Ecologists seek to understand how animal distributions arise from their small-scale movements. Data are collected by attaching tags to animals to record their locations (e.g., GPS collars), and statistical approaches fall into two categories: small-scale models of animals’ movement decisions, and large-scale models of spatial distributions. The movement decisions give rise, in the long term, to large-scale distributions, but this mechanism is usually ignored, and it has been difficult to describe distributions as emerging properties of habitat-driven movements. I will show that irreversible processes with explicit stationary distributions are promising multiscale models for the movement of animals. This approach can be used to understand how animals’ distributions are affected by the environment (e.g., habitat fragmentation), and makes it possible to combine data sources that have previously been analysed separately.