暖温带落叶阔叶林功能多样性与生产力关系及其对气候变化响应

Understanding the link between biodiversity and ecosystem functioning (BEF) is pivotal in the context of global biodiversity loss. There are contrasting reports whether and how biodiversity influences stand productivity in forests, and no clear evidence has been found regarding the underlying mechanisms, especially under the future climate change scenarios. Based on the data of two large-scale forest dynamic monitoring plots, we firstly examined the existence of a relationship between biodiversity and tree productivity in a warm-temperate deciduous broad-leaved forest of the North China, and to test whether functional diversity is a better predictor of productivity than species richness alone. General mechanisms proposed to explain diversity effects include complementary resource use and invoke a key role for species’ functional traits. Here we set up two field manipulative experiments to explore different tree species’ functional traits response to climate changes and adaptation mechanisms, and the change of consequences of community productivity. Using the model data fusion method, we finally optimize the structure and parameters of the two individual-based dynamic global vegetation model (LPJ-GUESS and LPJmL-FIT) to evaluate the relative importance of biodiversity and environmental factors in future productivity long-term dynamics by comparing the productivity of ecosystems in response to each driver. These result would be to test the hypotheses that biodiversity fosters the productivity of forest ecosystems and, consequently, the provision of crucial ecosystem services that support human well-being and quality of life. . According to CTFS norms, we have established two large-scale forest dynamic monitoring plots in the Beijing forest ecosystem station, and survey once every 5 years. These data provide a solid foundation for the project. Replacement series experiment design is embedded in the climate change manipulative experiments to reduce the space needed for trees to interact allowing a thorough set of mixtures varying over different diversity gradients (specific, functional) and environmental conditions to be tested in the field. The project use the dynamic global vegetation models combined with field manipulative experiments and long term monitoring in large-scale forest dynamic plots to set up a coordinated approach for multi-scale process studies. It can provide new evidence for the in-depth understanding of the role of biological diversity in the forest response to the climate change, and provide scientific support for the policy makers to biodiversity conservation and tackle climate change strategy.

人们对森林生物多样性与生产力关系还缺乏足够认识，更不了解在未来气候变化条件下这两者之间的关系将会如何改变。本项目依托于北京森林站，综合利用大型森林动态监测样地、野外控制实验与基于个体的动态全球植被模型(LPJ-GUESS和LPJmL-FIT)，定量分析暖温带落叶阔叶林物种/功能多样性与生产力的关系，探索未来不同气候情景下生物多样性与生产力关系变化规律，阐明生物多样性是暖温带落叶阔叶林生产力形成和维持的重要机制。严格按CTFS规范建立和复查的5公顷和20公顷暖温带落叶阔叶林样地为本项目奠定坚实基础。在气候变化控制实验内嵌入取代系列实验设计为探讨不同树种组合形成的群落对气候变化差异性响应成为可能。项目的实施将观测、实验和模型有机结合起来，建立一套适应于整合多尺度水平生态过程的协同研究方法，其研究成果可为政府制定和实施华北地区生物多样性保护政策和林业气候变化适应性措施的科学依据。

人们对森林生物多样性与生产力关系还缺乏足够认识，更不了解在未来气候变化条件下这两者之间的关系将会如何改变。本项目依托于北京森林站，动态监测东灵山20公顷样地内17种优势树种779个个体的径向生长量，通过结构方程模型，定量评估暖温带落叶阔叶林优势树种个体生长与地形因子、群落特征、植物功能性状等的关系，阐明暖温带落叶阔叶林生产力的形成机制。在此基础上，以典型的暖温带落叶阔叶林和亚热带常绿阔叶林为例，利用野外植物群落调查和主要物种功能性状观测对LPJ-GUESS模型进行参数化和验证；借鉴BEF实验设置不同物种水平组合，在不同气候变化情景下驱动LPJ-GUESS模型，阐明森林生物多样性与生产力之间关系及其对气候变化响应的机制。此外，本项目还调查和观测了我国东部南北森林样带其它9个站点的群落和功能性状数据，并以此来驱动和验证BIOME-BGC模型，探索出一套高效利用野外群落调查数据和功能性状观测数据对基于过程的生态系统模型进行参数化的途径，提高生态系统模型在我国东部南北森林样带全球气候变化响应与适应研究中的应用能力。