A multivariate network analysis of ring- and diffuse-porous tree xylem vasculature segmented by convolutional neural networks

Abstract

The xylem network, the water conduction system in wood determines the ability of trees to avoid hydraulic failure during drought stress. The capability to withstand embolisms, disruptions of the water column by gas bubbles that contribute to hydraulic failure, is mainly determined by the anatomical arrangement and connectedness (topology) of xylem vessels. However, the quantification of xylem network characteristics has been difficult, so that relating network properties and topology to hydraulic vulnerability and predicting xylem function remains challenging. We studied the xylem vessel networks of three diffuse- (Fagus sylvatica, Liriodendron tulipifera, Populus x canadensis) and three ring-porous (Carya ovata, Fraxinus pennsylvatica, Quercus montana) tree species using volumetric images of xylem from laser ablation tomography (LATscan). Using convolutional neural networks for image segmentation, we generated three-dimensional, high-resolution maps of xylem vessels, with detailed measurements of morphology and topology. We studied the network topologies by incorporating multiple network metrics into a multidimensional analysis and simulated the robustness of these networks against the loss of individual vessel elements that mimic the obstruction of water flow from embolisms. This analysis suggested that networks in Populus x canadensis and Carya ovata are quite similar despite being different wood types. Similar networks had comparable experimental measurements of P50 values (pressure inducing 50% hydraulic conductivity loss) obtained from hydraulic vulnerability curves, a common tool to quantify the cavitation resistance of xylem networks. This work produced novel data on plant xylem vessel networks and introduces new methods for analyzing the biological impact of these network structures.