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Consistent stabilizing effects of plant diversity across spatial scales and climatic gradients

Nature Ecology & Evolution volume 6, pages 1669–1675 (2022)Cite this article

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Abstract

Biodiversity has widely been documented to enhance local community stability but whether such stabilizing effects of biodiversity extend to broader scales remains elusive. Here, we investigated the relationships between biodiversity and community stability in natural plant communities from quadrat (1 m2) to plot (400 m2) and regional (5−214 km2) scales and across broad climatic conditions, using an extensive plant community dataset from the National Ecological Observatory Network. We found that plant diversity provided consistent stabilizing effects on total community abundance across three nested spatial scales and climatic gradients. The strength of the stabilizing effects of biodiversity increased modestly with spatial scale and decreased as precipitation seasonality increased. Our findings illustrate the generality of diversity–stability theory across scales and climatic gradients, which provides a robust framework for understanding ecosystem responses to biodiversity and climate changes.

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Fig. 1: Biodiversity and ecosystem stability across multiple spatial scales along climatic gradients.
Fig. 2: The DSRs across multiple spatial scales.
Fig. 3: The SEM depicting the relationships among climatic factors, species diversity and ecosystem stability across multiple spatial scales.
Fig. 4: The DSRs within sites and their dependencies on climatic factors.

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Data availability

Raw datasets are available from the NEON (https://data.neonscience.org/data-products/DP1.10058.001). The data used in this study are available via GitHub (https://github.com/mwliang/NEON_stability).

Code availability

R code of all analyses is available at GitHub (https://github.com/mwliang/NEON_stability).

References

  1. Transforming our World: The 2030 Agenda for Sustainable Development (UN, 2015).

  2. May, R. M. Will a large complex system be stable? Nature 238, 413–414 (1972).

    CAS  PubMed  Google Scholar 

  3. Pimm, S. L. The complexity and stability of ecosystems. Nature 307, 321–326 (1984).

    Google Scholar 

  4. McCann, K. S. The diversity–stability debate. Nature 405, 228–233 (2000).

    CAS  PubMed  Google Scholar 

  5. Ives, A. R. & Carpenter, S. R. Stability and diversity of ecosystems. Science 317, 58–62 (2007).

    CAS  PubMed  Google Scholar 

  6. Donohue, I. et al. Navigating the complexity of ecological stability. Ecol. Lett. 19, 1172–1185 (2016).

    PubMed  Google Scholar 

  7. Yachi, S. & Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Proc. Natl Acad. Sci. USA 96, 1463–1468 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).

    CAS  PubMed  Google Scholar 

  9. Tilman, D., Reich, P. B. & Knops, J. M. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).

    CAS  PubMed  Google Scholar 

  10. Loreau, M. et al. Biodiversity as insurance: from concept to measurement and application. Biol. Rev. 96, 2333–2354 (2021).

    PubMed  Google Scholar 

  11. Thibaut, L. M. & Connolly, S. R. Understanding diversity–stability relationships: towards a unified model of portfolio effects. Ecol. Lett. 16, 140–150 (2013).

    PubMed  Google Scholar 

  12. Xu, Q. et al. Consistently positive effect of species diversity on ecosystem, but not population, temporal stability. Ecol. Lett. 24, 2256–2266 (2021).

    PubMed  Google Scholar 

  13. Hector, A. et al. General stabilizing effects of plant diversity on grassland productivity through population asynchrony and overyielding. Ecology 91, 2213–2220 (2010).

    CAS  PubMed  Google Scholar 

  14. Hautier, Y. et al. Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science 348, 336–340 (2015).

    CAS  PubMed  Google Scholar 

  15. Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).

    CAS  PubMed  Google Scholar 

  16. Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).

    Google Scholar 

  17. Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Gonzalez, A. et al. Scaling-up biodiversity–ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).

    PubMed  PubMed Central  Google Scholar 

  19. Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).

    Google Scholar 

  20. Wang, S. & Loreau, M. Ecosystem stability in space: alpha, beta and gamma variability. Ecol. Lett. 17, 891–901 (2014).

    PubMed  Google Scholar 

  21. Wang, S. & Loreau, M. Biodiversity and ecosystem stability across scales in metacommunities. Ecol. Lett. 19, 510–518 (2016).

    PubMed  PubMed Central  Google Scholar 

  22. Wang, S. et al. Biotic homogenization destabilizes ecosystem functioning by decreasing spatial asynchrony. Ecology 102, e03332 (2021).

  23. Zhang, Y., He, N., Loreau, M., Pan, Q. & Han, X. Scale dependence of the diversity–stability relationship in a temperate grassland. J. Ecol. 106, 1227–1285 (2018).

    PubMed  Google Scholar 

  24. Wang, S., Lamy, T., Hallett, L. M. & Loreau, M. Stability and synchrony across ecological hierarchies in heterogeneous metacommunities: linking theory to data. Ecography 42, 1200–1211 (2019).

    Google Scholar 

  25. Hautier, Y. et al. General destabilizing effects of eutrophication on grassland productivity at multiple spatial scales. Nat. Commun. 11, 5375 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Liang, M., Liang, C., Hautier, Y., Wilcox, K. R. & Wang, S. Grazing-induced biodiversity loss impairs grassland ecosystem stability at multiple scales. Ecol. Lett. 24, 2054–2064 (2021).

    PubMed  Google Scholar 

  27. Qiao, X. et al. Spatial asynchrony matters more than alpha stability in stabilizing ecosystem productivity in a large temperate forest region. Glob. Ecol. Biogeogr. 31, 1133–1146 (2022).

    Google Scholar 

  28. Catano, C. P., Fristoe, T. S., LaManna, J. A. & Myers, J. A. Local species diversity, beta-diversity and climate influence the regional stability of bird biomass across North America. Proc. R. Soc. B 287, 20192520 (2020).

    PubMed  PubMed Central  Google Scholar 

  29. Patrick, C. J. et al. Multi‐scale biodiversity drives temporal variability in macrosystems. Front. Ecol. Environ. 19, 47–56 (2021).

    Google Scholar 

  30. Wilcox, K. R. et al. Asynchrony among local communities stabilises ecosystem function of metacommunities. Ecol. Lett. 20, 1534–1545 (2017).

    PubMed  PubMed Central  Google Scholar 

  31. Zhang, Y. et al. Nitrogen addition does not reduce the role of spatial asynchrony in stabilising grassland communities. Ecol. Lett. 22, 563–571 (2019).

    PubMed  PubMed Central  Google Scholar 

  32. Garcia, R. A., Cabeza, M., Rahbek, C. & Araujo, M. B. Multiple dimensions of climate change and their implications for biodiversity. Science 344, 1247579 (2014).

    PubMed  Google Scholar 

  33. García-Palacios, P., Gross, N., Gaitán, J. & Maestre, F. T. Climate mediates the biodiversity–ecosystem stability relationship globally. Proc. Natl Acad. Sci. USA 115, 8400–8405 (2018).

    PubMed  PubMed Central  Google Scholar 

  34. Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).

    PubMed  Google Scholar 

  35. Qian, H. & Ricklefs, R. E. A latitudinal gradient in large-scale beta diversity for vascular plants in North America. Ecol. Lett. 10, 737–744 (2007).

    PubMed  Google Scholar 

  36. Kraft, N. J. B. et al. Disentangling the drivers of β diversity along latitudinal and elevational gradients. Science 333, 1755–1758 (2011).

    CAS  PubMed  Google Scholar 

  37. Ma, Z. et al. Climate warming reduces the temporal stability of plant community biomass production. Nat. Commun. 8, 15378 (2017).

    PubMed  PubMed Central  Google Scholar 

  38. Song, J. et al. A meta-analysis of 1,119 manipulative experiments on terrestrial carbon-cycling responses to global change. Nat. Ecol. Evol. 3, 1309–1320 (2019).

    PubMed  Google Scholar 

  39. Valencia, E. et al. Synchrony matters more than species richness in plant community stability at a global scale. Proc. Natl Acad. Sci. USA 117, 24345–24351 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Gilbert, B. et al. Climate and local environment structure asynchrony and the stability of primary production in grasslands. Glob. Ecol. Biogeogr. 29, 1177–1188 (2020).

    Google Scholar 

  41. Hallett, L. M. et al. Biotic mechanisms of community stability shift along a precipitation gradient. Ecology 95, 1693–1700 (2014).

    PubMed  Google Scholar 

  42. Hong, P. et al. Biodiversity promotes ecosystem functioning despite environmental change. Ecol. Lett. 25, 555–569 (2022).

    PubMed  Google Scholar 

  43. . Plant presence and percent cover, RELEASE-2021. NEON (National Ecological Observatory Network) https://doi.org/10.48443/abge-r811 (2021).

  44. Barnett, D. T. et al. The plant diversity sampling design for The National Ecological Observatory. Netw. Ecosphere 10, e02603 (2019).

    Google Scholar 

  45. Lasky, J. R., Uriarte, M. & Muscarella, R. Synchrony, compensatory dynamics, and the functional trait basis of phenological diversity in a tropical dry forest tree community: effects of rainfall seasonality. Environ. Res. Lett. 11, 115003 (2016).

    Google Scholar 

  46. Inchausti, P. & Halley, J. Investigating long-term ecological variability using the global population dynamics database. Science 293, 655–657 (2001).

    CAS  PubMed  Google Scholar 

  47. Luo, M. et al. The effects of dispersal on spatial synchrony in metapopulations differ by timescale. Oikos 130, 1762–1772 (2021).

    Google Scholar 

  48. Pimm, S. L. & Redfearn, A. The variability of population densities. Nature 334, 613–614 (1988).

    Google Scholar 

  49. Craven, D. et al. Multiple facets of biodiversity drive the diversity–stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).

    PubMed  Google Scholar 

  50. Peet, R. K., Wentworth, T. R. & White, P. S. A flexible, multipurpose method for recording vegetation composition and structure. Castanea 63, 262–274 (1998).

    Google Scholar 

  51. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).

    Google Scholar 

  52. Loreau, M. & de Mazancourt, C. Species synchrony and its drivers: neutral and nonneutral community dynamics in fluctuating environments. Am. Nat. 172, E48–E66 (2008).

    PubMed  Google Scholar 

  53. Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling inr for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).

    Google Scholar 

  54. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and nonlinear mixed effects models. R package v.3.1–152 https://CRAN.R-project.org/package=nlme (2021).

  55. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (31988102 and 32122053). The NEON is a programme sponsored by the National Science Foundation and operated under cooperative agreement by Battelle. This material is based in part upon work supported by the National Science Foundation through the NEON Program. Funding was also provided by the National Science Foundation (no. 1926567 to P.L.Z.; no. 1926568 to S.R.; no. 1926569 to B.B.). We thank the numerous scientists, ecologists and staff who managed the NEON observations and collected plant community data.

Author information

Authors and Affiliations

  1. Institute of Ecology, College of Urban and Environmental Science and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing, P. R. China

    Maowei Liang & Shaopeng Wang

  2. Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA

    Benjamin Baiser

  3. Department of Biology and Environmental Studies Program, University of Oregon, Eugene, OR, USA

    Lauren M. Hallett

  4. Ecology and Biodiversity Group, Department of Biology, Utrecht University, Utrecht, the Netherlands

    Yann Hautier

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

    Lin Jiang

  6. Theoretical and Experimental Ecology Station, CNRS, Moulis, France

    Michel Loreau

  7. Department of Wildlife, Fisheries, and Conservation Biology, University of Maine, Orono, ME, USA

    Sydne Record

  8. National Ecological Observatory Network (NEON), Battelle, Boulder, CO, USA

    Eric R. Sokol

  9. Institute of Arctic and Alpine Research (INSTAAR), University of Colorado Boulder, Boulder, CO, USA

    Eric R. Sokol

  10. Department of Integrative Biology, Michigan State University, East Lansing, MI, USA

    Phoebe L. Zarnetske

  11. Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI, USA

    Phoebe L. Zarnetske

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Contributions

M. Liang and S.W. conceived the idea, analysed the data and wrote the first draft. B.B., L.M.H., Y.H., L.J., M. Loreau, S.R., E.R.S. and P.L.Z. contributed to the development and revision of the manuscript.

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Correspondence to Shaopeng Wang.

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Extended data

Extended Data Fig. 1 Geographic and climatic information of 36 NEON sites.

Shown are the geographic distribution across the United States (A), distribution in the Whittaker biomes (B), the mean annual temperature (C, MAT: F1,34 = 176.50, P < 0.0001) and mean annual precipitation (D, MAP: F1,34 = 4.70, P = 0.037) along the latitude gradient, as well as (E) their relationship between MAT and MAP (E, F1,34 = 6.43, P = 0.016). In (A − B), the abbreviation of the site name is provided in Appendix data 1. In (C − E), shaded areas are the error bands and denote 95% confidence intervals and significance levels are as follows: ‘*’: P ≤ 0.05 and ‘***’: P ≤ 0.0001.

Extended Data Fig. 2 A hypothesized structural equation modelling (SEM) illustrating the direct and indirect effects of climatic factors (as well as spatial configuration of plots) on biodiversity and stability at different scales.

Details about the rationales of each pathway in the SEM are provided in Supplementary Table 15.

Extended Data Fig. 3 The diversity − stability relationships (DSRs) across multiple spatial scales, based on partial regression models after controlling for the effects of climatic factors (N = 36 sites).

Shown are the log−log relationships between α diversity and α stability at the quadrat level (A, F1,34 = 10.2, R2 = 0.23, P = 0.003), between γ diversity and γ stability at the plot level (B, F1,34 = 9.00, R2 = 0.21, P = 0.005), between τ diversity and τ stability at the site level (C, F1,34 = 10.02, R2 = 0.23, P = 0.003), between \(\beta _D^{\alpha \to \gamma }\) and \(\beta _S^{\alpha \to \gamma }\) across quadrats (D, F1,34 = 7.92, R2 = 0.19, P = 0.008), between \(\beta _D^{\gamma \to \tau }\) and \(\beta _S^{\gamma \to \tau }\) across plots (E, F1,34 = 6.57, R2 = 0.16, P = 0.015), and a comparison of regression slopes across scales using ANCOVA (F, F2,102 = 0.77, P = 0.466 among quadrat, plot, and site levels; F1,68 = 0.13, P = 0.722 between across-quadrat and across-plot levels), respectively. Lines represent DSRs from the partial linear regression models (p-LMs) after accounting for the effects of mean annual precipitation and mean annual temperature. Shaded areas are the error bands and denote 95% confidence intervals; and significance levels are as follows: ‘*’: P ≤ 0.05 and ‘**’: P ≤ 0.001. In (F), bars and error bars are regression coefficients and standard errors from p-LMs (n = 36 for all) in (A − E). Note that in (F), pairwise comparisons between quadrat, plot, and site levels are non-significant (P > 0.1 for all). Information about the fitted models is provided in Supplementary Table 2.

Extended Data Fig. 4 The diversity − stability relationships (DSRs) across multiple spatial scales, after excluding woody plant species (N = 36 sites).

Shown are the log−log relationships between α diversity and α stability at the quadrat level (A, F1,34 = 12.27, R2 = 0.26, P = 0.001), between γ diversity and γ stability at the plot level (B, F1,34 = 5.58, R2 = 0.14, P = 0.024), between τ diversity and τ stability at the site level (C, F1,34 = 8.67, R2 = 0.20, P = 0.006), between \(\beta _D^{\alpha \to \gamma }\) and \(\beta _S^{\alpha \to \gamma }\) across quadrats (D, F1,34 = 2.09, R2 = 0.06, P = 0.158), between \(\beta _D^{\gamma \to \tau }\) and \(\beta _S^{\gamma \to \tau }\) across plots (E, F1,34 = 17.01, R2 = 0.33, P = 0.0002), and a comparison of regression slopes across scales using ANCOVA (F, F2,102 = 1.67, P = 0.193 among quadrat, plot, and site levels; F1,68 = 4.69, P = 0.034 between across-quadrats and across-plots levels). In (A − E), lines represent the overall relationships between biodiversity and community stability from the best-fit linear regression models (LMs), and shaded areas are the error bands and denote 95% confidence intervals. Significance levels are indicated as follows: ‘*’: P ≤ 0.05 and ‘**’: P ≤ 0.001. R2 is the explained variance in LMs. In (F), bars and error bars are regression coefficients and standard errors from p-LMs (n = 36 for all) in (A − E). Note that in (F), pairwise comparisons between quadrat, plot, and site levels are non-significant (P > 0.1 for all).

Extended Data Fig. 5 The diversity − stability relationships (DSRs) across multiple spatial scales, using sites with ≥ 5 years records (N = 24 sites).

Shown are the log−log slopes between α diversity and α stability at the quadrat level (A, F1,22 = 12.34, R2 = 0.36, P = 0.002), between γ diversity and γ stability at the plot level (B, F1,22 = 9.79, R2 = 0.31, P = 0.005), between τ diversity and τ stability at the site level (C, F1,22 = 16.99, R2 = 0.44, P = 0.001), between \(\beta _D^{\alpha \to \gamma }\) and \(\beta _S^{\alpha \to \gamma }\) across quadrats (D, F1,22 = 11.13, R2 = 0.34, P = 0.003), between \(\beta _D^{\gamma \to \tau }\) and \(\beta _S^{\gamma \to \tau }\) across plots (E, F1,22 = 8.51, R2 = 0.28, P = 0.008), and a comparison of regression slopes across scales using ANCOVA (F, F2,66 = 0.889, P = 0.416 among quadrat-, plot-, and site levels; F1,44 = 0.233, P = 0.632 between across-quadrats and across-plots levels). In (A − E), lines represent the overall significant relationships between biodiversity and community stability from the best-fit linear regression models (LMs), and shaded areas are the error bands and denote 95% confidence intervals. Significance levels are indicated as follows: ‘*’: P ≤ 0.05 and ‘**’: P ≤ 0.001. R2 is the explained variance in LMs. In (F), bars and error bars are regression coefficients and standard errors from p-LMs (n = 36 for all) in (A − E). Note that in (F), pairwise comparisons between quadrat, plot, and site levels are non-significant (P > 0.1 for all). More information about the fitted models and partial regression models is provided in Supplementary Tables 1–2.

Extended Data Fig. 6 The diversity − stability relationships (DSRs) across multiple spatial scales, using sites with ≥ 6 years records (N = 14 sites).

Shown are the log−log slopes between α diversity and α stability at the quadrat level (A, F1,12 = 5.84, R2 = 0.33, P = 0.033), between γ diversity and γ stability at the plot level (B, F1,12 = 5.46, R2 = 0.31, P = 0.038), between τ diversity and τ stability at the site level (C, F1,12 = 5.52, R2 = 0.32, P = 0.037), between \(\beta _D^{\alpha \to \gamma }\) and \(\beta _S^{\alpha \to \gamma }\) across quadrats (D, F1,12 = 10.29, R2 = 0.46, P = 0.008), between \(\beta _D^{\gamma \to \tau }\) and \(\beta _S^{\gamma \to \tau }\) across plots (E, F1,34 = 1.70, R2 = 0.12, P = 0.216), and a comparison of regression slopes across scales using ANCOVA (F, F2,36 = 0.120, P = 0.887 among quadrat-, plot-, and site- levels; F1,24 = 0.970, P = 0.335 between across-quadrats and across-plots levels). In (A − E), solid lines represent the overall significant relationships between biodiversity and community stability from the linear regression models (LMs), and shaded areas are the error bands and denote 95% confidence intervals; a dashed line denotes insignificant (P > 0.05). Significance levels are indicated as follows: ‘*’: P ≤ 0.05. R2 is the explained variance in LMs. In (F), bars and error bars are regression coefficients and standard errors from p-LMs (n = 36 for all) in (A − E). Note that in (F), pairwise comparisons between quadrat, plot, and site levels are non-significant (P > 0.1 for all). More information about the fitted models and partial regression models is provided in Supplementary Tables 1–2.

Extended Data Fig. 7 Effects of climatic factors on biodiversity and stability across multiple spatial scales.

Shown are the standardized regression coefficients between climatic factors and biodiversity or stability at different spatial scales (N = 36). Filled dots indicate significant effects (P ≤ 0.05). Bars denote 95% confidential intervals, respectively. More information about the fitted model is provided in Supplementary Tables 8–11. Abbreviations: MAP = mean annual precipitation; MAT = mean annual temperature.

Extended Data Fig. 8 The subordinate structural equation model (sub-SEM) depicting the relationships between climatic factors and plant diversity and community stability across multiple spatial scales within 36 NEON sites.

Shown are the final sub-SEM with significant pathways (P < 0.05) and the standardized path correlation coefficients (that is, the values). Black and red arrows denote positive and negative associations, respectively, and grey arrows indicate correlations. Fisher’s C = 14.528; df = 16; p = 0.559; AIC = 64.528; n = 945. Note that diversity and stability metrics have been log-transformed. Abbreviations: MAP = mean annual precipitation, and MAT = mean annual temperature. Information about the priori SEM and the unstandardized direct effects are provided in Extended Data Fig. 2 and Supplementary Tables 6–7, respectively.

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Liang, M., Baiser, B., Hallett, L.M. et al. Consistent stabilizing effects of plant diversity across spatial scales and climatic gradients. Nat Ecol Evol 6, 1669–1675 (2022). https://doi.org/10.1038/s41559-022-01868-y

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