The carbon storage in forest ecosystems is closely linked to biomass, and its dynamic changes are of significant importance for assessing forest structure and function, as well as their response to global climate change. Recently, the research on the influencing mechanism of forest carbon storage has been a hotpot in the field of forest ecology. However, it remains unclear on the relationships among stand structure, stand stability, and carbon storage. The issues needed to be answered are as follows: How are tree density, tree species diversity, stand structural characteristics, stand stability, and carbon storage correlated? Is there a direct or indirect effect between tree density, tree species diversity, stand structural characteristics, stand stability, and carbon storage? Do these factors have an impact on stand stability, and, subsequently, carbon storage? What is the crucial factor in the mechanism that influences carbon storage? Here, the natural Quercus mongolica forests in the Xiaolong Mountains were taken as the research object. Several methods, including Pearson’s correlation, the best-fitting SEM, and multiple regression, were used to examine the relationships among tree density, tree species diversity, stand structural characteristics, stand stability, and carbon storage. Our results show that there were correlations between tree density, tree species diversity, stand structural characteristics, stand stability, and carbon storage. Tree density not only directly affects stand stability but also indirectly influences it through the mediation of tree species diversity and stand structural characteristics. Meanwhile, tree density also indirectly influences carbon storage through the mediation of tree species diversity, stand structural characteristics, and stand stability. Crown volume exerts the greatest influence on stand stability, while carbon storage is mostly impacted by stand stability. Overall, the combination of tree density, tree species diversity, stand structural characteristics, and stand stability influences carbon storage (66.4%). Therefore, it is important to consider stand stability when assessing carbon sequestration potentials; furthermore, the importance of tree density, tree species composition, and stand structural characteristics should be emphasized. Our research provides a scientific basis for conservation and management decision-making in natural forests and offers novel insights as well as a scientific reference for future large-scale carbon storage investigations.

As climate change continues to alter species distributions, Pinus bungeana, an endangered conifer of significant ecological and ornamental value, faces heightened vulnerability, underscoring the critical need to understand and predict its future habitat shifts. Here, we used 83 effective geographic distribution records, along with climate, topography, soil, and drought indices, to simulate the potential distribution of suitable ecological niches for P. bungeana under current conditions and across three future time periods (2040–2060, 2060–2080, and 2080–2100) under two shared socioeconomic pathways: SSP126 (low emissions) and SSP585 (high emissions), using the maximum entropy (MaxEnt) model. The results show that the area under the receiver operating characteristic curve (AUC) for all simulations exceeded 0.973, indicating high predictive accuracy. Soil moisture, the minimum temperature of the coldest month, temperature seasonality, isothermality, the precipitation of the wettest quarter, and altitude were identified as key environmental factors limiting the distribution of P. bungeana, with soil moisture and the minimum temperature of the coldest month being the most important factors. Under the current climatic conditions, the potentially suitable ecological niches for P. bungeana were primarily located in Shaanxi Province, southern Shanxi Province, southeastern Gansu Province, northeastern Sichuan Province, Henan Province, and northwestern Hubei Province, covering approximately 75.59 × 10⁴ km². However, under the future climate scenarios, highly suitable areas were projected to contract, with the rate of decline varying significantly between scenarios. Despite this, the total area of potentially suitable ecological niches was predicted to expand in the future periods. Additionally, a pronounced eastward shift in P. bungeana’s distribution was projected, especially under the high-emission SSP585 scenario. These findings provide insights into the potential impacts of climate change on the distribution of P. bungeana, and they offer valuable guidance for its conservation strategies and habitat management in the context of climate change.

The dynamics and plant composition of toxic weeds in alpine meadows are strongly influenced by management practices such as livestock grazing. Here, the effect of grazing management on vegetation and soil characteristics within an alpine meadow ecosystem was assessed over a 5-year period. The experimental grazing treatments comprised no grazing (control), light grazing (5 sheep/ha), moderate grazing (10 sheep/ha), and heavy grazing (15 sheep/ha). The characteristics of both edible grass and toxic weeds, along with the soil’s physicochemical and biological properties, were evaluated. Under heavy grazing, the biomass of toxic weeds increased by 15.0%, while the biomass of edible species decreased by 57.0% compared to the control. The findings indicated that after 5 years, the plant composition changed significantly, with edible species such as Taraxacum mongolicum and Tibetia himalaica decreasing and disappearing under moderate and heavy grazing treatments. Conversely, toxic weeds like Stellera chamaejasme and Euphorbia micractina emerged under moderate or heavy grazing. Additionally, the richness of toxic weeds increased from 6.3 under the control to 14.2 under heavy grazing. Regarding soil properties, the levels of soil glucosidase, amylase, and cellulose decreased by 39.0%, 53.0%, and 40.0%, respectively. The amount of available potassium initially decreased and then increased under heavy grazing. The results demonstrated that the quality of the vegetation cover and a soil’s properties directly depend on land management. Overall, light to moderate grazing kept the soil in a better chemical and biological state and kept the biomass of palatable plants at a desirable level, which also controlled the abundance and biomass of toxic weeds. Enhancing soil nutrient conditions, such as by adding nitrate fertilizers, can be effective in restoring grasslands that have been severely degraded by grazing.

The synergistic effects of climate change and human activities have profoundly influenced the spatiotemporal dynamics of vegetation in arid and semi-arid regions. In this study, MODIS NDVI data and an integrated methodological approach, including trend analysis, partial correlation, residual regression, and geographical detector modeling, were used to analyze the variations in NDVI in Gansu Province from 2000 to 2022. The results showed the following: (1) The growing-season NDVI in Gansu Province exhibited a significant increasing trend overall (0.0029 per year, p < 0.05). (2) Both the NDVI values and their increasing rates presented a spatial pattern of “higher in the southeast and lower in the northwest”; although low vegetation coverage dominated the entire province, 49.47% of the area showed an extremely significant increasing trend in NDVI (p < 0.01). (3) In the future, the area ratio of regions with improved NDVI to those with degraded NDVI in Gansu Province will be approximately 45.5%:54.5%. (4) The contribution rate of human activities to the spatiotemporal variations in NDVI was higher than that of climate change; however, the synergistic effect of the two factors was greater than their individual effects. (5) Precipitation and solar radiation were the two primary climatic factors affecting NDVI variations in Gansu Province, while human activities played a regulatory role in mediating climate–vegetation interactions. Therefore, we suggest implementing more proactive ecological management and restoration measures to mitigate the impacts of future climate change, particularly in regions where NDVI may degrade in the future.

Epimedium brevicornu is an important medicinal plant in China, whose main bioactive components are flavonoid glycosides. UDP-glycosyltransferases (UGTs) play key roles in flavonoid glycosylation and metabolic diversification. In this study, transcriptome data from four representative production regions in Gansu Province were used to systematically identify and analyze the UGT gene family in E. brevicornu. A total of 359 UGT members were identified, and 168 homologous genes with clear expression evidence were obtained from four geographical populations. Molecular evolutionary analysis showed that most UGT genes were under purifying selection, whereas UGT2, UGT52, UGT57, UGT241, UGT269, and UGT271 exhibited significant signals of positive selection in specific lineages (p < 0.05). Protein interaction analysis indicated that many UGT proteins were closely associated with key enzymes involved in flavonoid biosynthesis, including CHS (TT4), CHI (TT5), F3H, FLS, and DFR, suggesting their potential involvement in flavonoid metabolism. Promoter analysis further revealed a high enrichment of ERF (11,169 occurrences) and MYB (7673 occurrences) transcription factor binding sites in the upstream regions of UGT genes. In addition, UGT57 and UGT241 showed significantly higher expression levels in the QLH population. Molecular docking analysis indicated relatively strong binding affinities with quercetin, with binding energies of −7.23 kcal/mol and −4.62 kcal/mol, respectively. These results suggest that the sequence variation and differential expression of UGT genes may be associated with flavonoid glycosylation and ecological adaptation in Epimedium. This study provides a basis for understanding the evolutionary characteristics and expression patterns of the UGT gene family and offers candidate genes for future studies on flavonoid metabolism.

We propose a novel two dimensional least mean square (2D-LMS) algorithm for sparse image de-noising, which exploits the sparsity information of images by utilizing a priori. To efficiently accelerate convergence speed, the proposed algorithm utilizes the image sparsity level information to modify the cost function of traditional 2D-LMS via a penalty of tap-weight. Since the largest q norm is not differentiable and its sub-gradient is difficult to be calculated, a novel method which contains two stages is proposed to produce the update function of the proposed algorithm. The proposed algorithm is implemented in both horizontal and vertical directions. Simulation results indicate that the proposed algorithm for sparse image de-noising can achieve higher peak signal-to-noise ratio (PSNR) than that of the two-dimensional zero-attracting least mean square (2D ZA-LMS) algorithm.