Wildlife movement forecasting improved with global gps and environment data
MoveBench: A Benchmark for Global-Scale Wildlife Movement Forecasting
Machine Learning
Summary
Predicting where animals move is important for protecting nature, but it is tricky because their paths depend on many changing environmental factors. The authors created MoveBench, a huge collection of GPS data from many animals worldwide, along with detailed environmental information. They also tested different prediction methods to see which work best for forecasting animal movements. Their findings show that current methods handle future time predictions better than predicting new animals, simple approaches can be as good as complex ones, and the choice of environmental data greatly affects accuracy.
What this means in practice
- •For conservation teams: Use large-scale animal movement and environmental datasets to improve habitat management and species protection plans.
- •For environmental monitoring groups: Implement standardized benchmarks to evaluate and select accurate wildlife movement prediction tools for ecosystem assessments.
Authors
Justin Kay, Shir Bar, Ellen O. Aikens, Martin Becker, Francesca Cagnacci, Juliet Cohen, Scott W. Forrest, Jessica Kendall-Bar, Madeleine Lucas, Macon Overcast, Meredith S. Palmer, Will Rogers, Nicholas J. Russo, Christian Rutz, Larissa T. Beumer, Michael Brown, Ying-Chi Chan, Sarah C. Davidson, Diego Ellis Soto, Anne G. Hertel, Roland Kays, Benjamin Koger, Guram Mikaberidze, Thomas Mueller, Ruth Oliver, Thorsten Papenbrock, Robert Patchett, Jared A. Stabach, Dane Taylor, Scott W. Yanco, Sara Beery
Abstract
Understanding and predicting wildlife movement is critical for ecology and conservation. While trajectory forecasting has advanced for human and vehicle movement, wildlife trajectories present distinct challenges: they are unconstrained in space, highly stochastic, and influenced by environmental conditions. We introduce MoveBench, the first large-scale benchmark for probabilistic wildlife movement forecasting, containing 2.6M GPS locations from 800+ individuals across 110 species in 127 countries, paired with 1.6B environmental raster tiles capturing 160 covariates known or hypothesized to influence movement. We propose a probabilistic evaluation protocol for movement trajectory forecasts, addressing limitations of point-prediction metrics for inherently stochastic phenomena. Through comprehensive empirical evaluation of four method families across multiple temporal and spatial scales, we reveal that: (1) existing predictive methods generalize better to future timepoints than to unseen individuals, (2) deep learning approaches do not consistently outperform simpler baselines, and (3) environmental covariate selection significantly impacts performance. MoveBench enables standardized evaluation of movement forecasting methods and provides a foundation for methodological advances on this ecologically important task.