Is vegetation composition or soil chemistry the best predictor of the soil microbial community?
- 1.1k Downloads
With the species composition and/or functioning of many ecosystems currently changing due to anthropogenic drivers it is important to understand and, ideally, predict how changes in one part of the ecosystem will affect another. Here we assess if vegetation composition or soil chemistry best predicts the soil microbial community. The above and below-ground communities and soil chemical properties along a successional gradient from dwarf shrubland (moorland) to deciduous woodland (Betula dominated) were studied. The vegetation and soil chemistry were recorded and the soil microbial community (SMC) assessed using Phospholipid Fatty Acid Extraction (PLFA) and Multiplex Terminal Restriction Fragment Length Polymorphism (M-TRFLP). Vegetation composition and soil chemistry were used to predict the SMC using Co-Correspondence analysis and Canonical Correspondence Analysis and the predictive power of the two analyses compared. The vegetation composition predicted the soil microbial community at least as well as the soil chemical data. Removing rare plant species from the data set did not improve the predictive power of the vegetation data. The predictive power of the soil chemistry improved when only selected soil variables were used, but which soil variables gave the best prediction varied between the different soil microbial communities being studied (PLFA or bacterial/fungal/archaeal TRFLP). Vegetation composition may represent a more stable ‘summary’ of the effects of multiple drivers over time and may thus be a better predictor of the soil microbial community than one-off measurements of soil properties.
KeywordsCo-correspondence analysis Ecosystem engineer Succession Moorland TRFLP PLFA
We are grateful to the late John Miles who identified these chronosequences. We would like to thank Angela Fraser and Tara Breedon for technical assistance. This work was funded by the Scottish Government, Rural and Environment Research and Analysis Directorate.
- Allen SE (1989) Chemical analysis of ecological material, 2nd edn. Blackwell Scientific, OxfordGoogle Scholar
- Bligh EG, Dyer WJ (1958) A rapid method of total lipid extraction and purification. Can J Biochem Physiol 37:911–917Google Scholar
- Casamayor EO, Massana R, Benlloch S, Øvreås L, Díez B, Goddard VJ, Gasol JM, Joint I, Rodríguez-Valera F, Perdrós-Alió C (2002) Changes in archaeal, bacterial and eukaryal assemblages along a salinity gradient by comparison of genetic fingerprinting methods in a multipond solar saltern. Environ Microbiol 4:338–348CrossRefPubMedGoogle Scholar
- Gardner WH (1965) Water content. In: Black C (ed) Methods of soil analysis. Part 1. Physical and mineralogical properties, including statistics of measurement and sampling. American Society of Agronomy, Madison, pp 82–127Google Scholar
- Goodwin J (1992) The role of mycorrhizal fungi in competitive interactions among native bunchgrasses and alien weeds—a review and synthesis. Northwest Sci 66:251–260Google Scholar
- Haseman JF, Marshall CE (1945) The use of heavy minerals in studies of the origin and development of soils. University of Missouri College of Agriculture Experimental Station Research Bulletin No. 387Google Scholar
- Janos DP (1980) Mycorrhizae influence tropical succession. Mycorrhizae 12:56–64Google Scholar
- Lavelle P, Bignell D, Lepage M, Wolters V, Roger P, Ineson P, Heal O, Dhillion S (1997) Soil function in a changing world: the role of invertebrate ecosystem engineers. Eur J Soil Biol 33:159–193Google Scholar
- Maindonald J, Braun WJ (2009) Data Analysis and Graphics. R package version 0.98. Published at http://www.R-project.org.
- McLean E (1982) Soil pH and lime requirement. In: Page A, Miller R, Keeney D (eds) Methods of soil analysis. Part 2. Chemical and microbiological properties. SSSA, Madison, pp 199–209Google Scholar
- Miles J, Young WF (1980) The effects on heathland and moorland soils in Scotland and northern England following colonisation by birch. Bulletin Societé ď Ecoloie France 11:233–242Google Scholar
- Pastor J, Cohen Y, Hobbs NT (2006) The roles of large herbivores in ecosystem nutrient cycles. In: Danell K, Bergstrom R, Duncan P, Pastor J (eds) Large herbivore ecology, ecosystem dynamics and conservation. Cambridge University Press, CambridgeGoogle Scholar
- Pella E, Colombo B (1973) Study of carbon, hydrogen and nitrogen by combustion gas chromatography. Mikrochim Acta 5:697–719Google Scholar
- R Development Core Team (2006) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria, Published at http://www.R-project.org.
- SAS (2005) SAS Software Version 9.1. SAS, CaryGoogle Scholar
- Simpson GL (2005) Cocorresp: co-correspondence analysis ordination methods for community ecology. R foundation for statistical computing, Vienna, Austria, published at http://www.R-project.org.
- Smith RS, Shield RS, Bardgettt RD, Millward D, Corkhill P, Rolph G, Hobbs PJ, Peacock S (2003) Diversification management of meadow grassland: plant species diversity and functional traits associated with change in meadow vegetation and soil microbial communities. J Appl Ecol 40:51–64CrossRefGoogle Scholar
- Ter Braak CJF, Smilauer P (2002) CANOCO reference manual and cano draw for windows user’s guide: software for canonical community ordination version 4.5. Microcomputer Power, IthacaGoogle Scholar
- Thomas G (1982) Exchangeable cations. In: Page A, Miller R, Keeney D (eds) Methods of soil analysis. Part 2. Chemical and microbiological properties. SSSA, Madison, pp 159–165Google Scholar
- White TJ, Bruns TD, Lee S, Taylor J (1990) Analysis of phylogenetic relationship by amplification and direct sequencing of ribosomal RNA genes. In: Innis MA, Gelford DH, Sninsky JJ, White TJ (eds) PCR protocols: a guide to methods and applications. Academic, New York, pp 315–322Google Scholar