent, hierarchical 3D movements and that underwater fine‐scale sinuous movements (i.e. VRS) were strongly linked to higher foraging success, particularly within nested VRS zones. We suggest that seals enhanced foraging success by employing hierarchical movements that possibly reflect the hierarchical property of prey distribution. Although recent studies advocate that optimal searching behaviour would be scale‐independent (e.g. Lévy walk), our study suggests that scale‐dependent processes are important components of successful foraging behaviour.

show abstract

“…; Iwata et al . ), or aspects of whole body acceleration, for example, longitudinal surge (Sakamoto et al . ).…”

Section: Accelerometer Datamentioning

confidence: 99%

Analysis of animal accelerometer data using hidden Markov models

et al. 2016

View full text Add to dashboard Cite

Summary Use of accelerometers is now widespread within animal biologging as they provide a means of measuring an animal's activity in a meaningful and quantitative way where direct observation is not possible. In sequential acceleration data, there is a natural dependence between observations of behaviour, a fact that has been largely ignored in most analyses. Analyses of acceleration data where serial dependence has been explicitly modelled have largely relied on hidden Markov models (HMMs). Depending on the aim of an analysis, an HMM can be used for state prediction or to make inferences about drivers of behaviour. For state prediction, a supervised learning approach can be applied. That is, an HMM is trained to classify unlabelled acceleration data into a finite set of pre‐specified categories. An unsupervised learning approach can be used to infer new aspects of animal behaviour when biologically meaningful response variables are used, with the caveat that the states may not map to specific behaviours. We provide the details necessary to implement and assess an HMM in both the supervised and unsupervised learning context and discuss the data requirements of each case. We outline two applications to marine and aerial systems (shark and eagle) taking the unsupervised learning approach, which is more readily applicable to animal activity measured in the field. HMMs were used to infer the effects of temporal, atmospheric and tidal inputs on animal behaviour. Animal accelerometer data allow ecologists to identify important correlates and drivers of animal activity (and hence behaviour). The HMM framework is well suited to deal with the main features commonly observed in accelerometer data and can easily be extended to suit a wide range of types of animal activity data. The ability to combine direct observations of animal activity with statistical models, which account for the features of accelerometer data, offers a new way to quantify animal behaviour and energetic expenditure and to deepen our insights into individual behaviour as a constituent of populations and ecosystems.

show abstract

The influence of preceding dive cycles on the foraging decisions of Antarctic fur seals

Cited by 16 publications

References 16 publications

How Elephant Seals (Mirounga leonina) Adjust Their Fine Scale Horizontal Movement and Diving Behaviour in Relation to Prey Encounter Rate

How Elephant Seals (Mirounga leonina) Adjust Their Fine Scale Horizontal Movement and Diving Behaviour in Relation to Prey Encounter Rate

Searching for prey in a three‐dimensional environment: hierarchical movements enhance foraging success in northern elephant seals

Analysis of animal accelerometer data using hidden Markov models

Contact Info

Product

Resources

About