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，由于气候变暖、酸化以及热浪和干旱等极端气候事件，生物多样性正在下降。每个物种都是一个相互作用网络的一部分，这个网络是生态系统功能的组成部分，即使一个物种的消失也会改变其消费者和资源的种群规模，从而导致整个食物网的连锁反应。幸存的物种也可能通过改变或扩大它们的饮食来适应生存，导致相互作用的复杂重组。因此，对生态网络模式的深入了解将极大地提高我们衡量生态系统如何响应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响应，或粗糙的网络水平指标，如连通性，但对更精细尺度的网络组织知之甚少。例如，子网络结构可以包括紧密交互节点的内聚“核心”和松散连接的“外围”。这些子结构已经在许多不同类型的人工网络（如电信和社会网络）中被观察到，它们对控制动态和稳定性的重要性被广泛认可。在生态学中，子网络结构揭示了干旱环境下食物网的重要细微变化，但这方面的研究在很大程度上尚未得到探索。因此，迫切需要科学进步来促进网络理论与生态系统生态学之间的耦合，使我们更好地了解我们的自然系统如何承受和适应外部压力因素的影响。该项目的总体目标是衡量和预测GEC对生态网络的影响，并开发一种更综合的建模方法，这种方法最终可以扩展和适应以覆盖所有生态系统。我们将通过分析全球范围内各种气候条件下600个高质量生态网络的数据库，并在半自然环境中进行对照实验，确定提供抗GEC弹性的子网络结构。了解这些网络中的亚结构模式是如何改变的，将为生态系统对气候变化的响应类型和程度分类提供新的途径。此外，我们通常局限于气候扰动前后生态系统状态的信息，对中间阶段知之甚少。生物多样性丧失的顺序会影响生存物种调整饮食的方式，这是由于不同阶段资源物种的可用性。因此，我们将使用计算模型来模拟生物多样性损失的现实顺序，并使用我们的操作实验进行验证和改进，帮助我们确定支撑生态系统对GEC响应的规则和机制。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.