STEPPY: Multi-scale, multi-species wildlife movement forecasting

Reliable forecasts of wildlife movement would enable numerous advances in ecology and conservation, but forecasting wildlife movement is a challenging predictive task, requiring models to represent complex, heterogeneous, and multi-scale movement across species and individual animals. Recent empirical studies indicate that even state-of-the-art wildlife movement forecasting methods only modestly outperform simple statistical approaches, even when leveraging complex environmental predictors. Further, existing methods are typically specialized for individual species and for specific predefined forecasting horizons, though research in ecology indicates that movement patterns may be shared across taxa and across different scales of movement. We propose a new movement forecasting model, STEPPY, that produces movement forecasts for any species and any time horizon dynamically at prediction time, leveraging ecologically-inspired architectural designs that enable it to learn useful and shared patterns across species and spatiotemporal scales. STEPPY outperforms existing state-of-the-art forecasting methods significantly, improving probabilistic forecasting by 18% on future trajectories from individuals who appear in the training data and 20% on trajectories for held-out individuals, even without incorporating any explicit environmental predictors. We will make our code, models, and data public upon publication.