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Summary Variation in leaf venation network architecture may reflect trade‐offs among multiple functions including efficiency, resilience, support, cost, and resistance to drought and herbivory. However, our knowledge about architecture‐function trade‐offs is mostly based on studies examining a small number of functional axes, so we still lack a more integrative picture of multidimensional trade‐offs.Here, we measured architecture and functional traits on 122 ferns and angiosperms species to describe how trade‐offs vary across phylogenetic groups and vein spatial scales (small, medium, and large vein width) and determine whether architecture traits at each scale have independent or integrated effects on each function.We found that generalized architecture‐function trade‐offs are weak. Architecture strongly predicts leaf support and damage resistance axes but weakly predicts efficiency and resilience axes. Architecture traits at different spatial scales contribute to different functional axes, allowing plants to independently modulate different functions by varying network properties at each scale.This independence of vein architecture traits within and across spatial scales may enable evolution of multiple alternative leaf network designs with similar functioning.
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Automated and accurate segmentation of leaf venation networks via deep learning
Summary Leaf vein network geometry can predict levels of resource transport, defence and mechanical support that operate at different spatial scales. However, it is challenging to quantify network architecture across scales due to the difficulties both in segmenting networks from images and in extracting multiscale statistics from subsequent network graph representations.Here we developed deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks. Thirty‐eight CNNs were trained on subsets of manually defined ground‐truth regions from >700 leaves representing 50 southeast Asian plant families. Ensembles of six independently trained CNNs were used to segment networks from larger leaf regions (c. 100 mm2). Segmented networks were analysed using hierarchical loop decomposition to extract a range of statistics describing scale transitions in vein and areole geometry.The CNN approach gave a precision‐recall harmonic mean of 94.5% ± 6%, outperforming other current network extraction methods, and accurately described the widths, angles and connectivity of veins. Multiscale statistics then enabled the identification of previously undescribed variation in network architecture across species.We provide aLeafVeinCNNsoftware package to enable multiscale quantification of leaf vein networks, facilitating the comparison across species and the exploration of the functional significance of different leaf vein architectures.
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- Award ID(s):
- 2025282
- PAR ID:
- 10380863
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- New Phytologist
- Volume:
- 229
- Issue:
- 1
- ISSN:
- 0028-646X
- Page Range / eLocation ID:
- p. 631-648
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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