MoveBench: A Benchmark for Global-Scale Wildlife Movement Forecasting
cs.LG
Submitted: 2026-09-14
Updated: 2026-09-14
Comments: Pre-release preprint
License: http://creativecommons.org/licenses/by/4.0/
The gist: Understanding and predicting wildlife movement is critical for ecology and conservation.
Terminology
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.
Sources
- AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data
- Analyzing animal movement using deep learning
- Towards Predicting Any Human Trajectory In Context
- LIMP: Large Language Model Enhanced Intent-aware Mobility Prediction
- PLMTrajRec: A Scalable and Generalizable Trajectory Recovery Method with Pre-trained Language Models
- UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces
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