JOURNAL ARTICLE
Root topological order drives variation of fine root vessel traits and hydraulic strategies in tropical trees.
Published In: Journal of Experimental Botany, 2024, v. 75, n. 10. P. 2951 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Zhang, Guangqi; Fortunel, Claire; Niu, Shan; Zuo, Juan; Maeght, Jean-Luc; Yang, Xiaodong; Xia, Shangwen; Mao, Zhun 3 of 3
Abstract
This article investigates the variation and co-variation of fine root vessel anatomical traits and their influence on theoretical specific xylem hydraulic conductivity (Kth) across five root topological orders in 20 tropical tree species from southwestern China. The study finds that with increasing root order, Kth increases primarily due to larger mean vessel diameter (MVD), greater vessel size heterogeneity (cvVD), stable vessel fraction (VF), and decreased vessel density (Vd), reflecting a shift in anatomical strategies for water transport from absorption to conduction. While vessel traits such as MVD and VF are positively associated with Kth across species, root diameter (RD) and stele: root diameter ratio (SDR) are poor predictors of Kth interspecifically, though stele diameter (SD) shows a moderate correlation in higher-order roots. These findings highlight the dominant role of root topological order in shaping fine root hydraulic function and suggest phenotypic plasticity in root xylem anatomy as an adaptive response to soil water availability in tropical forests.
Additional Information
- Source:Journal of Experimental Botany. 2024/05, Vol. 75, Issue 10, p2951
- Document Type:Article
- Subject Area:Anatomy and Physiology
- Publication Date:2024
- ISSN:0022-0957
- DOI:10.1093/jxb/erae083
- Accession Number:177357863
- Copyright Statement:Copyright of Journal of Experimental Botany is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.