Estimation of Fungal Diversity and Identification of Major Abiotic Drivers Influencing Fungal Richness and Communities in Northern Temperate and Boreal Quebec Forests

Abstract

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.

Boreal Forest Multifunctionality Is Promoted by Low Soil Organic Matter Content and High Regional Bacterial Biodiversity in Northeastern Canada

“…Mill.). The 156 polygons represent the boreal forest subset of the polygons used by Laperriere et al (2019) [28].…”

“…We used air-dried soil to measure the following soil characteristics: water holding capacity (WHC) (adapted from [29]), organic matter concentration (Mo) by loss on ignition (adapted from [30]), soil texture (Fritsch Analyzer 22 microtech plus), and pH and Eh using a platinum probe (5 × 5 mm) and an Ag/AgCl electrode coupled to a voltmeter (based on [31], refer to [28]). Soil porosity and water-filled pore space (WFP) were calculated using WHC and bulk density [29,32].…”

“…For fungal diversity, extractions were amplified by PCR using the primer pair fITS7-ITS4. Each PCR was done at three different temperatures (48,51, and 58 • C) to allow the primers to combine with as many species as possible (see [28]). For bacterial diversity, the same DNA extractions were amplified by PCR at a single temperature of 48 • C with the 16S specific 515F-805R primer pair with a CS1 (ACACTGACGACATGGTTCTACAGTGCCA GCMGCCGCGG) and CS2 (TACGGTAGCAGAGACTTGGTCTGACTACHVGGGTATCTAATCC) tag added at the final 5' extremities [39].…”

“…In order to calculate the microbial diversity index, taxonomy was obtained by comparing our DNA sequences to those of public databases. For fungi, bioinformatics was performed with Vsearch (see [28]), while for bacteria, it was done using the DADA2 package on R software (version 3.5.1). Specifically, DADA2 was used as a pipeline to cut and filter DNA sequences.…”

“…The merging of forward and reverse sequences took place in Galaxy [40]. The combined DNA sequences were then downloaded and manipulated again in R to perform dereplication, remove chimeras, and assign taxonomy (see [28] for details). The public databases used were utax_reference (UNITE datasets v.7.2) and SILVA (SILVA_132_SSURef_tax_silva) for fungi and bacteria, respectively.…”