DOI: 10.5061/dryad.p8cz8wb3f
title: Imposing primary colonisation success of wood-decomposing fungi in birch wood alters microbiome composition and carbon release rates

description: Fungi dominate the decomposition of deadwood, with white rot-type species
 removing more lignin than brown rot-type species to gain access to wood
 carbohydrates. These fungi often compete to colonise the same tree
 species, and globally, a small shift in the success of either rot type
 could have massive greenhouse gas implications. For this reason,
 we need to know what controls white vs brown rot outcomes, starting with
 field studies that track fungi and the fate of wood lignin under
 real-world conditions. Such experiments, however, have had skewed,
 white-rot-only outcomes and have lacked representation of bacteria that
 compete for wood sugars. To address this, we pre-inoculated
 small-diameter birch Betula papyrifera stem sections with a brown rot
 fungus Fomitopsis betulina and compared these to non-inoculated birch in a
 treatment design that had been dominated by white rot fungi in past field
 studies. This approach encouraged more brown rot, widening the
 range of wood physicochemical outcomes (density, pH, lignin, and
 carbohydrate profiles). Achieving the brown rot outcome allowed
 us to better connect the fungal rot type as a trait to its functional
 outcomes. Specifically, it enabled us to test for definitive links between
 dominant fungi (identified via ITS2 amplicon sequencing), their C release
 patterns, and their bacterial associates (identified via 16S rRNAV4
 amplicon sequencing). We observed a clear link between rot type and fungal
 dominance but did not find parallel bacterial codominance. While the
 Shannon index for fungi was lower in brown-rotted wood than in
 white-rotted wood, the pattern was reversed for bacteria. Bacterial beta
 diversity, too, was different between brown- and white-rotted wood. This
 fungal influence on bacterial diversity may be due to increased niche
 space (i.e., higher availability of soluble sugars) for bacteria when
 brown rot fungi dominate. Collectively, achieving these more
 directed wood rot-type outcomes enabled us to clearly link fungal
 dominance to wood physicochemical changes, including C release, while also
 revealing a lack of relationship between dominant fungi and the success of
 associated bacteria.
description: # Imposing primary colonisation success of wood-decomposing fungi in birch
 wood alters microbiome composition and carbon release rates Dataset DOI:
 [10.5061/dryad.p8cz8wb3f](10.5061/dryad.p8cz8wb3f) ## Description of the
 data and file structure Density loss of wood post-decay was measured and
 lignin-loss-to-density-loss (L:D) ratio was calculated. pH of decayed and
 non-decayed (i.e., sound) wood was measured. Wood sugars (glucose, xylose,
 galactose, arabinose, and mannose) were quantified.  ### Files and
 variables #### File: sugars_dryad.csv **Description:** The quantification
 of sugars in wood using high-performance liquid chromatography #####
 Variables * sample (ID of each sample) * sugar (ID of sugar) *
 adjusted_percent_polysacc (percentage of structural carbohydrates in a
 given sample, adjusted for density loss) * soil (independent variable):
 whether or not the soil pads directly below each wood sample were
 sterilised in the autoclave at Gravity30, twice, with 48 h rest period  *
 wood (independent variable): whether or not the given wood sample
 was sterilised in the autoclave at Gravity30, twice, with 48 h rest period
 * inoculum (independent variable): whether or not fungal biomass
 of *Fomitopsis betulina* brown rot fungus was added to each wood sample
 #### File: density_ligninloss_ph_dryad.csv **Description:** Density loss,
 lignin-loss-to-density-loss ratio, and pH for each sample. ##### Variables
 * sample (ID of each sample) * soil (independent variable): whether or not
 the soil pads directly below each wood sample were sterilised in the
 autoclave at Gravity30, twice, with 48 h rest period  * wood (independent
 variable): whether or not the given wood sample was sterilised in the
 autoclave at Gravity30, twice, with 48 h rest period *
 inoculum (independent variable): whether or not fungal biomass
 of *Fomitopsis betulina* brown rot fungus was added to each wood sample *
 density_loss_percentage (calculated from density of the sample as fraction
 of density of sound wood) * lignin_loss_to_density_loss (ratio of lignin
 loss to density loss, relative to sound wood). Values of lignin loss to
 density loss ratio are "N/A" for 8 of the 72 total samples
 because those 8 samples are "time-zero"/"sound wood"
 samples. Their density loss is zero, because density loss for all other
 samples was calculated as a difference between density of each sample and
 the average density of the 8 "sound wood"/"time-zero"
 samples. The lignin loss values were also calculated as the difference
 between lignin amounts in each sample and the average of the 8 "sound
 wood"/"time-zero" samples. So if we were to calculate a
 value for each "time-zero" sample, it would be 0/0, which is
 undefined in mathematics. This makes sense, because we cannot get
 the lignin loss to density loss ratio for the 8 "time-zero"
 samples, as they would have to be calculated from their own values. * pH