Mechanistic niche predictive modelling of plant invasion at the range front

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.