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

Mohammad S Farhadinia¹, Théo Michelot², Paul J Johnson³

¹1Oxford Martin School and Department of Zoology, University of Oxford, 34 Broad St, Oxford, OX1 3BD UK.

Movement Ecology

|February 20, 2020

Summary

Hidden Markov models reveal life stage influences predator behavior and energy use. This study shows how Persian leopards adjust movement strategies to balance predation with avoiding risks like human presence.

Keywords:

Caching behaviour Hidden Markov models Life-stage Multistate animal movement Panthera pardus saxicolor Range residency Satellite telemetry Viterbi algorithm

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Published on: August 2, 2018

Area of Science:

Movement ecology
Behavioral ecology
Conservation biology

Background:

Understanding animal decision-making requires linking spatial-temporal behavior to environmental factors.
Hidden Markov models (HMMs) are valuable for analyzing movement trajectories and inferring unobservable behavioral states.
Field verification of HMM-derived behavioral ontogeny in wild animals remains limited.

Purpose of the Study:

To investigate how life stage and environmental factors influence the multi-state behavior and activity budgets of Persian leopards.
To apply HMMs to GPS tracking data to understand behavioral plasticity in a large carnivore.
To validate inferred behavioral states, specifically 'caching phases,' through field observations.

Main Methods:

Utilized HMMs to analyze GPS relocation data, biotelemetry, and environmental data for Persian leopards (Panthera pardus saxicolor) in Iran.
Decomposed activity budgets into distinct movement states, including a novel 'caching phase' definition.
Examined the influence of intrinsic (life stage) and extrinsic (temperature, diel period, predation) drivers on behavioral states.

Main Results:

Life stage significantly affected behavioral states and time budgets in Persian leopards.
Non-resident leopards adjusted behavior based on environmental covariates and predation, shifting to less energetically costly states.
Resident leopards exhibited consistent crepuscular/nocturnal patterns and maintained energetically demanding mobile behavior, suggesting a risk-avoidance strategy.

Conclusions:

Predator behavior is plastic, adapting to trade-offs between predation success and risks (conspecifics, humans, territory defense).
Range residency in territorial predators incurs high energetic costs, potentially overriding responses to thermoregulation or foraging needs.
HMMs offer valuable insights into the spatial behavior and decision-making of leopards and other large felids in complex terrains.