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Sökning: WFRF:(Li Jianrong) > (2022) > Soil moisture regul...

Soil moisture regulates warming responses of autumn photosynthetic transition dates in subtropical forests

Fu, Yongshuo H. (författare)
Beijing Normal University,University of Antwerp
Li, Xinxi (författare)
Beijing Normal University
Chen, Shouzhi (författare)
Beijing Normal University
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Wu, Zhaofei (författare)
Beijing Normal University
Su, Jianrong (författare)
Chinese Academy of Forestry, Beijing
Li, Xing (författare)
Seoul National University
Li, Shuaifeng (författare)
Chinese Academy of Forestry, Beijing
Zhang, Jing (författare)
Beijing Normal University
Tang, Jing (författare)
Lund University,Lunds universitet,MERGE: ModElling the Regional and Global Earth system,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,Institutionen för naturgeografi och ekosystemvetenskap,Centre for Environmental and Climate Science (CEC),Faculty of Science,Dept of Physical Geography and Ecosystem Science,University of Copenhagen
Xiao, Jingfeng (författare)
University of New Hampshire, Durham
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 (creator_code:org_t)
2022-06
2022
Engelska 12 s.
Ingår i: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 28:16, s. 4935-4946
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • Autumn phenology plays a key role in regulating the terrestrial carbon and water balance and their feedbacks to the climate. However, the mechanisms underlying autumn phenology are still poorly understood, especially in subtropical forests. In this study, we extracted the autumn photosynthetic transition dates (APTD) in subtropical China over the period 2003–2017 based on a global, fine-resolution solar-induced chlorophyll fluorescence (SIF) dataset (GOSIF) using four fitting methods, and then explored the temporal–spatial variations of APTD and its underlying mechanisms using partial correlation analysis and machine learning methods. We further predicted the APTD shifts under future climate warming conditions by applying process-based and machine learning-based models. We found that the APTD was significantly delayed, with an average rate of 7.7 days per decade, in subtropical China during 2003–2017. Both partial correlation analysis and machine learning methods revealed that soil moisture was the primary driver responsible for the APTD changes in southern subtropical monsoon evergreen forest (SEF) and middle subtropical evergreen forest (MEF), whereas solar radiation controlled the APTD variations in the northern evergreen-broadleaf deciduous mixed forest (NMF). Combining the effects of temperature, soil moisture and radiation, we found a significantly delayed trend in APTD during the 2030–2100 period, but the trend amplitude (0.8 days per decade) was much weaker than that over 2003–2017. In addition, we found that machine learning methods outperformed process-based models in projecting APTD. Our findings generate from different methods highlight that soil moisture is one of the key players in determining autumn photosynthetic phenological processes in subtropical forests. To comprehensively understand autumn phenological processes, in-situ manipulative experiments are urgently needed to quantify the contributions of different environmental and physiological factors in regulating plants' response to ongoing climate change.

Ämnesord

NATURVETENSKAP  -- Biologi -- Ekologi (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Ecology (hsv//eng)

Nyckelord

autumn phenology
chlorophyll fluorescence
climate change
machine learning
soil moisture
subtropical forests

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