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Land-use intensification causes multitrophic homogenization of grassland communities

Abstract

Land-use intensification is a major driver of biodiversity loss1,2. Alongside reductions in local species diversity, biotic homogenization at larger spatial scales is of great concern for conservation. Biotic homogenization means a decrease in β-diversity (the compositional dissimilarity between sites). Most studies have investigated losses in local (α)-diversity1,3 and neglected biodiversity loss at larger spatial scales. Studies addressing β-diversity have focused on single or a few organism groups (for example, ref. 4), and it is thus unknown whether land-use intensification homogenizes communities at different trophic levels, above- and belowground. Here we show that even moderate increases in local land-use intensity (LUI) cause biotic homogenization across microbial, plant and animal groups, both above- and belowground, and that this is largely independent of changes in α-diversity. We analysed a unique grassland biodiversity dataset, with abundances of more than 4,000 species belonging to 12 trophic groups. LUI, and, in particular, high mowing intensity, had consistent effects on β-diversity across groups, causing a homogenization of soil microbial, fungal pathogen, plant and arthropod communities. These effects were nonlinear and the strongest declines in β-diversity occurred in the transition from extensively managed to intermediate intensity grassland. LUI tended to reduce local α-diversity in aboveground groups, whereas the α-diversity increased in belowground groups. Correlations between the β-diversity of different groups, particularly between plants and their consumers, became weaker at high LUI. This suggests a loss of specialist species and is further evidence for biotic homogenization. The consistently negative effects of LUI on landscape-scale biodiversity underscore the high value of extensively managed grasslands for conserving multitrophic biodiversity and ecosystem service provision. Indeed, biotic homogenization rather than local diversity loss could prove to be the most substantial consequence of land-use intensification.

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Figure 1: The effect of LUI on α-diversity above- and belowground.
Figure 2: Effects of LUI on β-diversity above- and belowground.
Figure 3: Effect of LUI on correlations between the β-diversities (βsim) of different trophic groups.

References

  1. Allan, E. et al. Interannual variation in land-use intensity enhances grassland multidiversity. Proc. Natl Acad. Sci. USA 111, 308–313 (2014)

    ADS  CAS  Article  Google Scholar 

  2. Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000)

    CAS  Article  Google Scholar 

  3. Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015)

    ADS  CAS  Article  Google Scholar 

  4. Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005)

    ADS  CAS  Article  Google Scholar 

  5. Anderson, M. J. et al. Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist. Ecol. Lett . 14, 19–28 (2011)

    ADS  Article  Google Scholar 

  6. Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 8568 (2015)

    ADS  Article  Google Scholar 

  7. Karp, D. S. et al. Intensive agriculture erodes β-diversity at large scales. Ecol. Lett. 15, 963–970 (2012)

    Article  Google Scholar 

  8. McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999)

    CAS  Article  Google Scholar 

  9. Smart, S. M. et al. Biotic homogenization and changes in species diversity across human-modified ecosystems. Proc. R. Soc. B 273, 2659–2665 (2006)

    Article  Google Scholar 

  10. Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014)

    ADS  CAS  Article  Google Scholar 

  11. Solar, R. R. C. et al. How pervasive is biotic homogenization in human-modified tropical forest landscapes? Ecol. Lett. 18, 1108–1118 (2015)

    Article  Google Scholar 

  12. Kleijn, D. et al. On the relationship between farmland biodiversity and land-use intensity in Europe. Proc. R. Soc. B 276, 903–909 (2009)

    CAS  Article  Google Scholar 

  13. Ferrier, S., Manion, G., Elith, J. & Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers. Distrib . 13, 252–264 (2007)

    Article  Google Scholar 

  14. Fitzpatrick, M. C. et al. Environmental and historical imprints on beta diversity: insights from variation in rates of species turnover along gradients. Proc. R. Soc. B 280, 2013 1201 (2013)

    Article  Google Scholar 

  15. Carvalho, J. C., Cardoso, P., Borges, P. A. V., Schmera, D & Podani, J. Measuring fractions of beta diversity and their relationships to nestedness: a theoretical and empirical comparison of novel approaches. Oikos 122, 825–834 (2013)

    Article  Google Scholar 

  16. Baselga, A. & Leprieur, F. Comparing methods to separate components of beta diversity. Methods Ecol. Evol . 6, 1069–1079 (2015)

    Article  Google Scholar 

  17. Rodrigues, J. L. M. et al. Conversion of the Amazon rainforest to agriculture results in biotic homogenization of soil bacterial communities. Proc. Natl Acad. Sci. USA 110, 988–993 (2013)

    ADS  CAS  Article  Google Scholar 

  18. Pellissier, L. et al. Turnover of plant lineages shapes herbivore phylogenetic beta diversity along ecological gradients. Ecol. Lett. 16, 600–608 (2013)

    Article  Google Scholar 

  19. 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)

    Article  Google Scholar 

  20. De Deyn, G. B. & Van der Putten, W. H. Linking aboveground and belowground diversity. Trends Ecol. Evol. 20, 625–633 (2005)

    Article  Google Scholar 

  21. Haimi, J., Fritze, H. & Moilanen, P. Responses of soil decomposer animals to wood-ash fertilisation and burning in a coniferous forest stand. For. Ecol. Manage . 129, 53–61 (2000)

    Article  Google Scholar 

  22. Bardgett, R., Hobbs, P. & Frostegård, Å. Changes in soil fungal:bacterial biomass ratios following reductions in the intensity of management of an upland grassland. Biol. Fertil. Soils 22, 261–264 (1996)

    Article  Google Scholar 

  23. Fenchel, T. & Finlay, B. J. The ubiquity of small species: patterns of local and global diversity. Bioscience 54, 777–784 (2004)

    Article  Google Scholar 

  24. Finlay, B. J. Global dispersal of free-living microbial eukaryote species. Science 296, 1061–1063 (2002)

    ADS  CAS  Article  Google Scholar 

  25. Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl Acad. Sci. USA 112, 10967–10972 (2015)

    ADS  CAS  Article  Google Scholar 

  26. van der Plas, F. et al. Biotic homogenization can decrease landscape-scale forest multifunctionality. Proc. Natl Acad. Sci. USA 113, 3557–3562 (2016)

    ADS  CAS  Article  Google Scholar 

  27. Perovic´, D. et al. Configurational landscape heterogeneity shapes functional community composition of grassland butterflies. J. Appl. Ecol. 52, 505–513 (2015)

    Article  Google Scholar 

  28. Manning, P. et al. Grassland management intensification weakens the associations among the diversities of multiple plant and animal taxa. Ecology 96, 1492–1501 (2015)

    Article  Google Scholar 

  29. Fischer, M. et al. Implementing large-scale and long-term functional biodiversity research: the Biodiversity Exploratories. Basic Appl. Ecol. 11, 473–485 (2010)

    Article  Google Scholar 

  30. Birkhofer, K. et al. General relationships between abiotic soil properties and soil biota across spatial scales and different land-use types. PLoS One 7, e43292 (2012)

    ADS  CAS  Article  Google Scholar 

  31. Dray, S. & Dufour, A. B. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007)

    Article  Google Scholar 

  32. vegan: Community Ecology Package. R package version 2.3-3. https://CRAN.R-project.org/package=vegan (2016)

  33. Socher, S. A. et al. Interacting effects of fertilization, mowing and grazing on plant species diversity of 1500 grasslands in Germany differ between regions. Basic Appl. Ecol. 14, 126–136 (2013)

    Article  Google Scholar 

  34. 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)

    Article  Google Scholar 

  35. Boch, S., Prati, D., Schöning, I. & Fischer, M. Lichen species richness is highest in non-intensively used grasslands promoting suitable microhabitats and low vascular plant competition. Biodivers. Conserv. 25, 225–238 (2016)

    Article  Google Scholar 

  36. Müller, J. et al. Impact of land-use intensity and productivity on bryophyte diversity in agricultural grasslands. PLoS One 7, e51520 (2012)

    ADS  Article  Google Scholar 

  37. Simons, N. K. et al. Resource-mediated indirect effects of grassland management on arthropod diversity. PLoS One 9, e107033 (2014)

    ADS  Article  Google Scholar 

  38. Simons, N. K. et al. Effects of land-use intensity on arthropod species abundance distributions in grasslands. J. Anim. Ecol. 84, 143–154 (2015)

    Article  Google Scholar 

  39. Weiner, C. N., Werner, M., Linsenmair, K. E. & Blüthgen, N. Land-use impacts on plant–pollinator networks: interaction strength and specialization predict pollinator declines. Ecology 95, 466–474 (2014)

    Article  Google Scholar 

  40. Börschig, C. Effects of land-use intensity in grasslands on diversity, life-history traits and multitrophic interactions Dr. rer. nat. thesis, Georg-August-Universität (2012)

  41. Börschig, C., Klein, A. M., von Wehrden, H. & Krauss, J. Traits of butterfly communities change from specialist to generalist characteristics with increasing land-use intensity. Basic Appl. Ecol . 14, 547–554 (2013). 10.1016/j.baae.2013.09.002

    Article  Google Scholar 

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

    Google Scholar 

  43. Renner, S. C. et al. Temporal changes in randomness of bird communities across central Europe. PLoS One 9, e112347 (2014)

    ADS  Article  Google Scholar 

  44. Rydell, J., Entwistle, A. & Racey, P. A. Timing of foraging flights of three species of bats in relation to insect activity and predation risk. Oikos 76, 243–252 (1996)

    Article  Google Scholar 

  45. Denzinger, A., Siemers, B. M., Schaub, A. & Schnitzler, H.-U. Echolocation by the barbastelle bat, Barbastella barbastellus. J. Comp. Physiol. A 187, 521–528 (2001)

    CAS  Article  Google Scholar 

  46. Russo, D. & Jones, G. Identification of twenty-two bat species (Mammalia: Chiroptera) from Italy by analysis of time-expanded recordings of echolocation calls. J. Zool . 258, 91–103 (2002)

    Article  Google Scholar 

  47. Obrist, M. K., Boesch, R. & Flückiger, P. F. Variability in echolocation call design of 26 Swiss bat species: consequences, limits and options for automated field identification with a synergetic pattern recognition approach. Mammalia 68, 307–322 (2004)

    Article  Google Scholar 

  48. Jung, K., Kaiser, S., Böhm, S., Nieschulze, J. & Kalko, E. K. V. Moving in three dimensions: effects of structural complexity on occurrence and activity of insectivorous bats in managed forest stands. J. Appl. Ecol . 49, 523–531 (2012). 10.1111/j.1365-2664.2012.02116.x

    Article  Google Scholar 

  49. Fenton, M. B. in Bat Echolocation Research: Tools, Techniques and Analysis (eds Brigham, M. et al. ) 133–140 (Bat Conservation International, 2004)

  50. Estrada-Villegas, S., Meyer, C. F. J. & Kalko, E. K. V. Effects of tropical forest fragmentation on aerial insectivorous bats in a land-bridge island system. Biol. Conserv. 143, 597–608 (2010)

    Article  Google Scholar 

  51. Lueders, T., Manefield, M. & Friedrich, M. W. Enhanced sensitivity of DNA- and rRNA-based stable isotope probing by fractionation and quantitative analysis of isopycnic centrifugation gradients. Environ. Microbiol . 6, 73–78 (2004)

    CAS  Article  Google Scholar 

  52. Bartram, A. K., Lynch, M. D. J., Stearns, J. C., Moreno-Hagelsieb, G. & Neufeld, J. D. Generation of multimillion-sequence 16S rRNA gene libraries from complex microbial communities by assembling paired-end illumina reads. Appl. Environ. Microbiol. 77, 3846–3852 (2011)

    CAS  Article  Google Scholar 

  53. Morris, E. K. et al. Land use and host neighbor identity effects on arbuscular mycorrhizal fungal community composition in focal plant rhizosphere. Biodivers. Conserv. 22, 2193–2205 (2013)

    Article  Google Scholar 

  54. Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009)

    CAS  Article  Google Scholar 

  55. Öpik, M. et al. The online database MaarjAM reveals global and ecosystemic distribution patterns in arbuscular mycorrhizal fungi (Glomeromycota). New Phytol . 188, 223–241 (2010)

    Article  Google Scholar 

  56. R: A language and environment for statistical computing v. 3.2.2. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2015)

  57. Chao, A. et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014)

    Article  Google Scholar 

  58. Chao, A. & Jost, L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93, 2533–2547 (2012)

    Article  Google Scholar 

  59. iNEXT: iNterpolation and EXTrapolation for species diversity. R package version 2.0, http://chao.stat.nthu.edu.tw/blog/software-download (2014)

  60. Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006)

    Article  Google Scholar 

  61. Jost, L. Partitioning diversity into independent alpha and beta components. Ecology 88, 2427–2439 (2007)

    Article  Google Scholar 

  62. Hill, M. O. Diversity and eveness: unifying notations and its consequences. Ecology 54, 427–432 (1973)

    Article  Google Scholar 

  63. Maurer, B. A. & McGill, B. J. in Biological Diversity: Frontiers in Measurement and Assessment Vol. 12 (eds Magurran, A. E. & McGill, B. J. ) 55–65 (Oxford Univ. Press, 2011)

    Google Scholar 

  64. Jost, L., Chao, A. & Chazdon, R. in Biological Diversity: Frontiers in Measurement and Assessment Vol. 12 (eds Magurran, A. E. & McGill, B. J. ) 66–84 (Oxford Univ. Press, 2011)

    Google Scholar 

  65. vegan: Community Ecology Package. R package version 2.2-1. (2015)

  66. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-125, http://CRAN.R-project.org/package=nlme (2016)

  67. lmPerm: Permutation Tests for Linear Models. R package version 2.1.0. https://CRAN.R-project.org/package=lmPerm (2016)

  68. gdm: Functions for Generalized Dissimilarity Modeling v. R-package version 1.1-7 (2016)

  69. Goslee, S. C. & Urban, D. L. The ecodist package for dissimilarity-based analysis of ecological data. J. Stat. Softw. 22, 1–19 (2007)

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to J. Chase and M. Fitzpatrick for their comments and suggestions on a previous version of the manuscript; B. Büche, R. Achtziger, T. Wagner, F. Köhler, T. Blick and M.-A. Fritze for arthropod species identification and U. Kern for creating the small icons of the 12 trophic groups used in the figures. We thank the managers of the three Exploratories, K. Hartwich, S. Gockel, K. Wiesner and M. Gorke for their work in maintaining the plot and project infrastructure; C. Fischer and S. Pfeiffer for giving support through the central office, M. Owonibi for managing the central data base; and E. Linsenmair, D. Hessenmöller, J. Nieschulze, I. Schöning and the late E. Kalko for their role in setting up the Biodiversity Exploratories project. We are also grateful to E. Kalko for her invaluable inspiration and for launching the studies on bats and birds. The work has been funded by the DFG Priority Program 1374 ‘Infrastructure-Biodiversity-Exploratories’. Field work permits were issued by the responsible state environmental offices of Baden-Württemberg, Thüringen and Brandenburg (according to §72 BbgNatSchG).

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Authors

Contributions

M.M.G. and E.A. conceived the idea for the manuscript, and defined the final analysis, M.M.G., E.A., C.P., T.M.L. and T.K. analysed the data, M.M.G. and E.A. wrote the first manuscript draft and finalized the manuscript. A.M.K., C.B., C.N.W., C.W., D.J.P., D.P., E.P., F.B., H.A., I.S., J.K., J.M., J.S., J.O., K.J., K.B., M.Tü., M.Ts., M.F., M.L., M.M.G., M.W., N.B., P.C.V., S.Bl., S.Bo., S.A.S., S.C.R, S.K., S.W., T.D., T.W., V.B., V.W., and W.W.W. contributed data. T.M.L., F.G., S.Bo., D.P., L.R.J., K.B., S.C.R., A.C.K., O.P., P.S., T.T., W.W.W. and J.S. contributed substantially to revisions. All authors commented on the manuscript.

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Correspondence to Martin M. Gossner.

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Nature thanks P. Barton, S. Prober and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 The effect of LUI on higher q-level α-diversity above- and belowground.

The partial effect of local LUI comes from a power law model fitted to the exponential Shannon diversity (q = 1) and reciprocal Simpson index (q = 2) of the seven aboveground (solid lines) and the five belowground trophic groups (dashed lines) (n = 105 plots; for more details see Methods). In the model, all parameters of the power law function depended on temporal variation in LUI (sdLUI) and isolation. LUI effects are plotted at the mean values of these two variables. α-diversity and land-use variables were corrected for differences due to region, pH and soil nutrients, by taking residuals, and were then scaled between 0 and 1. The models for protists (q = 1 and q = 2) and mycorrhizae (q = 2) failed to converge and are therefore not shown. Note that plant pathogens are missing because, for this group, no data on abundance was available.

Extended Data Figure 2 Effects of LUI on turnover of aboveground species.

Scatter plots showing the effects of mean LUI and ΔLUI, between plot pairs (n = 105 plots), on the species turnover component of β-diversity for seven aboveground groups. Regression lines show predictions from linear models.

Extended Data Figure 3 Effects of LUI on turnover of belowground species.

Scatter plots showing the effects of mean LUI and ΔLUI, between plot pairs (n = 105 plots), on the species turnover component of β-diversity for five belowground groups. Regression lines show predictions from linear models.

Extended Data Figure 4 Effects of LUI on total β-diversity above- and belowground.

a, c, e, Partial effects of mean LUI and ΔLUI, between plot pairs, on total β-diversity (a, Sørensen q = 0; c, Morisita q = 1; e, Morisita–Horn q = 2) for seven aboveground and five belowground groups from linear models. Negative effects of mean LUI indicate that land-use intensification reduces β-diversity. The bars show coefficients from the models. Numbers adjoining bars are the proportion of explained variance uniquely explained by mean LUI or ΔLUI. b, d, f, Results from the GDMs are shown for total β-diversity (b, Sørensen q = 0; d, Morisita q = 1; f, Morisita–Horn q = 2) for the same trophic groups. The figures show the effect of differences in LUI on β-diversity (calculated between all plot pairs). Effects of differences in LUI can vary nonlinearly along the gradient of LUI. Higher maximum curves indicate larger effects of differences in LUI on β-diversity. The values in the legend are the percentage of deviance that is explained uniquely by LUI. Effects of both linear models and GDMs are corrected for other drivers of β-diversity, and response and explanatory variables are scaled to allow comparisons across trophic groups (n = 105 plots; for details see Methods).

Extended Data Figure 5 Partial effects of geographic and environmental distances and temporal variation in LUI on β-diversity above- and belowground.

a, b, Results from GDMs are shown for seven aboveground and five belowground groups, with total β-diversity measured as the Sørensen index βsor (a) or as the species turnover component βsim (b). The figures show the effect of differences in each of the five variables on β-diversity (calculated between all plot pairs; n = 105 plots). Effects of differences in each explanatory variable can vary nonlinearly along the gradient of that variable and each is corrected for all other variables in the model. Higher maximum curves indicate larger effects of differences in a given variable on β-diversity. Soil nutrients refer to the scores of the first PCA axis. Temporal variation in LUI is shown as s.d. Geographic distance has to be multiplied by 100 km.

Extended Data Figure 6 Uncertainty of effects of LUI on β-diversity above (AG) and belowground (BG).

The uncertainty is calculated on the basis of 100 bootstraps for each model, each time removing 30% of the plot pairs, then fitting a GDM and extracting the predictions. Predictions are shown as fitted lines and s.d. Uncertainty is shown for all seven above- and five belowground trophic groups based on species turnover βsim (n = 105 plots). PriPro, primary producers; PlPa, plant pathogens; Herb, herbivores; Poll, pollinators; InvDec, invertebrate decomposers; SecCon, secondary consumers; TerCon, tertiary consumers; Myco, Mycorrhizae; MicDec, microbial decomposers; Bact, bacterivores.

Extended Data Figure 7 The relative importance of LUI as a driver of β-diversity.

The bar plot shows the importance of LUI (in terms of total effect size) relative to the most important variable in the GDM. Results are shown for each trophic group, for the species turnover component (βsim) and total β-diversity (Sørensen index) (n = 105 plots).

Extended Data Figure 8 Effects of single land-use components on β-diversity above- and belowground.

a, c, e, Partial effects of minimum LUI (min LUI) and ΔLUI between plot pairs (n = 105 plots), on the species turnover component of β-diversity (βsim) for seven aboveground and five belowground groups based on linear models. Negative effects of minimum LUI indicate that land-use intensification reduces β-diversity. The bars show coefficients from the models. Numbers adjoining bars are the proportion of the total explained variance that is uniquely explained by minimum LUI orΔLUI among plot pairs, on the basis of hierarchical partitioning. b, d, f, Results from GDMs are shown for the turnover component βsim for the same trophic groups. The figures show the effect of ΔLUI on β-diversity (calculated between all plot pairs). Effects of ΔLUI can vary nonlinearly along the gradient of LUI. Higher maximum curves indicate larger effects of ΔLUI on β-diversity. The values in the legend are the percentage of deviance that is explained uniquely by LUI. Effects of both linear models and GDMs are corrected for other drivers of β-diversity, and response and explanatory variables are scaled to allow comparisons across trophic levels (see Methods).

Extended Data Figure 9 Sample coverage of above- and belowground trophic groups based on species incidences.

Sample coverage was calculated for low (52 plots) and high (53) LUI plots based on refs 57, 58. Coverage is defined as the proportion of the total number of individuals in an assemblage that belong to species represented in the sample, and is therefore a measure of sampling completeness. Means and 95% confidence intervals based on 200 bootstraps are shown.

Extended Data Figure 10 The effect of LUI on the correlation between the β-diversities of different trophic groups.

Each dot represents the correlation (R2) between two trophic groups. Correlations are R2 values from matrix regressions between β-diversity values of different groups (corrected for effects of differences in LUI on β-diversity). Significant correlations (P < 0.05) are marked in red. Upward and downward triangles indicate significance under low or high LUI only. Interactions with R2 values higher than 0.2 in one of the LUI-categories are illustrated by icons. β-diversity was calculated as the Sørensen index (βsor, total β-diversity) and as the species turnover component (βsim) (n = 105 plots). For statistical details see Supplementary Information Section 5.

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Gossner, M., Lewinsohn, T., Kahl, T. et al. Land-use intensification causes multitrophic homogenization of grassland communities. Nature 540, 266–269 (2016). https://doi.org/10.1038/nature20575

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