Data from: Exploiting Poisson additivity to predict fire frequency from maps of fire weather and land cover in boreal forests of Québec, Canada

Predictive models of fire frequency conditional on weather and land cover are essential to assess how future cover-type distributions and weather conditions may influence fire regimes. We modelled the effects of bottom-up variables (e.g. land cover) and top-down variables (e.g. fire weather) simultaneously with data aggregated or interpolated to spatial and temporal units of 100 km2 and 1yr in the boreal forest of Québec, Canada. For models of human-caused fires, we used road density as a surrogate for human access and behaviour. We exploited the additive property of Poisson distributions to estimate cover-type specific fire count rates, which would normally not be possible with data of this spatial resolution. We used piecewise linear functions to model nonlinear relations between fire weather and fire frequency for each cover-type simultaneously. The estimated conditional rates may be considered as expected mean counts per unit area and time. It follows that these rates can be rescaled to arbitrary spatial and temporal extents. Our results showed fire frequency increased nonlinearly as aridity increased and more quickly in disturbed areas than other types. Road density exerted the strongest influence on the frequency of human-caused fires, which were positively correlated with road density. The estimates may be used to parameterize the fire ignition component of spatial simulation models, which often have a resolution different from that at which the data were collected. This is an essential step in incorporating biotic and abiotic feedbacks, land-cover dynamics, and climate projections into ecological forecasting. The insight into the power of Poisson additivity to reveal high-resolution ecological processes from low-resolution data could have applications in other areas of ecology.

以天气与土地覆盖为条件的火灾发生频率预测模型，对于评估未来植被类型分布与天气状况如何影响火灾制度至关重要。我们以加拿大魁北克（Québec）的北方针叶林（boreal forest）为研究区域，采用聚合或插值至100平方千米、1年时空单元的数据集，同时建模了自下而上变量（如土地覆盖）与自上而下变量（如火灾天气）的效应。针对人为火源火灾的模型，我们采用道路密度（road density）作为人类活动可达性与行为的替代指标。我们借助泊松分布（Poisson distribution）的可加性特性，估算了特定植被类型的火灾计数率——这在该空间分辨率的数据条件下通常难以实现。我们采用分段线性函数（piecewise linear function），同时为每种植被类型建模火灾天气与火灾发生频率间的非线性关联。估算得到的条件发生率可视为单位面积与单位时间内的期望平均起火次数，因此可将其重新缩放至任意时空尺度。研究结果显示，火灾发生频率随干旱度提升呈非线性增长，且在受干扰区域的增长速度快于其他植被类型。道路密度对人为火源火灾的发生频率影响最为显著，二者呈正相关关系。该估算结果可用于为空间模拟模型的火灾引燃组件参数化，而这类空间模拟模型的分辨率往往与原始数据的采集分辨率存在差异。这是将生物与非生物反馈、土地覆盖动态及气候预测纳入生态预报的关键步骤。本次研究中，利用泊松分布可加性从低分辨率数据中揭示高分辨率生态过程的思路，有望在生态学其他研究领域得到应用。