Photoperiod as the primary determinant regulating the onset and cessation of wood formation in temperate trees

作者
Nipeng Qian,Gangdun Li,Linxu Wang,Chunchao Dong,Zhenzhao Xu,Qijing Liu,Guang Zhou
出处
期刊:Forest Ecosystems [Springer Science+Business Media]
卷期号:15: 100395-100395
标识
DOI:10.1016/j.fecs.2025.100395
摘要

Accurate prediction of xylem phenology is essential for evaluating the long-term impacts of climate change on carbon sequestration, forest productivity, and ecosystem resilience. However, the environmental controls on xylem phenology remain poorly clarified in terms of quantification, particularly for broadleaf species and the process of growth cessation. In this study, we monitored the onset and cessation of wood formation in 19 temperate tree species over periods of up to six years (2019–2024, with observation length varying among species) to assess the impact of environmental changes on the timing of wood formation. Linear mixed-effects models were used to evaluate and quantify the relative importance of photoperiod, forcing, chilling, precipitation, SPEI (standardized precipitation-evapotranspiration index), cold degree days, and MAT (mean annual temperature) to the onset and cessation of wood formation. Photoperiod and forcing temperature were identified as the key drivers of wood formation onset, while photoperiod was the primary factor regulating its cessation. Wood formation onset was less sensitive to photoperiod compared with cessation, but exhibited greater sensitivity to temperature. Conifers were more responsive to changes in day length at onset than broadleaf species, while broadleaf species appeared to rely solely on photoperiod to regulate the cessation, with conifers showing additional modulation by temperature. Moreover, ring-porous species exhibited stronger photoperiodic control of both onset and cessation than diffuse-porous species. These findings highlight the critical role of photoperiod, temperature, or their interactions in regulating xylem phenology, providing insights for improving process-based models that predict xylem growth dynamics.
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