Search

Wild edible mushrooms are an important food source globally and have a crucial role in forest ecosystems. However, there is limited research on the growth characteristics and the contribution of agronomic traits to biomass, and the environmental factors affecting mushroom growth are limited. This study was conducted in the Qilian Mountains, China, and focused on investigating the growth patterns and agronomic traits of Lyophyllum decastes and Coprinus comatus. The results revealed that the growth of these mushrooms followed a logical growth curve. By calculating the model parameters, we obtained the maximum daily growth of height (PH), pileus diameter (PD), and cluster perimeter (CP) of L. decastes on the 5th, 7th, and 7th days, respectively, with values of 0.55 cm d−1, 0.54 cm d−1, and 4.54 cm d−1, respectively. However, the maximum daily growth of PH, pileus length (PL), and PD of the C. comatus appeared on the 3rd day, 2nd day, and 2nd day of the observation, respectively. This study identified near-surface relative humidity, air relative humidity, and rainfall as the primary factors influencing mushroom growth, as indicated by Pearson's correlation analysis, redundancy analysis (RDA), and multiple linear and stepwise regression. Additionally, land surface temperature and air temperature were also identified as important factors affecting mushroom growth. By utilizing random forest and stepwise regression analysis, this study identified PH and stipe diameter (SD) as the most crucial agronomic traits affecting mushroom biomass. Overall, this study offers insights for industrial mushroom cultivation and basic fungal research. [ABSTRACT FROM AUTHOR]

In this paper, based on the requirement of military integrated information systems and the characteristic of cloud computing, we design a model of platform. This model combines the cloud platform OpenStack, the large data processing framework Spark, in-memory-database and other latest tools, and then the ablity of high scalability and computing of the model is proved, by the testing of data service permornace and scalability. Expermimental results show that the model can meet the requriments of large scale data storage, processing and service in the future battlefield.

Forest litter is an important guarantee for maintaining forest soil fertility and circulating material in forest ecosystems. The input of litter plays an important role in soil organic matter formation and biogeochemical cycles in forest ecosystems. However, the production and elements concentrations of Juniperus przewalskii (JP) litter in the Qilian Mountains are still unknown. In this study, we investigated the production of needle, branch and bark, cone, and impurity litters. We determined the concentrations and fluxes of major (K, Mg, Al, and Fe) and trace (Na, Mn, Zn, Cr, Ni, Cu, Pb, Co, Cd, and Ag) elements in needle litter of JP from September 2020 to August 2021. The results showed that the annual litter production was 4040.74 ± 495.96 kg ha−1 a−1. Needle and cone litters were the main components of the total litter production. The major elements (MEs) and trace elements (TEs) fluxes of litter were consistent with the litter production trend. The concentrations and fluxes of MEs and TEs in needle litter decreased in the order: K > Mg > Al > Fe > Na > Mn > Zn > Cr > Ni > Cu > Pb > Co > Cd > Ag. These results have important implications for understanding the migration processes of MEs and TEs in forest ecosystems of the Qilian Mountains. [ABSTRACT FROM AUTHOR]

Due to climate change and human activities, the eco-environment quality (EEQ) of eco-fragile regions has undergone massive change, especially in the Tibet Plateau. The Qilian Mountains (QLM) region is an essential ecological function zone in the northeastern Tibet Plateau, which plays a vital role in northwestern China's eco-environmental balance. However, EEQ changes in the QLM during the 21st century remain poorly understood. In this study, the spatiotemporal variations of the EEQ in the QLM were analyzed from 2000 to 2020 using a remote sensing ecological index (RSEI). The EEQ driving factors are identified by the geographic detector, and the spatial influence of critical factors is represented by a geographically weighted regression model. The results show low EEQ in the QLM. From 2000 to 2020, the EEQ initially slightly improved, then deteriorated, and finally gradually recovered. Spatially, the EEQ shows an increasing trend from northwest to southeast. Moran's I of EEQ remains at around 0.95, representing high spatial aggregation. "High–High" and "Low–Low" clustering features dominate in the local spatial autocorrelation, indicating the EEQ of the QLM is polarized. Precipitation is the dominant positive factor in the EEQ, with a q statistics exceeding 0.644. Furthermore, the key factors (precipitation, distance to towns, distance to roads) affecting EEQ in different periods vary significantly in space. From results we can draw the conclusion that the natural factors mainly control the spatial patterns of EEQ, while the human factors mainly impact the temporal trend of EEQ, the EEQ in the QLM has been significantly improved since 2015. Our findings can provide theoretical support for future eco-environmental protection and restoration in the QLM. [ABSTRACT FROM AUTHOR]