J. R. Potts

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.

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.

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.

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.

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

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.