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Canopy structure regulates autumn phenology by mediating the microclimate in temperate forests

Nature Climate Change volume 14, pages 1299–1305 (2024)Cite this article

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Abstract

Autumn phenology plays a critical role in shaping the carbon sequestration capacity of temperate forests. Notable local-scale variations in autumn phenology have drawn increasing attention recently, potentially introducing substantial uncertainty when predicting temperate forest productivity. Yet the underpinning mechanisms driving these variations remain inadequately elucidated. Here we observed significant and consistent relationships between canopy structure and autumn phenology across six temperate forest sites, induced by the regulation effect of canopy structure on microclimate conditions. Incorporating the identified ‘canopy structure–microclimate–autumn phenology’ pathway into existing autumn phenology models significantly improves the prediction accuracy and reduces the projected delay in the start of autumn over the remainder of the century. These findings offer a new perspective for interpreting the local variations of autumn phenology in temperate forests and emphasize the urgent need to integrate the identified pathway into the Earth system and vegetation models, especially considering the asynchronous changes of macroclimate and microclimate conditions.

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Fig. 1: Study site locations, spatial variations of autumn phenology metrics and relationships between canopy structure and autumn phenology.
Fig. 2: Relationships between canopy structure and microclimate factors and relative contributions of microclimate factors to autumn phenology.
Fig. 3: Relationships between microclimate factors and autumn phenology within the five NEON sites.
Fig. 4: A conceptual diagram of the influence of canopy structure on autumn phenology in temperate forests by mediating microclimate conditions.
Fig. 5: Model accuracy comparisons and future projections of autumn phenology under SSP1-2.6, SSP3-7.0 and SSP5-8.5.

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Data availability

The generated autumn phenology data, canopy structural attributes, microclimate simulation results, and terrain features, as well as the collected macroclimate records and in situ microclimate observations, are accessible via Figshare at https://doi.org/10.6084/m9.figshare.26064097 (ref. 54). The original NEON field measurements and airborne lidar data can be accessed via https://www.neonscience.org/, the National Forest Type Dataset through https://data.fs.usda.gov/geodata/rastergateway/forest_type/, the MODIS MCD43A1, MCD12Q2, and MOD15A2H data through https://www.earthdata.nasa.gov/, the ERA5 macroclimate data through https://cds.climate.copernicus.eu/ and the projected future macroclimate data through https://esgf-node.llnl.gov/projects/cmip6/ and https://cds.climate.copernicus.eu/.

Code availability

The complete R (version 4.2.3) and python (version 3.8) code used for the calculation and visualization of the results are accessible via Figshare at https://doi.org/10.6084/m9.figshare.26064097 (ref. 54).

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Acknowledgements

This study was supported by National Key R&D Program of China grant 2022YFF0803100, National Natural Science Foundation of China grants 42201408 and 31922090, Office of China Postdoc Council International Postdoctoral Exchange Fellowship YJ20210322 and the Innovation and Technology Fund (funding support to State Key Laboratories in Hong Kong of Agrobiotechnology) of the HKSAR, China. The tree elements in Fig. 4 were adapted from the design of Freepik.

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Authors and Affiliations

  1. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China

    Xiaoyong Wu, Chunyue Niu, Xiaoqiang Liu, Tianyu Hu, Lingli Liu & Yanjun Su

  2. China National Botanical Garden, Beijing, China

    Xiaoyong Wu, Chunyue Niu, Xiaoqiang Liu, Tianyu Hu, Lingli Liu & Yanjun Su

  3. University of Chinese Academy of Sciences, Beijing, China

    Xiaoyong Wu, Chunyue Niu, Xiaoqiang Liu, Tianyu Hu, Lingli Liu & Yanjun Su

  4. Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing, China

    Yuhao Feng & Qinghua Guo

  5. School of Biological Sciences and Institute for Climate and Carbon Neutrality, The University of Hong Kong, Pokfulam, China

    Yingyi Zhao, Shuwen Liu, Zhonghua Liu & Jin Wu

  6. Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China

    Guanhua Dai

  7. Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Yao Zhang

  8. Division of Forest, Nature and Landscape, Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium

    Koenraad Van Meerbeek

  9. KU Leuven Plant Institute, KU Leuven, Leuven, Belgium

    Koenraad Van Meerbeek

  10. Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China

    Qinghua Guo

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Contributions

Y.S. and X.W. designed this study. X.W. and C.N. developed the methodology and conducted the investigation, with contributions from Y.S. Y.S. and X.W. handled the visualization. The study was supervised by Y.S. X.W., C.N. and Y.S. wrote the original draft, with all authors contributing to the review and editing process.

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Correspondence to Yanjun Su.

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Nature Climate Change thanks David H. Klinges and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Location, drone imagery, and autumn phenology metrics of the Changbai Mountain (CBS) site.

a, Map of the CBS site with the background illustrating forest types, derived from moderate resolution imaging spectroradiometer (MODIS) MCD12Q1 product. ENF, EBF, DNF, DBF, and MF represent evergreen needleleaf forest, evergreen broadleaf forest, deciduous needleleaf forest, deciduous broadleaf forest, and mixed forest. The boundary line was obtained from the National Catalogue Service For Geographic Information. b, Drone image collected in the day of year (DOY) of 235 exhibiting the vegetation conditions before the start of autumn (SOA). c, Drone image collected in the DOY of 295 exhibiting the vegetation conditions after the end of autumn. d, Map of SOA for the CBS site, derived from drone images. e, Map of the duration of autumn (DOA) for the CBS site, derived from drone images. f, Box plots of SOA and DOA within the CBS site. Grey dots in the background represent data points of 10 × 10 m grid cells (number of samples = 109). The central vertical lines in each box represent the mean, and whiskers limits were calculated as 1.5 times of the interquartile range from the box limits.

Extended Data Fig. 2 Relationships between canopy structure, microclimate and autumn phenology metrics within the CBS site.

a, Relationships of SOA and DOA with plant area index (PAI) and canopy height. b, Relationships of SOA and DOA with global light index (GLI) and temperature buffering (Tbuffer). GLI and Tbuffer were derived from the simulated microclimate data (average values from 1 July to 31 August). All relationships were evaluated using the linear mixed-effects method with tree species as the random effect. The significance of the correlations was evaluated using a two-tailed t-test. Solid lines represent significant fitted lines (P < 0.05), and a confidence interval of 95% is shown (grey error bands).

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Supplementary methods, Tables 1–7 and Figs. 1–12.

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Wu, X., Niu, C., Liu, X. et al. Canopy structure regulates autumn phenology by mediating the microclimate in temperate forests. Nat. Clim. Chang. 14, 1299–1305 (2024). https://doi.org/10.1038/s41558-024-02164-2

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