The cambial response of most plants varies with exposure to different environmental stresses, affecting their morphological and physiological features. The timing of cambial reactivation contributes to the quality of wood produced and the ability of plants to adapt to environmental conditions. Cambial structure and function provide insights into the genetic components or mechanisms underlying the secondary growth of plants. This study aimed to investigate how environmental stresses such as temperature, water, and phosphorus affect cambial activity mechanisms and xylem differentiation in different plant species. Under temperature regulation, cambial reactivation and xylem differentiation occur earlier in young cambium than in mature cambium, even under the same environmental conditions. Studies suggest that the cambial response to temperature regulation is related to the age of the cambium. During high heat waves and extended periods despite the rain, the number of new tracheids on trees seemed to decrease, and their radial diameters diminished. In low phosphorus (P) soils, the increasing timber density was therefore connected with the inhibited stem cambial activity, which resulted in a higher amount of photoassimilates for the secondary wall thickening of fibre cells. Under P deficiency, root anatomy changes with respect to stele diameter, root diameter, and meta-xylem vessels. Ultimately, changes in climatic conditions have a significant impact on the physiological characteristics of particular plant genera.

The purpose of this study was to characterize the endogenous cellulase gene MaCel1 of Monochamus alternatus, which is an important vector of Bursaphelenchus xylophilus, a pine wood nematode, which causes pine wilt disease (PWD). In this study, MaCel1 was cloned by rapid amplification of cDNA end (RACE), and its expression analyzed by RT-qPCR (real-time quantitative PCR detecting). A total of 1778 bp of cDNA was obtained. The encoding region of this gene was 1509 bp in length, encoding a protein containing 502 amino acids with a molecular weight of 58.66 kDa, and the isoelectric point of 5.46. Sequence similarity analysis showed that the amino acids sequence of MaCel1 had high similarity with the β-Glucosinolate of Anoplophoraglabripennis and slightly lower similarity with other insect cellulase genes (GH1). The β-D-Glucosidase activity of MaCel1 was 256.02 ± 43.14 U/L with no β-Glucosinolate activity. MaCel1 gene was widely expressed in the intestine of M. alternatus. The expression level of MaCel1 gene in male (3.46) and female (3.51) adults was significantly higher than that in other developmental stages, and the lowest was in pupal stage (0.15). The results will help reveal the digestive mechanism of M. alternatus and lay the foundation for controlling PWD by controlling M. alternatus.

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.