DOI: 10.5061/dryad.z8w9ghxhn
title: Integrating animal tracking datasets at a continental scale for mapping Eurasian lynx habitat

description: Aim:   The increasing availability of animal tracking datasets
 collected across many sites provides new opportunities to move beyond
 local assessments to enable detailed and consistent habitat mapping at
 biogeographic scales. However, integrating wildlife datasets across large
 areas and study sites is challenging, as species’ varying responses to
 different environmental contexts must be reconciled. Here, we compare
 approaches for large-area habitat mapping and assess available habitat for
 a recolonizing large carnivore, the Eurasian lynx (Lynx lynx).  
 Location: Europe   Methods:   We use a continental-scale
 animal tracking database (450 individuals from 14 study sites) to
 systematically assess modeling approaches, comparing (1) global strategies
 that pool all data for training vs. building local, site-specific models
 and combining them, (2) different approaches for incorporating regional
 variation in habitat selection, and (3) different modeling algorithms,
 testing nonlinear mixed effects models as well as machine-learning
 algorithms.   Results:   Both global and local modeling
 strategies allowed building transferable habitat models with overall
 similar predictive performance. Model performance was the highest using
 flexible machine-learning algorithms and when incorporating variation in
 habitat selection as a function of environmental variation. Our
 best-performing model used a weighted combination of local, site-specific
 habitat models. Our habitat maps identified large areas of suitable, but
 currently unoccupied lynx habitat, with many of the most suitable
 unoccupied areas located in regions that could foster connectivity between
 currently isolated populations.   Main conclusions:   We
 demonstrate that global and local modeling strategies can achieve robust
 habitat models at the continental scale and that considering regional
 variation in habitat selection improves broad-scale habitat mapping. More
 generally, we highlight the promise of large wildlife tracking databases
 for large-area habitat mapping. Our maps provide the first
 high-resolution, yet continental assessment of lynx habitat across Europe,
 providing a consistent basis for conservation planning for restoring the
 species within its former range.
description: # Integrating animal tracking datasets at a continental scale for mapping
 Eurasian lynx habitat *** This dataset is a product of the Conservation
 Biogeography Lab at Humbolt-Universität zu Berlin (hu.berlin/biogeography)
 and is a result of the following publication: Oeser, J., Heurich, M.,
 Kramer-Schadt, S., Mattisson, J., Krofel, M., Krojerová-Prokešová, J.,
 Zimmermann, F., Anders, O., Andrén, H., Bagrade, G., Belotti, E.,
 Breitenmoser-Würsten, C., Bufka, L., Černe, R., Drouet-Hoguet, N., Duľa,
 M., Fuxjäger, C., Gomerčić, T., Jędrzejewski, W. … Kuemmerle, T. (2023).
 Integrating animal tracking datasets at a continental scale for mapping
 Eurasian lynx habitat. *Diversity and Distributions*, 00, 1–15.
 [https://doi.org/10.1111/ddi.13784](https://doi.org/10.1111/ddi.13784) ##
 Description of the data and file structure GEOTIFF files contain habitat
 suitability raster maps of the best-performing global and local habitat
 models, respectively, using an algorithm ensemble of the best-performing
 modeling algorithms (Maxent and random forest). Maps represent
 scale-integrated predictions obtained by combining (i.e., multiplying)
 habitat models built at the landscape and home range levels of selection
 (selection of home ranges in the wider landscape and selection of
 locations within home ranges). This means that areas with suitable habitat
 too small for the establishment of lynx home ranges will be 'masked
 out' in the scale-integrated prediction. Predictions represent a
 relative habitat suitability index rescaled to a 0-1000 scale, best
 understood as a relative ranking of raster cells in terms of their habitat
 suitability. Raster maps are in EPSG 3035 projection and have 100m spatial
 resolution. CSV tables contain data frames used for building habitat
 models. Column description: "animals\_id" - ID of the lynx
 individual "site" - name of the study site "occ" -
 occurrence status: 1 for presence locations and 0 for available locations
 (background points)"sex" - sex of the
 individual"acquisition\_time" - date and time of observation
 "ruggedness" - terrain ruggedness "forest" - forest
 cover"forest\_integrity" - forest integrity
 "human\_modification" - human modification index
 "accessibility" - accessibility (travel time to cities)
 "snow\_cover" - snow cover frequency "road\_density" -
 road density "greenness\_median" - median Landsat tasseled cap
 greenness "greenness\_variability" - amplitude of Landsat
 tasseled cap greenness "brightness\_median" - median Landsat
 tasseled cap brightness "brightness\_variability" - amplitude of
 Landsat tasseled cap brightness "wetness\_median" - median
 Landsat tasseled cap wetness "wetness\_variability" - amplitude
 of Landsat tasseled cap wetness The numbers following variable names
 indicate spatial scale of the model variables (e.g., \_22000 for 22km
 diameter moving window).