Modeling the past or future spread patterns of invasive plant species is challenging and in an ideal case requires multi-temporal and spatially explicit data on the occurrences of the target species as well as information on the habitat suitability of the areas at risk of being invaded. Most studies either focus on modeling the habitat suitability of a given area for an invasive species or try to model the spreading behavior of an invasive species based on temporally or spatially limited occurrence data and some environmental variables. Here we suggest a workflow that combines habitat suitability maps, occurrence data from multiple time steps collected from remote sensing data, and cellular automata models to first reconstruct the spreading patterns of the invasive shrub Ulex europaeus on the island Chiloé in Chile and then make predictions for the future spread of the species. First, U. europaeus occurrences are derived for four time steps between 1988 and 2020 using remote sensing data and a supervised classification. The resulting occurrence data is combined with occurrence data of the native range of U. europaeus from the GBIF database and selected environmental variables to derive habitat suitability maps using Maxent. Then, cellular automata models are calibrated using the occurrence estimates of the four time steps, the suitability map, and some additional geo-layer containing information about soils and human infrastructure. Finally, a set of calibrated cellular automata models are used to predict the potential spread of U. europaeus for the years 2070 and 2100 using climate scenarios. All individual steps of the workflow where reference data was available led to sufficient results (supervised classifications Overall Accuracy > 0.97; Maxent AUC > 0.85; cellular automata Balanced Accuracy > 0.91) and the spatial patterns of the derived maps matched the experiences collected during the field surveys. Our model predictions suggest a continuous expansion of the maximal potential range of U. europaeus, particularly in the Eastern and Northern part of Chiloé Island. We deem the suggested workflow to be a good solution to combine the static habitat suitability information—representing the environmental constraints—with a temporally and spatially dynamic model representing the actual spreading behavior of the invasive species. The obtained understanding of spreading patterns and the information on areas identified to have a high invasion probability in the future can support land managers to plan prevention and mitigation measures.
