Predicting the Impacts of Global Environmental Change on Ecological Networks

全球环境变化(GEC)正在对我们的自然环境产生深远的影响，气候变暖、酸化以及热浪和干旱等极端气候事件导致生物多样性下降。每个物种都是生态系统功能不可或缺的相互作用网络的一部分，即使一个物种的丧失也会改变其消费者和资源的种群规模，导致影响波及整个食物网。幸存的物种也可能通过改变或扩大饮食来适应，以求生存，导致复杂的相互作用重组。因此，对生态网络模式的深入了解将大大提高我们衡量生态系统将如何响应GEC的能力，并有助于指导保护工作。现有的GEC生态评估主要集中在少数关键物种的种群水平的反应，或粗略的网络水平指标，如连通性，但对更精细规模的网络组织知之甚少。例如，子网络结构可以包括由紧密交互的节点组成的内聚“核心”和松散连接的“外围”。在许多不同类型的人工网络(如电信和社会网络)中都观察到了这些亚结构，它们对治理动态和稳定性的重要性得到了广泛的承认。在生态学方面，子网络结构揭示了暴露在干旱中的食物网的重要细微尺度变化，但这条线的研究在很大程度上还没有被探索。因此，迫切需要科学进步来促进网络理论和生态系统生态学之间的耦合，使我们能够更好地了解我们的自然系统如何承受和适应外部压力的影响。这个项目的总体目标是评估和预测GEC对生态网络的影响，并开发一种更综合的建模方法，最终可以扩展和调整，以涵盖所有生态系统。我们将通过建立全球各种气候条件下600个优质生态网络的数据库，并在半自然环境中进行受控实验，确定具有抗GEC能力的子网络结构。了解这些网络中的子结构模式是如何改变的，将为分类生态系统对GEC的响应类型和大小提供一种新的途径。此外，我们通常仅限于关于气候扰动之前和之后生态系统状态的信息，对中间阶段知之甚少。由于不同阶段资源物种的可获得性，生物多样性丧失的顺序可能会影响幸存物种调整其饮食的方式。因此，我们将使用计算模型来模拟生物多样性丧失的现实顺序，并通过我们的操纵实验进行验证和改进，帮助我们确定支撑生态系统对GEC的反应的规则和机制。这些发现将为保护我们的自然资源提供重要信息，并使科学家能够更准确地预测未来生态系统的健康状况。

Global Environmental Change (GEC) is having profound effects on our natural environment, with declining biodiversity as a result of warming, acidification, and extreme climatic events such as heatwaves and droughts. Every species is part of a network of interactions that is integral to how ecosystems function and the loss of even one species can alter the population size of its consumers and resources, causing effects to ripple through the entire food web. The surviving species may also adapt by switching or expanding their diet in order to survive, resulting in complex reassembly of interactions. Thus, a deeper understanding of ecological network patterns will greatly enhance our ability to gauge how ecosystems will respond to GEC and help guide conservation efforts.Existing ecological assessment of GEC primarily focuses on the population-level response of a few key species, or coarse network-level metrics such as connectance, but little is known about network organisation at finer scales. For example, sub-network structures can include a cohesive 'core' of closely interacting nodes and a loosely connected 'periphery'. These sub-structures have been observed in many different types of artificial network (e.g. telecommunications and social networks), and their significance for governing dynamics and stability is widely acknowledged. In ecology, sub-network structures have revealed important fine scale changes in food webs exposed to drought, but this line of research is largely unexplored. Hence, there is an urgent need for scientific advances to facilitate the coupling between network theory and ecosystem ecology, allowing us to better understand how our natural systems withstand and adapt to the effects of external stressors.The overall aim of this project is to gauge and forecast the impact of GEC on ecological networks, and develop a more integrated modelling approach that can ultimately be expanded and adapted to cover all ecosystems. We will identify sub-network structures that provide resilience against GEC by profiling a database of 600 high-quality ecological networks from a wide range of climatic conditions across the globe, and conducting controlled experiments in semi-natural environments. Understanding how the sub-structural patterns have been altered in these networks will provide a new avenue for categorising the type and magnitude of ecosystem responses to GEC.In addition, we are typically limited to information about the state of an ecosystem before and after a climatic disturbance, with little known about the intermediate stages. The order of biodiversity loss can affect the way in which the surviving species adjust their diet, due to the availability of resource species at different stages. Thus, we will use computational models to simulate realistic orders of biodiversity loss, validated and refined using our manipulative experiments, helping us to identify the rules and mechanisms that underpin ecosystem responses to GEC. These findings will provide vital information for protecting our natural resources and enable scientists to predict the future health of ecosystems more accurately.