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Root traits explain plant species distributions along climatic gradients yet challenge the nature of ecological trade-offs

Abstract

Ecological theory is built on trade-offs, where trait differences among species evolved as adaptations to different environments. Trade-offs are often assumed to be bidirectional, where opposite ends of a gradient in trait values confer advantages in different environments. However, unidirectional benefits could be widespread if extreme trait values confer advantages at one end of an environmental gradient, whereas a wide range of trait values are equally beneficial at the other end. Here, we show that root traits explain species occurrences along broad gradients of temperature and water availability, but model predictions only resembled trade-offs in two out of 24 models. Forest species with low specific root length and high root tissue density (RTD) were more likely to occur in warm climates but species with high specific root length and low RTD were more likely to occur in cold climates. Unidirectional benefits were more prevalent than trade-offs: for example, species with large-diameter roots and high RTD were more commonly associated with dry climates, but species with the opposite trait values were not associated with wet climates. Directional selection for traits consistently occurred in cold or dry climates, whereas a diversity of root trait values were equally viable in warm or wet climates. Explicit integration of unidirectional benefits into ecological theory is needed to advance our understanding of the consequences of trait variation on species responses to environmental change.

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Fig. 1: The ecological consequences of trait variation on species distributions along climatic gradients.
Fig. 2: Specific root length and root diameter are related to species occurrences along climatic gradients.
Fig. 3: RTD and root N are related to species occurrences along climatic gradients.

Data availability

All of the data needed to reproduce the model results can be accessed at https://doi.org/10.25829/idiv.3475-8-2316.

Code availability

All of the code needed to reproduce the model results can be accessed at https://doi.org/10.25829/idiv.3475-8-2316.

References

  1. 1.

    Agrawal, A. A. A scale-dependent framework for trade-offs, syndromes, and specialization in organismal biology. Ecology 101, e02924 (2020).

    PubMed  Article  Google Scholar 

  2. 2.

    Agrawal, A. A., Conner, J. K. & Rasmann, S. in Evolution After Darwin: The First 150 Years (eds Bell, M. et al.) 243–268 (Sinauer Associates, 2010).

  3. 3.

    Futuyma, D. J. & Moreno, G. The evolution of ecological specialization. Annu. Rev. Ecol. Syst. 19, 207–233 (1988).

    Article  Google Scholar 

  4. 4.

    Grime, J. P. & Pierce, S. The Evolutionary Strategies that Shape Ecosystems (John Wiley & Sons, 2012).

  5. 5.

    Fry, J. D. Detecting ecological trade-offs using selection experiments. Ecology 84, 1672–1678 (2003).

    Article  Google Scholar 

  6. 6.

    Grubb, P. J. Trade-offs in interspecific comparisons in plant ecology and how plants overcome proposed constraints. Plant Ecol. Divers. 9, 3–33 (2016).

    Article  Google Scholar 

  7. 7.

    Kneitel, J. M. & Chase, J. M. Trade-offs in community ecology: linking spatial scales and species coexistence. Ecol. Lett. 7, 69–80 (2004).

    Article  Google Scholar 

  8. 8.

    Tilman, D. Plant Strategies and the Dynamics and Structure of Plant Communities (Princeton Univ. Press, 1988).

  9. 9.

    Lusk, C. H. & Jorgensen, M. A. The whole-plant compensation point as a measure of juvenile tree light requirements. Funct. Ecol. 27, 1286–1294 (2013).

    Article  Google Scholar 

  10. 10.

    Ho, M. D., Rosas, J. C., Brown, K. M. & Lynch, J. P. Root architectural tradeoffs for water and phosphorus acquisition. Funct. Plant Biol. 32, 737–748 (2005).

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Forister, M. L. & Jenkins, S. H. A neutral model for the evolution of diet breadth. Am. Nat. 190, E40–E54 (2017).

    PubMed  Article  Google Scholar 

  12. 12.

    Laughlin, D. C., Strahan, R. T., Adler, P. B. & Moore, M. M. Survival rates indicate that correlations between community-weighted mean traits and environments can be unreliable estimates of the adaptive value of traits. Ecol. Lett. 21, 411–421 (2018).

    PubMed  Article  Google Scholar 

  13. 13.

    Pollock, L. J., Morris, W. K. & Vesk, P. A. The role of functional traits in species distributions revealed through a hierarchical model. Ecography 35, 716–725 (2012).

    Article  Google Scholar 

  14. 14.

    Mason, N. W. H. et al. Changes in coexistence mechanisms along a long-term soil chronosequence revealed by functional trait diversity. J. Ecol. 100, 678–689 (2012).

    CAS  Article  Google Scholar 

  15. 15.

    Gompert, Z. et al. The evolution of novel host use is unlikely to be constrained by trade-offs or a lack of genetic variation. Mol. Ecol. 24, 2777–2793 (2015).

    PubMed  Article  Google Scholar 

  16. 16.

    Laliberté, E. Below-ground frontiers in trait-based plant ecology. New Phytol. 213, 1597–1603 (2017).

    PubMed  Article  Google Scholar 

  17. 17.

    Bergmann, J. et al. The fungal collaboration gradient dominates the root economics space in plants. Sci. Adv. 6, eaba3756 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Tedersoo, L., Bahram, M. & Zobel, M. How mycorrhizal associations drive plant population and community biology. Science 367, eaba1223 (2020).

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Kong, D. et al. Leading dimensions in absorptive root trait variation across 96 subtropical forest species. New Phytol. 203, 863–872 (2014).

    PubMed  Article  Google Scholar 

  20. 20.

    Ma, Z. et al. Evolutionary history resolves global organization of root functional traits. Nature 555, 94–97 (2018).

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Weemstra, M. et al. Towards a multidimensional root trait framework: a tree root review. New Phytol. 211, 1159–1169 (2016).

    CAS  PubMed  Article  Google Scholar 

  22. 22.

    Kramer-Walter, K. R. et al. Root traits are multidimensional: specific root length is independent from root tissue density and the plant economic spectrum. J. Ecol. 104, 1299–1310 (2016).

    Article  Google Scholar 

  23. 23.

    Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).

    PubMed  Article  CAS  Google Scholar 

  24. 24.

    Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).

    PubMed  Article  CAS  Google Scholar 

  25. 25.

    Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest–tree symbioses. Nature 569, 404–408 (2019).

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  27. 27.

    Kytöviita, M.-M. Asymmetric symbiont adaptation to Arctic conditions could explain why high Arctic plants are non-mycorrhizal. FEMS Microbiol. Ecol. 53, 27–32 (2005).

    PubMed  Article  CAS  Google Scholar 

  28. 28.

    Augé, R. M., Toler, H. D. & Saxton, A. M. Arbuscular mycorrhizal symbiosis alters stomatal conductance of host plants more under drought than under amply watered conditions: a meta-analysis. Mycorrhiza 25, 13–24 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Gill, R. A. & Jackson, R. B. Global patterns of root turnover for terrestrial ecosystems. New Phytol. 147, 13–31 (2000).

    Article  Google Scholar 

  30. 30.

    Butterfield, B. J., Bradford, J. B., Munson, S. M. & Gremer, J. R. Aridity increases below-ground niche breadth in grass communities. Plant Ecol. 218, 385–394 (2017).

    Article  Google Scholar 

  31. 31.

    Bruelheide, H. et al. sPlot—a new tool for global vegetation analyses. J. Veg. Sci. 30, 161–186 (2019).

    Article  Google Scholar 

  32. 32.

    Guerrero-Ramírez, N. R. et al. Global root traits (GRooT) database. Glob. Ecol. Biogeogr. 30, 25–37 (2021).

    Article  Google Scholar 

  33. 33.

    Valverde-Barrantes, O. J., Freschet, G. T., Roumet, C. & Blackwood, C. B. A worldview of root traits: the influence of ancestry, growth form, climate and mycorrhizal association on the functional trait variation of fine-root tissues in seed plants. New Phytol. 215, 1562–1573 (2017).

    PubMed  Article  Google Scholar 

  34. 34.

    Kong, D. et al. Nonlinearity of root trait relationships and the root economics spectrum. Nat. Commun. 10, 2203 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. 35.

    Fort, F. & Freschet, G. T.Plant ecological indicator values as predictors of fine-root trait variations. J. Ecol. 108, 1565–1577 (2020).

    Article  Google Scholar 

  36. 36.

    Purcell, A. S. T., Lee, W. G., Tanentzap, A. J. & Laughlin, D. C. Fine root traits are correlated with flooding duration while aboveground traits are related to grazing in an ephemeral wetland. Wetlands 39, 291–302 (2019).

    Article  Google Scholar 

  37. 37.

    Laughlin, D. C., Fulé, P. Z., Huffman, D. W., Crouse, J. & Laliberté, E. Climatic constraints on trait-based forest assembly. J. Ecol. 99, 1489–1499 (2011).

    Article  Google Scholar 

  38. 38.

    Simpson, A. H., Richardson, S. J. & Laughlin, D. C. Soil–climate interactions explain variation in foliar, stem, root and reproductive traits across temperate forests. Glob. Ecol. Biogeogr. 25, 964–978 (2016).

    Article  Google Scholar 

  39. 39.

    Chen, W., Zeng, H., Eissenstat, D. M. & Guo, D. Variation of first-order root traits across climatic gradients and evolutionary trends in geological time. Glob. Ecol. Biogeogr. 22, 846–856 (2013).

    Article  Google Scholar 

  40. 40.

    Freschet, G. T. et al. Climate, soil and plant functional types as drivers of global fine-root trait variation. J. Ecol. 105, 1182–1196 (2017).

    Article  Google Scholar 

  41. 41.

    Ostonen, I. et al. Adaptive root foraging strategies along a boreal–temperate forest gradient. New Phytol. 215, 977–991 (2017).

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Wang, R. et al. Different phylogenetic and environmental controls of first-order root morphological and nutrient traits: evidence of multidimensional root traits. Funct. Ecol. 32, 29–39 (2018).

    Article  Google Scholar 

  43. 43.

    Craine, J. M. & Lee, W. G. Covariation in leaf and root traits for native and non-native grasses along an altitudinal gradient in New Zealand. Oecologia 134, 471–478 (2003).

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Craine, J. M., Lee, W. G., Bond, W. J., Williams, R. J. & Johnson, L. C. Environmental constraints on a global relationship among leaf and root traits of grasses. Ecology 86, 12–19 (2005).

    Article  Google Scholar 

  45. 45.

    Zadworny, M. et al. Patterns of structural and defense investments in fine roots of Scots pine (Pinus sylvestris L.) across a strong temperature and latitudinal gradient in Europe. Glob. Change Biol. 23, 1218–1231 (2017).

    Article  Google Scholar 

  46. 46.

    Oliverio, A. M. et al. The global-scale distributions of soil protists and their contributions to belowground systems. Sci. Adv. 6, eaax8787 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Bennett, A. E., Grussu, D., Kam, J., Caul, S. & Halpin, C. Plant lignin content altered by soil microbial community. New Phytol. 206, 166–174 (2015).

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    Moore, B. D. & Johnson, S. N. Get tough, get toxic, or get a bodyguard: identifying candidate traits conferring belowground resistance to herbivores in grasses. Front. Plant Sci. 7, 1925 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Delgado-Baquerizo, M. et al. The proportion of soil-borne pathogens increases with warming at the global scale. Nat. Clim. Change 10, 550–554 (2020).

    Article  Google Scholar 

  50. 50.

    De la Riva, E. G. et al. Root traits across environmental gradients in Mediterranean woody communities: are they aligned along the root economics spectrum? Plant Soil 424, 35–48 (2018).

    CAS  Article  Google Scholar 

  51. 51.

    Hacke, U. G., Sperry, J. S. & Pittermann, J. Drought experience and cavitation resistance in six shrubs from the Great Basin, Utah. Basic Appl. Ecol. 1, 31–41 (2000).

    Article  Google Scholar 

  52. 52.

    Wright, I. J., Reich, P. B. & Westoby, M. Strategy shifts in leaf physiology, structure and nutrient content between species of high- and low-rainfall and high- and low-nutrient habitats. Funct. Ecol. 15, 423–434 (2001).

    Article  Google Scholar 

  53. 53.

    Wang, B. et al. Presence of three mycorrhizal genes in the common ancestor of land plants suggests a key role of mycorrhizas in the colonization of land by plants. New Phytol. 186, 514–525 (2010).

    PubMed  Article  Google Scholar 

  54. 54.

    Grubb, P. in Handbook of Vegetation Science Vol. 3 (ed. White, J.) 595–621 (Dr. W. Junk Publishers, 1985).

  55. 55.

    Laughlin, D. C. et al. Quantifying multimodal trait distributions improves trait-based predictions of species abundances and functional diversity. J. Veg. Sci. 26, 46–57 (2015).

    Article  Google Scholar 

  56. 56.

    Pfahl, S., O’Gorman, P. A. & Fischer, E. M. Understanding the regional pattern of projected future changes in extreme precipitation. Nat. Clim. Change 7, 423–427 (2017).

    Article  Google Scholar 

  57. 57.

    Read, D. J. Mycorrhizas in ecosystems. Experientia 47, 376–391 (1991).

    Article  Google Scholar 

  58. 58.

    Bruelheide, H. et al. Global trait–environment relationships of plant communities. Nat. Ecol. Evol. 2, 1906–1917 (2018).

    PubMed  Article  Google Scholar 

  59. 59.

    Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).

    CAS  Article  Google Scholar 

  60. 60.

    Kumordzi, B. B. et al. Geographic scale and disturbance influence intraspecific trait variability in leaves and roots of North American understorey plants. Funct. Ecol. 33, 1771–1784 (2019).

    Article  Google Scholar 

  61. 61.

    Velázquez, E., Paine, C. E. T., May, F. & Wiegand, T. Linking trait similarity to interspecific spatial associations in a moist tropical forest. J. Veg. Sci. 26, 1068–1079 (2015).

    Article  Google Scholar 

  62. 62.

    Butterfield, B. J. Environmental filtering increases in intensity at both ends of climatic gradients, though driven by different factors, across woody vegetation types of the southwest USA. Oikos 124, 1374–1382 (2015).

    Article  Google Scholar 

  63. 63.

    Iversen, C. M. et al. A global fine-root ecology database to address below-ground challenges in plant ecology. New Phytol. 215, 15–26 (2017).

    PubMed  Article  Google Scholar 

  64. 64.

    Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).

    Article  Google Scholar 

  65. 65.

    Pakeman, R. J. & Quested, H. M. Sampling plant functional traits: what proportion of the species need to be measured? Appl. Veg. Sci. 10, 91–96 (2007).

    Article  Google Scholar 

  66. 66.

    Karger, D. N. et al. Climatologies at high resolution for the Earth’s land surface areas. Sci. Data 4, 170122 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  67. 67.

    Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric. Ecosyst. Environ. 126, 67–80 (2008).

    Article  Google Scholar 

  68. 68.

    Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).

    Article  Google Scholar 

  69. 69.

    Jamil, T., Ozinga, W. A., Kleyer, M. & Ber Braak, C. J. F. Selecting traits that explain species–environment relationships: a generalized linear mixed model approach. J. Veg. Sci. 24, 988–1000 (2013).

    Article  Google Scholar 

  70. 70.

    Miller, J. E. D., Damschen, E. I. & Ives, A. R. Functional traits and community composition: a comparison among community-weighted means, weighted correlations, and multilevel models. Methods Ecol. Evol. 10, 415–425 (2018).

    Google Scholar 

  71. 71.

    R Development Core Team R: A language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).

  72. 72.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  73. 73.

    Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article  Google Scholar 

  74. 74.

    Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).

    Article  Google Scholar 

  75. 75.

    Lüdecke, D., Makowski, D. & Waggoner, P. performance: Assessment of regression models performance. R package version 0.4.2 (2019).

  76. 76.

    Stefan, V. & Levin, S. plotbiomes: Plot Whittaker biomes with ggplot2. R package version 0.0.0.9001 (2020).

  77. 77.

    Roberts, D. W. labdsv: Ordination and multivariate analysis for ecology. R package version 1.8.0 https://CRAN.R-project.org/package=labdsv (2016).

  78. 78.

    Anderson, D. R. Model Based Inference in the Life Sciences: a Primer on Evidence (Springer Science & Business Media, 2008).

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Acknowledgements

We thank the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig for supporting the sRoot and sPlot working groups and the University of Wyoming Advanced Research Computing Center for technical support. sPlot was initiated by sDiv and funded by the German Research Foundation (FZT 118) and is now a platform of iDiv. The sRoot workshops and L.M. were also supported by NWO-Vidi grant 864.14.006. C.M.I. and the Fine-Root Ecology Database were supported by the Biological and Environmental Research program in the US Department of Energy’s Office of Science. J.B. was supported by Deutsche Forschungsgemeinschaft (DFG) project 432975993. N.R.G.-R. thanks the Dorothea Schlözer Postdoctoral Programme of the Georg-August-Universität.

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A.W., L.M., H.B. and D.C.L. conceived of the idea for the project. All authors were involved with collecting datasets, developing the conceptual framework and interpreting the results. D.C.L., F.M.S. and H.B. performed the statistical analyses. D.C.L. wrote the first draft of the manuscript. All authors commented on and agreed with the final version of the manuscript.

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Correspondence to Daniel C. Laughlin.

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Peer review information Nature Ecology & Evolution thanks Benjamin Delory and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Geographic and climatic distribution of vegetation plots.

Distribution of vegetation plots (A) across the globe, and (B) in climate space represented by mean annual temperature (MAT) and mean annual precipitation (MAT) superimposed onto Whittaker biomes78. The legend for color codes of vegetation types (black=forest, gold=grassland, blue=wetland) can be seen in panels C and D. Note the heavy bias toward North America, Europe, and Asia. Plots are located in all major biomes except tropical rainforest, but the majority of plots are found in temperate grasslands, temperate forests and woodlands, and boreal forest biomes. Note that we do not use the Whittaker biomes in our classification of plots into forest, grassland, and wetlands but rather use the composition data to do so (see Methods). These three vegetation types span a broad range of climate space and it is common to find grassland plots in a forest biome and forest plots within a grassland biome. (C) Plots in climate space using the climate variables that were used in the models (minimum temperature of the coldest month, and the precipitation-to-potential evapotranspiration ratio). (D) Principal coordinates analysis (PCoA) of vegetation composition using Bray-Curtis distances. The first axis explains 12% of the variation and the second axis explains 5%. Plots are color-coded according to how they were classified (that is, forest, grassland, wetland) and we illustrate 80% confidence ellipses for each vegetation type. This plot illustrates a random sample of 15,461 plots because analysis of >100,000 observations with >600 species was not computationally feasible within the time limits imposed by high-performance computer clusters.

Extended Data Fig. 2 Species distributions along climatic gradients in relation to their specific root length (SRL) and root tissue density (RTD) in forests and grasslands combined.

The left-column illustrates the modelled distributions of species using quadratic polynomials in the random effects. The y-axis of modeled probabilities of occurrence were square root transformed to amplify distributions of less common species to make them more visible. The right-column illustrates the relationship between optimum climatic conditions and root functional traits for each species, where the dark line illustrates the fitted regression line and the dotted lines represent 95% confidence intervals. Trait values for each species are color-coded using two different color ramps for each trait where dark colors are low trait values and light colors are high trait values. Size of the species symbols is proportional to their occurrence in the dataset. See Supplementary Table 1 for numbers of species in each model.

Extended Data Fig. 3 The occurrences of species-level average trait values of specific root length and root tissue density along two climatic gradients.

Illustration of occurrences of specific root length and root tissue density along the gradients of minimum temperature in the coldest month and the precipitation-to-potential evapotranspiration ratio. The climate gradients are scaled to unit variance below and plotted in their native scale above. The traits are scaled to unit variance on the left and plotted in their native scale to the right.

Extended Data Fig. 4 The occurrences of species-level average trait values of root diameter and root nitrogen along two climatic gradients.

Illustration of occurrences of root diameter and root nitrogen along the gradients of minimum temperature in the coldest month and the precipitation-to-potential evapotranspiration ratio. The climate gradients are scaled to unit variance below and plotted in their native scale above. The traits are scaled to unit variance on the left and plotted in their native scale to the right.

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Laughlin, D.C., Mommer, L., Sabatini, F.M. et al. Root traits explain plant species distributions along climatic gradients yet challenge the nature of ecological trade-offs. Nat Ecol Evol (2021). https://doi.org/10.1038/s41559-021-01471-7

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