Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of Northeast China

Understanding the spatial patterns of fire occurrence and its response to climate change is vital to fire risk mitigation and vegetation management. Focusing on boreal forests in Northeast China, we used spatial point pattern analysis to model fire occurrence reported from 1965 to 2009. Our objectives were to quantitate the relative importance of biotic, abiotic, and human influences on patterns of fire occurrence and to map the spatial distribution of fire occurrence density (number of fires occurring over a given area and time period) under current and future climate conditions. Our results showed human‐caused fires were strongly related to human activities (e.g. landscape accessibility), including proximity to settlements and roads. In contrast, fuel moisture and vegetation type were the most important controlling factors on the spatial pattern of lightning fires. Both current and future projected spatial distributions of the overall (human‐ + lightning‐caused) fire occurrence density were strongly clustered along linear components of human infrastructure. Our results demonstrated that the predicted change in overall fire occurrence density is positively related to the degree of temperature and precipitation change, although the spatial pattern of change is expected to vary spatially according to proximity to human ignition sources, and in a manner inconsistent with predicted climate change. Compared to the current overall fire occurrence density (median value: 0.36 fires per 1000 km² per year), the overall fire occurrence density is projected to increase by 30% under the CGCM3 B1 scenario and by 230% under HadCM3 A2 scenario in 2081–2100, respectively. Our results suggest that climate change effects may not outweigh the effects of human influence on overall fire occurrence over the next century in this cultural landscape. Accurate forecasts of future fire‐climate relationships should account for anthropogenic influences on fire ignition density, such as roads and proximity to settlements.     

Historic distribution and driving factors of human-caused fires in the Chinese boreal forest between 1972 and 2005

Aims The pattern and driving factors of forest fires are of interest for fire occurrence prediction and forest fire management. The aims of the study were: (i) to describe the history of human-caused fires by season and size of burned area over time; (ii) to identify the spatial patterns of human-caused fires and test for the existence of 'hotspots' to determine their exact locations in the Daxing'an Mountains; (iii) to determine the driving factors that determine the spatial distribution and the possibility of human-caused fire occurrence.Methods In this study, K -function and Kernel density estimation were used to analyze the spatial pattern of human-caused fires. The analysis was conducted in S-plus and ArcGIS environments, respectively. The analysis of driving factors was performed in SPSS 19.0 based on a logistic regression model. The variables used to identify factors that influence fire occurrence included vegetation types, meteorological conditions, socioeconomic factors, topography and infrastructure factors, which were extracted and collected through the spatial analysis mode of ArcGIS and from official statistics, respectively.Important findings The annual number of human-caused fires and the area burnt have declined since 1987 due to the implementation of a forest fire protection act. There were significant spatial heterogeneity and seasonal variations in the distribution of human-caused fires in the Daxing'an Mountains. The heterogeneity was caused by elevation, distance to the nearest railway, forest type and temperature. A logistic regression model was developed to predict the likelihood of human-caused fire occurrence in the Daxing'an Mountains; its global accuracy attained 64.8%. The model was thus comparable to other relevant studies.     

我国北方针叶林人为火发生的预测模型

我国北方针叶林带是重要的森林资源储藏地,也是林火发生的重灾区,其自然火和人为火所占比例相当. 气象因子、地形特征、植被条件、人为基础设施等因素对人为火发生具有显著影响,国内目前应用空间分析技术对北方针叶林带人为火影响因子的研究还存在一定不确定性. 本文基于1974—2009年间人为火的空间地理坐标,结合研究地的气象因子、基础地理信息及矢量化林相图,应用ArcGIS 10.0中的空间分析工具和SPSS 19.0的逻辑斯蒂回归模型对影响人为火发生的主要驱动因子进行分析,并建立人为火发生的概率模型. 利用HADCM2模式下研究区域未来气象数据对塔河地区2015年人为火发生情况进行计算.结果表明： 距离铁路距离（x₁）和平均相对湿度（x₂）对研究区域人为火发生具有显著影响,并得到火险概率模型P=1/［1+e^{-(3.026-0.00011x1-0.047x₂)}］. 模型校验结果显示,模型的准确度可达到80%.林火发生预测结果表明,塔河地区2015年 4—6月、8月为人为火高发期,其中,4—5月的林火发生概率最高.从火险空间分布来看,高火险主要集中在塔河西部和西南部,铁路线路主要包含在此区域.     

森林生态系统性状的空间格局与影响因素研究进展——基于中国东部样带的整合分析

性状(Trait)或功能性状(Functional trait)是植物、动物和微生物等对外界环境长期适应和进化后所呈现出来的可量度的特征,也是人们认识自然、利用或改造自然的重要途径和技术手段。近几十年来,科学家对植物、动物和微生物功能性状的研究取得了令人瞩目的成就,尤其在物种水平的植物叶片和根性状的研究领域;然而,自然生态系统是复杂的,植物、动物和微生物自身的多种性状间及其不同生物间性状的相互作用是广泛存在的,因此需要跨学科、系统性、集成式地调查和研究。以中国东部南北样带(NSTEC)森林生态系统为对象开展了植物、微生物和土壤性状的综合测定;基于其核心的研究结论并适当整合NSTEC前期的相关研究成果,希望能给性状研究提供新的调查模式和分析思路。沿NSTEC从热带雨林到寒温带针叶林3700km样带选取了9个地带性森林生态系统,在群落结构调查基础上对群落内所有植物种类(总计1177物种)开展了系统性的性状测定(叶-枝-干-根多元素含量,叶片形态性状-气孔性状-解剖性状-叶绿素含量-多元素含量-非结构性碳水化合物、细根形态性状-解剖性状-多元素含量等),测定了土壤微生物群落结构、酶活性、土壤有机质结构与组成、土壤碳氮周转及其温度敏感性等参数。基于上述数据,不仅按传统途径系统性地探讨了植物、微生物和土壤多种性状的纬度变异规律与影响因素;还从不同角度探讨了"如何科学地将器官水平测定性状推导至天然森林群落水平"科学难题,并从多个性状角度建立了自然森林生态系统中性状与功能的定量关系。在此基础上提出"性状网络"和"生态系统性状"概念,以其更好地用于揭示自然界复杂的森林生态系统,为验证和发展生态学理论、探讨多种性状间协同(权衡)的生态系统生产力优化机制提供重要的数据支撑。希望通过解决性状尺度拓展的技术难题,未来将传统性状研究拓展至群落或生态系统水平,并与高速发展的宏观观测手段(遥感观测、通量观测、模型模拟)有机结合,使性状研究更好地服务于区域乃至全球性的生态环境问题。