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Leaf venation network evolution across clades and scales

Nature Plants volume 11, pages 1127–1141 (2025)Cite this article

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An Author Correction to this article was published on 11 July 2025

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Abstract

Leaf venation architecture varies greatly among living and fossil plants. However, we still have a limited understanding of when, why and in which clades new architectures arose and how they impacted leaf functioning. Using data from 1,000 extant and extinct (fossil) plants, we reconstructed approximately 400 million years of venation evolution across clades and vein sizes. Overall, venation networks evolved from having fewer veins and less smooth loops to having more veins and smoother loops, but these changes only occurred in small and medium vein sizes. The diversity of architectural designs increased biphasically, first peaking in the Paleozoic, then decreasing during the Cretaceous, then increasing again in the Cenozoic, when recent angiosperm lineages initiated a second and ongoing phase of diversification. Vein evolution was not associated with temperature and CO2 fluctuations but was associated with insect diversification. Our results highlight the complexity of the evolutionary trajectory and potential drivers of venation network architecture.

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Fig. 1: Time-calibrated phylogeny of 1,000 vascular plant taxa.
Fig. 2: Examples of leaf networks with low and high values for four architecture traits.
Fig. 3: Evolutionary trends in four leaf venation architecture traits across vein sizes (small, medium, large) for the entire phylogeny of vascular plants.
Fig. 4: Evolutionary trends in individual leaf venation architecture traits across vein sizes and plant clades.
Fig. 5: The venation architectural space of vascular plants and its variation over time.
Fig. 6: Variation in leaf venation architecture compared to abiotic and biotic proxies across the Phanerozoic eon.

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Data availability

The data needed to reproduce all analyses are publicly available on Zenodo at https://doi.org/10.5281/zenodo.13300782 (ref. 66). The original cleared leaf images, leaf masks, extracted networks and all LeafVeinCNN outputs for the 122 leaf samples collected at the UCBG at Berkeley are available on Dryad at https://doi.org/10.5061/dryad.1g1jwsv36 (ref. 67). Network segmentations and leaf masks of all other 878 samples are available on Zenodo at https://doi.org/10.5281/zenodo.13300782 (ref. 66) and https://doi.org/10.5281/zenodo.15217651 (ref. 68). These three previous datasets also contain high-resolution cleared leaf images from Ecuador, Costa Rica, Ghana and UCMP collections. Original leaf cleared images of all other extant species are available via Wilf collection at https://phytokeys.pensoft.net/article/72350/ and Smithsonian National Cleared Leaf Collection at https://collections.peabody.yale.edu/pb/nclc/. Original fossil leaf images are publicly available at https://peabody.yale.edu/, www.gbif.org, https://www.museumfuernaturkunde.berlin/, https://ucmpdb.berkeley.edu/, https://www.floridamuseum.ufl.edu/, https://search.museums.ualberta.ca/, https://ucmp.berkeley.edu/collections/paleobotany-collection/, https://phytokeys.pensoft.net/article/72350/, https://doi.org/10.1016/j.palwor.2017.01.003, https://doi.org/10.1080/14772019.2014.936974, https://doi.org/10.1080/11035890902857846, https://doi.org/10.1016/j.revpalbo.2009.08.004, https://doi.org/10.1016/j.revpalbo.2017.08.003, https://doi.org/10.1515/acpa-2017-0012 and https://api.semanticscholar.org/CorpusID:135128574. World Plants (https://worldplants.de/), Paleobiology (https://paleobiodb.org/) and Fossilworks (fossilworks.org) databases were used to standardize species names. TimeTree: the timescale of life (https://timetree.org/) database was used to recalibrate the age of internal nodes of the phylogenetic tree.

Code availability

The R code needed to reproduce all analyses and figures is publicly available on Zenodo at https://doi.org/10.5281/zenodo.13300782 (ref. 66).

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Acknowledgements

This study was supported by the US National Science Foundation (grant DEB-2025282, B.B.) with supplements from the Research Experience for Undergraduates, Research Experience for Post-Baccalaureate Students and Research Experience for Teachers programs, the University of California at Berkeley Sponsored Projects for Undergraduate Research program, and the UK Natural Environment Research Council (NE/M019160/1, M.F.). Ghana samples were collected under permit of the Forestry Research Institute of Ghana (FORIG, M.B.) at Kumasi, Ghana. Costa Rica samples were collected under permit ACOPAC-INV-RES-002-11, issued to B.B. by the Ministerio de Ambiente, Energía y Telecomunicaciones, Área de Conservación del Pacífico Central. Ecuador samples were collected under Ministerio Del Ambiente permit 004-2019-IC-PNY-DPAO/AVS and exported under permit MAE-DPAO-2019-1275-O. Ecuador field sampling efforts were funded by CTFS ForestGeo grants program and the Arizona State University WAESO program, which funded the undergraduate research technicians that processed the Ecuador leaf samples. We acknowledge P. Wilf for organizing and publishing a large image database of extant cleared and fossil leaves and thank him for providing useful feedback on a first draft of this paper. We thank S. Castiglione and P. Raia for providing input on RRphylo, and S. Rifai for providing computational support for the bootstrapping analysis. We are grateful to all staff of the UCBG at Berkeley for the logistical support, especially to the horticulturists E. Fenner, E. Hupperts, J. Fong, N. Gapsis, G. Dollarhide, S. Warsh, J. Bonham and C. Rieder, who helped us with sample collection.

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Authors and Affiliations

  1. Department of Environmental Science, Policy, and Management, University of California Berkeley, Berkeley, CA, USA

    Ilaine Silveira Matos, Bradley Vu, Joseph Mann, Emily Xie, Srinivasan Madhavan, Satvik Sharma, Izzi Niewiadomski, Andrea Echevarria, Connor Tomaka, Sonoma Carlos, Monica Antonio, Ashley Chu, Meg Scudder, Nicole Yokota, Hailey J. Park, Mickey Boakye, Caroline Pechuzal, Mia B. Franco, Ryan Jen Wong, Jocelyn Liu & Benjamin W. Blonder

  2. School of Biological Sciences, The University of Adelaide, Adelaide, South Australia, Australia

    Ilaine Silveira Matos

  3. Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA

    Hailey J. Park

  4. Department of Biology, University of Waterloo, Waterloo, Ontario, Canada

    Natalie Vuong

  5. School of Life Sciences, Arizona State University, Tempe, AZ, USA

    Miguel A. Duarte & Emily Guevara Heredia

  6. School of Biological Sciences, The University of Utah, Salt Lake City, UT, USA

    Luiza Maria T. Aparecido

  7. Escuela de Ciencias Biológicas, Pontificia Universidad Católica del Ecuador, Quito, Ecuador

    Emily Guevara Heredia & Rafael E. Cárdenas

  8. Department of Ecology & Evolutionary Biology, The University of Arizona, Tucson, AZ, USA

    Brad Boyle & Brian J. Enquist

  9. Department of Plants & Soil Sciences, University of Delaware, Newark, DE, USA

    Martha Ryan

  10. University of California Museum of Paleontology, Berkeley, CA, USA

    Diane M. Erwin

  11. University of California Botanical Garden at Berkeley, Berkeley, CA, USA

    Holly Forbes

  12. School of GeoSciences, University of Edinburgh, Edinburgh, UK

    Kyle Dexter

  13. Royal Botanic Garden Edinburgh, Edinburgh, UK

    Kyle Dexter

  14. Department of Life Sciences and Systems Biology, University of Turin, Turin, Italy

    Kyle Dexter

  15. Department of Biology, University of Oxford, Oxford, UK

    Mark Fricker

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  1. Ilaine Silveira Matos
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Contributions

B.W.B., M.F., L.M.T.A. and B.J.E. acquired funding. I.S.M. and B.W.B. designed the study. M.F. developed the program for leaf venation extraction, which was improved by B.W.B., S.S., S.M. and C.T., using venation images hand-traced by C.P., N.V. and H.J.P. H.F. and D.M.E. provided logistical support to access the UCBG living collections and the UCMP cleared leaf collections, respectively. I.N. led a lab team group responsible for clearing (S.C., M.A., A.C., M.S., N.Y.) and imaging (A.E.) the UCBG leaf samples. L.M.T.A., E.G.H., R.E.C., M.R. and M.A.D. collected and prepared the Ecuador leaf samples. M.B. collected the Ghana leaf samples. B.B. collected the Costa Rica leaf samples with funding from B.J.E. E.G.H. collected the Ecuador leaf samples with funding from L.M.T.A. E.X. selected and imaged the UCMP leaf samples. B.V., J.M., N.V., M.B., I.S.M. and C.P. hand-traced leaf images and prepared leaf masks of extant species, while M.B.F., R.J.W. and J.L. hand-traced fossil leaf images. B.V., J.M., M.S. and I.S.M. used the LeafVeinCNN program to extract the leaf venation networks and the leaf architectural traits. K.D. provided critical support for the data analysis. I.S.M. analysed the data and drafted the paper. All authors contributed to and revised the paper.

Corresponding author

Correspondence to Ilaine Silveira Matos.

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Nature Plants thanks Christopher Baraloto and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Evolutionary rates of leaf venation architecture traits across vein sizes.

a, Vein density - VD (F: - −0.72; CI: −0.16/0.55; P-value < 0.01); b, Minimum spanning tree ratio - MST (F: −1.43; CI: −0.007/1.43; P-value < 0.01); c, Loop elongation ratio - ER (F: 0.58; CI: 1.44/0.87; P-value < 0.01); d, Loop circularity ratio - CR (F: −1.20; CI: 0.15/1.35; P-value < 0.01). Letters indicate significant differences (p-value < 0.05) in two-sided Wilcoxon signed-rank test followed by Bonferroni p-value correction. Data is presented as median and the Kernel Density Estimation boundary.

Extended Data Fig. 2 Classification of veins into three size classes (small, medium, large).

(a) Density plot of scaled vein width for all 1,000 species evaluated in this study. The red lines indicate the limits used to define the three vein size classes. (b) Detail of the leaf venation network for the species Aextoxicon punctatum, showing the small, medium, and large veins. Distribution of (c) Vein density (VD), (d) Minimum spanning tree ratio (MST), (e) Loop circularity ratio (CR), and (f) Loop elongation ratio (ER) across vein widths for A. punctatum. Panels d-g show the median values for each venation trait at each vein size class.

Extended Data Fig. 3 Evolutionary trends in leaf venation architecture traits across plant clades and vein sizes.

a-c, Vein density (VD). d-f, Minimum spanning tree ratio (MST). g-i, Loop elongation ratio (ER). j-l, Loop circularity ratio (CR). In each panel the bottom axis and jittered points show the venation trait distribution in each plant clade, with the median value highlighted by the hollow black circle. The top axis and dark red bars show the mean trait evolutionary rate per 10 million years. An ‘*’ indicates a significant linear evolutionary trend (α = 0.05) of increase (positive values) or decrease (negative values) in mean trait values over time according to phylogenetic ridge regressions.

Extended Data Fig. 4 Results of disparity through time analysis under a gradual model of evolution for each vascular plant clade.

a, ferns; b, gymnosperms; c, basal angiosperms; d, monocots; e, basal eudicots; f, rosids; g, asterids. Each panel shows the temporal dynamics of two complementary metrics of disparity, the sum of variances (describing the position occupied in the venation architectural) and the median of centroids (describing the extent of space occupied). Lines represent the median values for each time-slice of ca. 10 million years of duration and the corresponding standard deviation.

Extended Data Fig. 5 Cumulative occupation of the venation architectural spaces for different plant clades over the geological time.

First (PC1) and second (PC2) principal components of leaf venation architecture traits (VD, MST, ER, CR) at three vein sizes (small, medium, large) across plant clades. (a) Present; (b) 5 million years ago (Ma); (c) 25 Ma; (d) 50 Ma; (e) 75 Ma; (f) 100 Ma; (g) 150 Ma; (h) 200 Ma; (i) 250 Ma; (j) 300 Ma; (k) 350 Ma; (l) PCA loadings separated from the PCA scored for ease of visualization. 95% confidence ellipses enclose the data at each clade. Colored inserts illustrate some of the changes in architectural designs over time.

Extended Data Fig. 6 Results of time-series analysis for identifying potential abiotic (global CO2 atmospheric concentration and global mean temperature) and biotic (insect diversification rates) drivers of leaf venation architecture evolution in vascular plants.

Vein density (VD, mm mm−2) of small (a-c) and medium (d-f) veins; Minimum spanning tree ratio (MST) of small (g-i) and medium (j-l) veins; Loop circularity ratio (CR) of small (m-o) and medium (p-r) veins; Disparity metrics of sum of variances (s-u) and median of centroids (v-y). Venation traits (VD, MST, CR) depicted in a-r were obtained by collating ancestral state estimates at the phylogenetic tree internal nodes to measured trait values at the tree tips.

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Supplementary Figs. 1–4 and Notes 1 and 2.

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Supplementary Data

Supplementary Data 1–8.

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Matos, I.S., Vu, B., Mann, J. et al. Leaf venation network evolution across clades and scales. Nat. Plants 11, 1127–1141 (2025). https://doi.org/10.1038/s41477-025-02011-y

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