Integrating animal tracking datasets at a continental scale for mapping Eurasian lynx habitat

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 eff..., , , # Integrating animal tracking datasets at a continental scale for mapping Eurasian lynx habitat

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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-perfo...