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Global synthesis of effects of plant species diversity on trophic groups and interactions

Nature Plants volume 6, pages 503–510 (2020)Cite this article

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Abstract

Numerous studies have demonstrated that plant species diversity enhances ecosystem functioning in terrestrial ecosystems, including diversity effects on insects (herbivores, predators and parasitoids) and plants. However, the effects of increased plant diversity across trophic levels in different ecosystems and biomes have not yet been explored on a global scale. Through a global meta-analysis of 2,914 observations from 351 studies, we found that increased plant species richness reduced herbivore abundance and damage but increased predator and parasitoid abundance, predation, parasitism and overall plant performance. Moreover, increased predator/parasitoid performance was correlated with reduced herbivore abundance and enhanced plant performance. We conclude that increasing plant species diversity promotes beneficial trophic interactions between insects and plants, ultimately contributing to increased ecosystem services.

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Fig. 1: Global distribution of study locations.
Fig. 2: Responses of four trophic groups to plant species diversity.
Fig. 3: Pairwise (bi-trophic) correlations of trophic group responses to plant species diversity.
Fig. 4: Path analysis for the effects of the increased plant species diversity on tri-trophic interactions.

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All data generated or analysed during this study are included in this Article and its Extended data, Supplementary tables and Supplementary methods.

References

  1. Li, L., Tilman, D., Lambers, H. & Zhang, F. S. Plant diversity and overyielding: insights from belowground facilitation of intercropping in agriculture. New Phytol. 203, 63–69 (2014).

    PubMed  Google Scholar 

  2. Tilman, D., Reich, P. B. & Knops, J. M. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).

    CAS  PubMed  Google Scholar 

  3. Isbell, F. et al. Benefits of increasing plant diversity in sustainable agroecosystems. J. Ecol. 105, 871–879 (2017).

    Google Scholar 

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

    CAS  PubMed  Google Scholar 

  5. Cardinale, B. J., Ives, A. R. & Inchausti, P. Effects of species diversity on the primary productivity of ecosystems: extending our spatial and temporal scales of inference. Oikos 104, 437–450 (2010).

    Google Scholar 

  6. Bright, M. B. H. et al. Long-term Piliostigma reticulatum intercropping in the Sahel: crop productivity, carbon sequestration, nutrient cycling, and soil quality. Agric. Ecosyst. Environ. 242, 9–22 (2017).

    Google Scholar 

  7. Damien, M. et al. Flowering crops in winter increases pest control but not trophic link diversity. Agric. Ecosyst. Environ. 247, 418–425 (2017).

    Google Scholar 

  8. Wan, N. F. et al. Increasing plant diversity with border crops reduces insecticide use and increases crop yield in urban agriculture. eLife 7, e35103 (2018).

    PubMed  PubMed Central  Google Scholar 

  9. Macfadyen, S. et al. Do differences in food web structure between organic and conventional farms affect the ecosystem service of pest control? Ecol. Lett. 12, 229–238 (2009).

    PubMed  Google Scholar 

  10. Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax0121 (2019).

    PubMed  PubMed Central  Google Scholar 

  11. Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).

    CAS  Google Scholar 

  12. Frank, K. T., Petrie, B., Choi, J. S. & Leggett, W. C. Trophic cascades in a formerly cod-dominated ecosystem. Science 308, 1621–1623 (2005).

    CAS  PubMed  Google Scholar 

  13. Knight, T. M., Mccoy, M. W., Chase, J. M., McCoy, K. A. & Holt, R. D. Trophic cascades across ecosystems. Nature 437, 880–883 (2005).

    CAS  PubMed  Google Scholar 

  14. Start, D. & Gilbert, B. Predator personality structures prey communities and trophic cascades. Ecol. Lett. 20, 366–374 (2017).

    PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  16. Ebeling, A. et al. Plant diversity effects on arthropods and arthropod-dependent ecosystem functions in a biodiversity experiment. Basic Appl. Ecol. 26, 50–63 (2018).

    Google Scholar 

  17. Bischoff, A. et al. Effects of spontaneous field margin vegetation and surrounding landscape on Brassica oleracea crop herbivory. Agric. Ecosyst. Environ. 223, 135–143 (2016).

    Google Scholar 

  18. Wan, N. F. et al. Plant diversification promotes biocontrol services in peach orchards by shaping the ecological niches of insect herbivores and their natural enemies. Ecol. Indic. 99, 387–392 (2019).

    Google Scholar 

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

    CAS  PubMed  Google Scholar 

  20. Seabloom, E. W. et al. Food webs obscure the strength of plant diversity effects on primary productivity. Ecol. Lett. 20, 505–512 (2017).

    PubMed  Google Scholar 

  21. Litsinger, J. A., Hasse, V., Barrion, A. T. & Schmutterer, H. Response of Ostrinia furnacalis (Guenée) (Lepidoptera: Pyralidae) to intercropping. Environ. Entomol. 20, 988–1004 (1991).

    Google Scholar 

  22. Hooks, C. R. R. & Johnson, M. W. Lepidopteran pest populations and crop yields in row intercropped broccoli. Agric. Forest Entomol. 4, 117–125 (2002).

    Google Scholar 

  23. Nitschke, N. et al. Plant diversity has contrasting effects on herbivore and parasitoid abundance in Centaurea jacea flower heads. Ecol. Evol. 7, 9319–9332 (2017).

    PubMed  PubMed Central  Google Scholar 

  24. Moreira, X. et al. Plant diversity effects on insect herbivores and their natural enemies: current thinking, recent findings, and future directions. Curr. Opin. Insect Sci. 14, 1–7 (2016).

    PubMed  Google Scholar 

  25. Gurevitch, J., Koricheva, J., Nakagawa, S. & Stewart, G. Meta-analysis and the science of research synthesis. Nature 555, 175–182 (2018).

    CAS  PubMed  Google Scholar 

  26. Chaplin-Kramer, R., O’Rourke, M. E., Blitzer, E. J. & Kremen, C. A meta-analysis of crop pest and natural enemy response to landscape complexity. Ecol. Lett. 14, 922–932 (2011).

    PubMed  Google Scholar 

  27. Shackelford, G. et al. Comparison of pollinators and natural enemies: a meta-analysis of landscape and local effects on abundance and richness in crops. Biol. Rev. 88, 1002–1021 (2013).

    PubMed  Google Scholar 

  28. Letourneau, D. K. et al. Does plant diversity benefit agroecosystems? A synthetic review. Ecol. Appl. 21, 9–21 (2011).

    PubMed  Google Scholar 

  29. Dassou, A. G. & Tixier, P. Response of pest control by generalist predators to local-scale plant diversity: a meta-analysis. Ecol. Evol. 6, 1143–1153 (2016).

    PubMed  PubMed Central  Google Scholar 

  30. Greenstone, M. H., Cornelius, M. L., Olsen, R. T. & Payton, M. E. Test of a natural enemy hypothesis on plant provenance: spider abundance in native and exotic ornamental landscapes. J. Entomol. Sci. 52, 340–351 (2017).

    Google Scholar 

  31. Novais, S. M. A., Macedo-Reis, L. E. & Neves, F. S. Predatory beetles in cacao agroforestry systems in Brazilian Atlantic forest: a test of the natural enemy hypothesis. Agroforestry Syst. 91, 201–209 (2017).

    Google Scholar 

  32. Root, R. B. Organization of a plant–arthropod association in simple and diverse habitats: the fauna of collards (Brassica oleracea). Ecol. Monogr. 43, 95–124 (1973).

    Google Scholar 

  33. Long, Z. T., Mohler, C. L. & Carson, W. P. Extending the resource concentration hypothesis to plant communities: effects of litter and herbivores. Ecology 84, 652–665 (2003).

    Google Scholar 

  34. Ebeling, A., Klein, A. M., Schumacher, J., Weisser, W. W. & Tscharntke, T. How does plant richness affect pollinator richness and temporal stability of flower visits? Oikos 117, 1808–1815 (2008).

    Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  36. Bernays, E. A., Bright, K. L., Gonzalez, N. & Angel, J. Dietary mixing in a generalist herbivore: tests of two hypotheses. Ecology 75, 1997–2006 (1994).

    Google Scholar 

  37. Srivastava, D. S. & Lawton, J. H. Why more productive sites have more species: an experimental test of theory using tree-hole communities. Am. Nat. 152, 510–529 (1998).

    CAS  PubMed  Google Scholar 

  38. Janssen, A., Sabelis, M. W., Magalhães, S., Montserrat, M. & Van der Hammen, T. Habitat structure affects intraguild predation. Ecology 88, 2713–2719 (2007).

    PubMed  Google Scholar 

  39. Coll, M. & Bottrell, D. G. Effects of nonhost plant on an insect herbivore in diverse habitats. Ecology 75, 723–731 (1994).

    Google Scholar 

  40. Petermann, J. S., Müller, C. B., Weigelt, A., Weisser, W. W. & Schmid, B. Effect of plant species loss on aphid–parasitoid communities. J. Anim. Ecol. 79, 709–720 (2010).

    PubMed  Google Scholar 

  41. Karp, D. S. et al. Crop pests and predators exhibit inconsistent responses to surrounding landscape composition. Proc. Natl Acad. Sci. USA 115, 7863–7870 (2018).

    Google Scholar 

  42. Martin, E. A. et al. The interplay of landscape composition and configuration: new pathways to manage functional biodiversity and agroecosystem services across Europe. Ecol. Lett. 22, 1047–1175 (2019).

    Google Scholar 

  43. Sirami, E. et al. Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. Proc. Natl Acad. Sci. USA 116, 16442–16447 (2019).

    CAS  PubMed  Google Scholar 

  44. Lu, M. et al. Responses of ecosystem nitrogen cycle to nitrogen addition: a meta-analysis. New Phytol. 189, 1040–1050 (2011).

    CAS  PubMed  Google Scholar 

  45. Treseder, K. K. Nitrogen additions and microbial biomass: a meta-analysis of ecosystem studies. Ecol. Lett. 11, 1111–1120 (2008).

    PubMed  Google Scholar 

  46. Liu, L. L. & Greaver, T. L. A global perspective on belowground carbon dynamics under nitrogen enrichment. Ecol. Lett. 13, 819–828 (2010).

    PubMed  Google Scholar 

  47. Liao, C. et al. Altered ecosystem carbon and nitrogen cycles by plant invasion: a meta-analysis. New Phytol. 177, 706–714 (2008).

    CAS  PubMed  Google Scholar 

  48. Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).

    Google Scholar 

  49. Nakagawa, S. & Santos, E. S. Methodological issues and advances in biological meta-analysis. Evol. Ecol. 26, 1253–1274 (2012).

    Google Scholar 

  50. Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 90, 363–368 (2009).

    PubMed  Google Scholar 

  51. Begg, C. B. & Mazumdar, M. Operating characteristics of a rank correlation test for publication bias. Biometrics 50, 1088–1101 (1994).

    CAS  PubMed  Google Scholar 

  52. Duval, S. & Tweedie, R. Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics 56, 455–463 (2000).

    CAS  PubMed  Google Scholar 

  53. Rosenberg, M. S. The file-drawer problem revisited: a general weighted method for calculating fail-safe numbers in meta-analysis. Evolution 59, 464–468 (2005).

  54. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).

  55. Lefcheck, J. S. piecewiseSEM: piecewise structural equation modeling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2015).

    Google Scholar 

  56. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team nlme: Linear and nonlinear mixed effects models. R package version 3.1–137 https://CRAN.R-project.org/package=nlme (2018).

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Acknowledgements

We thank M. van Kleunen, E. Siemann, X. Chen and Z.-W. Ren for useful suggestions on early versions of this paper, J. Weiner for providing the inspiration for this study, and all of the people whose data and work have been included in this meta-analysis. This study was financially supported by the Agriculture Research System of Shanghai, China (grant no. 201908), the National Natural Science Foundation of China (31401751 and 11971117), the Shanghai Academy of Agricultural Sciences Program for Excellent Research Team (2018[B-01]) and the European Union’s Horizon 2020 research and innovation programme (grant no. 727284).

Author information

Author notes

  1. These authors contributed equally: Nian-Feng Wan, Xiang-Rong Zheng.

Authors and Affiliations

  1. Eco-environmental Protection Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai Key Laboratory of Protected Horticultural Technology, Shanghai Engineering Research Centre of Low-carbon Agriculture, Shanghai, China

    Nian-Feng Wan, Jie-Xian Jiang & You-Ming Cai

  2. Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Coastal Ecosystems Research Station of the Yangtze River Estuary, Institute of Biodiversity Science, School of Life Sciences, Fudan University, Shanghai, China

    Nian-Feng Wan, Shi-Yun Qiu, Ming Nie, Rui-Ting Ju & Bo Li

  3. State Key Laboratory of Genetic Engineering, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China

    Xiang-Rong Zheng, Li-Wan Fu, Yue-Qing Hu & Wei-Dong Tian

  4. Department of Plant and Environmental Sciences, University of Copenhagen, Copenhagen, Denmark

    Lars Pødenphant Kiær

  5. Department of Biology, University of Konstanz, Konstanz, Germany

    Zhijie Zhang

  6. Natural Capital Project, Woods Institute for the Environment, Stanford University, Stanford, CA, USA

    Rebecca Chaplin-Kramer

  7. Institute on the Environment, University of Minnesota, Minneapolis, MN, USA

    Rebecca Chaplin-Kramer

  8. Institute for Alpine Environment, EURAC Research, Bolzano, Italy

    Matteo Dainese

  9. Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA

    Jiaqi Tan

  10. Department of Plant Protection, School of Agriculture and Food Science, Zhejiang Agriculture and Forestry University, Hangzhou, China

    Jian-Yu Deng

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Contributions

N.-F.W., X.-R.Z., J.-X.J., Y.-M.C. and B.L. conceived the idea. N.-F.W., X.-R.Z., Y.-M.C., J.-X.J. and B.L. collected the data. N.-F.W., M.D. and S.-Y.Q. made the maps. N.-F.W., X.-R.Z., L.-W.F., L.P.K., Z.Z., R.C.-K., M.D., J.T., S.-Y.Q., Y.-Q.H., W.-D.T., M.N., R.-T.J., J.-Y.D., J.-X.J., Y.-M.C. and B.L. analysed the data. N.-F.W., X.-R.Z., L.P.K., Z.Z., J.-X.J., Y.-M.C. and B.L. drafted the article. N.-F.W., X.-R.Z., L.-W.F., L.P.K., Z.Z., R.C.-K., M.D., J.T., J.-X.J., Y.-M.C. and B.L. wrote the manuscript. All authors prepared and edited the final drafts.

Corresponding authors

Correspondence to Jie-Xian Jiang, You-Ming Cai or Bo Li.

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The authors declare no competing interests.

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Peer review information Nature Plants thanks Forest Isbell and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Mean effect sizes of 12 response categories for the 4 trophic groups in agroecosystems, grasslands and forests, separately.

Numbers in brackets indicate the numbers of observations and studies, respectively. The horizontal lines indicate the 95% confidence intervals around the means. Black, red, green, and blue lines represent predators, parasitoids, herbivores and plants, respectively.

Extended Data Fig. 2 Response of 12 response categories for 4 trophic groups to herbaceous and woody plants.

Numbers in brackets indicate the numbers of observations and studies, respectively. The horizontal lines indicate the 95% confidence intervals around the means. Black, red, green, and blue lines represent predators, parasitoids, herbivores and plants, respectively.

Extended Data Fig. 3 Response of 12 response categories for 4 trophic groups to tropical and temperate biomes.

Numbers in brackets indicate the numbers of observations and studies, respectively. The horizontal lines indicate the 95% confidence intervals around the means. Black, red, green, and blue lines represent predators, parasitoids, herbivores and plants, respectively.

Extended Data Fig. 4 Scatter plots showing the relationship between log-transformed number of added species in the plant species diversity treatment over the control and the effect sizes along with the fitted meta-regression line.

a, Scatter plot for predator performance (571 observations/122 studies). b, Scatter plot for parasitoid performance (135 observations/57 studies). c, Scatter plot for herbivore performance (947 observations/214 studies). d, Scatter plot for plant performance (1041 observations/161 studies). Predator performance included predator abundance and predation, parasitoid performance included parasitoid abundance and parasitism, herbivore performance was involved in herbivore abundance and herbivore damage and plant performance was related with plant growth, quality and reproduction. The dark and light shaded regions indicate respectively the 95% confidence interval for the predicted average SMD and the 95% credible/prediction interval. The regression model intercepts, slopes and the P-values for the slopes are presented.

Extended Data Fig. 5 Scatter plots showing the relationship between number of added plant species over control and effect sizes for predator, parasitoid, herbivore and plant performances.

a–d, Scatter plots for agroecosystems. e–h, Scatter plots for grasslands. i–l, Scatter plots for forests. Sample sizes for Fig. S5a-5l are 533, 124, 732, 541, 64, 29, 133, 391, 112, 7, 139 and 109, respectively. The fitted meta-regression lines are also presented. The dark shaded and the light shaded regions indicate the 95% confidence interval for the predicted average SMD and the 95% credible/prediction interval respectively. The regression model intercepts, slopes, and the P-values for the slopes are presented.

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Wan, NF., Zheng, XR., Fu, LW. et al. Global synthesis of effects of plant species diversity on trophic groups and interactions. Nat. Plants 6, 503–510 (2020). https://doi.org/10.1038/s41477-020-0654-y

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