Assessing the long-term consequences of sub-lethal anthropogenic disturbance on wildlife populations requires integrating data on fine-scale individual behavior and physiology into spatially and temporally broader, population-level inference. A typical behavioral response to disturbance is the cessation of foraging, which can be translated into a common metric of energetic cost. However, this necessitates detailed empirical information on baseline movements, activity budgets, feeding rates and energy intake, as well as the probability of an individual responding to the disturbance-inducing stressor within different exposure contexts. Here, we integrated data from blue whales (Balaenoptera musculus) experimentally exposed to military active sonar signals with fine-scale measurements of baseline behavior over multiple days or weeks obtained from accelerometry loggers, telemetry tracking and prey sampling. Specifically, we developed daily simulations of movement, feeding behavior and exposure to localized sonar events of increasing duration and intensity and predicted the effects of this disturbance source on the daily energy intake of an individual. Activity budgets and movements were highly variable in space and time and among individuals, resulting in large variability in predicted energetic intake and costs. In half of our simulations, an individual’s energy intake was unaffected by the simulated source. However, some individuals lost their entire daily energy intake under brief or weak exposure scenarios. Given this large variation, population-level models will have to assess the consequences of the entire distribution of energetic costs, rather than only consider single summary statistics. The shape of the exposure-response functions also strongly influenced predictions, reinforcing the need for contextually explicit experiments and improved mechanistic understanding of the processes driving behavioral and physiological responses to disturbance. This study presents a robust approach for integrating different types of empirical information to assess the effects of disturbance at spatio-temporal and ecological scales that are relevant to management and conservation.

中文翻译：

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蓝鲸干扰的预计每日能量成本中的上下文相关变异

要评估亚致死性人为干扰对野生动植物种群的长期后果，需要将关于小规模个体行为和生理的数据整合到时空范围更广的种群水平的推断中。对干扰的典型行为响应是停止觅食，这可以转化为精力成本的通用指标。但是，这需要有关基线运动，活动预算，进食速率和能量摄入以及个人在不同暴露环境下对诱发干扰的应激源做出反应的可能性的详细经验信息。在这里，我们整合了蓝鲸（Balaenoptera musculus）的数据）通过加速度计记录仪，遥测跟踪和猎物采样获得的军事活动声纳信号进行实验，可以在数天或数周内对基线行为进行精细测量。具体而言，我们开发了运动，进食行为以及暴露于持续时间和强度不断增加的局部声纳事件的日常模拟，并预测了该干扰源对个人每日能量摄入的影响。活动预算和活动在空间和时间上以及个人之间变化很大，导致预计的能量摄入和费用差异很大。在我们的一半模拟中，一个人的能量摄入不受模拟源的影响。但是，有些人在短暂或微弱的暴露情况下会失去全部的每日能量摄入。鉴于变化很大，人口模型必须评估整个能源成本分配的后果，而不是仅考虑单一的汇总统计数据。暴露响应函数的形状也极大地影响了预测，从而增强了对上下文明确实验的需求，并增强了对对行为进行行为和生理响应的过程的机械理解。这项研究为整合不同类型的经验信息提供了一种鲁棒的方法，以评估与管理和保护相关的时空和生态规模的干扰影响。曝光响应函数的形状也极大地影响了预测，从而增强了对上下文明确实验的需求，并增强了对对行为进行行为和生理响应的过程的机械理解。这项研究为整合不同类型的经验信息提供了一种鲁棒的方法，以评估与管理和保护相关的时空和生态规模的干扰影响。暴露响应函数的形状也极大地影响了预测，从而增强了对上下文明确实验的需求，并增强了对对行为进行行为和生理响应的过程的机械理解。这项研究为整合不同类型的经验信息提供了一种鲁棒的方法，以评估与管理和保护相关的时空和生态规模的干扰影响。