Understanding decision making in a food-caching predator using hidden Markov models

Mohammad S Farhadinia,Paul J Johnson,Théo Michelot,Luke T B Hunter,David W Macdonald

doi:10.1186/s40462-020-0195-z

Abstract

BackgroundTackling behavioural questions often requires identifying points in space and time where animals make decisions and linking these to environmental variables. State-space modeling is useful for analysing movement trajectories, particularly with hidden Markov models (HMM). Yet importantly, the ontogeny of underlying (unobservable) behavioural states revealed by the HMMs has rarely been verified in the field.MethodsUsing hidden Markov models of individual movement from animal location, biotelemetry, and environmental data, we explored multistate behaviour and the effect of associated intrinsic and extrinsic drivers across life stages. We also decomposed the activity budgets of different movement states at two general and caching phases. The latter - defined as the period following a kill which likely involves the caching of uneaten prey - was subsequently confirmed by field inspections. We applied this method to GPS relocation data of a caching predator, Persian leopard Panthera pardus saxicolor in northeastern Iran.ResultsMultistate modeling provided strong evidence for an effect of life stage on the behavioural states and their associated time budget. Although environmental covariates (ambient temperature and diel period) and ecological outcomes (predation) affected behavioural states in non-resident leopards, the response in resident leopards was not clear, except that temporal patterns were consistent with a crepuscular and nocturnal movement pattern. Resident leopards adopt an energetically more costly mobile behaviour for most of their time while non-residents shift their behavioural states from high energetic expenditure states to energetically less costly encamped behaviour for most of their time, which is likely to be a risk avoidance strategy against conspecifics or humans.ConclusionsThis study demonstrates that plasticity in predator behaviour depending on life stage may tackle a trade-off between successful predation and avoiding the risks associated with conspecifics, human presence and maintaining home range. Range residency in territorial predators is energetically demanding and can outweigh the predator’s response to intrinsic and extrinsic variables such as thermoregulation or foraging needs. Our approach provides an insight into spatial behavior and decision making of leopards, and other large felids in rugged landscapes through the application of the HMMs in movement ecology.

Highlights

Tackling behavioural questions often requires identifying points in space and time where animals make decisions and linking these to environmental variables
We investigated how intrinsic and extrinsic factors interact to shape movement patterns, and how that is affected by life stage
We considered hidden Markov models (HMM) with three behavioural states, based on our prior knowledge of another caching felid, puma Puma concolor, suggesting that 3-state models are generally statistically well-supported and biologically interpretable

Summary

Introduction

Tackling behavioural questions often requires identifying points in space and time where animals make decisions and linking these to environmental variables. State-space modeling is useful for analysing movement trajectories, with hidden Markov models (HMM). The ontogeny of underlying (unobservable) behavioural states revealed by the HMMs has rarely been verified in the field. One popular state-space model is the hidden Markov model (HMM), which can be used to describe animal movement as arising from a finite number of hidden behavioural states [5, 6]. The behavioural state process is defined as a Markov chain, i.e. the state at the time step depends only on the current state. It is parameterized by its transition probabilities and an initial distribution [6, 7]. The observation process most often comprises the step lengths and turning angles of the animal, assumed to be driven by the underlying unobserved states [1, 7]

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Conclusion