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Vertical structural complexity of plant communities represents the combined effects of resource acquisition and environmental stress on the Tibetan Plateau
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  • Changjin Cheng,
  • MingXu Li,
  • Congcong Liu,
  • Li Xu,
  • Nianpeng He
Changjin Cheng
School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China

Corresponding Author:[email protected]

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MingXu Li
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences
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Congcong Liu
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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Li Xu
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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Nianpeng He
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences
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Abstract

Knowledge of vertical structural complexity (VSC) is important, because the resulting spatial partitioning is closely linked to resource utilization and environmental adaptation. How VSC responds to environmental changes on large scales and its mechanisms are poorly understood. We investigated 2,013 plant communities on the Tibetan Plateau (TP). VSC was quantified as the maximum height (Height-max), height variation (Height-var), and height evenness (Height-even). Precipitation dominated the VSC variation in forests and shrublands, supporting the classic physiological tolerance and hydraulic limitation hypotheses. In contrast, for alpine grasslands in extreme environments, non-resource limiting factors dominate VSC variation. Generally, with the shifting of climate from optimal to extreme, the effect of resource availability gradually decreases, but the effect of non-resource limiting factors increases. Using machine learning models, maps of VSC at 1-km resolution were firstly produced for the TP. These findings provide new insights into macroecological studies, especially for adaptation mechanisms and model optimization.