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影响，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。每个物种都是相互作用网络的一部分，而这个网络对于生态系统的运作至关重要，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，从而对整个食物网产生连锁反应。幸存的物种也可能通过改变或扩大饮食来生存，从而导致复杂的相互作用重组。因此，对生态网络模式的更深入了解将极大地增强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生态评估主要集中于少数关键物种的种群水平响应，或粗网络级指标，例如连接性，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的节点的内聚“核心”和松散连接的“外围”。这些子结构已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，它们对于控制动态和稳定性的重要性得到了广泛认可。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源的影响。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，最终可以扩展和适应以覆盖所有生态系统。我们将通过对全球各种气候条件下的 600 个高质量生态网络的数据库进行分析，并在半自然环境中进行对照实验，来确定能够抵御 GEC 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，将为对 GEC 生态系统响应的类型和程度进行分类提供新的途径。此外，我们通常只能了解有关气候变化前后生态系统状态的信息。扰，对中间阶段知之甚少。由于资源物种在不同阶段的可用性，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，并使用我们的操作性实验进行验证和完善，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生态系统未来的健康状况。