Fungi participate in driving home-field advantage of litter decomposition in a subtropical forest
Background and aims
Home-field advantage (HFA) hypothesis predicts that plant litter decomposes faster beneath the plant species from which it was derived than beneath other plant species. However, it remains unclear, which groups of soil organisms drive HFA effects across a wide range of litter quality and forest types.
We set up a reciprocal transplant decomposition experiment to quantify the HFA effects of broadleaf, coniferous and bamboo litters. Litterbags of different mesh sizes and high-throughput pyrosequencing of microbial rRNA gene were used to test the contribution of different decomposer groups to HFA effect.
The recalcitrant broadleaf litter and the labile bamboo litter exhibited HFA. Presence of meso-and macrofauna did not substantially change the HFA effects. Bacterial and fungal community composition on litters were significantly influenced by litter type. Bacterial community composition remained unchanged when the same litter was decomposed in different forest types, whereas fungal community composition on broadleaf and bamboo litters were significantly influenced by incubation site.
Our data demonstrate specific association between fungal community composition and faster litter decomposition in the home site, suggesting that fungi probably participate in driving the HFA effect of broadleaf and bamboo litters.
KeywordsHome-field advantage Litter-decomposer interactions Litter traits Local adaptation Functional redundancy
We thank Pei Wang and Yan Liu for their help in perparing litterbags, and Zhenxi Lai, Pengpeng Dou and Fang Wang for their help in the field and laboratory. We would also like to thank Alison Beamish at the University of British Columbia for her assistance with English language and grammatical editing of the manuscript, and anonymous reviewers for constructive comments on the manuscript. This work was supported by the National Natural Science Foundation of China [No. 31500356], Chongqing Research Program of Basic Research and Frontier Technology [No. cstc2016jcyjA0004], Fundamental Research Funds for the Central Universities [No. 2018CDXYCH0014] and the 111 Project [No. B13041].
- Fanin N, Fromin N, Bertrand I (2016) Functional breadth and home-field advantage generate functional differences among soil microbial decomposers. Ecology 97:1023–1037Google Scholar
- Graça MAS, Bärlocher F, Gessner MO (2005) Methods to study litter decomposition: a practical guide. In: Springer. Dordrecht, New YorkGoogle Scholar
- Hunt HW, Coleman DC, Ingham ER et al (1987) The detrital food web in a shortgrass prairie. Biol Fertil Soils 3:57–68Google Scholar
- Jones JB (2001) Laboratory guide for conducting soil tests and plant analysis. CRC Press, Boca RatonGoogle Scholar
- Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2:18–22Google Scholar
- Makkar HPS (2003) Quantification of tannins in tree and shrub foliage: a laboratory manual. In: Kluwer academic publishers. Dordrecht, BostonGoogle Scholar
- R Development Core Team (2016) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
- SAS Institute (2010) SAS for Windows, version 9.3. SAS Institute, Cary, North Carolina. USAGoogle Scholar
- Swift MJ, Heal OW, Anderson JM (1979) Decomposition in terrestrial ecosystems. University of California Press, BerkeleyGoogle Scholar
- Wall DH, Bradford MA, St John MG et al (2008) Global decomposition experiment shows soil animal impacts on decomposition are climate-dependent. Glob Chang Biol 14:2661–2677Google Scholar