Using individual metabolism and body size to predict climate warming impacts on aquatic food webs

全球变暖正以人类历史上前所未有的速度发生，导致许多物种的分布和丰度发生显著变化。到目前为止，大多数研究都集中在“更简单”的生物组织层面上的影响(例如物种种群的极地迁徙；北极北极熊数量的减少)，而不是对复杂的多物种生态系统的影响，在那里气候变化的影响可能特别深远。食物网是包含多个物种(网络中的节点)及其相互作用(节点之间的链接)的信息的生态网络。因此，它们代表了特定生态系统内从基础资源(例如浮游植物)到顶级捕食者(例如北极熊或商业渔业中的人类)的物种相互联系的方式。然而，当食物网受到环境压力，如气候变暖时，它们的行为方式可能是孤立地研究物种无法预测的。此外，尽管营养相互作用发生在个体之间，但大多数食物网是使用更粗糙的物种平均数据构建的。因此，需要一种新的、基于网络的视角来补充科学家预测气候变暖对地球生态系统影响的现有方法。我们建议使用水生食物网作为模型系统来解决这一知识鸿沟。我们的主要关注点将是淡水，因为“陆地海洋中的岛屿”特别容易受到环境压力的影响，我们拥有异常详细的食物网络数据。我们的方法将在一个独特的、全生态系统的实地实验中进行关键测试，我们将在该实验中改变冰岛地热流的温度--因为这些流靠近北极圈，它们很可能是第一批对全球变暖做出反应的人之一，有效地充当气候变化的“预警”哨兵。该项目将与由美国国家科学基金会资助的、授予我们的项目合作伙伴的为期4年的冰岛食物网研究拨款形成合作联系，这将使我们的成本效益最大化(有效地将NERC的成本降低约30%)，因为作为NSF项目的一部分收集的数据将提供给我们的提案的一部分，在该提案中，我们将对食物网结构和动态的新预测模型进行参数化和测试。总而言之，我们将：1)开发一种新的方法，通过使用关于身体大小、新陈代谢和觅食生物学的新数据来丰富现有信息，考虑个体在食物网中的作用(而不是简单地将物种种群作为网络中的节点)；2)使用这些数据和新兴的生态学理论来创建新的预测模型，以预测食物网将如何响应变暖；3)使用模拟气候变暖影响的操纵实验来测试我们的模型预测。

Global warming is occurring at an unprecedented rate in human history, causing marked changes in the distribution and abundance of many species. Most research to date has focused on the impacts at the 'simpler' levels of biological organisation (e.g. polewards migrations of species populations; declining abundance of Polar bears in the Arctic) rather than on complex, multispecies ecosystems, where the effects of climate change are likely to be particularly far-reaching. Food webs are ecological networks that contain information on multiple species (the nodes in the web) and their interactions (links between nodes). As such, they represent how species are connected to one another within a given ecosystem, from the basal resources (e.g. phytoplankton) to the top predators (e.g. Polar bears, or humans in commercial fisheries). However, when food webs are subjected to environmental stress, such as climate warming, they can behave in ways that cannot be predicted from studying species in isolation. Further, although trophic interactions occur between individuals, most food webs have been constructed using coarser, species-averaged data. Consequently, a new, network-based perspective is needed to complement scientists' existing approaches to predicting the impacts of climate warming on the planet's ecosystems. We propose to address this knowledge gap, using aquatic food webs as model systems. Our principal focus will be on freshwaters, which, as 'islands in a terrestrial sea' are particularly vulnerable to environmental stress and for which we possess exceptionally detailed food web data. A key test of our approach will be performed in a unique, whole-ecosystem field experiment in which we will alter the temperature of Icelandic geothermal streams - because these streams are close to the Arctic Circle, they are likely to be among the first to respond to global warming, effectively acting as 'early-warning' sentinels of climate change. This project will form a collaborative link with a 4-year research grant awarded to our Project Partners characterising Icelandic food webs funded by the U.S. National Science Foundation, which will maximise our cost-effectiveness (effectively reducing the cost to NERC by about 30%) since the data collected as part of the NSF project will feed into part of our proposal, in which we will parameterise and test new predictive models of food web structure and dynamics. In summary, we will: 1) develop a new approach, where we consider the role of individuals within food webs (rather than simply using species populations as nodes in the network) by enriching existing information with new data on body-size, metabolism and foraging biology; 2) use these data and emerging ecological theories to create novel predictive models of how food webs will respond to warming; 3) test our model predictions using manipulative experiments that simulate the effects of climate warming.