DOI: 10.5061/dryad.tmpg4f5bs
title: Data from: Distinct diversity Trajectories of Boreal wood-inhabiting fungi following fire vs. clear-cutting

description: Here, we describe the data and methods supporting a study on how
 disturbances such as forest fires and clear-cutting influence
 wood-inhabiting fungal diversity trajectories in boreal Scots pine (Pinus
 sylvestris) forests in Northern Sweden. The research contrasts two
 chronosequences, one under rotational management (clear-cutting, soil
 scarification, and thinning) and one unmanaged and fire-origin. Two
 contrasting disturbance regimes – wildfire and clear-cutting – are common
 in boreal forests, and create fundamentally different conditions for
 succession of wood-inhabiting fungi. We investigated (i) how species
 richness and community composition change after these two disturbances,
 and (ii) which stand-level characteristics drive diversity trajectories.
 In two chronosequences – managed clear-cut (1–109yr since clear-cut; n=18)
 and unmanaged fire (4–375yr since fire; n=18) – we combined fruiting-body
 surveys with DNA metabarcoding to quantify species richness of
 wood-inhabiting fungi, including total number of species, Agaricomycete
 and red-listed species. To identify drivers, we measured deadwood
 attributes and forest structural complexity using terrestrial laser
 scanning. Species richness, including red-listed species, was highest in
 unmanaged fire stands. Unmanaged fire stands had ~55 more total species
 than managed clear-cut stands at comparable time since disturbance
 (≤109yr), and ~156 more species in stands &gt;109yr. Communities
 differed markedly between chronosequence types. Managed clear-cut stands
 harbored a subset of species found in unmanaged fire stands, and nearly
 all red-listed and indicator species were exclusive to unmanaged fire
 stands. Total and Agaricomycete species richness increased with time in
 both chronosequences without saturating. Red-listed species richness
 remained low and did not increase with time in managed clear-cut stands,
 but was higher and increased with time in unmanaged fire stands.
 Conditional random forest models identified spruce deadwood percentage,
 deadwood volume, and forest structural complexity as dominant diversity
 drivers, with deadwood quality replacing forest structure as the best
 predictor for red-listed species. Species richness rose steadily with
 deadwood volume, leveling at ~50m³ha⁻¹ for total species and
 &gt;100m³ha⁻¹ for red-listed species. Synthesis: Clear-cutting altered
 fungal recovery trajectories differently from fire. While fires leave
 standing and fallen dead trees that host fungal communities for centuries,
 clear-cutting removes these legacies and simplifies forest structure,
 resulting in a lack of recovery of red-listed species. These contrasting
 disturbance pathways shape boreal fungal communities through their effects
 on deadwood and structural continuity. Retaining high deadwood volumes and
 structural complexity can help maintain fungal diversity in managed
 forests, however maintaining old-growth stands is essential for conserving
 highly diverse communities and red-listed species.
description: # Data from: Distinct diversity Trajectories of Boreal wood-inhabiting
 fungi following fire vs. clear-cutting Dataset DOI:
 [10.5061/dryad.tmpg4f5bs](10.5061/dryad.tmpg4f5bs) ## Description of the
 data and file structure This dataset supports the research article
 "Distinct Diversity Trajectories of Boreal Wood-Inhabiting Fungi
 Following Fire vs. Clear-Cutting" submitted to *Journal of Ecology*.
 The study examines how two contrasting disturbance regimes - natural fire
 and rotational clear-cutting - create fundamentally different conditions
 for successional development of wood-inhabiting fungal communities in
 boreal Scots pine (Pinus sylvestris) forests in Northern Sweden
 (approximately 64.8-66.5 degrees N and 17.1-20.5 degrees E). The dataset
 contains comprehensive stand-level measurements from 36 forest stands
 representing different stages of forest development following disturbance:
 18 rotational management stands (1-109 years since clear-cut) subjected to
 typical even-aged forestry practices including clear-cutting, soil
 scarification, planting, and 2-3 thinning operations 18 unmanaged fire
 stands (4-375 years since fire) with varying fire severity from
 non-stand-replacing ground fires to stand-replacing wildfire. ### Files
 and variables #### FILES INCLUDED Stand_Level_Data.xlsx - Complete dataset
 with all stand-level variables and fungal species occurrences (37 rows
 including header x 2631 columns) Stand_Level_Data_Agaricomycetes.xlsx -
 Subplot-level dataset containing Agaricomycete fungi only (180 rows
 including header x 550 columns). Each row represents one subplot (5
 subplots per stand, 36 stands; one subplot excluded due to insufficient
 sequencing depth). Metadata columns differ from Stand_Level_Data.xlsx as
 described below. Script_1_-_Laser_scanning_indices.R - Processes
 terrestrial laser scanning (.laz) point clouds to calculate forest
 structural complexity metrics Script_2_-_Diversity_trajectories.R -
 Analyzes fungal species richness trajectories using negative binomial
 GAMs, compares detection methods, and performs post-hoc group comparisons
 Script_3_-_Community_analysis.R - Performs NMDS ordination of fungal
 communities, indicator species analysis, and environmental vector fitting
 Script_4_-_Random_forest_analysis.R - Identifies key habitat drivers of
 fungal diversity using conditional random forests with cross-validation
 Script_5_-_Threshold_analysis.R - Detects ecological thresholds in fungal
 richness responses to habitat gradients using GAMs with bootstrap
 confidence intervals \==================== #### VARIABLE DEFINITIONS FOR
 Stand_Level_Data.xlsx Empty cells indicate true absence of measurement or
 non-applicability (e.g. variable not relevant for a given stand), not zero
 values. STAND CHARACTERISTICS (Columns A-C) * Stand: Stand identifier
 (format: M[number][letter] for managed, F[number] for fire) - Management:
 Management type * M = Managed (rotational forestry with clear-cutting) * F
 = Fire (unmanaged, fire-origin) - Time: Years since disturbance event
 (clear-cut or fire) FUNGAL DIVERSITY METRICS (Columns D-H) * TotalSpecies:
 Total number of wood-inhabiting fungal species (DNA + fruiting-body
 combined) * TotalAgaricomycetes: Total number of Agaricomycete species
 (DNA + fruiting-body combined) * SpeciesDNA: Species detected through DNA
 metabarcoding * SpeciesSporocarp: Species detected through fruiting-body
 surveys * RedListedSpeciesDNA: Red-listed species detected through DNA
 metabarcoding * RedListedSpeciesSporocarp: Red-listed species detected
 through fruiting-body surveys * RedListedSpecies: Total red-listed species
 according to Swedish Red List (Artdatabanken, 2020) STAND AND SAMPLING
 VARIABLES (Columns I-J) * StandSize: Total stand area (hectares) *
 SeqDepth: DNA sequencing depth (number of high-quality reads after
 filtering) FOREST STRUCTURE METRICS (Columns K-P) * Derived from
 terrestrial laser scanning using handheld MLS (ZEB Horizon, GeoSLAM Ltd,
 UK): * CanopyRugosity: Surface roughness of the canopy calculated at 2m
 grid resolution (dimensionless, ranges ~0.4-3.7) * TopRugosity: Surface
 roughness of tree tops calculated at 2m grid resolution (dimensionless,
 ranges ~1.1-5.4) * DeepGapFraction: Proportion of 2m grid cells classified
 as deep canopy gaps, defined as cells where canopy point density falls
 below max(1, 5% of mean cell density) above 2m height (dimensionless,
 ranges ~0.03-0.70) * CoverFraction: Proportion of 2m grid cells with
 canopy cover, defined as cells with &gt;=30 return points above 2m height
 (dimensionless, ranges ~0.65-1.00) * VerticalGini: Gini coefficient of
 vertical vegetation distribution (ranges ~0.54-0.96, higher values
 indicate more uneven distribution) * BoxDimension: Fractal dimension of
 the three-dimensional point cloud estimated via box-counting voxelization
 (dimensionless, ranges ~2.10-2.18). DEADWOOD CHARACTERISTICS (Columns
 Q-AA) All measured within 17.8m radius plots (1,000 m2 area, 5 subplots
 per stand): * TotalDeadwoodVolume: Total deadwood volume calculated as
 truncated cones for logs and standing deadwood (m3/ha). Equivalent to
 DeadwoodQuantity; retained as the variable name referenced in Script 4. *
 ShareDeciduousDeadwood: Percentage of deadwood volume from deciduous
 species (Betula pendula, Populus tremula, Sorbus aucuparia, Salix spp.)
 (%) * ShareSpruceDeadwood: Percentage of deadwood volume from Norway
 spruce (Picea abies) (%) * MeanDecayClass: Mean decay class following
 Swedish National Forest Inventory protocol (1-5 scale; 1=fresh wood,
 5=highly decomposed) * MeanDiameter: Mean diameter of deadwood items
 measured at both ends (cm) * LateDecay: Proportion of deadwood items in
 decay classes 3-5 (%) * ProportionLargeLogs: Proportion of logs with
 diameter &gt;=30cm (%) * ProportionStanding: Proportion of standing
 deadwood (snags and stumps) versus lying logs (%) * DeadwoodDiversity:
 Shannon diversity index calculated from deadwood categories (tree species
 x size class x decay stage) * DeadwoodQuantity: Total deadwood volume
 calculated as truncated cones for both logs and standing deadwood (m3/ha)
 * DeadwoodQuality: First principal component (PC1) from PCA of
 MeanDiameter, ProportionLargeLogs, and LateDecay (explains 63.4% variance;
 higher values indicate larger, more decayed logs) FUNGAL SPECIES
 OCCURRENCE DATA (Columns AB onwards) * Column headers: Species names
 following Swedish Taxonomic Database (Artdatabanken, 2025) nomenclature *
 Values for DNA-detected species: Number of sequence reads after quality
 filtering and rarefaction * Values for fruiting-body detected species:
 Presence (&gt;0) or absence (0) * Species naming format:
 Genus_species_scata[number] for DNA sequences, Genus_species for fruiting
 bodies SUMMARY COLUMNS (after species occurrence data) * SpeciesDNA: Total
 species detected through DNA metabarcoding * SpeciesSporocarp: Total
 species detected through fruiting-body surveys \==================== ####
 VARIABLE DEFINITIONS FOR Stand_Level_Data_Agaricomycetes.xlsx This file is
 organized at the subplot level. Variable definitions are the same as for
 Stand_Level_Data.xlsx except for the following: * Stand: Stand identifier
 (same as above) * Subplot: Subplot identifier within each stand (A-E) *
 Management: Management type (same as above) * Time: Years since
 disturbance event (same as above) * TotalSpecies: Total number of
 wood-inhabiting fungal species per subplot * TotalAgaricomycetes: Total
 number of Agaricomycete species per subplot * RedListedSpeciesDNA:
 Red-listed species detected through DNA metabarcoding per subplot *
 RedListedSpeciesSporocarp: Red-listed species detected through
 fruiting-body surveys per subplot * RedListedSpecies: Total red-listed
 species per subplot * SeqDepth_subplot: DNA sequencing depth per subplot
 (number of high-quality reads after filtering) * Forest structure metrics
 (CanopyRugosity, TopRugosity, DeepGapFraction, CoverFraction,
 VerticalGini, BoxDimension) are at the stand level and repeated across
 subplots within a stand. * Species occurrence columns contain
 Agaricomycete fungi only, with the same naming conventions as
 Stand_Level_Data.xlsx. * DeadwoodDiversity, DeadwoodQuantity,
 DeadwoodQuality, SpeciesDNA, and SpeciesSporocarp appear after the species
 occurrence columns. \==================== ## DETAILED METHODS FIELD
 SAMPLING DESIGN Sampling period: September 2023 Stand selection: Based on
 established chronosequence from Buness et al. (2025) Plot configuration: 5
 subplots per stand arranged as 1 central subplot plus 4 subplots at 35m
 distance in intercardinal directions (NE, SE, SW, NW) Deadwood assessment
 area: 17.8m radius circular plots (1,000 m2 per subplot, 5,000 m2 total
 per stand) DNA sampling area: 10m radius circular plots (314 m2 per
 subplot) nested within deadwood plots Deadwood inclusion criteria: Minimum
 10cm diameter at larger end and minimum 1m length Deadwood volume
 calculation: Truncated cone formula for all deadwood pieces: V = (pi x
 L)/12(D1^2 + D1D2 + D2^2) DNA METABARCODING PROTOCOL Sample collection:
 Wood cores drilled from deadwood using 30cm increment drill (8mm diameter)
 along 8 transects per subplot Drilling pattern: Three samples per log
 (0.5m left, center, 0.5m right of transect); for snags/stumps at base,
 0.5m, and 1m height Sample processing: Pooled by subplot (180 composite
 samples total), freeze-dried, ball-milled DNA extraction: NucleoSpin Soil
 kit with SL1 buffer + enhancer, 200mg material PCR amplification: ITS2-LSU
 region using primers gITS7 and TW13 Sequencing: PacBio Revio Platform (25M
 SMRT cell) at SciLifeLab NGI Uppsala Bioinformatics: SCATA pipeline with
 99.25% similarity clustering threshold Quality filtering: Mean Q &gt;= 20,
 removal of chimeras and singletons using MUMU Rarefaction: 2,000 reads per
 sample for community analyses Final dataset: 1,800 fungal species
 hypotheses after quality control FRUITING-BODY SURVEY PROTOCOL Target
 taxa: All polypore species plus tooth fungi genera Odonticium and
 Asterodon Survey effort: ~30 minutes per subplot by two experienced
 surveyors Identification: Field identification when possible, microscopic
 verification using Melzer's reagent, 3-10% KOH, iron sulfate, and
 cotton blue at 400-1000x magnification References: Ryvarden et al. (2017),
 Christensen & Heilmann-Clausen (2013), Laessoe & Petersen (2019),
 Larsson & Ryvarden (2021) External validation: ~20 specimens verified
 by taxonomic experts Total detected: 60 species by fruiting-body surveys,
 19 species detected by both methods TERRESTRIAL LASER SCANNING Equipment:
 Handheld mobile laser scanner (MLS, ZEB Horizon, GeoSLAM Ltd, UK) Scanning
 pattern: Circular paths at 12m, 24m, and 35m radii from stand center Point
 cloud processing: 1cm voxelization, 5cm DTM resolution, height
 normalization Structural metrics: Calculated using forestr R package
 following Atkins et al. (2018) Original data: Raw .laz point cloud files
 available at
 [https://doi.org/10.5061/dryad.2547d7x1f](https://doi.org/10.5061/dryad.2547d7x1f). Raw .laz point cloud files can be viewed using free software such as CloudCompare or LAStools (lasview). Processing infrastructure: National Academic Infrastructure for Supercomputing in Sweden (NAISS) \==================== ## Code/software STATISTICAL ANALYSES OVERVIEW SCRIPT WORKFLOW Script 1: Processes raw laser scanning data (.laz files) to derive structural complexity metrics. Requires substantial computational resources (NAISS infrastructure recommended) Script 2: Models species richness trajectories using GAMs, compares management types and successional stages, analyzes detection method performance Script 3: Analyzes community composition patterns using NMDS ordination and identifies environmental drivers through vector fitting Script 4: Identifies key habitat predictors using conditional random forests with 50 x 10-fold cross-validation Script 5: Determines ecological thresholds for management targets using GAM derivatives with bootstrap confidence intervals KEY STATISTICAL APPROACHES GAMs: Negative binomial family with sequencing depth offset for modeling richness trajectories NMDS: Jaccard dissimilarity for community ordination (stress = 0.194) Conditional Random Forests: 500 trees per model, variable importance assessed through permutation Indicator Species Analysis: IndVal.g function with 999 permutations for six successional classes Threshold Detection: First derivatives of GAM smooths with parametric bootstrap (200 iterations) for uncertainty \==================== SOFTWARE REQUIREMENTS R: Version 4.3.0 or higher Essential R packages: Core: tidyverse (v2.0.0+), readxl, writexl Statistical modeling: mgcv (GAMs), vegan (ordination), party (random forests), multcomp (post-hoc tests) Visualization: ggplot2, patchwork, gridExtra, cowplot, ggsignif LiDAR processing: lidR, raster, data.table, stringr Additional: indicspecies (indicator analysis), broom (model tidying), MuMIn (model selection) \==================== USAGE INSTRUCTIONS File paths: Update all paths marked with "/.../" in scripts to match your directory structure Execution order: Run scripts in numerical sequence (1-5) for complete reproduction Script 1 requirements: Access to high-performance computing infrastructure for processing .laz files Scripts 2-5: Can be run on standard desktop computers Output: Scripts generate figures and supplementary tables as described in the manuscript \==================== DATA COMPLEMENTARITY The DNA metabarcoding and fruiting-body survey methods are complementary: DNA metabarcoding detected 1,800 species (primarily capturing cryptic diversity) Fruiting-body surveys detected 60 species (excelling at red-listed species detection) Only 19 species were detected by both methods, highlighting the importance of dual approaches \==================== REFERENCES Artdatabanken (2020) Rodlista 2020: Svampar (Fungi). SLU Artdatabanken. [https://www.slu.se/globalassets/ew/subw/artd/6-publikationer/31.-rodlista-2020/rodlista-2020.pdf](https://www.slu.se/globalassets/ew/subw/artd/6-publikationer/31.-rodlista-2020/rodlista-2020.pdf) Artdatabanken (2025) Swedish Taxonomic Database. Swedish University of Agricultural Sciences. [https://www.artdatabanken.se](https://www.artdatabanken.se) Atkins JW, Bohrer G, Fahey RT, Hardiman BS, Morin TH, Stovall AEL, Zimmerman N, Gough CM (2018) Quantifying vegetation and canopy structural complexity from terrestrial LiDAR data using the forestr r package. Methods in Ecology and Evolution 9:2057-2066 Buness V, Sundqvist MK, Ali ST, Annighofer P, Aragon CM, Lanzrein I, Metcalfe DB, Nilsson M-C, Gundale MJ (2025) Resource quantity and heterogeneity drive successional plant diversity in managed and unmanaged boreal forests. Ecography e07676 Christensen M, Heilmann-Clausen J (2013) The genus Tricholoma. Danish Mycological Society Laessoe T, Petersen JH (2019) Fungi of temperate Europe. Princeton University Press Larsson K-H, Ryvarden L (2021) Corticioid fungi of Europe. Volume 1: Acanthobasidium - Gyrodontium. FUNGIFLORA Ryvarden L, Melo I, Niemela T (2017) Poroid fungi of Europe (Second revised edition). Fungiflora Swedish University of Agricultural Sciences (2023) Faltinstruktion 2023: Riksinventeringen av skog. Institutionen for skoglig resurshushallning, SLU \==================== FUNDING This study was supported by a grant from FORMAS (Swedish Research Council for Sustainable Development; Project Formas grant no. 2021-02116) awarded to MJG. \==================== CONTACT INFORMATION Corresponding authors: Vincent Buness ([vincent.buness@slu.se](mailto:vincent.buness@slu.se)) Michael J. Gundale ([michael.gundale@slu.se](mailto:michael.gundale@slu.se)) \==================== LICENSE This dataset is made available under CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. \==================== ACKNOWLEDGMENTS We thank Sveaskog AB for providing access to forest stands and their database. We extend our gratitude to Länsstyrelsen for allowing us to conduct research in protected areas. We acknowledge Morgan Karlsson, Per Schönning, and Carlos Miguel Aragon for their assistance with fieldwork, and Ilse van Duuren and Yasaman Najafi for laboratory work. The Centre for Statistics (SLU), particularly Magnus Ekström, provided valuable statistical advice. We also thank Mikael Brandström Durling for establishing and maintaining the bioinformatics pipeline. The data handling and processing was enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council through grant agreement no. 2022-06725. The authors declare no conflicts of interest.