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bacterial ratios in a pH gradient using physiological and PLFA-based techniques
Soil Biology & Biochemistry 35 (2003) 955–963 www.elsevier.com/locate/soilbio
Comparison of soil fungal/bacterial ratios in a pH gradient using physiological and PLFA-based techniques E. Ba˚a˚tha,*, T.-H. Andersonb a
Department of Microbial Ecology, Ecology Building, Lund University, Helgonavagen 5, Lund SE-223 62, Sweden b Institute of Agroecology, BFAL, Bundesallee 50, Braunschweig DE-38116, Germany Received 7 October 2002; received in revised form 19 March 2003; accepted 27 March 2003
Abstract We have compared the total microbial biomass and the fungal/bacterial ratio estimated using substrate-induced respiration (SIR) in combination with the selective inhibition technique and using the phospholipid fatty acid (PLFA) technique in a pH gradient (3.0– 7.2) consisting of 53 mature broad-leaved forest soils. A fungal/bacterial biomass index using the PLFA technique was calculated using the PLFA 18:2v6,9 as an indicator of fungal biomass and the sum of 13 bacterial specific PLFAs as indicator of the bacterial biomass. Good linear correlation ðp , 0:001Þ was found between the total microbial biomass estimated with SIR and total PLFAs (totPLFA), indicating that 1 mg biomass-C was equivalent to 130 nmol totPLFA. Both biomass estimates were positively correlated to soil pH. The fungal/bacterial ratio measured using the selective inhibition technique decreased significantly with increasing pH from about 9 at pH 3 to approximately 2 at pH 7, while the fungal/bacterial biomass index using PLFA measurements tended to increase slightly with increasing soil pH. Good correlation between the soil content of ergosterol and of the PLFA 18:2v6,9 indicated that the lack of congruency between the two methods in estimating fungal/bacterial ratios was not due to PLFA 18:2v6,9-related non-fungal structures to any significant degree. Several PLFAs were strongly correlated to soil pH (R2 values . 0.8); for example the PLFAs 16:1v5 and 16:1v7c increased with increasing soil pH, while i16:0 and cy19:0 decreased. A principal component analysis of the total PLFA pattern gave a first component that was strongly correlated to soil pH (R2 ¼ 0:85; p , 0:001) indicating that the microbial community composition in these beech/beech-oak forest soils was to a large extent determined by soil pH. q 2003 Elsevier Science Ltd. All rights reserved. Keywords: Fungal/bacterial ratios; Phospholipid fatty acid; Substrate-induced respiration; Ergosterol; pH Gradient; Forest soils
1. Introduction The microbial biomass in soil is not only the catalyst of all microbial transformations in soil, but also constitutes a pool of nutrients that has a rapid turnover compared with soil organic matter. Bacteria and fungi are the main constituents of this soil microbial biomass. Apart from specific members, such as mycorrhizal fungi and nitrifying bacteria, the two groups (bacteria and fungi) have the same main function in soil, that is, to decompose organic material. However, there will be differences depending on which group dominates in a particular soil. For example, fungi and bacteria are believed to have different C/N ratios (e.g. De Ruiter et al., 1993). Variations in the relative biomass of * Corresponding author. Tel.: þ 46-46-222-4264; fax: þ46-46-222-4158. E-mail address: [email protected] (E. Ba˚a˚th).
these groups will therefore affect the C/N ratio of the whole microbial biomass, a variable that is thought to be important in explaining different N mineralization patterns (De Ruiter et al., 1994). Thus, there is a need for reliable methods of estimating the fungal/bacterial ratio as complement to total biomass measurements. Several techniques have been used to differentiate between fungi and bacteria in soil. One of the most commonly used methods is the selective inhibition technique (Anderson and Domsch, 1973, 1975). The method is based on inhibition of the substrate-induced respiration (SIR) using antibiotics selective against bacterial and fungal respiration. The method is time-consuming but precise. Few comparisons have been made with direct microscopy to differentiate between fungal and bacterial biomass. Some have indicated that the two techniques give comparable results (Beare et al., 1990; Lin and Brookes, 1999), while
0038-0717/03/$ - see front matter q 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0038-0717(03)00154-8
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others have revealed considerable differences (West, 1986; Velvis, 1997). A different approach to estimating the fungal and bacterial biomass in soil is to measure chemical components that are specific for these two microbial groups. One such approach relies on bacteria and fungi having different fatty acid compositions in their phospholipids (Hardwood and Russell, 1984; Tunlid and White, 1992). Frostega˚rd and Ba˚a˚th (1996) suggested that a fungal/bacterial biomass ratio could be estimated by using the PLFA 18:2v6,9 as a measure of fungal biomass and the sum of 13 bacteriaspecific PLFAs as a measure of bacterial biomass. It must, however, be emphasized that this ratio does not reflect absolute biomass values, since no conversion factors from PLFA concentration to actual biomass are applied. Instead, this ratio is a biomass index, changes in which indicate relative changes in the ratio of fungal to bacterial biomass. Although the use of the PLFA technique to estimate a fungal/bacterial biomass index has become increasingly popular (Pennanen et al., 1998b; Bardgett and McAlister, 1999; Donnison et al., 2000; Khan and Scullion, 2000; Kozdro´j and van Elsas, 2001), systematic comparisons between the selective inhibition technique and the PLFA technique are scarce. However, they appeared to exhibit similar changes following various intensities of management of an upland grassland (Bardgett et al., 1996), although this was studied following only three types of treatment. One situation, in which there may be differences in the measured fungal/bacterial ratio depending on the method used, is that of varying pH. In a study of a natural pH gradient of forest soils, Blagodatskaya and Anderson (1998) found that the fungal/bacterial ratio (estimated using the selective inhibition technique) decreased as the pH increased. On the other hand, hardly any differences were found between the fungal/bacterial biomass index in coniferous forest humus that had been limed, ash-treated or alkaline polluted and natural forest soils, when using the PLFA method, although all these treatments increased soil pH considerably (Ba˚a˚th et al., 1992, 1995; Frostega˚rd et al., 1993). There may be several explanations of this difference in the effect of soil pH, apart from the different techniques used. In the first study the pH gradient was a natural one (that is, the soils have had a long period of constant pH), while in the others the changes in soil pH were brought about rapidly by man-made intervention. In the former case a mineral soil was studied and in the other cases a forest humus was studied. However, the conflicting results of these studies demonstrate the need to compare the selective inhibition and PLFA techniques in a more stringent way. The aims of the present study were several. Most importantly, we wanted to compare the selective inhibition technique with the PLFA method with respect to estimating both the total biomass and fungal/bacterial ratios. This comparison was performed in 53 samples of broad-leaved forest soils with a pH gradient from 3 to 7.2. Such a difference in pH has been shown to significantly affect both
total biomass (Anderson and Domsch, 1993; Anderson and Joergensen, 1997) and the fungal/bacterial ratio using the selective inhibition method (Blagodatskaya and Anderson, 1998). We also wanted to use this pH gradient to study more closely the effect of natural pH differences on the structure of the soil microbial community using the PLFA technique. Most previous studies on pH effects in this respect have been performed on soils in which the pH was changed through anthropogenic intervention, for example liming and ash application, or alkaline or acidifying pollution (Ba˚a˚th et al., 1992, 1995; Frostega˚rd et al., 1993; Pennanen et al., 1998a,b).
2. Materials and methods 2.1. Soils and sampling Soil from 53 mature broad-leaved forest stands (40 beech and 13 beech/oak stands) belonging to four different forest sites was sampled in spring (April). The forest stands were at least 70 years old. The sites were within a radius of 25 km of Braunschweig, Lower Saxony, Northern Germany. Prior to sampling, the litter (L) and humus layer (Oh) were removed with a hand-rake and a scraper. From an area of 2500 m2 per stand, five samples were taken from the mineral horizon (Ah, 0– 10 cm) with a spade, bulked and stored field-fresh in polyethylene bags sealed with cottonwool plugs for a maximum of 5 months at 4 8C. Prior to soil analysis the samples were sieved (, 2 mm) and the moisture adjusted to approx. 2 240 kPa, which corresponds to a water content range of 15– 57% (wet weight) in the samples. Soil pH was measured in 1 M KCl with a soil-to-solution ratio of 1:2. The percentage soil organic C (Corg) was estimated by dry combustion (C-IR 12, Leco) after having removed the inorganic C with 10% HCl (drop wise) and drying the samples on a sand bath at 70 8C. 2.2. Determination of microbial biomass and the fungal/bacterial ratio using the respiration technique Microbial biomass-C (Cmic) was determined using the SIR technique of Anderson and Domsch (1978). Samples of 25 g (dry weight) were amended with a powder mixture containing 200 mg glucose and 500 mg talcum, which warranted an initial maximal CO2-flush in all soils. The CO2 production rate was measured hourly at 22 8C using an automated infrared gas analyser system (Heinemeyer et al., 1989). Microbial biomass-C was calculated according to the equation of Anderson and Domsch (1978) where biomass-C (mg g21 soil) ¼ (ml CO2 g21 soil h21) £ 40.04. Fungal/bacterial respiratory ratios were determined using the selective inhibition method of Anderson and Domsch (1973, 1975). Selectivity of the inhibitor streptomycin (bacterial respiratory inhibitor) and cycloheximide (fungal respiratory inhibitor) was achieved with the following
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concentrations: 1 –2 mg g21 streptomycin plus 6– 8 mg g21 cycloheximide for acidic soils, and 2– 4 mg g21 streptomycin plus 6– 8 mg g21 cycloheximide for neutral soils. The inhibitors were applied to 25 g (dry weight) soil samples together with the glucose – talcum mixture (see above) and 16 mg g21 Na2HPO4. CO2 evolution rates were recorded hourly (see above). A computer program for selectivity analysis (‘DBF-SBA’ by Heinemeyer, O.; copyright 1992, MarCO Analytik, Hildesheim, Germany) was used to determine the fungal/bacterial respiratory ratio based on the criteria prescribed by Anderson and Domsch (1975): (1) proof of no unselective inhibition, and (2) proof of no onedirectional growth (shifts in the biosynthesis rates of bacteria and fungi in favour of one group). Inhibitor combinations were run in duplicate, uninhibited controls in triplicate. 2.3. Determination of biomass and fungal/bacterial ratio using PLFA analysis Phospholipid fatty acids (PLFAs) were extracted and analysed using a procedure described by Frostega˚rd et al. (1993). Briefly, the soil was extracted in a single-phase mixture of chloroform:methanol:citrate buffer (1:2:0.8 v/v/ v). After extraction the lipids were separated into neutral lipids, glycolipids and polar lipids (phospholipids) on a silicic acid column. The phospholipids were methylated and separated on a gas chromatograph equipped with a flame ionisation detector. Peak areas were quantified by adding methyl nonadecanoate fatty acid (19:0) as the internal standard before the methylation step. All solvents and chemicals used were of analytical grade. To remove lipid contaminants all glassware used was heated overnight at 400 8C. The fatty acid nomenclature used is as follows; total number of carbon atoms:number of double bonds, followed by the position (v) of the double bond from the methyl end of the molecule. Cis and trans configurations are indicated by c and t, respectively. Anteiso- and isobranching are designated by the prefix a or i. 10Me is a methyl group on the 10th carbon atom from the carboxyl end of the molecule. Cy indicates cyclopropane fatty acids. Br indicates a branched fatty acid with unknown branching configuration. xi denotes an unidentified fatty acid. The sum of the following PLFAs was used a measure of the bacterial biomass: i14:0, i15:0, a15:0, 15:0, i16:0, 10Me16:0, i17:0, a17:0, cy17:0, 17:0, br18, 10Me17:0, 18:1v7, 10Me18:0 and cy19:0 (Frostega˚rd and Ba˚a˚th, 1996). The PLFA 18:2v6,9 was used as a measure of fungal biomass.
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block at 70 8C for 90 min. After the heat treatment, 1 ml distilled water and 2 ml cyclohexane were added, the tubes were vortexed for 30 s, centrifuged and the top phase removed. The remaining solution was washed once with 2 ml cyclohexane and the combined cyclohexane fractions evaporated under N2 at 40 8C. Before measuring the amount of ergosterol with HPLC using a Nova Pak C 18 reverse-phase column (Waters, Millipore Corporation, USA), the samples were dissolved in methanol by heating at 40 8C for 15 min, and then filtered through a 0.45 mm filter. The mobile phase consisted of methanol at a flow rate of 1 ml min21 and ergosterol was detected using UV light at 282 nm. 2.5. Statistical analysis The mole percent of the individual PLFAs was standardized to unit variance (scaling) before being subjected to principal component analysis (PCA).
3. Results The soils varied in pH from 3.0 to 7.2 and in organic-C content from 3.5 to 8.9% (apart from four soil samples with . 10% organic-C). There was a weak tendency for the % organic-C to decrease with increasing pH (R2 ¼ 0:124; p , 0:05; n ¼ 53). A linear correlation (R2 ¼ 0:803; n ¼ 53; p , 0:001) was found between biomass-C estimated with the SIR technique and the total amount of PLFA (totPLFA) (Fig. 1). The relationship indicated that 1 mg biomass-C was equivalent to 130 nmol totPLFA. However, the regression line did not go through the origin, indicating that about 50 nmol totPLFA g21 dw of soil was not due to microbial biomass measured with SIR.
2.4. Determination of fungal biomass using ergosterol Ergosterol was extracted by adding 1 ml cyclohexane and 4 ml 10% KOH in methanol to 1 g of soil. After 15 min ultrasonic treatment, the test-tubes were placed in a heating
Fig. 1. Regression between total biomass-C measured with substrate induced respiration (SIR) and the total amount of phospholipid fatty acids (totPLFAs) in 52 beech/beech-oak forest soils with pH ranging from 3 to 7.2.
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Fig. 2. The effect of soil pH in beech/beech-oak forest soils on the soil microbial biomass estimated as: (A) biomass-C with the substrate induced respiration (SIR) technique and (B) as the total amount of phospholipid fatty acids (totPLFAs).
Both biomass measurements were positively correlated to the soil pH (R2 ¼ 0:666 and 0.548 for SIR and totPLFA, respectively, p , 0:001; Fig. 2A and B). The biomass-C estimated with SIR was about 0.5% of the soil organic-C in soils with pH 3, but increased to about 2.5% at pH 7 (Fig. 2A). The concentration of totPLFA increased from about 1.5 mmol totPLFA g21 soil organic-C at pH 3 to roughly 4 mmol totPLFA g21 soil organic-C at pH 7 (Fig. 2B). The fungal respiration was higher than the bacterial respiration in all soils when determined with the selective inhibition technique. In the low-pH soils, bacterial respiration was only 10% of the total antibiotic-inhibited respiration (Fig. 3A) giving a fungal/bacterial ratio of almost 9, while at pH 7 30% was bacterial respiration, giving a fungal/bacterial ratio of roughly 2. There was a good linear relation between soil pH and the fraction of SIR that was accounted for by bacterial respiration (R2 ¼ 0:708; n ¼ 53; p , 0:001; Fig. 3A). The fungal/bacterial biomass index ratio calculated using the PLFA technique varied between 0.01 and 0.07, and there
Fig. 3. The effect of soil pH in beech/beech-oak forest soils on the relation between fungal and bacterial biomass. (A) The fraction of bacterial respiration measured using the selective inhibition technique, (B) the fungal/bacterial biomass index using the PLFA 18:2v6,9 as an indicator of fungal biomass and the sum of 13 bacteria-specific PLFAs as an indicator of bacterial biomass.
was a tendency for this ratio to increase slightly with increasing pH (R2 ¼ 0:234; n ¼ 52; p , 0:01; Fig. 3B). Ergosterol (considered to be specific to fungi) was also measured in a subset of 35 soil samples covering the whole range of soil pH values. This was done in order to investigate whether the differences in the effect of pH on the fungal/bacterial ratios estimated with the two techniques was due to the PLFA used as a fungal marker, 18:2v6,9, being present in non-fungal structures such as plants roots. There was, however, a significant linear correlation between the soil content of ergosterol and the content of the PLFA 18:2v6,9 (R2 ¼ 0:671; p , 0:001; Fig. 4), indicating that this was not the case. The relation indicated that 1 nmol of the PLFA 18:2v6,9 was equivalent to 0.62 mg ergosterol. The data concerning the individual relative concentration (mol%) of the 33 most common PLFAs were subjected to a principal component analysis. The first principal component (PC1) explained over 50% of the variation in the data set, while the second, PC2, only explained 13.7%. The first PC appeared to be related to soil pH (R2 ¼ 0:845; n ¼ 52;
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Fig. 4. Correlation between the soil content of the PLFA 18:2v6,9 and ergosterol as indicators of fungal biomass. Only 35 of the 53 soil samples were used for the ergosterol measurements.
p , 0:001) (Fig. 5A). The loading plot of the individual PLFAs (Fig. 5B) shows that the main effect of soil pH on the PLFA pattern was higher concentrations of the PLFAs i16:0, cy19:0, 10Me17:0, i15:0, x3, 17:0 and 20:0 at low pH, while at higher pH, several mono-unsaturated PLFAs, such as 18:1v9, 17:1v8, 16:1v5, 18:1v7 and 16:1v7c, were more common. The effects of pH on the relative amounts of selected PLFAs are shown in Fig. 6. The PLFA 16:1v5, indicative of arbuscular mycorrhiza (Olsson et al., 1995, 1999), increased from 1.5 mol% at pH 3 to 5 mol% at pH 7 (Fig. 6A). The PLFAs 16:1v7c and 18:1v7c increased from 4 and 5 mol%, respectively, to 9 and 11 mol%, respectively (Fig. 6B and C), while cy19:0, also indicative of Gramnegative bacteria, decreased from 13 mol% at low soil pH to 3 mol% at high pH (Fig. 6D). The relative amounts of the PLFAs i15:0 and i16:0, indicative of Gram-positive bacteria, were lower at high than at low pH (Fig. 6E and F). Other PLFAs that increased markedly over the pH gradient were i14:0 (from 0.5 to 2 mol%), a15:0 (from 4 to 7 mol%), a17:0 (from 1.2 to 2 mol%) and 17:1v8 (from 0.2 to 0.6 mol%). Other PLFAs that decreased over the pH gradient were 16:1v7t (from 1.5 to 0.7 mol%) and 10Me17:0 (from 1.7 to 0.5 mol%).
4. Discussion To be able to calculate actual biomass values from the fungal/bacterial ratios one needs reliable conversion factors. Such conversion factors will be different for fungi and bacteria, but also different for the PLFA and the selective inhibition technique. Since there are currently no reliable conversion factors, it is meaningless to compare the actual values of the fungal/bacterial ratios estimated with the two methods. However, changes in the ratios due to environmental factors, such as pH, should give similar results, irrespective of the method used. However, the main results
Fig. 5. The effect of soil pH on the PLFA pattern of the soil microbial community. (A) Regression between soil pH and the scores along the first component of the principal component analysis of the PLFA data. (B) The loadings of the individual PLFAs from the principal component analysis of the PLFA data. PLFAs to the right in the plot indicate those that are more common in high-pH soils, while those to the left indicate those that are more common in the more acid soils.
of the present comparative study between the PLFA technique and the selective inhibition technique showed a lack of congruence in this respect (Fig. 2A and B). The pH gradient caused a change in the fungal/bacterial ratio estimated with the selective inhibition technique, but this was not reflected by the PLFA technique. This does not mean that either of the two methods is inadequate, but it clearly indicates that they reflect different aspects of fungi and bacteria, despite earlier studies, in which a correlation was found between the measurements (e.g. Bardgett et al., 1996). The lack of correlation found in a recent study on five different soil types (Bailey et al., 2002b) also indicates that these techniques reflect different aspects.
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Fig. 6. The effect of soil pH on the relative amount (mol%) of selective PLFAs. (A) 16:1v5 (arbuscular mycorrhiza), (B) 16:1v7c (Gram-negative bacteria), (C) 18:1v7 (Gram-negative bacteria), (D) cy19:0 (Gram-negative bacteria), (E) i15:0 (Gram-positive bacteria), (F) i16:0 (Gram-positive bacteria).
There are several possible explanations of the lack of congruence between the two methods, which we can only speculate about. In the PLFA technique there might be fatty acids not associated with the living biomass that interfere with the measurements. Interference from humic-acidderived fatty acids in PLFA analysis was reported by
Nielsen and Petersen (2000), although they estimated that this fraction constituted only 5– 10% of the total PLFA extracted from the soil. Also, the regression line in Fig. 1 not going through the origin indicated the presence of nonmembrane bound PLFA. The selective inhibition technique, on the other hand, will not include dormant cells, since
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the method requires an immediate response of cells to the addition of glucose. The method could also be selective against slow-growing organisms not responding rapidly to the addition of glucose. This might include ectomycorrhizal fungi which, deprived of their food base (the tree) might show little increased activity due to glucose addition. All fungal cells not responding to glucose would, however, still have intact PLFAs and would thus be included in PLFA measurements. Since the selective inhibition method is not a direct measurement of biomass, although most often used as such, it only provides information about the respiratory activity of the fungal and bacterial components of the total biomass after carbon amendment. It should therefore perhaps be regarded as an activity indicator, and the resulting ratio should be called a fungal/bacterial respiration ratio or a fungal/bacterial activity ratio (as used by Bailey et al., 2002). Modern microscopic techniques such as image analysis together with epi-fluorescence videomicroscopy (Anderson, 2001) for the quantification of the ratio of active to total microbial biomass as recently suggested by Klein and Paschke (2000), could be of additional help in explaining the lack of congruence between fungal/bacterial activity and biomass ratios. Despite the lack of congruence between the fungal/bacterial ratios estimated with the two methods in relation to soil pH, the total biomass appeared to be significantly correlated, indicating that in this respect the two methods are complementary (Fig. 1). Bailey et al. (2002a) also found a correlation between microbial biomass estimated with the PLFA and the SIR techniques in a study on five different soil types. The PLFA technique has usually been employed to give only a relative value (usually given as nmol totPLFA) of the total biomass, since a conversion factor is seldom applied to calculate absolute biomass values. A conversion factor of 340 nmol PLFA mg21 biomass C, based on few soils, was suggested by Frostega˚rd et al. (1991). The value obtained in the present study, 130 nmol PLFA mg21 biomass C, is considerably lower. However, although in our case the conversion factor was based on 53 soil samples, they were all from beech or beech-oak forests. The need for a more thorough comparison of biomass values using PLFA and other methods in many different soils is needed before a more accurate conversion factor can be presented. We also calculated a conversion factor between ergosterol and the PLFA indicative of fungi, 18:2v6,9 (Fig. 4). Few such comparisons have been made, but Frostega˚rd and Ba˚a˚th (1996) found a similar conversion factor to that in the present study, when comparing the two methods in 12 different soils. The fungal biomass can now be estimated from PLFA measurements, using our conversion factor (1 nmol 18:2v6,9 ¼ 0.62 mg ergosterol) and reported values of the ergosterol content of fungal biomass (ranging from 1.9 to 7 mg mg21 dry weight mycelium depending on species composition and growth conditions (e.g. Davis and Lamar, 1992; Djajakirana et al., 1996; Nout et al., 1997;
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Montgomery et al., 2000) or to 11 mg mg21 fungal biomassC (Scheu and Parkinson, 1994)). Marstorp et al. (2000) and Gong et al. (2001) related the amount of ergosterol (a measure of fungal biomass) to the total amount of soil microbial-C in a pH gradient from 3.8 to 7.3. In their case the change in pH was due to long-term addition of fertilizers and manure to agricultural soil. They found that the ergosterol/biomass-C ratio (mg/mg) decreased with increasing pH from 0.012 at low pH to 0.005 at high pH (Gong et al., 2001) and from 0.027 to less than 0.01 (Marstorp et al., 2000). Similar calculations with the present data set showed no relation between the ergosterol/biomassC ratio and pH (data not shown). This is important in that the use of ergosterol (or the PLFA 18:2v6,9 since their amounts were correlated) in some cases can indicate proportionally more fungi at low pH (Marstorp et al., 2000; Gong et al., 2001), while in other cases this is not the case (present study). Thus, it is not the methodology per se that is the reason for the lack of correlation between PLFAs and the physiological, SIR-based method of determining fungal/ bacterial ratios. In pH gradients of non-forest soils with less mycorrhiza present they may well give similar results. However, the complete absence of correlation in the present study clearly shows that the two methods measure different aspects of fungi and bacteria and that earlier correlations between the two techniques (e.g. Bardgett et al., 1996) might have been due to different underlying soil communities. Although the PLFAs indicating fungi (18:1v9, as well as 18:2v6,9) were little affected by pH, the relative concentrations of several PLFAs were closely related to pH (Fig. 6). The PLFAs most affected by pH in this study were the same as those affected in a similar way in earlier studies of the effect of pH on the PLFA pattern in different types of soil. This includes studies of alkaline pollution of coniferous forest humus (Ba˚a˚th et al., 1992), liming of coniferous forest humus (Ba˚a˚th et al., 1995), liming of agricultural soil (Schutter and Fuhrmann, 2001), primary succession transects (Merila¨ et al., 2002) and natural pH gradients in forest and meadow soil (M. Tabor and E. Ba˚a˚th, unpublished). In almost all these cases (including the present), where the specific PLFAs were reported, the relative concentrations of i14:0, 16:1v7c, 16:1v5 and 18:1v7 increased, while those of i15:0, i16:0, 16:1v7t and cy 19:0 decreased with increasing soil pH. Most of the other PLFAs also increased/decreased in the same way due to changes in pH in many of these studies. Thus, not only does pH appear to have a profound effect on the microbial community composition as measured with the PLFA technique (Fig. 5A), but the changes due to pH were similar in very different soils and when very different causes of the pH gradient were studied. Although one cannot directly translate changes in the PLFA pattern into a specific species composition, these results still indicate that similar microbial communities are induced in different soils at the same pH.
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The close correlation between the PLFA pattern and pH in various soils has an important implication, when evaluating PLFA data. The effect of pH must always be taken into consideration as an important explanation of changes in the PLFA pattern, even if the actual pH difference is rather small. For example, cy19:0 decreased by about 2.5 mol% per pH unit (Fig. 6D). Thus, a pH difference of only 0.5 units would, in most cases, induce a change of more than 1 mol% in the content of cy19:0, a difference that is usually considered significant. On the other hand, since changes in pH induced very similar changes in different soil habitats (see references above), one can use these changes as a way of detecting pH effects. Thus, if an investigation results in an altered PLFA pattern similar to that shown in Fig. 5B, one could suspect this to be due to variation in pH.
Acknowledgements This study was supported by grants from the Swedish Natural Science Research Council and the Swedish Research Council to E.B. We would like to thank Maria Bota and Kurt Steffens for their reliable technical assistance.
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Bailey, V.L., Smith, J.L. Jr., Bolton, H., 2002b. Fungal-to-bacterial ratios in soils investigated for enhanced C sequestration. Soil Biology and Biochemistry 34, 997–1008. Bardgett, R.D., McAlister, E., 1999. The measurement of soil fungal:bacterial biomass ratios as an indicator of ecosystem self-regulation in temperate meadow grassland. Biology and Fertility of Soils 29, 282–290. ˚ ., 1996. Changes in soil Bardgett, R.D., Hobbs, P.J., Frostega˚rd, A fungal:bacterial biomass ratios following reductions in the intensity of management of an upland grassland. Biology and Fertility of Soils 22, 261–264. Beare, M.H., Neely, C.L., Coleman, D.C., Hargrove, W.L., 1990. A substrate-induced respiration (SIR) method for measurement of fungal and bacterial biomass on plant residues. Soil Biology and Biochemistry 22, 585–594. Blagodatskaya, E.V., Anderson, T.-H., 1998. Interactive effects of pH and substrate quality on the fungal-to-bacterial ratio and QCO2 of microbial communities in forest soils. Soil Biology and Biochemistry 30, 1269–1274. Davis, M.W., Lamar, R.T., 1992. Evaluation of methods to extract ergosterol for quantification of soil fungal biomass. Soil Biology and Biochemistry 24, 189–198. De Ruiter, P.C., Moore, J.C., Zwart, K.B., Bouwman, L.A., Hassink, J., Bloem, J., de Vos, J.A., Marinissen, J.C.Y., Didden, W.A.M., Lebbink, G., Brussaard, L., 1993. Simulation of nitrogen mineralization in the below-ground food webs of two winter wheat fields. Journal of Applied Ecology 30, 95–106. De Ruiter, P.C., Bloem, J., Bouwman, L.A., Didden, W.A.M., Hoenderboom, G.H.J., Lebbink, G., Marinissen, J.C.Y., de Vos, J.A., VreekenBuijs, M.J., Zwart, K.B., Brussaard, L., 1994. Simulation of dynamics in nitrogen mineralisation in the belowground food webs of two arable farming systems. Agriculture, Ecosystems and Environment 51, 199 –208. Djajakirana, G., Joergensen, R.G., Meyer, B., 1996. Ergosterol and microbial biomass in soil. Biology and Fertility of Soils 22, 299– 304. Donnison, L.M., Griffith, G.S., Hedger, J., Hobbs, P., Bardgett, R.D., 2000. Management influences on soil microbial communities and their function in botanically diverse haymeadows of northern England and Wales. Soil Biology and Biochemistry 32, 253 –263. ˚ ., Ba˚a˚th, E., 1996. The use of phospholipid fatty acid analysis Frostega˚rd, A to estimate bacterial and fungal biomass in soil. Biology and Fertility of Soils 22, 59 –65. ˚ ., Tunlid, A., Ba˚a˚th, E., 1991. Microbial biomass measured as Frostega˚rd, A total lipid phosphate in soils of different organic content. Journal of Microbiological Methods 14, 151–163. ˚ ., Ba˚a˚th, E., Tunlid, A., 1993. Shifts in the structure of soil Frostega˚rd, A microbial communities in limed forests as revealed by phospholipid fatty acid analysis. Soil Biology and Biochemistry 25, 723– 730. Gong, P., Guan, X., Witter, E., 2001. A rapid method to extract ergosterol from soil by physical disruption. Applied Soil Ecology 17, 285 –289. Hardwood, J.L., Russell, N.J., 1984. Lipids in Plants and Microbes, George Allen & Unwin, London. Heinemeyer, O., Insam, H., Kaiser, E.-A., Walenzik, G., 1989. Soil microbial biomass and respiration measurements: an automated technique based on infrared gas analysis. Plant and Soil 116, 191 –195. Khan, M., Scullion, J., 2000. Effect of soil on microbial responses to metal contamination. Environmental Pollution 110, 115–125. Klein, D.A., Paschke, M.W., 2000. A soil microbial community structural– functional index: the microscopy-based total/active/active fungal/bacterial (TA/AFB) biovolumes ratio. Applied Soil Ecology 14, 257–268. Kozdro´j, J., van Elsas, J.D., 2001. Structural diversity of microorganisms in chemically perturbed soil assessed by molecular and cytochemical approaches. Journal of Microbiological Methods 43, 197 –212. Lin, Q., Brookes, P.C., 1999. Comparison of substrate induced respiration, selective inhibition and biovolume measurements of microbial biomass and its community structure in unamended, ryegrass-amended, fumigated and pesticide-treated soils. Soil Biology and Biochemistry 31, 1999–2014.
E. Ba˚a˚th, T.-H. Anderson / Soil Biology & Biochemistry 35 (2003) 955–963 Marstorp, H., Guan, X., Gong, P., 2000. Relationship between dsDNA chloroform labile C and ergosterol in soils of different organic matter contents and pH. Soil Biology and Biochemistry 32, 879–882. Merila¨, P., Stro¨mmer, R., Fritze, H., 2002. Soil microbial activity and community structure along a primary succession transect on the landuplift coast in western Finland. Soil Biology and Biochemistry 34, 1647–1654. Montgomery, H.J., Monreal, C.M., Young, J.C., Seifert, K.A., 2000. Determination of soil fungal biomass from soil ergosterol analyses. Soil Biology and Biochemistry 32, 1207–1217. Nielsen, P., Petersen, S.O., 2000. Ester-linked polar lipid fatty acid profiles of soil microbial communities: a comparison of extraction methods and evaluation of interference from humic acids. Soil Biology and Biochemistry 32, 1241–1249. Nout, M.J.R., Rinzema, A., Smits, J.P., 1997. Biomass and productivity estimates in solid substrate fermentation. In: Wicklow, D.T., So¨derstro¨m, B. (Eds.), The Mycota. Environmental and Microbial Relationships. IV, Springer, Berlin, pp. 323–345. Olsson, P.A., Ba˚a˚th, E., Jakobsen, I., So¨derstro¨m, B., 1995. The use of phospholipid and neutral lipid fatty acids to estimate biomass of arbuscular mycorrhizal fungi in soil. Mycological Research 99, 623–629. Olsson, P.A., Tingstrup, I., Jakobsen, I., Ba˚a˚th, E., 1999. Estimation of the biomass of arbuscular mycorrhizal fungi in a linseed field. Soil Biology and Biochemistry 31, 1879–1887.
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Pennanen, T., Fritze, H., Vanhala, P., Kiikkila¨, O., Neuvonen, S., Ba˚a˚th, E., 1998a. Structure of a microbial community in soil after prolonged addition of low levels of simulated acid rain. Applied and Environmental Microbiology 64, 2173–2180. Pennanen, T., Perkio¨ma¨ki, J., Kiikkila¨, O., Vanhala, P., Neuvonen, S., Fritze, H., 1998b. Prolonged, simulated acid rain and heavy metal deposition: separated and combined effects on forest soil microbial community structure. FEMS Microbiology Ecology 27, 291–300. Scheu, S., Parkinson, D., 1994. Changes in bacterial and fungal biomass C, bacterial and fungal biovolume and ergosterol content after drying, remoistening and incubation of different layers of cool temperate forest soils. Soil Biology and Biochemistry 26, 1515–1525. Schutter, M.E., Fuhrmann, J.J., 2001. Soil microbial community responses to fly ash amendment as revealed by analyses of whole soils and bacterial isolates. Soil Biology and Biochemistry 33, 1947–1958. Tunlid, A., White, D.C., 1992. Biochemical analysis of biomass, community structure, nutritional status, and metabolic communities in soil. In: Stotzky, G., Bollag, J.-M. (Eds.), Soil Biochemistry, vol. 7. Marcel Dekker, New York, pp. 229– 262. Velvis, H., 1997. Evaluation of the selective respiratory inhibition method for measuring the ratio of fungal:bacterial activity in acid agricultural soils. Biology and Fertility of Soils 25, 354–360. West, A.W., 1986. Improvement of the selective respiratory inhibition technique to measure eukaryote:prokaryote ratios in soil. Journal of Microbiological Methods 5, 125 –138.
Comparison of soil fungal/bacterial ratios in a pH gradient using physiological and PLFA-based techniques E. Ba˚a˚tha,*, T.-H. Andersonb a
Department of Microbial Ecology, Ecology Building, Lund University, Helgonavagen 5, Lund SE-223 62, Sweden b Institute of Agroecology, BFAL, Bundesallee 50, Braunschweig DE-38116, Germany Received 7 October 2002; received in revised form 19 March 2003; accepted 27 March 2003
Abstract We have compared the total microbial biomass and the fungal/bacterial ratio estimated using substrate-induced respiration (SIR) in combination with the selective inhibition technique and using the phospholipid fatty acid (PLFA) technique in a pH gradient (3.0– 7.2) consisting of 53 mature broad-leaved forest soils. A fungal/bacterial biomass index using the PLFA technique was calculated using the PLFA 18:2v6,9 as an indicator of fungal biomass and the sum of 13 bacterial specific PLFAs as indicator of the bacterial biomass. Good linear correlation ðp , 0:001Þ was found between the total microbial biomass estimated with SIR and total PLFAs (totPLFA), indicating that 1 mg biomass-C was equivalent to 130 nmol totPLFA. Both biomass estimates were positively correlated to soil pH. The fungal/bacterial ratio measured using the selective inhibition technique decreased significantly with increasing pH from about 9 at pH 3 to approximately 2 at pH 7, while the fungal/bacterial biomass index using PLFA measurements tended to increase slightly with increasing soil pH. Good correlation between the soil content of ergosterol and of the PLFA 18:2v6,9 indicated that the lack of congruency between the two methods in estimating fungal/bacterial ratios was not due to PLFA 18:2v6,9-related non-fungal structures to any significant degree. Several PLFAs were strongly correlated to soil pH (R2 values . 0.8); for example the PLFAs 16:1v5 and 16:1v7c increased with increasing soil pH, while i16:0 and cy19:0 decreased. A principal component analysis of the total PLFA pattern gave a first component that was strongly correlated to soil pH (R2 ¼ 0:85; p , 0:001) indicating that the microbial community composition in these beech/beech-oak forest soils was to a large extent determined by soil pH. q 2003 Elsevier Science Ltd. All rights reserved. Keywords: Fungal/bacterial ratios; Phospholipid fatty acid; Substrate-induced respiration; Ergosterol; pH Gradient; Forest soils
1. Introduction The microbial biomass in soil is not only the catalyst of all microbial transformations in soil, but also constitutes a pool of nutrients that has a rapid turnover compared with soil organic matter. Bacteria and fungi are the main constituents of this soil microbial biomass. Apart from specific members, such as mycorrhizal fungi and nitrifying bacteria, the two groups (bacteria and fungi) have the same main function in soil, that is, to decompose organic material. However, there will be differences depending on which group dominates in a particular soil. For example, fungi and bacteria are believed to have different C/N ratios (e.g. De Ruiter et al., 1993). Variations in the relative biomass of * Corresponding author. Tel.: þ 46-46-222-4264; fax: þ46-46-222-4158. E-mail address: [email protected] (E. Ba˚a˚th).
these groups will therefore affect the C/N ratio of the whole microbial biomass, a variable that is thought to be important in explaining different N mineralization patterns (De Ruiter et al., 1994). Thus, there is a need for reliable methods of estimating the fungal/bacterial ratio as complement to total biomass measurements. Several techniques have been used to differentiate between fungi and bacteria in soil. One of the most commonly used methods is the selective inhibition technique (Anderson and Domsch, 1973, 1975). The method is based on inhibition of the substrate-induced respiration (SIR) using antibiotics selective against bacterial and fungal respiration. The method is time-consuming but precise. Few comparisons have been made with direct microscopy to differentiate between fungal and bacterial biomass. Some have indicated that the two techniques give comparable results (Beare et al., 1990; Lin and Brookes, 1999), while
0038-0717/03/$ - see front matter q 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0038-0717(03)00154-8
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others have revealed considerable differences (West, 1986; Velvis, 1997). A different approach to estimating the fungal and bacterial biomass in soil is to measure chemical components that are specific for these two microbial groups. One such approach relies on bacteria and fungi having different fatty acid compositions in their phospholipids (Hardwood and Russell, 1984; Tunlid and White, 1992). Frostega˚rd and Ba˚a˚th (1996) suggested that a fungal/bacterial biomass ratio could be estimated by using the PLFA 18:2v6,9 as a measure of fungal biomass and the sum of 13 bacteriaspecific PLFAs as a measure of bacterial biomass. It must, however, be emphasized that this ratio does not reflect absolute biomass values, since no conversion factors from PLFA concentration to actual biomass are applied. Instead, this ratio is a biomass index, changes in which indicate relative changes in the ratio of fungal to bacterial biomass. Although the use of the PLFA technique to estimate a fungal/bacterial biomass index has become increasingly popular (Pennanen et al., 1998b; Bardgett and McAlister, 1999; Donnison et al., 2000; Khan and Scullion, 2000; Kozdro´j and van Elsas, 2001), systematic comparisons between the selective inhibition technique and the PLFA technique are scarce. However, they appeared to exhibit similar changes following various intensities of management of an upland grassland (Bardgett et al., 1996), although this was studied following only three types of treatment. One situation, in which there may be differences in the measured fungal/bacterial ratio depending on the method used, is that of varying pH. In a study of a natural pH gradient of forest soils, Blagodatskaya and Anderson (1998) found that the fungal/bacterial ratio (estimated using the selective inhibition technique) decreased as the pH increased. On the other hand, hardly any differences were found between the fungal/bacterial biomass index in coniferous forest humus that had been limed, ash-treated or alkaline polluted and natural forest soils, when using the PLFA method, although all these treatments increased soil pH considerably (Ba˚a˚th et al., 1992, 1995; Frostega˚rd et al., 1993). There may be several explanations of this difference in the effect of soil pH, apart from the different techniques used. In the first study the pH gradient was a natural one (that is, the soils have had a long period of constant pH), while in the others the changes in soil pH were brought about rapidly by man-made intervention. In the former case a mineral soil was studied and in the other cases a forest humus was studied. However, the conflicting results of these studies demonstrate the need to compare the selective inhibition and PLFA techniques in a more stringent way. The aims of the present study were several. Most importantly, we wanted to compare the selective inhibition technique with the PLFA method with respect to estimating both the total biomass and fungal/bacterial ratios. This comparison was performed in 53 samples of broad-leaved forest soils with a pH gradient from 3 to 7.2. Such a difference in pH has been shown to significantly affect both
total biomass (Anderson and Domsch, 1993; Anderson and Joergensen, 1997) and the fungal/bacterial ratio using the selective inhibition method (Blagodatskaya and Anderson, 1998). We also wanted to use this pH gradient to study more closely the effect of natural pH differences on the structure of the soil microbial community using the PLFA technique. Most previous studies on pH effects in this respect have been performed on soils in which the pH was changed through anthropogenic intervention, for example liming and ash application, or alkaline or acidifying pollution (Ba˚a˚th et al., 1992, 1995; Frostega˚rd et al., 1993; Pennanen et al., 1998a,b).
2. Materials and methods 2.1. Soils and sampling Soil from 53 mature broad-leaved forest stands (40 beech and 13 beech/oak stands) belonging to four different forest sites was sampled in spring (April). The forest stands were at least 70 years old. The sites were within a radius of 25 km of Braunschweig, Lower Saxony, Northern Germany. Prior to sampling, the litter (L) and humus layer (Oh) were removed with a hand-rake and a scraper. From an area of 2500 m2 per stand, five samples were taken from the mineral horizon (Ah, 0– 10 cm) with a spade, bulked and stored field-fresh in polyethylene bags sealed with cottonwool plugs for a maximum of 5 months at 4 8C. Prior to soil analysis the samples were sieved (, 2 mm) and the moisture adjusted to approx. 2 240 kPa, which corresponds to a water content range of 15– 57% (wet weight) in the samples. Soil pH was measured in 1 M KCl with a soil-to-solution ratio of 1:2. The percentage soil organic C (Corg) was estimated by dry combustion (C-IR 12, Leco) after having removed the inorganic C with 10% HCl (drop wise) and drying the samples on a sand bath at 70 8C. 2.2. Determination of microbial biomass and the fungal/bacterial ratio using the respiration technique Microbial biomass-C (Cmic) was determined using the SIR technique of Anderson and Domsch (1978). Samples of 25 g (dry weight) were amended with a powder mixture containing 200 mg glucose and 500 mg talcum, which warranted an initial maximal CO2-flush in all soils. The CO2 production rate was measured hourly at 22 8C using an automated infrared gas analyser system (Heinemeyer et al., 1989). Microbial biomass-C was calculated according to the equation of Anderson and Domsch (1978) where biomass-C (mg g21 soil) ¼ (ml CO2 g21 soil h21) £ 40.04. Fungal/bacterial respiratory ratios were determined using the selective inhibition method of Anderson and Domsch (1973, 1975). Selectivity of the inhibitor streptomycin (bacterial respiratory inhibitor) and cycloheximide (fungal respiratory inhibitor) was achieved with the following
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concentrations: 1 –2 mg g21 streptomycin plus 6– 8 mg g21 cycloheximide for acidic soils, and 2– 4 mg g21 streptomycin plus 6– 8 mg g21 cycloheximide for neutral soils. The inhibitors were applied to 25 g (dry weight) soil samples together with the glucose – talcum mixture (see above) and 16 mg g21 Na2HPO4. CO2 evolution rates were recorded hourly (see above). A computer program for selectivity analysis (‘DBF-SBA’ by Heinemeyer, O.; copyright 1992, MarCO Analytik, Hildesheim, Germany) was used to determine the fungal/bacterial respiratory ratio based on the criteria prescribed by Anderson and Domsch (1975): (1) proof of no unselective inhibition, and (2) proof of no onedirectional growth (shifts in the biosynthesis rates of bacteria and fungi in favour of one group). Inhibitor combinations were run in duplicate, uninhibited controls in triplicate. 2.3. Determination of biomass and fungal/bacterial ratio using PLFA analysis Phospholipid fatty acids (PLFAs) were extracted and analysed using a procedure described by Frostega˚rd et al. (1993). Briefly, the soil was extracted in a single-phase mixture of chloroform:methanol:citrate buffer (1:2:0.8 v/v/ v). After extraction the lipids were separated into neutral lipids, glycolipids and polar lipids (phospholipids) on a silicic acid column. The phospholipids were methylated and separated on a gas chromatograph equipped with a flame ionisation detector. Peak areas were quantified by adding methyl nonadecanoate fatty acid (19:0) as the internal standard before the methylation step. All solvents and chemicals used were of analytical grade. To remove lipid contaminants all glassware used was heated overnight at 400 8C. The fatty acid nomenclature used is as follows; total number of carbon atoms:number of double bonds, followed by the position (v) of the double bond from the methyl end of the molecule. Cis and trans configurations are indicated by c and t, respectively. Anteiso- and isobranching are designated by the prefix a or i. 10Me is a methyl group on the 10th carbon atom from the carboxyl end of the molecule. Cy indicates cyclopropane fatty acids. Br indicates a branched fatty acid with unknown branching configuration. xi denotes an unidentified fatty acid. The sum of the following PLFAs was used a measure of the bacterial biomass: i14:0, i15:0, a15:0, 15:0, i16:0, 10Me16:0, i17:0, a17:0, cy17:0, 17:0, br18, 10Me17:0, 18:1v7, 10Me18:0 and cy19:0 (Frostega˚rd and Ba˚a˚th, 1996). The PLFA 18:2v6,9 was used as a measure of fungal biomass.
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block at 70 8C for 90 min. After the heat treatment, 1 ml distilled water and 2 ml cyclohexane were added, the tubes were vortexed for 30 s, centrifuged and the top phase removed. The remaining solution was washed once with 2 ml cyclohexane and the combined cyclohexane fractions evaporated under N2 at 40 8C. Before measuring the amount of ergosterol with HPLC using a Nova Pak C 18 reverse-phase column (Waters, Millipore Corporation, USA), the samples were dissolved in methanol by heating at 40 8C for 15 min, and then filtered through a 0.45 mm filter. The mobile phase consisted of methanol at a flow rate of 1 ml min21 and ergosterol was detected using UV light at 282 nm. 2.5. Statistical analysis The mole percent of the individual PLFAs was standardized to unit variance (scaling) before being subjected to principal component analysis (PCA).
3. Results The soils varied in pH from 3.0 to 7.2 and in organic-C content from 3.5 to 8.9% (apart from four soil samples with . 10% organic-C). There was a weak tendency for the % organic-C to decrease with increasing pH (R2 ¼ 0:124; p , 0:05; n ¼ 53). A linear correlation (R2 ¼ 0:803; n ¼ 53; p , 0:001) was found between biomass-C estimated with the SIR technique and the total amount of PLFA (totPLFA) (Fig. 1). The relationship indicated that 1 mg biomass-C was equivalent to 130 nmol totPLFA. However, the regression line did not go through the origin, indicating that about 50 nmol totPLFA g21 dw of soil was not due to microbial biomass measured with SIR.
2.4. Determination of fungal biomass using ergosterol Ergosterol was extracted by adding 1 ml cyclohexane and 4 ml 10% KOH in methanol to 1 g of soil. After 15 min ultrasonic treatment, the test-tubes were placed in a heating
Fig. 1. Regression between total biomass-C measured with substrate induced respiration (SIR) and the total amount of phospholipid fatty acids (totPLFAs) in 52 beech/beech-oak forest soils with pH ranging from 3 to 7.2.
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Fig. 2. The effect of soil pH in beech/beech-oak forest soils on the soil microbial biomass estimated as: (A) biomass-C with the substrate induced respiration (SIR) technique and (B) as the total amount of phospholipid fatty acids (totPLFAs).
Both biomass measurements were positively correlated to the soil pH (R2 ¼ 0:666 and 0.548 for SIR and totPLFA, respectively, p , 0:001; Fig. 2A and B). The biomass-C estimated with SIR was about 0.5% of the soil organic-C in soils with pH 3, but increased to about 2.5% at pH 7 (Fig. 2A). The concentration of totPLFA increased from about 1.5 mmol totPLFA g21 soil organic-C at pH 3 to roughly 4 mmol totPLFA g21 soil organic-C at pH 7 (Fig. 2B). The fungal respiration was higher than the bacterial respiration in all soils when determined with the selective inhibition technique. In the low-pH soils, bacterial respiration was only 10% of the total antibiotic-inhibited respiration (Fig. 3A) giving a fungal/bacterial ratio of almost 9, while at pH 7 30% was bacterial respiration, giving a fungal/bacterial ratio of roughly 2. There was a good linear relation between soil pH and the fraction of SIR that was accounted for by bacterial respiration (R2 ¼ 0:708; n ¼ 53; p , 0:001; Fig. 3A). The fungal/bacterial biomass index ratio calculated using the PLFA technique varied between 0.01 and 0.07, and there
Fig. 3. The effect of soil pH in beech/beech-oak forest soils on the relation between fungal and bacterial biomass. (A) The fraction of bacterial respiration measured using the selective inhibition technique, (B) the fungal/bacterial biomass index using the PLFA 18:2v6,9 as an indicator of fungal biomass and the sum of 13 bacteria-specific PLFAs as an indicator of bacterial biomass.
was a tendency for this ratio to increase slightly with increasing pH (R2 ¼ 0:234; n ¼ 52; p , 0:01; Fig. 3B). Ergosterol (considered to be specific to fungi) was also measured in a subset of 35 soil samples covering the whole range of soil pH values. This was done in order to investigate whether the differences in the effect of pH on the fungal/bacterial ratios estimated with the two techniques was due to the PLFA used as a fungal marker, 18:2v6,9, being present in non-fungal structures such as plants roots. There was, however, a significant linear correlation between the soil content of ergosterol and the content of the PLFA 18:2v6,9 (R2 ¼ 0:671; p , 0:001; Fig. 4), indicating that this was not the case. The relation indicated that 1 nmol of the PLFA 18:2v6,9 was equivalent to 0.62 mg ergosterol. The data concerning the individual relative concentration (mol%) of the 33 most common PLFAs were subjected to a principal component analysis. The first principal component (PC1) explained over 50% of the variation in the data set, while the second, PC2, only explained 13.7%. The first PC appeared to be related to soil pH (R2 ¼ 0:845; n ¼ 52;
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Fig. 4. Correlation between the soil content of the PLFA 18:2v6,9 and ergosterol as indicators of fungal biomass. Only 35 of the 53 soil samples were used for the ergosterol measurements.
p , 0:001) (Fig. 5A). The loading plot of the individual PLFAs (Fig. 5B) shows that the main effect of soil pH on the PLFA pattern was higher concentrations of the PLFAs i16:0, cy19:0, 10Me17:0, i15:0, x3, 17:0 and 20:0 at low pH, while at higher pH, several mono-unsaturated PLFAs, such as 18:1v9, 17:1v8, 16:1v5, 18:1v7 and 16:1v7c, were more common. The effects of pH on the relative amounts of selected PLFAs are shown in Fig. 6. The PLFA 16:1v5, indicative of arbuscular mycorrhiza (Olsson et al., 1995, 1999), increased from 1.5 mol% at pH 3 to 5 mol% at pH 7 (Fig. 6A). The PLFAs 16:1v7c and 18:1v7c increased from 4 and 5 mol%, respectively, to 9 and 11 mol%, respectively (Fig. 6B and C), while cy19:0, also indicative of Gramnegative bacteria, decreased from 13 mol% at low soil pH to 3 mol% at high pH (Fig. 6D). The relative amounts of the PLFAs i15:0 and i16:0, indicative of Gram-positive bacteria, were lower at high than at low pH (Fig. 6E and F). Other PLFAs that increased markedly over the pH gradient were i14:0 (from 0.5 to 2 mol%), a15:0 (from 4 to 7 mol%), a17:0 (from 1.2 to 2 mol%) and 17:1v8 (from 0.2 to 0.6 mol%). Other PLFAs that decreased over the pH gradient were 16:1v7t (from 1.5 to 0.7 mol%) and 10Me17:0 (from 1.7 to 0.5 mol%).
4. Discussion To be able to calculate actual biomass values from the fungal/bacterial ratios one needs reliable conversion factors. Such conversion factors will be different for fungi and bacteria, but also different for the PLFA and the selective inhibition technique. Since there are currently no reliable conversion factors, it is meaningless to compare the actual values of the fungal/bacterial ratios estimated with the two methods. However, changes in the ratios due to environmental factors, such as pH, should give similar results, irrespective of the method used. However, the main results
Fig. 5. The effect of soil pH on the PLFA pattern of the soil microbial community. (A) Regression between soil pH and the scores along the first component of the principal component analysis of the PLFA data. (B) The loadings of the individual PLFAs from the principal component analysis of the PLFA data. PLFAs to the right in the plot indicate those that are more common in high-pH soils, while those to the left indicate those that are more common in the more acid soils.
of the present comparative study between the PLFA technique and the selective inhibition technique showed a lack of congruence in this respect (Fig. 2A and B). The pH gradient caused a change in the fungal/bacterial ratio estimated with the selective inhibition technique, but this was not reflected by the PLFA technique. This does not mean that either of the two methods is inadequate, but it clearly indicates that they reflect different aspects of fungi and bacteria, despite earlier studies, in which a correlation was found between the measurements (e.g. Bardgett et al., 1996). The lack of correlation found in a recent study on five different soil types (Bailey et al., 2002b) also indicates that these techniques reflect different aspects.
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Fig. 6. The effect of soil pH on the relative amount (mol%) of selective PLFAs. (A) 16:1v5 (arbuscular mycorrhiza), (B) 16:1v7c (Gram-negative bacteria), (C) 18:1v7 (Gram-negative bacteria), (D) cy19:0 (Gram-negative bacteria), (E) i15:0 (Gram-positive bacteria), (F) i16:0 (Gram-positive bacteria).
There are several possible explanations of the lack of congruence between the two methods, which we can only speculate about. In the PLFA technique there might be fatty acids not associated with the living biomass that interfere with the measurements. Interference from humic-acidderived fatty acids in PLFA analysis was reported by
Nielsen and Petersen (2000), although they estimated that this fraction constituted only 5– 10% of the total PLFA extracted from the soil. Also, the regression line in Fig. 1 not going through the origin indicated the presence of nonmembrane bound PLFA. The selective inhibition technique, on the other hand, will not include dormant cells, since
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the method requires an immediate response of cells to the addition of glucose. The method could also be selective against slow-growing organisms not responding rapidly to the addition of glucose. This might include ectomycorrhizal fungi which, deprived of their food base (the tree) might show little increased activity due to glucose addition. All fungal cells not responding to glucose would, however, still have intact PLFAs and would thus be included in PLFA measurements. Since the selective inhibition method is not a direct measurement of biomass, although most often used as such, it only provides information about the respiratory activity of the fungal and bacterial components of the total biomass after carbon amendment. It should therefore perhaps be regarded as an activity indicator, and the resulting ratio should be called a fungal/bacterial respiration ratio or a fungal/bacterial activity ratio (as used by Bailey et al., 2002). Modern microscopic techniques such as image analysis together with epi-fluorescence videomicroscopy (Anderson, 2001) for the quantification of the ratio of active to total microbial biomass as recently suggested by Klein and Paschke (2000), could be of additional help in explaining the lack of congruence between fungal/bacterial activity and biomass ratios. Despite the lack of congruence between the fungal/bacterial ratios estimated with the two methods in relation to soil pH, the total biomass appeared to be significantly correlated, indicating that in this respect the two methods are complementary (Fig. 1). Bailey et al. (2002a) also found a correlation between microbial biomass estimated with the PLFA and the SIR techniques in a study on five different soil types. The PLFA technique has usually been employed to give only a relative value (usually given as nmol totPLFA) of the total biomass, since a conversion factor is seldom applied to calculate absolute biomass values. A conversion factor of 340 nmol PLFA mg21 biomass C, based on few soils, was suggested by Frostega˚rd et al. (1991). The value obtained in the present study, 130 nmol PLFA mg21 biomass C, is considerably lower. However, although in our case the conversion factor was based on 53 soil samples, they were all from beech or beech-oak forests. The need for a more thorough comparison of biomass values using PLFA and other methods in many different soils is needed before a more accurate conversion factor can be presented. We also calculated a conversion factor between ergosterol and the PLFA indicative of fungi, 18:2v6,9 (Fig. 4). Few such comparisons have been made, but Frostega˚rd and Ba˚a˚th (1996) found a similar conversion factor to that in the present study, when comparing the two methods in 12 different soils. The fungal biomass can now be estimated from PLFA measurements, using our conversion factor (1 nmol 18:2v6,9 ¼ 0.62 mg ergosterol) and reported values of the ergosterol content of fungal biomass (ranging from 1.9 to 7 mg mg21 dry weight mycelium depending on species composition and growth conditions (e.g. Davis and Lamar, 1992; Djajakirana et al., 1996; Nout et al., 1997;
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Montgomery et al., 2000) or to 11 mg mg21 fungal biomassC (Scheu and Parkinson, 1994)). Marstorp et al. (2000) and Gong et al. (2001) related the amount of ergosterol (a measure of fungal biomass) to the total amount of soil microbial-C in a pH gradient from 3.8 to 7.3. In their case the change in pH was due to long-term addition of fertilizers and manure to agricultural soil. They found that the ergosterol/biomass-C ratio (mg/mg) decreased with increasing pH from 0.012 at low pH to 0.005 at high pH (Gong et al., 2001) and from 0.027 to less than 0.01 (Marstorp et al., 2000). Similar calculations with the present data set showed no relation between the ergosterol/biomassC ratio and pH (data not shown). This is important in that the use of ergosterol (or the PLFA 18:2v6,9 since their amounts were correlated) in some cases can indicate proportionally more fungi at low pH (Marstorp et al., 2000; Gong et al., 2001), while in other cases this is not the case (present study). Thus, it is not the methodology per se that is the reason for the lack of correlation between PLFAs and the physiological, SIR-based method of determining fungal/ bacterial ratios. In pH gradients of non-forest soils with less mycorrhiza present they may well give similar results. However, the complete absence of correlation in the present study clearly shows that the two methods measure different aspects of fungi and bacteria and that earlier correlations between the two techniques (e.g. Bardgett et al., 1996) might have been due to different underlying soil communities. Although the PLFAs indicating fungi (18:1v9, as well as 18:2v6,9) were little affected by pH, the relative concentrations of several PLFAs were closely related to pH (Fig. 6). The PLFAs most affected by pH in this study were the same as those affected in a similar way in earlier studies of the effect of pH on the PLFA pattern in different types of soil. This includes studies of alkaline pollution of coniferous forest humus (Ba˚a˚th et al., 1992), liming of coniferous forest humus (Ba˚a˚th et al., 1995), liming of agricultural soil (Schutter and Fuhrmann, 2001), primary succession transects (Merila¨ et al., 2002) and natural pH gradients in forest and meadow soil (M. Tabor and E. Ba˚a˚th, unpublished). In almost all these cases (including the present), where the specific PLFAs were reported, the relative concentrations of i14:0, 16:1v7c, 16:1v5 and 18:1v7 increased, while those of i15:0, i16:0, 16:1v7t and cy 19:0 decreased with increasing soil pH. Most of the other PLFAs also increased/decreased in the same way due to changes in pH in many of these studies. Thus, not only does pH appear to have a profound effect on the microbial community composition as measured with the PLFA technique (Fig. 5A), but the changes due to pH were similar in very different soils and when very different causes of the pH gradient were studied. Although one cannot directly translate changes in the PLFA pattern into a specific species composition, these results still indicate that similar microbial communities are induced in different soils at the same pH.
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The close correlation between the PLFA pattern and pH in various soils has an important implication, when evaluating PLFA data. The effect of pH must always be taken into consideration as an important explanation of changes in the PLFA pattern, even if the actual pH difference is rather small. For example, cy19:0 decreased by about 2.5 mol% per pH unit (Fig. 6D). Thus, a pH difference of only 0.5 units would, in most cases, induce a change of more than 1 mol% in the content of cy19:0, a difference that is usually considered significant. On the other hand, since changes in pH induced very similar changes in different soil habitats (see references above), one can use these changes as a way of detecting pH effects. Thus, if an investigation results in an altered PLFA pattern similar to that shown in Fig. 5B, one could suspect this to be due to variation in pH.
Acknowledgements This study was supported by grants from the Swedish Natural Science Research Council and the Swedish Research Council to E.B. We would like to thank Maria Bota and Kurt Steffens for their reliable technical assistance.
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