Li Ying, Liu Congcong, Sack Lawren, Xu Li, Li Mingxu, Zhang Jiahui, He Nianpeng
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
School of Ecology and Nature Conservation, Beijing Forestry University, Beijing, China.
Ecol Lett. 2022 Jun;25(6):1442-1457. doi: 10.1111/ele.14009. Epub 2022 Apr 9.
Variation in the architecture of trait networks among ecosystems has been rarely quantified, but can provide high resolution of the contrasting adaptation of the whole phenotype. We constructed leaf trait networks (LTNs) from 35 structural, anatomical and compositional leaf traits for 394 tree species in nine forests from tropical to cold-temperate zones in China. Our analyses supported the hypothesis that LTNs would increase in modular complexity across forests in parallel with species-richness and climatic warmth and moisture, due to reduced phenotypic constraints and greater opportunities for niche differentiation. Additionally, we found that within LTNs, leaf economics traits including leaf thickness would have central importance, acting as hub traits with high connectivity due to their contributions to multiple functions. Across the continent, the greater species richness and trait diversity observed in forests under resource-rich climates enable greater complexity in whole phenotype structure and function as indicated by the trait network architecture.
生态系统间性状网络结构的变化很少被量化，但它能够为整个表型的对比适应性提供高分辨率信息。我们基于中国从热带到寒温带九个森林中的394种树种的35个叶片结构、解剖和组成性状构建了叶片性状网络（LTN）。我们的分析支持了这样一个假设，即由于表型限制的减少和生态位分化机会的增加，LTN的模块化复杂性会随着森林物种丰富度以及气候温暖和湿润程度的增加而提高。此外，我们发现，在LTN中，包括叶片厚度在内的叶片经济性状具有核心重要性，由于它们对多种功能的贡献，这些性状作为具有高连通性的枢纽性状发挥作用。在整个大陆范围内，资源丰富气候下的森林中观察到的更高物种丰富度和性状多样性，使得性状网络结构所表明的整个表型结构和功能具有更高的复杂性。
