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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
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
Transforming our World: The 2030 Agenda for Sustainable Development (UN, 2015).
May, R. M. Will a large complex system be stable? Nature 238, 413–414 (1972).
Pimm, S. L. The complexity and stability of ecosystems. Nature 307, 321–326 (1984).
McCann, K. S. The diversity–stability debate. Nature 405, 228–233 (2000).
Ives, A. R. & Carpenter, S. R. Stability and diversity of ecosystems. Science 317, 58–62 (2007).
Donohue, I. et al. Navigating the complexity of ecological stability. Ecol. Lett. 19, 1172–1185 (2016).
Yachi, S. & Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Proc. Natl Acad. Sci. USA 96, 1463–1468 (1999).
Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).
Tilman, D., Reich, P. B. & Knops, J. M. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).
Loreau, M. et al. Biodiversity as insurance: from concept to measurement and application. Biol. Rev. 96, 2333–2354 (2021).
Thibaut, L. M. & Connolly, S. R. Understanding diversity–stability relationships: towards a unified model of portfolio effects. Ecol. Lett. 16, 140–150 (2013).
Xu, Q. et al. Consistently positive effect of species diversity on ecosystem, but not population, temporal stability. Ecol. Lett. 24, 2256–2266 (2021).
Hector, A. et al. General stabilizing effects of plant diversity on grassland productivity through population asynchrony and overyielding. Ecology 91, 2213–2220 (2010).
Hautier, Y. et al. Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science 348, 336–340 (2015).
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).
Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).
Gonzalez, A. et al. Scaling-up biodiversity–ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).
Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Wang, S. & Loreau, M. Ecosystem stability in space: alpha, beta and gamma variability. Ecol. Lett. 17, 891–901 (2014).
Wang, S. & Loreau, M. Biodiversity and ecosystem stability across scales in metacommunities. Ecol. Lett. 19, 510–518 (2016).
Wang, S. et al. Biotic homogenization destabilizes ecosystem functioning by decreasing spatial asynchrony. Ecology 102, e03332 (2021).
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).
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).
Hautier, Y. et al. General destabilizing effects of eutrophication on grassland productivity at multiple spatial scales. Nat. Commun. 11, 5375 (2020).
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).
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).
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).
Patrick, C. J. et al. Multi‐scale biodiversity drives temporal variability in macrosystems. Front. Ecol. Environ. 19, 47–56 (2021).
Wilcox, K. R. et al. Asynchrony among local communities stabilises ecosystem function of metacommunities. Ecol. Lett. 20, 1534–1545 (2017).
Zhang, Y. et al. Nitrogen addition does not reduce the role of spatial asynchrony in stabilising grassland communities. Ecol. Lett. 22, 563–571 (2019).
Garcia, R. A., Cabeza, M., Rahbek, C. & Araujo, M. B. Multiple dimensions of climate change and their implications for biodiversity. Science 344, 1247579 (2014).
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).
Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).
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).
Kraft, N. J. B. et al. Disentangling the drivers of β diversity along latitudinal and elevational gradients. Science 333, 1755–1758 (2011).
Ma, Z. et al. Climate warming reduces the temporal stability of plant community biomass production. Nat. Commun. 8, 15378 (2017).
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).
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).
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).
Hallett, L. M. et al. Biotic mechanisms of community stability shift along a precipitation gradient. Ecology 95, 1693–1700 (2014).
Hong, P. et al. Biodiversity promotes ecosystem functioning despite environmental change. Ecol. Lett. 25, 555–569 (2022).
. Plant presence and percent cover, RELEASE-2021. NEON (National Ecological Observatory Network) https://doi.org/10.48443/abge-r811 (2021).
Barnett, D. T. et al. The plant diversity sampling design for The National Ecological Observatory. Netw. Ecosphere 10, e02603 (2019).
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).
Inchausti, P. & Halley, J. Investigating long-term ecological variability using the global population dynamics database. Science 293, 655–657 (2001).
Luo, M. et al. The effects of dispersal on spatial synchrony in metapopulations differ by timescale. Oikos 130, 1762–1772 (2021).
Pimm, S. L. & Redfearn, A. The variability of population densities. Nature 334, 613–614 (1988).
Craven, D. et al. Multiple facets of biodiversity drive the diversity–stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).
Peet, R. K., Wentworth, T. R. & White, P. S. A flexible, multipurpose method for recording vegetation composition and structure. Castanea 63, 262–274 (1998).
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).
Loreau, M. & de Mazancourt, C. Species synchrony and its drivers: neutral and nonneutral community dynamics in fluctuating environments. Am. Nat. 172, E48–E66 (2008).
Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling inr for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).
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).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
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
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.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Ecology & Evolution thanks Christopher Catano, Frank Pennekamp and Qiang Yang for their contribution to the peer review of this work. Peer reviewer reports are available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
Supplementary information
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41559-022-01868-y
This article is cited by
-
Environmental conditions are the dominant factor influencing stability of terrestrial ecosystems on the Tibetan plateau
Communications Earth & Environment (2023)
-
Stable or unstable? Landscape diversity and ecosystem stability across scales in the forest–grassland ecotone in northern China
Landscape Ecology (2023)