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。

Patterns and influencing factors of traits in forest ecosystems: Synthesis and perspectives on the synthetic investigation from the north-east transect of eastern China (NETEC)

Traits or functional traits are measurable properties that can reflect long-term adaptations to the environment,driving the evolution of organisms (plants, animals, and microbes). There are many remarkable achievements on studies of the traits of plants, animals, and microbes; however, studies of plants focus on either leaves or roots, with few integrating the two, paticularly at the ecosystem and regional levels. Interdisciplinary, systematic, and integrated investigations are needed to reveal the characteristics of complex natural ecosystems (including various traits of plants, animals, microbes, and their interactions) more clearly. With the support of the National Natural Science Foundation of China, comprehensive research on the traits of forest ecosystems (plants, microbes, and soil) along the North-South Transect of Eastern China (NSTEC) have been investigated from 2013 to 2017. This approach aimed to establish a new survey mode (interdisciplinary, systematic, and integrative), facilitating the analysis of new concepts to promote the development of related studies. For the current study, we selected nine typical forest ecosystems along the NSTEC (3700 km), extending from the tropical rainforest to cold temperate coniferous forests. After investigating the community structure, researchers measured various plant traits systematically, including multiple elements of the leaf, branch, trunk, and root, and leaf morphological traits, stomatal traits, anatomical traits, chlorophyll contents, and the contents of multiple elements, non structural carbohydrate content, fine root morphology and anatomy. Furthermore, soil microbial community structure, enzyme activity, the structure and composition of soil organic matter, and the rate and temperature sensitivity of soil carbon and nitrogen mineralization were measured and calculated. Based on the systematic data, the latitudinal patterns and influencing factors of a series of traits for plants, microbes, and the soil were evaluated, in parallel to resolving the major challenge of how to scale the traits from the organ level to the community level objectively. This study was the first to elucidate the quantitative relationships between traits and ecosystem functions in natural forest ecosystems with respect to certain components. Based on the integration of data, we tentatively put forward two new concepts:"Trait network" and "Ecosystem traits". We anticipate that this type of systematic data, combined with new concepts, will help to reveal the mechanisms of complex forest ecosystems. Furthermore, the acquisition of similar systematic data are important for developing and verifying ecological theories in the future. Using systematic data, data collected at the organ level may be scaled up to the community or ecosystem level easily, making it is possible to bridge trait research using rapid-development macro-observation technologies, such as remote sensing, flux observation, and ecological models. The integration of such approach could help us to resolve environmental problems at regional or global scales in future.