The authors train sequence models to identify animal species from GPS movement trajectories alone, using data from seven species tracked on Movebank. They test how well models generalize when entire studies or geographic regions are held out (a harder test than random splitting). Transformers beat LSTM, CNN, and temporal convolutional baselines by 8–22 percentage points in balanced accuracy, and adding richer movement features (speed, direction, turning angle) beyond simple displacement helps especially for rare species like lions and zebras.
Main takeaways:
- Transformers consistently outperform recurrent and convolutional architectures on wildlife trajectory classification, achieving 0.83 balanced accuracy and 0.92 AUC on elephant detection
- Testing with whole studies or regions held out is much harder than random splits, but better reflects real deployment scenarios
- Enriching trajectories with speed, direction, and turning features (not just position changes) significantly improves performance, especially for underrepresented species
- One-hour resolution works better than 30-minute resolution across studies, likely because it reduces missing data and ensures consistent temporal coverage
- The approach shows promise for species identification from movement patterns alone, useful when visual identification is impractical