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

能量被称为生命的通用货币。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）与种群内更广泛的地理和环境格局（营养联系、种群分布）之间的基本联系。因此，能量流的知识对于理解物种如何持续存在以及它们的生存如何受到环境变化的影响至关重要，特别是对于那些变化的条件可能对其生存能力产生重大影响的脆弱物种。虽然理论上很明显，但动物能量收支与生态系统过程之间的关系缺乏经验证据（2,3）。到目前为止，科学界在确定野生动物的瞬时空间和时间特定能量消耗方面的装备还很差。动物携带记录仪硬件和软件的最新技术进步现在意味着我们现在可以测量野生动物的活动模式，并使用这个变量作为能量消耗和行为的代理。在哺乳动物群体中，某些物种似乎特别容易受到能量限制，因此数量迅速下降。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。其他担忧也强调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，我们必须了解每种种群压力源以及压力源之间的相互作用对野生动物生态的影响。诸如栖息地丧失和人类与野生动物冲突等驱动因素被广泛接受，也很容易证明。然而，由于栖息地特征、疾病状况、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，行为改变与能量消耗之间的微妙和多重相互作用尚未得到很好的探索。因此，这种相互作用值得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）在数据分析研究和开发方面的合作，将获得宝贵的经验，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。也有可能与其他软件包（如R或MatLab）进行接口，以进行额外的统计分析。将主要软件与外部软件包接口将极大地扩展内部软件功能。如果不允许用户完全控制他们的分析，没有一个软件工具可以做所有的事情。现有的数据集将用于开发所需的技能，然后将设计一个研究方案来收集额外的实地数据。

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.