Fungi play important roles in forest ecosystems and understanding fungal diversity is crucial to address essential questions about species conservation and ecosystems management. Changes in fungal diversity can have severe impacts on ecosystem functionality. Unfortunately, little is known about fungal diversity in northern temperate and boreal forests, and we have yet to understand how abiotic variables shape fungal richness and composition. Our objectives were to make an overview of the fungal richness and the community composition in the region and identify their major abiotic drivers. We sampled 262 stands across the northern temperate and boreal Quebec forest located in the region of Abitibi-Témiscamingue, Mauricie, and Haute-Mauricie. At each site, we characterized fungal composition using Illumina sequencing, as well as several potential abiotic drivers (e.g., humus thickness, soil pH, vegetation cover, etc.). We tested effects of abiotic drivers on species richness using generalized linear models, while difference in fungal composition between stands was analyzed with permutational multivariate analysis of variance and beta-diversity partitioning analyses. Fungi from the order Agaricales, Helotiales, and Russulales were the most frequent and sites from the north of Abitibi-Témiscamingue showed the highest OTUs (Operational Taxonomic Unit) richness. Stand age and moss cover were the best predictors of fungal richness. On the other hand, the strongest drivers of fungal community structure were soil pH, average cumulative precipitation, and stand age, although much of community variance was left unexplained in our models. Overall, our regional metacommunity was characterized by high turnover rate, even when rare OTUs were removed. This may indicate strong environmental filtering by several unmeasured abiotic filters, or stronger than expected dispersal limitations in soil fungal communities. Our results show how difficult it can be to predict fungal community assembly even with high replication and efforts to include several biologically relevant explanatory variables.
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