![]() The method is tested with an ~8 × 8 km 2 grid containing floral alien species presence and several potentially exploratory indices of climatic, habitat, land use, and soil property covariates for the Mediterranean island of Crete, Greece. RF results are processed to estimate variable importance and model performance. A bootstrapping scheme is designed to account for sub-setting uncertainty, and subsets are used to train a sufficiently large number of RF models. This paper presents a method that relies on Random Forest (RF) models to distribute alien species presence counts at a finer resolution grid, thus achieving spatial downscaling. However, these data are often unavailable. Spatially explicit assessments of alien species environmental and socio-economic impacts, and subsequent management interventions for their mitigation, require large scale, high-resolution data on species presence distribution. ![]() 4School of Biological and Chemical Sciences, Queen Mary University of London, London, United Kingdom.3Department of Marine Sciences, University of the Aegean, Mytilene, Greece.2School of Environmental Engineering, Technical University of Crete, Crete, Greece.1TM Solutions, Specialized Health and Environmental Services, Crete, Greece. ![]() Daliakopoulos 1,2 *, Stelios Katsanevakis 3 and Aristides Moustakas 4
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