Upscaling Invasion Impact Prediction

扩大入侵影响预测

• 批准号: MR/X035662/1
• 负责人: Josie South
• 金额: $ 166.43万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Fellowship
• 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=MR%2FX035662%2F1
• 关键词: Upscaling; Invasion; Impact; Prediction

This fellowship proposal plans to increase our capacity to predict the ways and magnitude in which non-native invasive species will cause damaging ecological impact and under what environmental scenarios. This will drastically enhance the efficiency of conservation interventions and proactive policy making to benefit biodiversity and human livelihoods. Biodiversity loss is occurring rapidly in all ecosystems across the globe. Freshwater systems have the highest extinction rates in any system and are losing populations at an alarming rate. This is due to the interacting effects of climate change, non-native invasive species, and habitat loss. Around 10% of established non-native species populations are thought to cause negative impact, however, this changes under different climatic scenarios as environmental change can trigger benign populations to become damaging. Due to the speed of change it is not practical to wait until a species is present and causing damage in the environment. Pre-emptive action will vastly benefit global biodiversity goals and economies which lose US$162.7 billion annually due to biological invasions. These costs are incurred through damage to infrastructure, flooding, loss of ecosystem services, damage to fisheries, as well as management costs undertaken to control species of concern and mitigate damage. To act pre-emptively to reduce these costs, we need accurate methods to predict which species will cause what negative impact, and under which environmental scenarios. Current methods are insufficient as they do not account for the complexity of natural environments nor the rate of global change. We need to use interdisciplinary approaches to unravel and predict the interacting twin threats of non-native invasive species and climate change at a global scale. Contemporary methods have not yet resolved this level of complexity which makes cohesive management action impossible. Food web interactions determine the structure and composition of biological communities. If interactions in the food web change, this can drastically alter the functioning of the ecosystem and have knock on effects for all levels of biodiversity, as well as human livelihoods. Food web interactions can be measured in a standardised manner across all species. I will use interaction strength as a currency to track change across communities over space and time. Foraging efficiency varies depending on physiology and morphology of both consumer and resource, and the outcomes are governed by of responses of both to environmental characteristics. Multi-disciplinary methods will be paired and tested in the laboratory and then upscaled to field campaigns under natural conditions. This will generate ecologically relevant data which will be used to develop a hypothesis testing model which will be used to predict the conditions in which an invasive species will cause negative ecological impact on native biodiversity regardless of invasion location or time since invasion. This will allow us to find general rules which control invasion dynamics and ecological impact regardless of climate and time, to be able to predict ecological outcome in advance and "future proof" legislation.I am an aquatic ecologist who has developed and pioneered multi-disciplinary impact prediction methodology. The current project builds on my previous innovations and leverages my international partnerships. My vision is that the capacity of my partnerships are built up to function as an International Freshwater Invasions Network to tackle future pressing challenges which can only be solved through global cooperation. This fellowship will provide the foundation for the formation of this network and contribute to guiding more effective legislation and targeted invasive species management approaches which account for global climate change. In doing so, the outcomes of this research will directly combat rapid biodiversity loss in freshwater ecosystems.

这项奖学金计划旨在提高我们预测非本土入侵物种将造成破坏性生态影响的方式和程度以及在何种环境情景下的能力。这将大大提高保护措施的效率和积极主动的政策制定，以造福生物多样性和人类生计。生物多样性正在地球仪的所有生态系统中迅速丧失。淡水系统在所有系统中灭绝率最高，并且正在以惊人的速度失去人口。这是由于气候变化、非本地入侵物种和栖息地丧失的相互作用。大约10%的已建立的非本地物种种群被认为会造成负面影响，然而，这种情况在不同的气候情景下会发生变化，因为环境变化会引发良性种群变得具有破坏性。由于变化的速度，等待一个物种出现并对环境造成破坏是不切实际的。先发制人的行动将极大地有利于全球生物多样性目标和经济，因为生物入侵每年损失1627亿美元。这些费用是通过基础设施的破坏、洪水、生态系统服务的损失、渔业的破坏以及为控制受关注物种和减轻损害而产生的管理费用而产生的。为了先发制人地降低这些成本，我们需要准确的方法来预测哪些物种会造成什么样的负面影响，以及在哪些环境情景下。目前的方法是不够的，因为它们没有考虑到自然环境的复杂性，也没有考虑到全球变化的速度。我们需要使用跨学科的方法来揭示和预测全球范围内非本地入侵物种和气候变化的相互作用的双重威胁。现代方法还没有解决这种复杂程度，这使得一致的管理行动是不可能的。食物网的相互作用决定了生物群落的结构和组成。如果食物网中的相互作用发生变化，这可能会极大地改变生态系统的功能，并对各级生物多样性以及人类生计产生连锁反应。食物网相互作用可以在所有物种中以标准化的方式进行测量。我将使用交互强度作为一种货币来跟踪社区在空间和时间上的变化。觅食效率的变化取决于生理和形态的消费者和资源，和结果是由两者对环境特征的反应。多学科方法将在实验室进行配对和测试，然后扩大到自然条件下的实地活动。这将产生与生态相关的数据，这些数据将用于开发一个假设检验模型，该模型将用于预测入侵物种对本地生物多样性造成负面生态影响的条件，而无论入侵地点或入侵时间如何。这将使我们能够找到控制入侵动力学和生态影响的一般规则，而不受气候和时间的影响，能够提前预测生态结果和“未来证明”立法。我是一名水生生态学家，开发并开创了多学科影响预测方法。目前的项目建立在我以前的创新基础上，并利用了我的国际合作伙伴关系。我的愿景是，我的伙伴关系的能力得到建设，作为一个国际淡水入侵网络，以应对未来只能通过全球合作解决的紧迫挑战。该研究金将为该网络的形成奠定基础，并有助于指导更有效的立法和有针对性的入侵物种管理方法，这是全球气候变化的原因。在这样做的过程中，这项研究的成果将直接应对淡水生态系统中生物多样性的迅速丧失。