J. R. Potts

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.

How to scale up from animal movement decisions to spatio-temporal patterns: an approach via step selection

This paper provides guidance on scaling step selection models to predict broader spatio-temporal animal space use patterns, with code examples in R and Python.

Beyond resource selection: emergent spatio-temporal distributions from animal movements and stigmergent interactions

The paper develops a stochastic individual-based model to study how animal movements and stigmergent interactions shape spatio-temporal distributions, showing how these interactions can lead to emergent patterns distinct from environmental factors alone.

Why did the animal turn? Time-varying step selection analysis for inference between observed turning points in high frequency data

This paper introduces tiSSA, a time-varying step selection analysis method for high-frequency animal movement data, showing it provides more accurate insights than traditional methods.

Solving the Sample Size Problem for Resource Selection Analysis

This paper provides mathematical expressions to determine optimal sample sizes for resource selection analyses, showing that fewer data may be sufficient for strong selection effects.

Periscope Proteins are variable length regulators of bacterial cell surface interactions

The paper identifies Periscope Proteins as variable-length bacterial surface regulators that influence host interactions through domain number variation.

Parametrising diffusion-taxis equations from animal movement trajectories using step selection analysis

This paper uses step selection analysis to parametrise diffusion-taxis equations from animal movement data, showing accurate results and linking fine-scale SSA with broad-scale RSA.