Waterlogging Stress Response and Physiological Prediction of Recovery Capability in 27 Woody Plants

Abstract

Screening waterlogging-tolerant woody plants is essential for urban greening, and predictive models based on physiological indicators offer an efficient approach for rapid selection. In this study, 27 woody plant species were subjected to 10 and 15 days of waterlogging stress. Chlorophyll fluorescence parameters and biomass-related traits were measured and subsequently analyzed using linear mixed models, principal component analysis, clustering, correlation analysis, and random forest modeling. The results were as follows: (1) Waterlogging significantly suppressed most photosynthetic and biomass-related traits, whereas initial fluorescence (Fo) and dry matter content (DMC) increased; the stress response of maximum photochemical efficiency of PSII (Fv/Fm) intensified with prolonged waterlogging. (2) The first four principal components explained 83.2% of total variance, with stressed and control groups separating distinctly along PC1, reflecting photochemical inhibition. (3) Based on post-recovery phenotypes and survival rates, the 27 species were classified into strong and weak recovery capability categories; physiological clustering showed strong concordance with these recovery categories (Cramér’s V = 0.714, accuracy = 85.2%). Representative strong-recovery species included Fraxinus chinensis, Ulmus pumila, and Lagerstroemia indica, which maintained high survival (≥80%) and good ornamental quality after recovery. In contrast, representative weak-recovery species such as Kerria japonica, Syringa oblata, and Forsythia suspensa displayed marked post-recovery deterioration despite relatively intact stress-period phenotypes. (4) Among the measured parameters, maximum fluorescence (Fm) emerged as the most robust indicator, maintaining significant associations with recovery-phase phenotypes and survival in both contemporaneous and cross-period analyses (cross-period r = 0.46 with survival). Other fluorescence parameters exhibited distinct time-dependent indicative patterns. (5) Random forest models achieved the best predictive performance for 15-day survival (R2 = 0.712), substantially outperforming 10-day models (R2 = 0.153). Collectively, these findings demonstrate that chlorophyll fluorescence and biomass-related indicators effectively capture post-waterlogging recovery capability in woody plants, and the random forest framework provides technical support for rapid screening of waterlogging-tolerant germplasm.