Data from: An intuitive method to calculate the utilisation distribution of an animal from step-selection analysis

Step-selection analysis (SSA) is a popular tool used by ecologists to
define a movement model based on an animal's locomotive capabilities
and selection for spatiotemporally explicit variables. The magnitudes and
directions of the estimated model parameters provide inference regarding
an individual's or species' resource selection, and the
resulting movement model can be simulated to infer space use patterns.
Despite the popularity of SSA, many studies focus exclusively on
interpreting parameter values without estimating the resulting spatial
patterns. We advocate here for the full predictive functionality of
step-selection analysis and provide methods to use a fitted step-selection
function to estimate an animal's space use. Specifically, we use
fundamental mathematical principles to construct a transition matrix from
the step selection movement process. We demonstrate that transition
matrices can efficiently produce the emergent spatial pattern from SSA
using two different methods - eigen decomposition and iterations -
alleviating the need for thousands of simulations of the underlying model.
We show how such matrices may be parameterized under competing definitions
of the underlying movement kernel and link these back to realistic
ecological processes. To emphasize the flexibility of this approach, we
apply these transition matrices to field data from two species with
markedly different movement capacities and strategies: wild pigs (Sus
scrofa) and long-tailed tits (Aegithalos caudatus). We discuss the caveats
in transition matrix parameterization and resulting predictions of space
use with respect to the underlying movement process and provide
suggestions for the application of this procedure to SSA. Our framework
represents a robust tool for space-use prediction from SSA while also
highlighting critical gaps in our current knowledge about, and tools to
describe, animal space-use processes. In particular, we discuss
higher-order Markovian processes and spatiotemporally dynamic variables as
particularly important topics for further development.