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. 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. 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 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. 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.

中文翻译：

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使用隐马尔可夫模型了解食物缓存捕食者的决策。

解决行为问题通常需要确定动物做出决定的空间和时间点，并将这些点与环境变量联系起来。状态空间建模对于分析运动轨迹非常有用，尤其是对于隐马尔可夫模型 (HMM)。然而重要的是，HMM 揭示的潜在（不可观察）行为状态的个体发育在该领域很少得到验证。使用来自动物位置、生物遥测和环境数据的个体运动隐藏马尔可夫模型，我们探索了多状态行为以及相关内在和外在驱动因素在整个生命阶段的影响。我们还在两个一般和缓存阶段分解了不同运动状态的活动预算。后者 - 定义为可能涉及未吃猎物缓存的杀戮之后的时期 - 随后被现场检查证实。我们将此方法应用于伊朗东北部缓存捕食者波斯豹 Panthera pardus saxicolor 的 GPS 重定位数据。多状态模型为生命阶段对行为状态及其相关时间预算的影响提供了强有力的证据。尽管环境协变量（环境温度和昼夜周期）和生态结果（捕食）影响了非居民豹的行为状态，但居民豹的反应尚不清楚，除了时间模式与黄昏和夜间运动模式一致。常住豹子大部分时间采取能量更高成本的移动行为，而非居民将其行为状态从高能量消耗状态转变为能量成本较低的营地行为，这可能是针对同种动物的风险规避策略或人类。这项研究表明，捕食者行为的可塑性取决于生命阶段，可以解决成功捕食与避免与同种动物、人类存在和维持家园范围相关的风险之间的权衡。领地掠食者的范围居住要求很高，并且可能超过掠食者对温度调节或觅食需求等内在和外在变量的反应。我们的方法提供了对豹的空间行为和决策的洞察，