Soil moisture regulates warming responses of autumn photosynthetic transition dates in subtropical forests
Fu, Yongshuo H. ; Li, Xinxi ; Chen, Shouzhi ; Wu, Zhaofei ; Su, Jianrong ; Li, Xing ; Li, Shuaifeng ; Zhang, Jing ; Tang, Jing LU
and Xiao, Jingfeng
(2022)
In Global Change Biology
28(16).
p.4935-4946
- Abstract
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... (More)
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.
(Less)
- author
- Fu, Yongshuo H.
; Li, Xinxi
; Chen, Shouzhi
; Wu, Zhaofei
; Su, Jianrong
; Li, Xing
; Li, Shuaifeng
; Zhang, Jing
; Tang, Jing
LU
and Xiao, Jingfeng
- organization
- publishing date
- 2022-08
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- autumn phenology, chlorophyll fluorescence, climate change, machine learning, soil moisture, subtropical forests
- in
- Global Change Biology
- volume
- 28
- issue
- 16
- pages
- 12 pages
- publisher
- Wiley-Blackwell
- external identifiers
-
- pmid:35642473
- scopus:85130999273
- ISSN
- 1354-1013
- DOI
- 10.1111/gcb.16227
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2022 John Wiley & Sons Ltd.
- id
- 919f6926-1660-4b68-8808-6cbd7d62ce61
- date added to LUP
- 2022-08-19 14:04:04
- date last changed
- 2024-06-13 10:15:42
@article{919f6926-1660-4b68-8808-6cbd7d62ce61,
abstract = {{<p>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.</p>}},
author = {{Fu, Yongshuo H. and Li, Xinxi and Chen, Shouzhi and Wu, Zhaofei and Su, Jianrong and Li, Xing and Li, Shuaifeng and Zhang, Jing and Tang, Jing and Xiao, Jingfeng}},
issn = {{1354-1013}},
keywords = {{autumn phenology; chlorophyll fluorescence; climate change; machine learning; soil moisture; subtropical forests}},
language = {{eng}},
number = {{16}},
pages = {{4935--4946}},
publisher = {{Wiley-Blackwell}},
series = {{Global Change Biology}},
title = {{Soil moisture regulates warming responses of autumn photosynthetic transition dates in subtropical forests}},
url = {{http://dx.doi.org/10.1111/gcb.16227}},
doi = {{10.1111/gcb.16227}},
volume = {{28}},
year = {{2022}},
}