Chen Yudong, Wang Jinlong, Jiang Lamei, Li Hanpeng, Wang Hengfang, Lv Guanghui, Li Xiaotong
College of Ecology and Environment, Xinjiang University, Urumqi, China.
Key Laboratory of Oasis Ecology of Education Ministry, Xinjiang University, Urumqi, China.
Front Plant Sci. 2023 Jun 2;14:1131778. doi: 10.3389/fpls.2023.1131778. eCollection 2023.
The relationship between plant functional traits and ecosystem function is a hot topic in current ecological research, and community-level traits based on individual plant functional traits play important roles in ecosystem function. In temperate desert ecosystems, which functional trait to use to predict ecosystem function is an important scientific question. In this study, the minimum data sets of functional traits of woody (wMDS) and herbaceous (hMDS) plants were constructed and used to predict the spatial distribution of C, N, and P cycling in ecosystems. The results showed that the wMDS included plant height, specific leaf area, leaf dry weight, leaf water content, diameter at breast height (DBH), leaf width, and leaf thickness, and the hMDS included plant height, specific leaf area, leaf fresh weight, leaf length, and leaf width. The linear regression results based on the cross-validations (, , , and ) for the MDS and TDS (total data set) showed that the (coefficients of determination) for wMDS were 0.29, 0.34, 0.75, and 0.57, respectively, and those for hMDS were 0.82, 0.75, 0.76, and 0.68, respectively, proving that the MDSs can replace the TDS in predicting ecosystem function. Then, the MDSs were used to predict the C, N, and P cycling in the ecosystem. The results showed that non-linear models RF and BPNN were able to predict the spatial distributions of C, N and P cycling, and the distributions showed inconsistent patterns between different life forms under moisture restrictions. The C, N, and P cycling showed strong spatial autocorrelation and were mainly influenced by structural factors. Based on the non-linear models, the MDSs can be used to accurately predict the C, N, and P cycling, and the predicted values of woody plant functional traits visualized by regression kriging were closer to the kriging results based on raw values. This study provides a new perspective for exploring the relationship between biodiversity and ecosystem function.
植物功能性状与生态系统功能之间的关系是当前生态学研究的热点话题，基于个体植物功能性状的群落水平性状在生态系统功能中发挥着重要作用。在温带沙漠生态系统中，使用哪种功能性状来预测生态系统功能是一个重要的科学问题。在本研究中，构建了木本植物（wMDS）和草本植物（hMDS）功能性状的最小数据集，并用于预测生态系统中碳、氮和磷循环的空间分布。结果表明，wMDS包括株高、比叶面积、叶干重、叶含水量、胸径（DBH）、叶宽和叶厚度，hMDS包括株高、比叶面积、叶鲜重、叶长和叶宽。基于MDS和TDS（总数据集）的交叉验证（、、、和）的线性回归结果表明，wMDS的（决定系数）分别为0.29、0.34、0.75和0.57，hMDS的分别为0.82、0.75、0.76和0.68，证明MDS在预测生态系统功能方面可以替代TDS。然后，利用MDS预测生态系统中的碳、氮和磷循环。结果表明，非线性模型RF和BPNN能够预测碳、氮和磷循环的空间分布，并且在水分限制下不同生活型之间的分布模式不一致。碳、氮和磷循环表现出很强的空间自相关性，并且主要受结构因素影响。基于非线性模型，MDS可用于准确预测碳、氮和磷循环，通过回归克里金法可视化的木本植物功能性状预测值更接近基于原始值的克里金结果。本研究为探索生物多样性与生态系统功能之间的关系提供了新的视角。
