Luyssaert, S. et al. Old-growth forests as global carbon sinks. Nature 455, 213–215 (2008).
Google Scholar
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).
Google Scholar
Rinne-Garmston, K. T. et al. Carbon flux from decomposing wood and its dependency on temperature, wood N2 fixation rate, moisture and fungal composition in a Norway spruce forest. Glob. Chang. Biol. 25, 1852–1867 (2019).
Google Scholar
Šamonil, P. et al. Convergence, divergence or chaos? Consequences of tree trunk decay for pedogenesis and the soil microbiome in a temperate natural forest. Geoderma 376, 114499 (2020).
Google Scholar
Tláskal, V. et al. Complementary roles of wood-inhabiting fungi and bacteria facilitate deadwood decomposition. mSystems 6, e01078–20 (2021).
Google Scholar
Odriozola, I. et al. Fungal communities are important determinants of bacterial community composition in deadwood. mSystems 6, e01017–20 (2021).
Google Scholar
Valášková, V., de Boer, W., Gunnewiek, P. J. A. K., Pospíšek, M. & Baldrian, P. Phylogenetic composition and properties of bacteria coexisting with the fungus Hypholoma fasciculare in decaying wood. ISME J. 3, 1218–1221 (2009).
Google Scholar
Brunner, A. & Kimmins, J. P. Nitrogen fixation in coarse woody debris of Thuja plicata and Tsuga heterophylla forests on northern Vancouver Island. Can. J. For. Res. 33, 1670–1682 (2003).
Google Scholar
Rinne, K. T. et al. Accumulation rates and sources of external nitrogen in decaying wood in a Norway spruce dominated forest. Funct. Ecol. 31, 530–541 (2016).
Google Scholar
Põlme, S. et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105, 1–16 (2020).
Google Scholar
Tláskal, V. & Baldrian, P. Deadwood-inhabiting bacteria show adaptations to changing carbon and nitrogen availability during decomposition. Front. Microbiol. 12, 685303 (2021).
Google Scholar
Lemos, L. N., Mendes, L. W., Baldrian, P. & Pylro, V. S. Genome-resolved metagenomics is essential for unlocking the microbial black box of the soil. Trends Microbiol. 29, 279–282 (2021).
Google Scholar
Větrovský, T. et al. GlobalFungi, a global database of fungal occurrences from high-throughput-sequencing metabarcoding studies. Sci. Data 7, 228 (2020).
Google Scholar
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).
Google Scholar
Anderson-Teixeira, K. J., Davies, S. J., Bennett, A. C., Muller-landau, H. C. & Wright, S. J. CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change. Glob. Chang. Biol. 21, 528–549 (2015).
Google Scholar
Baldrian, P. et al. Fungi associated with decomposing deadwood in a natural beech-dominated forest. Fungal Ecol. 23, 109–122 (2016).
Google Scholar
Smyth, C. E. et al. Patterns of carbon, nitrogen and phosphorus dynamics in decomposing wood blocks in Canadian forests. Plant Soil 9, 46–62 (2016).
Král, K. et al. Local variability of stand structural features in beech dominated natural forests of Central Europe: Implications for sampling. For. Ecol. Manage. 260, 2196–2203 (2010).
Google Scholar
Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).
Google Scholar
Lanzén, A. et al. CREST – Classification resources for environmental sequence tags. PLoS One 7, e49334 (2012).
Google Scholar
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
Google Scholar
Žifčáková, L., Větrovský, T., Howe, A. & Baldrian, P. Microbial activity in forest soil reflects the changes in ecosystem properties between summer and winter. Environ. Microbiol. 18, 288–301 (2016).
Google Scholar
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Google Scholar
Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).
Google Scholar
Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).
Google Scholar
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
Google Scholar
Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).
Google Scholar
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).
Google Scholar
Lee, M. D. GToTree: A user-friendly workflow for phylogenomics. Bioinformatics 35, 4162–4164 (2019).
Google Scholar
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).
Google Scholar
Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).
Google Scholar
Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
Google Scholar
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: A tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).
Google Scholar
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS One 5, e9490 (2010).
Google Scholar
Ihrmark, K. et al. New primers to amplify the fungal ITS2 region – evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677 (2012).
Google Scholar
Větrovský, T., Baldrian, P. & Morais, D. SEED 2: A user-friendly platform for amplicon high-throughput sequencing data analyses. Bioinformatics 34, 2292–2294 (2018).
Google Scholar
Aronesty, E. Comparison of sequencing utility programs. Open Bioinforma. J. 7, 1–8 (2013).
Google Scholar
Nilsson, R. H. et al. An open source software package for automated extraction of ITS1 and ITS2 from fungal ITS sequences for use in high-throughput community assays and molecular ecology. Fungal Ecol. 3, 284–287 (2010).
Google Scholar
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
Google Scholar
Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).
Google Scholar
Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classification. Nucleic Acids Res. 47, D259–D264 (2018).
Google Scholar
Wright, E. S. Using DECIPHER v2.0 to analyze big biological sequence data in R. R J. 8, 352–359 (2016).
Google Scholar
Murali, A., Bhargava, A. & Wright, E. S. IDTAXA: A novel approach for accurate taxonomic classification of microbiome sequences. Microbiome 6, 140 (2018).
Google Scholar
NCBI BioProject https://identifiers.org/ncbi/bioproject:PRJNA603240 (2020).
NCBI Sequence Read Archive, https://identifiers.org/ncbi/bioproject:PRJNA672674 (2020).
Sutela, S., Poimala, A. & Vainio, E. J. Viruses of fungi and oomycetes in the soil environment. FEMS Microbiol. Ecol. 95, fiz119 (2019).
Google Scholar
Woodcroft, B. J. et al. Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018).
Google Scholar
Mackelprang, R. et al. Microbial community structure and functional potential in cultivated and native tallgrass prairie soils of the Midwestern United States. Front. Microbiol. 9, 1775 (2018).
Google Scholar
Hervé, V. et al. Phylogenomic analysis of 589 metagenome-assembled genomes encompassing all major prokaryotic lineages from the gut of higher termites. PeerJ 8, e8614 (2020).
Google Scholar
Clissmann, F. et al. First insight into dead wood protistan diversity: a molecular sampling of bright-spored Myxomycetes (Amoebozoa, slime-moulds) in decaying beech logs. FEMS Microbiol. Ecol. 91, fiv050 (2015).
Google Scholar
Urich, T. et al. Simultaneous assessment of soil microbial community structure and function through analysis of the meta-transcriptome. PLoS One 3, e2527 (2008).
Google Scholar
Geisen, S. et al. Metatranscriptomic census of active protists in soils. ISME J. 9, 2178–2190 (2015).
Google Scholar
Tláskal, V., Zrůstová, P., Vrška, T. & Baldrian, P. Bacteria associated with decomposing dead wood in a natural temperate forest. FEMS Microbiol. Ecol. 93, fix157 (2017).
Google Scholar
Moll, J. et al. Bacteria inhabiting deadwood of 13 tree species reveal great heterogeneous distribution between sapwood and heartwood. Environ. Microbiol. 20, 3744–3756 (2018).
Google Scholar
Christofides, S. R., Hiscox, J., Savoury, M., Boddy, L. & Weightman, A. J. Fungal control of early-stage bacterial community development in decomposing wood. Fungal Ecol. 42, 100868 (2019).
Google Scholar
Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509 (2021).
Google Scholar
Seibold, S. et al. Experimental studies of dead-wood biodiversity — A review identifying global gaps in knowledge. Biol. Conserv. 191, 139–149 (2015).
Google Scholar
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