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Species richness is more important for ecosystem functioning than species turnover along an elevational gradient

Abstract

Many experiments have shown that biodiversity enhances ecosystem functioning. However, we have little understanding of how environmental heterogeneity shapes the effect of diversity on ecosystem functioning and to what extent this diversity effect is mediated by variation in species richness or species turnover. This knowledge is crucial to scaling up the results of experiments from local to regional scales. Here we quantify the diversity effect and its components—that is, the contributions of variation in species richness and species turnover—for 22 ecosystem functions of microorganisms, plants and animals across 13 major ecosystem types on Mt Kilimanjaro, Tanzania. Environmental heterogeneity across ecosystem types on average increased the diversity effect from explaining 49% to 72% of the variation in ecosystem functions. In contrast to our expectation, the diversity effect was more strongly mediated by variation in species richness than by species turnover. Our findings reveal that environmental heterogeneity strengthens the relationship between biodiversity and ecosystem functioning and that species richness is a stronger driver of ecosystem functioning than species turnover. Based on a broad range of taxa and ecosystem functions in a non-experimental system, these results are in line with predictions from biodiversity experiments and emphasize that conserving biodiversity is essential for maintaining ecosystem functioning.

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Fig. 1: Quantifying the effect of diversity on ecosystem functioning.
Fig. 2: Effect of diversity on ecosystem functioning within and across ecosystem types.
Fig. 3: Environmental heterogeneity controls the effect of diversity on ecosystem functioning.

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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.

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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.

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Correspondence to Jörg Albrecht or Marcell K. Peters.

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The authors declare no competing interests.

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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 be 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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