Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions

Habitat selection models frequently use data collected from a small
geographic area over a short window of time to extrapolate patterns of
relative abundance to unobserved areas or periods of time. However, these
types of models often poorly predict how animals will use habitat beyond
the place and time of data collection because space-use behaviors vary
between individuals and are context-dependent. Here, we present a
modelling workflow to advance predictive distribution performance by
explicitly accounting for individual variability in habitat selection
behavior and dependence on environmental context. Using global positioning
system (GPS) data collected from 238 individual pronghorn, (Antilocapra
americana), across 3 years in Utah, we combine
individual-year-season-specific exponential habitat-selection models with
weighted mixed-effects regressions to both draw inference about the
drivers of habitat selection and predict space-use in areas/times
where/when pronghorn were not monitored. We found a tremendous amount of
variation in both the magnitude and direction of habitat selection
behavior across seasons, but also across individuals, geographic regions,
and years. We were able to attribute portions of this variation to season,
movement strategy, sex, and regional variability in resources, conditions,
and risks. We were also able to partition residual variation into inter-
and intra-individual components. We then used the results to predict
population-level, spatially and temporally dynamic, habitat-selection
coefficients across Utah, resulting in a temporally dynamic map of
pronghorn distribution at a 30x30m resolution but an extent of 220,000km2.
We believe our transferable workflow can provide managers and researchers
alike a way to turn limitations of traditional habitat selection models -
variability in habitat selection - into a tool to understand and predict
species-habitat associations across space and time.