Dataset Information

Topological Phenotypes Constitute a New Dimension in the Phenotypic Space of Leaf Venation Networks.

ABSTRACT: The leaves of angiosperms contain highly complex venation networks consisting of recursively nested, hierarchically organized loops. We describe a new phenotypic trait of reticulate vascular networks based on the topology of the nested loops. This phenotypic trait encodes information orthogonal to widely used geometric phenotypic traits, and thus constitutes a new dimension in the leaf venation phenotypic space. We apply our metric to a database of 186 leaves and leaflets representing 137 species, predominantly from the Burseraceae family, revealing diverse topological network traits even within this single family. We show that topological information significantly improves identification of leaves from fragments by calculating a "leaf venation fingerprint" from topology and geometry. Further, we present a phenomenological model suggesting that the topological traits can be explained by noise effects unique to specimen during development of each leaf which leave their imprint on the final network. This work opens the path to new quantitative identification techniques for leaves which go beyond simple geometric traits such as vein density and is directly applicable to other planar or sub-planar networks such as blood vessels in the brain.

SUBMITTER: Ronellenfitsch H

PROVIDER: S-EPMC4699199 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Topological Phenotypes Constitute a New Dimension in the Phenotypic Space of Leaf Venation Networks.

Ronellenfitsch Henrik H Lasser Jana J Daly Douglas C DC Katifori Eleni E

PLoS computational biology 20151223 12

The leaves of angiosperms contain highly complex venation networks consisting of recursively nested, hierarchically organized loops. We describe a new phenotypic trait of reticulate vascular networks based on the topology of the nested loops. This phenotypic trait encodes information orthogonal to widely used geometric phenotypic traits, and thus constitutes a new dimension in the leaf venation phenotypic space. We apply our metric to a database of 186 leaves and leaflets representing 137 specie ...[more]

PMID: 26700471

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Project description:BackgroundLeaf venation traits are important for many research fields such as systematics and evolutionary biology, plant physiology, climate change, and paleoecology. In spite of an increasing demand for vein trait data, studies are often still data-limited because the development of methods that allow rapid generation of large sets of vein data has lagged behind. Recently, non-destructive X-ray technology has proven useful as an alternative to traditional slow and destructive chemical-based methods. Non-destructive techniques more readily allow the use of herbarium specimens, which provide an invaluable but underexploited resource of vein data and related environmental information. The utility of 2D X-ray technology and microfocus X-ray computed tomography, however, has been compromised by insufficient image resolution. Here, we advanced X-ray technology by increasing image resolution and throughput without the application of contrast agents.ResultsFor 2D contact microradiography, we developed a method which allowed us to achieve image resolutions of up to 7 µm, i.e. a 3.6-fold increase compared to the industrial standard (25 µm resolution). Vein tracing was further optimized with our image processing standards that were specifically adjusted for different types of leaf structure and the needs of higher imaging throughput. Based on a test dataset, in 91% of the samples the 7 µm approach led to a significant improvement in estimations of minor vein density compared to the industrial standard. Using microfocus X-ray computed tomography, very high-resolution images were obtained from a virtual 3D-2D transformation process, which was superior to that of 3D images.ConclusionsOur 2D X-ray method with a significantly improved resolution advances rapid non-destructive bulk scanning at a quality that in many cases is sufficient to determine key venation traits. Together with our high-resolution microfocus X-ray computed tomography method, both non-destructive approaches will help in vein trait data mining from museum collections, which provide an underexploited resource of historical and recent data on environmental and evolutionary change. In spite of the significant increase in effective image resolution, a combination of high-throughput and full visibility of the vein network (including the smallest veins and their connectivity) remains challenging, however.

Dataset Information

Topological Phenotypes Constitute a New Dimension in the Phenotypic Space of Leaf Venation Networks.

Publications

Topological Phenotypes Constitute a New Dimension in the Phenotypic Space of Leaf Venation Networks.

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