Imposing primary colonisation success of wood-decomposing fungi in birch wood alters microbiome composition and carbon release rates
Data files
Aug 01, 2025 version files 30.93 KB
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density_ligninloss_ph_dryad.csv
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README.md
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sugars_dryad.csv
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Abstract
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.
Dataset DOI: 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