Abstract
One of the most notable ecological trends—described more than 2,300 years ago by Theophrastus—is the association of small leaves with dry and cold climates, which has recently been recognized for eudicotyledonous plants at a global scale1,2,3. For eudicotyledons, this pattern has been attributed to the fact that small leaves have a thinner boundary layer that helps to avoid extreme leaf temperatures4 and their leaf development results in vein traits that improve water transport under cold or dry climates5,6. However, the global distribution of leaf size and its adaptive basis have not been tested in the grasses, which represent a diverse lineage that is distinct in leaf morphology and that contributes 33% of terrestrial primary productivity (including the bulk of crop production)7. Here we demonstrate that grasses have shorter and narrower leaves under colder and drier climates worldwide. We show that small grass leaves have thermal advantages and vein development that contrast with those of eudicotyledons, but that also explain the abundance of small leaves in cold and dry climates. The worldwide distribution of leaf size in grasses exemplifies how biophysical and developmental processes result in convergence across major lineages in adaptation to climate globally, and highlights the importance of leaf size and venation architecture for grass performance in past, present and future ecosystems.
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Data availability
All data are available in the Article and its Supplementary Information. Leaf trait data for the 1,752 grass species was provided by the published Kew Grassbase Database (http://www.kew.org/data/grassbase/). Climate data for species were extracted from WorldClim 2 5-arc minute resolution (https://www.worldclim.org/) and from CRU TS4.01 01 (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.01/) on the basis of the geographical records for each species (http://www.gbif.org). Photosynthetic trait data and field locations were extracted for the 13 C3 grass species for which this was available in GLOPNET (http://bio.mq.edu.au/~iwright/glopian.htm). Source data are provided with this paper.
Code availability
Custom-written R code is available on GitHub (https://github.com/smuel-tylor/grass-leaf-size-).
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Acknowledgements
We thank T. Cheng, W. Deng, A. C. Diener, A. Kooner, M. McMaster, C. Muir, S. Moshrefi, A. J. Patel, A. Sayari and M. S. Vorontsova for logistical assistance. Funding was provided by the US National Science Foundation (grants 1457279, 1951244 and 2017949), the Natural Environment Research Council (grants NE/DO13062/1 and NE/T000759/1) and a Royal Society University Research Fellowship (grant URF\R\180022).
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The project was conceptualized by A.S.B., S.H.T., C.P.O. and L.S. A.S.B., S.H.T., J.P.-K., C.V., Y.Z., T.W., C.S., E.J.E., P.-A.C., C.P.O. and L.S. performed data curation, and reviewed and edited the manuscript. A.S.B., S.H.T., J.P.-K., C.V., Y.Z., T.W., C.S., P.-A.C. and L.S. undertook formal analyses. C.P.O. and L.S. acquired funding. A.S.B., S.H.T., J.P.-K., T.W., C.S., E.J.E., P.-A.C., C.P.O. and L.S. performed the investigations. A.S.B., S.H.T., J.P.-K., T.W., C.S., E.J.E., P.-A.C., C.P.O. and L.S. developed the methodology. A.S.B., S.H.T., J.P.-K., C.P.O. and L.S. administered the project. A.S.B., S.H.T., J.P.-K., T.W., C.S., E.J.E., P.-A.C., C.P.O. and L.S. provided resources. A.S.B., S.H.T., T.W. and P.-A.C. wrote the software. A.S.B., S.H.T., J.P.-K., C.P.O. and L.S. supervised the project. A.S.B., S.H.T., C.P.O. and L.S. validated the data. A.S.B., S.H.T., T.W., C.V. and P.-A.C. provided the data visualization. A.S.B., S.H.T. and L.S. wrote the original draft.
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Extended data figures and tables
Extended Data Fig. 1 Time-calibrated phylogenetic trees for 1,752 worldwide grass species and for 27 grass species grown in a greenhouse common garden.
a, Phylogeny for 1,752 species, trimmed from a previous publication132, used for analyses of global scaling of leaf size with climate. C3 and C4 species are in black and red, respectively (n = 840 and n = 912, respectively). b, Phylogeny for 27 species used for analyses of leaf vein scaling (black branches, 11 C3 grasses; red branches, 16 C4 grasses), emphasizing the inclusion of 11 independent C4 origins. c, d, Map of the distributions of the 11 C3 species (c) and 16 C4 species (d).
Extended Data Fig. 2 Worldwide relationships of grass leaf and plant dimensions with the native climate of species, the global distribution of grass leaf size, and the scaling of grass leaf and plant dimensions.
a–l, Relationship of leaf length (a–c), leaf width (d–f), leaf area (leaf width × leaf length) (g–i) and culm height (j–l) with MAT, MAP and the aridity index (AI). m–o, Average across species of leaf area for each country in the global database (International Working Group on Taxonomic Databases for Plant Sciences, TDWG level-3 spatial units165), including countries for which >20 species occur in the global database (21–547 species for each country; grey for countries with <20 species represented); that is, mean leaf area (m), median leaf area (n) and leaf area for the largest leafed species (o). p–u, The scaling of leaf area with leaf length (p) and leaf width (q), leaf area with culm height (r), culm height with leaf length (s) and leaf width (t), and leaf width with leaf length (u). Leaf trait and climate data are provided in Supplementary Table 2. n = 1,752 globally distributed grass species in a–i, p, q, u, and 1,729 in j–l, r–t. Corresponding regression coefficients for ahistorical analyses of relationships in a–l: 0.14, 0.17, 0.14, 0.26, 0.34, 0.28, 0.24, 0.31, 0.26, 0.24, 0.29 and 0.3. Two-tailed PRMA regressions were fitted for log(trait) = log(a) + b log(trait) in a–l, p–u. ***P < 0.001, **P < 0.01. P = 0.0099 (a), 7.8 × 10−9 (b), 4.2 × 10−9 (c), 0.004 (d), 1.8 × 10−8 (e), 2.4 × 10−11 (f), 0.0014 (g), 2.9 × 10−11 (h), 2.2 × 10−13 (i), 1.7 × 10−6 (j), 4.0 × 10−7 (k), 1.1 × 10−5 (l), about 0 (p), about 0 (q), 3.17 × 10−219 (r), 1.92 × 10−205 (s), 7.92 × 10−106 (t), 2.7 × 10−96 (u). C3 and C4 species are shown in red and blue, respectively.
Extended Data Fig. 3 Worldwide association of grass leaf size with the native climate of the species in 3D, and binned by 1/3rd lowest, middle and highest MAT or MAP in 2D.
a–d, Leaf area versus climate variables (that is, x = MAT and y = MAP) (a, c); horizontal axes are flipped (that is, leaf area versus x = MAP and y = MAT) in b, d. e–p, Relationship of leaf length (e–g), leaf width (h–j), leaf area (k–m) and culm height (n–p) to MAP. n = 584 globally distributed grass species in e–m, and 576 in n–p. q–z, aa, bb, Relationships of leaf length (q–s), leaf width (t–v), leaf area (w–y) and culm height (z, aa, bb) with MAT. n = 584 globally distributed grass species in e–m, q–y, and 576 for n–p, z, aa, bb. In a, b, data for all species in the global database (n = 1,752) are presented; in c, d, 29 species with MAT <0 °C are excluded, for a clearer view of the bulk of the species. Projected grey shadows in a–d represent the bivariate relationships. Parameters from multiple regression analysis are presented in Supplementary Table 8. Two-tailed ordinary least square regressions were fitted for log(trait) = log (a) + b log(climate variable) in e–z, aa, bb. ***P < 0.001, **P < 0.01. P = 8.1 × 10−5 (e), 2.2 × 10–5 (f), 0.0002 (g), 0.0094 (h), 8.4 × 10−28 (i), 1.7 × 10–18 (j), 0.0002 (k), 1.1 × 10−20 (l), 1.8 × 10−15 (m), 0.0028 (n), 4.7 × 10–22 (o), 2.2 × 10−10 (p), 0.0106 (q), 2.9 × 10−6 (r), 7.0 × 10−5 (t), 6.7 × 10−6 (u), 1.5 × 10−17 (v), 0.0001 (w), 7.9 × 10−8 (x), 2.6 × 10−11 (y), 1.3 × 10−5 (z), 1.7 × 10−9 (aa), 8.5 × 10−10 (bb). C3 and C4 species are shown in red and blue, respectively.
Extended Data Fig. 4 Quantile regression analyses of worldwide associations of grass leaf traits with the native climate of species.
a–l, Relationship of leaf length (a–c), leaf width (d–f), leaf area (g–i) and culm height (j–l) with MAT, MAP and the aridity index. n = 1,752 globally distributed grass species in a–i, and 1,729 in j–l. Two-tailed ordinary least square (solid lines) and 95% and 5% quantile regressions (dotted lines) were fitted for log(trait) = log(a) + b log(climate variable); quantile lines are drawn if significantly different in slope at P < 0.05. C3 and C4 species are in red and blue, respectively.
Extended Data Fig. 5 Worldwide associations of grass leaf and plant dimensions with the native climate of species for species with leaf width <8.16 cm or <4.47 cm (below the modelled threshold for damage owing to night-time chilling or overheating) and modelled leaf temperature difference from air temperature for amiphistomatous grass leaves under different air temperatures.
a–h, Relationship of leaf length (a, b), leaf width (c, d), leaf area (e, f) and culm height (g, h) to MAT and MAP for species with leaf width <8.16 cm. i–p, Relationships of leaf length (i, j), leaf width (k, l), leaf area (m, n) and culm height (o, p) to MAT and MAP for species with leaf width <4.47 cm. n = 1,748 globally distributed grass species for a–f, 1,725 for g, h, 1,716 for i–n and 1,694 for o, p. q–z, aa, bb, Simulations were run with stomatal conductance (mol m−2 s−1) 0.1 (q–t), 0.2 (u–x) and 0.4 (y, z, aa, bb), and wind speed (m s−1), at 0.1 (q, u, y), 0.5 (r, v, z), 1 (s, w, aa) and 2 (t, x, bb), with leaf width (cm) of 0.04, 0.1, 0.5, 0.9, 1.5, 2.7 and 11 shown as increasing darker blue lines. No difference in leaf temperature from air temperature line in red. Two-tailed ordinary least square regressions were fitted for log(trait) = log(a) + b log(climate variable) in a–p. ***P < 0.001, **P < 0.01, *P < 0.05. P = 2.1 × 10−8 (a), 6.2 × 10−13 (b), 4.7 × 10−29 (c), 6.2 × 10−48 (d), 2.0 × 10−24 (e), 6.8 × 10−40 (f), 1.9 × 10−24 (g), 1.3 × 10−33 (h), 2.4 × 10−7 (i), 7.4 × 10−11 (j), 1.0 × 10−26 (k), 3.4 × 10−39 (l), 5.4 × 10−22 (m), 9.8 × 10−33 (n), 4.4 × 10−22 (o), 3.8 × 10−29 (p). C3 and C4 species are shown in red and blue, respectively.
Extended Data Fig. 6 Worldwide scaling of grass VLA and vein diameter with leaf size and aridity of the native climate of the species, and of vein xylem conduit diameter with vein diameter.
a–d, Relationship of major VLA to leaf width (a, c), leaf area (b, d) and the aridity index (e) (in which lower values correspond to greater climatic aridity). f–q, Relationship of vein diameters to leaf length (f, i, l, o), leaf width (g, j, m, p) and leaf area (h, k, n, q). r–z, aa, bb, cc, Relationship of VLA to leaf length (r, u, x, aa), leaf width (s, v, y, bb) and leaf area (t, w, z, cc). dd, ee, ff, gg, Relationships of vein xylem conduit diameters with vein diameter of first-order veins (dd), second-order veins (ee), third-order veins (ff) and fourth-order veins (gg). n = 616 species in a, 600 in b, 170 in c, 166 in d, 21 in e, 27 in f–z, aa, bb, cc, dd, ee, ff and 7 in gg. Two-tailed ordinary least square regressions, PGLS or PRMA regressions were fitted for log(trait) = log(a) + b log(trait or climate variable) in a and b, c and d or e, respectively. PRMA or PGLS regressions were fitted for log(vein diameter or VLA) = log(a) + b log(leaf length, width or leaf area) in f–q and r–z, aa, bb, cc, respectively. PRMA regressions were fitted for log(xylem conduit diameter) = log(a) + b log(vein diameter) in dd, ee, ff, gg. *P < 0.05, **P < 0.01, ***P < 0.001. P = 9.4 × 10−250 (a), 1.6 × 10−139 (b), 7.0 × 10−46 (c), 1.0 × 10−31 (d), 0.0051 (e), 0.0007 (f), 3.0 × 10−5 (h), 3.9 × 10−6 (i), 0.0003 (k), 1.2 × 10−34 (s), 7.0 × 10−4 (t), 1.4 × 10−7 (v), 0.0167 (w), 0.0020 (bb), 0.0110 (dd) and 0.0004 (ee). Line parameters for f–z, aa, bb, cc are given in Table 1, Supplementary Table 10; line parameters for dd, ee, ff, gg are given in Supplementary Table 11. Significant relationships are plotted with PRMA to illustrate the central trends (Methods). C3 and C4 species are shown in white and grey, respectively. The s.e. for species trait values are given in Supplementary Table 3.
Extended Data Fig. 7 Scaling of leaf vein projected area, vein surface area and vein volume of given vein orders with leaf dimensions across 27 grass species grown experimentally.
a–l, Relationship of VPA to leaf length (a, d, g, j), leaf width (b, e, h, k) and leaf area (c, f, i, l). m–x, Relationship of VSA to leaf length (m, p, s, v), leaf width (n, q, t, w) and leaf area (o, r, u, x). y, z, aa, bb, cc, dd, ee, ff, gg, ii, Relationship of VVA to leaf length (y, bb, ee, hh), leaf width (z, cc, ff, ii) and leaf area (aa, dd, gg, jj). Two-tailed PGLS regressions were fitted for log(VPA, VSA or VVA) = log(a) + b log(leaf length, width or area) and drawn when significant. *P < 0.05, **P < 0.01, ***P < 0.001; line parameters are given in Supplementary Table 10. P = 0.0011 (a), 1.2 × 10−12 (b), 0.0011 (d), 7.0 × 10−5 (e), 0.0335 (g), 0.0161 (h), 0.0167 (k), 0.0011 (m), 1.2 × 10−12 (n), 0.0011 (p), 7.0 × 10−5 (q), 0.0335 (s), 0.0161 (t), 0.0167 (w), 8.2 × 10−6 (y), 5.4 × 10−6 (z), 5.2 × 10−5 (bb), 0.0037 (cc), 0.0093 (ff). Significant trends are plotted with PRMA to illustrate the central trends (Methods). The s.e. for species trait values are given in Supplementary Table 3. C3 and C4 species are in white and grey, respectively.
Extended Data Fig. 8 Partitioning of the contributions of given vein orders of the venation architecture of C3 and C4 grasses, with minor veins accounting for the differences in VLA.
a, Triticum aestivum, a C3 species. b, Aristida ternipes, a C4 species without fourth-order veins (C4–3L) (that is, third-order veins are the highest longitudinal vein order). c, Paspalum dilatum, a C4 species with fourth-order veins (C4–4L) (that is, fourth-order veins are the highest longitudinal vein order). d, VLA (cm per cm2) distribution across vein orders for each type (C3 n = 11, C4–3L = 9, C4–4L = 7). e–h, VLA (e), VSA (f), VPA (g) and VVA (h) distribution across vein orders for each type (C3, n = 11; C4, n = 16). Statistical comparisons by phylogenetic ANOVA are given in Supplementary Table 3.
Extended Data Fig. 9 Associations between light-saturated leaf photosynthetic rate and native climate and vein traits for terrestrial C3 species, and the scaling of VLA of transverse fifth-order veins with major VLA in 27 C3 and C4 grass species grown experimentally.
a–c, Relationship of area-based light-saturated photosynthetic rate (Aarea) measured with photosynthesis systems and MAT (a), MAP (b) and growing season length (GSL) (c). d–f, Relationship of light-saturated photosynthetic rate per area and VLAmajor (cm per cm2) (d) and major VSA (VSAmajor, unitless) (e), and transverse VLA (VLAtransverse) (cm per cm2) with VLAmajor. Points and lines in red represent eight terrestrial C3 grasses (from this study) grown in a greenhouse common garden related to the mean climate of their native distribution, supporting the assumption of a higher photosynthetic rate in colder and drier climates with shorter growing seasons. Open points represent 13 Northern Hemisphere temperate terrestrial C3 grass species from the global plant trait network (GLOPNET126) measured in the field, as related to the mean climate at their field site. Black lines represent the significant trend through all the points in a, c, which—given the disparate data sources combined here (and the consideration of field site rather than native range climate for the GLOPNET species) —provides strong support for the generality of the relationships of Aarea to MAT and growing season length. Notably, these are conservative tests of the relationships of photosynthetic rate with native climate, as measurements of Aarea that use the photosynthesis system chamber do not include the effect of the boundary layer conductance (which is made very high and invariant)27. Under natural conditions (and especially under slow wind speeds), smaller leaves would have a boundary layer conductance higher than that of larger leaves (as shown in the simulation in Extended Data Fig. 5), and thus—under natural conditions that included the effects of boundary layer—a stronger trend would be expected for small-leafed species in colder and drier climates to have higher photosynthetic rates than larger-leafed species of warm, moist climates. Two-tailed ordinary least square regressions or PRMA were fitted for log(trait) = log(a) + b log(trait or climate variable) in a–e and f, respectively. *P < 0.05, **P < 0.01, xP = 0.04 in a one-tailed test of the hypothesized positive correlation. P = 0.0301 (red line in a), 0.0071 (black line in a), 0.0183 (b), 0.0474 (red line in c), 0.0021 (black line in c), 0.0794 (d), 0.0138 (e), 0.0061 (f). Error bars represent s.e. in a–e. The s.e. for species trait values in f are given in Supplementary Table 3. C3 and C4 species are shown in white and grey, respectively, in e.
Extended Data Fig. 10 Estimating leaf size from venation traits that can be measured on small samples or fragments of grass leaves.
a, b, Leaf area (a) and leaf width (b) predicted from VLA of second-order veins. n = 600 and 616 species in a and b, respectively (Grassbase dataset, Supplementary Table 2). The relationships were fitted with two-tailed ordinary least square regressions. These relationships enable the determination of intact leaf size from fragments that include at least two second-order veins (including fragmentary fossil remains). The 95% confidence intervals are in blue and 95% prediction intervals in red. ***P < 0.001. P = 1.4 × 10−127 (a), 7.6 × 10−227 (b).
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Baird, A.S., Taylor, S.H., Pasquet-Kok, J. et al. Developmental and biophysical determinants of grass leaf size worldwide. Nature 592, 242–247 (2021). https://doi.org/10.1038/s41586-021-03370-0
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DOI: https://doi.org/10.1038/s41586-021-03370-0
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