Predicting the Ecological Impacts of Future Fire Activity on a Global Scale

野火是影响陆地生物圈的最重要的自然干扰类型。火灾的性质在多个空间和时间尺度上影响植被：小火灾可以在局部尺度上触发间隙动态，而火灾相关特征综合征的表达则受到大火的频率和强度的强烈控制。野火的流行也与特定植被类型的维持有关，特别是稀树草原和草原。野火的发生受多种因素的影响，包括长期气候条件、短期天气、植被类型和生产力以及影响土地利用的人为因素。这些因素可能对火灾的不同方面产生不同的影响：例如，人为点火会影响火灾的数量及其季节性分布，但对火灾蔓延和燃烧总面积的影响很小。这种复杂性意味着仅凭经验证据很难预测未来气候变化、气候引起的植被变化和人类活动对火灾状况的影响，而且由于这些不同控制因素之间存在多种反馈，情况进一步复杂化。耦合火-植被模型是预测未来大尺度火情变化和探索这些变化如何影响区域植被的唯一途径。然而，尽管最近开发了几个这样的模型，但它们对过去和未来的预测存在很大差异1，而且模型结构和参数化的不确定性使得很难知道现代火灾状况是否因为正确的原因而得到了正确的模拟。该项目的首要目标是提高建模能力，以解决未来野火变化对生态的影响。您将使用先进的统计技术，包括广义线性和混合效应建模，以及遥感观测（例如2）来探索控制野火制度的不同方面（火灾开始数量，火灾类型，火灾季节性，频率和强度，燃烧面积和排放）以及这些控制是否因地区而不同。您将使用这些分析来设计一个简单的全局火灾模型（INFERNO3）的“扰动参数”实验。在这些实验中，使用可能的参数值的合理范围来指定关键模式过程，以研究哪些不确定性对野火状态的影响最大。这些分析将有助于量化预测中的不确定性，但也将导致对火灾模型的改进。在项目的最后一步，您将把INFERNO与植被生产力的简单模型（P4）结合起来，以调查未来气候和其他火灾控制的变化如何影响火灾制度，与火灾相关的恢复特征（再生、服务、重新播种）的表达以及草和木覆盖之间的平衡（从而影响稀树草原、森林和草原的范围）。

Wildfire is the most important type of natural disturbance impacting the terrestrial biosphere. The nature of the fire regime affects vegetation on multiple space and time scales: small fires can trigger gap dynamics at a local scale, while the expression of fire-related trait syndromes is strongly controlled by the frequency and intensity of large fires. The prevalence of wildfire is also implicated in the maintenance of specific vegetation types, notably savannas and grasslands. The incidence of wildfire is influenced by multiple factors, including long-term climate conditions, short-term weather, vegetation type and productivity, and human factors affecting land-use. These factors may have different effects on different aspects of the fire regime: human ignitions affect the number of fires and their seasonal distribution, for example, but have little impact on fire spread and the total area burned. This complexity means it is difficult to predict the impact of future changes in climate, climate-induced changes in vegetation, and human activities on fire regimes solely using empirical evidence, and the situation is further complicated because there are multiple feedbacks between these different controls. Coupled fire-vegetation models are the only way of predicting future changes in large-scale fire regimes and exploring how these will affect regional vegetation. However, although several such models have recently been developed, there are large differences in their past and future predictions1 and uncertainties in model structure and parameterisation that make it hard to know whether modern fire regimes are correctly simulated for the right reasons.The overarching goal of this project is to improve modelling capacity to address the ecological impacts of future change in wildfire. You will use advanced statistical techniques, including generalized linear and mixed-effect modelling, with remote-sensing observations (e.g.2) to explore what controls different aspects of wildfire regimes (numbers of fire starts, fire type, fire seasonality, frequency and intensity, burned area, and emissions) and whether these controls vary regionally. You ill use these analyses to inform the design of "perturbed-parameter" experiments with a simple global fire model (INFERNO3). In these experiments, key model processes are specified using a plausible range of possible parameter values to investigate which uncertainties have the largest impact on wildfire regimes. These analyses will help quantify uncertainties in projections but will also lead to improvements to the fire model. In the final step of the project you will couple INFERNO to a simple model of vegetation productivity (P4) to investigate how future change in climate and other fire controls might affect fire regimes, the expression of fire-related recovery traits (resprouting, serotiny, re-seeding) and the balance between grass and woody cover (and thus the extent of savannas, forests and grasslands).