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Using specific PLFAs as biomarkers (see Section 2), we can quantify the relative abundances of specific microbial groups within the soil profiles (Table 4). In both profiles, Fig. 3. Principal component analysis (PCA) of PLFA signatures (mol percentages) from soil samples collected at different depths within the two profiles. The same symbols are used for the different depth increments in the valley and terrace profiles. The valley profile samples are represented by the filled symbols. PC1 explains 42% of the variance in the PLFA data, PC2 explains 12%, the individual scores are unit less. Error bars indicate + 1 SE (N = 3).

Table 4 Using specific PLFAs as biomarkers (see Section 2), we can quantify the relative abundances of specific microbial groups within the soil profiles (Table 4). In both profiles, Fig. 3. Principal component analysis (PCA) of PLFA signatures (mol percentages) from soil samples collected at different depths within the two profiles. The same symbols are used for the different depth increments in the valley and terrace profiles. The valley profile samples are represented by the filled symbols. PC1 explains 42% of the variance in the PLFA data, PC2 explains 12%, the individual scores are unit less. Error bars indicate + 1 SE (N = 3).

Related Figures (6)

General physical and chemical characteristics of the soil profiles at the two study sites. Average temperatures and temperature ranges obtained from date collected between March 2001 and September 2001. Moisture content data (% of dry weight) were obtained from 4 sampling times (Fall 2000, Winter 2001 late Spring 2001, and late summer 2001). One standard error reported in parentheses. N = 3 for each sampling depth
General physical and chemical characteristics of the soil profiles at the two study sites. Average temperatures and temperature ranges obtained from date collected between March 2001 and September 2001. Moisture content data (% of dry weight) were obtained from 4 sampling times (Fall 2000, Winter 2001 late Spring 2001, and late summer 2001). One standard error reported in parentheses. N = 3 for each sampling depth
Microbial abundances within the two soil profiles, as estimated by SIR, summing extractable PLFAs, and chloroform fumigation-extraction. One standard error in parantheses, VN = 3 for each sampling depth
Microbial abundances within the two soil profiles, as estimated by SIR, summing extractable PLFAs, and chloroform fumigation-extraction. One standard error in parantheses, VN = 3 for each sampling depth
Fig. 1. The relationships between total microbial biomass estimates obtained by summing extractable PLFAs and the biomass estimates obtained by chloroform fumigation extraction (A) and SIR (B) for all samples. Separate regression lines have been drawn for the high biomass samples collected from the 0—5 cm depth and the lower biomass samples from the 5—200 cm depths.
Fig. 1. The relationships between total microbial biomass estimates obtained by summing extractable PLFAs and the biomass estimates obtained by chloroform fumigation extraction (A) and SIR (B) for all samples. Separate regression lines have been drawn for the high biomass samples collected from the 0—5 cm depth and the lower biomass samples from the 5—200 cm depths.
Fig. 2. The number of detectable PLFAs extracted from the soil samples (PLFA richness) with soil depth in the two profiles. Error bars = 1 SE, N=3.
Fig. 2. The number of detectable PLFAs extracted from the soil samples (PLFA richness) with soil depth in the two profiles. Error bars = 1 SE, N=3.
* Mol% below the detection limit of the instrument. The distribution of notional microbial groups, as identified using PLFA biomarkers, through the two soil profiles. Also shown are two PLFA ratios which may be indicative of the nutritional status of microbial communities. See Section 2 for identification of the PLFAs used to calculate the abundances of the specific microbial groups and the two ratios. One standard error indicated in parentheses, N = 3 in all cases
* Mol% below the detection limit of the instrument. The distribution of notional microbial groups, as identified using PLFA biomarkers, through the two soil profiles. Also shown are two PLFA ratios which may be indicative of the nutritional status of microbial communities. See Section 2 for identification of the PLFAs used to calculate the abundances of the specific microbial groups and the two ratios. One standard error indicated in parentheses, N = 3 in all cases
Individual PLFAs most responsible for the changes in PLFA signatures with sample depth along PC1. A positive loading score is driven by 2 decrease in proportional abundance with depth, a negative loading score indicates an increase in proportional abundance. We selected the PLFAs with loading scores that were relatively high in magnitude and had simila1 scores in both profiles. PLFAs belonging to notional microbial groups (see Section 2) are identified as follows
Individual PLFAs most responsible for the changes in PLFA signatures with sample depth along PC1. A positive loading score is driven by 2 decrease in proportional abundance with depth, a negative loading score indicates an increase in proportional abundance. We selected the PLFAs with loading scores that were relatively high in magnitude and had simila1 scores in both profiles. PLFAs belonging to notional microbial groups (see Section 2) are identified as follows
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MicrobiologyBiodiversitySoil HorizonsMonounsaturated Fatty Acid
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