Frontiers in Plant Science (Sep 2023)

Overwintering performance of bamboo leaves, and establishment of mathematical model for the distribution and introduction prediction of bamboos

  • Yufang Wu,
  • Yufang Wu,
  • Jing Li,
  • Jing Li,
  • Lixia Yu,
  • Lixia Yu,
  • Shuguang Wang,
  • Shuguang Wang,
  • Shuguang Wang,
  • Zhuo Lv,
  • Zhuo Lv,
  • Hao Long,
  • Hao Long,
  • Jingyu Zhai,
  • Shuyan Lin,
  • Yong Meng,
  • Zhihua Cao,
  • Hui Sun

DOI
https://doi.org/10.3389/fpls.2023.1255033
Journal volume & issue
Vol. 14

Abstract

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Bamboo has great economic values and is used extensively in many industries, and their natural distribution range was divided into 12 zones in China according to the temperature of their geographical distribution in previous works. Different bamboo species had significantly different abilities in low-temperature tolerance, which need to be considered carefully during ex-situ introduction. In this paper, we observed and evaluated the low-temperature damage of 19 bamboo species in winter, and measured the physiological changes of bamboo leaves. A total of 3060 leaf samples were obtained from 102 core collections in 34 bamboo species from the 5 regions of Chinese mainland for anatomical comparison, in order to screen out the key anatomical indicators related to their low-temperature tolerance and to establish a mathematical prediction model for bamboo introduction. The results showed that the low-temperature resistance of clustered bamboos was generally lower than that of the scattered bamboos. The decreased temperature led to the constant decrease of net photosynthetic rate and transpiration rate, but the increase of soluble sugar content in all bamboo species. There was no dormancy for all bamboo species in winter. The temperate bamboos showed lower photosynthesis as compared to tropical bamboos in winter. The leaf shape of bamboos was closely related to their distribution. A total of 13 leaf indicators were screened and more suitable to estimate the low-temperature tolerant abilities of bamboos and to predict their distribution. The MNLR (multiple nonlinear regression) mathematical model showed the highest fitting degree and the optimal prediction ability in the potential northernmost introduction range of bamboos. This study lay a foundation for bamboo introduction, and could also reduce the economic losses caused by the wrong introduction.

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