Mechanistic niche predictive modelling of plant invasion at the range front

山脉前沿植物入侵的机械生态位预测模型

• 批准号: EP/Y028473/1
• 负责人: Erola Fenollosa
• 金额: $ 25.55万
• 依托单位: University of Oxford
• 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=EP%2FY028473%2F1
• 关键词: Mechanistic; niche; predictive; modelling; plant

Invasive species constitute one of the main threats to global biodiversity. Controlling or eradicating invasive species is strongly costly and, in some cases, impossible. As such, impact mitigation and expansion prevention are the most effective strategy against them. Mitigation strategies against invasive species require knowledge of their potential for expansion, especially under changing environments. However, the spatiotemporal complexity of biological invasions hinders invasion forecasts. Species distribution models (SDM) are currently the main tools to derive spatially explicit predictions of environmental suitability for species combining observations of species occurrences with environmental estimates. Yet, correlative species distribution models have strong limitations, especially regarding predicting dynamic distribution processes (such as those found in invasive species) and under changing environmental conditions. FrontStress proposes a new framework for the modelling of invasive plant species distribution and expansion potential by tackling the main limitations of SDMs. Firstly, I will build correlative SDMs for the 31 most aggressive invasive plants considering central and peripheric individuals to explore expansion potential. Secondly, from three selected European invasive plant species I will collect data of intraspecific variability in functional traits, vital rates and fundamental niche from eight populations covering their European distribution. Finally, I will use the parametrized mechanistic models to simulate their distribution under future climates. This will allow me to describe range-edge dynamics considering the species stress tolerance but also to understand basic aspects of species adaptation and expansion. FrontStress will build for the first-time mechanistic niche models that overcome correlative SDMs main limitations, as they consider process-explicit dynamics in relation to the environmental conditions and its geographic variability.

入侵物种是全球生物多样性的主要威胁之一。控制或根除入侵物种成本高昂，在某些情况下甚至是不可能的。因此，减轻影响和防止扩大是应对这些问题的最有效策略。针对入侵物种的缓解策略需要了解其扩张潜力，特别是在不断变化的环境下。然而，生物入侵的时空复杂性阻碍了入侵预测。物种分布模型（SDM）是目前将物种发生观测与环境估计相结合得出物种环境适宜性空间明确预测的主要工具。然而，相关物种分布模型具有很大的局限性，特别是在预测动态分布过程（例如入侵物种中发现的分布过程）和不断变化的环境条件下。 FrontStress 通过解决 SDM 的主要局限性，提出了一个新框架，用于对入侵植物物种分布和扩张潜力进行建模。首先，我将为 31 种最具攻击性的入侵植物建立相关的 SDM，考虑中心和外围个体，以探索扩张潜力。其次，我将从三个选定的欧洲入侵植物物种中收集覆盖其欧洲分布的八个种群的功能性状、生命率和基本生态位的种内变异数据。最后，我将使用参数化机械模型来模拟它们在未来气候下的分布。这将使我能够在考虑物种胁迫耐受性的情况下描述范围边缘动态，同时也了解物种适应和扩张的基本方面。 FrontStress 将首次构建克服相关 SDM 主要局限性的机械利基模型，因为它们考虑了与环境条件及其地理变化相关的过程显式动态。