Li Xu

Background: The mangrove ecosystem serves as a vital coastal ecotone, providing essential ecological services, such as water purification, shoreline protection, and biodiversity maintenance. Fungal pathogens threaten mangrove health and contribute to ecosystem degradation, but the molecular mechanisms underlying disease resistance in mangroves remain poorly explored. Introdustion: Thaumatin-like proteins (TLPs), belonging to the pathogenesis-related-5 (PR-5) family, play a crucial role in antifungal defense in plants. Method and results: In this study, we identified 23 TLP family members in the mangrove Aegiceras corniculatum. The genes encoding TLP family members were unevenly distributed on chromosomes. Collinearity analyses showed that TLP family members in A. corniculatum underwent multiple gene duplication events, and Ka/Ks calculations revealed that these duplicated genes were predominantly under purifying selection. Among the 23 TLPs, AcTLP19 was significantly upregulated after Botrytis cinerea infection. Subcellular localization prediction and experiments revealed the extracellular localization of AcTLP19. Conclusions: Heterologous expression and antibacterial tests showed that recombinant AcTLP19 had no direct antifungal activity against B. cinerea or Fusarium oxysporum under the tested conditions, leaving open the possibility that it contributes to mangrove defense through indirect mechanisms, or that its antifungal activity was not captured under the specific assay conditions. This study advances our knowledge of mangrove stress responses and may contribute to future conservation strategies.

Leaf Area Index (LAI) is the total leaf area per unit of land surface area and is a crucial parameter for assessing vegetation growth and productivity. Machine learning regression algorithms are widely applied for LAI estimation. Due to spectral response variations among sensors and susceptibility of mangrove-derived variables to environmental noise suppression, obtaining sensitivity indices and optimal machine learning regression models is essential for retrieving mangrove LAI at the population scale. This study proposes a novel approach to processing and retrieving mangrove LAI data by integrating multispectral indices with machine learning methods. Box–Cox transformation and CatBoost-based feature selection were employed to obtain the optimal dataset. Random Forest (RF), Gradient Boosting Regression Trees (GBRT), and Categorical Boosting (CatBoost) algorithms were used to evaluate the accuracy of LAI retrieval from Unmanned Aerial Vehicle (UAV) and Gaofen-6 (GF-6) data. Results indicate that when LAI > 3, LAI does not immediately saturate as CVI, MTVI 2, and other indices increase, demonstrating higher sensitivity. UAV data outperformed GF-6 data in retrieving LAI for diverse mangrove populations; during model training, RF proved more suitable for small-sample datasets, while CatBoost effectively suppressed environmental noise. Both RF and CatBoost demonstrated higher robustness in estimating Avicennia marina (AM) (RF: R² = 0.704) and Aegiceras corniculatum (AC) (R² = 0.766), respectively. Spatial distribution analysis of LAI indicates that healthy AM and AC cover 85.36% and 96.67% of the area, respectively. Spartina alterniflora and aquaculture wastewater may be among the factors affecting the health of mangrove forests in the study area. LAI retrieval holds significant importance for mangrove health monitoring and risk early warning.