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 when key phenological stages of invasive species are reached. This includes knowing, for example, when 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 phenological stages of relevance for the detection 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 using direct predictors of phenological drivers and trained on thousands of observation records outperforming those relying on indirect predictors and only a few hundred training records. The analysis of daily predictions also allowed mapping European-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. By enhancing our current ability to anticipate the phenological stages of invasive species, this approach has the potential to significantly improve decision-making in invasion surveillance and monitoring activities.