Why is the strength of relationships between pairs of methods for estimating soil microbial biomass often so variable?

Pergamon

0038-0717(94)00229-O

Soil Biol. Biochem.Vol. 27, No. 6, pp. 821-828, 1995 Copyright 0 1995 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0038-0717/95 $9.50 + 0.00

WHY IS THE STRENGTH OF RELATIONSHIPS BETWEEN PAIRS IOF METHODS FOR ESTIMATING SOIL MICROBIAL BIOMASS OFTEN SO VARIABLE? D. A. WARDLE* AgResearch,

Ruakura

Agricultural

and A. GHAN1

Centre,

Private

Bag 3123, Hamilton,

New Zealand

(Accepted 12 October 1994) Summary-Physiological and biochemical methods for estimating soil microbial biomass are usually calibrated against other methods and parameters. However, while calibrations are usually made over soils with a very wide range of microbial biomass values (across a wide geographical range) they are often used to assess relatively small differences in microbial biomass across a narrower range of microbial biomass values (across a smaller geographical area, e.g. within a single field), where their reliability may be considerably less. We investigated the abilities of three methods of quantifying microbial biomass, i.e. substrate-induced respiration (SIR), fumigation-incubation (FI) and fumigation-extraction (FE) to serve as predictors of each other across two geographical scales, i.e. across 12 sites over an area of z 100 x 100 km; and within each of these sites (12 samples per site) over an area of 0.3 ha each. Over the larger scale, relationships between pairs of methods were strong, with R* values always > 0.90. However, over the smaller scale, correlations between pairs ofmethods were variable and only significant for those sites in which spatial variability in soil organic matter was relatively high. Uncertain relationships between SIR and the fumigation-based methods may be expected because they apply to different subsets of the soil biomass (i.e. glucose-responsive vs chloroform-sensitive). However, we suggest that FI and FE are sometimes weakly correlated because the FI decomposition constant kc and the FE constant RECvary differently relative to each other across underlying gradients. Calibration equations for estimating microbial biomass are most accurate when restricted to situations where the range of biomass values is comparable to that from which the calibration was first derived, and to similar soil types. In our study, it appears that calibration equations for predicting microbial biomass are only likely to provide reliable relative estimates in situations where the coefficient of variation (standard deviation/mean) of soil organic C is > 15%.

Three physiological and biochemical methods are now in wide use for assessing microbial biomass viz. fumigation-incubation (FI), fumigation-extraction (FE) (Voroney, 1985; Vance et al., 1987) and substrate-induced respiration (SIR) (Anderson and Domsch, 1978; West and Sparling, 1986) and these methods are all calibrated against other methods or parameters. However, these calibrations are frequently problematic, and the apparent strength of some widely used calibration equations diminishes when correction is made for statistical artefact (Wardle and Parkinson, 1991). Correlations between pairs of methods for measuring microbial biomass across several soil samples or soil types are highly variable, with some studies showing very strong relationships (e.g. Martens, 1987; Chander and Brookes, 1991; Kaiser et al., 1992) and others revealing weak or non-existent relationships (e.g. Ross et al., 1984; Wardle et al., 1993). The strongest and most widely used calibration equations for quantifying microbial biomass are based on soil samples with a very wide range of biomass values (e.g. Anderson and Domsch, 1978; Vance et al., 1987). It is reasonable to expect that calibration equations derived over a wide range of values (and

‘INTRODUCTION

The soil microbial biomass is an essential component of most terrestrial ecosystems because it is responsible for regulating nutrient cycling, and acts as a highly labile source of plant-available nutrients (Jenkinson and Ladd, 1981; Okano et al., 1989; Singh er al., 1989). It exerts a major influence on other components of the ecosystem, because it controls flow of energy to higher trophic levels in the decomposer food-web (Wardle, 1995) and is closely linked to ecosystem primary productivity (Zak et al., 1990; Jenkinson et al., 1992). Since the development of the fumigation-incubation technique for quantifying total microbial biomass (Jenkinson and Plswlson, 1976) there has been a substantial (and rapidly increasing) number of studies investigating properties of the soil microbial biomass as well as its response to environmental and land management factors (Smith and Paul, 1990). This has been accompanied by an increasing interest in devising new and improved methodology for assessing microbial biomass (e.g. Jenkinson, 1988; Ross, 1990; Sparling et al., 1990; Schmidt, 1992), so as to generate more reliable biomass estimates. *Author for correspondence. 821

822

D. A. Wardle and A. Ghani

over a wider geographical area) would be stronger than those over a narrower range (and smaller geographical area). Yet many studies which have quantified microbial biomass using such equations have assessed biomass over a much narrower range of values than that for which the equations were themselves derived. It is of concern that this strategy may be inappropriate for providing relative estimates of the microbial biomass, especially when treatment effects on the microbial biomass with only relatively small differences are being compared. In this light, our aim was to assess the effects of soil sample variability at two spatial scales on the reliability of three microbial biomass methods as predictors of each other, and to identify in which circumstances the use of calibration equations for estimating microbial biomass are likely to be appropriate.

MATERIALS

AND METHODS

The soils chosen for our study were all collected from managed grassland regions between Taupo and Rotorua, New Zealand (mean grid coordinates 3830’s. 176”12’E.) over an area of approximately 100 x 100 km. Twelve sites were selected from the 40 studied by Perrott et al. (1989). All soil forming factors except management were similar for all sites. All sites had a mean rainfall of approximately 1300 mm y - ’ and a flat gently rolling topography, and all soils were volcanic yellow-brown pumice (Typic Vitrandept) and derived from the Taupo eruption (186 A.D.) Characteristics of the sites are given in Table 1. In August and September 1993, 12 random samples were collected from each site over an area of 0.3 ha. Each sample consisted of 20 cores, each 7.5 cm deep x 2.5 cm dia, and collected over an area of 1 m2. Our study therefore contained two vastly different spatial scales, viz. the 12 separate sites and the 12 samples within each site. All samples were sieved (4 mm) and gravimetric soil moisture determined. Percent organic C was determined for each sample using the method of Walkley and Black (1934). Analyses were made of relative microbial biomass using the following three methods. SIR. This was assessed using the approach of Anderson and Domsch (1978) and West and Sparling (1986), following the experimental details of Wardle

Table 1. Properties Site code

K L

Land use Sheep Sheep and beef Dairy cattle Deer Beef cattle Mown area Dairy cattle Dairy cattle Deer and sheep Deer and sheep Dairy cattle Dairy cattle

et al. (1993). Duplicate soil samples (10 g dry wt) were amended to 55% soil moisture content (dry wt basis; i.e. approximately equivalent to -33 kPa), either by rewetting with a fine mist immediately before assessment or by gradual air-drying. Samples were amended with 60 mg glucose, placed in airtight containers and incubated at 22°C. The total CO*-C released between 1 and 4 h in the headspace was measured by injecting 1 ml subsamples into an IR gas analyser. The mean rate of CO?-release over this time was used as a measure of SIR. FI. This used the method of Jenkinson and Powlson (1976) basically as modified by Wardle and Parkinson (1990). Soil moisture was adjusted to 55% (dry wt basis) as described for the SIR method. Duplicate subsamples (10 g dry wt) were then fumigated with chloroform for 18-24 h and a further pair of subsamples (11.1 g dry wt) left unfumigated. After fumigation, the fumigated subsamples were reinoculated with 1.11 g (dry wt) soil. The fumigated and non-fumigated subsamples were then incubated for 7 d at 22°C in 500 ml airtight containers, each with vials containing 10 ml 1.0 M NaOH. These incubation conditions have been demonstrated to provide consistent and reliable estimates (Wardle et al., 1993). Total CO,-C released over 7 d in each container was assessed by titrating this NaOH against 0.5 M HCI. Because some studies have advocated using non-fumigated controls (e.g. Voroney and Paul, 1984) two measures of FI flush of CO,-C were used, viz. with and without subtraction of the non-fumigated (control) CO?-C values. FE. A modified version of the method of Vance et al. (1987) was used. Duplicate subsamples (5 g dry wt) were fumigated with chloroform as described above. Fumigated and corresponding non-fumigated (5 g dry wt; duplicate) subsamples were then extracted with 0.5 M KzS04 (1 : 5 soil solution ratio) for 2 h on an end-over-end shaker at 40 rev min - ’ and 20°C. Suspended samples were centrifuged for 5 min at 3500 rev min’ and filtered through Whatman 42 filter paper. Two ml of each extracted solution was reacted with 1 ml of 150 mM KZCr207 and 5 ml of distilled water and 15 ml of cont. HzS04 was added to facilitate oxidation of C by dichromate. After a 2 h cooling period, chromate remaining in the samples was titrated potentiometrically with 30 IIIM Fe(NH4)(S04)

of the 12 locations

selected

Soil c (%)

Soil N (%)

Soil pH

Clay (%)

Silt (%)

Age of pasture (y)

8.25 9.91 7.13 7.53 7.57 5.09 7.36 7.76 8.88 9.82 9.11 LO.16

0.59 0.72 0.63 0.49 0.53 0.32 0.66 n 71 0.70 0.82 0.78 0.81

5.77 5.52 5.40 5.57 5.16 5.99 i.64 5.59 5.75 5.46 6.15 5.77

10.2 12.2 11.2 5.0 11.2 12.8 15.8 5.8 4.8 8.4 6.4 7.4

6.0 4.2 4.6 18.0 11.4 8.2 11.2 17.2 11.2 8.0 15.4 15.6

24 46 49 38 38 0 61 61 24 44 59 37

Microbial

biomass

methods

823

Table 2. Relative microbial biomass values (mean + SD) for the 12 sites outlined in Table I, assessed using FE, FI and SIR; each value presented is based on 12 independent data points

Site code?

J

K L

--

FE (pg C,g soil-‘) 146 f 586 * 595 * 579 * 671 f 416 + 668 * 646 k 798 k 891 * 776 + 814 +

‘17 97 :37 54 G49 56 ,41 53 ,41 14 38 12

FE-NF (fig C.g soil - ‘)

FI (pg C0&.7d - ‘.g soil - ‘)

479 f 72 339 + 92 390 k 36 359 * 50 424 If: 50 267 k 52 432 + 40 451 * 47 525 + 46 617 + 42 502 * 47 563 f 62

843 f 67 733 + 82 786 f 55 152 + 39 805 k 31 658 + 38 752 + 26 806 * 43 838 + 31 944+44 844+25 894 f 43

FI-NF (pg COz-C.7d - ‘.g soil - ‘) 510 + 433 + 480 + 448 + 496 + 402 f 451 * 506 f 547 5 618 + 553 f 587 *

SIR (pg COI-C,h - ‘.g soil- I) 24.4 + 3.6 18.4 + 3.0 20.1 + 1.6 18.9 k 1.5 21.9 f 1.6 15.9 + 1.4 20.3 + 0.9 22.2 + 1.8 23.3 5 1.4 27.1 &-1.5 23.6 rt 0.7 27.5 f 1.0

63 70 52 29 28 33 17 35 25 40 17 39

tSite code as for Table I. $FE and FI, non-fumigated controls not subtracted; FE-NF and FI-NF, non-fumigated control subtracted.

with an end point of 860 mV. As for FI, two measures of microbial C assessment were used, viz. with and without subtraction of the non-fumigated C-values. Correlation analyses were used to elucidate the relationships between SIR, FI and FE both between the 12 sites (larger spatial scale) and within each of the 12 sites (smaller spartial scale). Analyses using FE or FI data were performed both with and without subtraction of the appropriate non-fumigated control values.

RESULTS

AND DISCUSSION

The 12 sites represented a relatively wide range of microbial biomass values, especially considering that they were from the same soil type (Table 2). For all methods, site F (which was the least developed) had the lowest microbial biomass while sites J and L had the highest micrsobial biomass. When each site represented one data point (i.e. the mean of 12 samples), all microbial biomass methods were very strongly correlated (Table 3). The results of the FI and FE methods were (closely related to each other and to the SIR method, regardless of whether or not non-fumigated controls were subtracted. Partial correlation coefficients between pairs of methods correcting for variability in soil C or N were only slightly less than those not correcting for these factors and were always > 0.88. This means that over a spatial scale of several km, these methods are correlated with each other independently of soil organic matter, which indicates that at the larger

spatial scale all methods apply more or less to the same fraction of the living component of the soil. We therefore propose that all methods considered in this study are reliable predictors of relative microbial biomass at this spatial scale of resolution. The soil microbial biomass over the larger spatial scale showed very strong relationships with soil C and N and non-significant relationships with soil C-to-N ratio, pH and clay content (Table 4). The very close agreement between methods suggests that over this scale, all methods are appropriate for assessing microbial biomass relationships with soil properties. However, when the microbial biomass is expressed per unit organic C [such as is often done when it is used as an ecological indicator (Insam and Domsch, 1988; Wardle, 1993)] the FI method demonstrated strong negative relationships with soil N and C which were not apparent with the other methods. Testing correlations between organic C and the microbial C-to-organic C ratio may be spurious because both terms are partially dependent on each other through including organic C as a component (Kenney, 1982; but see Prairie and Bird, 1989). Despite this limitation our results provide some evidence that the response of this variable to gradients in organic matter content is very closely influenced by selection of microbial biomass method. We therefore propose that over a broad geographical scale, the ratios of microbial C-to-organic C estimated using FI, FE and SIR all respond differently to underlying gradients of organic matter at least across the sites considered in our study.

Table 3. Correlation analysis between pairs ofmethods across all 12 sites with each site representing an independent data point Partial correlation coefficient correcting for: hdethods being comparedt F’I vs FE (FI-NF) vs (FE-NF) SIR vs FI S’IR vs (FI-NF) EmIRvs FE SIR vs (FE-NF)

Correlation coefficient

Organic C

Total N

0.949 0.969 0.975 0.959 0.967 0.967

0.886 0.939 0.948 0.919 0.932 0.935

0.884 0.929 0.945 0.911 0.927 0.935

iAbb reviations for methods as for Table 2.

824

D.

A. Wardle and A. Ghani

Table 4. Correlation coefficients between microbial biomass variables and soil chemical and physical properties Soil properties Microbial variable

Biomass methodt FE FE-NF FI FI-NF

Biomass C

Biomass C-to-organic

Correlation

C

SIR FE FE-NF FI FI-NF SIR

coefficient is sienificantlv

%C 0.75** 0.70” 0.74** 0.71** 0.72** - 0.35 -0.07 -0.91*** -0.77** -0.58*

%N

C-to-N ratio

0.75** 0.77** 0.76** 0 751’ 0.73** -0.17 -0.19 -0.73** -0.57 -0.3s*

-0.47 -0.57 -0.49 -0.49 -0.48 -0.06 -0.41 0.32 0.20 - 0.04

PH

%Clay

0.08 0.02 -0.09 0.03 0.01 0.27 0.10 0.13 0.22 0.16

-0.41 -0.44 -0.48 -0.53 -0.43 0.04 -0.20 0.24 0.07 0.06

different to 0 at *P = 0.05, **P = 0.01 and ***P = 0.001

TAbbreviations for method; as for fable 2.

On the smaller spatial scale (i.e. within sites) relationships between pairs of methods were far more uncertain. For all comparisons, sites with higher variability in soil organic matter content (estimated using the coefficient of variation or standard deviation : mean ratio for soil organic C content) across the 12 samples usually yielded strong relationships between methods while those with low variability did not (Fig. 1). This is presumably because in soils that are more spatially heterogeneous, biomass values will have been stretched over a wider range. Thus, all pairs of methods were strongly correlated for soils A, B and F, while none were for soils C, G and K. This means that at a within-site level of resolution (i.e. within a single field) pairs of methods are probably only good predictors of each other when the coefficient of variation of soil C is > 20%, at least in the sites we considered. Therefore, in circumstances where spatial variability of organic matter is low, including cases where differences in treatments are small, we suggest that estimation of microbial biomass response to management or environmental factors is likely to be less reliable than in circumstances where background variability is higher. Using a calibration equation derived for soil samples over an extremely wide range (e.g. that of Anderson and Domsch, 1978) to characterize and quantify very small differences in total microbial biomass on a within-field scale is therefore dubious. It should also be noted that studies which have found weak or non-existent relationships between pairs of methods (e.g. Sparling, 1981; Wardle and Parkinson, 1990) probably included a narrower range of values than those which have typically found stronger relationships. There is no consistent evidence from Fig. 1 that any pair of methods is significantly (at least in a statistical sense) more strongly related to each other than any other pair of methods, meaning that they appear to

perform roughly equally as predictors of one another. However, two soils with intermediate coefficients of variation for soil organic C (viz. soils D and H) demonstrated stronger relationships between SIR and FI than SIR and FE. SIR may be expected to be only partially correlated with the other approaches because it measures the microbial biomass which is glucose-responsive, rather than that which is chloroform-susceptible (Wardle and Parkinson, 1991). It has also been suggested that SIR measures only active microbial biomass while fumigation-based methods measure total microbial biomass (van de Werf and Verstraete, 1987; Ross, 1991). If the proportion of the microbial biomass which is active is variable, then weak relationships are expected between SIR and FI or FE. In addition, SIR assumes that all organisms release an equivalent amount of CO? per unit weight biomass in response to glucose, which may be unrealistic given that r-selected organisms respond much more rapidly to added substrates than K-selected organisms (Wardle and Parkinson, 1990). Variability in the composition of the microbial community could therefore conceivably weaken apparent relationships between SIR and the fumigation-based methods. We suggest that SIR is best used as a relative measure of glucose responsive (and metabolically active) microbial biomass, particularly whenever the range of microbial biomass values is relatively small. Problems with FI or FE may also account in part for the poor relationships sometimes observed with SIR. One possible problem is the efficiency of fumigation (Ingham and Horton, 1987) although most studies have found chloroform to work as a reasonably effective fumigant (Jenkinson and Powlson, 1976; Wardle and Parkinson, 1990; Santrukova, 1992). The criticisms of the effectiveness of FI outlined by Ingham er al. (1991) are based on data from forests containing

(Fig. 1 Opposiie) Fig. 1. Coefficients of variation (SD/mean) for % soil C, plotted against correlation coefficients between pairs of methods. Each data point on each graph is based on 12 independent soil samples within each site. Points above the lines marked P = 0.05, P = 0.01 and P = 0.001 indicate correlation coefficients between pairs of methods which are significantly different to zero at these probability levels. Letters for each point correspond to the sites listed in Table 1; abbreviations for methods are as for Table 2. The points labelled as “ALL SOILS” represent analyses using each site (=mean of 12 samples) as an independent data point, i.e. “across-site” analyses.

825

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826

D. A. Wardle and A. Ghani

thick hyphal mats, and in our view are not of wide applicability and probably of little significance in our study [indeed the laboratory responsible for developing FI caution against using that method in such circumstances, e.g. Vance et aI. (1987)]. Another problem often identified with FI and FE is selection of appropriate non-fumigated controls, and there are several points of view as to how this should be dealt with (Chaussod and Nicolardot, 1982; Voroney and Paul, 1984; Jenkinson, 1988; Ross and Tate, 1993). In our study correlations of FI or FE with SIR were equivalent regardless of whether a control was used. This suggests that selection of a control would not affect relative estimates of microbial biomass, although absolute microbial biomass values are still obviously dependent on the use of a control. Some studies, e.g. Ross and Tate (1993) and Wardle et al. (1993) have generated reliable microbial biomass data using FI without subtracting non-fumigated control values and in some soils, particularly those with high respiratory activity, sensible biomass estimates are only obtained when control values are not subtracted (Wardle et al., 1993). Controls are undoubtedly necessary in soils where a high proportion of microbial biomass is dead prior to fumigation because decomposition of dead microbes will also produce a C02-flush which needs to be corrected for (Wardle and Parkinson, 1990). However, this did not appear to be problematic in the sites we considered. In the case of FE, there is no prior recommendation we are aware of that controls may be unnecessary. However, if we define the reliability of a given method for providing relative microbial biomass values as its strength of correlation with other methods, then in our study FE is equally reliable regardless of whether a control is subtracted or not. As in the case of FI, absolute FE measurements are dependent upon the use of a control, but relative measurements are not. Although we are unaware of the applicability of the conclusions to other situations, in our soils at least there may be circumstances where not using a control may be permissible. The absence of any detectable relationship between FI and FE for several of the sites considered raises questions about the efficiency of the two methods in measuring C released by fumigation. Since for both methods comparable non-fumigated controls (or lack of controls) were used for all analyses, and fumigation conditions were almost identical, any discrepancy between them is most likely because the kc factor (i.e. the proportion of fumigated biomass C released as CO1 over 7 d) and the kECfactor (i.e. the proportion of biomass C extracted by K2S04 after 24 h fumigation) vary differently between samples. Our results demonstrate that in most soils measurement of C by extraction is less efficient than by incubation (a lower proportion of the total microbial C is detected) but that this difference in efficiency decreases (and eventually becomes non-existent) with increasing microbial biomass (Fig. 2). Thus, kc and kECcan both

vary across underlying gradients, and will even vary relative to each other. The generally greater efficiency of FI compared to FE is also apparent from data of Sparling and West (1988a,b) and Ross and Tate (1993) but not that of Kaiser et al. (1992) or Zagel (1993). Since greater efficiency of any measurement procedure is obviously a desirable feature (in this case recovery of microbial carbon from the soil), in our sites FI would be the preferred method especially for those sites with lower amounts of microbial biomass. Our results provide evidence that microbial biomass calibrations work well as relative predictors of each other over large spatial scales and over small (within field) spatial scales wherever spatial variability is high. These are situations in which the range of microbial biomass values is likely to be stretched, making a linear relationship more likely. However, they do not appear to work well as relative predictors in situations of low variability. Our results suggest that, in some grassland soils at least, attempts to characterize differences in microbial biomass in situations where the coefficient of variation is <20% is likely to be unreliable and may not give a true representation of microbial biomass differences. It is likely that in many studies, differences smaller than this are often comparatively unimportant, at least in terms of soil ecological function, and attempts to characterise relatively small differences may be unnecessary. This is particularly relevant is studies on seasonal variability. Upon reviewing the literature, Wardle (1992) found that only those studies with considerable interseasonal climatic variability [e.g. dry tropical forests (Singh et al., 1989) or boreal forests (Flanagan and van Cleve, 1977)] had I. non-fumigated controls not subtracted 1.0 0

2 ii

1.0

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Fig. 2. Relationship between relative microbial biomass (following rank-transformation) and the ratio of FE-C to FIX. Microbial biomass rank (horizontal axis variate) was determined by ranking the microbial biomass of the 12 sites independently for each of the three methods (FI, FE and SIR) and averaging these three ranks for each site.

827

Microbial biomass methods consistently high interseasonal variations in microbial biomass, and it is likely that in many studies where smaller fluctuations had been “identified”, the differences between seasons were less than that which could be explained in terms of the error components of the calibrations, of the methods used. We are cautious, however, in extrapolating our conclusions to other situations, a.nd it may be that in different ecosystems and soil types, the strength of relationships between methods rnay be either greater or less than what we have found. If our results suggest that only comparatively large relative differences in microbial biomass can be estimated reliably, then this means that use of microbial biomass as a bioindicator of soil quality has certain limitations. Over large spatial scales microbial biomass is very strongly correlated with soil C and N and there are circumstances where measurement of organic matter contents may provide as reliable an estimate of soil quality as measurement of microbial biomass; the literalture is replete with examples of where microbial biomass data and soil organic matter data are of comparable value in contributing to the understanding of soil response. We should also note, though, that there are other circumstances in which microbial biomass can respond dramatically to soil amendments even when little or no change in soil organic matter status is observed (e.g. Powlson et al., 1987). Ultimately the desirability or otherwise of using microbial biomass measurements in a study will depend on the kinds of questions being answered, and some of the more promising applications for microbial biomass studies may be in terms of understanding trophic relationships in the decomposer food-web (e.g. Wolters, 1989), the role of primary production in influencing soil processes (Jenkinson et al., 1992), in understanding patterns of nutrient release for plant growth (Singh et al., 1989) and in understanding nutrient turnover (Smith and Paul, 1990). In our view it seems somewhat unnecessary, though, to conduct experiments with the primary goal simply consisting of how microbial biomass responds to a given set of treatments, without acknowledging how this links to other aspects of the ecosystem being considered. In other words, we see microbial biomass measurements as a means to an end, rather than an end in themselves. Part of the problem with microbial biomass calibrations can be overcome by acknowledging that different methods do not necessarily measure the same thing, and simply expressing measurements in the units in which they were quantified. For example, we usually express SIlR data as units of respired CO* per weight of soil per unit time (rather than converting this to biomass units) and describe it as a relative measure of glucose-responsive microbial biomass (e.g. Ghani et al., 1995). We appreciate, however, that there are instances where it is clearly necessary to express microbial biomass as an absolute value and this will depend upon the objectives of a given study. Another point is that microbial biomass may be less desirable

as a bioindicator than other measurements based on microbial biomass data. The metabolic quotient (respiration : biomass ratio) (Anderson and Domsch, 1985) and ratio of microbial C-to-organic C (Insam and Domsch, 1988) are both strongly indicative of soil quality and are often more sensitive to ecological changes than microbial biomass by itself. It is important to note, also, that calculation of these variables does not depend upon absolute microbial values, and relative measures can also be used; ratios of basal respiration-to-SIR and SIR-to-organic C can both provide useful comparative measurements, at least within studies (Wardle, 1993). In conclusion, microbial biomass methods may be poor relative predictors of each other where variability is low or the range of microbial biomass values is limited; in these circumstances at least two and possibly several methods are preferred (see Jenkinson, 1988). Our results also suggest that it may be desirable to evaluate the reliability of a given calibration relationship prior to using it in a new situation. We believe that calibration equations for quantifying microbial biomass are most accurately used when restricted to studies where the range of values is similar to that from which the calibration was first derived. Acknowledgements-We

thank Bruce Thorrold for the

location of appropriate sites and for valuable discussions, Moira Dexter and Moinuddin Ahmed for technical help and Kathy Nicholson for preparing the figures. We also ihank L. G. Greenfield and G. P. Sparling for helpful and constructive comments at various stages of this manuscript. REFERENCES

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