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Decadal-scale observations are key to detecting the stabilizing effects of plant diversity in natural ecosystems

Nature Plants volume 12, pages 37–48 (2026) Cite this article

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Abstract

Extensive experimental and theoretical evidence demonstrates the positive effects of plant diversity on the temporal stability of productivity, yet how the diversity-stability relationship varies across timescales and different diversity dimensions in natural ecosystems remains unclear. By integrating a comprehensive regional vegetation survey conducted in Tibetan alpine grasslands with the global plant diversity and productivity databases, we revealed a consistent temporal pattern at regional and global scales: the stabilizing effect of plant diversity on productivity strengthened over time, approaching saturation at 10 to 13 years. Notably, plant phylogenetic diversity emerged as the dominant biotic driver of long-term stability. In contrast, plant community height exerted a stronger positive influence on short-term stability. These findings highlight the critical role of timescales in shaping diversity-stability relationships and underscore the necessity of decadal-scale studies. Our results further support integrating phylogenetic diversity into conservation and management strategies to sustain ecosystem functioning under global change.

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Fig. 1: Locations of sampling sites and temporal stability of productivity across timescales.
The alternative text for this image may have been generated using AI.
Fig. 2: Biotic and environmental drivers of productivity stability across timescales.
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Fig. 3: Biotic and climatic drivers of productivity stability across timescales.
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Fig. 4: Global patterns of GPP temporal stability and three plant diversity dimensions.
The alternative text for this image may have been generated using AI.
Fig. 5: Temporal dynamics of the effects of global plant diversity on GPP stability.
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Data availability

ET data were obtained from the Penman-Monteith-Leuning Version 2 (PML V2) dataset on Google Earth Engine (https://developers.google.com/earth-engine/datasets/catalog/CAS_IGSNRR_PML_V2_v018). EVI data were from the MODIS MOD13Q1 product on Google Earth Engine (https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13Q1). FLUXCOM GPP data are available from the Max Planck Institute for Biogeochemistry (https://www.bgc-jena.mpg.de/geodb/projects/Home.php). All raw and processed data generated in this study are deposited in Figshare (https://doi.org/10.6084/m9.figshare.28643762). Source data are provided with this paper.

Code availability

The analysis code used in this study is deposited in Figshare (https://doi.org/10.6084/m9.figshare.28643762).

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Acknowledgements

We thank all the researchers whose data were incorporated in this study. This work was supported by the National Natural Science Foundation of China (grant 32588202 to S.N.), the National Key Research and Development Program of China (grant 2022YFF0802100 to S.N., R.Z., D.T. and S.W.) and the National Natural Science Foundation of China (grant 32322055 to D.T. and grant 32571824 to R.Z.).

Author information

Authors and Affiliations

  1. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, PR China

    Ruiyang Zhang, Dashuan Tian, Jinsong Wang, Juntao Zhu, Junxiao Pan, Guang Zhao, Yunlong He, Jinlong Peng & Shuli Niu

  2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, PR China

    Ruiyang Zhang, Chenfang Su, Dashuan Tian, Jinsong Wang, Juntao Zhu, Junxiao Pan, Guang Zhao, Jinlong Peng & Shuli Niu

  3. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, PR China

    Chenfang Su

  4. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, PR China

    Yiheng Wang

  5. Institute of Ecology, College of Urban and Environmental Sciences, and State Key Laboratory for Vegetation Structure, Function and Construction (VegLab), Peking University, Beijing, PR China

    Shaopeng Wang

  6. School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA

    Lin Jiang

  7. State Key Laboratory for Development and Utilization of Forest Food Resources, Zhejiang A&F University, Hangzhou, PR China

    Xinli Chen

  8. School of Integrative Plant Science, Cornell University, Ithaca, NY, USA

    Quan Quan

  9. Department of Botany, University of Wisconsin-Madison, Madison, WI, USA

    Pu Yan

  10. Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, PR China

    Yang Li

  11. Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Ecology, Northeast Forestry University, Harbin, PR China

    Lei Song

  12. School of Grassland Science, Beijing Forestry University, Beijing, PR China

    Yingjie Yan

  13. College of Grassland Science and Technology, China Agricultural University, Beijing, PR China

    Yicheng He

  14. College of Animal Science, Xizang Agricultural and Animal Husbandry University, Linzhi, PR China

    Xuehong Wei

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Contributions

R.Z. and S.N. conceived and designed the study. R.Z. conducted the research with assistance from S.W. and L.J., who contributed to theoretical analyses. R.Z., C.S. and Y.W. performed data analyses. R.Z., C.S., Y.W., D.T., J.W., J.Z., J. Pan, G.Z., Y.L., L.S., Y.Y., Y.H. and X.W. contributed experimental and field data. R.Z. wrote the initial draft of the manuscript. S.N., S.W., D.T., J.W., L.J., X.C., C.S., Y.W., J.Z., J. Pan, G.Z., Q.Q., P.Y., Y.H., Y.L., L.S. and J. Peng contributed substantially to manuscript revision.

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Correspondence to Shuli Niu.

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Extended data

Extended Data Fig. 1 Temporal stability of global gross primary productivity (GPP) across timescales.

Points represent the mean stability values at each timescale, and vertical bars indicate standard errors (n = 47,079 grid cells). The solid blue line shows the fitted segmented linear regression, with the shaded area representing 95% confidence intervals. The vertical dashed line denotes the identified temporal threshold (year 13). Regression coefficients were tested using two-sided t-tests, and breakpoint significance was evaluated with two-sided F-tests (P < 2×10-16).

Source data

Extended Data Fig. 2 Individual effects of environmental and biotic factors on productivity stability across extended time scales.

Individual effects of environmental (elevation and climatic aridity) and biotic factors (phylogenetic, taxonomic, and functional diversity: FEve, FRic, FDiv; and community-weighted mean traits) on productivity stability across extended time scales (ranging from 5 to 20 years). Effect sizes are represented by standardized regression slopes derived from linear regression models (n = 160 independent sites).

Source data

Extended Data Fig. 3 Effects of all biotic and environmental factors on productivity stability over 5-, 13-, and 20-year periods.

Effects of biotic and environmental factors on productivity stability over 5- (a), 13- (b), and 20-year (c) periods (n = 160 independent sites). The environmental factors include elevation and climatic aridity, and the multiple facets of diversity include taxonomic diversity (TD), phylogenetic diversity (PD), functional traits (CWM.HT: community-weighted mean height; CWM.LW: community-weighted mean leaf water content; CWM.SPAD: community-weighted mean SPAD; CWM.SLA: community-weighted mean SLA) and functional diversity (Feve: functional evenness; Fdiv: functional divergence; Fric: functional richness). Bars represent the average standardized regression coefficients (effect sizes) from multiple linear regression models, with error bars indicating 95% confidence intervals. The relative importance of each predictor is expressed as the percentage of explained variance. Significance levels were determined using two-sided t-tests: *P < 0.001, P < 0.01, P < 0.05, ^P < 0.1. All variables were standardized (z-scores) to enable comparison of effect sizes. No adjustment for multiple comparisons was applied.

Source data

Extended Data Fig. 4 Effects of biotic and environmental factors on productivity stability over time, assessed using a non-overlapping 5-year window approach.

Effects of biotic and environmental factors on productivity stability over time, assessed using a non-overlapping 5-year window approach (n = 160 independent sites). Time periods are defined as D1 (2018-2022) (a), D2 (2013-2017) (b), D3 (2008-2012) (c), and D4 (2003-2007) (d). Environmental predictors include elevation and climatic aridity. Biotic predictors comprise taxonomic diversity (TD), phylogenetic diversity (PD), functional diversity (FD), and community-weighted mean traits: plant height (CWM.HT) and specific leaf area (CWM.SLA). Bars represent the average standardized regression coefficients (effect sizes) of model predictors, with error bars indicating 95% confidence intervals. The relative importance of each predictor is expressed as the percentage of explained variance. Significance levels were determined using two-sided t-tests: ***P < 0.001, **P < 0.01, *P < 0.05, and ^P < 0.1. All variables were standardized (z-scores) to enable comparison of effect sizes. No adjustment for multiple comparisons was applied.

Source data

Extended Data Fig. 5 Effects of biotic and climatic factors on productivity stability over time, analyzed using a non-overlapping 5-year window approach.

Effects of biotic and climatic factors on productivity stability over time, analyzed using a non-overlapping 5-year window approach (n = 160 independent sites). Four time periods were examined: D1 (2018-2022) (a), D2 (2013-2017) (b), D3 (2008-2012) (c), and D4 (2003-2007) (d). Environmental predictors comprise elevation and interannual climate variability, represented by the coefficient of variation in precipitation (Prep CV) and temperature (Temp CV). Biotic predictors include taxonomic diversity (TD), phylogenetic diversity (PD), functional diversity (FD), and community-weighted mean traits for plant height (CWM.HT) and specific leaf area (CWM.SLA). Bars display average standardized regression coefficients with 95% confidence intervals, while the relative importance of each predictor is shown as the percentage of explained variance. Significance levels were determined using two-sided t-tests: ***P < 0.001, **P < 0.01, *P < 0.05, and ^P < 0.1. All variables were standardized (z-scores) to enable comparison of effect sizes. No adjustment for multiple comparisons was applied.

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Zhang, R., Su, C., Wang, Y. et al. Decadal-scale observations are key to detecting the stabilizing effects of plant diversity in natural ecosystems. Nat. Plants 12, 37–48 (2026). https://doi.org/10.1038/s41477-025-02189-1

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