Is vegetation composition or soil chemistry the best predictor of the soil microbial community

• Published in: Plant and soil Vol. 333; no. 1-2; pp. 417 - 430
• Main Authors: Mitchell, Ruth J, Hester, Alison J, Campbell, Colin D, Chapman, Stephen J, Cameron, Clare M, Hewison, Richard L, Potts, Jackie M
• Format: Journal Article
• Language: English
• Published: Dordrecht Dordrecht : Springer Netherlands 01.08.2010; Springer; Springer Netherlands; Springer Nature B.V
• Subjects: Acid soils; Agronomy. Soil science and plant productions; Analysis; Animal and plant ecology; Animal, plant and microbial ecology; Anthropogenic factors; Betula; Bickham, Jack M; Biochemistry and biology; Biological and medical sciences; Biomedical and Life Sciences; Botany; Chemical composition; Chemical properties; Chemical, physicochemical, biochemical and biological properties; Co-correspondence analysis; data collection; dwarfing; Ecology; Ecosystem engineer; Ecosystems; Fatty acids; Fundamental and applied biological sciences. Psychology; fungi; General agronomy. Plant production; Geochemistry; Grassland soils; heathlands; Life Sciences; Markvetenskap; Microbial activity; Microbiology; Motion picture theaters; phospholipids; Physics, chemistry, biochemistry and biology of agricultural and forest soils; Plant communities; Plant Physiology; Plant Sciences; Plant species; Plants; PLFA; prediction; Rare species; Regular Article; restriction fragment length polymorphism; shrublands; soil chemical properties; Soil chemistry; Soil composition; Soil ecology; Soil microbiology; Soil microorganisms; Soil properties; Soil Science; Soil Science & Conservation; Soil sciences; Soil-plant relationships. Soil fertility; Soil-plant relationships. Soil fertility. Fertilization. Amendments; Species composition; species diversity; Succession; Synecology; TRFLP; Vegetation; Woodlands; United Kingdom; Moorland; Succession; PLFA; TRFLP; Co-correspondence analysis; Ecosystem engineer; Microbial activity; Predictor; Phospholipid; Correspondence analysis; Ecosystem; Soil science; Vegetation structure; Microbial community; Organic compounds; Soil chemistry; Heathland and moor; Data analysis; Floristic composition; Aliphatic compound; Property of soil; Complex lipid; Fatty acids; Biological activity; Statistical method; Soils; Restriction fragment length polymorphism; Applied mathematics; Soil plant relation
• DOI: 10.1007/s11104-010-0357-7
• ISSN: 0032-079X; 1573-5036

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.