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Dominant species predict plant richness and biomass in global grasslands

Nature Ecology & Evolution volume 9, pages 924–936 (2025)Cite this article

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Abstract

The bidirectional relationship between plant species richness and community biomass is often variable and poorly resolved in natural grassland ecosystems, impeding progress in predicting impacts of environmental changes. Most biological communities have long-tailed species abundance distributions (for example, biomass, cover, number of individuals), a general property that may provide predictive power for species richness and community biomass. Here we show mathematical relationships between community characteristics and the abundance of dominant species arising from long-tailed distributions and test these predictions using observational and experimental data from 76 grassland sites across 6 continents. We find that community biomass provides little predictive ability for community richness, consistent with previous findings. By contrast, the relative abundance of dominant species quantitatively predicts species richness, whereas their absolute abundance quantitatively predicts community biomass under both ambient and altered environmental conditions, as expected mathematically. These results are robust to the type of abundance measure used. Three types of simulated data further show the generality of these results. Our integrative framework, arising from a few dominant species and mathematical properties of species abundance distributions, fills a persistent gap in our ability to predict community richness and biomass under ambient and anthropogenically altered conditions.

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Fig. 1: Structural relationships and species abundance patterns across global grassland sites.
Fig. 2: Grassland richness and biomass relationships under ambient conditions.
Fig. 3: Grassland richness and biomass relationships under altered environmental conditions.
Fig. 4: The regression model for ambient conditions predicts the outcome for global grasslands under altered environmental conditions.
Fig. 5: Structural relationships across empirical and simulated grassland data under ambient and altered conditions.

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Data availability

All the data that support the findings of this article are freely available via the Environmental Data Initiative (EDI) Data Portal (https://doi.org/10.6073/pasta/442895326274ea09942bd04e6ea92df2)71.

Code availability

The R code used to perform the analyses is freely available via the EDI Data Portal (https://doi.org/10.6073/pasta/442895326274ea09942bd04e6ea92df2)71.

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Acknowledgements

We thank each of the researchers who have contributed data and ideas to NutNet (http://www.nutnet.org). Grants to P.Z. came from the National Natural Science Foundation of China (grant number 32101267) and the Start-Up Funds of Introduced Talent in Lanzhou University (grant number 561120205). Azi.cn and azitwo.cn were conducted at the Gansu Gannan Grassland Ecosystem National Observation and Research Station. Coordination and data management in NutNet have been supported by funding to E.T.B. and E.W.S. from the National Science Foundation Research Coordination Network (NSF-DEB-1042132) and Long-Term Ecological Research (NSF-DEB-1234162 to Cedar Creek LTER) programmes, and the Institute on the Environment (DG-0001-13). We also thank the Minnesota Supercomputer Institute for hosting project data and the Institute on the Environment for hosting network meetings. N.E. was funded by the German Research Foundation (DFG‚ FZT 118, 202548816; Ei 862/29-1). Y.L. was funded by MPG Ranch; N.G.S. was supported by the US National Science Foundation (DEB-2045968) and Texas Tech University. S.M.P. was supported by the Australian government through the Great Western Woodlands TERN SuperSite. G.M.W. and C.R.D. were supported by the Australian Government through the desert ecology TERN sites funded by NCRIS and the ARC. P.T. was funded by UBACyT20020190100212BA, PICT-2019-2019-02324 and Familia Bordeu and Pepo. A.J. was funded by Federal Ministry of Education and Research (BMBF) (grant numbers FKZ 031B0516C and 031B1067C). J.A.C. was funded by European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant number 101002987).

Author information

Authors and Affiliations

  1. State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Ecology, Lanzhou University, Lanzhou, People’s Republic of China

    Pengfei Zhang, Zhengwei Ren & Guozhen Du

  2. Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN, USA

    Pengfei Zhang, Eric W. Seabloom & Elizabeth T. Borer

  3. School of Mathematics, University of Minnesota, Minneapolis, MN, USA

    Jasmine Foo

  4. Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada

    Andrew S. MacDougall

  5. Department of Physiological Diversity, Helmholtz Center for Environmental Research—UFZ, Leipzig, Germany

    W. Stanley Harpole & Christiane Roscher

  6. German Centre for Integrative Biodiversity Research (iDiv), Leipzig, Germany

    W. Stanley Harpole, Nico Eisenhauer & Christiane Roscher

  7. Martin Luther University Halle-Wittenberg, Halle, Germany

    W. Stanley Harpole

  8. Department of Wildland Resources and the Ecology Center, Utah State University, Logan, UT, USA

    Peter B. Adler

  9. Ecology and Biodiversity Group, Department of Biology, Utrecht University, Utrecht, The Netherlands

    Yann Hautier

  10. Institute of Biology, Leipzig University, Leipzig, Germany

    Nico Eisenhauer

  11. Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden

    Marie Spohn

  12. School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA

    Jonathan D. Bakker

  13. MPG Ranch Missoula, Florence, MT, USA

    Ylva Lekberg

  14. Department of Ecosystem and Conservation Sciences, W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, USA

    Ylva Lekberg

  15. Department of Biology, University of North Carolina Greensboro, Greensboro, NC, USA

    Alyssa L. Young

  16. School of Life Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa

    Clinton Carbutt & Michelle J. Tedder

  17. Scientific Services, Ezemvelo KZN Wildlife, Pietermaritzburg, South Africa

    Clinton Carbutt

  18. Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland

    Anita C. Risch

  19. Instituto Nacional de Tecnología Agropecuaria (INTA)–Universidad Nacional de la Patagonia Austral (UNAP)–CONICET, Río Gallegos, Argentina

    Pablo L. Peri

  20. Department of Biological Sciences, Texas Tech University, Lubbock, TX, USA

    Nicholas G. Smith

  21. Lancaster Environment Centre, Lancaster University, Lancaster, UK

    Carly J. Stevens

  22. CSIRO Environment, Canberra, Australian Capital Territory, Australia

    Suzanne M. Prober

  23. Health and Environmental Sciences Department, Xi’an Jiaotong-Liverpool University, Suzhou, People’s Republic of China

    Johannes M. H. Knops

  24. Desert Ecology Research Group, School of Life and Environmental Sciences and ARC Training Centre in Data Analytics for Resources and Environments (DARE), The University of Sydney, Sydney, New South Wales, Australia

    Glenda M. Wardle & Christopher R. Dickman

  25. Institute of Ecology and Evolution, University Jena, Jena, Germany

    Anne Ebeling

  26. Department of Biology, McDaniel College, Westminster, MD, USA

    Holly M. Martinson

  27. Department of Biology, Texas State University, San Marcos, TX, USA

    Jason P. Martina

  28. Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia

    Sally A. Power

  29. Department of Disturbance Ecology and Vegetation Dynamics, Bayreuth Center of Ecology and Environmental Research (Bayceer), University of Bayreuth, Bayreuth, Germany

    Yujie Niu & Anke Jentsch

  30. College of Grassland Science, Key Laboratory of Grassland Ecosystem of the Ministry of Education, Gansu Agricultural University, Lanzhou, People’s Republic of China

    Yujie Niu

  31. Ecology & Genetics, University of Oulu, Oulu, Finland

    Risto Virtanen

  32. IFEVA, Facultad de Agronomía, Universidad de Buenos Aires-CONICET, Buenos Aires, Argentina

    Pedro Tognetti

  33. Department of Geography, King’s College London, London, UK

    Jane A. Catford

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Contributions

Conceptualization: P.Z., E.T.B. and E.W.S. conceived and developed the study with substantial input from J.F., A.S.M., W.S.H., P.B.A., Y.H., N.E. and M.S. Formal analysis: P.Z. performed the analysis, J.F. developed the mathematical formula derivation and J.D.B. contributed to the analysis. Writing—original draft: P.Z. and E.T.B. wrote the paper with substantial input from E.W.S., J.F., A.S.M., W.S.H., P.B.A., N.E. and M.S. Writing—review and editing: all co-authors contributed data and reviewed, approved and had the opportunity to comment on the paper. An author contribution matrix is provided in Supplementary Table 5.

Corresponding author

Correspondence to Pengfei Zhang.

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The authors declare no competing interests.

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Nature Ecology & Evolution thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Climatic (a) and geographic (b) distribution of 76 experimental sites from NutNet data; and (c) structural equation meta-model to characterize the possible relationships among log(mean absolute biomass of dominant species), log(mean relative biomass of dominant species), log(mean community biomass), and log(mean species richness); and the link between these four variables in the structural equation model and one site-level patterns of (d) cumulative absolute biomass curve and (e) cumulative relative biomass curve taking azi.cn site under ambient conditions as an example.

Here, species-level abundance is estimated based on species-level biomass. The red, gray, and blue dots in Fig. a and b mean that the relationship between log(species richness) and log(community biomass) on the site-level under ambient conditions (year = 0) is significantly positive, non-significant correlated, and significantly negative correlated, respectively (see Extended Data Fig. 3a).

Extended Data Fig. 2 (a) The relationship between directly measured species-level absolute biomass and species-level absolute cover for the 7 sites that simultaneously collected these data; and (b-g) patterns of these 7 sites based on species level biomass data (black) and species level cover data (purple).

All data were natural log-transformed. Each regression curve in Fig. a represents one year (in different colors). Relationships between mean richness and (b) mean community biomass, and (c) mean absolute biomass of the two most dominant species, and (d) mean relative biomass of the two most dominant species; and (e) between mean relative biomass and mean absolute biomass of the two most dominant species; and between mean community biomass and (f) mean absolute biomass of the two most dominant species, and (g) mean relative biomass of the two most dominant species, of these 7 sites. The black dots and lines in Fig. b to g are the results of each pattern by directly calculating the absolute and relative biomass of the two most dominant species based on the species level biomass data. The purple dots and lines in Fig. b to g are the results of each pattern by indirect calculations of the absolute and relative biomass of the two dominant species based on species level cover data. The dashed and solid lines indicate that the overall relationship is not significant (P > 0.05) and significant (P < 0.05), respectively, with shaded areas indicating 95% confidence intervals. Different sites are represented by points of different shapes. Slopes in Fig. d and f are reported as the mean ± SEM. All statistical tests are conducted as two-sided.

Extended Data Fig. 3 Site-level relationships in NutNet under (a-f) ambient conditions and (g-l) altered conditions.

The relationship between richness and (a, g) community biomass, and (b, h) absolute biomass of the two most dominant species, and (c, i) relative biomass of the two most dominant species; and (d, j) between relative biomass and absolute biomass of the two most dominant species; and between community level biomass and (e, k) absolute biomass of the two most dominant species, and (f, l) relative biomass of the two most dominant species of each site under ambient and altered conditions (76 sites; each site ≈ 3 blocks; each block ≈ 10 plots). All data were natural log-transformed to improve normality. The black dashed and solid lines indicate that the worldwide relationship is not significant (P > 0.05) and significant (P < 0.05), respectively. Both marginal R2 (R2m) and conditional R2 (R2c) are presented in the figures. All statistical tests are conducted as two-sided.

Extended Data Fig. 4 Worldwide relationships between log(gamma diversity) and log(mean relative biomass of the two most dominant species) across 76 NutNet sites under (a) ambient and (b) altered conditions.

All data were natural log-transformed to improve normality. The solid lines indicate that the overall relationship is significant (P < 0.05), with shaded areas indicating 95% confidence intervals. All statistical tests are conducted as two-sided.

Extended Data Fig. 5 Overall relationships generated from simulated lognormal distribution data under simulated ambient (purple lines) and altered environmental (dots and black lines) conditions.

The relationship between mean of the number of all values and (a) mean of the sum of all values, and (b) mean of the sum of the two largest values, and (c) mean of the proportion of the two largest values; and (d) mean of the proportion of the two largest values and mean of the sum of the two largest values; and between mean of the sum of all values and (e) mean of the sum of the two largest values, and (f) mean of the proportion of the two largest values, of 100 simulated sites under simulated altered environmental conditions (100 simulated sites; simulated year > 0; each simulated site includes 3 simulated blocks; each simulated block includes 10 simulated plots). All data were natural log-transformed to improve normality. The red, gray, and blue dots mean that the relationship between the y-axis and x-axis variables of each panel on the simulated site-level is significantly positive, non-significant correlated, and significantly negative correlated under simulated altered conditions, respectively. The purple lines are regression curves for the ambient conditions. The purple fonts are R2 and P values for the ambient conditions. The black lines are regression curves for the altered environmental conditions. The black fonts are R2 and P values for the altered environmental conditions. The dashed and solid lines indicate that the overall relationship is not significant (P > 0.05) and significant (P < 0.05), respectively, with shaded areas indicating 95% confidence intervals. All statistical tests are conducted as two-sided.

Extended Data Fig. 6 Overall relationships generated from simulated Fisher’s log series distribution data under simulated ambient (purple lines) and altered environmental (dots and black lines) conditions.

The relationship between mean of the number of all values and (a) mean of the sum of all values, and (b) mean of the sum of the two largest values, and (c) mean of the proportion of the two largest values; and (d) mean of the proportion of the two largest values and mean of the sum of the two largest values; and between mean of the sum of all values and (e) mean of the sum of the two largest values, and (f) mean of the proportion of the two largest values, of 100 simulated sites under simulated ambient and altered environmental conditions (100 simulated sites; simulated year > 0; each simulated site includes 3 simulated blocks; each simulated block includes 10 simulated plots). All data were natural log-transformed to improve normality. The red, gray, and blue dots mean that the relationship between the y-axis and x-axis variables of each panel on the simulated site-level is significantly positive, non-significant correlated, and significantly negative correlated under simulated altered conditions, respectively. The purple lines are regression curves for the ambient conditions. The purple fonts are R2 and P values for the ambient conditions. The black lines are regression curves for the altered environmental conditions. The black fonts are R2 and P values for the altered environmental conditions. The dashed and solid lines indicate that the overall relationship is not significant (P > 0.05) and significant (P < 0.05), respectively, with shaded areas indicating 95% confidence intervals. All statistical tests are conducted as two-sided.

Extended Data Fig. 7 Overall relationships generated from simulated multinomial distribution data under simulated ambient (purple lines) and altered environmental (dots and black lines) conditions.

The relationship between mean of the number of all values and (a) mean of the sum of all values, and (b) mean of the sum of the two largest values, and (c) mean of the proportion of the two largest values; and (d) mean of the proportion of the two largest values and mean of the sum of the two largest values; and between mean of the sum of all values and (e) mean of the sum of the two largest values, and (f) mean of the proportion of the two largest values, of 100 simulated sites under simulated ambient and altered environmental conditions (100 simulated sites; simulated year > 0; each simulated site includes 3 simulated blocks; each simulated block includes 10 simulated plots). All data were natural log-transformed to improve normality. The red, gray, and blue dots mean that the relationship between the y-axis and x-axis variables of each panel on the simulated site-level is significantly positive, non-significant correlated, and significantly negative correlated under simulated altered conditions, respectively. The purple lines are regression curves for the ambient conditions. The purple fonts are R2 and P values for the ambient conditions. The black lines are regression curves for the altered environmental conditions. The black fonts are R2 and P values for the altered environmental conditions. The dashed and solid lines indicate that the overall relationship is not significant (P > 0.05) and significant (P < 0.05), respectively, with shaded areas indicating 95% confidence intervals. All statistical tests are conducted as two-sided.

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Zhang, P., Seabloom, E.W., Foo, J. et al. Dominant species predict plant richness and biomass in global grasslands. Nat Ecol Evol 9, 924–936 (2025). https://doi.org/10.1038/s41559-025-02701-y

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