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Tree bark constitutes an ideal habitat for microbial communities, because it is a stable substrate, rich in micro-niches. Bacteria, fungi, and terrestrial microalgae together form microbial communities, which in turn support more bark-associated organisms, such as mosses, lichens, and invertebrates, thus contributing to forest biodiversity. We have a limited understanding of the diversity and biotic interactions of the bark-associated microbiome, as investigations have mainly focused on agriculturally relevant systems and on single taxonomic groups. Here we implemented a multi-kingdom metabarcoding approach to analyze diversity and community structure of the green algal, bacterial, and fungal components of the bark-associated microbial communities of beech, the most common broadleaved tree of Central European forests. We identified the most abundant taxa, hub taxa, and co-occurring taxa. We found that tree size (as a proxy for age) is an important driver of community assembly, suggesting that environmental filtering leads to less diverse fungal and algal communities over time. Conversely, forest management intensity had negligible effects on microbial communities on bark. Our study suggests the presence of undescribed, yet ecologically meaningful taxa, especially in the fungi, and highlights the importance of bark surfaces as a reservoir of microbial diversity. Our results constitute a first, essential step toward an integrated framework for understanding microbial community assembly processes on bark surfaces, an understudied habitat and neglected component of terrestrial biodiversity. Finally, we propose a cost-effective sampling strategy to study bark-associated microbial communities across large spatial or environmental scales.
Questions: Habitat islands are often characterized by the presence of more or less sharp boundaries to adjacent matrix habitats. However, knowledge on boundaries of natural habitat islands is scarce, especially regarding patterns of beta diversity and its two underlying components: species turnover and nestedness. We therefore aim to quantify the effects of fine-scaled and sharp boundaries of quartz islands (quartz gravel-covered soils) on the different components of plant beta diversity and how they are linked to different soil environmental drivers. Location: Knersvlakte, Western Cape, South Africa. Methods: We sampled plant species richness in 56 fine-scale transects of 6 m × 1 m plots across eight different boundary types (four quartz island to matrix, four between habitats on quartz islands). Soil depth and chemistry (pH, electrical conductivity) were analyzed for each 1 m2 plot. Differences in the two beta diversity components (turnover and nestedness) for each boundary type were tested by t tests. We used linear models to test relationships between species and environmental dissimilarity. Results: All boundary types showed high beta diversity. Species turnover was the prevailing component for six boundary types, the nestedness component was only important for two boundary types. We found a significant linear increase of species dissimilarity with increasing dissimilarity in soil pH and distinct plant communities for the habitat types, but no significant increase for electrical conductivity or soil depth. Conclusions: The spatial distinctiveness of the quartz islands leads to sharp boundaries, which result in high beta diversity, mainly through species turnover. This reflects the high levels of diversification and adaptation of the local plant communities. Nestedness occurred at two boundaries to the matrix, indicating that the latter does not necessarily represent an impermeable boundary for all species of the respective ecosystem. Studying diversity patterns across boundaries contributes to the question of applicability of island biogeography theory to habitat islands.
High-throughput metabarcoding studies on fungi and other eukaryotic microorganisms are rapidly becoming more frequent and more complex, requiring researchers to handle ever increasing amounts of raw sequence data. Here, we provide a flexible pipeline for pruning and analyzing fungal barcode (ITS rDNA) data generated as paired-end reads on Illumina MiSeq sequencers. The pipeline presented includes specific steps fine-tuned for ITS, that are mostly missing from pipelines developed for prokaryotes. It (1) employs state of the art programs and follows best practices in fungal high-throughput metabarcoding; (2) consists of modules and scripts easily modifiable by the user to ensure maximum flexibility with regard to specific needs of a project or future methodological developments; and (3) is straightforward to use, also in classroom settings. We provide detailed descriptions and revision techniques for each step, thus giving the user maximum control over data treatment and avoiding a black-box approach. Employing this pipeline will improve and speed up the tedious and error-prone process of cleaning fungal Illumina metabarcoding data.