Deciphering the variability of leaf phosphorus‐allocation strategies using leaf economic traits and reflectance spectroscopy across diverse forest types

作者：Tingting Dong, Fengqi Wu, Yuki Tsujii, Philip A. Townsend, Nan Yang, Weiying Xu, Shuwen Liu, Nathan G. Swenson, Julien Lamour, Wenxuan Han, Nicholas G. Smith, Yue Shi, Yan Li, Di Tian, Mingkai Jiang, Zhihui Wang, Xiaojuan Liu, Guanhua Dai, Jinlong Dong, Jordi Sardans, Peter B. Reich, Hans Lambers, Shawn Serbin, Josep Peñuelas, Jin Wu, Zhengbing Yan · 发表于：New Phytologist · 年份：2025 · DOI：10.1111/nph.70219 · 被引用次数：9 · 研究领域：Plant Water Relations and Carbon Dynamics、Soil Carbon and Nitrogen Dynamics、Horticultural and Viticultural Research

Summary Allocation of leaf phosphorus (P) among different functional fractions represents a crucial adaptive strategy for optimizing P use. However, it remains challenging to monitor the variability in leaf P fractions and, ultimately, to understand P‐use strategies across diverse plant communities. We explored relationships between five leaf P fractions (orthophosphate P, P i ; lipid P, P L ; nucleic acid P, P N ; metabolite P, P M ; and residual P, P R ) and 11 leaf economic traits of 58 woody species from three biomes in China, including temperate, subtropical and tropical forests. Then, we developed trait‐based models and spectral models for leaf P fractions and compared their predictive abilities. We found that plants exhibiting conservative strategies increased the proportions of P N and P M , but decreased the proportions of P i and P L , thus enhancing photosynthetic P‐use efficiency, especially under P limitation. Spectral models outperformed trait‐based models in predicting cross‐site leaf P fractions, regardless of concentrations ( R 2 = 0.50–0.88 vs 0.34–0.74) or proportions ( R 2 = 0.43–0.70 vs 0.06–0.45). These findings enhance our understanding of leaf P‐allocation strategies and highlight reflectance spectroscopy as a promising alternative for characterizing large‐scale leaf P fractions and plant P‐use strategies, which could ultimately improve the physiological representation of the plant P cycle in land surface models.