Forests

The exacerbation of Aluminum (Al) toxicity is a leading cause of forest degradation. However, the effects of Al on clone bamboo are not well-characterized. This study examined the influence of Al on bamboo growth using one-year-old Phyllostachys edulis seedlings subjected to control Al treatments, which aim to provide theoretical support for improving the soil quality of bamboo forests. The results indicated that the Al content in the seedlings increased by 86.42% to 162.79% compared to the control. However, it remained within a relatively stable range, with the root being the primary site of accumulation. Among the treatments, the 0.3 mM Al group (Al³⁺) exhibited the highest values in biomass indexes (LB, RB and AGB). In contrast, the 2.0 mM Al treatment led to a significantly higher root-to-shoot ratio (RSR) than other groups. Physiological analyses revealed coordinated responses in key antioxidant enzymes (POD, SOD, CAT) and osmotic adjustment substances (Pro, SP, Bet). These findings demonstrate that P. edulis possesses considerable tolerance to Al, with a significant phenotypic inhibitory effect that was not observed with 2.0 mM Al treatment. Bamboo responds to Al stress through controlling Al absorption, optimizing resource reallocation, and enhancing adaptability physiology capacity, illustrating a comprehensive collaboration adaptive mechanism.

Chinese fir (Cunninghamia lanceolata) is a cornerstone timber species in southern China. However, yet its plantation productivity frequently declines under successive rotations, threatening long-term sustainability. While belowground processes are suspected drivers, the mechanisms—particularly plant–soil–microbe interactions—remain poorly resolved. To address this, we examined a chronosequence of C. lanceolata plantations (5, 15, 20, and 30 years) in Jingdezhen, Jiangxi Province, integrating soil physicochemical assays, high-throughput sequencing, and extracellular enzyme activity profiling. We found that near-mature stands (20 years) exhibited a 60.7% decline in mean annual volume increment relative to mid-aged stands (15 years), despite continued increases in individual tree volume—suggesting a strategic shift from resource-acquisitive to nutrient-conservative growth. Peak values of soil organic carbon (32.87 g·kg⁻¹), total nitrogen (2.51 g·kg⁻¹), microbial biomass carbon (487.33 mg·kg⁻¹), and phosphorus (25.65 mg·kg⁻¹) coincided with this stage, reflecting accelerated nutrient turnover and intensified plant–microbe competition. Microbial communities shifted markedly over time: Basidiomycota and Acidobacteria became dominant in mature stands, replacing earlier Ascomycota and Proteobacteria. Random Forest and Partial Least Squares Path Modeling (PLS-SEM) identified total nitrogen, ammonium nitrogen, and total phosphorus as key predictors of productivity. PLS-SEM further revealed that stand age directly enhanced productivity (β = 0.869) via improved soil properties, but also indirectly suppressed it by stimulating microbial biomass (β = 0.845)—a “dual-effect” that intensified nutrient competition. Fungal and bacterial functional profiles were complementary: under phosphorus limitation, fungi upregulated acid phosphatase to enhance P acquisition, while bacteria predominately mediated nitrogen mineralization. Our results demonstrate a coordinated “soil–microbe–enzyme” feedback mechanism regulating productivity dynamics in C. lanceolata plantations. These insights advance a mechanistic understanding of rotation-associated decline and underscore the potential for targeted nutrient and microbial management to sustain long-term plantation yields.

Biological invasions are major threats to global biodiversity, and mapping their distribution is essential to prioritizing management efforts. The Pinaceae family (hereafter pines) includes invasive trees, particularly in Southern Hemisphere regions where they are non-native. These invasions can increase the severity of fires in wildland–urban interfaces (WUIs). We mapped pine invasion in the Bariloche WUI (≈150,000 ha, northwest Patagonia, Argentina) using supervised land cover classification of Sentinel-2 imagery with a Random Forest algorithm on Google Earth Engine, achieving 90% overall accuracy but underestimating the pine invasion area by about 25%. We then assessed in which main vegetation context pine invasions occurred relying on major vegetation units across the precipitation gradient of our study area. Invasions cover 2% of the study area, mainly in forests (61%), steppes (25.4%), and shrublands (13.4%). Most invaded areas (89.1%) are on private land; nearly 70% are on large properties (>10 ha), where state financial incentives could support removal. Another 13.5% occur on many small properties (<1 ha), where awareness campaigns could enable decentralized, low-effort control. Our land cover map can be developed further to integrate invasion dynamics, inform fire risk and behavior models, optimize management actions, and guide territorial planning. Overall, it provides a valuable tool for targeted, scale-appropriate strategies to mitigate ecological and fire-related impacts of invasive pines.