The mining of manganese brings excellent wealth to humankind. However, it destroys the ecological environment, mainly manifested as heavy metal pollution and vegetation destruction. The restoration of ecological vegetation in manganese mining areas has become an important work after mineral exploitation. The effect of bryophytes on ecological restoration in mining areas is irreplaceable. The bryophytes diversity and its pioneering role in two types of manganese waste residue areas were investigated in Guizhou province, China. The results showed that there were 24 species of mosses in mine waste slag areas, and all of them belonged to 6 families and 15 genera; the species Gymnostomum subrigidulum, Pohlia gedeana, and Bryum atrovirens were the dominant mosses. There were 6 species of mosses in electrolytic manganese slag areas, and all of them belonged to 5 families and 5 genera. The dominant moss was B. atrovirens. The bryophytes diversity in the electrolytic manganese slag areas with lower pH was poorer than that in mine slag areas. The accumulation of heavy metals in mosses showed that B. atrovirens collected from two types of areas had a strong ability to accumulate Mn with the cumulants 5588.00μg/g and 4283.41μg/g, respectively. All mosses had a strong enrichment ability to Cd. It indicated that mosses had strong tolerance to heavy metals. Bryophytes increased the available nutrients and bacterial community diversity of mosses growth substrates in two types of areas. Besides, we studied the relationships between bacterial community structure and soil factors. The main soil factor affecting the bacterial community structure was available nitrogen (AN) in mine waste slag areas, while it was pH in the electrolytic manganese residue areas. The systematic study suggested that bryophytes increased the available nutrients and the microbial community diversity of the growth substrates in manganese waste residue areas, which provided the basic conditions for the growth of vascular plants.

Betula luminifera H. Winkler, a fast-growing broad-leaved tree species native to China’s subtropical regions, possesses significant ecological and economic value. The species’ adaptability and ornamental characteristics make it a crucial component of forest ecosystems. However, the impacts of global climate change on its geographical distribution are not well understood, necessitating research to predict its potential distribution shifts under future climate scenarios. Our aims were to forecast the impact of climate change on the potential suitable distribution of B. luminifera across China using the MaxEnt model, which is recognized for its high predictive accuracy and low sample data requirement. Geographical coordinate data of B. luminifera distribution points were collected from various databases and verified for redundancy. Nineteen bioclimatic variables were selected and screened for correlation to avoid overfitting in the model. The MaxEnt model was optimized using the ENMeval package, and the model accuracy was evaluated using the Akaike Information Criterion Correction (delta.AICc), Training Omission Rate (OR10), and Area Under the Curve (AUC). The potential distribution of B. luminifera was predicted under current and future climate scenarios based on the Shared Socio-economic Pathways (SSPs). The optimized MaxEnt model demonstrated high predictive accuracy with an AUC value of 0.9. The dominant environmental variables influencing the distribution of B. luminifera were annual precipitation, minimum temperature of the coldest month, and standard deviation of temperature seasonality. The potential suitable habitat area and its geographical location were predicted to change significantly under different future climate scenarios, with complex dynamics of habitat expansion and contraction. The distribution centroid of B. luminifera was also predicted to migrate, indicating a response to changing climatic conditions. Our findings underscore the importance of model optimization in enhancing predictive accuracy and provide valuable insights for the development of conservation strategies and forest management plans to address the challenges posed by climate change.

Climate change continues to threaten global biodiversity, making it essential to assess how keystone species may shift their distributions and to use these findings to inform conservation planning. This study evaluated the current and future habitat suitability of D. macropodum, an important tree species within subtropical evergreen broad-leaved forests in China, using 354 occurrence records and a suite of environmental variables. A parameter-optimized MaxEnt model (calibrated with ENMeval; RM = 4, FC = QHPT) was applied to simulate the species’ present distribution and projected changes under three climate scenarios (SSP126, SSP245, SSP585). The main factors influencing distribution were determined to be moisture and temperature seasonality, with the precipitation of the coldest quarter (Bio19, 36.3%), the mean diurnal range (Bio2, 37.5%), and the precipitation of the warmest quarter (Bio18, 14.2%) jointly contributing 88.0% of the total influence. The model projections indicated a 40.1% reduction in the total number of suitable habitats under high-emission scenarios (SSP585) by the 2090s, including a loss of over 80% of highly suitable areas. Centroid movements also diverged across the scenarios: a southwestern shift under SSP126 and SSP245 contrasted with a southeastern shift under SSP585, with each accompanied by significant habitat fragmentation. Key climate refugia were identified primarily in central Taiwan Province and the mountainous zones of Zhejiang and Fujian Provinces, which should be prioritized for conservation activities. These insights offer a foundational understanding for the conservation of D. macropodum and other ecologically similar subtropical evergreen species. However, direct extrapolation to other taxa should be made cautiously, as specific responses may vary based on differing ecological tolerances and dispersal capacities. Further research is needed to test the generalizability of these patterns across diverse plant functional types.

Climate change poses unprecedented challenges to forest ecosystems. Betula alnoides, a tree species with significant ecological and economic value in southern China, has been the subject of studies on its distribution pattern and response to climate change. However, research on the distribution pattern of B. alnoides and its response to climate change remains relatively limited. In this study, we developed a MaxEnt model incorporating multiple environmental variables, including climate, topography, soil, vegetation, and human activities, to evaluate model performance, identify key factors influencing the distribution of B. alnoides, and project its potential distribution under various future climate scenarios. Species occurrence data and environmental layers were compiled for China, and model parameters were optimized using the ENMeval package. The results showed that the optimized model achieved an AUC value of 0.956, indicating extremely high predictive accuracy. The four key factors affecting the distribution of B. alnoides were standard deviation of temperature seasonality (Bio4), normalized difference vegetation index (NDVI), mean temperature of driest quarter (Bio9), and annual precipitation (Bio12). Among them, the cumulative contribution rate of climatic factors reached 68.9%, but the influence of NDVI was significantly higher than that of precipitation factors. The current suitable habitat of B. alnoides is mainly concentrated in the southwestern region, covering an area of 179.32 × 104 km2, which accounts for 18.68% of China’s land area. Under the SSP126 scenario, the suitable habitat area first decreases and then increases in the future, while under the SSP370 and SSP585 scenarios, the suitable habitat area continues to shrink, with significant losses in high-suitability areas. In addition, the centroid of the suitable habitat of B. alnoides shows an overall trend of shifting northwestward. This indicates that B. alnoides is highly sensitive to climate change and its distribution pattern will undergo significant changes in the future. In conclusion, the distribution pattern of B. alnoides shows a significant response to climate change, with particularly prominent losses in high-suitability areas in the future. Therefore, it is recommended to strengthen the protection of high-suitability areas in the southwestern region and consider B. alnoides as an alternative tree species for regions facing warming and drying trends to enhance its climate adaptability.

Global warming is accelerating the poleward and upward shifts in climatically suitable ranges of species. Panicum virgatum (switchgrass) is recognized for its dual value in China’s dual-carbon strategy: mitigating food–energy land competition and restoring marginal ecosystems. However, the accuracy of habitat projections is constrained by three limitations: reliance on North American provenance data, uncalibrated model parameters, and insufficient scenario coverage. To address these, 48 switchgrass occurrence records and 22 climatic–topographic variables were integrated. The MaxEnt model was optimized with ENMeval (RM = 4.0, FC = LQH) and coupled with three SSP scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5) to quantify habitat area changes and centroid shifts across China. The key findings were as follows: (1) The mean temperature of the coldest quarter (Bio11) and elevation were identified as the key limiting factors for the suitable distribution of switchgrass, with their corresponding optimal thresholds determined as −8.79 to 8.11 °C and 0 to 2893 m, respectively. (2) The current suitable habitat covers 583.58 × 10⁴ km², concentrated in the North China Plain. (3) Under SSP5-8.5, the high-suitability habitat is projected to reach 229.44 × 10⁴ km² by the 2090s, with the centroid migrating 305 km northwestward to the Inner Mongolia–Jilin belt. This study highlights the climate–topography coupling that drives northward migration and proposes cold-tolerant cultivar development, priority zoning of marginal lands, and ecological corridor establishment to inform climate-smart biomass energy planning in China.

Microplastic pollution has become a major environmental challenge for terrestrial ecosystems; however, the mechanisms by which it affects the growth of medicinal plants and their rhizosphere microecology remain unclear. This study selected the important medicinal herb P. capitata as the research subject. A pot experiment was conducted to investigate the effects of different concentrations (0%, 1%, 4%, and 8% w/w) of polyethylene microplastics (PE-MPs) on its growth performance, physiological responses, and rhizosphere bacterial communities. The main findings are as follows: (1) PE-MPs exhibited a hormetic effect on the biomass of P. capitata. A low concentration (1%) slightly promoted total biomass (44.78 g) compared to the control (40.94 g), whereas higher concentrations caused significant inhibition. Total biomass decreased by 28.5% and 53.5% under the 4% and 8% treatments, respectively, indicating that the transition from stimulation to inhibition occurred between the 1% and 4% PE-MPs. (2) Chlorophyll a was more sensitive to stress, with its content significantly increasing under the 1% treatment but declining at higher concentrations. (3) Regarding the antioxidant system, POD activity was significantly inhibited at low and medium concentrations, while MDA content increased significantly only under the 8% treatment (by 72%). SOD and CAT showed no significant changes, indicating that POD and MDA were more sensitive indicators of oxidative damage. (4) PE-MPs significantly reduced rhizosphere bacterial community richness (Chao1 index), and the loss of microbial diversity was highly coupled with the decline in plant biomass and the exacerbation of oxidative damage. In conclusion, this study confirms a threshold effect in the toxicity of PE-MPs to P. capitata, with the transition from hormetic stimulation to toxic inhibition occurring between 1% and 4% (w/w). Furthermore, rhizosphere microecological imbalance is identified as a key indirect mechanism underlying phytotoxicity. These findings provide a new theoretical basis for understanding the potential impacts of microplastic pollution on medicinal plants and agroecosystems.