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Species richness is more important for ecosystem functioning than species turnover along an elevational gradient - Nature Ecology & Evolution

  • ️Schleuning, Matthias
  • ️Mon Sep 20 2021

Data availability

The data that support the findings of this study are available in Figshare93 with the identifier https://doi.org/10.6084/m9.figshare.14544207.

Code availability

The computer code of the analyses is available in Figshare93 with the identifier https://doi.org/10.6084/m9.figshare.14544207. The JAGS code for the Bayesian hierarchical structural equation model is also given in Supplementary Note 1.

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Acknowledgements

We thank the Tanzanian Commission for Science and Technology, the Tanzania Wildlife Research Institute and the Tanzania National Parks Authority for their support and for granting us access to the Kilimanjaro National Park area. We are grateful to all companies and private farmers that allowed us to work on their land. We thank the KiLi field staff for helping with data collection at Mt Kilimanjaro. This study was conducted within the framework of the Research Unit FOR1246 (‘Kilimanjaro ecosystems under global change: linking biodiversity, biotic interactions and biogeochemical ecosystem processes’, https://www.kilimanjaro.biozentrum.uni-wuerzburg.de) funded by the Deutsche Forschungsgemeinschaft.

Author information

Author notes

  1. These authors contributed equally: Jörg Albrecht, Marcell K. Peters.

  2. These authors jointly supervised this work: Ingolf Steffan-Dewenter, Matthias Schleuning.

Authors and Affiliations

  1. Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany

    Jörg Albrecht, Stefan W. Ferger, Ulf Pommer, Maximilian G. R. Vollstädt, Hamadi I. Dulle, Claudia Hemp, Peter Manning, Thomas Mueller, Katrin Böhning-Gaese, Markus Fischer & Matthias Schleuning

  2. Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, Würzburg, Germany

    Marcell K. Peters, Alice Classen, Friederike Gebert, William J. Kindeketa, Antonia V. Mayr, Henry K. Njovu, Jie Zhang & Ingolf Steffan-Dewenter

  3. Department of Soil Science of Temperate Ecosystems, and Department of Agricultural Soil Science, University of Göttingen, Göttingen, Germany

    Joscha N. Becker, Holger Pabst & Yakov Kuzyakov

  4. Institute of Soil Science, CEN Center for Earth System Research and Sustainability, University of Hamburg, Hamburg, Germany

    Joscha N. Becker

  5. Institute for Evolutionary Ecology and Conservation Genomics, University of Ulm, Ulm, Germany

    Christina Behler, Maria Helbig-Bonitz, Antonia V. Mayr, Anna Vogeler & Marco Tschapka

  6. Institute of Plant Sciences, University of Bern, Bern, Switzerland

    Andreas Ensslin, Gemma Rutten & Markus Fischer

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

    Friederike Gebert

  8. Regional Council Freiburg, State Office for Geology, Raw Materials and Mining, Soil Science, Freiburg, Germany

    Friederike Gerschlauer

  9. Tanzania Forestry Research Institute, Morogoro, Tanzania

    William J. Kindeketa

  10. Bavarian State Research Centre for Agriculture, Institute for Organic Farming, Soil and Resource Management, Freising, Germany

    Anna Kühnel

  11. Department of Ecology, Animal Ecology, University of Marburg, Marburg, Germany

    Juliane Röder & Roland Brandl

  12. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

    Gemma Rutten & David Schellenberger Costa

  13. Institute of Biology and Environmental Sciences, University Oldenburg, Oldenburg, Germany

    David Schellenberger Costa & Michael Kleyer

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

    David Schellenberger Costa

  15. Plant Ecology and Ecosystems Research, University of Göttingen, Göttingen, Germany

    Natalia Sierra-Cornejo, Dietrich Hertel & Christoph Leuschner

  16. Agroecology, Department of Crop Sciences, University of Göttingen, Göttingen, Germany

    Maximilian G. R. Vollstädt

  17. Center for Macroecology, Evolution and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark

    Maximilian G. R. Vollstädt

  18. College of African Wildlife Management, Moshi, Tanzania

    Hamadi I. Dulle

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

    Connal D. Eardley

  20. Department of Zoology and Wildlife Conservation, University of Dar es Salaam, Dar es Salaam, Tanzania

    Kim M. Howell

  21. Cellular and Organismic Networks, Faculty of Biology, Ludwig-Maximilians-University Munich, Planegg-Martinsried, Germany

    Alexander Keller

  22. Zoological Research Museum Alexander Koenig, Department Arthropoda, Bonn, Germany

    Ralph S. Peters

  23. Tanzania Wildlife Research Institute, Arusha, Tanzania

    Victor Kakengi

  24. Department of Biological Sciences, Johann Wolfgang Goethe-University Frankfurt, Biologicum, Frankfurt am Main, Germany

    Thomas Mueller & Katrin Böhning-Gaese

  25. Ecosystem Research Group, Institute of Geography, Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany

    Christina Bogner

  26. Bayreuth Centre of Ecology and Environmental Research, University of Bayreuth, Bayreuth, Germany

    Bernd Huwe

  27. Institute of Meteorology and Climate Research, Department of Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT)—Campus Alpin, Garmisch-Partenkirchen, Germany

    Ralf Kiese

  28. Agro-Technological Institute, RUDN, Moscow, Russia

    Yakov Kuzyakov

  29. Environmental Informatics, Faculty of Geography, University of Marburg, Marburg, Germany

    Thomas Nauss

  30. Smithsonian Tropical Research Institute, Balboa Ancón, Panama

    Marco Tschapka

  31. Department of Plant Systematics, University of Bayreuth, Bayreuth, Germany

    Andreas Hemp

Authors

  1. Jörg Albrecht

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  2. Marcell K. Peters

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  3. Joscha N. Becker

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  4. Christina Behler

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  5. Alice Classen

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  6. Andreas Ensslin

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  7. Stefan W. Ferger

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  8. Friederike Gebert

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  9. Friederike Gerschlauer

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  10. Maria Helbig-Bonitz

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  11. William J. Kindeketa

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  12. Anna Kühnel

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  13. Antonia V. Mayr

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  14. Henry K. Njovu

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  15. Holger Pabst

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  16. Ulf Pommer

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  17. Juliane Röder

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  18. Gemma Rutten

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  19. David Schellenberger Costa

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  20. Natalia Sierra-Cornejo

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  21. Anna Vogeler

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  22. Maximilian G. R. Vollstädt

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  23. Hamadi I. Dulle

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  24. Connal D. Eardley

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  25. Kim M. Howell

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  26. Alexander Keller

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  27. Ralph S. Peters

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  28. Victor Kakengi

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  29. Claudia Hemp

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  30. Jie Zhang

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  31. Peter Manning

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  32. Thomas Mueller

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  33. Christina Bogner

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  34. Katrin Böhning-Gaese

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  35. Roland Brandl

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  36. Dietrich Hertel

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  37. Bernd Huwe

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  38. Ralf Kiese

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  39. Michael Kleyer

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  40. Christoph Leuschner

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  41. Yakov Kuzyakov

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  42. Thomas Nauss

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  43. Marco Tschapka

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  44. Markus Fischer

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  45. Andreas Hemp

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  46. Ingolf Steffan-Dewenter

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Contributions

J.A., M.K.P., I.S.-D. and M.S. conceived the study. M.F., A.H. and I.S.-D. initiated the research unit at Mt Kilimanjaro. A.H. established the study sites. C. Bogner, K.B.-G., R.B., M.F., A.H., D.H., B.H., R.K., M.K., Y.K., C.L., T.N., M.K.P., M.S., I.S.-D. and M.T. conceptualized and supervised the data collection. J.N.B., C. Behler, A.C., H.I.D., C.D.E., A.E., S.W.F., F. Gebert, F. Gerschlauer, M.H.-B., A.H., V.K., A. Keller, W.J.K., A. Kühnel, A.V.M., H.K.N., H.P., M.K.P., R.S.P., U.P., J.R., G.R., D.S.C., N.S.-C., A.V., M.G.R.V. and J.Z. collected the data. A.H., C.H., H.I.D., K.M.H., V.K. and J.Z. supported the data collection and fieldwork. J.A. and M.K.P. processed the data. J.A. developed the analytical tools with input from M.S. J.A. analysed the data and wrote the first version of the manuscript with input from M.K.P., I.S.-D., M.S., P.M. and T.M. All authors contributed to subsequent versions of the manuscript.

Corresponding authors

Correspondence to Jörg Albrecht or Marcell K. Peters.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Kathryn Barry, Yahuang Luo and Jean-François Arnoldi 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 The environmental gradients covered by the 71 study sites on Mt Kilimanjaro, Tanzania.

a, Elevational distribution of the six near natural and seven anthropogenic ecosystem types. b, Variation in mean annual temperature (MAT, °C) and annual precipitation (MAP, mm yr−1) along the elevational gradient.

Extended Data Fig. 2 Taxonomic sampling completeness for the 22 ecosystem functions related to process rates and biomass stocks at different sampling grains.

For each function we provide the mean number of individuals sampled across sites (nsite), ecosystem types (neco) and the total number of individuals (ntot) sampled across the elevational gradient; the mean observed species richness across sites (Ssite), ecosystem types (Seco) and the total observed species richness across the elevational gradient (Stot); as well as the mean sample coverage (proportion of expected taxa sampled) across sites (SCsite), ecosystem types (SCeco) and the total sample coverage across the elevational gradient (SCtot). The sample coverage estimator quantifies the proportion of the total number of individuals in a community that belong to the species represented in the sample. The intensity of the green colour scale for sample coverage reflects sampling completeness with more intense colours indicating higher sampling completeness.

Extended Data Fig. 3 Species richness–ecosystem function relationships across the 22 functions.

All relationships are shown on log–log scales. Regression lines are shown when the relationship is significant at P < 0.05. Note the standardization of values of species richness and ecosystem functions by their observed maxima.

Extended Data Fig. 4 Comparison of β-diversity and its components, as well as the diversity effect within and across ecosystem types.

a-c, Comparison of (a) total β-diversity, (b) variation in species richness and (c) species turnover between sites within the same ecosystem types, as well as between sites across ecosystem types. d, Comparison of the contribution of diversity to variation in ecosystem functioning between study sites within and across ecosystem types. Each pair of dots represents the measures within and across ecosystem types for one of the 22 ecosystem functions. Note that the within-ecosystem comparison for biomass stocks of microorganisms is missing, because data were only available for one replicate per ecosystem type.

Extended Data Fig. 5 Relationship between the number of ecosystem types and environmental heterogeneity across the 22 ecosystem functions.

Shown is the mean environmental distance between study sites as a function of the number of ecosystem types. The mean environmental distance was computed based on the Gower distance on the basis of a combination of 11 variables related to climatic conditions (mean annual temperature, mean annual precipitation and relative humidity), land-use (biomass removal, agricultural inputs and landscape composition), and soil properties (soil organic carbon, pH, C/N-ratio, N/P-ratio, available water capacity). Note that noise has been added to the positions of single data points along the x-axis to improve visibility. Sample size is n = 43 (n = 21 within and n = 22 across ecosystem types, respectively).

Extended Data Fig. 6 Comparison of environmental heterogeneity within and across ecosystem types.

a-l, Comparison of the mean environmental distance (based on the Gower distance) between study sites within the same ecosystem types and between study sites across ecosystem types based on (a) a combination of all 11 environmental variables, and (b-l) after excluding each of the 11 environmental variables from the composite index of environmental heterogeneity. b-d, variables reflecting climate parameters. e-g, variables reflecting land-use dimensions. h-l, variables reflecting soil properties. Each pair of dots represents the measures within and across ecosystem types for one of the 22 ecosystem functions. Note that the within-ecosystem comparison for biomass stocks of microorganisms is missing, because data were only available for one replicate per ecosystem type.

Extended Data Fig. 7 Summary of Bayesian hierarchical structural equation model.

The structural equation model tested for direct effects of environmental heterogeneity on the contribution of diversity to variation in ecosystem functioning (that is, the diversity effect), as well as for indirect effects that were mediated by variation in species richness and species turnover. a, Pairs of predictor and response variables (y ~ x); variance explained by the random factor for ecosystem function id (σf2); residual variance (σε2) and residual covariance (Turnover ~~ Richness); variance for the path coefficients (σ𝛽2); marginal variance explained by fixed effects (rm2); conditional variance explained by fixed and random effects combined (rc2). b, The direct, indirect and total effects of environmental heterogeneity on the diversity effect, as well as contrasts between richness- and turnover-mediated effects of environmental heterogeneity and effects of variation in species richness and species turnover. a,b Given are median effect sizes (with shrinkage), as well as the 50% and 95% credible intervals (CrIs), the posterior selection probability (Ppost), the prior selection probability (Pprior), 2logeBF as a measure of support for a given effect, the effective sample size (Neff) and the potential scale reduction factor (PSRF). Values of PSRF < 1.1 indicate that MCMC chains have converged on the same posterior distribution. Neff indicates approximate sample size of posterior samples after accounting for temporal autocorrelation between posterior samples. Values of 2logeBF < 2 indicate no support; values between 2 and 6 indicate positive support; values between 6 and 10 indicate strong support; and values >10 indicate decisive support. Effects that were supported by the variable selection with 2logeBF > 2 are highlighted in bold. Sample size is n = 43 (n = 21 within and n = 22 across ecosystem types, respectively).

Extended Data Fig. 8 Partial residual plots of the indirect species richness- and species turnover-mediated effects of environmental heterogeneity on the contribution of diversity to variation in ecosystem functioning.

a, Topology of the Bayesian hierarchical structural equation model with the strongest support. Solid paths were supported by the Bayesian variable selection, whereas dotted paths were not (see Extended Data Fig. 7). Plots b–e visualize partial relationships indicated by the bold letters of the structural equation model in (a). Relationships of (b) variation in species richness and (c) species turnover with environmental heterogeneity. Relationships of the diversity effect with (d) variation in species richness and (e) species turnover. All variables were scaled to zero mean and unit variance before analysis. Units on the y-axes are standardized residual deviations from predicted partial scores after conditioning on all predictor variables except for the one shown on the x-axis and after conditioning on the random effects (function ID, nfunction = 22). The colors of the circles represent the two types of comparisons (orange, within ecosystem types; black, across ecosystem types). The grey arrows indicate the directional change in the predictor and response variable with increasing environmental heterogeneity (that is, from comparisons within to comparisons across ecosystem types). The black lines depict the partial regression slopes. Sample size is n = 43 (n = 21 within and n = 22 across ecosystem types, respectively). Note that the within-ecosystem comparison for biomass stocks of microorganisms is missing, because data were only available for one replicate per ecosystem type.

Extended Data Fig. 9 Summary of sensitivity analysis.

Shown are effect sizes θ estimated by the Bayesian hierarchical structural equation model (medians and 95% credible intervals (CrIs) based on the posterior distribution of estimated model parameters) for several subsets of the data and for several treatments of the data prior to analyses; 2logeBF (2loge[Bayes Factor]) as a measure of evidence for a given effect. Effect sizes reflect the expected change in the response variable for a 1% change in the predictor variable (for example, θR→Y = 1.7 means that an increase of 10% in variation in species richness causes an increase of 17% in the diversity effect). Values of 2logeBF < 2 indicate no support; values between 2 and 6 indicate positive support; values between 6 and 10 indicate strong support; and values >10 indicate decisive support.

Extended Data Fig. 10 Relationships between elevation and ecosystem functioning across the 22 functions.

Regression lines represent best-fit generalized additive models with a thin plate spline (second-order penalty of m = 2 and basis dimension of k = 5 if n > 40 and k = 4 otherwise). Note the log-scale on the y-axes. r2dev, proportion of deviance explained by the model. ns, P > 0.1;’P < 0.1; *P < 0.05; **P < 0.01; ***P < 0.001. Subscripts of F-values indicate the effective degrees of freedom of the smooth term and the residual degrees of freedom.

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Albrecht, J., Peters, M.K., Becker, J.N. et al. Species richness is more important for ecosystem functioning than species turnover along an elevational gradient. Nat Ecol Evol 5, 1582–1593 (2021). https://doi.org/10.1038/s41559-021-01550-9

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  • Received: 02 February 2021

  • Accepted: 09 August 2021

  • Published: 20 September 2021

  • Issue Date: December 2021

  • DOI: https://doi.org/10.1038/s41559-021-01550-9