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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.

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.

References

  1. Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).

    Article  CAS  PubMed  Google Scholar 

  2. Hautier, Y. et al. Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science 348, 336–340 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Balvanera, P. et al. Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol. Lett. 9, 1146–1156 (2006).

    Article  PubMed  Google Scholar 

  4. Hector, A. et al. Plant diversity and productivity experiments in european grasslands. Science 286, 1123–1127 (1999).

    Article  CAS  PubMed  Google Scholar 

  5. Tilman, D. et al. Diversity and productivity in a long-term grassland experiment. Science 294, 843–845 (2001).

    Article  CAS  PubMed  Google Scholar 

  6. Hector, A., Beale, A., Minns, A., Otway, S. & Lawton, J. Consequences of the reduction of plant diversity for litter decomposition: effects through litter quality and microenvironment. Oikos 90, 357–371 (2000).

    Article  Google Scholar 

  7. Milcu, A., Partsch, S., Scherber, C., Weisser, W. W. & Scheu, S. Earthworms and legumes control litter decomposition in a plant diversity gradient. Ecology 89, 1872–1882 (2008).

    Article  PubMed  Google Scholar 

  8. Ebeling, A. et al. Plant diversity impacts decomposition and herbivory via changes in aboveground arthropods. PLoS ONE 9, e106529 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Barnes, A. D. et al. Energy flux: the link between multitrophic biodiversity and ecosystem functioning. Trends Ecol. Evol. 33, 186–197 (2018).

  10. Duffy, J. E. et al. The functional role of biodiversity in ecosystems: incorporating trophic complexity. Ecol. Lett. 10, 522–538 (2007).

    Article  PubMed  Google Scholar 

  11. Lindeman, R. L. The trophic-dynamic aspect of ecology. Ecology 23, 399–417 (1942).

    Article  Google Scholar 

  12. Juday, C. The annual energy budget of an inland lake. Ecology 21, 438–450 (1940).

    Article  Google Scholar 

  13. Getz, W. M. Biomass transformation webs provide a unified approach to consumer-resource modelling. Ecol. Lett. 14, 113–124 (2011).

    Article  PubMed  Google Scholar 

  14. Barnes, A. D. et al. Consequences of tropical land use for multitrophic biodiversity and ecosystem functioning. Nat. Commun. 5, 5351 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. Ghedini, G., Loreau, M., White, C. R. & Marshall, D. J. Testing MacArthur’s minimisation principle: do communities minimise energy wastage during succession? Ecol. Lett. 21, 1182–1190 (2018).

    Article  PubMed  Google Scholar 

  16. Gamfeldt, L. et al. Marine biodiversity and ecosystem functioning: what’s known and what’s next? Oikos 124, 252–265 (2015).

    Article  Google Scholar 

  17. Unsicker, S. B. et al. Invertebrate herbivory along a gradient of plant species diversity in extensively managed grasslands. Oecologia 150, 233–246 (2006).

    Article  PubMed  Google Scholar 

  18. Hertzog, L. R., Ebeling, A., Weisser, W. W. & Meyer, S. T. Plant diversity increases predation by ground-dwelling invertebrate predators. Ecosphere 8, e01990 (2017).

    Article  Google Scholar 

  19. Odum, E. P. Trends expected in stressed ecosystems. BioScience 35, 419–422 (1985).

    Article  Google Scholar 

  20. Margalef, R. On certain unifying principles in ecology. Am. Nat. 97, 357–374 (1963).

    Article  Google Scholar 

  21. Weisser, W. W. et al. Biodiversity effects on ecosystem functioning in a 15-year grassland experiment: patterns, mechanisms, and open questions. Basic Appl. Ecol. 23, 1–73 (2017).

    Article  Google Scholar 

  22. Eisenhauer, N., Reich, P. B. & Scheu, S. Increasing plant diversity effects on productivity with time due to delayed soil biota effects on plants. Basic Appl. Ecol. 13, 571–578 (2012).

    Article  Google Scholar 

  23. Ludovisi, A., Pandolfi, P. & Taticchi, M. I. The strategy of ecosystem development: specific dissipation as an indicator of ecosystem maturity. J. Theor. Biol. 235, 33–43 (2005).

    Article  PubMed  Google Scholar 

  24. Poisot, T., Mouquet, N. & Gravel, D. Trophic complementarity drives the biodiversity–ecosystem functioning relationship in food webs. Ecol. Lett. 16, 853–861 (2013).

    Article  PubMed  Google Scholar 

  25. Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).

    Article  Google Scholar 

  26. Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336, 589–592 (2012).

    Article  CAS  PubMed  Google Scholar 

  27. Meyer, S. T. et al. Effects of biodiversity strengthen over time as ecosystem functioning declines at low and increases at high biodiversity. Ecosphere 7, e01619 (2016).

  28. Fagan, W. F. et al. Nitrogen in insects: implications for trophic complexity and species diversification. Am. Nat. 160, 784–802 (2002).

    Article  PubMed  Google Scholar 

  29. Scherber, C. et al. Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment. Nature 468, 553–556 (2010).

    Article  CAS  PubMed  Google Scholar 

  30. Bessler, H. et al. Aboveground overyielding in grassland mixtures is associated with reduced biomass partitioning to belowground organs. Ecology 90, 1520–1530 (2009).

    Article  PubMed  Google Scholar 

  31. Lange, M. et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat. Commun. 6, 6707 (2015).

    Article  CAS  PubMed  Google Scholar 

  32. Berlow, E. Strong effects of weak interactions in ecological communities. Nature 398, 330–334 (1999).

    Article  CAS  Google Scholar 

  33. Moore, J. C., de Ruiter, P. C. & Hunt, H. W. Influence of productivity on the stability of real and model ecosystems. Science 261, 906–908 (1993).

    Article  CAS  PubMed  Google Scholar 

  34. Pfisterer, A. B. & Schmid, B. Diversity-dependent production can decrease the stability of ecosystem functioning. Nature 416, 84–86 (2002).

    Article  CAS  PubMed  Google Scholar 

  35. Pilette, R. Evaluating direct and indirect effects in ecosystems. Am. Nat. 133, 303–307 (1989).

    Article  Google Scholar 

  36. Eisenhauer, N. et al. Plant diversity effects on soil food webs are stronger than those of elevated CO2 and N deposition in a long-term grassland experiment. Proc. Natl Acad. Sci. USA 110, 6889–6894 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hines, J. et al. Towards an integration of biodiversity–ecosystem functioning and food web theory to evaluate relationships between multiple ecosystem services. Adv. Ecol. Res. 53, 161–199 (2015).

    Article  Google Scholar 

  38. Hines, J. et al. A meta food web for invertebrate species collected in a European grassland. Ecology 100, e02679 (2019).

    Article  PubMed  Google Scholar 

  39. Giling, D. P. et al. Plant diversity alters the representation of motifs in food webs. Nat. Commun. 10, 1226 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Huang, Y. et al. Impacts of species richness on productivity in a large-scale subtropical forest experiment. Science 362, 80–83 (2018).

    Article  CAS  PubMed  Google Scholar 

  41. Roscher, C. et al. The role of biodiversity for element cycling and trophic interactions: an experimental approach in a grassland community. Basic Appl. Ecol. 5, 107–121 (2004).

    Article  Google Scholar 

  42. Peters, R. H. The Ecological Implications of Body Size (Cambridge Univ. Press, 1986).

  43. Eisenhauer, N. et al. Plant diversity surpasses plant functional groups and plant productivity as driver of soil biota in the long term. PLoS ONE 6, e16055 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Ravenek, J. M. et al. Long-term study of root biomass in a biodiversity experiment reveals shifts in diversity effects over time. Oikos 123, 1528–1536 (2014).

    Article  Google Scholar 

  45. Swift, M. J., Heal, O. W. & Anderson, J. M. Decomposition in Terrestrial Ecosystems (Univ. California Press, 1979).

  46. Kempson, D., Lloyd, M. & Ghelardi, R. A new extractor for woodland litter. Pedobiologia 3, 1–21 (1963).

    Google Scholar 

  47. Sechi, V., Brussaard, L., De Goede, R. G. M., Rutgers, M. & Mulder, C. Choice of resolution by functional trait or taxonomy affects allometric scaling in soil food webs. Am. Nat. 185, 142–149 (2015).

    Article  PubMed  Google Scholar 

  48. Cortois, R. et al. Possible mechanisms underlying abundance and diversity responses of nematode communities to plant diversity. Ecosphere 8, e01719 (2017).

    Article  Google Scholar 

  49. Andrássy, I. Die rauminhalst and gewichtsbestimmung der fadenwürmer (Nematoden). Acta Zool. Acad. Sci. 2, 1–15 (1956).

    Google Scholar 

  50. Yeates, G. W., Bongers, T., De Goede, R. G., Freckman, D. W. & Georgieva, S. S. Feeding habits in soil nematode families and genera—an outline for soil ecologists. J. Nematol. 25, 315–331 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Ferris, H. NEMAPLEX The Nematode-Plant Information System. A Virtual Encyclopedia on Soil and Plant Nematodes (Univ. California, 1999); http://nemaplex.ucdavis.edu/

  52. Sieriebriennikov, B., Ferris, H. & de Goede, R. G. M. NINJA: an automated calculation system for nematode-based biological monitoring. Eur. J. Soil Biol. 61, 90–93 (2014).

    Article  Google Scholar 

  53. Scheu, S. Automated measurement of the respiratory response of soil microcompartments: active microbial biomass in earthworm faeces. Soil Biol. Biochem. 24, 1113–1118 (1992).

    Article  Google Scholar 

  54. Strecker, T. et al. Effects of plant diversity, functional group composition, and fertilization on soil microbial properties in experimental grassland. PLoS ONE 10, e0125678 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Beck, T. et al. An inter-laboratory comparison of ten different ways of measuring soil microbial biomass C. Soil Biol. Biochem. 29, 1023–1032 (1997).

    Article  CAS  Google Scholar 

  56. Eisenhauer, N. et al. Plant diversity effects on soil microorganisms support the singular hypothesis. Ecology 91, 485–496 (2010).

    Article  CAS  PubMed  Google Scholar 

  57. Steinbeiss, S. et al. Plant diversity positively affects short-term soil carbon storage in experimental grasslands. Glob. Change Biol. 14, 2937–2949 (2008).

    Article  Google Scholar 

  58. Loranger, H. et al. Invertebrate herbivory increases along an experimental gradient of grassland plant diversity. Oecologia 174, 183–193 (2014).

    Article  PubMed  Google Scholar 

  59. Vogel, A., Eisenhauer, N., Weigelt, A. & Scherer-Lorenzen, M. Plant diversity does not buffer drought effects on early-stage litter mass loss rates and microbial properties. Glob. Change Biol. 19, 2795–2803 (2013).

    Article  Google Scholar 

  60. Engelmann, M. D. The role of soil arthropods in the energetics of an old field community. Ecol. Monogr. 31, 221–238 (1961).

    Article  Google Scholar 

  61. De Ruiter, P. C., Van Veen, J. A., Moore, J. C., Brussaard, L. & Hunt, H. W. Calculation of nitrogen mineralization in soil food webs. Plant Soil 157, 263–273 (1993).

    Article  Google Scholar 

  62. Sneath, P. H. A. Longevity of micro-organisms. Nature 195, 643–646 (1962).

    Article  CAS  PubMed  Google Scholar 

  63. Ehnes, R. B., Rall, B. C. & Brose, U. Phylogenetic grouping, curvature and metabolic scaling in terrestrial invertebrates. Ecol. Lett. 14, 993–1000 (2011).

    Article  PubMed  Google Scholar 

  64. Reich, P. B., Tjoelker, M. G., Machado, J.-L. & Oleksyn, J. Universal scaling of respiratory metabolism, size and nitrogen in plants. Nature 439, 457–461 (2006).

    Article  CAS  PubMed  Google Scholar 

  65. Eisenhauer, N., Milcu, A., Sabais, A. C. W. & Scheu, S. Animal ecosystem engineers modulate the diversity-invasibility relationship. PLoS ONE 3, e3489 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Eisenhauer, N. Earthworms in a Plant Diversity Gradient: Direct and Indirect Effects on Plant Competition and Establishment. PhD thesis, TU Darmstadt (2008).

  67. Kazanci, C. EcoNet: a new software for ecological modeling, simulation and network analysis. Ecol. Modell. 208, 3–8 (2007).

    Article  Google Scholar 

  68. MATLAB—the language of technical computing. v.8.1, 2013a (MathWorks, 2013); https://www.mathworks.com/products/matlab/

  69. Borrett, S. R. & Lau, M. K. enaR: an R package for ecosystem network analysis. Methods Ecol. Evol. 5, 1206–1213 (2014).

    Article  Google Scholar 

  70. Borrett, S. R., Whipple, S. J. & Patten, B. C. Rapid development of indirect effects in ecological networks. Oikos 119, 1136–1148 (2010).

    Article  Google Scholar 

  71. Borrett, S. R. & Scharler, U. M. Walk partitions of flow in ecological network analysis: review and synthesis of methods and indicators. Ecol. Indic. 106, 105451 (2019).

    Article  Google Scholar 

  72. Wiegert, R. G. & Kozlowski, J. Indirect causality in ecosystems. Am. Nat. 124, 293–298 (1984).

    Article  Google Scholar 

  73. Hines, D. E., Ray, S. & Borrett, S. R. Uncertainty analyses for ecological network analysis enable stronger inferences. Environ. Modell. Softw. 101, 117–127 (2018).

    Article  Google Scholar 

  74. Soetaert, K., Van den Meersche, K. & van Oevelen, D. limSolve: solving linear inverse models in R. R package v.1.5.1 (2009).

  75. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017); http://www.R-project.org/

  76. Murray, A. A Chaos of Delight. The Wonderful World of Soil Mesofauna (accessed 2 December 2019); https://www.chaosofdelight.org/

  77. IAN/UMCES Symbol and Image Libraries (Integration and Application Network, Univ. Maryland Center for Environmental Science, accessed 2 December 2019); https://ian.umces.edu/symbols/

  78. Smith, M. et al. NodeXL: A Free and Open Network Overview, Discovery and Exploration add-in for Excel Version NodeXL Basic (The Social Media Research Foundation, 2010); https://www.smrfoundation.org

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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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  4. Nico Eisenhauer
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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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Competing interests

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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