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.

入侵物种构成了全球生物多样性的主要威胁之一。控制或根除入侵物种的成本很高，在某些情况下是不可能的。因此，减轻影响和预防效果是针对它们的最有效策略。针对入侵物种的缓解策略需要了解其扩展潜力，尤其是在不断变化的环境下。但是，生物入侵的时空复杂性阻碍了入侵预测。物种分布模型（SDM）当前是对环境适用性的空间明确预测的主要工具，该物种与环境估计的观察结果结合了观察结果。但是，相关物种分布模型具有强大的局限性，尤其是在预测动态分布过程（例如在入侵物种中发现的过程）以及在不断变化的环境条件下。 Frontstress提出了一个新的框架，用于通过应对SDM的主要局限性来建模侵入性植物物种分布和扩展潜力。首先，我将为31种最具侵略性的侵入性植物建立相关的SDM，以考虑中心和外围个体来探索扩张潜力。其次，从三种选定的欧洲入侵植物物种中，我将收集功能性状，生命率和基本生态位的种内变异性数据，这些数据涵盖了欧洲分布。最后，我将使用参数化的机械模型在未来的气候下模拟它们的分布。这将使我可以描述考虑物种胁迫耐受性的范围边缘动态，同时也了解物种适应和扩张的基本方面。前者将为克服相关SDMS主要限制的首次机械利基模型建立，因为它们考虑了与环境条件及其地理变异性有关的过程解释动态。