Fungal-bacterial interactions play a key role in the functioning of many ecosystems. Thus, understanding their interactive dynamics is of central importance for gaining predictive knowledge on ecosystem functioning. However, it is challenging to disentangle the mechanisms behind species associations from observed co-occurrence patterns, and little is known about the directionality of such interactions. Here, we applied joint species distribution modeling to high-throughput sequencing data on co-occurring fungal and bacterial communities in deadwood to ask whether fungal and bacterial co-occurrences result from shared habitat use (i.e., deadwood's properties) or whether there are fungal-bacterial interactive associations after habitat characteristics are taken into account. Moreover, we tested the hypothesis that the interactions are mainly modulated through fungal communities influencing bacterial communities. For that, we quantified how much the predictive power of the joint species distribution models for bacterial and fungal community improved when accounting for the other community. Our results show that fungi and bacteria form tight association networks (i.e., some species pairs co-occur more frequently and other species pairs co-occur less frequently than expected by chance) in deadwood that include common (or opposite) responses to the environment as well as (potentially) biotic interactions. Additionally, we show that information about the fungal occurrences and abundances increased the power to predict the bacterial abundances substantially, whereas information about the bacterial occurrences and abundances increased the power to predict the fungal abundances much less. Our results suggest that fungal communities may mainly affect bacteria in deadwood.IMPORTANCE Understanding the interactive dynamics between fungal and bacterial communities is important to gain predictive knowledge on ecosystem functioning. However, little is known about the mechanisms behind fungal-bacterial associations and the directionality of species interactions. Applying joint species distribution modeling to high-throughput sequencing data on co-occurring fungal-bacterial communities in deadwood, we found evidence that nonrandom fungal-bacterial associations derive from shared habitat use as well as (potentially) biotic interactions. Importantly, the combination of cross-validations and conditional cross-validations helped us to answer the question about the directionality of the biotic interactions, providing evidence that suggests that fungal communities may mainly affect bacteria in deadwood. Our modeling approach may help gain insight into the directionality of interactions between different components of the microbiome in other environments.

Department of Agricultural Sciences University of Helsinki Helsinki Finland

Laboratory of Environmental Microbiology Institute of Microbiology of the Czech Academy of Sciences Prague Czech Republic

Organismal and Evolutionary Biology Research Programme University of Helsinki Helsinki Finland

Zobrazit více v PubMed

Deveau A, Bonito G, Uehling J, Paoletti M, Becker M, Bindschedler S, Hacquard S, Hervé V, Labbé J, Lastovetsky OA, Mieszkin S, Millet LJ, Vajna B, Junier P, Bonfante P, Krom BP, Olsson S, van Elsas JD, Wick LY. 2018. Bacterial–fungal interactions: ecology, mechanisms and challenges. FEMS Microbiol Rev 42:335–352. doi:10.1093/femsre/fuy008. PubMed DOI

Götzenberger L, de Bello F, Bråthen KA, Davison J, Dubuis A, Guisan A, Lepš J, Lindborg R, Moora M, Pärtel M, Pellissier L, Pottier J, Vittoz P, Zobel K, Zobel M. 2012. Ecological assembly rules in plant communities–approaches, patterns and prospects. Biol Rev Camb Philos Soc 87:111–127. doi:10.1111/j.1469-185X.2011.00187.x. PubMed DOI

Ovaskainen O, Abrego N. 2020. Joint species distribution modelling: with applications in R. Cambridge University Press, Cambridge, United Kingdom.

García-Baquero G, Crujeiras RM. 2015. Can environmental constraints determine random patterns of plant species co-occurrence? Ecol Evol 5:1088–1099. doi:10.1002/ece3.1349. PubMed DOI PMC

Warton DI, Blanchet FG, O'Hara RB, Ovaskainen O, Taskinen S, Walker SC, Hui FKC. 2015. So many variables: joint modeling in community ecology. Trends Ecol Evol 30:766–779. doi:10.1016/j.tree.2015.09.007. PubMed DOI

Zurell D, Pollock LJ, Thuiller W. 2018. Do joint species distribution models reliably detect interspecific interactions from co-occurrence data in homogenous environments? Ecography 41:1812–1819. doi:10.1111/ecog.03315. DOI

Ovaskainen O, Hottola J, Siitonen J. 2010. Modeling species co-occurrence by multivariate logistic regression generates new hypotheses on fungal interactions. Ecology 91:2514–2521. doi:10.1890/10-0173.1. PubMed DOI

Baldrian P. 2017. Forest microbiome: diversity, complexity and dynamics. FEMS Microbiol Rev 41:109–130. doi:10.1093/femsre/fuw040. PubMed DOI

Lladó S, López-Mondéjar R, Baldrian P. 2017. Forest soil bacteria: diversity, involvement in ecosystem processes, and response to global change. Microbiol Mol Biol Rev 81:e00063-16. doi:10.1128/MMBR.00063-16. PubMed DOI PMC

Gómez-Brandón M, Probst M, Siles JA, Peintner U, Bardelli T, Egli M, Insam H, Ascher-Jenull J. 2020. Fungal communities and their association with nitrogen-fixing bacteria affect early decomposition of Norway spruce deadwood. Sci Rep 10:8025. doi:10.1038/s41598-020-64808-5. PubMed DOI PMC

Johnston SR, Boddy L, Weightman AJ. 2016. Bacteria in decomposing wood and their interactions with wood-decay fungi. FEMS Microbiol Ecol 92:fiw179. doi:10.1093/femsec/fiw179. PubMed DOI

Watkinson S, Bebber D, Darrah P, Fricker M, Tlalka M, Boddy L. 2006. The role of wood decay fungi in the carbon and nitrogen dynamics of the forest floor, p 151–181. In Gadd GM (ed), Fungi in biogeochemical cycles. Cambridge University Press, Cambridge, United Kingdom.

de Boer W, Folman LB, Klein Gunnewiek PJA, Svensson T, Bastviken D, Öberg G, del Rio JC, Boddy L. 2010. Mechanism of antibacterial activity of the white-rot fungus Hypholoma fasciculare colonizing wood. Can J Microbiol 56:380–388. doi:10.1139/w10-023. PubMed DOI

Folman LB, Klein Gunnewiek PJA, Boddy L, De Boer W. 2008. Impact of white-rot fungi on numbers and community composition of bacteria colonizing beech wood from forest soil. FEMS Microbiol Ecol 63:181–191. doi:10.1111/j.1574-6941.2007.00425.x. PubMed DOI

Valášková V, de Boer W, Klein Gunnewiek PJA, Pospíšek M, Baldrian P. 2009. Phylogenetic composition and properties of bacteria coexisting with the fungus Hypholoma fasciculare in decaying wood. ISME J 3:1218–1221. doi:10.1038/ismej.2009.64. PubMed DOI

Rinne KT, Rajala T, Peltoniemi K, Chen J, Smolander A, Mäkipää R. 2017. Accumulation rates and sources of external nitrogen in decaying wood in a Norway spruce dominated forest. Funct Ecol 31:530–541. doi:10.1111/1365-2435.12734. DOI

Murray AC, Woodward S. 2003. In vitro interactions between bacteria isolated from Sitka spruce stumps and Heterobasidion annosum. Forest Pathol 33:53–67. doi:10.1046/j.1439-0329.2003.00307.x. DOI

De Boer W, Wagenaar A-M, Klein Gunnewiek PJA, Van Veen JA. 2007. In vitro suppression of fungi caused by combinations of apparently non-antagonistic soil bacteria. FEMS Microbiol Ecol 59:177–185. doi:10.1111/j.1574-6941.2006.00197.x. PubMed DOI

Caldeira AT, Feio SS, Arteiro JMS, Coelho AV, Roseiro JC. 2008. Environmental dynamics of Bacillus amyloliquefaciens CCMI 1051 antifungal activity under different nitrogen patterns. J Appl Microbiol 104:808–816. doi:10.1111/j.1365-2672.2007.03601.x. PubMed DOI

Brabcová V, Nováková M, Davidová A, Baldrian P. 2016. Dead fungal mycelium in forest soil represents a decomposition hotspot and a habitat for a specific microbial community. New Phytol 210:1369–1381. doi:10.1111/nph.13849. PubMed DOI

Hervé V, Ketter E, Pierrat J-C, Gelhaye E, Frey-Klett P. 2016. Impact of Phanerochaete chrysosporium on the functional diversity of bacterial communities associated with decaying wood. PLoS One 11:e0147100. doi:10.1371/journal.pone.0147100. PubMed DOI PMC

Hervé V, Junier T, Bindschedler S, Verrecchia E, Junier P. 2016. Diversity and ecology of oxalotrophic bacteria. World J Microbiol Biotechnol 32:28. doi:10.1007/s11274-015-1982-3. PubMed DOI

Nazir R, Tazetdinova DI, van Elsas JD. 2014. Burkholderia terrae BS001 migrates proficiently with diverse fungal hosts through soil and provides protection from antifungal agents. Front Microbiol 5:598. doi:10.3389/fmicb.2014.00598. PubMed DOI PMC

Tecon R, Or D. 2017. Biophysical processes supporting the diversity of microbial life in soil. FEMS Microbiol Rev 41:599–623. doi:10.1093/femsre/fux039. PubMed DOI PMC

Kohlmeier S, Smits THM, Ford RM, Keel C, Harms H, Wick LY. 2005. Taking the fungal highway: mobilization of pollutant-degrading bacteria by fungi. Environ Sci Technol 39:4640–4646. doi:10.1021/es047979z. PubMed DOI

Johnston SR, Hiscox J, Savoury M, Boddy L, Weightman AJ. 2019. Highly competitive fungi manipulate bacterial communities in decomposing beech wood (Fagus sylvatica). FEMS Microbiol Ecol 95:fiy225. doi:10.1093/femsec/fiy225. PubMed DOI PMC

Christofides SR, Hiscox J, Savoury M, Boddy L, Weightman AJ. 2019. Fungal control of early-stage bacterial community development in decomposing wood. Fungal Ecol 42:100868. doi:10.1016/j.funeco.2019.100868. DOI

Hoppe B, Kahl T, Karasch P, Wubet T, Bauhus J, Buscot F, Krüger D. 2014. Network analysis reveals ecological links between N-fixing bacteria and wood-decaying fungi. PLoS One 9:e88141. doi:10.1371/journal.pone.0088141. PubMed DOI PMC

Kielak AM, Scheublin TR, Mendes LW, van Veen JA, Kuramae EE. 2016. Bacterial community succession in pine-wood decomposition. Front Microbiol 7:231. doi:10.3389/fmicb.2016.00231. PubMed DOI PMC

Rinta-Kanto JM, Sinkko H, Rajala T, Al-Soud WA, Sørensen SJ, Tamminen MV, Timonen S. 2016. Natural decay process affects the abundance and community structure of Bacteria and Archaea in Picea abies logs. FEMS Microbiol Ecol 92:fiw087. doi:10.1093/femsec/fiw087. PubMed DOI

Purahong W, Wubet T, Krüger D, Buscot F. 2018. Molecular evidence strongly supports deadwood-inhabiting fungi exhibiting unexpected tree species preferences in temperate forests. ISME J 12:289–295. doi:10.1038/ismej.2017.177. PubMed DOI PMC

Küffer N, Gillet F, Senn-Irlet B, Job D, Aragno M. 2008. Ecological determinants of fungal diversity on dead wood in European forests. Fungal Divers 30:83–95.

Baldrian P, Zrůstová P, Tláskal V, Davidová A, Merhautová V, Vrška T. 2016. Fungi associated with decomposing deadwood in a natural beech-dominated forest. Fungal Ecol 23:109–122. doi:10.1016/j.funeco.2016.07.001. DOI

Hoppe B, Krüger D, Kahl T, Arnstadt T, Buscot F, Bauhus J, Wubet T. 2015. A pyrosequencing insight into sprawling bacterial diversity and community dynamics in decaying deadwood logs of Fagus sylvatica and Picea abies. Sci Rep 5:9456. doi:10.1038/srep09456. PubMed DOI PMC

Moll J, Kellner H, Leonhardt S, Stengel E, Dahl A, Bässler C, Buscot F, Hofrichter M, Hoppe B. 2018. Bacteria inhabiting deadwood of 13 tree species are heterogeneously distributed between sapwood and heartwood. Environ Microbiol 20:3744–3756. doi:10.1111/1462-2920.14376. PubMed DOI

Tlaskal V, Zrustova P, Vrska T, Baldrian P. 2017. Bacteria associated with decomposing dead wood in a natural temperate forest. FEMS Microb Ecol 93:fix157. doi:10.1093/femsec/fix157. PubMed DOI

Leibold MA, Chase JM. 2017. Metacommunity ecology. Princeton University Press, Princeton, NY.

Purahong W, Wubet T, Lentendu G, Schloter M, Pecyna MJ, Kapturska D, Hofrichter M, Krüger D, Buscot F. 2016. Life in leaf litter: novel insights into community dynamics of bacteria and fungi during litter decomposition. Mol Ecol 25:4059–4074. doi:10.1111/mec.13739. PubMed DOI

Worrich A, König S, Miltner A, Banitz T, Centler F, Frank K, Thullner M, Harms H, Kästner M, Wick LY. 2016. Mycelium-like networks increase bacterial dispersal, growth, and biodegradation in a model ecosystem at various water potentials. Appl Environ Microbiol 82:2902–2908. doi:10.1128/AEM.03901-15. PubMed DOI PMC

Tecon R, Or D. 2016. Bacterial flagellar motility on hydrated rough surfaces controlled by aqueous film thickness and connectedness. Sci Rep 6:19409. doi:10.1038/srep19409. PubMed DOI PMC

Banitz T, Fetzer I, Johst K, Wick LY, Harms H, Frank K. 2011. Assessing biodegradation benefits from dispersal networks. Ecol Modell 222:2552–2560. doi:10.1016/j.ecolmodel.2010.07.005. DOI

Anderson-Teixeira KJ, Davies SJ, Bennett AC, Gonzalez-Akre EB, Muller-Landau HC, Wright SJ, Abu Salim K, Almeyda Zambrano AM, Alonso A, Baltzer JL, Basset Y, Bourg NA, Broadbent EN, Brockelman WY, Bunyavejchewin S, Burslem DFRP, Butt N, Cao M, Cardenas D, Chuyong GB, Clay K, Cordell S, Dattaraja HS, Deng X, Detto M, Du X, Duque A, Erikson DL, Ewango CEN, Fischer GA, Fletcher C, Foster RB, Giardina CP, Gilbert GS, Gunatilleke N, Gunatilleke S, Hao Z, Hargrove WW, Hart TB, Hau BCH, He F, Hoffman FM, Howe RW, Hubbell SP, Inman-Narahari FM, Jansen PA, Jiang M, Johnson DJ, Kanzaki M, Kassim AR, et al.. 2015. CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change. Glob Chang Biol 21:528–549. doi:10.1111/gcb.12712. PubMed DOI

Král K, Valtera M, Janík D, Šamonil P, Vrška T. 2014. Spatial variability of general stand characteristics in central European beech-dominated natural stands – effects of scale. For Ecol Manage 328:353–364. doi:10.1016/j.foreco.2014.05.046. DOI

Král K, Janík D, Vrška T, Adam D, Hort L, Unar P, Šamonil P. 2010. Local variability of stand structural features in beech dominated natural forests of Central Europe: implications for sampling. For Ecol Manage 260:2196–2203. doi:10.1016/j.foreco.2010.09.020. DOI

Přívětivý T, Janík D, Unar P, Adam D, Král K, Vrška T. 2016. How do environmental conditions affect the deadwood decomposition of European beech (Fagus sylvatica L.)? For Ecol Manage 381:177–187. doi:10.1016/j.foreco.2016.09.033. DOI

Větrovský T, Baldrian P. 2015. An in-depth analysis of actinobacterial communities shows their high diversity in grassland soils along a gradient of mixed heavy metal contamination. Biol Fertil Soils 51:827–837. doi:10.1007/s00374-015-1029-9. DOI

Kirk TK, Obst JR. 1988. Lignin determination. Methods Enzymol 161:87–101. doi:10.1016/0076-6879(88)61014-7. DOI

Ihrmark K, Bödeker ITM, Cruz-Martinez K, Friberg H, Kubartova A, Schenck J, Strid Y, Stenlid J, Brandström-Durling M, Clemmensen KE, Lindahl BD. 2012. New primers to amplify the fungal ITS2 region - evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol Ecol 82:666–677. doi:10.1111/j.1574-6941.2012.01437.x. PubMed DOI

Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Lozupone CA, Turnbaugh PJ, Fierer N, Knight R. 2011. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A 108:4516–4522. doi:10.1073/pnas.1000080107. PubMed DOI PMC

Větrovský T, Baldrian P, Morais D. 2018. SEED 2: a user-friendly platform for amplicon high-throughput sequencing data analyses. Bioinformatics 34:2292–2294. doi:10.1093/bioinformatics/bty071. PubMed DOI PMC

Aronesty E. 2013. Comparison of sequencing utility programs. Open Bioinform J 7:1–8. doi:10.2174/1875036201307010001. DOI

Nilsson RH, Veldre V, Hartmann M, Unterseher M, Amend A, Bergsten J, Kristiansson E, Ryberg M, Jumpponen A, Abarenkov K. 2010. 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. doi:10.1016/j.funeco.2010.05.002. DOI

Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. 2011. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27:2194–2200. doi:10.1093/bioinformatics/btr381. PubMed DOI PMC

Edgar RC. 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods 10:996–998. doi:10.1038/nmeth.2604. PubMed DOI

Kõljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AFS, Bahram M, Bates ST, Bruns TD, Bengtsson-Palme J, Callaghan TM, Douglas B, Drenkhan T, Eberhardt U, Dueñas M, Grebenc T, Griffith GW, Hartmann M, Kirk PM, Kohout P, Larsson E, Lindahl BD, Lücking R, Martín MP, Matheny PB, Nguyen NH, Niskanen T, Oja J, Peay KG, Peintner U, Peterson M, Põldmaa K, Saag L, Saar I, Schüßler A, Scott JA, Senés C, Smith ME, Suija A, Taylor DL, Telleria MT, Weiss M, Larsson K-H. 2013. Towards a unified paradigm for sequence-based identification of fungi. Mol Ecol 22:5271–5277. doi:10.1111/mec.12481. PubMed DOI

Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, Brown CT, Porras-Alfaro A, Kuske CR, Tiedje JM. 2014. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res 42:D633–D642. doi:10.1093/nar/gkt1244. PubMed DOI PMC

Tikhonov G, Opedal ØH, Abrego N, Lehikoinen A, Jonge MMJ, Oksanen J, Ovaskainen O. 2020. Joint species distribution modelling with the r‐package Hmsc. Methods Ecol Evol 11:442–447. doi:10.1111/2041-210X.13345. PubMed DOI PMC

Ovaskainen O, Tikhonov G, Norberg A, Guillaume Blanchet F, Duan L, Dunson D, Roslin T, Abrego N. 2017. How to make more out of community data? A conceptual framework and its implementation as models and software. Ecol Lett 20:561–576. doi:10.1111/ele.12757. PubMed DOI

Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H. 2019. vegan: community ecology package. R package version 2.5–6.

R Core Team. 2017. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Examining the co-occurrences of human threats within terrestrial protected areas

Factors Controlling Dead Wood Decomposition in an Old-Growth Temperate Forest in Central Europe

Metagenomes, metatranscriptomes and microbiomes of naturally decomposing deadwood

Deadwood-Inhabiting Bacteria Show Adaptations to Changing Carbon and Nitrogen Availability During Decomposition

Ecological Divergence Within the Enterobacterial Genus Sodalis: From Insect Symbionts to Inhabitants of Decomposing Deadwood