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

全球变暖正在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着变化。迄今为止，大多数研究都集中在生物组织的“简单”层面上的影响（例如物种种群的向极地迁移;北极北极熊数量的减少），而不是复杂的多物种生态系统，气候变化的影响可能特别深远。食物网是一种生态网络，包含多个物种（网络中的节点）及其相互作用（节点之间的联系）的信息。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孤立地研究物种来预测。此外，虽然营养相互作用发生在个体之间，但大多数食物网都是使用较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方法，以预测气候变暖对地球生态系统的影响。我们建议，以解决这一知识差距，水生食物网作为模型系统。我们的主要重点将放在淡水，作为“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，我们将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流，有效地充当气候变化的“早期预警”哨兵。该项目将与授予我们的项目合作伙伴的为期4年的研究赠款形成合作联系，该项目由美国国家科学基金会资助，将最大限度地提高我们的成本效益（有效地将NERC的成本降低了约30%），因为作为NSF项目的一部分收集的数据将纳入我们提案的一部分，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。总之，我们将：1）开发一种新的方法，我们考虑个人在食物网中的作用（而不是简单地使用物种种群作为网络中的节点），用身体大小，新陈代谢和觅食生物学的新数据丰富现有信息; 2）使用这些数据和新兴的生态理论来创建食物网如何应对变暖的新预测模型; 3）使用模拟气候变暖影响的操纵性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.