Understanding the role of root cell walls in the mechanism of plant tolerance to salinity requires elucidation of the changes caused by salinity in the interactions between the mechanical properties of the cell walls and root growth, and between the chemical composition of the cell walls and root growth. Here, we investigated cell wall composition and extensibility of roots by growing a halophyte (Suaeda salsa) and a glycophyte (Spinacia oleracea) species under an NaCl concentration gradient. Root growth was inhibited by increased salinity in both species. However, root growth was more strongly reduced in S. oleracea than in S. salsa. Salinity reduced cell wall extensibility in S. oleracea significantly, whereas treatment with up to 200 mM NaCl increased it in S. salsa. Meanwhile, S. salsa root cell walls exhibited relatively high cell wall stiffness under 300 mM NaCl treatment, which resist wall deformation under such stress conditions. There was no decrease in pectin content with salinity treatment in the cell walls of the elongation zone of S. salsa roots. Conversely, a decrease in pectin content was noted with increasing salinity in S. oleracea, which might be due to Na⁺ accumulation. Cellulose content and uronic acid proportions in pectin increased with salinity in both species. Our results suggest that (1) cell wall pectin plays important roles in cell wall extension in both species under salinity, and that the salt tolerance of glycophyte S. oleracea is affected by the pectin; (2) cellulose limits root elongation under saline conditions in both species, but in halophytes, a high cell wall content and the proportion of cellulose in cell walls may be a salt tolerance mechanism that protects the stability of cell structure under salt stress; and (3) the role of the cell wall in root growth under salinity is more prominent in the glycophyte than in the halophyte.

In understanding the role of root cell wall mechanisms in plant tolerance to salinity, it is important to elucidate the changes in the pectin composition and physical properties of the cell wall. Two salt-sensitive (Helan 3 and Prius β) and one salt-tolerant (R7) spinach cultivars were used to investigate the pectin polysaccharides, the characteristics of pectin, including the degree of pectin methy-lesterification, the HG:RG-I ratio, neutral side chains (galactan/arabinangalactan), and elasticity and viscosity parameters in the root elongation zone under salinity. Root growth was inhibited by salinity, whereas the root diameter was thickened in all cultivars. Salinity significantly reduced cell wall extensibility in all cultivars, and increased cell wall viscosity in Helan 3 and R7 relative to Prius β. Pectin was significantly increased under salinity stress. Cell wall viscosity was affected by pectin due to the molar proportion of uronic acid and/or pectin characteristics (HG:RG-I ratio). The molar proportion of uronic acid in pectin was reduced in Helan 3 and R7 compared with Prius β. The length and degree of pectin methy-lesterification of neutral side chains were significantly decreased in the R7 cultivar, with no significant changes in the other two cultivars. Demethylation of pectin could alter root growth and boost salt tolerance in the R7 cultivar. In this study, it is shown that cell wall pectin played important roles in regulating the root growth of Spinacia oleracea L. under salinity stress.

Reliable quantification of forest aboveground biomass (AGB) and growing stock volume (GSV) underpins carbon stock assessment and ecosystem monitoring. However, the complex mountainous terrain makes it difficult to estimate forest parameters, and research on the simultaneous estimation of forest AGB and GSV remains limited. To this end, this study proposed an eco-constrained explainable multi-task deep learning framework (Eco-AMT-MLP) that integrated Sentinel-2 data obtained from the Google Earth Engine platform with topographic, soil, and climatic factors to jointly estimate forest AGB and GSV while incorporating an ecological constraint linking the two prediction tasks in the complex mountainous forests of northwestern Yunnan Province. The model’s performance was evaluated against Random Forest (RF), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and K-Nearest Neighbor Regression (KNN), and ablation experiments were conducted to assess the contribution of the ecological constraint module. The Eco-AMT-MLP model demonstrated the best overall predictive performance among the compared models, achieving an R² of 0.64 ± 0.08 and an RMSE of 132.99 ± 19.96 m³ ha⁻¹ for GSV prediction, and an R² of 0.58 ± 0.07 and an RMSE of 87.37 ± 13.10 Mg ha⁻¹ for AGB prediction. Compared with the MLP model, Eco-AMT-MLP increased the R² values for GSV and AGB predictions by 0.04 and 0.11, respectively, while reducing the corresponding RMSE values by 7.46 m³ ha⁻¹ and 10.12 Mg ha⁻¹, respectively. However, the model shows only limited improvement in AGB estimation accuracy, indicating that its enhancement of AGB estimation is less pronounced than its improvement of GSV estimation. SHAP analysis further identified environmental covariates and Sentinel-2 spectral features as important predictors of forest AGB and GSV. By integrating joint estimation of forest AGB and GSV with ecological constraints and interpretable predictor analysis, this framework provides a methodological reference for regional forest carbon-stock assessment, forest resource inventories, and ecosystem monitoring in complex mountainous regions.