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.

中文翻译：

---

阿尔卑斯山和地中海盆地火灾发生的跨区域建模

近几十年来，欧洲已经观察到火灾活动的变化。火灾会对生态系统服务的提供和人类福祉产生重大影响。因此，了解火灾发生的驱动因素，提高火灾发生模型的预测能力至关重要。迄今为止，大多数研究都集中在空间分辨率较低的单个区域，缺乏将模型应用于不同区域的能力。在这里，物种分布建模方法 (Maxent) 用于在两个空间分辨率下使用多个环境变量对地中海盆地和阿尔卑斯山的四个地区的火灾发生进行建模。此外，还开发了一个跨区域模型并测试了空间可转移性。大多数模型表现出良好的性能，精细分辨率模型的性能总是比粗糙分辨率模型高一些。当跨区域转移时，区域模型的性能只有在相似的环境条件下才能表现良好。跨区域模型在转移测试中表现出比区域模型更高的性能。结果表明，跨区域方法在旨在使用区域尺度但超出当前环境条件的火灾发生模型时最为稳健，例如在气候变化影响的情景分析中。跨区域模型在转移测试中表现出比区域模型更高的性能。结果表明，跨区域方法在旨在使用区域尺度但超出当前环境条件的火灾发生模型时最为稳健，例如在气候变化影响的情景分析中。跨区域模型在转移测试中表现出比区域模型更高的性能。结果表明，跨区域方法在旨在使用区域尺度但超出当前环境条件的火灾发生模型时最为稳健，例如在气候变化影响的情景分析中。