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）对我们的自然环境产生了深远的影响，由于温暖，酸化和极端气候事件（如热浪和干旱），生物多样性下降。每个物种都是相互作用网络的一部分，它是生态系统功能和甚至一个物种的损失的重要组成部分，它可以改变其消费者和资源的人口规模，从而导致效果在整个食物网中荡漾。幸存的物种还可以通过切换或扩大饮食来适应以生存，从而使相互作用的复杂复杂。因此，对生态网络模式的更深入的理解将极大地增强我们评估生态系统将如何响应GEC并帮助指导保护工作的能力。对GEC的生态评估的存在主要集中在几个关键物种或粗线级别的人群级别的响应上，或者诸如连接之类的粗糙网络级别的响应，但对网络组织的众所周知，对网络的组织却一无所知。例如，子网络结构可以包括紧密相互作用节点的凝聚力“核心”和一个松散连接的“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）中观察到，并且广泛认识到它们在管理动态和稳定性方面的意义。在生态学中，子网络结构揭示了暴露于干旱的食物网的重要规模变化，但是这一研究基本上没有探索。 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.我们将通过分析来自全球各种气候条件的600个高质量生态网络的数据库，并在半天然环境中进行受控的实验，从而确定对GEC提供弹性的子网络结构。了解这些网络中如何改变了子结构模式将为对GEC的生态系统响应的类型和大小提供新的途径。此外，我们通常仅限于气候干扰之前和之后有关生态系统状态的信息，对中间阶段却一无所知。由于资源物种在不同阶段的可用性，生物多样性损失的顺序会影响存活物种调整饮食的方式。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，并使用我们的操纵实验对生物多样性损失进行了验证和精致，从而帮助我们确定了基于GEC生态系统响应的规则和机制。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生态系统的未来健康。