The positive compound leaf–drought association is shrub specific in Chinese forests
Year: 2026
Type: article
Abstract: BACKGROUND AND AIMS: The classic hypothesis that compound leaves are associated with drought has largely treated all woody plants as a single functional group, overlooking potential ecological divergence among shrubs, trees and lianas. METHODS: Using 2727 species across 63 forest plots in China, we quantified compound leaf proportion within shrubs, trees and lianas, respectively, and assessed their climatic associations and used partial least squares structural equation modelling to disentangle direct and indirect pathways of drought influence. KEY RESULTS: Compound leaf distribution differed markedly among life forms. The shrub compound-leaf proportion showed strong positive correlations with drought index and negative correlations with mean annual precipitation and temperature. Trees exhibited a weak negative correlation with mean annual precipitation and no significant relationship with mean annual temperature or drought index, whereas lianas displayed no climatic relationships; instead, their proportion varied geographically. Structural equation modelling revealed that drought is associated with woody compound leaf proportion primarily through indirect pathways: increasing shrub proportion (which carries high compound leaf proportion) while reducing tree proportion, together with direct positive effects on compound leaf proportion within shrubs and trees. CONCLUSIONS: Our findings demonstrate that compound leaf-drought relationships are life-form specific, challenging the view of compound leaves as a universal drought-associated trait. Integrating life-form information into ecological models is essential for accurately predicting the responses of vegetation to climate change.
Source: Annals of Botany
DOI: 10.1093/aob/mcag256
Authors: Wanli Zhao, Haoyu Zhang, Peili Fu
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: green
Awards: G2298262262, G7976870257, G8422262770
OpenAlex ID: W7203615683
