He Nian-Peng, Yan Pu, Guo Hong-Bo
Institute of Carbon Neutrality/School of Ecology, Northeast Forestry University, Harbin 150040, China.
Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin 150040, China.
Ying Yong Sheng Tai Xue Bao. 2025 Jul;36(7):1941-1951. doi: 10.13287/j.1001-9332.202507.019.
Plants contribute significantly to ecosystem primary productivity, serving as the basis of material cycling and energy flow. How to improve the accuracy of ecosystem productivity predictions is a classic topic in ecology. For decades, researchers have employed radiation-based remote sensing models or big-leaf-based process models to predict the spatiotemporal variations in ecosystem productivity. However, large discrepancies among model outputs constrain our understanding of the carbon sequestration capacity of ecosystems under global change. Recently, plant functional traits, as key parameters in next-generation process models, have received extensive attention. However, the scale mismatch between traditionally measured individual-level traits and community-level productivity constitutes an important source of model uncertainties. To address these challenges, we introduced the classical engine power model from physics and developed a novel trait-based productivity (TBP) framework centered on the two-dimensionality of plant community traits (quantity traits and efficiency traits). Contrary to the traditional models, all parameters in the TBP framework were defined at the community scale, with environmental factors influencing ecosystem productivity both directly and indirectly by regulating plant community traits. On this basis, using an in situ multi-trait database of Chinese ecosystems (including leaf chlorophyll concentration, leaf area, specific leaf area, leaf dry mass, and leaf nitrogen and phosphorus concentrations), we used three empirical studies to demonstrate the application scenarios of TBP theory. The TBP framework effectively bridges the scale gap between traits at individual level and ecosystem primary productivity. This framework is compatible with massive spatial data generated by flux observations, hyperspectral sensing, remote sensing, machine learning technologies, thus holding considerable application potential. Currently, the TBP framework is at an early stage. Besides requiring further theoretical innovation and methodological improvement, it also necessitates extensive support and validation from ground-based and remote sensing data. This will lay the foundation for the development of new-generation mechanistic process models and effectively improve the prediction accuracy of ecosystem productivity.
植物对生态系统的初级生产力有重大贡献，是物质循环和能量流动的基础。如何提高生态系统生产力预测的准确性是生态学中的一个经典话题。几十年来，研究人员一直采用基于辐射的遥感模型或基于大叶的过程模型来预测生态系统生产力的时空变化。然而，模型输出之间的巨大差异限制了我们对全球变化下生态系统碳固存能力的理解。最近，植物功能性状作为下一代过程模型中的关键参数受到了广泛关注。然而，传统测量的个体水平性状与群落水平生产力之间的尺度不匹配是模型不确定性的一个重要来源。为应对这些挑战，我们引入了物理学中的经典发动机功率模型，并开发了一个以植物群落性状二维性（数量性状和效率性状）为核心的基于性状的新型生产力（TBP）框架。与传统模型不同，TBP框架中的所有参数都在群落尺度上定义，环境因素通过调节植物群落性状直接或间接地影响生态系统生产力。在此基础上，利用中国生态系统的原位多性状数据库（包括叶片叶绿素浓度、叶面积、比叶面积、叶片干质量以及叶片氮磷浓度），我们通过三项实证研究展示了TBP理论的应用场景。TBP框架有效地弥合了个体水平性状与生态系统初级生产力之间的尺度差距。该框架与通量观测、高光谱传感、遥感、机器学习技术产生的大量空间数据兼容，因此具有相当大的应用潜力。目前，TBP框架尚处于早期阶段。除了需要进一步的理论创新和方法改进外，还需要地面和遥感数据的广泛支持与验证。这将为新一代机理过程模型的发展奠定基础，并有效提高生态系统生产力的预测准确性。
