Supporting early detection of biological invasions through short-term spatial forecasts of detectability

Abstract Early detection of invasive species is crucial to prevent biological invasions. To increase the success of detection efforts, it is often essential to know the phenological stages in which the invasive species are found. This includes knowing, for example, if invasive insect species are in their adult phase, invasive plants are flowering, or invasive mammals have finished their hibernation. Unfortunately, this kind of information is often unavailable or is provided at very coarse temporal and spatial resolutions. On the other hand, opportunistic records of the location and timing of observations of these stages are increasingly available from biodiversity data repositories. Here, we demonstrate how to apply these data for predicting the timing of phenological stages of invasive species. The predictions are made across Europe, at a daily temporal resolution, including in near real time and for multiple days ahead. We apply this to detectability-relevant phenological stages of four well-known invasive species: the freshwater jellyfish, the geranium bronze butterfly, the floating primrose-willow, and the garden lupine. Our approach uses machine learning and statistical-based algorithms to identify the set of temporal environmental conditions (e.g., temperature values and trends, precipitation, snow depth, and wind speed) associated with the observation of each phenological stage, while accounting for spatial and temporal biases in recording effort. Correlation between predictions from models and the actual timing of observations often exceeded values of 0.9. However, some inter-taxa variation occurred, with models trained on several thousands of observation records performing consistently better than those based on a few hundred records. The analysis of daily predictions also allowed mapping EU-wide regions with similar phenological dynamics (i.e., ‘phenoregions’). Our results underscore the significant potential of opportunistic biodiversity observation data in developing models capable of predicting and forecasting species phenological stages across broad spatial extents. This information has the potential to significantly improve decision-making in invasion surveillance and monitoring activities. Competing Interest Statement The authors have declared no competing interest.