The utility of ergosterol as a bioindicator of fungi in temperate
soils
Stepan Ruzicka*, Deborah Edgerton, Mark Norman, Tom Hill
Department of Environmental Sciences, University of East London, Romford Road, London E15 4LZ, UK
Accepted 10 January 2000
Give me a place to stand and I will move the earth. Archimedes
Abstract
In this paper we evaluate the utility of ergosterol as a measure of fungal biomass in temperate soils. We summarise published ®ndings and compare them with data from our own broad-scale assessment of the relationship between ergosterol and ATP in a range of temperate soils. Two hundred and ninety ®ve plots (three cores taken from each 1010 m plot) in seven ecotypes were sampled. Soils ranged from entirely mineral to entirely organic (0.01±46% Corg and sites comprised two primary successions,
one on shingle ridge on the south coast of England and one in the slack of a dune blow-out on the south coast of Wales, various meadow, pasture (some restored after opencast mining) and ancient woodland soils throughout England and acid forest soils in Central Europe. We found a strong relationship between ergosterol and ATP r20:80), which was largely unaected by the key soil properties of Corg, C/N ratio, moisture and pH. The sources and implications of the 20% of residual variance
were explored by assuming that the error was compounded from three sources: the inaccuracies in methods of analysis of ergosterol and ATP, the failings of each of the variables to estimate their underlying populations (i.e., fungal and total biomass, respectively) evar), and the non-equivalence of these populations (i.e., their incomplete overlap) epop). By partitioning the
residual variance into components corresponding to the levels of sampling, we estimated that the sum of the systematic portions ofevar andepopformed as much as three quarters of the 20% of residual variance in the ATP±ergosterol correlation, leaving just
5% mostly due to random error. Despite this close relationship, the attainment of a universal conversion factor between ergosterol and fungal biomass, applicable to all temperate soils, remains elusive and problematic. Many problems are caused by a lack of comparability between the various measures of fungal and total biomass used and the reliability, or otherwise, of extrapolations based on measures of axenic cultures (in contrast to in-situ measurements). The issue is further complicated by the non-linearity of the relationship between fungal biomass and fungal surface area; ergosterol is more correctly an index of the latter since it is a principal membrane sterol. We conclude that ergosterol is likely to be a reliable indicator of the extent of fungal membranes in temperate soils, if not an accurate measure of fungal biomass. 7 2000 Elsevier Science Ltd. All rights reserved.
Keywords:Temperate soils; Ergosterol; Fungi; ATP; Microbial biomass; Bacteria:fungi ratio
1. Introduction
To date, the measurement of the ergosterol concen-tration in natural substrates is arguably the most e-cient method for estimating their fungal biomass. However, despite an accumulating body of research on the potential of the ergosterol assay, few soil ecologists have either used or relied upon it because of persistent uncertainty over its utility. This is because the true
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* Corresponding authors. S. Ruzicka: Present address: Waterman Environmental Versailles Court, 3 Paris Garden, London SE1 8ND, UK. Tel.: +44-207-928-7888; fax: +44-207-928-0656; T. Hill. Tel. +44-208-590-7722.
error in the relationship between ergosterol concen-tration and fungal biomass is obscured by the errors and uncertainties associated with the various methods used to measure both quantities. This problem has been exacerbated by the limited comparability of the existing studies due to their narrow ecological scope.
As with other soil biomass assays, errors arising from the measurement of ergosterol are dicult to quantify, especially since each researcher also contrib-utes an unknown amount of human bias. Before extraction, varying amounts of ergosterol are lost during sample preparation and storage, with losses tending to be ordered: storage in methanolRfreezing followed by lyophilisation < lyophilisationRfreezing at ÿ208C << air or oven drying (West et al., 1987a; Newell et al., 1988; Zelles et al., 1991; Davis and Lamar, 1992). Sample storage and/or conditioning, usually at refrigerator or room temperature (48C and 258C, respectively), may conceivably lead to increases as well as decreases in ergosterol content due to changes in the membrane composition of existing mycelia, and replacement of the original fungal domi-nants by species able to grow below 58C and favoured by the disturbance of sampling and sieving.
Extraction of ergosterol from the sample is associ-ated with several further sources of variation. Because ergosterol occurs not only as free alcohols, but also as esters and glycosides (LoÈsel, 1988; Weete, 1989), sapo-ni®cation of the whole sample is necessary to release these bound forms. However, in soil this step is ac-companied by loss of free ergosterol, caused by soil-catalysed thermal degradation (Davis and Lamar, 1992; Martin, 1990; Djajakirana et al., 1996). E-ciency of ergosterol extraction is also aected by whether an alkaline or neutral extractant is used, whether partitioning of ergosterol into non-polar sol-vent (hexane) from the hydrolysis reagents is ecient and whether the internal standard is added to the soil or to the extract (Zelles et al., 1987; Newell et al., 1988; Davis and Lamar, 1992). To complicate matters further, the recovery of pure ergosterol added to soil may, paradoxically, be lower than the recovery of ergosterol from fungal tissue added to soil (Davis and Lamar, 1992).
Errors associated with the other fungal biomass measures against which ergosterol is compared are also high since all have major ¯aws. Newell (1992) has given a detailed account of an abundance of problems that aict direct observation, including the destruction of hyphae during sample homogenisation, positive bias with increasing magni®cation, diculty in distinguish-ing between live and dead hyphae and between thin hyphae and actinomycete ®laments, the inecient and non-selective binding of ¯uourochromes and problems with masking of ¯uorescence in melanised hyphae, and the variability, and hence inaccuracy, of the
volume-to-mass conversion factor. For example, Scheu and Parkinson (1994) found that the proportion of non-¯u-orescent fungi, stained with calco¯uor white, ranged from 5 to 93% in dierent horizons of the same forest pro®le. Stahl et al. (1995) showed that human bias accounted for 83% of the total variance in fungal bio-mass measures using direct microscopic counting, and actually suggested using ergosterol to correct for this error between laboratories. Measurement of fungal biomass by selective inhibition of substrate-induced respiration has its own suite of problems, including dierent fungal biomass C conversion factors in dier-ent soils (e.g. West, 1986; Hintze et al., 1994), a lack of response to glucose addition (Scheu and Parkinson, 1994) and imperfect inhibition of the target group combined with stimulation of the non-target group by each antibiotic (Stamatiadis et al., 1990; Scheu and Parkinson, 1994; Alphei et al., 1995).
In soil studies and surveys, the above uncertainties are further ampli®ed by the addition of a non-meth-odological, ecological error. Soil ergosterol concen-tration alone will vary across space and time due to natural variability in the composition and diversity of the fungal community because dierent fungi syn-thesise dierent amounts of the sterol (see Table 1). Moreover, some of this variation may be systemati-cally aected by properties of the soil environment, such as the resource quality of the organic matter aecting fungal membrane composition and altering fungal community composition.
The sought ergosterol-to-fungal biomass relationship contains errors compounded from all these sources. Not surprisingly, the variability of derived conversion factors is high while the comparability of studies is low. Averaging all published ratios serves little purpose since Ð apart from the limited statistical value of an average obtained from a highly variable, multimodal, and asymmetrically distributed dataset Ð this does not answer the fundamental question of whether ergos-terol can really be adopted as a measure of fungal bio-mass in natural substrates and soil. Rather, the answer lies in understanding the relationship between the in situ ergosterol concentration and the biomass of indi-genous fungi across a range of soil environments. In the absence of an independent and reliable measure of fungal biomass to which ergosterol could be compared, a clear-cut resolution to this problem will remain elu-sive. Only an approximation of the relationship is feas-ible and hence the approach one adopts to make this approximation is critical for supplying an answer. As with any model, a ®ne balance between depth, i.e., ac-curacy, and breadth, i.e., applicability, of the approxi-mation is crucial. We can accurately measure both ergosterol and biomass of axenically grown fungi (for reviews see Newell, 1992; Djajakirana et al., 1996) but the likelihood that the data can be extrapolated to the S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
thousands of species of indigenous soil fungi in their natural environment is remote. This is because in the absence of a broad approximation Ð a study invol-ving a wide range of natural environments Ð these data have ``no place to stand'' in relation to the ecol-ogy of the soil environment.
In this study we aim to ®ll this gap, seeking a broad approximation of the ergosterol±fungal biomass re-lationship by comparing in situ ergosterol concen-tration with the concenconcen-tration of ATP, a measure of total microbial biomass. Since the microbial biomass of temperate soils is dominated typically by fungi (see Table 2), ATP can thus be adopted as an approximate ``benchmark'' co-variate with which changes in ergos-terol concentration can be compared.
We have amassed a considerable dataset of ergos-terol and ATP concentrations in diverse temperate soils, ranging from recently-colonised, calcareous dune soils in south Wales to extremely acid forest soils in Bohemia, and including various meadow, pasture (some restored after opencast mining) and ancient woodland soils throughout England. Ergosterol con-centrations in these soils span more than three orders of magnitude. Since essentially the same methods were used for sampling, sample preparation and storage, and the analyses were performed using the same re-agents and equipment, many of the systematic errors associated with the methodology were ®xed. This enabled us to investigate the ``ecological'' errors arising from random and systematic variation in the ergos-terol±total biomass ratio, and extrapolate it to the in situ relationship between ergosterol and fungal bio-mass.
2. Materials and methods
A summary of site characteristics and major proper-ties of the soils sampled are presented in Table 3. All soils were sampled between 1990 and 1994, the ma-jority of them in the winter period between November and March. Sites were typically sampled using ®ve, randomly-placed, 10 10 m plots, with three ran-domly-placed soil cores (47 mm or 80 mm diameter, to a depth as given in Table 3) extracted from each; i.e.,
n15 for each site. Sites were divided into seven eco-types, based on their vegetation and soil type. Soils were chopped, mixed and larger stones and roots were removed by hand. Shingle ridge samples were sieved using a 4 mm diameter sieve (pebbles, typically 4±10 mm, formed up to 95% of the volume in this soil). The homogenised samples were kept at 48C prior to analysis. For ergosterol, a subsample was taken im-mediately after soil homogenisation and stored at ÿ208C. Most analyses, including ergosterol determi-nation, were performed within four months after sampling. Here it should be noted that for long-term storage, direct placement of freshly collected and hom-ogenised soil in a methanol or methanol/ethanol mix-ture would be preferable.
Soil pH was measured in a 1:2.5 w/w slurry of soil and water, or soil and 1M KCl. Texture was deter-mined either hydrometrically or by wet-sieving fol-lowed by sedimentography (SA-CP2, Shimadzu, Japan). Organic C and total N were determined using an elemental analyser (CHN 2400, Perkin Elmer, UK), with alkaline soils (pH > 6) being preconditioned with 1 M HCl to remove carbonates.
Table 1
Ergosterol contents in known fungi (the kingdom Eukaryota) derived from data presented by Weete (1989) and LoÈsel (1988). The classi®cation scheme of Hawksworth et al., (1995) was used. The data must be considered provisional because only a very minor fraction of all fungi have been describedaand fewer still analysed for their ergosterol content. Further, among ergosterol producing genera, ergosterol content may vary considerably between species and strains (e.g., Nylund and Wallander, 1992)
Fungal phylum or groupb No. of species
Ergosterol abundance Comments
Ascomycota 132,300 Mostly 70±100% of total
sterols
Much variability, some species do not produce ergosterol
Basidiomycota 122,200 40±85% of total sterols Data is for the Basidiomycete class only, which account for 62% of species in the phylum
Chytridiomycota 1790 Not known to produce
ergosterol
Zygomycota 11060 Ergosterol content variable Surveying incomplete
Mitosporic fungi (lacking a sexual phase)
114,100 Typically the principal sterol Includes many common soil fungi, such asAspergillus niger, Alternaria alternata, Fusarium oxysporum, F. moniliforme, F. culmorumandRhizoctonia solani.Ergosterol also detected in severalPenicilliumspp.
aThe170,000 of known fungi comprise less than 5% of the total in the fungal kingdom, which may total as many as 1.5 million species (Hawksworth, 1994).
Table 2
Estimates of relative fungal biomass in temperate soils obtained using direct microscopy (DM), selective inhibition of respiration (SI) and phos-pholipid fatty acids (PLFA). For standardisation, fungal to bacterial biomass ratios have been converted to percent fungal biomasses (e.g., the ratios of 1.0 and 1.2 given by West (1986) are presented as relative fungal biomass values of 50% and 55%, respectively). For the selective inhi-bition method the use of a ratio would, however, be preferable since the method estimates only those fractions of the total masses of fungi and bacteria that are ``active'' at the time of measurement and which are stimulated to synthesise protein by glucose and inhibited from doing so by the antibiotics
Reference Method Vegetation/land use Soil detailsa Fungi (%)b
Shields et al. (1973) DM Arable Brown chernozem 85±92c
Jordan et al. (1995) DM Arable ZL (0±10 cm) 62±88
Stamatiadis et al. (1990) DM Arable S (Ap, 0±13 cm) 74 (total)
" " " 75 (active)
" " SCL (A, 0±30 cm) 96 (total)
" " " 15 (active)d
SI " S (Ap, 0±13 cm) 53
" " SCL (A, 0±30 cm) 48
Anderson and Domsch (1973) SI Arable Brown podzol (A) 78
Anderson and Domsch (1975) SI Arable Brown soil & chernozem 65±90
VancÆura and Kunc (1977) SI ± (probably arable) Chernozem & brown soil 67±82
Domsch et al. (1979) SI Arable Limed bog 75
" Chernozem 70
" (26 y bare fallow) Parabrown soil 80
Anderson and Domsch (1980) SI Arable ± 70±90
West et al. (1987b) SI Arable (stored for 2±10 w) SL (0±7.5 cm) 81
Killham et al. (1988) SI Arable (no amendment) CL, SCL & SL 31±55
Arable (straw incorporation) " 62±69
Wardle and Parkinson (1990a) SI Arable (matric potentialÿ3000 to
ÿ6 kPa)
Dark brown chernozem, SL 70±80
Wardle and Parkinson (1990b) SI Arable (soil dosed with herbicides) Dark brown chernozem, SL 61±84
Wardle et al. (1993) SI Arable (weed control: sawdust
mulch, cultivation, hoeing & herbicides)
SL (0±5 cm) 162±92 (median176)
" (5±10 cm) 154±86 (median171)
Federle (1986) PLFA ± (probably arable) L, SL, ZL & CL 13±33e
Nannipieri et al. (1978) DM Grassland Dark. brown chernozems, SL & C 84
Jordan et al. (1995) DM Pasture ZL (0±10 cm) 89±91
Virgin prairie ± (0±10 cm) 82
Domsch et al. (1979) SI Grassland Brown soil, SL 70
West (1986) SI Pasture (fertilized) ZL (0±7.5 cm) 55
Pasture (unfertilized) " 50
West et al. (1987b) SI Pasture (unfertilized, stored for 2± 10 weeks)
ZL (0±7.5 cm) 54±56
Pasture (fertilized, stored for 2±10 weeks)
ZL (0±7.5 cm) 56±70
Bardgett et al. (1996) SI Pasture (fertilized & limed) Brown earth (0±15 cm) 33
Pasture (limed only) " 34
Pasture (no input) " 44
grassland (no input) " 30
Fñgri et al. (1977) DM Subalpine vegetation O 68
Alpine vegetation A (0±10 cm) 96
Callunaheath O/A 95
Alphei et al. (1995) SI Fagus sylvaticaforest Ah 53±58
Anderson and Domsch (1975) SI Fagus sp.forest Ol&h 70(l) & 60(h)
Quercus/Carpinusforest Ah(0±10 cm) 80
Tate (1991) SI Quercusspp. forest O 82
" A 69
Pinus rigidaforest O 82
" A 62
Roberts et al. (1980) SI Pinus sylvestrisforest O/A 90
Bewley and Parkinson (1985) SI Pinussp. forest Of/h 82±95
" A (0±5 cm) 62±70
2.1. Ergosterol
The analytical technique used to determine ergos-terol evolved during the course of this study, starting with the assay used by Bentham et al. (1992) and end-ing with the assay described by Ruzicka et al. (1995) which was developed as a time and labour saving alternative. The majority of samples were analysed using the former method, while samples from the for-est ¯oor and forfor-est mineral ecotypes (except those from the Hainault sites) were analysed using the latter one. Both methods were shown to give identical results (Ruzicka et al., 1995; S. Ruzicka's unpublished data). However, see Ruzicka et al. (1995) for a discussion of the relative merits of each approach.
(Bentham et al., 1992): Soil (5±30 g) was weighed into 250 ml glass tubes and re¯uxed for 90 min at 908C in a methanol:ethanol:KOH mixture (80:20:8 v/v/ w); 50 g of the mixture was added to every 10 g of the sample. The product was vacuum ®ltered (Whatman, No. 42) with a methanol rinse (10±20 ml), then 20 ml of H2O added per 50 ml of the re¯uxing mixture. This was partition-extracted with two 60 ml aliquots of hex-ane. The combined hexane extracts were evaporated at 408C to a few millilitres using a Turbovap evaporator (Zymark, UK) before being evaporated to dryness under a stream of nitrogen, and redissolved in 2 ml of hexane:propan-2-ol (97:3, v/v). HPLC analysis was performed using an Applied Chromatography Systems model 352 delivery system. Each sample (20 ml) was injected into a 150 mm (4.6 mm i.d.) Lichrosorb Si 60
(10 mm) column preceded by a 10 mm guard column and eluted with hexane:propan-2-ol (97:3, v/v) at 1.5 ml minÿ1with absorbance measured at 272 or 282 nm. Prior to extraction, a duplicate of every third sample was `spiked' with 100 mg of ergosterol (added to the soil and steeped for 15 min before extraction) in order to determine recovery. The recovery was almost 100 % in sands and organic soils and ranged from 80 to 90 % in silty and clayey soils. The hexane:propan-2-ol ratio was progressively changed to 97.5:2.5 (v/v) and 98:2 (v/v) to enable separation of ergosterol from an occasional interfering co-elutant during the HPLC analysis (possibly acetone).
(Ruzicka et al., 1995): Soil (3±10 g) was weighed into a 50 ml polyethylene tube. Duplicate samples were `spiked' with 100 mg of ergosterol in 1 ml hexa-ne:propan-2-ol (98:2, v/v). After steeping for 15 min, 10 ml of a methanol:ethanol mixture (4:1, v/v) was dis-pensed into each tube and samples were kept at 48C for 2 h. Each sample then received 20 ml (the spikes, 19 ml) of hexane:propan-2-ol (98:2, v/v) and was im-mediately ultrasonicated at 150 W for 200 s with a Sonics and Material Vibra-cell probe (USA) while kept on ice. After 30 s, to allow the sample to settle, ap-proximately 2 ml of the top layer (hexane:propan-2-ol) were transferred into a microfuge tube and centrifuged at 7000g for 10 min. HPLC analysis was performed on the supernatant as above, except that 100ml instead of 20 ml was injected. The recovery of added ergosterol ranged from 70 to 95%.
Table 2 (continued)
Reference Method Vegetation/land use Soil detailsa Fungi (%)b
Scheu and Parkinson (1994) SI Pinus contortaforest Of/h 57
Populus tremuloidesforest Ol,f&h 85 (l), 67 (f), 55 (h)
" Ah 50
Parkinson (1986) SI Pinus contortaforest Of/h 82±83
Populus tremuloidesforest " 80
Abies lasiocarpaforest " 87
Picea glaucaforest " 82±85
Flanagan and van Cleve (1977) SI Picea marianaforest O/A 85
Parkinson et al. (1978) SI Picea abiesforest Ol,f&h 80 (l), 78 (f), 67 (h)
Domsch et al. (1979) SI Picea abiesforest Oh 70 & 75
Parkinson et al. (1980) SI Picea abiesforest O/A 80
aType, texture or horizon sampled if known; S, sand; Z, silt; L, loam; and C, clay (e.g. SZL is silty clay loam). b
For selective inhibition studies that involved lengthy incubations, only the ®rst days results have been included since antibiotics start to fail after this period.
c
Experiment was a 104 day soil incubation. Ratios from the ®rst month were excluded to allow for equilibration after initial addition of glu-cose and NH4NO3.
dAccumulation of ¯uorescein and formazan were used to measure amounts of metabolically active fungi and bacteria, respectively. Incubation times in the stains diered: fungi were incubated for 3 min, whereas bacteria were incubated for 60 min.
Table 3
Characteristics of the soils used in this study; nd Ð not determined, na Ð not applicable, OCCM Ð opencast coal mine. Data presented are based on site means (n= 3±5) and are expressed on a dry weight basis (for textural symbols see Table 2)
Ecotype Location (no. of sites)
Vegetation Soil type, Avery (1990) (FAO, 1974)
Depth (cm)
Texture pHa C orgb
(g kgÿ1)
TotalN
(g kgÿ1)
Dune slack Ken®g (6) Dune slack sere Calcaric sandy regosol (Sandy Regosol) 0±30 S 7.8±8.4 1.0±34.3 0.04±2.0 Restored grassland/pasture Erin OCCM (5) Improved pasture Disturbed 0±30 ZL/CL 6.4±6.8 19±31 1.6±1.7
Butterwell OCCM (3) Improved pasture Disturbed 0±30 CL/L 7.2±7.6 20±26 1.8±2.1 Lounge OCCM (2) Improved pasture Disturbed 0±30 ZCL/CL 7.3 60 2.5 Derbyshire (3) Improved pasture Disturbed 0±30 nd 6.0±7.3 nd 1.3±2.9 Cumbria Improved pasture Disturbed 0±30 nd 6.9 nd 2.4 Meadow Oxford (3) Floodmeadow sere Pelocalcaric alluvial gley (Calcaric Fluvisol) 0±30 ZCL 6.4±7.3 56±73 6.2±7.4
Hainault (4) Low input meadow Pelo-orthic Gley (Eutric Gleysol) 0±5 ZCL 4.8±5.4 59±nd 4.4±nd Hainault Low input meadow Pelo-orthic Gley (Eutric Gleysol) 5±30 ZCL 5.5 26 2.3 Epping Low input meadow Pelo-orthic Gley (Eutric Gleysol) 0±30 ZCL 6.0 38 3.3 Krusne hory Calcifuge montane meadow Colluvial orthic brown soil (Humic Cambisol) 0±30 nd 3.8 216 15.0 Whitehouse Norman Low input pasture Stagnogley (Humic Gleysol) 0±30 nd 7.3 nd 2.6 Bryngwyn Low input pasture Stagnogley (Humic Gleysol) 0±30 nd 6.2 nd 1.8 Shingle ridge Dungeness (3) Gorse scrub/calcifuge grassland sere Non-calcaric rego-alluvial (Dystric Fluvisol) 0±30 na 4.1±6.2 272±456 13±30 Forest full pro®le Hainault (2) Birch woodland/hawthorn shrub Pelo-orthic gley (Dystric Gleysol) 0±30 ZCL 4.0±4.5 31±32 2.1±2.2
Epping Ancient oak/hornbeam woodland Pelo-orthic gley 0±30 ZCL 3.7 43 2.4 Epping Birch woodland Pelo-orthic gley 0±30 ZCL 4.1 41 3.0 Slavkovsky les Spruce forest Luvic podzol (Orthic Podzol) 0±30 LS 3.7 60 3.1 Krusne hory Mature spruce forest Typ. podzolic brown soil (Podzoluvisol) 0±30 L 3.6 107 4.9 Krusne hory Dead spruce Typ. podzolic brown soil (Podzoluvisol) 0±30 SL 3.6 88 4.7 Krusne hory Birch/mountain ash shrub Gleyic podzolic brown soil (Leptic Podzol) 0±30 SL 3.9 71 3.6 Forest ¯oor Hainault (2) Ancient oak/hornbeam woodland Mull O/Ac na 3.8 216 11
Hainault Hawthorn scrub Mull/moder Horizons na 4.5 nd nd Slavkovsky les (2) Spruce forest Mor " na 2.8±3.1
255±257 11±13 Krusne hory (3) Mature/dead/young spruce sere Mull " na 2.5±3.0
267±328 12±16 Sumava Ancient spruce forest Moder " na 2.9
302 14 Sumava (2) Spruce forest Mor " na 2.7±2.8
128±147 6 Forest mineral Hainault forest Ancient oak/hornbeam woodland Pelo-orthic gley (Dystric Gleysol) A/Bd ZCL 4.2 27 1.8
Slavkovsky les Spruce forest Luvic podzol (Orthic Podzol) Horizons LS 3.7 10 0.8 Slavkovsky les Spruce forest Typ. gleyic brown soil (Gleyic Cambisol) " SL 4.4 8 0.6 Krusne hory (2) Dead/young spruce Typ. podzolic brown soil (Podzoluvisol) " SL 3.4±3.7 21±29 1.4±2.0 Krusne hory Spruce forest Typ. podzolic brown soil (Podzoluvisol) " L 3.6
29 1.4 Sumava Ancient spruce forest Typ. orthic brown soil (Dystric Cambisol) " SL 3.4
88 4.2 Sumava (2) spruce forest Typ. orthic-brown soil (Dystric Cambisol) " SL 3.5±3.9
16±31 0.9±1.4
aSoils marked with asterisk were determined in 1 M KCl.
bIt should be noted that soils from grasslands restored after opencast coal mining could be contaminated with coal particles, thus biasing the organic C values.
cPredominantly O fand Oh. dPredominantly B.
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2.2. ATP assay (after Inubushi et al., 1989b)
Before analysis, soils were incubated for one week at 258C at 50% of their maximum moisture retention. Samples (1.5±3 g of soil), were ultrasonicated at 150 W for 2 min with a Sonics and Material Vibra-cell probe (USA) in 25 ml of tri-chloroacetic acid:di-sodium hydrogen orthophosphate extractant. Dupli-cate samples, used to calculate ATP recovery, had 1.2
mg lÿ1 ATP added to the extractant. After adjustment to pH 7.75 with Tris:EDTA (dilution 1:100), lumines-cence was integrated with a LKB 1251 luminometer (Finland) for 30 s at 258C, 5 s after the addition of 50
ml of puri®ed luciferin-luciferase enzyme (AMR-5000, Labsystems, UK) to 150 ml of solution. Recoveries of added ATP ranged between 40 and 95%.
2.3. Statistical analysis
As most of the data were log-normal, the following transformation was used where appropriate: y
ln xa: The relationship between ergosterol and ATP was assessed using a general linear model in the form: ergosterol = ATP + SE + RE, where SE and
RE represent systematic and random eects, respect-ively. The systematic eects of soil properties and sampling designs were investigated using multiple re-gression models (see Tables 5 and 7 for further details).
3. Results and discussion
The studied soils were diverse, varying from strongly acidic (pH < 3) to fairly alkaline (pH > 8), and
includ-ing pure sands with Corg below 0.01%, a range of silty
and clay loams, as well as highly organic soils with
Corg contents as high as 46% (Table 3). This diversity
was re¯ected by a wide span in concentrations of both ergosterol and ATP: ergosterol varied from 0.08 to 230.4 mg gÿ1and ATP from 0.10 to 33.6 mg gÿ1of dry soil (Table 4). Both datasets were strongly log-normal and highly variable. As a result, for ergosterol the standard deviation of the upper tail of the distribution formed 362% of the geometric mean, while for ATP it was 254%. Low values of ergosterol and ATP were recorded in disturbed grasslands, forest B horizons and dune slacks. In these ecotypes, average ergosterol concentrations oscillated around 2 mg gÿ1 while ATP concentrations remained below 1mg gÿ1of dry soil. In forest ¯oor samples Of and Oh horizons), the values
were 20-fold higher for ergosterol and 9-fold higher for ATP. However, the highest concentrations were recorded in samples from the shingle ridge±grassland sere, where respective values of ergosterol and ATP were 230 and 34 mg gÿ1 dry soil. These samples were entirely organic, the shingle has been excluded by siev-ing of each sample through a 4 mm sieve on site.
3.1. Optimising the relationship between ergosterol and ATP
The relationship between ergosterol and ATP is shown in Fig. 1. The scatterplot, which contains 295 data points (each point being the mean of three repli-cate sets of measures from cores taken within a 10 10 m plot) indicates a strong correlation between the two variables accounting for 80% of the total var-iance. Although highly signi®cant P<0:001), suggesting that ergosterol was a good predictor of
Table 4
Basic statistics of ergosterol and ATP in the soils of the individual ecotypes. All values are inmg gÿ1soil. Each replicate is a mean of three sub-samples from 1010 m quadrat
Ecotype Variable n Range Geometric mean Standard deviation
Upper tail Lower tail
Dune slack ATP 28 0.12±3.36 0.95 1.47 0.58
ergosterol 28 0.08±9.71 2.17 7.67 1.69
Restored grassland ATP 66 0.10±1.67 0.55 0.54 0.27
ergosterol 66 0.54±5.21 1.77 1.17 0.70
Meadow ATP 50 0.82±9.61 3.94 3.89 1.96
ergosterol 50 1.74±18.2 6.46 6.24 3.17
Shingle ridge ATP 15 12.3±33.60 17.40 5.04 3.91
ergosterol 15 67.5±230.00 132.00 59.20 40.80
Forest full pro®le ATP 40 0.94±2.78 1.61 0.53 0.40
ergosterol 40 3.57±10.8 5.41 1.64 1.26
Forest ¯oor ATP 51 3.96±16.5 8.83 3.75 2.63
ergosterol 51 18.6±175.00 46.30 30.00 18.20
Forest mineral ATP 45 0.24±2.05 0.80 0.53 0.32
total microbial biomass, its ecological signi®cance must be carefully considered. Soil is a complex, non-linear (i.e., potentially chaotic) environment containing a network of strongly and weakly interconnected el-ements. As a result, an observed correlation between two variables does not necessarily indicate that a func-tional link underlies it. We tested for the in¯uence of the most common causes of incidental correlation between the measures of soil micro¯ora by including the following soil physicochemical properties in the re-gression: the amount of organic substrate Corg), one
aspect of its quality (C/N ratio), soil moisture status (moisture content) and soil acidity (pH). Having cho-sen ergosterol as the dependent variable, the multiple regression explained 85% of its variance, only 5% more than ATP alone, and with soil moisture content being the only other signi®cant predictor (Table 5). Although not strictly signi®cant P0:053 pH is likely to be another predictor; as would be expected, it was negatively correlated with ergosterol concen-tration.
While the modest in¯uence of soil moisture content may have been a genuine eect, it could also be an artifact of the methodology. Whereas Corg, C/N ratio
and pH would have been the same in samples used for both ergosterol and ATP, the soil moisture contents typically diered between the two; ergosterol was measured in ®eld-moist samples whereas ATP was
measured in samples adjusted to 50% of the maximum water retention and incubated for 7 days. These dier-ences in moisture contents may have introduced a sys-tematic error that resulted in a positive weighting given to wetter soils (at the time of sampling) in the multiple regression. By contrast, Corg, for example,
would have in¯uenced both variables to a similar degree and hence had no in¯uence upon their mutual relationship P0:84), despite being a good single predictor of either ergosterol or ATP, explaining 41 and 44% of their respective variation. Incidentally, moisture content was also quite a strong single predic-tor of ergosterol concentration r20:51).
Two other aspects of the regression, presented in Table 5, contain important information on the
ergos-Fig. 1. Scatterplot of ergosterol and ATP concentrations in temperate soils (n= 295).
Table 5
Multiple regressionyBixcpredicting ergosterol from ATP and soil physicochemical properties, whereirepresents the predictors (r2 = 0.847;F[5, 216] = 239.5,P< 0.001)
B SE ofB t(216) P
Intercept 0.898 0.269 3.34 0.001
ATP 0.987 0.053 18.8 < 0.001
Moisture content 0.014 0.006 2.41 0.017
pH ÿ0.071 0.036 ÿ1.96 0.053
Corg 0.013 0.063 0.21 0.838
C/N 0.002 0.007 0.24 0.812
terol±ATP relationship. These are, that the B coe-cient for ATP did not dier from one, and the signi®-cance and value of the regression constant. Because both ergosterol and ATP were log transformed, the former suggests that the relationship was linear, while the latter predicts a slope of 2.45.
3.2. Three sources of error: methodology, the variables and the populations they estimate
Before addressing this slope to judge the quality of the relationship, ®rst we need to assess its validity by examining the sources of error that aect it. These errors have their random and systematic parts and combine to form the 20% of residual variance in a simple regression between ergosterol and ATP. As shown in Table 6, the sources can be divided into three generic types: the failings in methods of analysis of ergosterol and ATP eanal), the failings of each of
the variables to estimate their underlying populations (i.e., fungal and total biomass, respectively) evar), and
the non-equivalence of these populations (i.e., their incomplete overlap) epop). This last source of error is
further complicated by dierences in methodology. Not only does ergosterol estimate fungal while ATP estimates total biomass, but ergosterol also assays the community that is present during soil homogenisation while ATP estimates the active community established following seven days of soil incubation Ð two dis-tinctly separate populations, notwithstanding their high degree of intersection.
The quanti®cation of these errors is central to clarify the relationship as well as to assess the utility of ergos-terol as a measure of fungal biomass. Ifepop was much
higher than evar, the actual ergosterol-to-fungal
bio-mass relationship would be much stronger than the observed correlation between ergosterol and ATP, as some of the residual error, the 20% uncertainty, would stem from the imperfect overlap of the populations underlying the two variables.
While it is impossible to directly quantify eanal, evar,
and epop in our dataset, it is possible to indirectly
gauge their signi®cance by scrutinising the behaviour of their sum, the residual of the ergosterol±ATP re-lationship, across a wide range of soils and ecotypes. A change in size of the residual would re¯ect a change in the amount of systematic error in one or more of
eanal, evar, and epop: We have already mentioned that
the methodological error eanal was essentially ®xed
throughout the study and hence its net eect on the two variables would be minimal. Therefore, and apart from random eects, the residual variance in the ergos-terol±ATP regression would be mostly attributable to changes in evar, the error in each variable as an index
of biomass, andepop, the error caused by lack of
equiv-alence between the two variables.
3.3. Inferring the size of population and variables errors from their eect upon ecotype, site and plot variability
In general, the distribution between the random and systematic eects is determined by the scope of the study and the rigour of the sampling design. In studies of the soil environment where a single variable is often subjected to a multitude of complex in¯uences and the terms ``systematic'' and ``random'' are often replaced by ``fathomable'' and ``unfathomable'', the systematic eects upon a variable are usually con®ned to its cov-ariation with general soil properties. This is a gross ap-proximation considering that we are yet to de®ne what the relevant soil properties are, notwithstanding the dicult task of accurately measuring them in a spatially heterogeneous environment.
In the ensuing analysis, we assess the in¯uence of in-dividual soil systems upon the ergosterol vs. ATP vari-ation, utilising the fact that the crucial parameters aecting sample variation are the size and the hierar-chy of sampling units and an appropriate level of repli-cation. Given that we maintained a uniform level of replication (1010 m plots) throughout the study, we
Table 6
Summary of errors aecting the ergosterol±ATP relationship in soils
Type of error
Description Examples Level of in¯uence in
this study
Population
epop)
The incomplete intersection of the populations estimated by ergosterol and ATP
Detection of mycorrhizal biomass by ergosterol but not ATP, and, conversely, the measurement of bacterial and protozoan biomass by ATP but not by ergosterol
Large
Variables
evar)
Failure of ergosterol or ATP to accurately measure each of their respective populations
Dominance of species not producing, or with atypical levels of, ergosterol in certain substrates; low levels of adenylate energy charge in certain environments, even following incubation
Medium
Analytical
eanal)
Inadequate sample processing and analysis of ergosterol and ATP
Incomplete extraction of ergosterol or ATP; losses of either chemical during analysis
may assume that the random variation will be mainly con®ned to this level while the systematic errors will appear on the higher sampling levels. It should be noted that although bothepop and evar contain a small
random element, we refer only to their systematic parts.
As the ergosterol±ATP relationship was approxi-mately linear, we can simplify the relationship and iso-late its residual error by standardising both ergosterol and ATP (already log-transformed) to a zero mean and unit variance, obtaining:
ergosterolsATPseSeR 1
with
eS1epopevar 2
where ergosterols and ATPs are standardised variables,
eS is systematic error and eR is random error. Note
that following the standardisation, eSeR, re¯ecting
the 20% uncertainty of the relationship, has a variance
s20:20 (cf. Table 7). As discussed, eS is eectively
the sum of the systematic fractions ofepop andevar:
The residual variance of the ergosterol±ATP re-lationship can then be partitioned into the error caused by dierences between ecotypes, into the error arising from dierences between sites within each ecotype, and into the error due to the random variation between plots within each site. We can therefore write:
s2seco2 ssite2 splot2 eSeR 3
with
seco2 ssite2 1eS1epopevar 4
wheres2 is the total variance of the residuals andseco2 ,
ssite2 and splot2 are the components of the variance at-tributable to ecotype, site and plot sampling levels, re-spectively.
Two nested regression models were used to progress-ively reduce, and thereby partition, s2: One accounted
for the in¯uence of ecotype (E) and the other for the eects of both ecotype and site (within each ecotype) ES). Each of the seven ecotypes contained 3±14 sites (Table 3). The amount of variance attributable to ecotype, site and plot was then found from the dier-ences between amounts of residual variance between models (Table 7).
The workings of the models are more readily visual-ised in Fig. 2. Fig. 2(a) shows the scatter of the re-siduals from the basic regression between ergosterol and ATP, whereas Fig. 2(b) shows the attenuated pat-tern of residuals obtained after the eects of ecotypes have been accounted for. Note that the residuals for the meadow sites (open squares) have been decreased by vertically shifting the group by a constant. One other ecotype, the shingle ridge succession (open dia-monds) showed a similarly large movement, but oppo-site to the meadows. Fig. 2(c) shows the further compression of the residuals achieved by accounting for the in¯uence of each site within each ecotype. The vertical shift of all plots within one of the dune slack sites (the youngest; the ®ve open circles on the left side of the graph) clearly illustrates this second stage of adjustment. Thirty six out of the 62 sites showed a sig-ni®cant movement P<0:05), the largest being made to the aforementioned 5-year-old dune slack site, two freshly restored pastures, three eutrophic meadow sites, and the mineral horizon of a forest site.
The inclusion of ecotype and ecotype site factors improved the prediction of ergosterol by ATP by 6.4% and 15.6%, respectively, leaving a residual attributable to the variation between plots of under 5%. The orig-inal residual of 20% was, thus, itself partioned into an ecotype error seco2 of 6.4/20 or 31%, a site component ssite2 of 9.2/20 or 45% and a residual plot variance splot2 of 4.8/20 or 24% (Table 7). Clearly, the soil dierences between ecotypes and sites generated errors (both evar and epop, see Eq. (4)) that compounded to
produce much of the 20% residual variance of the simple regression between ergosterol and ATP.
The relative contributions of the seven ecotypes to
Table 7
Partitioning of residual variance of the ergosterol±ATP relationship into components representing individual sampling levels
Modela Residuals Components of variance
n Min. Max. Variance Step s2
i %
ergSATPS 295 ÿ1.290 1.227 0.204 Total 0.204 100
ergSATPSE 295 ÿ1.153 1.053 0.141 Ecotype 0.064 31
ergSATPSES 295 ÿ0.769 0.909 0.048 Site 0.092 45
Plot 0.048 24
aThe ecotype factor (E) consisted of a suite of seven dummy variables Ei), one for each ecotype, and used in the regression according to the following,b2EP7i1b2iEi, whereb2i is a regression constant for each ecotype andEiis de®ned as 1 for all cases from ecotypeiand 0 for all the remaining cases. The site factor (S) was generated in the same way using 14 dummy variables (the maximum number of sites in the restored grassland ecotype). The eect of site within each ecotype was then computed as theESinteraction.
the within-ecotype variation (site and plot eects), after the weighting caused by the number of sites in each class was taken into account, are presented in Table 8. Logically, the highest within-ecotype variation occurred among the successional ecotypes of restored grassland, dune slack and shingle ridge, accounting for 21, 20 and 18% of the total, respectively. For the two classic primary successions, in the valley of the dune blowout and on the accreting shingle headland, further breakdown of the variance showed that most was due to between-site dierences (84 and 79%, respectively). These large site contributions to the variance were to be expected considering that over the 180-year course
of the dune slack succession the site means forCorg, C/N
ratio and pH changed from 1.0 to 34 mg gÿ1, from 43 to 17 and from 8.4 to 7.8, respectively, driving a corresponding six-fold increase in ergosterol/ATP ratio from 0.6 to 3.6. Similarly, over the 1155-year course of the shingle ridge successionCorg, C/N and pH
chan-ged from 272 to 450 mg gÿ1, from 21 to 15 and from 6.2 to 4.1; concurrently the ergosterol/ATP ratio rose from 4.4 to 10.1 (Hill et al., 1993).
By contrast, in comparatively static ecosystems such as forest (0±30 cm samples) and meadow the within-ecotype variation in ergosterol/ATP ratio was only 4 and 5%, respectively, of the total contributed by all ecotypes (Table 8). Within this, the amount attribu-table to between-site dierences was also lower, at 29 and 65%, respectively, further reinforcing the seeming homogeneity of these two equilibrial ecotypes (of course, the equilibrium of meadows is dynamic and inherently unstable, maintained only by the repeated disturbances of cutting and grazing). The low inter-site variability of the forest (0±30 cm) ecotype necessitates that the remainder, 71%, be due to inter-plot variabil-ity; a substantial level, but reasonable considering that the properties of forest and woodland soils vary at the patch scale of the trees. This patchiness produces both horizontal and vertical heterogeneity, as is apparent when organic ¯oor (Of + Oh) and mineral (B) hor-izons of forests and woodlands are considered separ-ately. Note that horizontal heterogeneity of these two ecotypes, approximated by the plot components of variance si2,plot, was similar (0.011) to the full pro®le (0.010), whereas their between-site components were ®ve times higher than that of full pro®le samples. It seems that bulking and mixing of the pro®le neutral-ises much of this between-site variation.
The forces that aect ecotype, site and plot variabil-ity of both ergosterol and ATP are primarily caused by the vegetation, via its control over the quality, amount and distribution (both vertically and horizon-tally) of organic matter, the size and properties of the rhizosphere and, associated with that, the mycorrhizal type, composition and abundance. pH and soil struc-ture, two further powerful drivers, have both biotic and abiotic sources.
3.4. Relative importance of population and variables errors
The 115% of systematic error in the regression between ergosterol and ATP that was revealed using the preceding analysis is composed, essentially, of two parts: epop, the error caused by the incomplete overlap
between the two populations estimated by the vari-ables, and evar, the error associated with each as an
index of biomass. epop is likely to contribute more to
this, primarily due to the implicit error arising from
Fig. 2. Eects of sampling level on the ergosterol±ATP relationship; (a) residuals from the regression of the raw variables (Fig. 1), (b) re-siduals left after the eect of ecotype has been accounted for, and, (c) residuals left after the combined eects of ecotype and site (within ecotype) have been accounted for. Both variables were standardised to a zero mean and unit variance sixiÿ
x
comparing a measure of total microbial biomass with a measure of a changeable subset of that biomass; for each sample, any departure from the mean level of relative fungal biomass will produce disparity, generat-ing a residual in the regression. Secondly, further uncoupling of it in the correlation between the two variables occurs because ergosterol re¯ects the fungal biomass present at the time of sample preparation whereas ATP measures the biomass present after incu-bation under standardised conditions. The in situ bio-mass ``seen'' by ergosterol may dier markedly, both in mass and composition, from that which dominates the soil sample after homogenisation and 7±10 days of moist incubation at 258C. This eect of incubation may be most pronounced in the humic soils of forest ¯oor (Of + Oh) and shingle ridge where extant ecto-mycorrhizae contribute to the ergosterol concentration (after homogenisation of the soil core a sub-sample was immediately frozen) but not to the ATP concen-tration of the sample, since after being severed from the host and damaged by mixing most would rapidly die during incubation (SoÈderstroÈm and Read, 1987; Read and Birch, 1988) to be replaced by both bacterial and fungal saprophytes, the biomass of which would amount to less than 20% of the original (van Veen et al., 1987). The recorded ergosterol/ATP ratio would thus be erroneously high.
The two sources of epop discussed above may have
combined to cause the sloping ``cloud'' of residuals formed by the three ecotypes on the right-hand side of Fig. 2(a). This was corrected, as shown in Fig. 2(b), by accounting for the ecotype variability; i.e., by vertically
shifting each ecotype-set by an amount equal to the distance from their centroids to the zero line of the re-siduals. A positive shift was observed for the meadow ecotype (open squares) because its samples had a rela-tively low mean ergosterol/ATP ratio, probably due to low relative abundance of fungi (note the moderate levels of fungal abundance found using the selective in-hibition method in the grasslands and pastures sub-section of Table 2). By contrast, signi®cant negative shifts were imposed upon the acidic, humic soils of for-est ¯oor Of + Oh (®lled circles) and shingle ridge (open diamonds) ecotypes, indicating that these samples contained atypically high amounts of ergos-terol, probably due to the dominance of the microbial biomass by fungi but perhaps also due to the mycor-rhizal contribution to the sterol concentration, but not to ATP. As discussed above, epop would also aect
within-ecoype variation, especially among those eco-types that encompassed ecological successions.
We cannot forsee such a dramatic eect for evar
upon the 115% of systematic error in the residual, although it is likely that the ergosterol concentration of the fungal biomass would dier in dierent ecosys-tems as a consequence of fundamental changes in the composition of the fungal dominants (itself driven by the organic matter resource quality), the presence of a large ectomycorrhizal population, pH, season (but see Wallander et al., 1997) and other factors. In addition, dierences in adenylate energy charge (AEC) of the microbial biomass after incubation could occur. For example, Inubushi et al. (1989a) found that soils (including a paddy soil) that had been incubated
anae-Table 8
Relative contribution of the ecotypes to the within-ecotype variance of the ergosterol±ATP relationship (indexx). The within-ecotype variance was further partitioned into site and plot components
Ecotype x(%)a Source df SS Components of variance
si2 %
Dune slack 19.7 Site 5 0.715 0.029 84
Plot 22 0.127 0.006 16
Restored grassland 20.6 Site 13 1.790 0.025 55
Plot 52 1.045 0.020 45
Meadow 5.4 Site 11 0.358 0.007 65
Plot 38 0.141 0.003 35
Shingle ridge 18.2 Site 2 0.342 0.032 79
Plot 12 0.106 0.009 21
Forest full pro®le 4.2 Site 7 0.219 0.004 29
Plot 32 0.328 0.010 71
Forest ¯oor 16.0 Site 10 1.073 0.021 65
Plot 40 0.440 0.011 35
Forest mineral 15.8 Site 8 0.936 0.021 65
Plot 36 0.423 0.011 35
ax i
Pj i1
nij zij:ÿzi::2 Ni:ÿ1 100 Pl
i1 Pj
j1
nij zij:ÿzi::2 Ni:ÿ1
,where zi::is a mean ergosterol/ATP value for ecotypei;zij: is a mean ergosterol/ATP value for sitejin ecotypei;
nijis the number of observations in sitejof ecotypei;Ni:is the total number of observations within ecotypei.
robically did not fully recover their pre-treatment AEC when subsequently aerated. Many of the restored grassland soils, which had lower than predicted ATP concentrations and which were seasonally waterlogged (samples were taken in winter) and composed of struc-tureless clay loams, may have been aected in this way. It is also possible that the ATP concentration in fungi and bacteria may not be equal, leading to appar-ent changes in biomass when the proportions of fungi and bacteria change. There is no clear evidence that eukaryotes and prokaryotes growing in situ contain the same concentration of ATP. Karl (1980) reviewed numerous studies and found little dierence in ATP content of the two groups, but essentially all were pure cultures growing under nutritionally abundant con-ditions and, at least for bacteria, measured during the exponential growth phase. By contrast, in sterilised soil inoculated with Pseudomonas paucimobilis the ATP/ bacterium content increased signi®cantly following the addition of glucose or ammonium sulphate (Fairbanks et al., 1984). Just as the ergosterol content of fungi grown in vitro diers from that grown in situ, it can-not be assumed that the ATP concentration of the fun-gal or bacterial biomass in ¯oodmeadow loam with a pH > 7 be identical to that in ancient spruce forest model with a pH < 3, irrespective of pre-incubation time.
3.5. The utility of ergosterol in estimating fungal biomass
Ergosterol should serve as a good indicator of
fun-gal biomass: its variation over a range of diverse soils explained all but 20% of the variation of microbial biomass with much of the residual attributable to the imperfect overlap between the fungal biomass mated by ergosterol and the microbial biomass esti-mated by ATP. Since most studies indicate that fungi dominate the decomposer biomass in temperate soils (Table 2) this close correlation would be expected across such a broad spectrum. It is surprising then that the utility of the ergosterol assay has been so often questioned and its usage limited. Scepticism among soil ecologists may have been fuelled, perhaps justi®ably, by the lack of agreement upon a reliable conversion factor between ergosterol concentration and fungal biomass and the sometimes high variation of ergosterol within single systems (e.g., Djajakirana et al., 1996).
Several recent studies attempted to derive a conver-sion factor between ergosterol and fungal biomass, with two producing a similar value of 5 mg ergosterol gÿ1 of dry mycelium (ergosterol/Cmic = 0.01) (Scheu and Parkinson, 1994; Djajakirana et al., 1996), see also Stahl and Parkin, 1996). However, this conversion, despite being well supported by data obtained from axenic cultures (Newell, 1996), seems unsustainable because the calculated fungal biomass often exceeds the total microbial biomass if measured by convention-al methods. When the conversion was applied to the soils in this study, predicted fungal biomass (dry weight, assuming fungal C content of 46% of dry weight) was 350 mg gÿ1 in disturbed grassland and 26400 mg gÿ1in the shingle ridges. However, respective total microbial biomasses were only 239 and 7500 mg gÿ1 (converted using a C
mic/ATP ratio of 200 and
mi-crobial C content of 46%). This basic inconsistency, wherein a subset of the biomass is predicted to be lar-ger than the whole, may be central in undermining con®dence in the utility of the biomarker. And yet it may simply be due to the two measures estimating dierent populations with diering degrees of accu-racy.
To illustrate the problem, we plotted ergosterol/Cmic ratios against dierent levels of total organic C in for-est soils (Fig. 3). The choice of forfor-est soils was both fortuitous and intentional: fortuitous because they have been the subject of the majority of the studies using ergosterol, and intentional because they display arguably the most inaccuracy, that is the greatest dis-parity between ergosterol±estimated fungal biomass and total biomass. They should, but do not, show close agreement between the two since fungi dominate the decomposer subsystem of most forests (Table 2). The plot shows high variability of ergosterol/Cmic ratios, with only a statistically trivial trend across a wide range of Corg concentrations, and with all but
three soils exceeding the proposed conversion factor of
0.011. Indeed, the only studies that contained soils with an ergosterol/Cmic ratio below 0.011 were those that used the data to derive the conversion (Scheu and Parkinson, 1994; Djajakirana et al., 1996). A relevant feature of both these studies was the long-term storage of the soils at or below 48C (over 6 months for the Djajakirana et al., (1996) study). Although cold, mi-crobial growth would not have stopped. For example, Parkinson (1986) reported that one of two annual peaks of fungal biomass in the Of=h layer of Populus tremuloides forest occurred just before the snow melt (see also SoÈderstroÈm, 1979), and Coleman et al. (1990) found that the decay rates of litter bags buried in
Pinus contorta forest varied little throughout winter, spring and summer, even though the site in winter was covered by 70±80 cm snow. Use of such lengthy sto-rage times would allow more time for microbial succes-sion and equilibration of a distinctly dierent community to the original, with the most signi®cant change being a loss of microbial symbionts, especially the mycorrhizae. Temperate forests are dominated by ectomycorrhizal trees. Low diversity of the trees con-trasts with high diversity of their predominantly basi-diomycete associates, which proliferate in the fermentation and humus layers (Read, 1994). Froste-gaÊrd et al. (1996) reported a 25 mol% decrease in 18:2o6, a phospholipid fatty acid (PLFA) indicative of fungal biomass (see Table 2, footnote e), relative to total PLFAs, in a mycorrhizae-rich forest soil after 2 months of storage, but not in a continually disturbed arable soil. Further, this decrease continued at a simi-lar rate over 6 months of storage of the forest soil, while total biomass, measured using total PLFAs and lipid phosphate, remained unchanged.
However, the decay of mycorrhizae cannot by itself account for all the discrepancies observed in Fig. 3. Even if mycorrhizae comprised as much as 50±60% of the total biomass (HaÊkan Wallander, personal com-munication) their loss could not account for ergos-terol/Cmic ratios > 0.030. Therefore, either the
conventional total biomass methods consistently underestimate the amount of microbial biomass pre-sent at the time of sampling or the indigenous in situ soil fungi contain more ergosterol per unit biomass than their axenically-grown counterparts. Presently we cannot discount either possibility; indeed both could be combining to widen the ratio. The standard total microbial biomass measures are far from reliable (see Ingham et al., 1991) because all involve soil precondi-tioning of one form or another (sieving, mixing, sto-rage, warm and moist pre-incubation) and because they share a history of circular calibration, one upon the other. Equally, the assumption that the ergosterol content in the membranes of fungi under ®eld con-ditions is similar to that of cultured isolates has never been tested. Since the sterol occurs mainly as a
com-ponent of the lipid bilayer in the plasma membrane (where along with other sterols it forms a close associ-ation with phospholipids, modifying the physical state of the membrane lipid and controlling its permeability, amongst other functions (Weete, 1989)) its relative abundance would undoubtedly be aected by the properties of the soil environment, an environment dis-similar to any agar. Its lability is highlighted by the potential range of ratios between free and bound ergosterol which may eect its extractability: the ratio ranges between 20±30:1 in axenic cultures but can reverse to 1:3±20 under anaerobic conditions or during transition from logarithmic growth to the stationary phase (LoÈsel, 1988; Weete, 1989). All this leads to a substantial small-scale variation in ergosterol, but also in biomass measures, which further fuels the doubts about ergosterol being a successful fungal biomarker.
At ®rst, this potential for high variation within a single system may not be apparent in our dataset as we demonstrated a close correlation between ergosterol and microbial biomass. However, the dataset was large and diverse, covering almost the entire range of values for both variables in temperate soils, and the corre-lation hence bene®ted from possessing maximal between-group variance, thus perhaps masking signi®-cant within-group (ecotype) variability. In fact the level of correlation was inconsistent within the individ-ual ecotypes; partial correlation coecients between the two variables ranged from 0.06 to 0.70, being sig-ni®cant in ®ve out of the seven (Table 9). The strength of the relationship was in¯uenced by the degree of variation of ergosterol within each ecotype, with low coecients of variance of ergosterol corresponding to low correlation. Wardle and Ghani (1995) found a similar in¯uence of the breadth of surveying upon the relationship between fumigation extraction, fumigation incubation and substrate induced respiration esti-mations of microbial biomass. The three were closely related when data from 12 managed grasslands across New Zealand were combined, but showed little or no correlation within individual sites.
Contributing to this variability may be another, and largely overlooked, factor. That is, that ergosterol may have been misappropriated as a rough index of bio-mass when in reality it should be used as a measure of fungal membrane area (Wallander et al., 1997). Data presented by FrostegaÊrd and BaÊaÊth (1996) support this view. They found a highly signi®cant relationship r20:85, P<0:001 between ergosterol and 18:2o6, the principal fatty acid of fungal membrane phospholi-pids, in 14 arable, grassland and forest soils. More-over, the quest for an index of fungal biomass may itself be a misplaced goal when a more relevant, holis-tic measure of the functioning of the network of hyphae that rami®es the soil Ð up to an extraordinary 600 km gÿ1 in ectomycorrhizal mats (Ingham et al., S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
1991) Ð may be surface area. Since the membrane controls the in¯ow and out¯ow of energy, nutrients and wastes its area should correlate closely with the functioning of the fungal mass.
Rayner (1997) proposed that organisms and ecosys-tems are controlled reciprocally from the top-down, by genes, as well as from the bottom-up, by cell bound-aries. The mycelium of a fungus, which may be regarded as a single cell with a large deformable boundary (since the septa are not complete), reacts to the amount of available free energy in its surroundings (the amount and properties of the organic substrate and the availability of nutrients) by altering its bound-ary, its surface area. Fungal boundaries are intrinsi-cally indeterminate and chaotic; they behave nonlinearly due to the counteraction between positive and negative feedbacks. They are highly deformable and expandable, able to coalesce and degenerate, and able to alter their permeability over a wide range. ``By dierentiating and integrating, recon®guring and recy-cling [their boundaries], a distributed power structure in which boundaries are opened or sealed o according to circumstances allows a versatile indeterminate sys-tem to develop. This syssys-tem gathers and stores, explores and redistributes as its components assert their autonomy [their genetic identity] and admit their interdependency in a continuing dynamic interplay.'' (Rayner, 1997, p. 292). Thus, predictability, and a neat conversion from surface area to biomass, will always be limited to a range of values rather than to a single ®gure. The relationship between fungal mass and fun-gal surface area will always be a loose one. This una-voidable random error translates to increased heterogeneity as the scale of sampling is reduced, with a corresponding fall in the closeness of the correlation between ergosterol and fungal biomass. It is ironic that ergosterol's shortcomings as a measure of fungal biomass may be the very reason for it being a poten-tially even more valuable index, that of the area of fungal semi-permeable membrane.
What next? Due to the above mentioned pitfalls, a
single conversion factor between ergosterol and fungal biomass is probably unachievable, even if an accurate alternative measure existed against which ergosterol could be judged (direct microscopic methods are bur-dened with as many disadvantages as bene®ts (Newell, 1992), and the lack of any emerging consenus about the relative superiority of active or total hyphal length measures only compounds the stasis). If ergosterol is shown to be, and used as, an index of fungal area, and if sampling strategies are changed to minimise patchi-ness (the ``pedon'' used to obtain each replicate soil sample should be adjusted to match the patch-scale of the vegetation and multiple cores bulked together from within each) then only the error arising from dier-ences in average ergosterol content of the fungal bio-mass in dierent soils remains to be adjusted for. Correction for this systematic bias would require a re-liable measure of fungal surface area. The closeness of the correlation between ergosterol and 18:2o6, and other eukaryote signature PLFAs, needs to be investi-gated more thoroughly, especially because the factors that control ergosterol concentration are unlikely to similarly aect PLFAs. This would allow the site-speci®c error to be adjusted for directly, dispensing with the need for an intermediate conversion of either measure to fungal surface area. Another promising approach, which uses 14C acetate uptake to measure ergosterol synthesis and fungal biomass production, has been developed by Newell (1996).
It should also be possible to measure the ergosterol content of the in situ fungal dominants by directly picking hyphae from the soil and extracting and measuring the minute amount of ergosterol so obtained. Warcup (1957) used direct plating of hyphae picked from broken clods to amass a collection of over 3000 isolates. In a humic forest soil containing roughly 10 km live hyphae gÿ1 and 200
mg ergosterol gÿ1, the average ergosterol content of the hyphae would be 20 pg mmÿ1. This is twice the on-column limit of detec-tion reported by Axellson et al. (1995) using GC-MS with samples of dust. Although requiring patience, it
Table 9
Partial correlation coecients, correcting for the in¯uence of physico-chemical soil properties, between ergosterol and ATP (i.e., the correlation that exists after each variable has been adjusted to remove the in¯uence of any linear regressions withCorg, C/N ratio, pH and moisture) and the coecient of variation of ergosterol within individual ecotypes (
P< 0.05,
P< 0.01,
P< 0.001)
N Partial correlation between ATP and ergosterol CV of ergosterol (%)a
Dune slack 28 0.70
353
Restored grassland 17 0.64
66
Meadow 33 0.44
96
Shingle 14 0.06 45
Forest full pro®le 40 0.11 30
Forest ¯oor 45 0.41
65
Forest mineral 45 0.50
105
a
should be possible to pick 100 or more relatively deb-ris-free hyphae from soil, measure the volume of cyto-plasm-®lled length, and then perform a small-scale ergosterol (and PLFA) determination.
Acknowledgements
We sincerely thank Helen Bentham, Gerard Farmer, Tom Gelsthorpe, Jim Harris, Eamon Keaveney, Ray Stoker, Rob Tempest and Kim Willis for their advice and technical assistance. We would also like to thank to two anonymous reviewers for their valuable advice and constructive criticism which greatly improved the manuscript.
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