Vianey Leos-Barajas

Sex-specific and individual preferences for hunting strategies in white sharks

1. Fine-scale predator movements may be driven by many factors including sex, habitat anddistribution of resources. There may also be individual preferences for certain movementstrategies within a population which can be hard to quantify.2. Within top predators, movements are also going to be directly related to the mode of hunting,for example sit-and-wait or actively searching for prey. Although there is mounting evidencethat different hunting modes can cause opposing trophic cascades, there has been littlefocus on the modes used by top predators, especially those in the marine environment.3. Adult white sharks (Carcharhodon carcharias) are well known to forage on marine mammalprey, particularly pinnipeds. Sharks primarily ambush pinnipeds on the surface, but there hasbeen less focus on the strategies they use to encounter prey.4. We applied mixed hidden Markov models to acoustic tracking data of white sharks in acoastal aggregation area in order to quantify changing movement states (area-restricted searching(ARS) vs. patrolling) and the factors that influenced them. Individuals were re-tracked overmultiple days throughout a month to see whether state-switching dynamics varied or if individualspreferred certain movement strategies.5. Sharks were more likely to use ARS movements in the morning and during periods of chummingby ecotourism operators. Furthermore, the proportion of time individuals spent in the two differentstates and the state-switching frequency, differed between the sexes and between individuals.6. Predation attempts/success on pinnipeds were observed for sharks in both ARS and patrollingmovement states and within all random effects groupings. Therefore, white sharks can use both a ‘sitand-wait’ (ARS) and ‘active searching’ (patrolling) movements to ambush pinniped prey on the surface.7. White sharks demonstrate individual preferences for fine-scale movement patterns, whichmay be related to their use of different hunting modes. Marine top predators are generallyassumed to use only one type of hunting mode, but we show that there may be a mix withinpopulations. As such, individual variability should be considered when modelling behaviouraleffects of predators on prey species.

Analysis of animal accelerometer data using hidden Markov models

1.Use of accelerometers is now widespread within animal biotelemetry as they provide a means of measuring an animals 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. 2.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. 3.We will 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. 4.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, energetic expenditure and deepen our insights into individual behaviour as a constituent of populations and ecosystems. This article is protected by copyright. All rights reserved.

Integrating behaviour into the pace-of-life continuum: Divergent levels of activity and information gathering in fast- and slow-living snakes

An animals life history, physiology, and behaviour can be shaped by selection in a manner that favours strong associations among these aspects of an integrated phenotype. Recent work combining animal personality and life-history theory proposes that animals with faster life-history strategies (i.e., fast growth, high annual reproductive rate, short lifespan) should exhibit higher general activity levels relative to those with slower life-history strategies, but empirical tests of within-species variation in these traits are lacking. In garter snakes from ecotypes which are known to differ in ecology, life-history strategy, and physiology, we tested for differences in tongue-flick rate as a measure of information gathering and movement patterns as a measure of general activity. Tongue flicks and movement were strongly positively correlated and both behaviours were repeatable across trials. Snakes from the fast-living ecotype were more active and showed evidence of habituation. The slow-living ecotype maintained low levels of activity throughout the trials. We propose that environmental factors, such as high predation, experienced by the fast-living ecotype select for both increased information-gathering and activity levels to facilitate efficient responses to repeated challenges. Thus, we offer evidence that behaviour is an important component of co-evolved suites of traits forming a general pace-of-life continuum in this system.

Activity seascapes highlight central place foraging strategies in marine predators that never stop swimming

BackgroundCentral place foragers (CPF) rest within a central place, and theory predicts that distance of patches from this central place sets the outer limits of the foraging arena. Many marine ectothermic predators behave like CPF animals, but never stop swimming, suggesting that predators will incur ‘travelling’ costs while resting. Currently, it is unknown how these CPF predators behave or how modulation of behavior contributes to daily energy budgets. We combine acoustic telemetry, multi-sensor loggers, and hidden Markov models (HMMs) to generate ‘activity seascapes’, which combine space use with patterns of activity, for reef sharks (blacktip reef and grey reef sharks) at an unfished Pacific atoll.ResultsSharks of both species occupied a central place during the day within deeper, cooler water where they were less active, and became more active over a larger area at night in shallower water. However, video cameras on two grey reef sharks revealed foraging attempts/success occurring throughout the day, and that multiple sharks were refuging in common areas. A simple bioenergetics model for grey reef sharks predicted that diel changes in energy expenditure are primarily driven by changes in swim speed and not body temperature.ConclusionsWe provide a new method for simultaneously visualizing diel space use and behavior in marine predators, which does not require the simultaneous measure of both from each animal. We show that blacktip and grey reef sharks behave as CPFs, with diel changes in activity, horizontal and vertical space use. However, aspects of their foraging behavior may differ from other predictions of traditional CPF models. In particular, for species that never stop swimming, patch foraging times may be unrelated to patch travel distance.

Visualizing communication patterns at DinoFun World

Two IDs are notable for their large volume of messages: 1278894 and 839736. These IDs are responsible for almost 80% of the message volume. Both these ids are stationary, sending messages from the Entry Corridor only. From the pattern of messages sent and received we are able to identify these ids as the parks help line (839736) and the Cindysaurus trivia game (1278894), which is part of the DinoFun World app (IEEE VAST Challenge 2015).

On the move at DinoFun world

Overall attendance at DinoFun World is characterized in figure 2. The number of moves park-goers make is charted for each minute of the day along a horizontal time axis. We can learn a couple of things from this plot: (1) the Scott Jones show was held from 10 to 11 during all mornings and from 3 to 4 in the afternoons of Friday and Saturday. We can see this from the dip in movements (orange lines are average number of moves during one hour) during this time, and the spikes immediately at the end of the show (when a lot of people move out of the area). The second show on Sunday was cancelled: the dip in movements is missing on Sunday afternoon. This is also visible in figure 2, showing check-ins by area: people check into the Grinosaurus Stage, where the Scott Jones show is hosted, before 10 am for all days but before 3 pm only on Friday and Saturday. (2) there is a spike in movements on Sunday at around 2:30 pm - judging from the movement pattern these are people on their way to the Scott Jones show who get turned away (because there are no check-ins to the Grinosaurus Stage), see also figure 3.