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Continental scale structuring of forest and soil diversity via functional traits

Nature Ecology & Evolution volume 3, pages 1298–1308 (2019)Cite this article

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An Author Correction to this article was published on 03 October 2019

This article has been updated

Abstract

Trait-based ecology claims to offer a mechanistic approach for explaining the drivers that structure biological diversity and predicting the responses of species, trophic interactions and ecosystems to environmental change. However, support for this claim is lacking across broad taxonomic groups. A framework for defining ecosystem processes in terms of the functional traits of their constituent taxa across large spatial scales is needed. Here, we provide a comprehensive assessment of the linkages between climate, plant traits and soil microbial traits at many sites spanning a broad latitudinal temperature gradient from tropical to subalpine forests. Our results show that temperature drives coordinated shifts in most plant and soil bacterial traits but these relationships are not observed for most fungal traits. Shifts in plant traits are mechanistically associated with soil bacterial functional traits related to carbon (C), nitrogen (N) and phosphorus (P) cycling, indicating that microbial processes are tightly linked to variation in plant traits that influence rates of ecosystem decomposition and nutrient cycling. Our results are consistent with hypotheses that diversity gradients reflect shifts in phenotypic optima signifying local temperature adaptation mediated by soil nutrient availability and metabolism. They underscore the importance of temperature in structuring the functional diversity of plants and soil microbes in forest ecosystems and how this is coupled to biogeochemical processes via functional traits.

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Fig. 1: Coordinated trade-offs in plant functional traits and their relation to microbial and ecosystem processes.
Fig. 2: Shifts in community-weighted leaf and microbial traits that influence soil nutrient availability across 30 forest plots at six sites spanning a broad latitudinal temperature gradient.
Fig. 3: Adaptive trait continuum for plants and microbes spanning a broad latitudinal temperature gradient.
Fig. 4: Relationships between microbial and plant functional traits that are relevant for ecosystem nutrient cycling.

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Data availability

Raw sequencing data have been deposited in the NCBI Sequence Read Archive under accession code PRJNA308872. The OTU tables and microarray data presented are available at http://www.ou.edu/ieg/publications/datasets. Additional data files and r-scripts are available at https://osf.io/thjxs/. Community-weighted mean trait data are available as Supplementary Data 1–3.

Code availability

R-script used for data formatting and statistics are available on the Open Science Framework website https://osf.io/thjxs/. Code for the simulation is available as Supplementary Data 4.

Change history

  • 03 October 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

We thank the members of the LTER Network and the Smithsonian Tropical Research Institute—Barro Colorado Island who participated in data collection. We thank C. Sides, T. Birt, L. Sloat, A. Henderson, J. Messier and B. Blonder for field assistance with vegetation sampling. We thank J. Wright for assistance with sampling at Barro Colorado Island. We also thank E. Brodie for helpful comments on earlier drafts. Funding for this study was provided by the US National Science Foundation MacroSystems Biology program NSF EF-1065844.

Author information

Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA

    Vanessa Buzzard, Sean T. Michaletz & Brian J. Enquist

  2. Los Alamos National Laboratory, Earth and Environmental Sciences Division, Los Alamos, NM, USA

    Sean T. Michaletz

  3. Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada

    Sean T. Michaletz

  4. Institute for Environmental Genomics and Department of Microbiology and Plant Biology, School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK, USA

    Ye Deng, Zhili He, Daliang Ning, Lina Shen, Qichao Tu, Joy D. Van Nostrand, James W. Voordeckers, Jianjun Wang & Jizhong Zhou

  5. Chinese Academy of Sciences, Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Beijing, China

    Ye Deng

  6. Environmental Microbiomics Research Center, School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou, China

    Zhili He

  7. Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, China

    Zhili He

  8. Institute of Marine Science and Technology, Shandong University, Qingdao, China

    Qichao Tu

  9. Geographical Ecology Group, Department of Biology, University of Oklahoma, Norman, OK, USA

    Michael D. Weiser & Michael Kaspari

  10. Smithsonian Tropical Research Institute, Balboa, Republic of Panama

    Michael Kaspari

  11. Department of Biology, University of New Mexico, Albuquerque, NM, USA

    Robert B. Waide

  12. Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Jizhong Zhou

  13. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China

    Jizhong Zhou

  14. The Santa Fe Institute, Santa Fe, NM, USA

    Brian J. Enquist

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  1. Vanessa Buzzard
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Contributions

All authors contributed to the intellectual development of this study. V.B., B.J.E., M.K., S.T.M. and M.D.W. collected the field experimental data. Y.D., Z.H., J.Z., D.N., L.S., Q.T., J.V. and J.W. collected the sequencing and GeoChip data, and performed statistical analyses and data integration for microbial communities. D.N. developed and ran the simulation comparing rarefied versus non-rarefied sequence data. V.B., B.J.E. and S.T.M. performed statistical analyses and drafted the manuscript with help from Z.H., J.Z., M.K., R.W. and M.D.W.

Corresponding author

Correspondence to Vanessa Buzzard.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–7, Box 1, Tables 1–6 and references.

Reporting Summary

Supplementary Data 1

All abundance weighted plant functional trait data for each community (plot). Site codes definitions are AND, HJ Andrews Experimental forest; BCI, Barro Colorado Island; CWT, Coweeta LTER; HFR, Harvard Forest; LUQ, Luquillo LTER; NWT, Niwot Ridge LTER with each plot labelled 1-5 at each site. Abund represents the abundance of woody plants observed within each plot. All traits are measured locally.

Supplementary Data 2

All abundance weighted fungal functional trait data for each community (plot). Site codes definitions are AND, HJ Andrews Experimental forest; BCI, Barro Colorado Island; CWT, Coweeta LTER; HFR, Harvard Forest; LUQ, Luquillo LTER; NWT, Niwot Ridge LTER with each plot labelled 1-5 at each site. Abund represents the abundance of fungi based on ITS observed within each plot. All traits are measured locally and definitions are available for GeoChip data in Supplementary Table 1.

Supplementary Data 3

All abundance weighted bacterial functional trait data for each community (plot). Site codes definitions are AND, HJ Andrews Experimental forest; BCI, Barro Colorado Island; CWT, Coweeta LTER; HFR, Harvard Forest; LUQ, Luquillo LTER; NWT, Niwot Ridge LTER with each plot labelled 1-5 at each site. Abund represents the abundance of bacteria based on 16S observed within each plot. All traits are measured locally and definitions are available for GeoChip data in Supplementary Table 1.

Supplementary Data 4

Script and data used for simulation to test use of rarified sequence data.

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Buzzard, V., Michaletz, S.T., Deng, Y. et al. Continental scale structuring of forest and soil diversity via functional traits. Nat Ecol Evol 3, 1298–1308 (2019). https://doi.org/10.1038/s41559-019-0954-7

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