Abstract

Losses and gains in species diversity affect ecological stability1,2,3,4,5,6,7 and the sustainability of ecosystem functions and services8,9,10,11,12,13. Experiments and models have revealed positive, negative and no effects of diversity on individual components of stability, such as temporal variability, resistance and resilience2,3,6,11,12,14. How these stability components covary remains poorly understood15. Similarly, the effects of diversity on overall ecosystem stability16, which is conceptually akin to ecosystem multifunctionality17,18, remain unknown. Here we studied communities of aquatic ciliates to understand how temporal variability, resistance and overall ecosystem stability responded to diversity (that is, species richness) in a large experiment involving 690 micro-ecosystems sampled 19 times over 40 days, resulting in 12,939 samplings. Species richness increased temporal stability but decreased resistance to warming. Thus, two stability components covaried negatively along the diversity gradient. Previous biodiversity manipulation studies rarely reported such negative covariation despite general predictions of the negative effects of diversity on individual stability components3. Integrating our findings with the ecosystem multifunctionality concept revealed hump- and U-shaped effects of diversity on overall ecosystem stability. That is, biodiversity can increase overall ecosystem stability when biodiversity is low, and decrease it when biodiversity is high, or the opposite with a U-shaped relationship. The effects of diversity on ecosystem multifunctionality would also be hump- or U-shaped if diversity had positive effects on some functions and negative effects on others. Linking the ecosystem multifunctionality concept and ecosystem stability can transform the perceived effects of diversity on ecological stability and may help to translate this science into policy-relevant information.

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Data availability

The experimental data that support the findings of this study are available at Github (https://github.com/pennekampster/Code_and_data_OverallEcosystemStability) with the identifier (https://doi.org/10.5281/zenodo.1345557). Source Data for Figs. 13 are provided in the online version of the paper.

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

F. De Laender and B. Schmid provided feedback on previous drafts of the Letter; I. Donohue provided the list of publications from his 2016 review paper. The University of Zurich Research Priority Programme on Global Change and Biodiversity supported this research. In addition, funding came from the Swiss National Science Foundation (grant PP00P3_150698 to F.A. and 31003A_159498 to O.L.P.). This is publication ISEM 2018-171 of the Institut des Sciences de l’Evolution, Montpellier.

Reviewer information

Nature thanks T. Bell, P. J. Morin and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

    • Emanuel A. Fronhofer

    Present address: ISEM, Université de Montpellier, CNRS, IRD, EPHE, Montpellier, France

    • Suzanne Greene

    Present address: MIT Center for Transportation & Logistics, Massachusetts Institute of Technology, Cambridge, MA, USA

    • Gian Marco Palamara

    Present address: Department of Systems Analysis, Integrated Assessment and Modelling, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland

    • Mathew Seymour

    Present address: Molecular Ecology and Fisheries Genetics Laboratory, School of Biological Sciences, Bangor University, Bangor, UK

Affiliations

  1. Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland

    • Frank Pennekamp
    • , Mikael Pontarp
    • , Andrea Tabi
    • , Florian Altermatt
    • , Roman Alther
    • , Yves Choffat
    • , Emanuel A. Fronhofer
    • , Pravin Ganesanandamoorthy
    • , Aurélie Garnier
    • , Suzanne Greene
    • , Katherine Horgan
    • , Thomas M. Massie
    • , Elvira Mächler
    • , Gian Marco Palamara
    •  & Owen L. Petchey
  2. Department of Ecology and Environmental Science, Umeå University, Umeå, Sweden

    • Mikael Pontarp
  3. Department of Aquatic Ecology, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland

    • Florian Altermatt
    • , Roman Alther
    • , Emanuel A. Fronhofer
    • , Pravin Ganesanandamoorthy
    • , Elvira Mächler
    •  & Mathew Seymour
  4. Department of Mathematics, University of Utah, Salt Lake City, UT, USA

    • Jason I. Griffiths

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Contributions

O.L.P., F.P. and F.A. conceived the study. O.L.P., F.P., M.S., E.A.F., F.A., G.-M.P., T.M.M. and M.P. designed the experiment. F.P. coordinated and led the experiment. The experimental sampling was performed by all authors, except J.G. and A.T. F.P., O.L.P. and J.G. prepared the data for analysis. F.P., O.L.P., M.P., A.T. and M.S. analysed the dataset. The first draft was written by F.P. and O.L.P. All authors contributed to revisions of the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Frank Pennekamp.

Extended data figures and tables

  1. Extended Data Fig. 1 Richness increased temporal stability across temperatures.

    a, The stabilizing effect of richness was present across all temperatures, although temperature had a negative effect on mean stability. The inverse coefficient of variation (ICV) is calculated as the mean biomass divided by the standard deviation of biomass. b, Results of the linear mixed-effects model of log richness, temperature and their interaction on temporal stability supporting the positive effects of richness and the negative effect of temperature on temporal stability (n = 681 independent microcosms). c, Results of the same analysis as in b but without the monocultures. Results are qualitatively the same, indicating that the relationship between richness and temporal stability is not driven only by the monocultures (n = 580 independent microcosms). CI, 95% confidence interval; DF, degrees of freedom; Std.Error, standard error of the estimate.

  2. Extended Data Fig. 2 The effect of richness on absolute and proportional resistance.

    a, c, Richness decreased resistance, regardless of whether it was measured on an absolute or proportional scale. b, d, Results of linear mixed-effects models of richness, temperature and their interaction on absolute and proportional richness (n = 567 independent microcosms).

  3. Extended Data Fig. 3 Niche complementarity and low response diversity probably caused the negative covariance of stability components.

    Niche complementarity and the resulting increase in total biomass with richness tended to increase temporal stability (Fig. 1). a, b, We found little evidence for an effect of population asynchrony on temporal stability (linear mixed-effects model with composition as random effect and log richness and temperature as fixed effects; n = 681 independent microcosms). c, d, By contrast, statistical averaging contributed to stabilization (linear regression between mean species biomass and the variance of species biomass; n = 2,077 species mean–variance biomass observations). e, Low response diversity was inferred because the biomass of most species decreased or was unaffected by temperature (linear regression between temperature and species biomass; n = 972 species biomass × temperature observations). Consequently, when there were more species, there was greater total biomass and greater temporal stability, but a greater biomass loss, with temperature increase. Therefore, niche complementarity (that is, effect diversity) probably not only caused a positive effect of diversity on temporal stability but also had a negative effect of diversity on resistance in the absence of high response diversity. However, this explanation cannot apply within richness levels, for which positive covariance among stability components was found.

  4. Extended Data Fig. 4 Overview of terms and the concept of overall ecosystem stability.

    Measured ecosystem functions (left-most top box) can each have multiple components of stability (for example, temporal variability, resistance and resilience of biomass production), each of which can be combined into a measure of overall stability. When—as in our study—there is only one ecosystem function, this overall stability of a specific function is also the overall ecosystem stability. In studies of more than one ecosystem function, the overall stability of several functions could be combined to give overall ecosystem stability. Alternatively, one could first calculate ecosystem multifunctionality and then measure its stability components.

  5. Extended Data Fig. 5 The effect of aggregating more than two stability components into overall ecosystem stability.

    The fraction of stability components with a negative sign influences whether or not a unimodal pattern will result for a total of 100 stability components. a, A unimodal relationship between diversity and OES will result if at least one stability component is negative. b, However, the strength of the pattern depends on the relative balance of positive and negative relationships.

  6. Extended Data Table 1 Richness increased, whereas temperature decreased, biomass production
  7. Extended Data Table 2 Positive temporal stability–resistance relationships within richness levels
  8. Extended Data Table 3 Overview of studies used for literature survey
  9. Extended Data Table 4 Putative mechanisms and type of evidence for bivariate diversity–stability relationships

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