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

Abstract

Background: 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, 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. Methods: Using 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. Results: Multistate 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 nonresidents 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. Conclusions: This 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.

The effect of competing carnivores on the feeding behaviour of leopards (Panthera pardus) in an African savanna

“…The African leopard ( Panthera pardus ) is a generalist predator (Hayward et al., 2006; Shehzad et al., 2015), actively hunting for its prey but also taking carrion whenever present (Bothma & Walker, 2013; Stuart & Stuart, 1993). Leopards usually cache their kills in bushes and consume them on the ground (Balme et al., 2007; Farhadinia et al., 2020; Karanth & Sunquist, 2000). However, competing guild predators such as spotted hyenas ( Crocuta crocuta ) and lions ( Panthera leo ) often discover and kleptoparasitize cached prey (Domínguez‐Rodrigo, 2001; Pitman et al., 2013; Volmer & Hertler, 2016).…”

“…hunting for its prey but also taking carrion whenever present (Bothma & Walker, 2013;Stuart & Stuart, 1993). Leopards usually cache their kills in bushes and consume them on the ground (Balme et al, 2007;Farhadinia et al, 2020;Karanth & Sunquist, 2000).…”

Analysis of central place foraging behaviour of wolves using hidden Markov models

“…Viterbi coded states also confirmed that homing behaviour typically ended at the den site. As noted Farhadinia et al (2020), this kind of verification of the behavioural states revealed by a hidden Markov model using field observations is relatively rare, but here we were able to do it. Therefore, our analysis provided new information on wolves foraging behaviour in summertime, which is relevant when, for example assessing the risk of human-wildlife conflict.…”

Behavioral plasticity allows ungulates to balance risk and reward following megafire

“…We assessed goodness of fit for the HMM using pseudo-residuals drawn from the fit model. Pseudo-residuals of the step length parameter should be normally distributed given good model fit (Farhadinia et al, 2020;Patterson et al, 2009). Therefore, we visually inspected step length pseudo residuals and used a Shapiro-Wilk normality test using a random subset of pseudo residual values (n = 1000).…”