Beyond the next step: A multi‐criteria generative validation framework for step selection functions

Abstract Step‐selection functions (SSFs), typically fitted using step‐selection analysis (SSA) or integrated step‐selection analysis (iSSA) are widely used to infer habitat selection and movement kernels from high‐frequency telemetry data, but most standard validation tools focus on one‐step‐ahead prediction and do not guarantee that fitted models generate realistic trajectories or emergent space‐use patterns. We propose a multi‐criteria generative validation framework for SSF‐based movement models (typically fitted via SSA/iSSA), built around four pillars that target emergent utilization distributions, mean squared displacement, path sinuosity and barrier crossing. For each pillar, we define an ecologically interpretable trajectory‐level summary (Wasserstein distance between utilization distributions, mean squared displacement, straightness index and barrier‐crossing counts) and embed it in a Monte Carlo rank‐testing scheme that propagates parameter uncertainty. Applied to six synthetic ‘stress tests’ (sedentary home range, hard barrier, corridor follower, multi‐state movement, orbiter and return‐conditioned sinuosity), the framework reveals distinct failure modes that may not be detected by conventional stepwise validation. An empirical application to a GPS‐tracked red deer illustrates partial generative realism at a 6‐h sampling interval: the fitted iSSA reproduces long‐horizon space‐use and path sinuosity but shows a detectable mismatch in displacement dynamics (mean squared displacement).