Predicting the Impacts of Global Environmental Change on Ecological Networks

预测全球环境变化对生态网络的影响

• 批准号: NE/Y001184/1
• 负责人: Eoin O'Gorman
• 金额: $ 107.07万
• 依托单位: University of Essex
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FY001184%2F1
• 关键词: Predicting; Impacts; Global; Environmental; Change

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.

全球环境变化（GEC）正在对我们的自然环境产生深远的影响，由于变暖，酸化和极端气候事件（如热浪和干旱），生物多样性正在下降。每一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如何运作的一个组成部分，即使是一个物种的消失也会改变其消费者和资源的人口规模，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，从而导致复杂的相互作用重组。因此，更深入地了解生态网络模式将大大提高我们的能力，以衡量生态系统将如何响应GEC，并帮助指导保护工作。现有的GEC生态评估主要集中在人口水平的几个关键物种的反应，或粗糙的网络水平的指标，如连接，但鲜为人知的是网络组织在更精细的尺度。例如，子网络结构可以包括紧密交互的节点的内聚“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（例如电信和社交网络）中被观察到，并且它们对管理动态和稳定性的重要性被广泛承认。在生态学中，子网络结构揭示了暴露于干旱的食物网中重要的精细尺度变化，但这一研究基本上未被探索。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。本项目的总体目标是测量和预测GEC对生态网络的影响，并制定一个更加综合的建模方法，最终可以扩大和调整，以涵盖所有生态系统。我们将通过分析来自地球仪广泛气候条件的600个高质量生态网络的数据库，并在半自然环境中进行受控实验，来确定提供对GEC恢复力的子网络结构。了解这些网络中的子结构模式是如何改变的，将为分类GEC.Also的生态系统响应的类型和幅度提供一个新的途径，我们通常仅限于气候扰动之前和之后的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幸存物种调整饮食的方式，因为资源物种在不同阶段的可用性。因此，我们将使用计算模型来模拟生物多样性丧失的现实秩序，使用我们的操纵实验进行验证和完善，帮助我们确定支持生态系统对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生态系统的未来健康状况。