State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China.
Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, 666303, China.
Sci China Life Sci. 2023 Feb;66(2):376-384. doi: 10.1007/s11427-021-2135-3. Epub 2022 Jul 20.
Plant communities in mountainous areas shift gradually as climatic conditions change with altitude. How trait structure in multivariate space adapts to these varying climates in natural forest stands is unclear. Studying the multivariate functional trait structure and redundancy of tree communities along altitude gradients is crucial to understanding how temperature change affects natural forest stands. In this study, the leaf area, specific leaf area, leaf carbon, nitrogen, and phosphorous content from 1,590 trees were collected and used to construct the functional trait space of 12 plant communities at altitudes ranging from 800 m to 3,800 m across three mountains. Hypervolume overlap was calculated to quantify species trait redundancy per community. First, hypervolumes of species exclusion and full species set were calculated, respectively. Second, the overlap between these two volumes was calculated to obtain hypervolume overlap. Results showed that the functional trait space significantly increased with mean annual temperature toward lower altitudes within and across three mountains, whereas species trait redundancy had different patterns between mountains. Thus, warming can widen functional trait space and alter the redundancy in plant communities. The inconsistent patterns of redundancy between mountains suggest that warming exerts varying influences on different ecosystems. Identification of climate-vulnerable ecosystems is important in the face of global warming.
随着海拔高度的变化,山区的植物群落逐渐发生变化,气候条件也随之变化。在自然林分中,多维空间中的性状结构如何适应这些变化的气候尚不清楚。研究树木群落沿海拔梯度的多维功能性状结构和冗余性,对于理解温度变化如何影响自然林分至关重要。在本研究中,我们收集了来自海拔 800 米至 3800 米的三座山上 12 个植物群落的 1590 棵树木的叶面积、比叶面积、叶片碳、氮和磷含量,用于构建功能性状空间。通过计算超体积重叠来量化每个群落的物种性状冗余度。首先,分别计算物种排斥的超体积和完整物种集的超体积。其次,计算这两个体积之间的重叠,以获得超体积重叠。结果表明,在三座山的内部和之间,随着年平均温度向低海拔方向的升高,功能性状空间显著增加,而物种性状冗余度在山之间呈现出不同的模式。因此,变暖可以拓宽功能性状空间,并改变植物群落的冗余性。山之间冗余性的不一致模式表明,变暖对不同的生态系统施加了不同的影响。在全球变暖的背景下,识别对气候敏感的生态系统非常重要。