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。火制度的性质影响植被在多个空间和时间尺度上：小火可以触发间隙动态在局部范围内，而火灾相关的性状综合征的表达强烈控制的频率和强度的大火。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地。野火的发生受到多种因素的影响，包括长期气候条件、短期天气、植被类型和生产力，以及影响土地使用的人为因素。这些因素可能对火灾状况的不同方面产生不同的影响：例如，人为点火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制度的影响，仅仅使用经验证据，而且情况更加复杂，因为这些不同的控制之间存在多重反馈。耦合火灾植被模型是预测未来大规模火灾制度的变化，并探讨这些将如何影响区域植被的唯一途径。然而，尽管最近已经开发了几个这样的模型，有很大的差异，在他们的过去和未来的预测1和模型结构和参数化的不确定性，使它很难知道是否正确地模拟现代火灾制度的正确原因。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（火灾开始的数量，火灾类型，火灾季节性，频率和强度，燃烧面积和排放量）以及这些控制是否因地区而异。您将使用这些分析来为使用简单全局火灾模型（INFERNO3）的“扰动参数”实验设计提供信息。在这些实验中，关键的模型过程指定使用一个合理的范围内可能的参数值，以调查哪些不确定性有最大的影响野火制度。这些分析将有助于量化预测中的不确定性，但也将导致改进火灾模型。在项目的最后一步，您将把INFERNO与一个简单的植被生产力模型（P4）结合起来，研究未来气候变化和其他火灾控制措施如何影响火灾状况，与火灾相关的恢复特征（重新发芽，重新播种）的表达以及草和木本覆盖之间的平衡（以及稀树草原，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).