Abstract Dynamic energy budget individual-based models (DEB-IBMs) provide a well-tested framework for modelling the acquisition and use of energy throughout the life cycle of organisms. These models are often developed using species-specific data to link behaviour and physiology and predict how individuals and populations perform in changing environments. Novel bio-logging technology, applied to free-ranging animals, provide detailed information about individual behavioural variations and responses to the environment. However, so far, no DEB-IBM has attempted to link and parameterise energy dynamics with the fine-scale activity data obtained by bio-logging to answer questions related to life history events in wild populations. The aim of this study is, therefore, to develop and test a DEB-IBM framework which integrates individuals behaviour obtained from high-resolution activity data. Our approach uses data collected with GPS and accelerometer tags attached to two muskox (Ovibos moschatus). The individuals had different fates: one survived the Arctic winter, while the other died. Applying hidden Markov models to accelerometer data, we quantified the variation in behavioural activity budgets (in stationary, feeding and transit) in relation to environmental conditions. We subsequently test how differences in time allocation affect fitness outcomes such as body mass, energy reserves and survival. The proportion of time spent feeding by each animal is used within the DEB-IBM framework to scale the functional response for food acquisition. Simulations using the seasonal feeding budget of the deceased individual lead to drastically reduced energy reserves and body weight during the Arctic winter, and to elevated probabilities of mortality by the end of winter. In contrast, simulations using the seasonal feeding pattern of the surviving muskox, result in much less pronounced losses in reserves and body weight, leading to negligible over-winter mortality. The framework we present here uses standardized modelling techniques, is applicable across species and can easily be extended to include other fitness measures or environmental stressors. Moreover, our framework provides the foundation needed for future studies to link individual movements, energetic processes, life history events and population dynamics in wildlife, which would open up possibilities to test how changing (future) environmental conditions influence animal decision-making and ultimately population level consequences.

中文翻译：

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能量学作为将高分辨率活动模式集成到基于个人的动态能量预算模型中的通用货币

摘要 基于个体的动态能量预算模型 (DEB-IBMs) 提供了一个经过良好测试的框架，用于对整个生物体生命周期中的能量获取和使用进行建模。这些模型通常使用特定物种的数据来开发，以将行为和生理联系起来，并预测个体和种群在不断变化的环境中的表现。应用于自由放养动物的新型生物记录技术可提供有关个体行为变化和对环境反应的详细信息。然而，到目前为止，还没有 DEB-IBM 尝试将能量动态与通过生物记录获得的精细活动数据联系起来和参数化，以回答与野生种群生活史事件相关的问题。因此，本研究的目的是，开发和测试 DEB-IBM 框架，该框架集成了从高分辨率活动数据中获得的个人行为。我们的方法使用 GPS 和加速度计标签收集的数据，这些标签附加到两个麝香牛 (Ovibos moschatus)。这些人有不同的命运：一个在北极冬天幸存下来，而另一个则死了。将隐马尔可夫模型应用于加速度计数据，我们量化了与环境条件相关的行为活动预算（在静止、进食和运输中）的变化。我们随后测试了时间分配的差异如何影响健康结果，例如体重、能量储备和生存。DEB-IBM 框架中使用每只动物喂食所花费的时间比例来衡量食物获取的功能响应。使用已故个体的季节性喂养预算进行的模拟导致北极冬季期间能量储备和体重急剧减少，并导致冬季结束时死亡的可能性增加。相比之下，使用幸存麝牛的季节性摄食模式进行的模拟，导致储备和体重的损失要小得多，导致过冬死亡率可以忽略不计。我们在此介绍的框架使用标准化建模技术，适用于跨物种，并且可以轻松扩展到包括其他健康措施或环境压力因素。此外，我们的框架提供了未来研究所需的基础，以将野生动物的个体运动、能量过程、生活史事件和种群动态联系起来，