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Biodiversity increases multitrophic energy use efficiency, flow and storage in grasslands

Nature Ecology & Evolution volume 4, pages 393–405 (2020)Cite this article

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Abstract

The continuing loss of global biodiversity has raised questions about the risk that species extinctions pose for the functioning of natural ecosystems and the services that they provide for human wellbeing. There is consensus that, on single trophic levels, biodiversity sustains functions; however, to understand the full range of biodiversity effects, a holistic and multitrophic perspective is needed. Here, we apply methods from ecosystem ecology that quantify the structure and dynamics of the trophic network using ecosystem energetics to data from a large grassland biodiversity experiment. We show that higher plant diversity leads to more energy stored, greater energy flow and higher community-energy-use efficiency across the entire trophic network. These effects of biodiversity on energy dynamics were not restricted to only plants but were also expressed by other trophic groups and, to a similar degree, in aboveground and belowground parts of the ecosystem, even though plants are by far the dominating group in the system. The positive effects of biodiversity on one trophic level were not counteracted by the negative effects on adjacent levels. Trophic levels jointly increased the performance of the community, indicating ecosystem-wide multitrophic complementarity, which is potentially an important prerequisite for the provisioning of ecosystem services.

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Fig. 1: Illustrative network and network measures.
Fig. 2: Conceptual diagram of the grassland trophic network models of this study.
Fig. 3: Effect of plant species richness and legume presence on grassland multitrophic network measures.
Fig. 4: Trophic network models for communities with low (1 species) and high (60 species) plant species richness and the differences among them.
Fig. 5: The effect of plant species richness and legume presence on standing biomass, energy flow and maintenance costs for each trophic compartment.
Fig. 6: The effect of plant species richness on the proportional allocation of total-network standing biomass and total energy flow to trophic levels.

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Data availability

The data used to support the conclusions of this study are available at PANGAEA (https://doi.pangaea.de/10.1594/PANGAEA.910659).

Code availability

The code for the analyses of this study is available from the corresponding authors on request.

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Acknowledgements

We thank B. C. Rall, Y. Oelmann, H. Hillebrand, A. Barnes, B. C. Patten, C. Kazanci, U. Scharler and S. S. Rudenko for discussions, which improved this research; G. Luo and P. G. Mellado-Vazquez for assistance with the Jena Experiment database and M. Fischer and E. De Luca for data on plant biomass; C. Kazanci for assistance in network balancing; C. Mulder for consultation about soil fauna body sizes; and members of the gardener team, technicians, student helpers and managers of the Jena Experiment for their work. This research was supported by a POINT fellowship to O.Y.B. from the Dahlem Research School Program at Freie Universität Berlin, co-financed by the German Excellence Initiative and the Marie Curie Program of the European Commission. O.Y.B. was also funded by the State Fund for Fundamental Research of Ukraine (GP/F 61053). The Jena Experiment is funded by the Deutsche Forschungsgemeinschaft (DFG FOR 1451) and the Swiss National Science Foundation (SNF).

Author information

Author notes

  1. These authors contributed equally: Oksana Y. Buzhdygan, Sebastian T. Meyer.

Authors and Affiliations

  1. Theoretical Ecology Group, Institute of Biology, Freie Universität Berlin, Berlin, Germany

    Oksana Y. Buzhdygan & Britta Tietjen

  2. Department of Ecology and Biomonitoring, Chernivtsi National University, Chernivtsi, Ukraine

    Oksana Y. Buzhdygan

  3. Terrestrial Ecology Research Group, School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany

    Sebastian T. Meyer & Wolfgang W. Weisser

  4. German Centre for Integrative Biodiversity Research (iDiv), Leipzig, Germany

    Nico Eisenhauer, Jes Hines, Anja Vogel & Alexandra Weigelt

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

    Nico Eisenhauer, Jes Hines & Anja Vogel

  6. Institute of Ecology and Evolution, Friedrich Schiller University of Jena, Jena, Germany

    Anne Ebeling & Anja Vogel

  7. Department of Biology and Marine Biology, University of North Carolina Wilmington, Wilmington, NC, USA

    Stuart R. Borrett

  8. Duke Network Analysis Center, Social Science Research Institute, Duke University, Durham, NC, USA

    Stuart R. Borrett

  9. Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland

    Nina Buchmann

  10. Department of Terrestrial Ecology, Netherlands Institute of Ecology, Wageningen, the Netherlands

    Roeland Cortois, Gerlinde B. De Deyn & Katja Steinauer

  11. Department of Soil Quality, Wageningen University, Wageningen, the Netherlands

    Gerlinde B. De Deyn

  12. Institute for Water and Wetland Research, Experimental Plant Ecology, Radboud University Nijmegen, Nijmegen, the Netherlands

    Hans de Kroon & Janneke Ravenek

  13. Max Planck Institute for Biogeochemistry, Jena, Germany

    Gerd Gleixner & Markus Lange

  14. Terrestrial Ecology Lab, Department of Biology, Ghent University, Gent, Belgium

    Lionel R. Hertzog

  15. Plant Ecology and Nature Conservation, Department of Environmental Sciences, Wageningen University, Wageningen, the Netherlands

    Liesje Mommer

  16. Institute of Landscape Ecology, University of Münster, Münster, Germany

    Christoph Scherber

  17. Faculty of Biology, Geobotany, University of Freiburg, Freiburg, Germany

    Michael Scherer-Lorenzen

  18. J. F. Blumenbach Institute of Zoology and Anthropology, University of Göttingen, Göttingen, Germany

    Stefan Scheu & Tanja Strecker

  19. Department of Geography, University of Zurich, Zurich, Switzerland

    Bernhard Schmid

  20. Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany

    Britta Tietjen

  21. Systematic Botany and Functional Biodiversity, Institute of Biology, University of Leipzig, Leipzig, Germany

    Alexandra Weigelt

  22. Department of Biosciences, University of Salzburg, Salzburg, Austria

    Jana S. Petermann

Authors
  1. Oksana Y. Buzhdygan
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  2. Sebastian T. Meyer
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  3. Wolfgang W. Weisser
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Contributions

O.Y.B., S.T.M. and J.S.P. conceptualized and designed the study and developed the analytical procedure. S.T.M., W.W.W., N.E., A.E., N.B., R.C., G.B.D.D., H.d.K., G.G., L.R.H., J.H., M.L., L.M., J.R., M.S.-L., S.S., B.S., K.S., T.S., A.V. and A.W. contributed data. O.Y.B. assembled the data and conducted network modelling and analysis. O.Y.B. performed statistical analysis with contributions from S.T.M. and J.S.P. O.Y.B. and S.R.B. performed uncertainty analysis. O.Y.B., S.T.M. and J.S.P. wrote the original draft. W.W.W., N.E., A.E., S.R.B., N.B., R.C., G.B.D.D., H.d.K., G.G., L.R.H., J.H., M.L., L.M., J.R., C.S., M.S.-L., S.S., B.S., K.S., T.S., B.T., A.V. and A.W. contributed substantially to reading and editing the paper.

Corresponding authors

Correspondence to Oksana Y. Buzhdygan, Sebastian T. Meyer or Jana S. Petermann.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1

Network metrics used for the analysis of the proxies of the multitrophic ecosystem functioning.

Extended Data Fig. 2 Conceptual diagram showing the data sources used for model parameterization.

Dashed arrows denote flows entering the system (carbon uptake) and flows leaving the system (respiration losses). Solid arrows are internal flows transferring energy from one ecosystem compartment to another via feeding (flows among trophic groups), excretion, and mortality (flows from trophic groups into detrital pools). Green and light brown sectors depict above- and belowground ecosystem parts, respectively. Photos: Christoph Scherber (spider in AG Carnivores, carabid beetle in AG Omnivores, geotrupid beetle in AG Decomposers, elaterid larva in BG Herbivores, millipede in BG Decomposers), Stefan Scheu (carabid larva in AG Carnivores), Nico Eisenhauer (earthworm in BG Decomposers), and Andy Murray76 (Diplura in BG Omnivores). The images of Plants, Surface Litter, and of grasshopper in AG Herbivores were created using the IAN Symbols77.

Extended Data Fig. 3

Summary of a least-squares linear model testing the effects of plant species richness and the presence of specific functional groups on multitrophic ecosystem functions and properties emerging from network analysis (n=80 plots): total energy flow (g m-2 d-1), total-network standing biomass (g m-2), community maintenance costs (d-1), and flow uniformity. Variable Block accounts for initial physico-chemical soil- and microclimate conditions. Plant species richness accounts for the number of plant species (1, 2, 4, 8, 16, and 60 species).

Extended Data Fig. 4

Standing biomass and energy flows (mean and standard error SE) for each compartment of the trophic networks within the 80 study plots with varying plant biodiversity. AG – aboveground; BG – belowground; SOM — soil organic matter.

Extended Data Fig. 5 Effect of plant species richness and legume presence on the network-wide metrics resulting from the flow uncertainty analysis (mean of the set of 10,000 parsimonious model solutions for each plot, n=80 plots).

(a) total energy flow, (b) community maintenance costs, and (c) flow uniformity. For response variables that show a significant legume effect (Supplementary Table 9), regression lines for plots containing legume (solid) and without legumes (dashed) are shown separately. Thick lines: significant effects (P<0.05; analysis of variance with sequential sum of squares, type I); thin lines: nonsignificant effects (P≥0.05) of plant species richness (Supplementary Table 3). Shaded areas around lines show 95% confidence intervals. Filled dots: plots containing legumes; open dots: plots without legumes.

Extended Data Fig. 6 Summary of linear model results for the effects of plant diversity on compartmental standing biomass (n=80 plots).

Significant effects (P < 0.05) are given in bold; marginally significant effects (0.05 < P < 0.09) are given in italic bold. See also Fig. 5.

Extended Data Fig. 7 Summary of linear model results for the effects of plant diversity on compartmental energy flow (n=80 plots).

Significant effects (P < 0.05) are given in bold; marginally significant effects (0.05 < P < 0.09) are given in italic bold. See also Fig. 5.

Extended Data Fig. 8 Summary of linear model results for the effects of plant diversity on compartmental maintenance costs (n=80 plots).

Significant effects (P < 0.05) are given in bold; marginally significant effects (0.05 < P < 0.09) are given in italic bold. See also Fig. 5.

Extended Data Fig. 9 Effect of plant species richness on maintenance costs (d-1) of plants (a) and consumers (b).

(Supplementary Table 12). Maintenance costs were only calculated for living ecosystem compartments, thus not for the litter and soil organic matter. Thick lines: significant effects (P<0.05; analysis of variance with sequential sum of squares, type I, n=80); thin lines: non-significant effects (P≥0.05) of plant species richness, Supplementary Table 12. Shaded areas around lines show 95% confidence intervals. Filled dots: plots containing legumes; open dots: plots without legumes.

Extended Data Fig. 10 Effect of plant species richness on flow uniformity (unitless) of consumers (a) and detritus (b).

(Supplementary Table 13). Solid lines depict regression lines with 95% confidence intervals (dashed lines). Flow uniformity of consumers is the ratio of the mean of throughflows of each consumer compartment (n=9) to its standard deviation. Flow uniformity of detritus is the ratio of the mean of throughflows of each detritus (n=2) compartment to its standard deviation. Flow uniformity cannot be calculated for plants because plants are represented by only one ecosystem compartment. None of the relationships shown was significant (analysis of variance with sequential sum of squares, type I, n=80; Supplementary Table 13). Shaded areas around regression lines show 95% confidence intervals. Filled dots: plots containing legumes; open dots: plots without legumes.

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Supplementary Figs. 1–6, Tables 1–13, discussion and references.

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Buzhdygan, O.Y., Meyer, S.T., Weisser, W.W. et al. Biodiversity increases multitrophic energy use efficiency, flow and storage in grasslands. Nat Ecol Evol 4, 393–405 (2020). https://doi.org/10.1038/s41559-020-1123-8

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