Modelling of habitat suitability and population dynamics of an invasive plant with advanced remote sensing data

Plant invasions contribute significantly to global changes by affecting biodiversity and ecosystem processes. The development of realistic modelling tools incorporating colonization, establishment, and invasion processes are urgently needed to predict future spread of invasive species and to propose efficient management strategies. Species distribution modelling (SDM) is an important approach to predict the current and potential distributions of invasive plant species. However, to ensure robust predictions of species distribution it is important to develop a multi-scale SDM given that the determinants of species distributions are hierarchically structured: climatic variables are among the most important determinants across large spatial extents of coarse resolutions, followed by land use, landscape structure, topographic complexities and biotic conditions across smaller spatial extents of finer resolutions. Beside, SDM is mostly carried out to characterise the suitability of a given environment for a species without taking into account the demographic dynamics of invasive species after their establishment. Addressing all these issues requires a better integration of SDMs with ecological theory. In this study, we propose the following hybrid model combining (i) a hierarchical SDM to model species-climate and species-habitat relationships and (ii) a demographic model to explore the local population dynamics of an invasive species. The invasion of a European temperate forest (i.e. the forest of Compiègne, France) by the American black cherry (Prunus serotina) has been chosen as a case study to assess the ability of this modelling framework to reconstruct the invasion dynamic of this long-lived species with a complex life cycle. The establishment of such an approach has always been limited by the availability of fine-resolution data appropriate for most species response. In this study we used high-resolution light detection and ranging (LiDAR) data to derive biotic, topographic and energy predictors at landscape scale. These data were combined with global and regional scale climatic predictors and species-specific data on demographic parameters. Here, we present the various components of the hybrid model, input data, and first results of our study. We suggest new avenues for incorporating population dynamics, dispersion and environmental filtering into SDMs at multiple spatial scales.