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数量发生显著变化。迄今为止，大多数研究都集中在“更简单”的生物组织层面（例如物种种群向极地迁移；北极北极熊数量下降）的影响上，而不是在复杂的多物种生态系统上，气候变化的影响可能特别深远。食物网是包含多种物种（网络中的节点）及其相互作用（节点之间的联系）信息的生态网络。因此，它们代表了特定生态系统中物种之间的联系，从基础资源（如浮游植物）到顶级捕食者（如北极熊，或商业渔业中的人类）。然而，当食物网受到气候变暖等环境压力时，它们的行为方式无法通过单独研究物种来预测。此外，尽管营养相互作用发生在个体之间，但大多数食物网都是使用更粗糙的物种平均数据构建的。因此，需要一个新的、基于网络的视角来补充科学家现有的预测气候变暖对地球生态系统影响的方法。我们建议使用水生食物网作为模型系统来解决这一知识差距。我们的主要重点将是淡水，作为“陆地海洋中的岛屿”，它特别容易受到环境压力的影响，我们拥有非常详细的食物网数据。我们的方法的一个关键测试将在一个独特的、全生态系统的现场实验中进行，在这个实验中，我们将改变冰岛地热流的温度——因为这些地热流靠近北极圈，它们可能是第一批对全球变暖做出反应的，有效地充当气候变化的“早期预警”哨兵。该项目将与美国国家科学基金会资助的冰岛食物网项目合作伙伴的4年研究补助金形成合作关系，这将最大限度地提高我们的成本效益（有效地降低NERC的成本约30%），因为作为国家科学基金会项目的一部分收集的数据将成为我们提案的一部分，其中我们将参数化和测试食物网结构和动态的新预测模型。总而言之，我们将：1)开发一种新的方法，我们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），通过对身体大小，新陈代谢和觅食生物学的新数据丰富现有信息；2)利用这些数据和新兴的生态理论，建立食物网如何应对气候变暖的新预测模型；3)利用模拟气候变暖影响的操纵实验来检验我们的模型预测。

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.