Soil bacterial community differences along a coastal restoration chronosequence
Restoration interventions such as revegetation are globally-important to combat biodiversity declines and land degradation. However, restoration projects are generally poorly monitored because current approaches to monitoring are limited in their ability to assess important components of biodiversity, such as belowground microbial diversity. Since soil bacterial communities mediate many belowground ecosystems processes and represent substantial biodiversity in their own right, bacteria are important components to monitor during ecosystem restoration. High-throughput amplicon sequencing (DNA metabarcoding) has been put forward as a potential cost-effective, scalable and easy-to-standardise partial solution to restoration’s monitoring problem. However, its application to restoration projects has to date been limited. Here, we used DNA metabarcoding of bacterial 16S rRNA gene from soil DNA to explore community differences across a 16-year restoration chronosequence. The bacterial composition in the oldest revegetation sites was comparable to the remnant sites. Proteobacteria and Acidobacteria were significantly higher in relative sequence abundance, while Actinobacteria was significantly lower, with time since revegetation. Classes Alphaproteobacteria and Acidobacteria were indicative of remnant and the oldest revegetation sites, while Deltaproteobacteria and Rubrobacteria were characteristic of younger revegetation sites. Changes in the soil physical and chemical characteristics associated with revegetation appear to shape bacterial community structure and composition. These findings provide evidence that revegetation can have positive effects on belowground microbial communities, and help demonstrate that the soil bacterial community can be restored towards its native state by revegetation, which may be useful in restoration monitoring.
KeywordsBacterial community Coastal restoration DNA metabarcoding Environmental microbiome Revegetation
This work was supported by funding from the China Scholarship Council (201408410176 awarded to DY). We thank L. Blake, M. Durant, I. Fox, F. Hutchings, C. Jackson, M. Laws, J. McDonald for technical and field assistance. We are grateful for the contribution of the Biomes of Australian Soil Environments (BASE) consortium (https://data.bioplatforms.com/organization/pages/bpa-base/acknowledgements) in the generation of data used in this publication. The BASE project is supported by funding from Bioplatforms Australia through the Australian Government National Collaborative Research Infrastructure Strategy.
Compliance with ethical standards
Conflict of interest
The authors declare no conflict of interest.
- Anderson MJ (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecol 26:32–46Google Scholar
- Balint M, Bahram M, Eren AM, Faust K, Fuhrman JA, Lindahl B, O'Hara RB, Opik M, Sogin ML, Unterseher M, Tedersoo L (2016) Millions of reads, thousands of taxa: microbial community structure and associations analyzed via marker genes. FEMS Microbiol Rev 40:686–700PubMedCrossRefPubMedCentralGoogle Scholar
- Bissett A, Fitzgerald A, Meintjes T, Mele PM, Reith F, Dennis PG, Breed MF, Brown B, Brown MV, Brugger J, Byrne M, Caddy-Retalic S, Carmody B, Coates DJ, Correa C, Ferrari BC, Gupta VV, Hamonts K, Haslem A, Hugenholtz P, Karan M, Koval J, Lowe AJ, Macdonald S, McGrath L, Martin D, Morgan M, North KI, Paungfoo-Lonhienne C, Pendall E, Phillips L, Pirzl R, Powell JR, Ragan MA, Schmidt S, Seymour N, Snape I, Stephen JR, Stevens M, Tinning M, Williams K, Yeoh YK, Zammit CM, Young A (2016) Introducing BASE: the biomes of Australian soil environments soil microbial diversity database. Gigascience 5:1–11CrossRefGoogle Scholar
- De Palma A, Sanchez-Ortiz K, Martin PA, Chadwick A, Gilbert G, Bates AE, Börger L, Contu S, Hill SLL, Purvis A (2018) Challenges with inferring how land-use affects terrestrial biodiversity: study design, time, space and synthesis. In: Bohan DA, Dumbrell AJ, Woodward G, Jackson M (eds) Advances in ecological research. Academic Press, Cambridge, pp 163–199Google Scholar
- Delgado-Baquerizo M, Powell JR, Hamonts K, Reith F, Mele P, Brown MV, Dennis PG, Ferrari BC, Fitzgerald A, Young A, Singh BK, Bissett A (2017) Circular linkages between soil biodiversity, fertility and plant productivity are limited to topsoil at the continental scale. New Phytol 215(3):1186–1196PubMedCrossRefGoogle Scholar
- Dufrêne M, Legendre P (1997) Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol Monogr 67:345–366Google Scholar
- Ficetola GF, Pansu J, Bonin A, Coissac E, Giguet-Covex C, De Barba M, Gielly L, Lopes CM, Boyer F, Pompanon F, Rayé G, Taberlet P (2015) Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data. Mol Ecol Resour 15:543–556PubMedCrossRefGoogle Scholar
- Ji Y, Ashton L, Pedley SM, Edwards DP, Tang Y, Nakamura A, Kitching R, Dolman PM, Woodcock P, Edwards FA, Larsen TH, Hsu WW, Benedick S, Hamer KC, Wilcove DS, Bruce C, Wang X, Levi T, Lott M, Emerson BC, Yu DW (2013) Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding. Ecol Lett 16:1245–1257PubMedCrossRefGoogle Scholar
- Lane D (1991) 16S/23S rRNA sequencing. In: Stackebrandt E, Goodfellow M (eds) Nucleic acid techniques in bacterial systematics. Wiley, New York, pp 125–175Google Scholar
- Luecker S, Wagner M, Maixner F, Pelletier E, Koch H, Vacherie B, Rattei T, Damste JSS, Spieck E, Le Paslier D, Daims H (2010) A Nitrospira metagenome illuminates the physiology and evolution of globally important nitrite-oxidizing bacteria. Proc Natl Acad Sci USA 107:13479–13484CrossRefGoogle Scholar
- Oksanen J., Blanchet F.G., Friendly M., Kindt R., Legendre P., McGlinn D., Minchin P.R., O’Hara R.B., Simpson G.L., Solymos P., Stevens M.H.S., Szoecs E. and Wagner H. 2018. Vegan: community ecology package. R package version 2.5-1Google Scholar
- R Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
- Rideout JR, He Y, Navas-Molina JA, Walters WA, Ursell LK, Gibbons SM, Chase J, McDonald D, Gonzalez A, Robbins-Pianka A (2014) Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences. PeerJ 2:e545PubMedPubMedCentralCrossRefGoogle Scholar
- Rodrigues JLM, Pellizari VH, Mueller R, Baek K, Jesus EC, Paula FS, Mirza B, Hamaoui GS Jr, Tsai SM, Feigl B, Tiedje JM, Bohannan BJ, Nuesslein K (2013) Conversion of the Amazon rainforest to agriculture results in biotic homogenization of soil bacterial communities. Proc Natl Acad Sci USA 110:988–993PubMedCrossRefGoogle Scholar
- Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75:7537–7541PubMedPubMedCentralCrossRefGoogle Scholar
- Vandamme P, Opelt K, Knoechel N, Berg C, Schoenmann S, De Brandt E, Eberl L, Falsen E, Berg G (2007) Burkholderia bryophila sp nov and Burkholderia megapolitana sp nov., moss-associated species with antifungal and plant-growth-promoting properties. Int J Syst Evol Microbiol 57:2228–2235PubMedCrossRefGoogle Scholar
- Wheeler B, Torchiano M (2016) lmPerm: permutation tests for linear models. R package version 2.1.0Google Scholar