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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The data used to support the conclusions of this study are available at PANGAEA (https://doi.pangaea.de/10.1594/PANGAEA.910659).
The code for the analyses of this study is available from the corresponding authors on request.
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
Hautier, Y. et al. Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science 348, 336–340 (2015).
Balvanera, P. et al. Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol. Lett. 9, 1146–1156 (2006).
Hector, A. et al. Plant diversity and productivity experiments in european grasslands. Science 286, 1123–1127 (1999).
Tilman, D. et al. Diversity and productivity in a long-term grassland experiment. Science 294, 843–845 (2001).
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).
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).
Ebeling, A. et al. Plant diversity impacts decomposition and herbivory via changes in aboveground arthropods. PLoS ONE 9, e106529 (2014).
Barnes, A. D. et al. Energy flux: the link between multitrophic biodiversity and ecosystem functioning. Trends Ecol. Evol. 33, 186–197 (2018).
Duffy, J. E. et al. The functional role of biodiversity in ecosystems: incorporating trophic complexity. Ecol. Lett. 10, 522–538 (2007).
Lindeman, R. L. The trophic-dynamic aspect of ecology. Ecology 23, 399–417 (1942).
Juday, C. The annual energy budget of an inland lake. Ecology 21, 438–450 (1940).
Getz, W. M. Biomass transformation webs provide a unified approach to consumer-resource modelling. Ecol. Lett. 14, 113–124 (2011).
Barnes, A. D. et al. Consequences of tropical land use for multitrophic biodiversity and ecosystem functioning. Nat. Commun. 5, 5351 (2014).
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).
Gamfeldt, L. et al. Marine biodiversity and ecosystem functioning: what’s known and what’s next? Oikos 124, 252–265 (2015).
Unsicker, S. B. et al. Invertebrate herbivory along a gradient of plant species diversity in extensively managed grasslands. Oecologia 150, 233–246 (2006).
Hertzog, L. R., Ebeling, A., Weisser, W. W. & Meyer, S. T. Plant diversity increases predation by ground-dwelling invertebrate predators. Ecosphere 8, e01990 (2017).
Odum, E. P. Trends expected in stressed ecosystems. BioScience 35, 419–422 (1985).
Margalef, R. On certain unifying principles in ecology. Am. Nat. 97, 357–374 (1963).
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).
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).
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).
Poisot, T., Mouquet, N. & Gravel, D. Trophic complementarity drives the biodiversity–ecosystem functioning relationship in food webs. Ecol. Lett. 16, 853–861 (2013).
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).
Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336, 589–592 (2012).
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).
Fagan, W. F. et al. Nitrogen in insects: implications for trophic complexity and species diversification. Am. Nat. 160, 784–802 (2002).
Scherber, C. et al. Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment. Nature 468, 553–556 (2010).
Bessler, H. et al. Aboveground overyielding in grassland mixtures is associated with reduced biomass partitioning to belowground organs. Ecology 90, 1520–1530 (2009).
Lange, M. et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat. Commun. 6, 6707 (2015).
Berlow, E. Strong effects of weak interactions in ecological communities. Nature 398, 330–334 (1999).
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).
Pfisterer, A. B. & Schmid, B. Diversity-dependent production can decrease the stability of ecosystem functioning. Nature 416, 84–86 (2002).
Pilette, R. Evaluating direct and indirect effects in ecosystems. Am. Nat. 133, 303–307 (1989).
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).
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).
Hines, J. et al. A meta food web for invertebrate species collected in a European grassland. Ecology 100, e02679 (2019).
Giling, D. P. et al. Plant diversity alters the representation of motifs in food webs. Nat. Commun. 10, 1226 (2019).
Huang, Y. et al. Impacts of species richness on productivity in a large-scale subtropical forest experiment. Science 362, 80–83 (2018).
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).
Peters, R. H. The Ecological Implications of Body Size (Cambridge Univ. Press, 1986).
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).
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).
Swift, M. J., Heal, O. W. & Anderson, J. M. Decomposition in Terrestrial Ecosystems (Univ. California Press, 1979).
Kempson, D., Lloyd, M. & Ghelardi, R. A new extractor for woodland litter. Pedobiologia 3, 1–21 (1963).
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).
Cortois, R. et al. Possible mechanisms underlying abundance and diversity responses of nematode communities to plant diversity. Ecosphere 8, e01719 (2017).
Andrássy, I. Die rauminhalst and gewichtsbestimmung der fadenwürmer (Nematoden). Acta Zool. Acad. Sci. 2, 1–15 (1956).
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).
Ferris, H. NEMAPLEX The Nematode-Plant Information System. A Virtual Encyclopedia on Soil and Plant Nematodes (Univ. California, 1999); http://nemaplex.ucdavis.edu/
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).
Scheu, S. Automated measurement of the respiratory response of soil microcompartments: active microbial biomass in earthworm faeces. Soil Biol. Biochem. 24, 1113–1118 (1992).
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).
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).
Eisenhauer, N. et al. Plant diversity effects on soil microorganisms support the singular hypothesis. Ecology 91, 485–496 (2010).
Steinbeiss, S. et al. Plant diversity positively affects short-term soil carbon storage in experimental grasslands. Glob. Change Biol. 14, 2937–2949 (2008).
Loranger, H. et al. Invertebrate herbivory increases along an experimental gradient of grassland plant diversity. Oecologia 174, 183–193 (2014).
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).
Engelmann, M. D. The role of soil arthropods in the energetics of an old field community. Ecol. Monogr. 31, 221–238 (1961).
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).
Sneath, P. H. A. Longevity of micro-organisms. Nature 195, 643–646 (1962).
Ehnes, R. B., Rall, B. C. & Brose, U. Phylogenetic grouping, curvature and metabolic scaling in terrestrial invertebrates. Ecol. Lett. 14, 993–1000 (2011).
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).
Eisenhauer, N., Milcu, A., Sabais, A. C. W. & Scheu, S. Animal ecosystem engineers modulate the diversity-invasibility relationship. PLoS ONE 3, e3489 (2008).
Eisenhauer, N. Earthworms in a Plant Diversity Gradient: Direct and Indirect Effects on Plant Competition and Establishment. PhD thesis, TU Darmstadt (2008).
Kazanci, C. EcoNet: a new software for ecological modeling, simulation and network analysis. Ecol. Modell. 208, 3–8 (2007).
MATLAB—the language of technical computing. v.8.1, 2013a (MathWorks, 2013); https://www.mathworks.com/products/matlab/
Borrett, S. R. & Lau, M. K. enaR: an R package for ecosystem network analysis. Methods Ecol. Evol. 5, 1206–1213 (2014).
Borrett, S. R., Whipple, S. J. & Patten, B. C. Rapid development of indirect effects in ecological networks. Oikos 119, 1136–1148 (2010).
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).
Wiegert, R. G. & Kozlowski, J. Indirect causality in ecosystems. Am. Nat. 124, 293–298 (1984).
Hines, D. E., Ray, S. & Borrett, S. R. Uncertainty analyses for ecological network analysis enable stronger inferences. Environ. Modell. Softw. 101, 117–127 (2018).
Soetaert, K., Van den Meersche, K. & van Oevelen, D. limSolve: solving linear inverse models in R. R package v.1.5.1 (2009).
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017); http://www.R-project.org/
Murray, A. A Chaos of Delight. The Wonderful World of Soil Mesofauna (accessed 2 December 2019); https://www.chaosofdelight.org/
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/
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
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).
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Network metrics used for the analysis of the proxies of the multitrophic ecosystem functioning.
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.
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).
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. 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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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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