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Surface and subsurface microbial biomass, community structure and metabolic activity as a function of soil depth and season
Applied Soil Ecology 20 (2002) 171–181
Surface and subsurface microbial biomass, community structure and metabolic activity as a function of soil depth and season E. Blume a , M. Bischoff d , J.M. Reichert a , T. Moorman c , A. Konopka b , R.F. Turco d,∗ a
Universidade Federal de Santa Maria, Campus Universitário 97105-900 Santa Maria, RS, Brazil b Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA c National Soil Tilth Laboratory, USDA, Agricultural Research Service, Ames, IA, USA d Department of Agronomy, Purdue University, West Lafayette, IN 47907-1150, USA Received 19 June 2001; accepted 22 February 2002
Abstract Microbial biomass, size and community structure along with an estimate of microbial activity and soil chemical parameters were determined at three depths in two soils (e.g. sandy loam Ultic Hapludalf and silt loam Mollic Hapludalf ) replicated three times under one winter and summer season. Microbial biomass and community structure were estimated from phospholipid-PO4 content and fatty acid methyl ester (FAME) measurements. Microbial activity and assimilative capacity were estimated using a 3 H-acetate incorporation into phospholipids and by incubating the soil samples at the average winter and summer temperatures, 3 and 20 ◦ C, respectively. We found that the size of the microbial biomass in both the surface and the subsurface soils was not significantly affected by the seasonal variation but activity increased by as much as 83% at the summer temperatures in the surface soil. We demonstrated using FAME analysis that for both soils seasonal changes in the subsurface microbial community occurred. These findings suggest that winter conditions will shift the population activity level in both the surface and subsurface systems and the biochemical structure of the community in the subsurface. In all cases, the inorganic chemical properties of the soil, as a function of season, remained constant. The greatly increased activity of microbial population at the higher temperature will favor the capacity of the system to utilize nutrients or organic materials that may enter soil. During low temperature seasons the capacity of either surface or subsurface soils to assimilate materials is generally diminished but the reduction reflects changes in metabolism and not a reduced biomass size. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Subsurface soils; Soil microbial activity; Seasonal response; Soil microbiology
1. Introduction While the existence and activity of microorganisms in the subsurface, particularly in deep soils and aquifers, have been demonstrated by Ghiorse and ∗ Corresponding author. Tel.: +1-765-496-3212; fax: +1-765-496-3210. E-mail address: [email protected] (R.F. Turco).
Wilson (1988), Dobbins et al. (1992), Crocker et al. (2000) and recently reviewed by Krumholz (2000), less information is available concerning the microbial populations in the shallow subsurface. This region is easily impacted by agricultural production or other surface perturbations including contaminant releases (Konopka and Turco, 1991; Doods et al., 1996; Ames and Hoyle, 1999; Lee et al., 1999). Few studies have examined how season affects the activity of the
0929-1393/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 9 - 1 3 9 3 ( 0 2 ) 0 0 0 2 5 - 2
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resident populations and their ability to respond to a perturbation such as the introduction of organic pollutants. Studies that have examined the seasonal effects on microbial activity and biomass size of the subsurface have been contradictory. Buchanan and King (1992) and Kaiser and Heinemeyer (1993) observed a greater microbial biomass size in the summer as compared to winter and suggested this is a direct consequence of higher temperatures. Bååth and Söderström (1982) and Sarathchandra et al. (1989) showed that soil microbial biomass was greatest in the spring and fall and lowest in the summer and winter. Holmes and Zac (1994) reported no differences in the size of the biomass as related to season. For surface soil, Buchanan and King (1992) found higher microbial activity in the summer than in the winter as did Palmisano et al. (1991) for freshwater streams. Palmisano et al. (1991) established that seasonal effects could be reversed by incubating environmental samples collected in the winter at a summer temperature of 22 ◦ C. Conrad et al. (1999) reported fluctuations of microbial activity that can be associated with seasonal changes in ground water levels. A positive relationship between microbial biomass and soil moisture was reported by Van Gestel et al. (1992). Work by Arnold et al. (1999) found that for a range of controlled temperatures (5, 15, 25 ◦ C) and moisture (20, 120, 220, 320% water holding capacity) microbial biomass size was greatest under cool wet conditions. In contrast, Srivastava (1992) observed lower biomass in the rainy season, attributing it to an increase in grazing of microbes by the soil fauna (protozoa). For a Florida soil sampled in late January, Tate (1979) observed a decrease in microbial activity of approximately 50% compared to summer sampling dates. Wood et al. (1993) obtained higher activities in the summer for two different sites but only in one site was the increased activity successfully expressed as a function of temperature. Others have shown the relationship between temperature and rates of mineralization of phenol to vary as a function of soil type and horizon (Thornton-Manning et al., 1987). In contrast, cresol mineralization in an estuarine environment appeared to be independent of temperature (Bartholomew and Pfaender, 1983). Many of these studies have concentrated on deep subsurface conditions and these deeper samples are protected from local temperature extremes because they lie beneath
the damping depth. The damping depth is a function of the soil’s bulk density and water content and represents the point in the soil profile where the effects of daily changes in surface temperature are lost or dampened. Samples collected above the damping depth can reflect the influence of the local air temperature. For soil collected beneath the damping depth, temperatures are modulated on an internal basis and are not as influenced by the local air temperature. The lack of information on seasonal effects on microbial biomass in the shallow subsurface has been a major limitation to advancing our understanding of the consequences and pathways of pollution (Conrad et al., 1999; Looney and Falta, 2001). In view of these results, conclusions about the effects of seasonal variation on the microbial activity (capacity to function) and biomass size in surface and subsurface soils above the damping depth cannot be made with any certainty. We hypothesize the effect of season will only be detectable at the soil surface and that biomass size is a poor indicator of seasonal effects. This study was designed to provide insight on the role of season in modulation of the size of the microbial biomass, the community structure and activity for two soils as a function of season and depth in the profile.
2. Material and methods 2.1. Soil sampling and characterization Two soils, a Tracy sandy loam (coarse-loamy, mixed, mesic Ultic Hapludalf) and a Lauramie silt loam (fine-loamy, mixed, mesic Mollic Hapludalf ) were collected at Purdue University’s Piney and O’Neil Agricultural Research Centers, respectively. Three cores were collected 1 m apart from each other, in both February and July. Both sites were planted to corn at the time of the July collection. Both sites were tile drained (∼1 m) to remove excess spring water but the actual water table was two to three meters below the lowest point of collection. The cores were collected using a truck-mounted D25 Drill Rig (Diedrich Drill Inc., La Porte, IN) continuous core sampler (10.16 cm i.d.). The sampler was first rinsed with water and then methanol between samplings to control contamination of subsurface soil with surface soil. After extrusion the cores were divided into sections
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at 105 ◦ C for 24 h and determining the loss of water from the samples (Table 1).
and to further control contamination the exterior 1 cm of each section was removed with a sterile spatula. The depths analyzed in the study were: 0.0–0.2, 0.7–0.9, and 1.5–1.7 m for the Tracy and 0.0–0.2, 0.5–0.7, and 1.1–1.3 m for the Lauramie. Following the exterior section removal, the cut sections were placed in sterile plastic bags and transported on ice to the Purdue Laboratory for Soil Microbiology. After mixing, a subsample was freeze-dried for further chemical and microbial biomass analysis. The remaining fresh soil was stored at 4 ◦ C for microbial activity analysis. In all cases, microbial activity analysis was started within 48 h of core collection. With a bulk density of 1.6 for the soils, damping depth is calculated to be 3.38 m. Therefore, the samples were collected above the damping depth and as a result the microbial populations in even the subsurface will be responding to the influences of the local air temperature. Total organic carbon (TOC) was analyzed with the NA 1500 NC carbon/nitrogen analyzer (Fisons Instruments, Beverly, MA) after removal of (1) inorganic carbon with a 50% hydrochloric acid solution, (2) inorganic N (NH4 + NO3 ) by extracting with 1N KCl and analyzing with a Quick Chem and Trace Analyzer (Lachat Instruments, Milwaukee, WI), (3) available P by the Bray P-1 and (4) exchangeable K by the NH4 OAc methods (Dahnke, 1988). Moisture was determined by placing the samples in an oven
2.2. Microbial biomass and microbial diversity Microbial biomass was estimated using the phospholipid-PO4 content of the soil. Extractions were done in triplicate for the surface soil and duplicates of four subsamples of 10 g for the subsurface soil. After extraction the four subsamples were combined to exceed the procedure’s detection limit. Phospholipids were extracted using the method of Findlay et al. (1989). Total lipid was extracted from the soils using methanol/chloroform/phosphate buffer (1:2:0.8) and phospholipids were fractionated using column chromatography (Findlay et al., 1989). A 100 l portion of the phospholipid extract was used to determine phospholipid-PO4 after potassium persulfate digestion using a colorimetric assay (Findlay et al., 1989). The remaining extract was used to characterize community structure by PLFA analysis. Phospholipid extract was esterified by mild alkaline methanolysis and the resulting fatty acid methyl esters (FAMEs) were analyzed by gas chromatography. FAMEs were determined using a Hewlett Packard Model 5890 GC equipped with a capillary column (DB-5, 60 m × 0.25 mm, 0.25 film, J&W Scientific, Folsom, CA), flame ionization detector (Tunlid et al.,
Table 1 Chemical analysis of samples collected from two soils at three depths, in the winter and in the summer Soil Tracy Winter
Summer
Lauramie Winter
Summer
Depth (m)
pH
Moisture (%)
Total organic C (%)
0.0–0.2 0.7–0.9 1.5–1.7 0.0–0.2 0.7–0.9 1.5–1.7
6.6 6.6 7.0 6.6 5.9 5.9
16.1 10.8 13.7 11.5 11.8 12.5
1.72 0.13 0.48 1.80 0.32 0.42
0.0–0.2 0.5–0.7 1.1–1.3 0.0–0.2 0.5–0.7 1.1–1.3
5.5 6.4 6.4 5.6 5.3 8.2
18.2 15.3 13.4 11.4 12.5 9.2
0.62 (0.02) 0.23 (0.03) 0.15 (0.02) 0.65 (0.03) 0.32 (0.07) n.d.b
(0.07)a a (0.02) b (0.28) b (0.17) a (0.05) b (0.19) b a b b a b
Inorganic N (mg kg−1 )
Available P Exchangeable K (kg ha−1 )
8.5 5.1 7.3 10.4 5.9 7.4
(1.51) a (0.86) b (0.89) ab (1.8) a (0.94) b (0.75) ab
105.3 13.4 25.4 103.8 10.5 17.9
(6.7) a (3.9) b (2.0) b (10.1) a (5.2) b (2.2) b
225.9 69.8 85.8 279.3 114.2 102.3
(53.3) a (16.5) b (5.6) b (55.1) a (19.4) b (31.4) b
11.8 12.2 5.8 11.1 12.1 7.1
(4.9) a (6.8) a (1.0) a (2.7) a (5.9) a (0.97) a
153.1 34.3 28.4 177.7 52.3 2.2
(23.3) a (23.5) b (22.7) b (15.9) a (7.8) b (0.0) b
285.6 209.4 157.5 321.1 228.5 76.2
(17.4) (19.4) (60.8) (13.7) (25.3) (14.3)
a ab b a ab b
a Numbers in parenthesis are the standard deviation of the mean. Within soil type, means followed by similar letters are not significantly different (P < 0.05). b n.d.: not determined.
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1989) and data collected with Varian Star Workstation software. Identification of fatty acids was based on comparison of retention times to purchased standards (Matreya Inc., Pleasant Gap, PA, Supelco, Bellefonte, PA) and quantified using an internal standard (nonadecanoic methyl ester). Characterization of microbial community structure was based on FAME profiles (Bobbie and White, 1980; Guckert et al., 1985). Classical fatty acid terminology is utilized: A:BωC where A indicates the total number of carbon atoms, B the number of unsaturations and C the number of carbon atoms between the closest unsaturation and the alpha carbon. Cy and OH indicate cyclopropyl and hydroxy groups, respectively. The suffixes c and t represent cis and trans isomers, and i and a indicate iso and anteiso methyl branching, respectively. Total bacterial phospholipid fatty acids are defined by the levels of 15:0, i15:0, a15:0, i16:0, 17:0, i17:0, a17:0, 16:1ω9c/t, Cy17:0 and Cy19:0 (Federle, 1986; Frostegård et al., 1993; Bardgett and McAlister, 1999); Gram positive bacteria are defined by the levels of i15:0, a15:0, i17:0, a17:0; and Gram negative bacteria are defined by Cy17:0 and Cy19:0. 2.3. Microbial activity Microbial activity was estimated directly from the incorporation of 3 H-acetate into phospholipids. Acetate is readily used by the resident microbial biomass and following its incorporation into the biomass, over a short time period, can provide information on the rate of intrinsic metabolism (Lovell and Konopka, 1985). 3 H-acetate was chosen because of its higher specific activity, as compared to 14 C-acetate. 3 H-acetate incorporation into microbial lipids is a highly sensitive method for assessing activity and this allowed for the addition of a lower concentration of the substrate in an effort to approximate an in situ incorporation situation. Triplicate 2 g samples of soil were weighed into 50 ml sterile plastic centrifuge tubes (Fisher Scientific, Pittsburgh, PA). To each tube, 3 H-acetate-sodium salt (specific activity of 6.08 Ci mmol−1 ; Dupont NEN Products, Boston, MA) diluted with cold sodium-acetate to achieve a 5 Ci addition of radiolabel was added in 1 ml aliquots. Soil slurries were created with excess water to ensure an even distribution of the label to the resident population. The tubes were vortexed and sets of tubes
incubated for 0, 2, 6, and 12 h. To simulate the soil temperature at the time of core collection, winter samples were incubated at 3 ± 1 ◦ C and summer samples at 20 ± 2 ◦ C. Analysis of available soil temperature (at 10 cm) data for the two locations indicated the average winter (November–March) soil temperature is approximately 3 ◦ C. The average temperature for the remainder of the year is approximately 20 ◦ C. It is also clear from the available information that incubation of samples collected in the winter at higher temperatures will result in increased rates of such parameters as degradation or incorporation of substrate (Palmisano et al., 1991). Therefore, our incubation temperatures were chosen to mimic in situ conditions and estimate the reaction potential for processes as occurring within the particular season. At each incubation time, activity was stopped by adding 5 ml of the phospholipids extraction solution (chloroform–methanol–phosphate buffer) to the corresponding set of tubes. The tubes were then frozen (−5 ◦ C) until continuation of phospholipids analysis. After thawing, phospholipids were extracted as described above, and the chloroform extracts evaporated under a stream of nitrogen (50 ◦ C). The phospholipid extract was resuspended in 1 ml chloroform, an aliquot of 0.5 ml was transferred to a scintillation vial and mixed with 15 ml of scintillation cocktail (BetamaxTM, ICN Biochemicals Inc., Costa Mesa, CA). The 3 H radioactivity was counted in a liquid scintillation counter (Tri-Carb 1600 TR, Packard Instruments, Downers Grove, IL). 2.4. Statistical analysis For the measurements of microbial biomass, TOC, inorganic available P, mineral N and exchangeable K, an analysis of variance was conducted using a block design and testing for the main effects of replication, soil type (location), depth, and season as well as possible interactions. Analysis was conducted using a general linear model (GLM) within Minitab (version 13). Initial analysis of the combined data set indicated significant (P < 0.05) differences between the two soils types. Data was then analyzed for each location, independently. Rates of incorporation of acetate into microbial biomass were calculated by first fitting the uptake data to zero-order kinetic model, using curve fitting software SPSS (SigmaPlot, San Rafael, CA)
E. Blume et al. / Applied Soil Ecology 20 (2002) 171–181
and then evaluating the rate using GLM procedures within Minitab (version 13). For characterization of the biomass population structure, at each soil sampling time, the FAME concentration data were converted to mol% of the total fatty acid concentration; principal component analysis (PCA) was used to compare fatty acid profiles for each sample (Systat 8, SPSS, Chicago, IL). PCA is useful in discerning patterns within the PLFA data (Bossio and Scow, 1995). Prior to analysis the raw data was arcsine transformed and extracted into a correlation matrix. The correlation matrix was evaluated and eigenvalues developed; from these values, eigenvectors were determined. Analysis was conducted with various rotations of the data in order to test different loadings on factor 1. Following these analyses an approach using no rotation of data was selected as it sequestered the highest amount of variance within factor 1. Where PCA indicated a fatty acid contributed to the loading on a factor, the general linear model (GLM) function of Systat version 8 was used to test between the effects of sampling locations, depths and seasons on the factor loading.
3. Results 3.1. Soil chemical properties An analysis of variance (P < 0.05) was conducted on the combined data set from the two locations and showed soil type and depth to be the main effects controlling the distribution of key physical and biological properties (Table 1). Soil data were divided between the two locations and a separate analysis of variance was conducted. Chemical analysis (Table 1) showed a numerical but non-significant increase in pH with depth for both soils, except for the deepest sample from the Lauramie soil where the pH increased significantly (P < 0.05). The high pH value (8.2) observed in the deepest layer of the Lauramie soil is due to a localized carbonate deposit which is known to exist in the region. P and K levels for both soils and both seasons showed a significant (P < 0.05) decline with depth. Available P and exchangeable K were significantly higher at the surface (P < 0.05) and were elevated slightly during the summer as a consequence of fertilizer application for the two soils. However, only organic carbon levels showed a significant interaction
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between depth and season (P < 0.05). Higher levels of N, P, and K, especially at the upper two depths, were observed for the Lauramie soil as compared to the Tracy soil. The Tracy soil contained approximately two times more organic carbon (1.72%) at the surface than the Lauramie soil (0.62%). 3.2. Microbial biomass Studies have not evaluated seasonal effects on subsurface microbial biomass but our findings suggest there is little impact on the size of community. Lower biomass values in the winter have been observed for the plough layer of several temperate soils (Lynch and Panting, 1980; Patra et al., 1990; Buchanan and King, 1992; Kaiser and Heinemeyer, 1993). Average microbial biomass levels (Table 2) as determined by phospholipid fatty acid (PLFA) were higher in the Tracy soil than in the Lauramie soil but in both soils the majority of the biomass occurred in the surface horizon (P < 0.01). For example, in the summer the average microbial biomass in the Tracy surface soil was approximately 22 and 78 times greater than at 0.7–0.9 m depth and 1.5–1.7 m, respectively. However, no significant (P = 0.05) changes in the size of the microbial biomass for the surface or subsurface were observed between the winter and summer samples for either soil. This is similar to the seasonal effects reported by Holmes and Zac (1994). A similar pattern of biomass decline with depth in the profile was found in the Lauramie soil (Table 2). However, in the Lauramie soil the magnitude of decline from the surface to 0.5–0.7 m was less (about 18-fold) than observed with the Tracy soil. Microbial biomass was similar between 0.5–0.7 m and the 1.5–1.7 m layer but the overall decline between the surface and the lowest depth was 49-fold. As was reported for the Tracy surface soil, microbial biomass was not altered by the winter conditions and no significant changes in the size of the biomass was observed between winter and summer for the subsurface. It is clear that seasonal affects were fairly limited in their impact on the size of the biomass and this supports earlier findings by Holmes and Zac (1994). The lower nutrient levels, in particular C, deeper in the profile (Table 1) may account for the lower biomass levels in the subsurface materials as we show a linear correlation (r = 0.84) between TOC and decrease in
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Table 2 Biochemical analysis of samples collected from two soils at three depths, in the winter and in the summer Soil
Tracy Winter
Summer
Lauramie Winter
Summer
Depth (m)
Bacterial PLFA (nmol g−1 dry soil)a
0.0–0.2 0.7–0.9 1.5–1.7 0.0–0.2 0.7–0.9 1.5–1.7
6.5 0.3 0.1 6.8 0.3 0.1
0.0–0.2 0.5–0.7 1.1–1.3 0.0–0.2 0.5–0.7 1.1–1.3
6.6 0.7 0.3 5.9 0.4 0.1
PLFA (nmol PLFA g−1 dry soil)
Trans:cis ratio Incorporation rate (C16:1ω9t/C16: (dpm nmol-per total 1ω9c) phospholipid h−1 )b
(1.6) ad (0.13) b (0.03) c (1.5) a (0.20) b (0.06) c
16.4 0.7 0.3 15.6 0.7 0.2
(2.9) a (0.3) b (0.06) b (3.29) a (0.43) b (0.12) b
1.81 2.77 6.98 1.51 1.82 1.90
1.7 8.5 3.8 2.3 1.9 1.7
× × × × × ×
105 104 102 105 105 104
(2.9 (3.9 (1.4 (1.2 (4.1 (7.4
× × × × × ×
104 ) 104 ) 102 ) 104 ) 104 ) 103 )
b c d a ab d
5.4 9.3 1.0 5.8 2.4 8.0
× × × × × ×
10−5 10−3 10−5 10−4 10−2 10−4
(9 × 10−5 ) c (1.1 × 10−2 ) (0) c (1.6 × 10−4 ) (7.9 × 10−2 ) (8.5 × 10−4 )
(1.8) a (0.51) b (0.16) b (0.90) a (0.18) b (0.06) c
14.6 1.5 0.6 14.7 0.8 0.3
(3.1) a (1.19) b (0.4) b (3.5) a (0.34) b (0.13) b
1.75 2.58 2.81 2.38 4.26 4.40
6.0 1.8 7.9 1.1 1.1 6.2
× × × × × ×
104 105 103 105 105 104
(1.8 (1.7 (5.8 (7.0 (3.3 (4.0
× × × × × ×
104 ) 105 ) 103 ) 104 ) 104 ) 104 )
bc a c ab ab ab
2.1 7.5 5.9 3.2 3.8 1.1
× × × × × ×
10−4 10−3 10−4 10−4 10−3 10−2
(4 × 10−5 ) b (8.1 × 10−3 ) ab (5.7 × 10−4 ) ab (2.4 × 10−5 ) b (7.0 × 10−4 ) ab (1.3 × 10−2 ) a
Specific activity (dpm 3 H per nmol-total phospholipid h−1 )c
c b a bc
a
Contribution from phospholipid fatty acids: 15:0, i15:0, a15:0, i16:0, 17:0, i17:0, a17:0, 16:1ω9c/t, Cy17:0 and Cy19:0, only. Total phospholipid from phospholipid-PO4 values. c Assumes 100 nmol PO -P per 3.14 × 109 cells using total nmol PO g−1 (Findlay et al., 1989). 4 4 d Numbers in parenthesis are the standard deviation of the mean. Within a soil type, means followed by similar letters are not significantly different (P < 0.05) within a depths and seasons. b
total PO4 -biomass. Others have reported decreases in microbial biomass with depth in the profile (Ghiorse and Wilson, 1988). Federle et al. (1986) observed a decrease in phospholipid-PO4 with depth for four different soils; however, the pattern and magnitude of decrease varied with soil. Kaiser and Heinemeyer (1993) reported microbial biomass at 0.5 m was six times lower than at the surface. Much evidence exists to support a conclusion that the higher biomass values found in the surface soil is a function of a higher level of available carbon. The presence of higher quantities of labile C (root exudates, etc.) as compared to the more complex substrates available from crop residues in the winter would contribute to increases in the microbial biomass. When biomass biochemical analysis is restricted to the bacterial indicator of PLFA (15:0, i15:0, a15:0, i16:0, 17:0, i17:0, a17:0, 16:1ω9c/t, Cy17:0 and Cy19:0) the pattern is similar but the magnitude of the change is reduced (Table 2). This exclusion removes the contribution from plants and algae, micro algae, diatoms and fungi (Vestal and White, 1989; Pankhurst et al., 2001). For example, in the winter sample for the Lauramie soil, the difference between the surface and the lowest horizon was 22-fold reduction compared to a 54-fold difference for the total phospholipid. This
provides some insight into the significant contribution of non-bacterial fatty acids to the total PLFA load in the surface soil. 3.3. Microbial community structure Use of PLFA analysis prevents bias in estimation of diversity that is typically encountered when using selective media (Palumbo et al., 1996) or other culture procedures (Konopka et al., 1998). For the recovered fatty acid, the resulting profiles indicate that changes in population structure can be represented by (∼50% of the total variance) three principal components (PC). A plot of factor 1 versus factor 2 (∼40% of the total variance) separates the samples by the depth and season in which the sample was collected (Fig. 1). The significance of these separations was confirmed using a GLM and mean separation for the individual fatty acids; a significant (P = 0.05) effect of season was also demonstrated. The positive loading along factor 1 (season) was driven by higher levels of the unsaturated fatty acids 20:1ω11, 22:1ω13, 16:1ω9c, as well as 20:0 17:0 22:0 and 10Me18:0 and Cy17:0. Negative loading on factor 1 was driven by changes in fatty acid 12:0, 19:1ω9c, 3OH14:0 i12:0 and 18:0 in the summer samples. The strongest positive loadings on factor
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Fig. 1. Principal component analysis (PCA) of PLFA profiles obtained from discrete soil samples collected in July or February, from one of three sample depths: W, winter (February) sample; S, summer sample (July); 1, surface soil; 2, root zone and 3, subsurface.
one were from the fatty acids 16:1ω9c, 10Me18:0 and Cy17:0 all of which are associated with bacteria. A comparison of Gram positive to Gram negative indicators shows that in both soils, regardless of season, the frequency of Gram positive bacteria increases with depth but not with season. The ratios of Gram positive to Gram negative for the surface soil ranged from 2.5 to 3.2:1 while for subsurface soils the ratio increased and ranged from 4.9 to 7:1. The changes in the fatty acid profiles, associated with sample depth are expressed along factors 2. Positive loading on factor 2 (depth) is driven by increased levels of Cy19:0, 16:1ω9t, 18:1ω9t i15:0 and two unidentified fatty acids that occurred in the surface samples. The negative loading on factor 2 is driven by decreases in the levels of 14:0, 15:0, 2OH12:0, 16:0 and 17:0 fatty acids occurring at depth in the profile. Fatty acid separation on factor 3 is also related to depth. Three fatty acids, an unidentified material, i17:0, and i16:0 are key in controlling distribution on this factor. However, statistical analysis (GLM) indicated the separations on factor 3 were not significant. When combined, the information in factors 2 and 3 indicate that differences between the upper soil samples and lower samples and show that seasonal
effects were strongest in the subsurface samples. More importantly, the ratio of 16:1ω9t to 16:1ω9c (trans:cis) which indicates the response of cells to stress conditions, shows that in the deeper portions of the profile the microbial populations are stressed as a ratio of trans:cis shows a lager fraction of the 16:1ω9t (Vestal and White, 1989). This difference may reflect the higher nutrient status in the surface soil as the occurrence of total organic C was increased. The total organic C levels indicate a contribution from plants and show an improved nutrient content; as is indicated in a lower 16:1ω9t to 16:1ω9c ratio. Overall, the fatty acid findings suggest two conclusions: 1) that subsurface (>1.5 m) samples are different from either surface or root zone materials and 2) that season has a stronger effect on sub-surface communities than on surface communities, as surface samples tended to group near each other in the PC analysis, regardless of season (Fig. 1). 3.4. Microbial activity Microbial activity was estimated by incubating soils collected from the different depths and seasons with 3 H-acetate and determining the rates of incorporation
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into microbial phospholipids. The incubations were conducted at the average seasonal temperature to allow an assessment of metabolic response to seasonal temperature. It is clear from other studies that incubation at warmer temperatures will increase reaction rates (Palmisano et al., 1991). What is less clear is how much the seasonal temperature will reduce the microbial reaction rates. By following the incorporation of 3 H-acetate into phospholipids we estimated microbial activity at the average seasonal temperature. Data were fitted to a zero-order model in order to achieve a biomass independent measurement of activity. While few differences in the size of the biomass were observed within the two soils (Table 2), activity was numerically higher in the Tracy soil than in the Lauramie and a significant seasonal response with depth was noted for some conditions. The cold temperature incorporation rate for the Tracy soil (Table 2) decreased three-fold from the surface to 0.7–0.9 m layer and was 137 times lower thereafter (P < 0.05). The differences in the rates of 3 H-acetate incorporation between depths for the Lauramie soil (Table 2) were less pronounced than for the Tracy soil. For the Lauramie soils, the summer samples were similar at all depths (P = 0.05). In the cold samples, a significant increase of two fold was observed from the surface to the 0.5–0.7 m layer and a threefold decrease from the surface to the subsurface layer. In all cases, the differences were statistically significant (P < 0.05). A calculation of the specific activity (Table 2) of the microbial population at the different depths was made by dividing the activity of the first time-point at each depth by the calculated number of cells (derived from the phospholipid-PO4 , after Findlay et al., 1989), to provide an estimate of the cellular specific activity (dpm 3 H-acetate incorporated per cell assuming 100 nmol PO4 -P per 3.14 × 109 cells) in the summer and winter samples. Maximum activity was generally observed in the middle profile (0.5–0.9 m). Studies with surface soil have demonstrated that higher incubation temperatures can increase microbial activity (Alexander, 1977). As significant seasonal variation in temperature occurs in the soil profile (Hilell, 1982) at depths up to 10 m (Ghiorse and Wilson, 1988), it would be expected that microbial activity at the depths evaluated in our study (up to 1.7 m) are responding to air temperature. Indeed, at
all depths the activity was higher in the summer than in the winter. For the winter to summer change, acetate incorporation activity in the Tracy soil (Table 2) had significant (P < 0.05) increases of 1.5 for the surface, 2.8 for the 0.7–0.9 m, and a non-significant increase of 49 times for the 1.5–1.7 m depths. Acetate incorporation rates in the Lauramie soil in the summer (Table 2) showed an increase, as compared to the winter, of 1.2 times for the surface, 1.30 times for the 0.5–0.7 m, and 4.9 times for the 1.1–1.3 m layers. In both cases the difference in the levels of mineral nutrients and moisture, especially for the subsurface soils, between the winter and the summer samples was minimal. A minor variation was noted for Lauramie soil that had a wintertime incorporation rate in the 0.5–0.7 level that was similar to the values found for summer samples and for the Tracy soil at 1.5–1.7 in which the rate was lower in the winter but not significantly different from the summer sample. The relation of microbial activity to chemical properties of the soils was different for each soil and season. For the Lauramie soil, the overall rates of acetate incorporation were highly correlated to TOC in both seasons (r > 0.95), to N, P, and K in the summer (r > 0.91), and to K and moisture in the winter (r > 0.91). For the Tracy soil, the correlation to TOC was lower in both seasons (r = 0.75 in the winter and 0.70 in the summer). Activity was more related to P and K in the winter (r = 0.86) than in the summer (r < 0.77); the relation to N was very poor in both seasons (r < 0.30); and a negative relation was observed with moisture in the summer (r = −1.00) but not in the winter (r = 0.55). No relation was found between 20 different soil factors and the mineralization of phenol in subsurface soils (Dobbins et al., 1987). On the other hand, Federle et al. (1986) showed that 88.6% of the variation in biomass and 82.4% of the variation in activity could be explained by edaphic factors.
4. Discussion Our general hypothesis had been that when compared to the subsurface soils, the surface soil would should significant seasonal effects in biomass size, structure and activity. Our work indicates a stable biomass size across seasons and this contrasts both
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Grayston et al., 2001 who showed total PLFA to change with season with the greater biomass size occurring in early winter and Bååth and Söderström (1982) who showed biomass to be higher in warmer conditions. However, our findings are similar to Holmes and Zac (1994) who showed that soil microbial populations were stable in forest systems. Grayston et al. (2001) indicate that the presence of plants and plant species is a significant factor governing the resident microbial community structure (size and composition). Our data suggests the nutrient status, as indicated by the level of organic C is greater at the soil surface regardless of season lessens the stress on the population as indicated by trans to cis ratios. Our data also shows the structure of the subsurface community is different from the surface, but when the response is adjusted for biomass size, the subsurface population is comparable in their respond to introduced substrates. While the size of the microbial biomass in the surface and subsurface of the Tracy and Lauramie was not dramatically affected by the seasonal changes, season did affect the microbial population’s structure and activity. PC-analysis of the fatty acid data indicates a major split in the population structure between the surface and the deeper subsurface for the two soils but that seasonal effects on the surface soil were limited (Fig. 1). The seasonal finding was somewhat unexpected, as the more pronounced environmental effect was hypothesized to be occurring on the soil surface, which is exposed, directly to the environment. Seasonal effects were manifested in the population at the lower depths and point out the importance of considering the damping depth in microbial ecology of soils. It is suggested that the improved nutrient status (i.e. trans:cis ratio and organic C level) of the population in the surface may have enhanced their ability to withstand environmental perturbations. It is also suggested that the overall size of the biomass in the surface soil masks observation of many of the subtle changes occurring in response to temperature. Changes in the subsurface biomass structure as affected by temperature maybe more apparent given the smaller size of the population. Phelps et al. (1989a,b) observed that incorporation of 14 C-acetate into microbial lipids, 3 H-thymidine into microbial DNA, and mineralization of 14 C-acetate and 14 C-glucose at 6 and 15 m was more than two orders
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of magnitude lower than at the surface. Federle et al. (1986) measured activity as the rate of FDA (fluorescein diacetate) hydrolysis in four different soils, and demonstrated that declines in hydrolysis rates with depth exhibited a unique pattern of activity throughout the soil profile. They also observed that the activity pattern did not always agree with the biomass pattern, which we observed in our biomass-adjusted data set. While our data supports these earlier studies provided we do not adjust for the smaller biomass size, our results also agree with those of Tate (1979) who reported that microbial activity per cell, towards organic compounds such as succinate, acetate, and salicylate increased with increasing depth although the total number of bacteria decreased. Data in Table 2 indicates a tendency for a larger specific activity at the mid-soil depth (0.5–0.9 m) in the summer, showing that the high temperatures or drier soil conditions may reduce activity on the surface. It is clear from our work that any assessments of degradation ability (rate) for subsurface systems should be compared to surface systems using caution as the biomass size is reduced in the subsurface and this smaller population size affects our assessment of the apparent system response. Reduced mineralization of phenol (Dobbins et al., 1987) and reduced microbial CO2 production (Wood et al., 1993) has been shown to occur in the subsurface environment. Many have reported profile-depth related decreases in activity towards other organic compounds and in general, these studies have not normalized for biomass size. However, our biomass normalized 3 H-acetate incorporation study shows that per unit of biomass, the subsurface population to be highly effective in utilization of added materials. A strong relation occurs between temperature and microbial activity but not between temperature and microbial biomass size, especially in the subsurface soils was shown to exist. Activity was significantly increased in the summer as compared to the winter for all depths. This is similar to the findings of Thornton-Manning et al. (1987) who obtained an 18% increase in rates of phenol mineralization at 20 ◦ C compared to 5 ◦ C for materials taken at 2 m deep for two different soil series. It could be expected the capacity of soils to assimilate organic materials would be diminished during low temperature seasons and this reflects the lessened activity of the biomass not a reduction in numbers in response to the cold.
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The ramifications of this finding suggest that any leached chemicals may have a longer life-time in the subsurface, especially if the leaching event occurs in the fall or winter. In contrast, that warmer summer conditions allowed a rapid response to the substrate especially near but not on the surface. This effect is generally unaccounted for in environmental fates models. Our work supports previous findings about the positive affects of increased temperature on transformation rates but surprisingly the most efficient area for transformation was often found to be in the layer below the surface.
Acknowledgements This work was supported by the USEPA Paper Number 16,776 of the Purdue Agricultural Experiment Station Series and Conselho Nacional de Desenvolvimento Técnico Cient´ıfico—CNPQ, Brazil. References Alexander, M., 1977. Introduction to Soil Microbiology, 2nd Edition. John Wiley, New York. Ames, R.A., Hoyle, B.L., 1999. Biodegradation and mineralization of atrazine in shallow subsurface sediments from Illinois. J. Environ. Qual. 28, 1674–1681. Arnold, S.S., Frenandez, I.J., Rustad, L.E., Zibilske, L.M., 1999. Microbial response of an acid forest soil to experimental soil warming. Biol. Fert. Soils 30, 239–244. Bååth, E., Söderström, B., 1982. Seasonal and spatial variation in fungal biomass in a forest soil. Soil Biol. Biochem. 14, 353–358. Bardgett, R.D., McAlister, E., 1999. The measurement of soil fungal:bacterial biomass ratios as an indicator of ecosystem self-regulation in temperate meadow grasslands. Biol. Fert. Soils 29, 282–290. Bartholomew, G.W., Pfaender, F.K., 1983. Influence of spatial and temporal variations on organic pollutant biodegradation rates in an estuarine environment. Appl. Environ. Microbiol. 45, 103–109. Bossio, D.A., Scow, K.M., 1995. Impact of carbon and flooding on the metabolic diversity of microbial communities in soils. Appl. Environ. Microbiol. 61, 4043–4050. Bobbie, R.J., White, D.C., 1980. Characterization of benthic microbial community structure by high-resolution gas chromatography of fatty acid methyl esters. Appl. Environ. Microbiol. 39, 1212–1222. Buchanan, M., King, L.D., 1992. Seasonal fluctuations in soil microbial biomass carbon, phosphorus, and activity in no-till and reduced-chemical-input maize agroecosystems. Biol. Fertility Soils 13, 211–217.
Crocker, F.H., Fredrickson, J.K., White, D.C., Ringelberg, D.B., Balkwill, D.L., 2000. Phylogenetic and physiological diversity of Athrobacter strains isolated from unconsolidated subsurface sediments. Microbiology UK 146, 1295–1310. Conrad, M.E., Templeton, A.S., Daley, P.F., Alvarez-Cohen, L., 1999. Seasonally-induced fluctuations in microbial production and consumption of methane during bioremediation of aged subsurface refinery contamination. Environ. Sci. Technol. 33, 4061–4068. Dahnke, W.C., 1988. Recommended Chemical Soil Test Procedures for the North Central Region. North Dakota Agricultural Experimentation Station Bulletin 499, North Dakota. Dobbins, D.C., Aelion, C.M., Pfaender, F.K., 1992. Subsurface, terrestrial microbial ecology and biodegradation of organic chemicals: a review. Crit. Rev. Environ. Control 22, 67–136. Dobbins, D.C., Thornton-Manning, J.R., Jones, D.D., Federle, T.W., 1987. Mineralization potential for phenol in subsurface soils. J. Environ. Qual. 16, 54–58. Doods, W.K., Banks, M.K., Clenan, C.S., Rice, C.W., Sotomayor, D., Strauss, E.A., Yu, W., 1996. Biological properties of soil and subsurface sediments under abandoned pasture and cropland. Soil Biol. Biochem. 28, 837–846. Federle, T.W., Dobbins, D.C., Thorton-Manning, J.R., Jones, D.D., 1986. Microbial biomass, activity, and community structure in subsurface soil. Ground Water 24, 365–374. Federle, T.W., 1986. Microbial distribution in soil—new techniques. In: Megusar F., Gantar M. (Eds.), Perspectives in Microbial Ecology. Solvene Society of Microbiology, Ljublijana, pp. 723–730. Findlay, R.H., King, G., Watling, L., 1989. Efficacy of phospholipid analysis in determining microbial biomass in sediments. Appl. Environ. Microbiol. 55, 2888–2893. Frostegård, Å., Bååth, E., Tunlid, A., 1993. Shifts in the structure of soil microbial communities in limed forests as revealed by phospholipid fatty acid analysis. Soil Biol. Biochem. 25, 723–730. Ghiorse, W.C., Wilson, J.T., 1988. Microbial ecology of the terrestrial subsurface. Adv. Appl. Microbiol. 33, 107–172. Grayston, S.J., Griffith, G.S., Mawdsley, J.L., Campbell, C.D., Bargett, R.D., 2001. Accounting for variability in soil microbial communities of temperate upland grassland ecosystems. Soil Biol. Biochem. 33, 533–551. Guckert, J.B., Antworth, C.B., Nichols, P.D., White, D.C., 1985. Phospholipid ester-linked fatty acid profiles as reproducible assays for changes in prokaryotic community structure of estuarine sediments. FEMS Microbiol. Ecol. 31, 147–158. Hilell, D., 1982. Introduction to Soil Physics. Academic Press, New York. Holmes, W.E., Zac, D.R., 1994. Soil microbial biomass dynamics and net nitrogen mineralization in Northern hardwood ecosystems. Soil Sci. Soc. Am. J. 58, 238–243. Kaiser, E.A., Heinemeyer, O., 1993. Seasonal variations of soil microbial biomass carbon within the plough layer. Soil Biol. Biochem. 25, 1649–1655. Konopka, A., Turco, R.F., 1991. Biodegradation of organic compounds in vadose zone and aquifer sediments. Appl. Environ. Microbiol. 57, 2260–2268.
E. Blume et al. / Applied Soil Ecology 20 (2002) 171–181 Konopka, A., Oliver, L.D., Turco, R.F., 1998. The use of carbon substrate utilization patterns in environmental and ecological microbiology. Microbial Ecol. 35, 103–115. Krumholz, L.R., 2000. Microbial communities in the deep subsurface. Hydrogeol. J. 8, 4–10. Lee, M.D., Sehayek, L., Sleep, B.E., Vandell, T.D., 1999. Investigation and Remediation of 1,2-dicholorethane spill. II. Documentation of Natural Attenuation. Lynch, J.M., Panting, L.M., 1980. Cultivation and the soil biomass. Soil Biol. Biochem. 12, 29–33. Looney, B.B., Falta, R.W. (Eds.), 2001. Vadose Zone: Science and Technology Solutions. Battelle Press, Columbus, OH, 2000. Vol. 2 (xxxix), 1540 p. Lovell, C.R., Konopka, A., 1985. Excretion of photosynthetically fixed organic carbon by metalimnetic phytoplankton. Microbial Ecol. 11, 1–9. Palmisano, A.C., Schwab, B.S., Maruscik, D.A., Ventullo, R.N., 1991. Seasonal changes in mineralization of xenobiotics by stream microbial communities. Canadian J. Microbiol. 37, 939–948. Pankhurst, C.E., Yu, S., Hawke, B.G., Harch, B.D., 2001. Capacity of fatty acid profiles and substrate utilization patterns to describe differences in soil microbial communities associated with increased salinity or alkalinity at three locations in South Australia. Biol. Fert. Soils. 33, 204–217. Patra, D.D., Brookes, P.C., Coleman, K., Jenkinson, D.S., 1990. Seasonal changes of soil microbial biomass in an arable and a grassland soil which have been under uniform management for many years. Soil Biol. Biochem. 22, 739–742. Palumbo, A.V., Zhang, C.L., Liu, S., Scarborough, S.P., Pfiffner, S.M., Phelps, T.J., 1996. Influence of media on measurement of bacterial populations in the subsurface—numbers and diversity. Appl. Biochem. Biotech. 57, 905–914.
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Phelps, T.J., Hedrick, D.B., Ringelberg, D., Fliermans, C.B., White, D.C., 1989a. Utility of radiotracer activity measurements for subsurface microbiology studies. J. Microbiol. Meth. 9, 15–27. Phelps, T.J., Rajone, E.G., White, D.C., Fliermans, C.B., 1989b. Microbial activities in deep subsurface environments. Geomicrobiol. J. 7, 79–91. Sarathchandra, S.U., Perrot, K.W., Littler, R.A., 1989. Soil microbial biomass: influence of simulated temperature changes on size, activity and nutrient content. Soil Biol. Biochem. 21, 987–993. Srivastava, S.C., 1992. Microbial C, N and P in dry tropical soils: seasonal changes and influence of soil moisture. Soil Biol. Biochem. 24, 711–714. Tate III, R.L., 1979. Microbial activity in organic soils as affected by soil depth and crop. Appl. Environ. Microbiol. 37, 1085–1090. Thornton-Manning, J.R., Jones, D.D., Federle, T.W., 1987. Effects of experimental manipulation of environmental factors on phenol mineralization in soil. Environ. Toxicol. Chem. 6, 615–621. Tunlid, A., Ringleberg, D., Phelps, T.J., Low, C., White, D.C., 1989. Biochemical analysis of biomass community structure, nutritional status, and metabolic activity of microbial communities in soil. In: Bollag, J.M., Stozky G. (Eds.), Soil Biochemistry, Vol. 6. Dekker, New York, pp. 229–262. Van Gestel, M., Lad, J.N., Amato, M., 1992. Microbial biomass responses to seasonal change and imposed drying regimes at increasing depths of undisturbed topsoil profiles. Soil Biol. Biochem. 24, 103–111. Vestal, R.J., White, D.C., 1989. Lipid analysis in microbial ecology. BioScience 39, 535–541. Wood, B.D., Keller, C.K., Johnstone, D.L., 1993. In situ measurement of microbial activity and controls on microbial CO2 production in the unsaturated zone. Water Resources Res. 29, 647–659.
Surface and subsurface microbial biomass, community structure and metabolic activity as a function of soil depth and season E. Blume a , M. Bischoff d , J.M. Reichert a , T. Moorman c , A. Konopka b , R.F. Turco d,∗ a
Universidade Federal de Santa Maria, Campus Universitário 97105-900 Santa Maria, RS, Brazil b Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA c National Soil Tilth Laboratory, USDA, Agricultural Research Service, Ames, IA, USA d Department of Agronomy, Purdue University, West Lafayette, IN 47907-1150, USA Received 19 June 2001; accepted 22 February 2002
Abstract Microbial biomass, size and community structure along with an estimate of microbial activity and soil chemical parameters were determined at three depths in two soils (e.g. sandy loam Ultic Hapludalf and silt loam Mollic Hapludalf ) replicated three times under one winter and summer season. Microbial biomass and community structure were estimated from phospholipid-PO4 content and fatty acid methyl ester (FAME) measurements. Microbial activity and assimilative capacity were estimated using a 3 H-acetate incorporation into phospholipids and by incubating the soil samples at the average winter and summer temperatures, 3 and 20 ◦ C, respectively. We found that the size of the microbial biomass in both the surface and the subsurface soils was not significantly affected by the seasonal variation but activity increased by as much as 83% at the summer temperatures in the surface soil. We demonstrated using FAME analysis that for both soils seasonal changes in the subsurface microbial community occurred. These findings suggest that winter conditions will shift the population activity level in both the surface and subsurface systems and the biochemical structure of the community in the subsurface. In all cases, the inorganic chemical properties of the soil, as a function of season, remained constant. The greatly increased activity of microbial population at the higher temperature will favor the capacity of the system to utilize nutrients or organic materials that may enter soil. During low temperature seasons the capacity of either surface or subsurface soils to assimilate materials is generally diminished but the reduction reflects changes in metabolism and not a reduced biomass size. © 2002 Elsevier Science B.V. All rights reserved. Keywords: Subsurface soils; Soil microbial activity; Seasonal response; Soil microbiology
1. Introduction While the existence and activity of microorganisms in the subsurface, particularly in deep soils and aquifers, have been demonstrated by Ghiorse and ∗ Corresponding author. Tel.: +1-765-496-3212; fax: +1-765-496-3210. E-mail address: [email protected] (R.F. Turco).
Wilson (1988), Dobbins et al. (1992), Crocker et al. (2000) and recently reviewed by Krumholz (2000), less information is available concerning the microbial populations in the shallow subsurface. This region is easily impacted by agricultural production or other surface perturbations including contaminant releases (Konopka and Turco, 1991; Doods et al., 1996; Ames and Hoyle, 1999; Lee et al., 1999). Few studies have examined how season affects the activity of the
0929-1393/02/$ – see front matter © 2002 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 9 - 1 3 9 3 ( 0 2 ) 0 0 0 2 5 - 2
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resident populations and their ability to respond to a perturbation such as the introduction of organic pollutants. Studies that have examined the seasonal effects on microbial activity and biomass size of the subsurface have been contradictory. Buchanan and King (1992) and Kaiser and Heinemeyer (1993) observed a greater microbial biomass size in the summer as compared to winter and suggested this is a direct consequence of higher temperatures. Bååth and Söderström (1982) and Sarathchandra et al. (1989) showed that soil microbial biomass was greatest in the spring and fall and lowest in the summer and winter. Holmes and Zac (1994) reported no differences in the size of the biomass as related to season. For surface soil, Buchanan and King (1992) found higher microbial activity in the summer than in the winter as did Palmisano et al. (1991) for freshwater streams. Palmisano et al. (1991) established that seasonal effects could be reversed by incubating environmental samples collected in the winter at a summer temperature of 22 ◦ C. Conrad et al. (1999) reported fluctuations of microbial activity that can be associated with seasonal changes in ground water levels. A positive relationship between microbial biomass and soil moisture was reported by Van Gestel et al. (1992). Work by Arnold et al. (1999) found that for a range of controlled temperatures (5, 15, 25 ◦ C) and moisture (20, 120, 220, 320% water holding capacity) microbial biomass size was greatest under cool wet conditions. In contrast, Srivastava (1992) observed lower biomass in the rainy season, attributing it to an increase in grazing of microbes by the soil fauna (protozoa). For a Florida soil sampled in late January, Tate (1979) observed a decrease in microbial activity of approximately 50% compared to summer sampling dates. Wood et al. (1993) obtained higher activities in the summer for two different sites but only in one site was the increased activity successfully expressed as a function of temperature. Others have shown the relationship between temperature and rates of mineralization of phenol to vary as a function of soil type and horizon (Thornton-Manning et al., 1987). In contrast, cresol mineralization in an estuarine environment appeared to be independent of temperature (Bartholomew and Pfaender, 1983). Many of these studies have concentrated on deep subsurface conditions and these deeper samples are protected from local temperature extremes because they lie beneath
the damping depth. The damping depth is a function of the soil’s bulk density and water content and represents the point in the soil profile where the effects of daily changes in surface temperature are lost or dampened. Samples collected above the damping depth can reflect the influence of the local air temperature. For soil collected beneath the damping depth, temperatures are modulated on an internal basis and are not as influenced by the local air temperature. The lack of information on seasonal effects on microbial biomass in the shallow subsurface has been a major limitation to advancing our understanding of the consequences and pathways of pollution (Conrad et al., 1999; Looney and Falta, 2001). In view of these results, conclusions about the effects of seasonal variation on the microbial activity (capacity to function) and biomass size in surface and subsurface soils above the damping depth cannot be made with any certainty. We hypothesize the effect of season will only be detectable at the soil surface and that biomass size is a poor indicator of seasonal effects. This study was designed to provide insight on the role of season in modulation of the size of the microbial biomass, the community structure and activity for two soils as a function of season and depth in the profile.
2. Material and methods 2.1. Soil sampling and characterization Two soils, a Tracy sandy loam (coarse-loamy, mixed, mesic Ultic Hapludalf) and a Lauramie silt loam (fine-loamy, mixed, mesic Mollic Hapludalf ) were collected at Purdue University’s Piney and O’Neil Agricultural Research Centers, respectively. Three cores were collected 1 m apart from each other, in both February and July. Both sites were planted to corn at the time of the July collection. Both sites were tile drained (∼1 m) to remove excess spring water but the actual water table was two to three meters below the lowest point of collection. The cores were collected using a truck-mounted D25 Drill Rig (Diedrich Drill Inc., La Porte, IN) continuous core sampler (10.16 cm i.d.). The sampler was first rinsed with water and then methanol between samplings to control contamination of subsurface soil with surface soil. After extrusion the cores were divided into sections
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at 105 ◦ C for 24 h and determining the loss of water from the samples (Table 1).
and to further control contamination the exterior 1 cm of each section was removed with a sterile spatula. The depths analyzed in the study were: 0.0–0.2, 0.7–0.9, and 1.5–1.7 m for the Tracy and 0.0–0.2, 0.5–0.7, and 1.1–1.3 m for the Lauramie. Following the exterior section removal, the cut sections were placed in sterile plastic bags and transported on ice to the Purdue Laboratory for Soil Microbiology. After mixing, a subsample was freeze-dried for further chemical and microbial biomass analysis. The remaining fresh soil was stored at 4 ◦ C for microbial activity analysis. In all cases, microbial activity analysis was started within 48 h of core collection. With a bulk density of 1.6 for the soils, damping depth is calculated to be 3.38 m. Therefore, the samples were collected above the damping depth and as a result the microbial populations in even the subsurface will be responding to the influences of the local air temperature. Total organic carbon (TOC) was analyzed with the NA 1500 NC carbon/nitrogen analyzer (Fisons Instruments, Beverly, MA) after removal of (1) inorganic carbon with a 50% hydrochloric acid solution, (2) inorganic N (NH4 + NO3 ) by extracting with 1N KCl and analyzing with a Quick Chem and Trace Analyzer (Lachat Instruments, Milwaukee, WI), (3) available P by the Bray P-1 and (4) exchangeable K by the NH4 OAc methods (Dahnke, 1988). Moisture was determined by placing the samples in an oven
2.2. Microbial biomass and microbial diversity Microbial biomass was estimated using the phospholipid-PO4 content of the soil. Extractions were done in triplicate for the surface soil and duplicates of four subsamples of 10 g for the subsurface soil. After extraction the four subsamples were combined to exceed the procedure’s detection limit. Phospholipids were extracted using the method of Findlay et al. (1989). Total lipid was extracted from the soils using methanol/chloroform/phosphate buffer (1:2:0.8) and phospholipids were fractionated using column chromatography (Findlay et al., 1989). A 100 l portion of the phospholipid extract was used to determine phospholipid-PO4 after potassium persulfate digestion using a colorimetric assay (Findlay et al., 1989). The remaining extract was used to characterize community structure by PLFA analysis. Phospholipid extract was esterified by mild alkaline methanolysis and the resulting fatty acid methyl esters (FAMEs) were analyzed by gas chromatography. FAMEs were determined using a Hewlett Packard Model 5890 GC equipped with a capillary column (DB-5, 60 m × 0.25 mm, 0.25 film, J&W Scientific, Folsom, CA), flame ionization detector (Tunlid et al.,
Table 1 Chemical analysis of samples collected from two soils at three depths, in the winter and in the summer Soil Tracy Winter
Summer
Lauramie Winter
Summer
Depth (m)
pH
Moisture (%)
Total organic C (%)
0.0–0.2 0.7–0.9 1.5–1.7 0.0–0.2 0.7–0.9 1.5–1.7
6.6 6.6 7.0 6.6 5.9 5.9
16.1 10.8 13.7 11.5 11.8 12.5
1.72 0.13 0.48 1.80 0.32 0.42
0.0–0.2 0.5–0.7 1.1–1.3 0.0–0.2 0.5–0.7 1.1–1.3
5.5 6.4 6.4 5.6 5.3 8.2
18.2 15.3 13.4 11.4 12.5 9.2
0.62 (0.02) 0.23 (0.03) 0.15 (0.02) 0.65 (0.03) 0.32 (0.07) n.d.b
(0.07)a a (0.02) b (0.28) b (0.17) a (0.05) b (0.19) b a b b a b
Inorganic N (mg kg−1 )
Available P Exchangeable K (kg ha−1 )
8.5 5.1 7.3 10.4 5.9 7.4
(1.51) a (0.86) b (0.89) ab (1.8) a (0.94) b (0.75) ab
105.3 13.4 25.4 103.8 10.5 17.9
(6.7) a (3.9) b (2.0) b (10.1) a (5.2) b (2.2) b
225.9 69.8 85.8 279.3 114.2 102.3
(53.3) a (16.5) b (5.6) b (55.1) a (19.4) b (31.4) b
11.8 12.2 5.8 11.1 12.1 7.1
(4.9) a (6.8) a (1.0) a (2.7) a (5.9) a (0.97) a
153.1 34.3 28.4 177.7 52.3 2.2
(23.3) a (23.5) b (22.7) b (15.9) a (7.8) b (0.0) b
285.6 209.4 157.5 321.1 228.5 76.2
(17.4) (19.4) (60.8) (13.7) (25.3) (14.3)
a ab b a ab b
a Numbers in parenthesis are the standard deviation of the mean. Within soil type, means followed by similar letters are not significantly different (P < 0.05). b n.d.: not determined.
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1989) and data collected with Varian Star Workstation software. Identification of fatty acids was based on comparison of retention times to purchased standards (Matreya Inc., Pleasant Gap, PA, Supelco, Bellefonte, PA) and quantified using an internal standard (nonadecanoic methyl ester). Characterization of microbial community structure was based on FAME profiles (Bobbie and White, 1980; Guckert et al., 1985). Classical fatty acid terminology is utilized: A:BωC where A indicates the total number of carbon atoms, B the number of unsaturations and C the number of carbon atoms between the closest unsaturation and the alpha carbon. Cy and OH indicate cyclopropyl and hydroxy groups, respectively. The suffixes c and t represent cis and trans isomers, and i and a indicate iso and anteiso methyl branching, respectively. Total bacterial phospholipid fatty acids are defined by the levels of 15:0, i15:0, a15:0, i16:0, 17:0, i17:0, a17:0, 16:1ω9c/t, Cy17:0 and Cy19:0 (Federle, 1986; Frostegård et al., 1993; Bardgett and McAlister, 1999); Gram positive bacteria are defined by the levels of i15:0, a15:0, i17:0, a17:0; and Gram negative bacteria are defined by Cy17:0 and Cy19:0. 2.3. Microbial activity Microbial activity was estimated directly from the incorporation of 3 H-acetate into phospholipids. Acetate is readily used by the resident microbial biomass and following its incorporation into the biomass, over a short time period, can provide information on the rate of intrinsic metabolism (Lovell and Konopka, 1985). 3 H-acetate was chosen because of its higher specific activity, as compared to 14 C-acetate. 3 H-acetate incorporation into microbial lipids is a highly sensitive method for assessing activity and this allowed for the addition of a lower concentration of the substrate in an effort to approximate an in situ incorporation situation. Triplicate 2 g samples of soil were weighed into 50 ml sterile plastic centrifuge tubes (Fisher Scientific, Pittsburgh, PA). To each tube, 3 H-acetate-sodium salt (specific activity of 6.08 Ci mmol−1 ; Dupont NEN Products, Boston, MA) diluted with cold sodium-acetate to achieve a 5 Ci addition of radiolabel was added in 1 ml aliquots. Soil slurries were created with excess water to ensure an even distribution of the label to the resident population. The tubes were vortexed and sets of tubes
incubated for 0, 2, 6, and 12 h. To simulate the soil temperature at the time of core collection, winter samples were incubated at 3 ± 1 ◦ C and summer samples at 20 ± 2 ◦ C. Analysis of available soil temperature (at 10 cm) data for the two locations indicated the average winter (November–March) soil temperature is approximately 3 ◦ C. The average temperature for the remainder of the year is approximately 20 ◦ C. It is also clear from the available information that incubation of samples collected in the winter at higher temperatures will result in increased rates of such parameters as degradation or incorporation of substrate (Palmisano et al., 1991). Therefore, our incubation temperatures were chosen to mimic in situ conditions and estimate the reaction potential for processes as occurring within the particular season. At each incubation time, activity was stopped by adding 5 ml of the phospholipids extraction solution (chloroform–methanol–phosphate buffer) to the corresponding set of tubes. The tubes were then frozen (−5 ◦ C) until continuation of phospholipids analysis. After thawing, phospholipids were extracted as described above, and the chloroform extracts evaporated under a stream of nitrogen (50 ◦ C). The phospholipid extract was resuspended in 1 ml chloroform, an aliquot of 0.5 ml was transferred to a scintillation vial and mixed with 15 ml of scintillation cocktail (BetamaxTM, ICN Biochemicals Inc., Costa Mesa, CA). The 3 H radioactivity was counted in a liquid scintillation counter (Tri-Carb 1600 TR, Packard Instruments, Downers Grove, IL). 2.4. Statistical analysis For the measurements of microbial biomass, TOC, inorganic available P, mineral N and exchangeable K, an analysis of variance was conducted using a block design and testing for the main effects of replication, soil type (location), depth, and season as well as possible interactions. Analysis was conducted using a general linear model (GLM) within Minitab (version 13). Initial analysis of the combined data set indicated significant (P < 0.05) differences between the two soils types. Data was then analyzed for each location, independently. Rates of incorporation of acetate into microbial biomass were calculated by first fitting the uptake data to zero-order kinetic model, using curve fitting software SPSS (SigmaPlot, San Rafael, CA)
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and then evaluating the rate using GLM procedures within Minitab (version 13). For characterization of the biomass population structure, at each soil sampling time, the FAME concentration data were converted to mol% of the total fatty acid concentration; principal component analysis (PCA) was used to compare fatty acid profiles for each sample (Systat 8, SPSS, Chicago, IL). PCA is useful in discerning patterns within the PLFA data (Bossio and Scow, 1995). Prior to analysis the raw data was arcsine transformed and extracted into a correlation matrix. The correlation matrix was evaluated and eigenvalues developed; from these values, eigenvectors were determined. Analysis was conducted with various rotations of the data in order to test different loadings on factor 1. Following these analyses an approach using no rotation of data was selected as it sequestered the highest amount of variance within factor 1. Where PCA indicated a fatty acid contributed to the loading on a factor, the general linear model (GLM) function of Systat version 8 was used to test between the effects of sampling locations, depths and seasons on the factor loading.
3. Results 3.1. Soil chemical properties An analysis of variance (P < 0.05) was conducted on the combined data set from the two locations and showed soil type and depth to be the main effects controlling the distribution of key physical and biological properties (Table 1). Soil data were divided between the two locations and a separate analysis of variance was conducted. Chemical analysis (Table 1) showed a numerical but non-significant increase in pH with depth for both soils, except for the deepest sample from the Lauramie soil where the pH increased significantly (P < 0.05). The high pH value (8.2) observed in the deepest layer of the Lauramie soil is due to a localized carbonate deposit which is known to exist in the region. P and K levels for both soils and both seasons showed a significant (P < 0.05) decline with depth. Available P and exchangeable K were significantly higher at the surface (P < 0.05) and were elevated slightly during the summer as a consequence of fertilizer application for the two soils. However, only organic carbon levels showed a significant interaction
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between depth and season (P < 0.05). Higher levels of N, P, and K, especially at the upper two depths, were observed for the Lauramie soil as compared to the Tracy soil. The Tracy soil contained approximately two times more organic carbon (1.72%) at the surface than the Lauramie soil (0.62%). 3.2. Microbial biomass Studies have not evaluated seasonal effects on subsurface microbial biomass but our findings suggest there is little impact on the size of community. Lower biomass values in the winter have been observed for the plough layer of several temperate soils (Lynch and Panting, 1980; Patra et al., 1990; Buchanan and King, 1992; Kaiser and Heinemeyer, 1993). Average microbial biomass levels (Table 2) as determined by phospholipid fatty acid (PLFA) were higher in the Tracy soil than in the Lauramie soil but in both soils the majority of the biomass occurred in the surface horizon (P < 0.01). For example, in the summer the average microbial biomass in the Tracy surface soil was approximately 22 and 78 times greater than at 0.7–0.9 m depth and 1.5–1.7 m, respectively. However, no significant (P = 0.05) changes in the size of the microbial biomass for the surface or subsurface were observed between the winter and summer samples for either soil. This is similar to the seasonal effects reported by Holmes and Zac (1994). A similar pattern of biomass decline with depth in the profile was found in the Lauramie soil (Table 2). However, in the Lauramie soil the magnitude of decline from the surface to 0.5–0.7 m was less (about 18-fold) than observed with the Tracy soil. Microbial biomass was similar between 0.5–0.7 m and the 1.5–1.7 m layer but the overall decline between the surface and the lowest depth was 49-fold. As was reported for the Tracy surface soil, microbial biomass was not altered by the winter conditions and no significant changes in the size of the biomass was observed between winter and summer for the subsurface. It is clear that seasonal affects were fairly limited in their impact on the size of the biomass and this supports earlier findings by Holmes and Zac (1994). The lower nutrient levels, in particular C, deeper in the profile (Table 1) may account for the lower biomass levels in the subsurface materials as we show a linear correlation (r = 0.84) between TOC and decrease in
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Table 2 Biochemical analysis of samples collected from two soils at three depths, in the winter and in the summer Soil
Tracy Winter
Summer
Lauramie Winter
Summer
Depth (m)
Bacterial PLFA (nmol g−1 dry soil)a
0.0–0.2 0.7–0.9 1.5–1.7 0.0–0.2 0.7–0.9 1.5–1.7
6.5 0.3 0.1 6.8 0.3 0.1
0.0–0.2 0.5–0.7 1.1–1.3 0.0–0.2 0.5–0.7 1.1–1.3
6.6 0.7 0.3 5.9 0.4 0.1
PLFA (nmol PLFA g−1 dry soil)
Trans:cis ratio Incorporation rate (C16:1ω9t/C16: (dpm nmol-per total 1ω9c) phospholipid h−1 )b
(1.6) ad (0.13) b (0.03) c (1.5) a (0.20) b (0.06) c
16.4 0.7 0.3 15.6 0.7 0.2
(2.9) a (0.3) b (0.06) b (3.29) a (0.43) b (0.12) b
1.81 2.77 6.98 1.51 1.82 1.90
1.7 8.5 3.8 2.3 1.9 1.7
× × × × × ×
105 104 102 105 105 104
(2.9 (3.9 (1.4 (1.2 (4.1 (7.4
× × × × × ×
104 ) 104 ) 102 ) 104 ) 104 ) 103 )
b c d a ab d
5.4 9.3 1.0 5.8 2.4 8.0
× × × × × ×
10−5 10−3 10−5 10−4 10−2 10−4
(9 × 10−5 ) c (1.1 × 10−2 ) (0) c (1.6 × 10−4 ) (7.9 × 10−2 ) (8.5 × 10−4 )
(1.8) a (0.51) b (0.16) b (0.90) a (0.18) b (0.06) c
14.6 1.5 0.6 14.7 0.8 0.3
(3.1) a (1.19) b (0.4) b (3.5) a (0.34) b (0.13) b
1.75 2.58 2.81 2.38 4.26 4.40
6.0 1.8 7.9 1.1 1.1 6.2
× × × × × ×
104 105 103 105 105 104
(1.8 (1.7 (5.8 (7.0 (3.3 (4.0
× × × × × ×
104 ) 105 ) 103 ) 104 ) 104 ) 104 )
bc a c ab ab ab
2.1 7.5 5.9 3.2 3.8 1.1
× × × × × ×
10−4 10−3 10−4 10−4 10−3 10−2
(4 × 10−5 ) b (8.1 × 10−3 ) ab (5.7 × 10−4 ) ab (2.4 × 10−5 ) b (7.0 × 10−4 ) ab (1.3 × 10−2 ) a
Specific activity (dpm 3 H per nmol-total phospholipid h−1 )c
c b a bc
a
Contribution from phospholipid fatty acids: 15:0, i15:0, a15:0, i16:0, 17:0, i17:0, a17:0, 16:1ω9c/t, Cy17:0 and Cy19:0, only. Total phospholipid from phospholipid-PO4 values. c Assumes 100 nmol PO -P per 3.14 × 109 cells using total nmol PO g−1 (Findlay et al., 1989). 4 4 d Numbers in parenthesis are the standard deviation of the mean. Within a soil type, means followed by similar letters are not significantly different (P < 0.05) within a depths and seasons. b
total PO4 -biomass. Others have reported decreases in microbial biomass with depth in the profile (Ghiorse and Wilson, 1988). Federle et al. (1986) observed a decrease in phospholipid-PO4 with depth for four different soils; however, the pattern and magnitude of decrease varied with soil. Kaiser and Heinemeyer (1993) reported microbial biomass at 0.5 m was six times lower than at the surface. Much evidence exists to support a conclusion that the higher biomass values found in the surface soil is a function of a higher level of available carbon. The presence of higher quantities of labile C (root exudates, etc.) as compared to the more complex substrates available from crop residues in the winter would contribute to increases in the microbial biomass. When biomass biochemical analysis is restricted to the bacterial indicator of PLFA (15:0, i15:0, a15:0, i16:0, 17:0, i17:0, a17:0, 16:1ω9c/t, Cy17:0 and Cy19:0) the pattern is similar but the magnitude of the change is reduced (Table 2). This exclusion removes the contribution from plants and algae, micro algae, diatoms and fungi (Vestal and White, 1989; Pankhurst et al., 2001). For example, in the winter sample for the Lauramie soil, the difference between the surface and the lowest horizon was 22-fold reduction compared to a 54-fold difference for the total phospholipid. This
provides some insight into the significant contribution of non-bacterial fatty acids to the total PLFA load in the surface soil. 3.3. Microbial community structure Use of PLFA analysis prevents bias in estimation of diversity that is typically encountered when using selective media (Palumbo et al., 1996) or other culture procedures (Konopka et al., 1998). For the recovered fatty acid, the resulting profiles indicate that changes in population structure can be represented by (∼50% of the total variance) three principal components (PC). A plot of factor 1 versus factor 2 (∼40% of the total variance) separates the samples by the depth and season in which the sample was collected (Fig. 1). The significance of these separations was confirmed using a GLM and mean separation for the individual fatty acids; a significant (P = 0.05) effect of season was also demonstrated. The positive loading along factor 1 (season) was driven by higher levels of the unsaturated fatty acids 20:1ω11, 22:1ω13, 16:1ω9c, as well as 20:0 17:0 22:0 and 10Me18:0 and Cy17:0. Negative loading on factor 1 was driven by changes in fatty acid 12:0, 19:1ω9c, 3OH14:0 i12:0 and 18:0 in the summer samples. The strongest positive loadings on factor
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Fig. 1. Principal component analysis (PCA) of PLFA profiles obtained from discrete soil samples collected in July or February, from one of three sample depths: W, winter (February) sample; S, summer sample (July); 1, surface soil; 2, root zone and 3, subsurface.
one were from the fatty acids 16:1ω9c, 10Me18:0 and Cy17:0 all of which are associated with bacteria. A comparison of Gram positive to Gram negative indicators shows that in both soils, regardless of season, the frequency of Gram positive bacteria increases with depth but not with season. The ratios of Gram positive to Gram negative for the surface soil ranged from 2.5 to 3.2:1 while for subsurface soils the ratio increased and ranged from 4.9 to 7:1. The changes in the fatty acid profiles, associated with sample depth are expressed along factors 2. Positive loading on factor 2 (depth) is driven by increased levels of Cy19:0, 16:1ω9t, 18:1ω9t i15:0 and two unidentified fatty acids that occurred in the surface samples. The negative loading on factor 2 is driven by decreases in the levels of 14:0, 15:0, 2OH12:0, 16:0 and 17:0 fatty acids occurring at depth in the profile. Fatty acid separation on factor 3 is also related to depth. Three fatty acids, an unidentified material, i17:0, and i16:0 are key in controlling distribution on this factor. However, statistical analysis (GLM) indicated the separations on factor 3 were not significant. When combined, the information in factors 2 and 3 indicate that differences between the upper soil samples and lower samples and show that seasonal
effects were strongest in the subsurface samples. More importantly, the ratio of 16:1ω9t to 16:1ω9c (trans:cis) which indicates the response of cells to stress conditions, shows that in the deeper portions of the profile the microbial populations are stressed as a ratio of trans:cis shows a lager fraction of the 16:1ω9t (Vestal and White, 1989). This difference may reflect the higher nutrient status in the surface soil as the occurrence of total organic C was increased. The total organic C levels indicate a contribution from plants and show an improved nutrient content; as is indicated in a lower 16:1ω9t to 16:1ω9c ratio. Overall, the fatty acid findings suggest two conclusions: 1) that subsurface (>1.5 m) samples are different from either surface or root zone materials and 2) that season has a stronger effect on sub-surface communities than on surface communities, as surface samples tended to group near each other in the PC analysis, regardless of season (Fig. 1). 3.4. Microbial activity Microbial activity was estimated by incubating soils collected from the different depths and seasons with 3 H-acetate and determining the rates of incorporation
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into microbial phospholipids. The incubations were conducted at the average seasonal temperature to allow an assessment of metabolic response to seasonal temperature. It is clear from other studies that incubation at warmer temperatures will increase reaction rates (Palmisano et al., 1991). What is less clear is how much the seasonal temperature will reduce the microbial reaction rates. By following the incorporation of 3 H-acetate into phospholipids we estimated microbial activity at the average seasonal temperature. Data were fitted to a zero-order model in order to achieve a biomass independent measurement of activity. While few differences in the size of the biomass were observed within the two soils (Table 2), activity was numerically higher in the Tracy soil than in the Lauramie and a significant seasonal response with depth was noted for some conditions. The cold temperature incorporation rate for the Tracy soil (Table 2) decreased three-fold from the surface to 0.7–0.9 m layer and was 137 times lower thereafter (P < 0.05). The differences in the rates of 3 H-acetate incorporation between depths for the Lauramie soil (Table 2) were less pronounced than for the Tracy soil. For the Lauramie soils, the summer samples were similar at all depths (P = 0.05). In the cold samples, a significant increase of two fold was observed from the surface to the 0.5–0.7 m layer and a threefold decrease from the surface to the subsurface layer. In all cases, the differences were statistically significant (P < 0.05). A calculation of the specific activity (Table 2) of the microbial population at the different depths was made by dividing the activity of the first time-point at each depth by the calculated number of cells (derived from the phospholipid-PO4 , after Findlay et al., 1989), to provide an estimate of the cellular specific activity (dpm 3 H-acetate incorporated per cell assuming 100 nmol PO4 -P per 3.14 × 109 cells) in the summer and winter samples. Maximum activity was generally observed in the middle profile (0.5–0.9 m). Studies with surface soil have demonstrated that higher incubation temperatures can increase microbial activity (Alexander, 1977). As significant seasonal variation in temperature occurs in the soil profile (Hilell, 1982) at depths up to 10 m (Ghiorse and Wilson, 1988), it would be expected that microbial activity at the depths evaluated in our study (up to 1.7 m) are responding to air temperature. Indeed, at
all depths the activity was higher in the summer than in the winter. For the winter to summer change, acetate incorporation activity in the Tracy soil (Table 2) had significant (P < 0.05) increases of 1.5 for the surface, 2.8 for the 0.7–0.9 m, and a non-significant increase of 49 times for the 1.5–1.7 m depths. Acetate incorporation rates in the Lauramie soil in the summer (Table 2) showed an increase, as compared to the winter, of 1.2 times for the surface, 1.30 times for the 0.5–0.7 m, and 4.9 times for the 1.1–1.3 m layers. In both cases the difference in the levels of mineral nutrients and moisture, especially for the subsurface soils, between the winter and the summer samples was minimal. A minor variation was noted for Lauramie soil that had a wintertime incorporation rate in the 0.5–0.7 level that was similar to the values found for summer samples and for the Tracy soil at 1.5–1.7 in which the rate was lower in the winter but not significantly different from the summer sample. The relation of microbial activity to chemical properties of the soils was different for each soil and season. For the Lauramie soil, the overall rates of acetate incorporation were highly correlated to TOC in both seasons (r > 0.95), to N, P, and K in the summer (r > 0.91), and to K and moisture in the winter (r > 0.91). For the Tracy soil, the correlation to TOC was lower in both seasons (r = 0.75 in the winter and 0.70 in the summer). Activity was more related to P and K in the winter (r = 0.86) than in the summer (r < 0.77); the relation to N was very poor in both seasons (r < 0.30); and a negative relation was observed with moisture in the summer (r = −1.00) but not in the winter (r = 0.55). No relation was found between 20 different soil factors and the mineralization of phenol in subsurface soils (Dobbins et al., 1987). On the other hand, Federle et al. (1986) showed that 88.6% of the variation in biomass and 82.4% of the variation in activity could be explained by edaphic factors.
4. Discussion Our general hypothesis had been that when compared to the subsurface soils, the surface soil would should significant seasonal effects in biomass size, structure and activity. Our work indicates a stable biomass size across seasons and this contrasts both
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Grayston et al., 2001 who showed total PLFA to change with season with the greater biomass size occurring in early winter and Bååth and Söderström (1982) who showed biomass to be higher in warmer conditions. However, our findings are similar to Holmes and Zac (1994) who showed that soil microbial populations were stable in forest systems. Grayston et al. (2001) indicate that the presence of plants and plant species is a significant factor governing the resident microbial community structure (size and composition). Our data suggests the nutrient status, as indicated by the level of organic C is greater at the soil surface regardless of season lessens the stress on the population as indicated by trans to cis ratios. Our data also shows the structure of the subsurface community is different from the surface, but when the response is adjusted for biomass size, the subsurface population is comparable in their respond to introduced substrates. While the size of the microbial biomass in the surface and subsurface of the Tracy and Lauramie was not dramatically affected by the seasonal changes, season did affect the microbial population’s structure and activity. PC-analysis of the fatty acid data indicates a major split in the population structure between the surface and the deeper subsurface for the two soils but that seasonal effects on the surface soil were limited (Fig. 1). The seasonal finding was somewhat unexpected, as the more pronounced environmental effect was hypothesized to be occurring on the soil surface, which is exposed, directly to the environment. Seasonal effects were manifested in the population at the lower depths and point out the importance of considering the damping depth in microbial ecology of soils. It is suggested that the improved nutrient status (i.e. trans:cis ratio and organic C level) of the population in the surface may have enhanced their ability to withstand environmental perturbations. It is also suggested that the overall size of the biomass in the surface soil masks observation of many of the subtle changes occurring in response to temperature. Changes in the subsurface biomass structure as affected by temperature maybe more apparent given the smaller size of the population. Phelps et al. (1989a,b) observed that incorporation of 14 C-acetate into microbial lipids, 3 H-thymidine into microbial DNA, and mineralization of 14 C-acetate and 14 C-glucose at 6 and 15 m was more than two orders
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of magnitude lower than at the surface. Federle et al. (1986) measured activity as the rate of FDA (fluorescein diacetate) hydrolysis in four different soils, and demonstrated that declines in hydrolysis rates with depth exhibited a unique pattern of activity throughout the soil profile. They also observed that the activity pattern did not always agree with the biomass pattern, which we observed in our biomass-adjusted data set. While our data supports these earlier studies provided we do not adjust for the smaller biomass size, our results also agree with those of Tate (1979) who reported that microbial activity per cell, towards organic compounds such as succinate, acetate, and salicylate increased with increasing depth although the total number of bacteria decreased. Data in Table 2 indicates a tendency for a larger specific activity at the mid-soil depth (0.5–0.9 m) in the summer, showing that the high temperatures or drier soil conditions may reduce activity on the surface. It is clear from our work that any assessments of degradation ability (rate) for subsurface systems should be compared to surface systems using caution as the biomass size is reduced in the subsurface and this smaller population size affects our assessment of the apparent system response. Reduced mineralization of phenol (Dobbins et al., 1987) and reduced microbial CO2 production (Wood et al., 1993) has been shown to occur in the subsurface environment. Many have reported profile-depth related decreases in activity towards other organic compounds and in general, these studies have not normalized for biomass size. However, our biomass normalized 3 H-acetate incorporation study shows that per unit of biomass, the subsurface population to be highly effective in utilization of added materials. A strong relation occurs between temperature and microbial activity but not between temperature and microbial biomass size, especially in the subsurface soils was shown to exist. Activity was significantly increased in the summer as compared to the winter for all depths. This is similar to the findings of Thornton-Manning et al. (1987) who obtained an 18% increase in rates of phenol mineralization at 20 ◦ C compared to 5 ◦ C for materials taken at 2 m deep for two different soil series. It could be expected the capacity of soils to assimilate organic materials would be diminished during low temperature seasons and this reflects the lessened activity of the biomass not a reduction in numbers in response to the cold.
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The ramifications of this finding suggest that any leached chemicals may have a longer life-time in the subsurface, especially if the leaching event occurs in the fall or winter. In contrast, that warmer summer conditions allowed a rapid response to the substrate especially near but not on the surface. This effect is generally unaccounted for in environmental fates models. Our work supports previous findings about the positive affects of increased temperature on transformation rates but surprisingly the most efficient area for transformation was often found to be in the layer below the surface.
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