943 disturbances in the forest of southern Switzerland have been visited and characterized with various general and specific parameters and the presence absence of woody neophyte species has also been recorded. A Generalized linear regression modelling approach with a binomial family (link function “logit”) was then used to analyse the effects of these parameters on the presence/absence of the six most widespread neophyte species separately (i.e Ailanthus altissima, Buddleja davidii, R. pseudoacacia, Paulownia tomentosa, Prunus laurocerasus, Trachycarpus fortunei). If needed, the models were refitted with the spmodel R-package to account for the spatial dependence. The best model for every species have been used to predict the risk of invasion on a 25 X 25m grid of 1’773’603 million of points covering the entire forest area under 1’500 m a.s.l. Predictions over this new set of points have been computed with the predict function (v4.2.1; R core Team, 2023) and using the best select model for every neophyte species. The resulting prediction are available as a raster tiff. These presence probability risk maps for the forest area of the entire canton Ticino provide a practical tool to be used in combination with the waldmonitoring.ch data allowing to efficiently monitor the spread of woody neophyte species in new disturbances in the forest.