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Drought shortens subtropical understory growing season by advancing leaf senescence.

Huanfa SunLiming YanZhao LiWanying ChengRuiling LuXingli XiaJiaye PingChenyu BianNing WeiCuihai YouSongbo TangYing DuJing WangYang QiaoErqian CuiXuhui ZhouJiangyang Xia
Published in: Global change biology (2024)
Subtropical forests, recognized for their intricate vertical canopy stratification, exhibit high resistance to extreme drought. However, the response of leaf phenology to drought in the species-rich understory remains poorly understood. In this study, we constructed a digital camera system, amassing over 360,000 images through a 70% throughfall exclusion experiment, to explore the drought response of understory leaf phenology. The results revealed a significant advancement in understory leaf senescence phenology under drought, with 11.75 and 15.76 days for the start and end of the leaf-falling event, respectively. Pre-season temperature primarily regulated leaf development phenology, whereas soil water dominated the variability in leaf senescence phenology. Under drought conditions, temperature sensitivities for the end of leaf emergence decreased from -13.72 to -11.06 days °C -1 , with insignificance observed for the start of leaf emergence. Consequently, drought treatment shortened both the length of the growing season (15.69 days) and the peak growth season (9.80 days) for understory plants. Moreover, this study identified diverse responses among intraspecies and interspecies to drought, particularly during the leaf development phase. These findings underscore the pivotal role of water availability in shaping understory phenology patterns, especially in subtropical forests.
Keyphrases
  • climate change
  • arabidopsis thaliana
  • heat stress
  • plant growth
  • endothelial cells
  • machine learning
  • deep learning
  • stress induced
  • single cell
  • convolutional neural network
  • high speed
  • electron transfer
  • low cost