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

• Main Authors: Oeser, Julian, Heurich, Marco, Kramer‐Schadt, Stephanie, Mattisson, Jenny, Krofel, Miha, Krojerová‐Prokešová, Jarmila, Zimmermann, Fridolin, Anders, Ole, Andrén, Henrik, Bagrade, Guna, Belotti, Elisa, Breitenmoser‐Würsten, Christine, Bufka, Luděk, Černe, Rok, Drouet‐Hoguet, Nolwenn, Duľa, Martin, Fuxjäger, Christian, Gomerčić, Tomislav, Jędrzejewski, Włodzimierz, Kont, Raido, Koubek, Petr, Kowalczyk, Rafał, Kusak, Josip, Kubala, Jakub, Kutal, Miroslav, Linnell, John D. C., Molinari‐Jobin, Anja, Männil, Peep, Middelhoff, Tomma Lilli, Odden, John, Okarma, Henryk, Oliveira, Teresa, Pagon, Nives, Persson, Jens, Remm, Jaanus, Schmidt, Krzysztof, Signer, Sven, Tám, Branislav, Vogt, Kristina, Kuemmerle, Tobias
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2024
• Subjects: 500 Naturwissenschaften und Mathematik::590 Tiere (Zoologie)::599 Mammalia (Säugetiere); animal tracking; Eurasian lynx; habitat suitability; large carnivore; large‐area mapping; Lynx lynx; Lynx; Lynx lynx lynx
• Online Access: https://depositonce.tu-berlin.de/handle/11303/21483; https://doi.org/10.14279/depositonce-20283

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 biogeographical 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 modelling approaches, comparing (1) global strategies that pool all data for training versus building local, site-specific models and combining them, (2) different approaches for incorporating regional variation in habitat selection and (3) different modelling algorithms, testing nonlinear mixed effects models as well as machine-learning algorithms. Results: Testing models on training sites and simulating model transfers, global and local modelling strategies achieved 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 modelling 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 ...