Fungal community assemblages in a high elevation desert environment: absence of dispersal limitation and edaphic effects in surface soil

Recent studies have shown the significant effects of environmental selection and possible dispersal limitation on soil fungal communities. However, less is known about the role of soil depth in fungal community assemblages, especially under soil environments that are intensely cold, infertile and water-deficient. In Ngari drylands of the Asiatic Plateau, we studied fungal assemblages at two soil depths, using Illumina sequencing of the ITS2 region for fungal identification (0–15 cm as the surface soil and 15–30 cm as the subsurface soil). Fungal diversity in the surface soil was much higher than that in the subsurface soil (P < 0.001), and communities differed significantly between the two layers (P = 0.001). Neither soil properties nor dispersal limitation could explain variation in the surface-soil fungal community. For the subsurface, by contrast, soil, climate and space explained 27% of variation in fungal community. Collectively, these results point to high dispersal rates and absence of edaphic effects in the surface-soil fungal community assemblage in Ngari drylands. It also suggests that for soil fungi with highly effective dispersal, regional distributions may fit with Bass-Becking's paradigm that ‘Everything is everywhere’

Age-associated microbiome shows the giant panda lives on hemicelluloses, not on cellulose

The giant panda feeds almost exclusively on bamboo, a diet highly enriched in lignin and cellulose, but is characterized by a digestive tract similar to carnivores. It is still large unknown if and how the giant panda gut microbiota contributes to lignin and cellulose degradation. Here we show the giant pandas’ gut microbiota does not significantly contribute to cellulose and lignin degradation. We found that no operational taxonomic unit had a nearest neighbor identified as a cellulolytic species or strain with a significant higher abundance in juvenile than cubs, a very low abundance of putative lignin and cellulose genes existed in part of analyzing samples but a significant higher abundance of genes involved in starch and hemicellulose degradation in juveniles than cubs. Moreover, a significant lower abundance of putative cellulolytic genes and a significant higher abundance of putative α-amylase and hemicellulase gene families were present in giant pandas than in omnivores or herbivores

Fungal Assemblages in Different Habitats in an Erman’s Birch Forest

Recent meta-analyses of fungal diversity using deeply sequenced marker genes suggest that most fungal taxa are locally distributed. However, little is known about the extent of overlap and niche partitions in total fungal communities or functional guilds within distinct habitats on a local forest scale. Here, we compared fungal communities in endosphere (leaf interior), phyllosphere (leaf interior and associated surface area) and soil samples from an Erman’s birch forest in Changbai Mountain, China. Community structures were significantly differentiated in terms of habitat, with soil having the highest fungal richness and phylogenetic diversity. Endophytic and phyllosphere fungi of Betula ermanii were more phylogenetically clustered compared with the corresponding soil fungi, indicating the ability of that host plants to filter and select their fungal partners. Furthermore, the majority of soil fungal taxa were soil specialists, while the dominant endosphere and phyllosphere taxa were aboveground generalists, with soil and plant foliage only sharing <8.2% fungal taxa. Most of the fungal taxa could be assigned to different functional guilds; however, the assigned guilds showed significant habitat specificity with variation in relative abundance. Collectively, the fungal assemblages in this Erman's birch forest were strictly niche specialized and constrained by weak migration among habitats. The findings suggest that phylogenetic relatedness and functional guilds’ assignment can effectively interpret the certain ecological processes

An adaptive segmentation method combining MSRCR and mean shift algorithm with K-means correction of green apples in natural environment

During the recognition and localization process of green apple targets, problems such as uneven illumination, occlusion of branches and leaves need to be solved. In this study, the multi-scale Retinex with color restoration (MSRCR) algorithm was applied to enhance the original green apple images captured in an orchard environment, aiming to minimize the impacts of varying light conditions. The enhanced images were then explicitly segmented using the mean shift algorithm, leading to a consistent gray value of the internal pixels in an independent fruit. After that, the fuzzy attention based on information maximization algorithm (FAIM) was developed to detect the incomplete growth position and realize threshold segmentation. Finally, the poorly segmented images were corrected using the K-means algorithm according to the shape, color and texture features. The users intuitively acquire the minimum enclosing rectangle localization results on a PC. A total of 500 green apple images were tested in this study. Compared with the manifold ranking algorithm, the K-means clustering algorithm and the traditional mean shift algorithm, the segmentation accuracy of the proposed method was 86.67%, which was 13.32%, 19.82% and 9.23% higher than that of the other three algorithms, respectively. Additionally, the false positive and false negative errors were 0.58% and 11.64%, respectively, which were all lower than the other three compared algorithms. The proposed method accurately recognized the green apples under complex illumination conditions and growth environments. Additionally, it provided effective references for intelligent growth monitoring and yield estimation of fruits. Keywords: Green fruit, Adaptive segmentation, MSRCR algorithm, Mean shift algorithm, K-means clustering algorithm, Manifold ranking algorith