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Fine-root traits in the global spectrum of plant form and function

Nature volume 597, pages 683–687 (2021)Cite this article

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Abstract

Plant traits determine how individual plants cope with heterogeneous environments. Despite large variability in individual traits, trait coordination and trade-offs1,2 result in some trait combinations being much more widespread than others, as revealed in the global spectrum of plant form and function (GSPFF3) and the root economics space (RES4) for aboveground and fine-root traits, respectively. Here we combine the traits that define both functional spaces. Our analysis confirms the major trends of the GSPFF and shows that the RES captures additional information. The four dimensions needed to explain the non-redundant information in the dataset can be summarized in an aboveground and a fine-root plane, corresponding to the GSPFF and the RES, respectively. Both planes display high levels of species aggregation, but the differentiation among growth forms, families and biomes is lower on the fine-root plane, which does not include any size-related trait, than on the aboveground plane. As a result, many species with similar fine-root syndromes display contrasting aboveground traits. This highlights the importance of including belowground organs to the GSPFF when exploring the interplay between different natural selection pressures and whole-plant trait integration.

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Fig. 1: The aboveground and fine-root planes.
Fig. 2: Patterns of dissimilarity on the aboveground and fine-root planes.
Fig. 3: Patterns of redundancy on the aboveground and fine-root planes.

Data availability

The datasets generated and analysed during the current study are available in the Figshare repository: https://doi.org/10.6084/m9.figshare.13140146.

Code availability

The R code used in the current study is available in the Figshare repository: https://doi.org/10.6084/m9.figshare.13140146.

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Acknowledgements

This study has been supported by the TRY initiative on plant traits (http://www.try-db.org). The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. Boenisch (Max Planck Institute for Biogeochemistry, Jena, Germany). TRY is currently supported by Future Earth/bioDISCOVERY and the German Centre for Integrative Biodiversity Research (iDiv) Halle–Jena–Leipzig. This study was financed by the Estonian Ministry of Education and Research (PSG293 for C.P.C., C.G.B., S.T. and R.T.; PRG609 for M.P. and R.T.; PSG505 for A.T.; and PRG1065 for M.M., M.Z. and C.G.B.), by the European Union through the European Regional Development Fund (Centre of Excellence EcolChange), the University of Tartu (PLTOM20903). S.D. received partial funding from CONICET, FONCyT and Universidad Nacional de Córdoba (Argentina), IAI SGP and the Newton Fund. Partial funding for A.D.M. is from the Natural Sciences and Engineering Research Council of Canada.

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Author notes

  1. These authors contributed equally: Carlos P. Carmona, C. Guillermo Bueno, Aurele Toussaint, Sabrina Träger, Riin Tamme

Authors and Affiliations

  1. Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia

    Carlos P. Carmona, C. Guillermo Bueno, Aurele Toussaint, Sabrina Träger, Mari Moora, Meelis Pärtel, Martin Zobel & Riin Tamme

  2. Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany

    Sabrina Träger

  3. Consejo Nacional de Investigaciones Científicas y Técnicas, Instituto Multidisciplinario de Biología Vegetal (IMBIV), CONICET, Universidad Nacional de Córdoba, Córdoba, Argentina

    Sandra Díaz

  4. Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba, Córdoba, Argentina

    Sandra Díaz

  5. Centre for Forest Research, Département des Sciences du bois et de la forêt, Université Laval, Quebec, Quebec, Canada

    Alison D. Munson

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Contributions

C.P.C., C.G.B., A.T., S.T., M.M. and R.T. conceived the study. R.T. and C.P.C. collected and processed the trait data. A.T. and S.T. collected and processed the biogeographical and climate data. C.P.C. and A.T. analysed the data with input from all authors. C.G.B., S.T. and R.T. performed a literature search. C.P.C. wrote a first draft of the manuscript, assisted by C.G.B., A.T., S.T. and R.T. S.D., M.M., A.M., M.P. and M.Z. contributed to the design of analyses, interpretation of results and article writing.

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Correspondence to Carlos P. Carmona.

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Peer review information Nature thanks Ian Wright 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 figures and tables

Extended Data Fig. 1 Correlations between traits.

Pairwise correlations between the considered traits in the different datasets (black: full dataset with 1,719 species, blue: imputed dataset with 1,218 species, orange: complete dataset with 301 species). The lower-left triangle of the matrix contains scatterplots of traits (after log-10 transformation) showing the relationship (including regression lines) between each pair of traits. The diagonal includes a probability density function showing the distribution of each individual trait. The upper-right triangle includes the value of the correlation coefficients and, in the case of the full dataset, the number of species with empirical data for both traits (imputed and complete dataset always considered the same numbers of species). Lines for each dataset have different thickness to allow visualization of the correlation and probability density function even when there is high overlap between lines corresponding to different datasets.

Extended Data Fig. 2 Geographical, climatic and phylogenetic cover of the datasets.

a, Global map (Robinson projection) showing the occurrences (according to GBIF: http://www.gbif.org) of the species in the imputed dataset (1,218 species with empirical information for at least three aboveground and two fine-root traits). b, Number of species present in the major biomes47 in the imputed dataset (in parentheses, number of species in the complete dataset). c, Distribution of species across the phylogeny of vascular plants excluding ferns (Polypodiopsida) and lycopods (Lycopodiopsida) in the complete (301 species) and imputed datasets.

Extended Data Fig. 3 Individual aboveground and fine-root functional spaces.

Probabilistic distributions of the 2,630 and 748 species with complete empirical information for aboveground (a) and fine-root (b) traits in the functional spaces defined by a PCA on the corresponding traits followed by varimax rotation. The colour gradient (red-yellow-white) depicts different density of species in the defined space (red areas are more densely populated). Arrow length is proportional to the loadings of the considered traits in the resulting space. Aboveground traits are represented in green tones and fine-root traits in brown tones. Thick contour lines indicate the 0.5 and 0.99 quantiles, and thinner lines indicate quantiles 0.6, 0.7, 0.8 and 0.9.

Extended Data Fig. 4 Functional space using the complete dataset.

Probabilistic distributions of the 301 species with complete empirical information in the functional space defined by a PCA followed by varimax rotation based on both aboveground and fine-root traits. Each panel shows a combination of two of the four components that define the full plant spectrum. The colour gradient (red-yellow-white) depicts different density of species in the defined space (red areas are more densely populated). Arrow length is proportional to the loadings of the considered traits in the resulting space. Only those traits that had a loading of at least 0.3 in any of the represented components are shown to improve visualization (see loadings of all components in table S2). Aboveground traits are represented in green tones and fine-root traits in brown tones. Thick contour lines indicate the 0.5 and 0.99 quantiles, and thinner lines indicate quantiles 0.6, 0.7, 0.8 and 0.9.

Extended Data Fig. 5 Comparison of the occupation of the functional space with multivariate normal distributions.

Functional richness profile (amount of functional space occupied by quantiles of the functional spectra), difference in % of functional space occupied with respect to the null models, and functional divergence (representing the degree to which the density of species in the trait space is distributed towards the extremes of the distribution of species; right column) considering the first and second (a), third and fourth (b) and all components (c). In the functional profile plots (top of the left column in each case) green lines represent the mean, 2.5% and 97.5% quantiles of the functional richness profiles of null models (n = 499) representing multivariate normal distributions with equivalent parameters (means and standard deviations) than the observed data; orange lines represent the functional richness profile of the observed spectra. The values of functional richness for the 0.5 and 0.99 quantiles of all profiles are shown for comparison. The difference plots (bottom of the left column in each case), represent the percentage of functional space occupied by each quantile in relation to the mean of the null models; negative percentages mean that the considered quantile of the observed distribution occupies less space than the average of the null models, and vice versa. Each repetition of the null model (n = 499) is represented with a thin green line, whereas thicker green lines represent the mean, 2.5% and 97.5% quantiles of the 499 null models, and the orange line represents the observed distribution. The right column of each case represents the observed and null values of functional divergence; two-sided p values were estimated by confronting the value of the Standardize Effect Size (SES) with the cumulative normal distribution with mean = 0 and standard deviation = 1. The centre, bounds of box, and whiskers of the boxplot represent the median, 25th and 75th percentiles, and 1.5 times the interquartile range, respectively.

Extended Data Fig. 6 Functional space using the imputed dataset.

Probabilistic distributions of the 1,218 species with information for at least three aboveground and two fine-root traits (imputed dataset) in the functional space defined by a PCA followed by varimax rotation based on both aboveground and fine-root traits of the subset of species with complete empirical information. Each panel shows a combination of two of the four components that define the full plant spectrum. The colour gradient (red-yellow-white) depicts different density of species in the defined space (red areas are more densely populated). Arrow lengths are proportional to the loadings of the considered traits in the resulting space. Only those traits that had a loading of at least 0.4 in any of the represented components are shown to improve visualization (see loadings of all components in Extended Data Table 2). Aboveground traits are represented in green tones and fine-root traits in brown tones. Thick contour lines indicate the 0.5 and 0.99 quantiles, and thinner lines indicate quantiles 0.6, 0.7, 0.8 and 0.9.

Extended Data Table 1 Traits considered in the study
Full size table
Extended Data Table 2 Functional spaces considering different datasets
Full size table
Extended Data Table 3 Angle between eigenvectors in the (non-rotated) PCA based on the complete dataset
Full size table
Extended Data Table 4 Functional redundancy patterns
Full size table

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Carmona, C.P., Bueno, C.G., Toussaint, A. et al. Fine-root traits in the global spectrum of plant form and function. Nature 597, 683–687 (2021). https://doi.org/10.1038/s41586-021-03871-y

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