Cross-regional modelling of fire occurrence in the Alps and the Mediterranean Basin

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Bekar I., TAVŞANOĞLU Ç., Pezzatti G. B., Vacik H., Pausas J. G., Bugmann H., ...More

INTERNATIONAL JOURNAL OF WILDLAND FIRE, vol.29, no.8, pp.712-722, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 29 Issue: 8
  • Publication Date: 2020
  • Doi Number: 10.1071/wf19158
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CAB Abstracts, Environment Index
  • Page Numbers: pp.712-722
  • Keywords: fire ignition, grain size, Maxent, spatial resolution, species distribution model, SPECIES DISTRIBUTION MODELS, ENVIRONMENTAL PREDICTORS, GRAIN-SIZE, WILDFIRES, CLIMATE, REGIME, SUSCEPTIBILITY, LANDSCAPE, SPACE
  • Hacettepe University Affiliated: Yes


In recent decades, changes in fire activity have been observed in Europe. Fires can have large consequences for the provisioning of ecosystem services and for human well-being. Therefore, understanding the drivers of fire occurrence and improving the predictive capability of fire occurrence models is of utmost importance. So far, most studies have focused on individual regions with rather low spatial resolution, and have lacked the ability to apply the models in different regions. Here, a species distribution modelling approach (Maxent) was used to model fire occurrence in four regions across the Mediterranean Basin and the Alps using several environmental variables at two spatial resolutions. Additionally, a cross-regional model was developed and spatial transferability tested. Most models showed good performance, with fine resolution models always featuring somewhat higher performance than coarse resolution models. When transferred across regions, the performance of regional models was good only under similar environmental conditions. The cross-regional model showed a higher performance than the regional models in the transfer tests. The results suggest that a cross-regional approach is most robust when aiming to use fire occurrence models at the regional scale but beyond current environmental conditions, for example in scenario analyses of the impacts of climate change.