DOI: 10.5061/dryad.m63xsj461
title: Understanding habitat selection of range-expanding populations of large carnivores: 20 years of grey wolves (Canis lupus) recolonizing Germany

description: 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.
description: Our wolf data comprised two complementary datasets. Our first
 dataset consisted of GPS telemetry locations of 20 collared resident
 wolves from 2009 to 2018. This data is provided at a 50km resolution due
 to species conservation concerns. Our final telemetry dataset consisted of
 3,841 locations from 21 home ranges (183 ± 104 locations per home range).
 Our second dataset consisted of centroids of known wolf territories
 monitored annually since 2000 (www.dbb-wolf.de/Wolfsvorkommen/territorien/karte-der-territorien). The centroids of the territories were assessed after the end of the monitoring year, as the central point of all activity signs (scats; camera trap images; telemetry data, when available; opportunistic sightings; hunt remains) that were assigned to the same territory. Territory areas for our study were delineated with a radius of 8 km around their centroids, resulting in territory sizes of approx. 200 km².
description: Datasets are specially optimised for R, but they can open with
 any software that reads .csv files.
description: # Data tables for the manuscript "Understanding range-expanding
 populations of large carnivores: 20 years of recolonizing grey wolves
 (Canis lupus) in Germany" ### Table monitoring territories Centroids
 of the wolf territories in Germany over the years. Coordinades are
 provided using epsg 3035. Average value of environmental variables is also
 provided for a buffer of 8km around the centroid, representing the
 territory. CLC2012: Corine Land Cover reclassied value (see Methods),
 Pop\_den: human population density, HFP: value of the Human FootPrint,
 Dist\_settl: Distance to settlements in meters, Dist\_roads: distance to
 roads in meters. ### Table telemetry 50km Telemetry data of the collared
 wolves. Data is provided at 50km resolution, as during the time that this
 study is carried out wolves are a threatened species. Cellcode refers to
 the Identification of the 50km grid cell in the reference grid by the
 European Environmental Agency (EEA,
 http://www.eea.europa.eu/legal/copyright). Centroids of the 50km cells are
 provided in epsg 3035, togehter with the environmental values of each wolf
 location that were used in the analyses -CLC2012: reclassified Corine Land
 Cover class, Pop\_den: human population density, HFP: value of the Human
 Footpring index, Dist\_settl: distance to settlements in meters,
 Dist\_roads:distance to roads in meters.