Michael Bode

This paper introduces deepSSF, a deep learning framework for predicting animal movement by learning from environmental and temporal data, showing it can capture complex habitat interactions and temporal dynamics.

The paper models optimal crown-of-thorns starfish control strategies on the Great Barrier Reef to maximize coral cover protection.

This study shows that incorporating fine-scale temporal dynamics improves both hourly and seasonal spatial predictions of water buffalo movement and habitat use.