A large body of research shows that biodiversity loss can reduce ecosystem functioning. However, much of the evidence for this relationship is drawn from biodiversity–ecosystem functioning experiments in which biodiversity loss is simulated by randomly assembling communities of varying species diversity, and ecosystem functions are measured. This random assembly has led some ecologists to question the relevance of biodiversity experiments to real-world ecosystems, where community assembly or disassembly may be non-random and influenced by external drivers, such as climate, soil conditions or land use. Here, we compare data from real-world grassland plant communities with data from two of the largest and longest-running grassland biodiversity experiments (the Jena Experiment in Germany and BioDIV in the United States) in terms of their taxonomic, functional and phylogenetic diversity and functional-trait composition. We found that plant communities of biodiversity experiments cover almost all of the multivariate variation of the real-world communities, while also containing community types that are not currently observed in the real world. Moreover, they have greater variance in their compositional features than their real-world counterparts. We then re-analysed a subset of experimental data that included only ecologically realistic communities (that is, those comparable to real-world communities). For 10 out of 12 biodiversity–ecosystem functioning relationships, biodiversity effects did not differ significantly between the full dataset of biodiversity experiments and the ecologically realistic subset of experimental communities. Although we do not provide direct evidence for strong or consistent biodiversity–ecosystem functioning relationships in real-world communities, our results demonstrate that the results of biodiversity experiments are largely insensitive to the exclusion of unrealistic communities and that the conclusions drawn from biodiversity experiments are generally robust.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Nature Communications Open Access 03 March 2022
Distinct effects of host and neighbour tree identity on arbuscular and ectomycorrhizal fungi along a tree diversity gradient
ISME Communications Open Access 20 August 2021
Communications Biology Open Access 25 June 2021
Subscribe to Nature+
Get immediate online access to Nature and 55 other Nature journal
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
The data supporting the findings of our study are available at https://doi.org/10.25829/idiv.1869-11-3082.
The R code to reproduce the findings and figures of our study is available at https://doi.org/10.25829/idiv.1869-11-3082.
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).
Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).
van der Plas, F. Biodiversity and ecosystem functioning in naturally assembled communities. Biol. Rev. 94, 1220–1245 (2019).
Schulze, E.-D. & Mooney, H. Biodiversity and Ecosystem Functioning (Springer, 1993).
Naeem, S., Thompson, L. J., Lawler, S. P., Lawton, J. H. & Woodfin, R. M. Declining biodiversity can alter the performance of ecosystems. Nature 368, 734–737 (1994).
Balvanera, P. et al. Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol. Lett. 9, 1146–1156 (2006).
Hines, J. et al. Mapping change in biodiversity and ecosystem function research: food webs foster integration of experiments and science policy. Adv. Ecol. Res. 61, 297–322 (2019).
Tilman, D., Wedin, D. & Knops, J. Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature 379, 718–720 (1996).
Roscher, C., Schumacher, J. & Baade, J. The role of biodiversity for element cycling and trophic interactions: an experimental approach in a grassland community. Basic Appl. Ecol. 121, 107–121 (2004).
Tilman, D. et al. Diversity and productivity in a long-term grassland experiment. Science 294, 843–845 (2001).
Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Cardinale, B. J. et al. The functional role of producer diversity in ecosystems. Am. J. Bot. 98, 572–592 (2011).
O’Connor, M. I. et al. A general biodiversity–function relationship is mediated by trophic level. Oikos 126, 18–31 (2017).
Loreau, M. et al. Biodiversity and ecosystem functioning: current knowledge and future challenges. Science 294, 804–808 (2001).
Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336, 589–592 (2012).
Huston, M. A. Hidden treatments in ecological experiments: re-evaluating the ecosystem function of biodiversity. Oecologia 110, 449–460 (1997).
Grime, J. P. Benefits of plant diversity to ecosystems: immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).
Wardle, D. A. et al. Biodiversity and ecosystem function: an issue in ecology. Bull. Ecol. Soc. Am. 81, 235–239 (2000).
Leps, J. What do the biodiversity experiments tell us about consequences of plant species loss in the real world? Basic Appl. Ecol. 5, 529–534 (2004).
Srivastava, D. S. & Vellend, M. Biodiversity–ecosystem function research: is it relevant to conservation? Annu. Rev. Ecol. Evol. Syst. 36, 267–294 (2005).
Duffy, J. E. Why biodiversity is important to the functioning of real-world ecosystems. Front. Ecol. Environ. 7, 437–444 (2008).
Duffy, J. E. Biodiversity effects: trends and exceptions—a reply to Wardle and Jonsson. Front. Ecol. Environ. 8, 11–12 (2010).
Wardle, D. A. & Jonsson, M. Biodiversity effects in real ecosystems—a response to Duffy. Front. Ecol. Environ. 8, 10–11 (2010).
Wardle, D. A. Do experiments exploring plant diversity–ecosystem functioning relationships inform how biodiversity loss impacts natural ecosystems? J. Veg. Sci. 27, 646–653 (2016).
Manning, P. et al. Transferring biodiversity-ecosystem function research to the management of ‘real-world’ ecosystems. Adv. Ecol. Res. 61, 323–356 (2019).
Wilsey, B. J. & Potvin, C. Biodiversity and ecosystem functioning: importance of species evenness in an old field. Ecology 81, 887–892 (2000).
Wilsey, B. J. & Polley, H. W. Realistically low species evenness does not alter grassland species-richness–productivity relationships. Ecology 85, 2693–2700 (2004).
Hillebrand, H., Bennett, D. & Cadotte, M. Consequences of dominance: a review of evenness effects on local and regional ecosystem processes. Ecology 89, 1510–1520 (2008).
Schmitz, M. et al. Consistent effects of biodiversity on ecosystem functioning under varying density and evenness. Folia Geobot. 48, 335–353 (2013).
Finn, J. A. et al. Ecosystem function enhanced by combining four functional types of plant species in intensively managed grassland mixtures: a 3-year continental-scale field experiment. J. Appl. Ecol. 50, 365–375 (2013).
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).
Schmid, B. & Hector, A. The value of biodiversity experiments. Basic Appl. Ecol. 5, 535–542 (2004).
Eisenhauer, N. et al. Biodiversity–ecosystem function experiments reveal the mechanisms underlying the consequences of biodiversity change in real world ecosystems. J. Veg. Sci. 27, 1061–1070 (2016).
Isbell, F. et al. Nutrient enrichment, biodiversity loss, and consequent declines in ecosystem productivity. Proc. Natl Acad. Sci. USA 110, 11911–11916 (2013).
Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).
Buchmann, T. et al. Connecting experimental biodiversity research to real-world grasslands. Perspect. Plant Ecol. Evol. Syst. 33, 78–88 (2018).
Tilman, D. et al. The influence of functional diversity and composition on ecosystem processes. Science 277, 1300–1302 (1997).
Tilman, D., Reich, P. B. & Isbell, F. Biodiversity impacts ecosystem productivity as much as resources, disturbance, or herbivory. Proc. Natl Acad. Sci. USA 109, 10394–10397 (2012).
Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).
Fischer, M. et al. Implementing large-scale and long-term functional biodiversity research: the biodiversity exploratories. Basic Appl. Ecol. 11, 473–485 (2010).
Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).
Tilman, D. Secondary succession and the pattern of plant dominance along experimental nitrogen gradients. Ecol. Monogr. 57, 189–214 (1987).
Clark, C. M. & Tilman, D. Loss of plant species after chronic low-level nitrogen deposition to prairie grasslands. Nature 451, 712–715 (2008).
Inouye, R. et al. Old-field succession on a Minnesota sand plain. Ecology 68, 12–26 (1987).
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2015).
Craven, D. et al. Multiple facets of biodiversity drive the diversity–stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).
Nakamura, G., Gonçalves, L. O. & da Silva Duarte, L. Revisiting the dimensionality of biological diversity. Ecography (Cop.) 43, 539–548 (2020).
Stevens, R. D. & Tello, J. S. On the measurement of dimensionality of biodiversity. Glob. Ecol. Biogeogr. 23, 1115–1125 (2014).
Manning, P. et al. Simple measures of climate, soil properties and plant traits predict national-scale grassland soil carbon stocks. J. Appl. Ecol. 52, 1188–1196 (2015).
Adler, D. & Kelly, T. vioplot: Violin plot. R package version 0.3.0 (2018).
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).
Allan, E. et al. Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition. Ecol. Lett. 18, 834–843 (2015).
Le Bagousse-Pinguet, Y. et al. Phylogenetic, functional, and taxonomic richness have both positive and negative effects on ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 116, 8419–8424 (2019).
Venail, P. et al. Species richness, but not phylogenetic diversity, influences community biomass production and temporal stability in a re-examination of 16 grassland biodiversity studies. Funct. Ecol. 29, 615–626 (2015).
Hillebrand, H. & Matthiessen, B. Biodiversity in a complex world: consolidation and progress in functional biodiversity research. Ecol. Lett. 12, 1405–1419 (2009).
Grace, J. B. et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature 529, 390–393 (2016).
Liang, J. et al. Positive biodiversity–productivity relationship predominant in global forests. Science 354, aaf8957 (2016).
Oehri, J., Schmid, B., Schaepman-Strub, G. & Niklaus, P. A. Biodiversity promotes primary productivity and growing season lengthening at the landscape scale. Proc. Natl Acad. Sci. USA 114, 10160–10165 (2017).
Díaz, S. et al. Incorporating plant functional diversity effects in ecosystem service assessments. Proc. Natl Acad. Sci. USA 104, 20684–20689 (2007).
Lavorel, S. et al. Using plant functional traits to understand the landscape distribution of multiple ecosystem services. J. Ecol. 99, 135–147 (2011).
Schmid, B. The species richness–productivity controversy. Trends Ecol. Evol. 17, 113–114 (2002).
Loreau, M. Biodiversity and ecosystem functioning: a mechanistic model. Proc. Natl Acad. Sci. USA 95, 5632–5636 (1998).
Maestre, F. T. et al. Plant species richness and ecosystem multifunctionality in global drylands. Science 335, 214–218 (2012).
van der Plas, F. et al. Jack-of-all-trades effects drive biodiversity–ecosystem multifunctionality relationships in European forests. Nat. Commun. 7, 11109 (2016).
Socher, S. A. et al. Direct and productivity-mediated indirect effects of fertilization, mowing and grazing on grassland species richness. J. Ecol. 100, 1391–1399 (2012).
Hobbs, R. J., Higgs, E. & Harris, J. A. Novel ecosystems: implications for conservation and restoration. Trends Ecol. Evol. 24, 599–605 (2009).
Klaus, V. H. et al. Do biodiversity–ecosystem functioning experiments inform stakeholders how to simultaneously conserve biodiversity and increase ecosystem service provisioning in grasslands? Biol. Conserv. 245, 108552 (2020).
Roscher, C. et al. Convergent high diversity in naturally colonized experimental grasslands is not related to increased productivity. Perspect. Plant Ecol. Evol. Syst. 20, 32–45 (2016).
Ellenberg, H. & Leuschner, C. Vegetation Mitteleuropas mit den Alpen: In Ökologischer, Dynamischer und Historischer Sicht (UTB, 2010).
Blüthgen, N. et al. A quantitative index of land-use intensity in grasslands: integrating mowing, grazing and fertilization. Basic Appl. Ecol. 13, 207–220 (2012).
Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).
Tilman, D. Community invasibility, recruitment limitation, and grassland biodiversity. Ecology 78, 81–92 (1997).
Catford, J. A. et al. Traits linked with species invasiveness and community invasibility vary with time, stage and indicator of invasion in a long-term grassland experiment. Ecol. Lett. 22, 593–604 (2019).
Fargione, J. et al. From selection to complementarity: shifts in the causes of biodiversity–productivity relationships in a long-term biodiversity experiment. Proc. R. Soc. B 274, 871–876 (2007).
Londo, G. The decimal scale for releves of permanent quadrats. Vegetatio 33, 61–64 (1976).
Roscher, C. et al. What happens to the sown species if a biodiversity experiment is not weeded? Basic Appl. Ecol. 14, 187–198 (2013).
Kattge, J. et al. TRY—a global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).
Cayuela, L., Stein, A. & Oksanen, J. Taxonstand: Taxonomic standardization of plant species names. R package version 2.1 (2017).
The Plant List version 1.1 (2013); http://www.theplantlist.org/
Qian, H. & Jin, Y. An updated megaphylogeny of plants, a tool for generating plant phylogenies and an analysis of phylogenetic community structure. J. Plant Ecol. 9, 233–239 (2016).
Martins, W. S., Carmo, W. C., Longo, H. J., Rosa, T. C. & Rangel, T. F. SUNPLIN: simulation with uncertainty for phylogenetic investigations. BMC Bioinform. 14, 324 (2013).
Rangel, T. F. et al. Phylogenetic uncertainty revisited: implications for ecological analyses. Evolution 69, 1301–1312 (2015).
Cornelissen, J. H. C. et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 51, 335–380 (2003).
Goolsby, E. W., Bruggeman, J. & Ane, C. Rphylopars: Phylogenetic comparative tools for missing data and within-species variation. R package version 0.2.9 (2016).
Penone, C. et al. Imputation of missing data in life-history trait datasets: which approach performs the best? Methods Ecol. Evol. 5, 961–970 (2014).
Oksanen, J. et al. Vegan: Community ecology package. R package version 2.3-4 (2016).
Hill, M. Diversity and evenness: a unifying notation and its consequences. Ecology 54, 427–432 (1973).
Smith, B. & Wilson, J. B. A consumer’s guide to evenness indices. Oikos 76, 70–82 (1996).
Magurran, A. Measuring Biological Diversity (Blackwell, 2004).
Morris, E. K. et al. Choosing and using diversity indices: insights for ecological applications from the German biodiversity exploratories. Ecol. Evol. 4, 3514–3524 (2014).
Tucker, C. M. et al. A guide to phylogenetic metrics for conservation, community ecology and macroecology. Biol. Rev. 92, 698–715 (2017).
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).
Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).
Laliberte, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).
Mouchet, M. A., Villéger, S., Mason, N. W. H. & Mouillot, D. Functional diversity measures: an overview of their redundancy and their ability to discriminate community assembly rules. Funct. Ecol. 24, 867–876 (2010).
Laliberté, E., Legendre, P. & Shipley, B. FD: Measuring functional diversity from multiple traits, and other tools for functional ecology. R package version 1.0-12 (2014).
R: A Language and Environment for Statistical Computing v.3.4.2 (R Core Team, 2019); https://doi.org/10.1007/978-3-540-74686-7
Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop.) 36, 27–46 (2013).
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
Jochum, M. et al. R-code and aggregated data from: The results of biodiversity-ecosystem functioning experiments are realistic. iDiv Data Repository https://doi.org/10.25829/idiv.1869-11-3082 (2020).
Fox, J. & Weisberg, S. An R Companion to Applied Regression (SAGE, 2011).
Pebesma, E. & Bivand, R. Classes and methods for spatial data in R. R News 5, 9–13 (2005).
Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied Spatial Data Analysis with R (Springer, 2013).
Habel, K., Grasman, R., Gramacy, R. B., Stahel, A. & Sterratt, D. C. geometry: Mesh generation and surface tessellation. R package version 0.4.1 (2019).
Blonder, B. & Harris, D. hypervolume: High dimensional geometry and set operations using kernel density estimation, support vector machines, and convex hulls. R package version 2.0.11 (2018).
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).
Brownrigg, R. mapdata: Extra map databases. R package version 2.3.0 (2018).
We thank the establishers, maintainers, coordinators, technical and research staff, and data owners of all involved projects, as well as the TRY initiative. We thank S. Soliveres and E. Allan for discussion; S. Thiel, G. Luo, D. Bahauddin and F. Schneider for help with data extraction and handling; and R. Junker and B. Blonder for assistance with the calculation of multidimensional hypervolumes. This study was funded through Jena Experiment SP 7 (Swiss National Science Foundation grant no. 310030E-166017/1). Further support came from the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation (grant no. FZT 118). The Jena Experiment was funded by the Deutsche Forschungsgemeinschaft (grant nos FOR 456 and FOR 1451) with additional support from Friedrich Schiller University Jena, the Max Planck Institute for Biogeochemistry in Jena and the Swiss National Science Foundation. All Cedar Creek studies are funded by the US National Science Foundation’s Long-Term Ecological Research (LTER) programme (grant no. DEB-1234162). F.I. acknowledges funding from the LTER Network Communications Office (grant no. DEB-1545288). We thank the iDiv Data Repository for hosting our R code and aggregated datasets and for performing the related quality checks.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Lists country, project name, project code used in this paper, main reference, number of plots we used, years we have vegetation data for, functions we used including years. Most of the raw data is openly available in various online repositories: Jena Experiment (http://jenaexperiment.uni-jena.de/index.php/data/), Biodiversity Exploratories (https://www.bexis.uni-jena.de/Login/Account.aspx), Cedar Creek (https://www.cedarcreek.umn.edu/research/data). Data from the Saale grasslands (Jena real world) were provided by Christiane Roscher and are currently not openly available. Aggregated datasets used for this study are now available online101.
Based on the PCA in Fig. 1a. Different shades of purple show Jena invasion communities across the years from 2003-2009. Orange and gray ellipses show 95% confidence intervals for Jena Experiment and combined real-world plots (but their communities are not plotted here), respectively. Note that while the points in different panels are from single years, the ellipses are fixed to the across-year comparison in Fig. 1a. The last panel shows the PCA factor loadings for the 12 vif-selected community properties (arrows scaled to improve visibility - “const=25” in R vegan “biplot” function87). Within six years of succession, the plant communities of Jena invasion plots fully “moved” into the core of the community property space defined by the combined real-world plots (German real world and Jena real world, respectively).
Experimental (E, Jena Experiment, strong orange, 82 plots), unrealistic experimental (unreal., intermediate orange, 59 plots), selected realistic experimental (real., weak orange, 23 plots) and combined real-world plots (German real world, Jena real world, gray, 164 plots), all averaged across years per plot. Combination of boxplot and rotated kernel density plot (R package “vioplot”51). Realistic plots were calculated based on the 12 vif-selected community properties and the convex hull volume method. Units: leaf area (mm²), leaf dry mass (mg), leaf dry matter content (LDMC, g/g), leaf nitrogen concentration (leaf N, mg/g), leaf phosphorus concentration (leaf P, mg/g), plant height (m), specific leaf area (SLA, mm²/mg) and seed mass (dry mass in mg). Other community properties are dimensionless.
Experimental (E, BioDIV, strong orange, 159 plots), unrealistic experimental (unreal., intermediate orange, 37 plots), selected realistic experimental (real., weak orange, 122 plots) and combined real-world plots (Fertilization 1 & 2, gray, 369 plots), all averaged across years per plot. Combination of boxplot and rotated kernel density plot (R package “vioplot”51). Realistic plots were calculated based on the 12 vif-selected community properties and the convex hull volume method. Units: leaf area (mm²), leaf dry mass (mg), leaf dry matter content (LDMC, g/g), leaf nitrogen concentration (leaf N, mg/g), leaf phosphorus concentration (leaf P, mg/g), plant height (m), specific leaf area (SLA, mm²/mg) and seed mass (dry mass in mg). Other community properties are dimensionless.
Values are presented for unconstrained (u) and constrained (c) models of Jena (J) and BioDIV BEF relationships. Constraining was done using the 12 vif-selected community properties and the convex hull method. Sample size (n), slope estimates (slop), lower (low) and upper (upp) 95% confidence intervals, p-values (p) and adjusted R2 values (R2). All values are rounded to two decimal places.
Percentage of total variance explained by each of the 12 PCA axes (PC’s, see Fig. 1) for each region (GER = Germany and US = USA). Rounded to two decimal places.
Scores have been produced using the scores() command of the “vegan” package87 in R and have been rounded to two decimal places.
About this article
Cite this article
Jochum, M., Fischer, M., Isbell, F. et al. The results of biodiversity–ecosystem functioning experiments are realistic. Nat Ecol Evol 4, 1485–1494 (2020). https://doi.org/10.1038/s41559-020-1280-9
This article is cited by
Nature Ecology & Evolution (2022)
Nature Ecology & Evolution (2022)
Nature Communications (2022)
Nature Ecology & Evolution (2022)
Reply to: “Results from a biodiversity experiment fail to represent economic performance of semi-natural grasslands”
Nature Communications (2021)