Understanding habitat selection of range-expanding populations of large carnivores: 20 years of grey wolves (Canis lupus) recolonizing Germany

Aim: The non-stationarity in habitat selection of expanding populations
poses a significant challenge for spatial forecasting. Focusing on the
grey wolf (Canis lupus) natural recolonization of Germany, we compared the
performance of different distribution modelling approaches for predicting
habitat suitability in unoccupied areas. Furthermore, we analysed whether
grey wolf showed non-stationarity in habitat selection in newly colonized
areas, which will impact the predictions for potential habitat. Location:
Germany Methods: Using telemetry data as presence points, we compared the
predictive performance of five modelling approaches based on combinations
of distribution modelling algorithms –GLMM, MaxEnt, and ensemble
modelling– and two background point selection strategies. We used a
homogeneous Poisson point process to draw background points from either
the minimum convex polygons derived from telemetry or the whole area known
to be occupied by wolves. Models were fit to the data of the first years
and validated against independent data representing the expansion of the
species. The best-performing approach was then used to further investigate
non-stationarity in the species’ response in spatiotemporal restricted
datasets that represented different colonization steps. Results: Whilst
all approaches performed similarly when evaluated against a subset of the
data used to fit the models, the ensemble model based on integrated data
performed best when predicting range expansion. Models for subsequent
colonization steps differed substantially from the global model,
highlighting the non-stationarity of wolf habitat selection towards human
disturbance during the colonization process. Main conclusions: While
telemetry-only data overfitted the models, using all available datasets
increased the reliability of the range expansion forecasts. The
non-stationarity in habitat selection pointed to wolves settling in the
best areas first, and filling in nearby lower-quality habitat as the
population increases. Our results caution against spatial extrapolation
and space-for-time substitutions in habitat models, at least with
expanding species.