The community composition variation of Russulaceae associated with the Quercus mongolica forest during the growing season at Wudalianchi City, China

Abstract

Background Most species of the Russulaceae are ectomycorrhizal (ECM) fungi, which are widely distributed in different types of forest ecology and drive important ecological and economic functions. Little is known about the composition variation of the Russulaceae fungal community aboveground and in the root and soil during the growing season (June–October) from a Quercus mongolica forest. In this study, we investigated the changes in the composition of the Russulaceae during the growing season of this type of forest in Wudalianchi City, China. Methods To achieve this, the Sanger sequencing method was used to identify the Russulaceae aboveground, and the high-throughput sequencing method was used to analyze the species composition of the Russulaceae in the root and soil. Moreover, we used the Pearson correlation analysis, the redundancy analysis and the multivariate linear regression analysis to analyze which factors significantly affected the composition and distribution of the Russulaceae fungal community. Results A total of 56 species of Russulaceae were detected in the Q. mongolica forest, which included 48 species of Russula, seven species of Lactarius, and one species of Lactifluus. Russula was the dominant group. During the growing season, the sporocarps of Russula appeared earlier than those of Lactarius. The number of species aboveground exhibited a decrease after the increase and were significantly affected by the average monthly air temperature (r = −0.822, p = 0.045), average monthly relative humidity (r = −0.826, p = 0.043), monthly rainfall (r = 0.850, p = 0.032), soil moisture (r = 0.841, p = 0.036) and soil organic matter (r = 0.911, p = 0.012). In the roots and soils under the Q. mongolica forest, the number of species did not show an apparent trend. The number of species from the roots was the largest in September and the lowest in August, while those from the soils were the largest in October and the lowest in June. Both were significantly affected by the average monthly air temperature (r2 = 0.6083, p = 0.040) and monthly rainfall (r2 = 0.6354, p = 0.039). Moreover, the relative abundance of Russula and Lactarius in the roots and soils showed a linear correlation with the relative abundance of the other fungal genera.

Fungal–Bacterial Networks in the Habitat of SongRong (Tricholoma matsutake) and Driving Factors of Their Distribution Rules

“…We marked five quadrants measuring 20 m × 20 m within a space of more than 100 m. The specimens were sampled by a five-point sampling method [ 11 ] in June–July and August–September (the fruiting season of T. matsutake ). All soil subsamples from the same plot and same sampling time were homogenized and pooled to obtain a composite sample, which was placed into a cooler containing dry ice and transported to the laboratory.…”

“…We know that various abiotic and biotic factors are important factors that influence fungi or other microbial structures and their distribution in forest ecosystems [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. The distribution of T. matsutake ’s fruiting bodies was closely related to their symbiotic partners, and Pinaceae are believed to be the main host of T. matsutake .…”

“…Despite the complexity of the food web of soil microorganisms, some correlation results have been reported as technology has improved. For example, analyses of the linear regression equation showed that the relative abundance of Russula was correlated with Lachnum (most species of which are saprophytic fungi) and Ilyonectria (some species of which can cause root rot disease) in a pure Q. mongolica forest [ 11 ]. In addition, the relative abundance of Suillus was shown to have certain relations with Umbelopsis in a Korean pine forest [ 12 ].…”

Response of bacterial community structure to different ecological niches and their functions in Korean pine forests

“…At the other collection sites, we designed four plots (20 m × 20 m, including more than five Korean pine trees) in different locations. We selected five trees from each plot and collected the fine root segments (approximately 20–50 g) at four locations that were about 1 m from the trunk of tree ( Xing et al, 2020 ) until we had 80 subsamples from the same collected site that we pooled into one sample. Finally, we had 10 root tip and 10 rhizosphere soil composite samples from the different sampling sites or at different sampling times for further processing.…”

Exploring the Relationships between Macrofungi Diversity and Major Environmental Factors in Wunvfeng National Forest Park in Northeast China

“…After removing impurities, we enclosed the fresh samples (20 g) from each clod in an aluminum box. We dried the samples to a constant weight in an oven at 105 • C to measure water content (SWC); we used natural air-dried composite samples (200 g) for each plot to analyze for pH, organic matter (SOM), available phosphorus (P), effective nitrogen (N), and available potassium (K) using the method described by Xing et al [39]. Finally, we averaged the data from three plots of the five fore for subsequent analysis.…”