Scott W. Forrest

Unexpected post-release behaviours of reintroduced kakapo

The paper analyzes the spatial behavior of reintroduced kakapo in a novel environment, finding unexpected mobility, synchronized movement, and habitat selection preferences.

Predicting animal movement with deepSSF: a deep learning step selection framework

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.

Predicting fine-scale distributions and emergent spatiotemporal patterns from temporally dynamic step selection simulations

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