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Integrative modelling reveals mechanisms linking productivity and plant species richness

Nature volume 529, pages 390–393 (2016)Cite this article

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Abstract

How ecosystem productivity and species richness are interrelated is one of the most debated subjects in the history of ecology1. Decades of intensive study have yet to discern the actual mechanisms behind observed global patterns2,3. Here, by integrating the predictions from multiple theories into a single model and using data from 1,126 grassland plots spanning five continents, we detect the clear signals of numerous underlying mechanisms linking productivity and richness. We find that an integrative model has substantially higher explanatory power than traditional bivariate analyses. In addition, the specific results unveil several surprising findings that conflict with classical models4,5,6,7. These include the isolation of a strong and consistent enhancement of productivity by richness, an effect in striking contrast with superficial data patterns. Also revealed is a consistent importance of competition across the full range of productivity values, in direct conflict with some (but not all) proposed models. The promotion of local richness by macroecological gradients in climatic favourability, generally seen as a competing hypothesis8, is also found to be important in our analysis. The results demonstrate that an integrative modelling approach leads to a major advance in our ability to discern the underlying processes operating in ecological systems.

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Figure 1: Comparison between low-dimension (top panel) and high-dimension (bottom panel) examinations of data.
Figure 2: Structural equation model representing connections between productivity and richness supported by the data.

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Acknowledgements

J.B.G. was supported by the US Geological Survey Ecosystems and Climate and Land use Change Programs. This work uses data from the Nutrient Network (http://nutnet.org) experiment, funded at the site scale by individual researchers. Coordination and data management were supported by funding to E.T.B. and E.W.S. from the National Science Foundation (NSF) Research Coordination Network (NSF-DEB-1042132) and Long Term Ecological Research (NSF-DEB-1234162 to Cedar Creek LTER) programs and the UMN Institute on the Environment (DG-0001-13). The Minnesota Supercomputer Institute hosts project data. The use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government. Support for site-level activities is acknowledged in the Supplementary Information. We thank D. Laughlin for comments on the manuscript.

Author information

Authors and Affiliations

  1. US Geological Survey, Wetland and Aquatic Research Center, 700 Cajundome Boulevard

    James B. Grace

  2. Lafayette, 70506, Louisiana, USA

    James B. Grace

  3. Department of Biology, 206 Winston Hall, Wake Forest University, Box 7325 Reynolda Station, Winston-Salem, 27109, North Carolina, USA

    T. Michael Anderson

  4. Ecology, Evolution, and Behavior, University of Minnesota, 1987 Upper Buford Circle, St Paul, 55108, Minnesota, USA

    Eric W. Seabloom, Elizabeth T. Borer & Eric M. Lind

  5. Department of Wildland Resources and the Ecology Center, Utah State University, 5230 Old Main, Logan, 84322, Utah, USA

    Peter B. Adler

  6. Department of Physiological Diversity, Helmholtz Center for Environmental Research – UFZ, Permoserstrasse 15, 04318, Leipzig, Germany

    W. Stanley Harpole

  7. German Centre for Integrative Biodiversity Research (iDiv), Deutscher Platz 5e, D-04103, Leipzig, Germany

    W. Stanley Harpole

  8. Martin Luther University Halle-Wittenberg, Am Kirchtor 1, Halle (Saale), 06108, Germany

    W. Stanley Harpole

  9. Department of Biology, Ecology and Biodiversity Group, Utrecht University, Padualaan 8, Utrecht, 3584 CH, The Netherlands

    Yann Hautier

  10. Institute for Chemistry and Biology of the Marine Environment, University of Oldenburg, Schleusenstrasse 1, Wilhelmshaven, D-26381, Germany

    Helmut Hillebrand

  11. Institute of Ecology and Earth Sciences, University of Tartu, Lai 40, Tartu, 51005, Estonia

    Meelis Pärtel

  12. School of Environmental and Forest Sciences, University of Washington, Seattle, Box 354115, 98195-4115, Washington, USA

    Jonathan D. Bakker

  13. School of Natural Sciences, Zoology, Trinity College Dublin, The University of Dublin, Dublin 2, Ireland

    Yvonne M. Buckley

  14. Department of Biological Sciences, Imperial College London, Silwood Park, Ascot, SL5 7PY, Berkshire, UK

    Michael J. Crawley

  15. Department of Zoology, University of Wisconsin, 430 Lincoln Drive, Madison, 53706, Wisconsin, USA

    Ellen I. Damschen & John L. Orrock

  16. Department of Ecology and Evolutionary Biology, UCB 334, University of Colorado, Boulder, 80309, Colorado, USA

    Kendi F. Davies & Brett A. Melbourne

  17. United States Department of Agriculture Agricultural Research Service, Grassland Soil and Water Research Laboratory, 808 East Blackland Road, Temple, 76502, Texas, USA

    Philip A. Fay

  18. #15 Queensland University of Technology, School of Earth, Environment and Biological Sciences, Brisbane, 4001, Queensland, Australia

    Jennifer Firn

  19. Department of Entomology, University of Maryland, College Park, 4112 Plant Sciences, College Park, 20742, Maryland, USA

    Daniel S. Gruner

  20. Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK

    Andy Hector

  21. School of Biological Sciences, 348 Manter Hall, University of Nebraska, Lincoln, 68588, Nebraska, USA

    Johannes M. H. Knops

  22. Department of Integrative Biology, University of Guelph, Guelph, N1G 2W1, Ontario, Canada

    Andrew S. MacDougall

  23. Department of Ecology, Environment, and Evolution, La Trobe University, Bundoora, 3083, Victoria, Australia

    John W. Morgan

  24. CSIRO Land and Water, Private Bag 5, Wembley, 6913, Western Australia, Australia

    Suzanne M. Prober

  25. Department of Biology, Colorado State University, 1878 Campus Delivery, Fort Collins, 80526, Colorado, USA

    Melinda D. Smith

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  1. James B. Grace
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Contributions

E.W.S., E.T.B., W.S.H. and E.M.L. are Nutrient Network coordinators. J.B.G. and T.M.A. developed and framed the research questions. T.M.A., E.W.S., E.T.B., P.B.A., W.S.H., Y.H., H.H., J.D.B., Y.M.B., M.J.C., E.I.D., K.F.D., P.A.F., J.F., D.S.G., A.H., J.M.H.K., A.S.M., B.A.M., J.W.M., J.L.O., S.M.P. and M.D.S. collected data used in this analysis. T.M.A. assembled the data and performed initial analyses. J.B.G. analysed the data and wrote the paper with contributions and input from all authors.

Corresponding author

Correspondence to James B. Grace.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Structural equation meta-model showing hypothesized probabilistic expectations based on literature related to the productivity–diversity debate.

Solid lines represent expected positive effects, dashed lines represent expected negative effects. Literature and meta-model development are discussed in the Supplementary Information. Specific implementations of this generalized model for particular cases will probably differ in detail as appropriate for the situation and available data.

Extended Data Table 1 Model variables and their indicators*
Full size table
Extended Data Table 2 Results of model dimensionality evaluations
Full size table
Extended Data Table 3 Basic information on the study sites included in the final analyses
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Materials and Methods, Supplementary Tables 1-2, Supplementary References and Supplementary Acknowledgements. (PDF 740 kb)

Supplementary Data 1

This file contains the computer code that accompanies the paper. (TXT 29 kb)

Supplementary Data 2

This file contains the plot-level data set. (CSV 222 kb)

Supplementary Data 3

This file contains the site-level dataset. (CSV 9 kb)

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Grace, J., Anderson, T., Seabloom, E. et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature 529, 390–393 (2016). https://doi.org/10.1038/nature16524

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