Links between energetics and disease in mammals: use of biologging and remote sensing to assess behaviour and movement

能源被称为生命的通用货币(1)。能量获取和消耗之间的平衡提供了在个体一级控制生理过程的能力(例如繁殖成功、质量平衡、免疫功能)和种群内更广泛的地理和环境模式(营养联系、种群分布)之间的重要联系。因此，对能量流动的了解对于理解物种如何持续以及它们的生存可能如何受到环境变化的影响至关重要，特别是对于环境变化可能对其生存能力产生重大影响的脆弱物种。尽管在理论上是显而易见的，但缺乏动物能量收支与生态系统过程之间关系的经验证据(2，3)。到目前为止，科学界在确定野生动物瞬时特定空间和时间的能量消耗方面一直装备不足。最近在动物携带的记录仪硬件和软件方面的技术进步意味着我们现在可以测量野生动物的活动模式，并将这个变量用作能量消耗和行为的替代。在哺乳动物协会中，某些物种似乎特别容易受到能量限制，因此迅速衰落。大型哺乳动物就是一个很好的例子。这可能是因为它们的分布有限和/或因为生物条件限制了它们的食物摄入量。其他担忧也突显了疾病和气候变化的影响。为了确保未来野生动物种群的可持续发展，我们必须了解每个种群应激源以及应激源之间的相互作用对野生动物生态的影响。栖息地丧失和人与野生动物冲突等驱动因素被广泛接受，并很容易得到证明。然而，由于栖息地特征、疾病状态、猎物的存在、同种生物、人类和/或其他环境扰动(包括气候变化)的差异，改变的行为和能量支出之间的微妙和乘性相互作用的研究较少。因此，这种相互作用需要更多地关注缓解规划和物种保护。学生将接受用于测量野生动物活动和能量消耗的分析技术的培训。通过与CASE合作伙伴(Wildbyte Technologies，见下文)合作研究和开发数据分析，包括多维可视化和过滤机制，以及增强现有内部软件的算法/参数，将获得宝贵的经验。此外，还有可能与其他软件包(如R或MatLab)对接，进行额外的统计分析。将主要软件与外部软件包连接将极大地扩展内部软件的功能。如果一个软件工具不允许用户完全控制他们的分析，那么没有一个软件工具可以做所有的事情。将使用现有的数据集来发展所需的技能，然后将设计一项研究方案来收集更多的实地数据。

Energy has been termed the universal currency of life (1). The balance between energy acquisition and expenditure provides an essential link between the ability to control physiological processes at the individual level (e.g. reproductive success, mass-balance, immune function) and wider-scale geographic and environmental patterns within populations (trophic linkages, population distributions). Knowledge of energy flow is, therefore, pivotal for understanding how species' persist and how their survival might be affected by environmental change, particularly for vulnerable species for which changing conditions may have large impacts on their ability to survive.Although theoretically apparent, there is scant empirical evidence of relationships between animal energy budgets and ecosystem processes (2,3). So far, the scientific community has been poorly equipped to determine instantaneous space- and time-specific energy expenditures of wild animals. Recent technological advances in animal-borne logger hardware and software now mean that we can now measure the activity patterns of wild animals and use this variable as a proxy both for energy expenditure and behaviour.Within mammal guilds, certain species seem to be particularly vulnerable to energetic constraints and are consequently in rapid decline. Large mammals are a case-in-point. This can be because their distributions are limited and/or because biotic conditions restrict their food intake. Other concerns also highlight the effects of disease and climate change. To ensure future sustainable wildlife populations, we must understand the impact that each population stressor and the interactions between stressors have on wild animal ecology. Drivers such as habitat loss and human-wildlife conflict are widely accepted and easily demonstrable. However, subtle and multiplicative interactions between altered behaviours and energy expenditures in response to differences in habitat characteristics, disease status, presence of prey, conspecifics, humans, and/or other environmental perturbations including changes in climate are less well explored. Such interactions therefore warrant more attention for mitigation planning and species conservation.The student will be trained in analytical techniques used to measure activity and energy expenditure in wild animals. Valuable experience will be acquired through working with the CASE partner (Wildbyte Technologies, see below) on the research and development of data analytics, including multidimensional visualisation and filtering mechanisms, and algorithms/parameters to enhance the existing in-house software. There is also potential to work on interfacing with other software packages such as R or MatLab for extra statistical analysis. Interfacing the primary software with external software packages would expand the in-house software capabilities immensely. No single software tool can do everything if it does not allow the user full control over their analysis. Existing datasets will be used to develop the skills required and then a research protocol will be designed to collect additional field data.