Learning to predict spatiotemporal movement dynamics from weather radar networks

Weather radar networks provide wide-ranging opportunities for ecologists to quantify and predict movements of airborne organisms over unprecedented geographical expanses. The typically sparse spatial distribution of radar measurements poses, however, a major challenge to spatiotemporal predictive modelling. We propose FluxRGNN, a recurrent graph neural network that is based on a generic mechanistic description of population-level movements across the Voronoi tessellation of radar sites. The resulting hybrid model capitalises on local associations between environmental conditions and animal density as well as on spatiotemporal dependencies inherent to the movement process. We applied FluxRGNN to make 72-h forecasts of nocturnal bird migration over Western Europe using simulated trajectories and measurements from the European weather radar network. For both datasets, FluxRGNN achieves higher predictive performance than baseline models based on environmental conditions alone. It effectively disentangles local take-off and landing dynamics from aerial movements and correctly predicts migration directions with an accuracy of 87%. Continental-scale forecasts of animal density and biomass fluxes have the potential to improve the impact and cost-effectiveness of wildlife management and conservation efforts. With FluxRGNN this becomes feasible for nocturnal bird migration. In the future, other migration systems could benefit from applying the proposed method to similar static sensor networks.