Chapter 3: EFFECTS OF LAND USE AND MANAGEMENT ON SOIL BACTERIAL
3.2 MATERIALS AND METHODS
3.2.1 Sites and soils
Two long-term experimental sites located in KwaZulu-Natal, South Africa, were selected for this study. These sites were chosen as all the land uses and management practices have been in place for many years, and the ecosystem was deemed to have achieved a degree of equilibrium before sampling took place.
For comparison of the effects of different land uses on soil microbial populations, fields on Baynesfield Estate (site 1) in the KwaZulu-Natal midlands (27º22’S and 30º 45’E) previously described by Dominy and Haynes (2002), were used. Cropping histories were: > 50 years permanent kikuyu grass pasture (Pennisetum clandestinum, Chiov) (KIK); > 30 years continuous, pre-harvest burnt sugarcane (Saccharum officinarum, Linn.) (SC); > 30 years continuous maize (Zea mays, Linn.) under conventional tillage (M); > 20 years pine plantation (Pinus patula, Schiede) (PF); 6 years wattle plantation (Acacia mearnsii, De Wild) (W) planted between the stumps of a harvested 20-year Eucalyptus plantation; and undisturbed native grassland (NAT). The native, high diversity grassland is situated in an area separated from agricultural land on one side by a roadway and on the other by old buildings. It has never been cultivated or fertilized and is typical of grasslands in the area. The vegetation is dominated by Themeda grassland, with grasses such as Monocymbium ceresiiforme, Trachypogon spicatus, Tristachya leucothrix, Eragrostis racemosa and Diheteropogon amplectens prominent (Low and Rebelo, 1996). All the agricultural fields had been fertilized with typical annual rates for arable and grassland sites of approximately 100–300 kg N ha-1, 50–150 kg K ha-1 and 25–50 kg P ha-1. For forestry plantation soils, typical fertilizer rates are approximately 50–100 kg N ha-1, 10–60 kg K ha-1 and 10–25 kg P ha-1,applied at the start of each rotation. The maize, sugarcane and pasture sites are irrigated during the dry winter period. Mean annual rainfall is 844 mm and mean monthly temperatures range from a maximum in January of 21.1ºC (maximum 25.9ºC, minimum 16.3ºC) to a minimum of 13.3ºC in June (maximum 16.3ºC, minimum 5.6ºC). The site is located on soils classified as Hutton form
(Farmingham series) (Soil Classification Working Group, 1991) or as Rhodic Ferralsols (IUSS Working Group WRB, 2006).
To compare trash management effects on the soil microbial communities, an experimental site situated at the South African Sugarcane Research Institute (SASRI) at Mount Edgecombe (31º 04’S and 29º 43’E) was used (site 2). All sugarcane varieties used at SASRI are hybrids, in this case Saccharum officinarum x S.
spontaneum var. N27. The site BT/139, previously described by Graham et al.
(2002a), was established in 1939 and is a long-term pre-harvest burning and crop residue (trash) retention trial. Experimental treatments studied were: (i) green cane harvested with retention of a trash mulch (100% cover), either fertilized [TF] or unfertilized [TFo]; and (ii) pre-harvest burnt cane with harvest residues (tops) raked off, either fertilized [BtoF], or unfertilized, [BtoFo]. Annual fertilizer applications are 160 kg N ha-1, 32 kg P ha-1 and 160 kg K ha-1. The experiment is replicated in a randomized split plot design, the main plots being trash management treatments.
Mean annual rainfall is approximately 950 mm. The soil is classified as Arcadia form, Lonehill family (Soil Classification Working Group, 1991) or as a Chromic Vertisol (IUSS Working Group WRB, 2006).
3.2.2 Site sampling
Soil samples from both sites were collected from a depth of 0–5 cm during the fallow period at the end of the dry winter season in 2004. For the different land uses at Baynesfield, each field or plantation was divided into three separate sampling areas (each approximately 60 m2). Composite (bulked) samples, consisting of ten random subsamples per sampling area were collected, resulting in three separate bulked samples per land use. For comparison of trash management at Mount Edgecombe, 10 soil samples (0–5 cm depth) were randomly taken from under the rows of three replicates of each treatment over the entire area. The subsamples from each replicate were bulked so that there were three independent samples for each land management.
All bulked, field-moist samples were thoroughly mixed and sieved (2 mm) in the laboratory. Subsamples for DNA extraction were stored in plastic bags at 4ºC and further subsamples for chemical analysis were again sieved (< 1 mm) and air-dried.
3.2.3 Chemical analysis
The air-dried, sieved soil samples were analysed by routine testing methods (Manson et al., 1993) by the Soil Fertility and Analytical Services Division of KwaZulu-Natal Department of Agriculture and Environmental Affairs, Cedara. Exchangeable acidity and exchangeable Ca and Mg were extracted with 1 M KCl (1:10 soil:solution ratio) for 10 minutes. The pH of the extract was measured with a glass electrode and exchange acidity by titration. Exchangeable K and extractable P, Zn and Mn were extracted with AMBIC reagent (0.025 M NH4HCO3; 0.01 M NH4F; 0.01 M EDTA [ethylenediaminetetra-acetic acid] at pH 8.0) using a 1:10 soil:solution ratio for 10 minutes. Total exchangeable Ca, Mg, K, Mn and Zn were analysed by atomic absorption spectrophotometry and P by the molybdenum blue method. Total exchangeable Ca, Mg, K and acidity were summed to give ‘total cations’, an approximation of the effective cation exchange capacity (ECEC). While N (the C/N ratio) is important in determining the structure and function of the soil microbial communities, this factor was not considered here as none of the current extraction methods can predict soil N supply accurately (Ros, in press). Organic C content was estimated by the Walkley-Black dichromate oxidation procedure (Walkley, 1947) in the University of KwaZulu-Natal laboratories.
3.2.4 DNA extraction
The UltraClean™ Soil DNA Isolation Kit (MO BIO Laboratories, Inc. CA, USA), was used to extract and purify DNA directly from the soil samples according to the manufacturer’s instructions (using the protocol designed to maximize yields). For the Baynesfield samples, 0.5 g field-moist soil from maize, sugarcane, native grassland, kikuyu pasture, pine and wattle respectively, were added to MO BIO bead-beating tubes. For the Mount Edgecombe samples, because the DNA concentration was low, 0.7 g field-moist soil from each replicate of the four treatments was added to the MO BIO tubes. All DNA samples were prepared in duplicate, one set being stored at -20ºC for subsequent analyses and the other at -80ºC as a long-term back up.
3.2.5 PCR amplification of 16S rDNA fragments
Initially, three primer pairs were used to amplify prokaryotic 16S rDNA from the different soil communities, namely, P63fGC/534r, 341fGC/534r, and 968fGC/1410r (Watanabe et al., 2001). While all primer pairs amplified efficiently, only 341fGC/534r consistently gave good results in subsequent DGGE analysis. Therefore, this universal bacterial primer pair, which amplifies the variable V3 region of bacterial 16S rDNA gene sequences, from nucleotides 341–534 (E. coli numbering) (Dilly et al., 2004), was chosen for PCR amplification of soil bacterial DNA. To ensure efficient separation of fragments in subsequent DGGE, a GC clamp was added to the 5' end of the forward primer, 341f (Muyzer et al., 1993, 1996) (Table 3.1).
TABLE 3.1 Primers used for PCR amplification of soil bacterial 16S rDNA
Primera Positionb Sequences (5'- 3') References 341 f GC 341-357 CCTACGGGAGGCAGCAG (GC clamp
attached to the 5' end of 341f)
Muyzer et al.
1993,1996
534 r 518-534 ATTACCGCGGCTGCTGG "
GC clamp CGCCCGCCGCGCGCGGCGGGC
GGGGCGGGGGCACGGGGGG
"
a f: forward primer; r: reverse primer.
b corresponding to the numbering in the Escherichia coli sequence.
Soil DNA was amplified in a Perkin Elmer Applied Biosystems Gene Amp 2400 thermal cycler (PE Corporation. Applied Biosystems, Foster City, CA, USA).
Reaction mixtures were prepared using the PCR Core Kit (Roche Diagnostics) according to the manufacturer’s instructions, and were optimized for the experimental soils. Each reaction contained 1.0 µl template DNA (Baynesfield soil samples) or 2.0 µl template DNA (Mount Edgecombe soil samples); 0.5 µM of each primer; 200 µM of each dNTP; 2.5 mM MgCl2; 1.25 U Taq DNA Polymerase (Roche Diagnostics);
sterile MilliQ H2O; and 1.0 µl (20 mg ml-1) Bovine Serum Albumen (BSA), added to
prevent inhibition of amplification, by organic compounds co-extracted from the soil (Pecku, 2003) in a buffered final volume of 50 µl.
PCR conditions were as described in a method, which had been adapted and optimized for environmental samples, in the laboratories of the University of KwaZulu-Natal (Pecku, 2003). These were: an initial denaturation step (94ºC/5 min), followed by 35 cycles comprising: a denaturing (92ºC/1 min), annealing (55ºC/1 min), and elongation (72ºC/1 min) step, and finally, a single elongation step (72ºC/10 min). Amplification products (~193 bp) were analyzed by electrophoresis of 5 µl aliquots in 2% agarose gels stained with ethidium bromide, and visualized on a UV transilluminator.
3.2.6 Community fingerprinting by DGGE
A DCode™ Universal Mutation Detection System (Bio-Rad Laboratories, Hercules, CA, USA) was used for DGGE. Equal amounts of PCR amplicons (visual estimation from agarose gels) (Pennanen et al., 2004), contained in 20 µl aliquots, were loaded onto 8% (v/v) polyacrylamide gels (Sigma, acrylamide/bisacrylamide 40% solution, mix ratio 19:1) containing a linear chemical gradient ranging from 30–70%
denaturant (100% denaturant corresponds to 7 M urea plus 40% (v/v) deionized formamide) in 1× TAE buffer (40 mM Tris base, 20 mM acetic acid, and 1 mM disodium EDTA, pH 8.3). The gels were allowed to polymerize for 1.5–2.5 hours, after which electrophoresis was run for 16 hours at 70 volts and 60ºC in 1× TAE buffer. After the run, the gels were silver-stained (H. van Verseveld, pers. comm.),† scanned and photographed with the Bio-Rad VersaDoc gel documentation system.
3.2.7 Data analysis
Image analysis of the one-dimensional (1-D) DGGE gels, by Bio-Rad Quantity One™
software (version 4.5), was used for band detection and band intensity quantification.
The Bio-Rad Versa Doc imaging device converts signals from biological samples into digital data. The signal intensity of the pixels in the bands is measured as OD units
† van Verseveld, H., 2001. Vrije Universiteit van Amsterdam.
(Bio-Rad Quantity One™ Instruction Manual, 2005). Bands were considered common if they migrated equidistantly in a gel (Nakatsu et al., 2000). After background subtraction of individual lanes using the ‘rolling disk’ method, the position and total number of bands was determined for each sample, and manually checked and adjusted (Wakelin et al., 2008a). Thereafter, banding patterns were scored as binary presence (1) or absence (0) matrices, and recorded in spreadsheets.
Average relative intensity of individual bands was then calculated and this data also transferred into spreadsheets (Hoshino and Matsumoto, 2007). Band intensity was expressed as a percentage of the summed average band intensities across each gel.
Each band was inferred to represent individual groups of species having similar melting domains, whereas the band intensity indicates the relative abundance of the group under the PCR conditions used (Eaton and Farrell, 2004; Wakelin et al., 2008a). Comparisons of lanes were confined to those within single gels because of variation between gels (Pennanen et al., 2004).
3.2.8 Statistical analysis
For practical reasons, as the sampling sites at the Baynesfield Estate are pre-existing land uses, the study was not established in a statistically rigorous experimental design (Lepš and Šmilauer, 2003). Accordingly, the data were analysed as a completely randomised design with three separate replications per land use, although the land use types were not truly replicated within a formal experimental design. Therefore, statistics were used only to describe the basic features of the study data and have limited application for extrapolation to other similar sites. By contrast, at Mount Edgecombe, the experiment is replicated in a statistically correct, randomized split plot design, so inferential statistics could be applied to this data.
Soil physicochemical properties were analysed using CANOCO 4.5 for Windows (Microcomputer Power, Ithaca, NY) (ter Braak and Šmilauer, 1997). Soil differences were tested by Multi-Response Permutation Procedures (MRPP) standardized to standard deviation = 1, using PC-ORD 4.25, and by Principal Component Analysis (PCA). Tests were done on the standardized data, where the original value for each soil variable from each site was divided by its standard deviation (across samples) to
allow comparisons of variables on a common scale (as was done in the PCAs).
Dissimilarity between samples was measured by Euclidian distance.
Canonical correspondence analysis (CCA) using CANOCO 4.5 for Windows (ter Braak and Šmilauer, 1997) was used to assess the effects of measured soil physicochemical variables [organic C; pH (KCl); exchange acidity; total cations (ECEC); exchangeable K, Ca and Mg; and extractable P] on soil microbial community structure (described by DGGE profiles), under the different land uses at Baynesfield and trash managements at Mount Edgecombe. Because the soil variables at site 2 were highly collinear (variance inflation factors > 20 for most of the variables), a sub-set of variables with a relatively independent significant effect on soil microbial composition, was selected through permutation testing (Monte Carlo Permutation test, n = 499) in a stepwise selection process. CCA is specifically designed to compare data in the form of contingency tables where variables (in this case, bands) are enumerated.
Microbial community composition (presence/absence of DGGE bands) in soils from fields with different land uses (Baynesfield) or trash management plots (Mount Edgecombe), was compared by non-metric multidimensional scaling (NMS), rotated by PCA to produce low-dimensional plots, with the horizontal axis aligned to the direction of maximum compositional variation, based on Sörensen’s distance coefficient, using the program WinKyst 1.0 (Šmilauer, 2002) and tested by MRPP, standard deviation = 1. The principle behind NMS is to find a graphical representation of the data in a few dimensions, the distance in the ordination of the samples reflecting the (dis)similarities between the respective DGGE patterns as closely as possible (Hernesmaa et al., 2005).
Genstat Release 9.1 (PC/Windows XP) 2006, Lawes Agricultural Trust (Rothamsted Experimental Station) was used for analyses of variance (ANOVA). One-way ANOVA of species richness (S) (number of bands present) of soil microbial communities was carried out. Relative band intensity (mass) and position were used to determine bacterial community evenness (J) and the Shannon Weaver Diversity Index (H') (Peet, 1974). The latter provides a composite measure of diversity, based on both species richness (S) (the number of bands present) and Pielou’s species
evenness (J') (a measure of the equitability of the relative band intensities across the gels) (Pielou, 1977). Pairwise comparisons were made, using the Ryan/Einot- Gabriel/Welsch multiple range test. For Mount Edgecombe data, a two-way ANOVA of soil variables, and another of the main effects and interaction of the trashing and fertilizer treatments on soil bacterial richness, evenness and diversity were carried out.
The results of ANOVA however, are not unequivocal, because neither the richness nor evenness parameter can be determined with certainty for prokaryotes in soil samples (Tebbe and Schloter, 2007). Least significant differences were calculated at the 5% level.