Abstract
The seasonal dynamics of the vegetation canopy strongly regulate the surface energy balance and terrestrial carbon fluxes, providing feedbacks to climate change. Whether the seasonal timing of maximum canopy structure was optimized to achieve a maximum photosynthetic carbon uptake is still not clear due to the complex interactions between abiotic and biotic factors. We used two solar-induced chlorophyll fluorescence datasets as proxies for photosynthesis and the normalized difference vegetation index and leaf area index products derived from the moderate resolution imaging spectroradiometer as proxies for canopy structure, to characterize the connection between their seasonal peak timings from 2000 to 2018. We found that the seasonal peak was earlier for photosynthesis than for canopy structure in >87.5% of the northern vegetated area, probably leading to a suboptimal maximum seasonal photosynthesis. This mismatch in peak timing significantly increased during the study period, mainly due to the increasing atmospheric CO2, and its spatial variation was mainly explained by climatic variables (43.7%) and nutrient limitations (29.6%). State-of-the-art ecosystem models overestimated this mismatch in peak timing by simulating a delayed seasonal peak of canopy development. These results highlight the importance of incorporating the mechanisms of vegetation canopy dynamics to accurately predict the maximum potential terrestrial uptake of carbon under global environmental change.
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Data availability
The CSIF dataset is from https://doi.org/10.17605/OSF.IO/8XQY6. The GOME-2 SIF dataset is from https://avdc.gsfc.nasa.gov/pub/data/satellite/MetOp/GOME_F/. The MODIS NDVI dataset is from https://lpdaac.usgs.gov/products/mod13c1v006/. The reprocessed LAI dataset is from http://globalchange.bnu.edu.cn/research/laiv6. The FLUXNET2015 dataset is from https://fluxnet.org/data/fluxnet2015-dataset/. The surface air temperature and Rad datasets are from https://rda.ucar.edu/datasets/ds314.3/. The SWC dataset is from https://disc.gsfc.nasa.gov/datasets/GLDAS_NOAH025_3H_2.1/summary?keywords=GLDAS. The SLA, Nm and Pm datasets are from https://github.com/abhirupdatta/global_maps_of_plant_traits. The canopy height and maximum rooting depth datasets are from https://webmap.ornl.gov/ogc/dataset.jsp?dg_id=10023_1 and https://wci.earth2observe.eu/thredds/catalog/usc/root-depth/catalog.html. The ASR and plant species datasets are from https://ecotope.org/anthromes/biodiversity/plants/data/ and https://databasin.org/datasets/43478f840ac84173979b22631c2ed672/. The tree density dataset is from https://elischolar.library.yale.edu/yale_fes_data/1/.
Code availability
All computer codes for the analysis of the data are available from the corresponding author on reasonable request.
Change history
11 January 2023
A Correction to this paper has been published: https://doi.org/10.1038/s41477-023-01342-y
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Acknowledgements
This study was supported by the National Natural Science Foundation of China (41988101), the Second Tibetan Plateau Scientific Expedition and Research Program (grant no. 2019QZKK0208) and the Xplorer Prize. Z.Z. was supported by the National Natural Science Foundation of China (41901122) and the Shenzhen Fundamental Research Program (GXWD20201231165807007-20200814213435001). J.P. was supported by the Spanish Government grant PID2019-110521GB-I00, the Fundación Ramón Areces grant CIVP20A6621 and the Catalan Government grants SGR2017-1005 and AGAUR-2020PANDE00117. We thank H. Vallicrosa from CSIC, Global Ecology Unit CREAF-CSIC-UAB for discussion. We also thank I. MacLachlan from Peking University for proofreading. We are grateful for the computational resources provided by the High-performance Computing Platform of Peking University’s supercomputing facility.
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S.P. and Z.Z. designed the study. Q.Z. performed the analysis. Q.Z., S.P. and Z.Z. wrote the initial draft. All authors, including H.Z., R.M., Y.Z. and J.P., contributed to the interpretation of the results and the writing of the paper.
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Extended data
Extended Data Fig. 1 Comparison between the timings of seasonal peak photosynthesis and canopy structure in northern ecosystems based on multiple proxies.
Spatial patterns of the seasonal peak timing difference between photosynthesis and canopy structure represented by δDOYGOME-2 SIF, NDVI (a) and δDOYCSIF, LAI (b).
Extended Data Fig. 2 Mean differences of seasonal peak timings between photosynthesis and canopy structure for different ecosystem types.
Northern ecosystems (n = 2578665), forests (n = 788343), shrublands (n = 765928), and grasslands (n = 344205). Boxplots show the median, maximum, minimum, 25th, and 75th quartiles values (without outliers). The coloured letters represent significant differences (all p values = 9.56 × 10−10, two-sided Tukey’s HSD test) in average δDOYCSIF, NDVI among ecosystems estimated by one-way analysis of variance (ANOVA).
Extended Data Fig. 3 Illustration of the optimal GPPmax conceptual model.
Coloured curves indicate the seasonal cycles of environmental resources (Resource, blue), photosynthesis (GPP, orange), and canopy structure (NDVI, green). The seasonal peak timing of the canopy structure is adjusted to match the highest availability of environmental resources (DOYNDVI = DOYResource), and therefore the optimized maximum seasonal GPP was achieved.
Extended Data Fig. 4 Attribution of the trends in absolute δDOYCSIF, NDVI in northern ecosystems during 2000–2017.
a, Trends in spatially averaged absolute δDOYCSIF, NDVI derived from satellite observation (OBS) and BRT models (Predicted), and attributed respectively to rising CO2 (CO2), climate change (Climate), and other factors (Others). b-e, Spatial patterns of the trends in absolute δDOYCSIF, NDVI corresponding to the columns in a. The satellite observation was resampled to 0.5° to match the spatial resolution of explanatory variables in the BRT model.
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Supplementary Figs. 1–7 and Tables 1–3.
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Zhao, Q., Zhu, Z., Zeng, H. et al. Seasonal peak photosynthesis is hindered by late canopy development in northern ecosystems. Nat. Plants 8, 1484–1492 (2022). https://doi.org/10.1038/s41477-022-01278-9
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DOI: https://doi.org/10.1038/s41477-022-01278-9
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