Invasive plants represent a significant global threat to natural ecosystems and biodiversity. The aim of this study was to classify alien woody plants according to their invasion status based on their phenological characteristics using machine learning. Data from phenological observations of 63 tree species, including both native and alien (introduced and invasive) species, were used for the analysis. The dataset contains information on long-term (1977–2024) observations of the timing of 18 phenological phases and the duration of 6 interphase intervals. The F1-score values for identifying the ‘introduced plant’ class in the ‘Native–Introduced’ pair were 81.3%. The values of this same indicator for identifying the ‘invasive plants’ class in the ‘Native–Invasive’ and ‘Introduced–Invasive’ pairs were 85.0% and 71.2% respectively. It has been shown that the invasive status of alien tree species can be predicted based on their phenological characteristics using machine learning algorithms. It has also been shown that information on six phenological phases may be sufficient for such predictions.
Kozlovsky et al. (Sun,) studied this question.