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.

这项奖学金建议计划提高我们预测非母体入侵物种会导致生态影响以及在哪些环境情景下的方式和规模的能力。这将大大提高保护干预措施和主动政策的效率，以使生物多样性和人类生计受益。在全球所有生态系统中，生物多样性丧失迅速发生。在任何系统中，淡水系统的灭绝率最高，并且以惊人的速度失去了种群。这是由于气候变化，非母体入侵物种和栖息地丧失的相互作用。大约有10％的既定非本地物种种群被认为会引起负面影响，但是，由于环境变化会触发良性种群的破坏，这种变化在不同的气候情况下。由于变化的速度，等到物种存在并在环境中造成损害是不切实际的。先发制人的行动将大大受益于全球生物多样性目标和经济，这些目标和经济因素每年由于生物学入侵而损失1627亿美元。这些成本是通过损害基础设施，洪水，生态系统服务的损失，对渔业损害的损失以及为控制关注和减轻损害的物种所承担的管理成本而产生的。为了降低这些成本，我们需要采取先发制人的行动，我们需要准确的方法来预测哪些物种会造成什么负面影响，以及在哪些环境方面。当前方法不足以说明自然环境的复杂性或全球变化速度。我们需要使用跨学科的方法来阐明并预测非本地入侵物种的相互作用的双胞胎威胁和在全球范围内的气候变化。当代方法尚未解决这种复杂程度，这使得凝聚力的管理行动不可能。食品网络互动决定了生物社区的结构和组成。如果食物网络中的相互作用变化，这会极大地改变生态系统的功能，并对所有级别的生物多样性以及人类生计产生震撼。可以在所有物种中以标准化的方式测量食物网络相互作用。我将使用互动强度作为一种货币来跟踪各个时空的变化。觅食效率取决于消费者和资源的生理和形态，结果受环境特征的反应的控制。多学科方法将在实验室配对和测试，然后在自然条件下进行扫描到现场运动。这将产生与生态相关的数据，该数据将用于开发假设测试模型，该模型将用于预测入侵物种将对天然生物多样性产生负面影响的条件，而不论入侵位置或入侵以来的时间如何。这将使我们能够找到控制入侵动态和生态影响的一般规则，无论气候和时间如何，能够预先预测生态结果以及“未来证明”立法。当前的项目以我以前的创新为基础，并利用了我的国际合作伙伴关系。我的愿景是，我的合作伙伴关系的能力是建立在国际淡水入侵网络中的工作，以应对未来的紧迫挑战，这只能通过全球合作来解决。这项奖学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，并针对造成全球气候变化的入侵物种管理方法。这样一来，这项研究的结果将直接与淡水生态系统中的快速生物多样性丧失作斗争。