关键词: 刺槐(Robinia pseudoacacia)人工林, 生物多样性生态系统功能(BEF), 退化生态系统, 气候变化, 功能多样性, 线性混合效应模型

Abstract: Functional traits play a vital role in mediating the responses of ecosystem services to environmental changes and in predicting the functioning of the ecosystem. However, the connection between functional traits and ecosystem services has become increasingly intricate due to climate change and human activities for degraded ecosystems. To investigate this relationship, we selected 27 sampling sites in the Yanhe River Basin of the Chinese Loess Plateau, each containing two types of vegetation ecosystems: natural vegetation and artificial vegetation ecosystems. At each sampling site, we measured ecosystem services and calculated the composition index of community traits. We established a response–effect trait framework that included environmental factors such as climate, elevation and human activities. Our results showed that leaf tissue density (LTD) was the overlapping response and effect trait when responding to climate change. LTD is positively correlated with mean annual temperature and negatively correlated with supporting services. Under the influence of human activities, leaf nitrogen content and leaf dry matter content were carriers of environmental change. Comparing the two vegetation ecosystems, the relationship between functional traits and ecosystem services showed divergent patterns, indicating that human activities increased the uncertainty of the relationship between functional traits and ecosystem services. Trait-based ecology holds promise for enhancing predictions of ecosystem services responses to environmental changes. However, the predictive ability is influenced by the complexity of environmental changes. In conclusion, our study highlights the importance of understanding the complex connection between functional traits and ecosystem services in response to climate changes and human activities.

Key words: Robinia pseudoacacia plantation,  BEF, degraded ecosystem, climate change, functional diversity, linear mixed-effect models