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Soil Biology & Biochemistry 32 (2000) 989±1005
www.elsevier.com/locate/soilbio
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 10 10 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 r 2 0:80), 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
of evar and epop formed 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
* 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.
E-mail addresses: stephan@ruzicka.freeserve.co.uk (S. Ruzicka),
t.c.j.hill@vel.ac.uk (T. Hill).
To date, the measurement of the ergosterol concentration in natural substrates is arguably the most ecient 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
0038-0717/00/$ - see front matter 7 2000 Elsevier Science Ltd. All rights reserved.
PII: S 0 0 3 8 - 0 7 1 7 ( 0 0 ) 0 0 0 0 9 - 2
990
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
error in the relationship between ergosterol concentration 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 contributes 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 methanol R freezing
followed by lyophilisation < lyophilisation R freezing
at ÿ208C 6) being preconditioned with
1 M HCl to remove carbonates.
992
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
Table 2
Estimates of relative fungal biomass in temperate soils obtained using direct microscopy (DM), selective inhibition of respiration (SI) and phospholipid 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 inhibition 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)
Jordan et al. (1995)
Stamatiadis et al. (1990)
Anderson and Domsch (1973)
Anderson and Domsch (1975)
VancÆura and Kunc (1977)
Domsch et al. (1979)
DM
DM
DM
"
"
"
SI
"
SI
SI
SI
SI
Anderson and Domsch (1980)
West et al. (1987b)
Killham et al. (1988)
SI
SI
SI
Wardle and Parkinson (1990a)
SI
Brown chernozem
ZL (0±10 cm)
S (Ap, 0±13 cm)
"
SCL (A, 0±30 cm)
"
S (Ap, 0±13 cm)
SCL (A, 0±30 cm)
Brown podzol (A)
Brown soil & chernozem
Chernozem & brown soil
Limed bog
Chernozem
Parabrown soil
±
SL (0±7.5 cm)
CL, SCL & SL
"
Dark brown chernozem, SL
85±92c
62±88
74 (total)
75 (active)
96 (total)
15 (active)d
53
48
78
65±90
67±82
75
70
80
70±90
81
31±55
62±69
70±80
Wardle and Parkinson (1990b)
Wardle et al. (1993)
SI
SI
Arable
Arable
Arable
"
"
"
"
"
Arable
Arable
± (probably arable)
Arable
"
" (26 y bare fallow)
Arable
Arable (stored for 2±10 w)
Arable (no amendment)
Arable (straw incorporation)
Arable (matric potential ÿ3000 to
ÿ6 kPa)
Arable (soil dosed with herbicides)
Arable (weed control: sawdust
mulch, cultivation, hoeing &
herbicides)
Dark brown chernozem, SL
SL (0±5 cm)
61±84
1 62±92 (median 1 76)
Federle (1986)
PLFA
± (probably arable)
" (5±10 cm)
L, SL, ZL & CL
1 54±86 (median 1 71)
13±33e
Nannipieri et al. (1978)
Jordan et al. (1995)
DM
DM
Domsch et al. (1979)
West (1986)
SI
SI
West et al. (1987b)
SI
Grassland
Pasture
Virgin prairie
Grassland
Pasture (fertilized)
Pasture (unfertilized)
Pasture (unfertilized, stored for 2±
10 weeks)
Pasture (fertilized, stored for 2±10
weeks)
Pasture (fertilized & limed)
Pasture (limed only)
Pasture (no input)
grassland (no input)
ZL (0±7.5 cm)
56±70
Brown earth (0±15 cm)
"
"
"
33
34
44
30
Dark. brown chernozems, SL & C 84
ZL (0±10 cm)
89±91
± (0±10 cm)
82
Brown soil, SL
70
ZL (0±7.5 cm)
55
"
50
ZL (0±7.5 cm)
54±56
Bardgett et al. (1996)
SI
Fñgri et al. (1977)
DM
Subalpine vegetation
Alpine vegetation
Calluna heath
O
A (0±10 cm)
O/A
68
96
95
Alphei et al. (1995)
Anderson and Domsch (1975)
SI
SI
Tate (1991)
SI
Roberts et al. (1980)
Bewley and Parkinson (1985)
SI
SI
Fagus sylvatica forest
Fagus sp. forest
Quercus/Carpinus forest
Quercus spp. forest
"
Pinus rigida forest
"
Pinus sylvestris forest
Pinus sp. forest
"
Ah
Ol&h
Ah (0±10 cm)
O
A
O
A
O/A
Of/h
A (0±5 cm)
53±58
70(l) & 60(h)
80
82
69
82
62
90
82±95
62±70
993
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
Table 2 (continued )
Reference
Method
Vegetation/land use
Soil detailsa
Fungi (%)b
Scheu and Parkinson (1994)
SI
Parkinson (1986)
SI
Flanagan and van Cleve (1977)
Parkinson et al. (1978)
Domsch et al. (1979)
Parkinson et al. (1980)
SI
SI
SI
SI
Pinus contorta forest
Populus tremuloides forest
"
Pinus contorta forest
Populus tremuloides forest
Abies lasiocarpa forest
Picea glauca forest
Picea mariana forest
Picea abies forest
Picea abies forest
Picea abies forest
Of/h
Ol,f&h
Ah
Of/h
"
"
"
O/A
Ol,f&h
Oh
O/A
57
85 (l), 67 (f), 55 (h)
50
82±83
80
87
82±85
85
80 (l), 78 (f), 67 (h)
70 & 75
80
a
Type, texture or horizon sampled if known; S, sand; Z, silt; L, loam; and C, clay (e.g. SZL is silty clay loam).
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 glucose and NH4NO3.
d
Accumulation 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.
e
Obtained from the abundance of the fungal fatty acid 18:2o6 relative to the combined abundance of all other detected PLFAs, and converted
to biomass by assuming that it comprised 43% of total fungal fatty acids based on studies of pure cultures. Neither FrostegaÊrd and BaÊaÊth (1996)
nor Bardgett et al. (1996, 1997) chose to extrapolate to biomass from PLFA ratios. They did, however, also record low fungal to bacterial PLFA
ratios, with medians in the range 0.05±0.08, by using the ratio of abundance of 18:2o6 to the combined abundance of 9±12 fatty acids representative of bacteria.
b
2.1. Ergosterol
The analytical technique used to determine ergosterol evolved during the course of this study, starting
with the assay used by Bentham et al. (1992) and ending 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 forest ¯oor and forest 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 hexane. 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 minÿ1 with 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 hexane:propan-2-ol (98:2, v/v). After steeping for 15 min,
10 ml of a methanol:ethanol mixture (4:1, v/v) was dispensed 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 immediately 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, approximately 2 ml of the top layer (hexane:propan-2-ol)
were transferred into a microfuge tube and centrifuged
at 7000 g for 10 min. HPLC analysis was performed
on the supernatant as above, except that 100 ml instead
of 20 ml was injected. The recovery of added ergosterol
ranged from 70 to 95%.
994
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)
Location
(no. of sites)
Vegetation
Soil type, Avery
(1990) (FAO, 1974)
Depth
(cm)
Texture
pHa
Corgb
(g kgÿ1)
Total N
(g kgÿ1)
Dune slack
Restored grassland/pasture
Ken®g (6)
Erin OCCM (5)
Butterwell OCCM (3)
Lounge OCCM (2)
Derbyshire (3)
Cumbria
Oxford (3)
Hainault (4)
Hainault
Epping
Krusne hory
Whitehouse Norman
Bryngwyn
Dungeness (3)
Hainault (2)
Epping
Epping
Slavkovsky les
Krusne hory
Krusne hory
Krusne hory
Hainault (2)
Hainault
Slavkovsky les (2)
Krusne hory (3)
Sumava
Sumava (2)
Hainault forest
Slavkovsky les
Slavkovsky les
Krusne hory (2)
Krusne hory
Sumava
Sumava (2)
Dune slack sere
Improved pasture
Improved pasture
Improved pasture
Improved pasture
Improved pasture
Floodmeadow sere
Low input meadow
Low input meadow
Low input meadow
Calcifuge montane meadow
Low input pasture
Low input pasture
Gorse scrub/calcifuge grassland sere
Birch woodland/hawthorn shrub
Ancient oak/hornbeam woodland
Birch woodland
Spruce forest
Mature spruce forest
Dead spruce
Birch/mountain ash shrub
Ancient oak/hornbeam woodland
Hawthorn scrub
Spruce forest
Mature/dead/young spruce sere
Ancient spruce forest
Spruce forest
Ancient oak/hornbeam woodland
Spruce forest
Spruce forest
Dead/young spruce
Spruce forest
Ancient spruce forest
spruce forest
Calcaric sandy regosol (Sandy Regosol)
Disturbed
Disturbed
Disturbed
Disturbed
Disturbed
Pelocalcaric alluvial gley (Calcaric Fluvisol)
Pelo-orthic Gley (Eutric Gleysol)
Pelo-orthic Gley (Eutric Gleysol)
Pelo-orthic Gley (Eutric Gleysol)
Colluvial orthic brown soil (Humic Cambisol)
Stagnogley (Humic Gleysol)
Stagnogley (Humic Gleysol)
Non-calcaric rego-alluvial (Dystric Fluvisol)
Pelo-orthic gley (Dystric Gleysol)
Pelo-orthic gley
Pelo-orthic gley
Luvic podzol (Orthic Podzol)
Typ. podzolic brown soil (Podzoluvisol)
Typ. podzolic brown soil (Podzoluvisol)
Gleyic podzolic brown soil (Leptic Podzol)
Mull
Mull/moder
Mor
Mull
Moder
Mor
Pelo-orthic gley (Dystric Gleysol)
Luvic podzol (Orthic Podzol)
Typ. gleyic brown soil (Gleyic Cambisol)
Typ. podzolic brown soil (Podzoluvisol)
Typ. podzolic brown soil (Podzoluvisol)
Typ. orthic brown soil (Dystric Cambisol)
Typ. orthic-brown soil (Dystric Cambisol)
0±30
0±30
0±30
0±30
0±30
0±30
0±30
0±5
5±30
0±30
0±30
0±30
0±30
0±30
0±30
0±30
0±30
0±30
0±30
0±30
0±30
O/Ac
Horizons
"
"
"
"
A/Bd
Horizons
"
"
"
"
"
S
ZL/CL
CL/L
ZCL/CL
nd
nd
ZCL
ZCL
ZCL
ZCL
nd
nd
nd
na
ZCL
ZCL
ZCL
LS
L
SL
SL
na
na
na
na
na
na
ZCL
LS
SL
SL
L
SL
SL
7.8±8.4
6.4±6.8
7.2±7.6
7.3
6.0±7.3
6.9
6.4±7.3
4.8±5.4
5.5
6.0
3.8
7.3
6.2
4.1±6.2
4.0±4.5
3.7
4.1
3.7
3.6
3.6
3.9
3.8
4.5
2.8±3.1
2.5±3.0
2.9
2.7±2.8
4.2
3.7
4.4
3.4±3.7
3.6
3.4
3.5±3.9
1.0±34.3
19±31
20±26
60
nd
nd
56±73
59±nd
26
38
216
nd
nd
272±456
31±32
43
41
60
107
88
71
216
nd
255±257
267±328
302
128±147
27
10
8
21±29
29
88
16±31
0.04±2.0
1.6±1.7
1.8±2.1
2.5
1.3±2.9
2.4
6.2±7.4
4.4±nd
2.3
3.3
15.0
2.6
1.8
13±30
2.1±2.2
2.4
3.0
3.1
4.9
4.7
3.6
11
nd
11±13
12±16
14
6
1.8
0.8
0.6
1.4±2.0
1.4
4.2
0.9±1.4
Meadow
Shingle ridge
Forest full pro®le
Forest ¯oor
Forest mineral
a
Soils marked with asterisk were determined in 1 M KCl.
It should be noted that soils from grasslands restored after opencast coal mining could be contaminated with coal particles, thus biasing the organic C values.
c
Predominantly Of and Oh.
d
Predominantly B.
b
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
Ecotype
995
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
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:disodium hydrogen orthophosphate extractant. Duplicate 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), luminescence 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 x a: 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, respectively. The systematic eects of soil properties and
sampling designs were investigated using multiple regression 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 gÿ1 and ATP from 0.10 to 33.6 mg gÿ1 of 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 1 mg gÿ1 of 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 sieving 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 replicate 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 variance. 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 in mg gÿ1 soil. Each replicate is a mean of three subsamples from 10 10 m quadrat
Ecotype
Dune slack
Restored grassland
Meadow
Shingle ridge
Forest full pro®le
Forest ¯oor
Forest mineral
Variable
ATP
ergosterol
ATP
ergosterol
ATP
ergosterol
ATP
ergosterol
ATP
ergosterol
ATP
ergosterol
ATP
ergosterol
n
28
28
66
66
50
50
15
15
40
40
51
51
45
45
Range
0.12±3.36
0.08±9.71
0.10±1.67
0.54±5.21
0.82±9.61
1.74±18.2
12.3±33.60
67.5±230.00
0.94±2.78
3.57±10.8
3.96±16.5
18.6±175.00
0.24±2.05
0.33±8.04
Geometric mean
0.95
2.17
0.55
1.77
3.94
6.46
17.40
132.00
1.61
5.41
8.83
46.30
0.80
1.82
Standard deviation
Upper tail
Lower tail
1.47
7.67
0.54
1.17
3.89
6.24
5.04
59.20
0.53
1.64
3.75
30.00
0.53
1.92
0.58
1.69
0.27
0.70
1.96
3.17
3.91
40.80
0.40
1.26
2.63
18.20
0.32
0.93
996
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
Fig. 1. Scatterplot of ergosterol and ATP concentrations in temperate soils (n = 295).
total microbial biomass, its ecological signi®cance
must be carefully considered. Soil is a complex, nonlinear (i.e., potentially chaotic) environment containing
a network of strongly and weakly interconnected elements. As a result, an observed correlation between
two variables does not necessarily indicate that a functional 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 regression: the amount of organic substrate Corg ), one
aspect of its quality (C/N ratio), soil moisture status
(moisture content) and soil acidity (pH). Having chosen 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 P 0:053 pH is
likely to be another predictor; as would be expected, it
was negatively correlated with ergosterol concentration.
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 dierences in moisture contents may have introduced a systematic 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 P 0:84), 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 predictor of ergosterol concentration r 2 0:51).
Two other aspects of the regression, presented in
Table 5, contain important information on the ergosTable 5
Multiple regression y Bi x c predicting ergosterol from ATP and
soil physicochemical properties, where i represents the predictors (r 2
= 0.847; F [5, 216] = 239.5, P < 0.001)
Intercept
ATP
Moisture content
pH
Corg
C/N
B
SE of B
t (216)
P
0.898
0.987
0.014
ÿ0.071
0.013
0.002
0.269
0.053
0.006
0.036
0.063
0.007
3.34
18.8
2.41
ÿ1.96
0.21
0.24
0.001
< 0.001
0.017
0.053
0.838
0.812
997
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
terol±ATP relationship. These are, that the B coecient 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 distinctly 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 ergosterol as a measure of fungal biomass. If epop was much
higher than evar , the actual ergosterol-to-fungal biomass 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 relationship, 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 ergosterol±ATP regression would be mostly attributable to
changes in evar , the error in each variable as an index
of biomass, and epop , the error caused by lack of equivalence 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 covariation with general soil properties. This is a gross approximation 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 individual soil systems upon the ergosterol vs. ATP variation, utilising the fact that the crucial parameters
aecting sample variation are the size and the hierarchy of sampling units and an appropriate level of replication. Given that we maintained a uniform level of
replication (10 10 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
Large
Variables
evar )
Failure of ergosterol or ATP to accurately
measure each of their respective populations
Analytical
eanal )
Inadequate sample processing and analysis of
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
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
Incomplete extraction of ergosterol or ATP; losses
of either chemical during analysis
Medium
Small (error remained
constant)
998
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
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 both epop and evar contain a small
random element, we refer only to their systematic
parts.
As the ergosterol±ATP relationship was approximately linear, we can simplify the relationship and isolate its residual error by standardising both ergosterol
and ATP (already log-transformed) to a zero mean
and unit variance, obtaining:
ergosterols ATPs eS eR
1
with
eS 1epop evar
2
where ergosterols and ATPs are standardised variables,
eS is systematic error and eR is random error. Note
that following the standardisation, eS eR , re¯ecting
the 20% uncertainty of the relationship, has a variance
s 2 0:20 (cf. Table 7). As discussed, eS is eectively
the sum of the systematic fractions of epop and evar :
The residual variance of the ergosterol±ATP relationship 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:
2
2
2
ssite
splot
eS eR
s 2 seco
3
with
2
2
ssite
1eS 1epop evar
seco
4
2
,
where s 2 is the total variance of the residuals and seco
2
2
ssite and splot are the components of the variance attributable to ecotype, site and plot sampling levels, respectively.
Two nested regression models were used to progressively reduce, and thereby partition, s 2 : One accounted
for the in¯uence of ecotype (E ) and the other for the
eects of both ecotype and site (within each ecotype)
E S). 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 dierences between amounts of residual variance between
models (Table 7).
The workings of the models are more readily visualised in Fig. 2. Fig. 2(a) shows the scatter of the residuals from the basic regression between ergosterol
and ATP, whereas Fig. 2(b) shows the attenuated pattern 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 diamonds) showed a similarly large movement, but opposite 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 signi®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 original residual of 20% was, thus, itself partioned into an
2
ecotype error seco
of 6.4/20 or 31%, a site component
2
ssite of 9.2/20 or 45% and a residual plot variance
2
of 4.8/20 or 24% (Table 7). Clearly, the soil
splot
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
ergS ATPS
ergS ATPS E
ergS ATPS E S
Residuals
Components of variance
n
Min.
Max.
Variance
Step
si2
295
295
295
ÿ1.290
ÿ1.153
ÿ0.769
1.227
1.053
0.909
0.204
0.141
0.048
Total
Ecotype
Site
Plot
0.204
0.064
0.092
0.048
%
100
31
45
24
a
The ecotype factor (E ) consisted of a suite of seven dummy variables Ei ), one for each ecotype, and used in the regression according to the
P
following, b2 E 7i1 b2i Ei , where b2i is a regression constant for each ecotype and Ei is de®ned as 1 for all cases from ecotype i and 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 the E S interaction.
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
Fig. 2. Eects of sampling level on the ergosterol±ATP relationship;
(a) residuals from the regression of the raw variables (Fig. 1), (b) residuals 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
i ÿx
to a zero mean and unit variance si xSD
). See Table 7 for details
on the individual regression models.
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
999
of the dune slack succession the site means for Corg , 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 succession Corg , C/N and pH changed 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 withinecotype variation in ergosterol/ATP ratio was only 4
and 5%, respectively, of the total contributed by all
ecotypes (Table 8). Within this, the amount attributable 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 variability; 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) horizons of forests and woodlands are considered separately. Note that horizontal heterogeneity of these two
ecotypes, approximated by the plot components of
variance si,2 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 neutralises much of this between-site variation.
The forces that aect ecotype, site and plot variability of both ergosterol and ATP are primarily caused
by the vegetation, via its control over the quality,
amount and distribution (both vertically and horizontally) of organic matter, the size and properties of the
rhizosphere and, associated with that, the mycorrhizal
type, composition and abundance. pH and soil structure, 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 variables, 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
1000
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
Table 8
Relative contribution of the ecotypes to the within-ecotype variance of the ergosterol±ATP relationship (index x). The within-ecotype variance
was further partitioned into site and plot components
x (%)a
Ecotype
Dune slack
19.7
Restored grassland
20.6
Meadow
5.4
Shingle ridge
18.2
Forest full pro®le
4.2
Forest ¯oor
16.0
Forest mineral
15.8
a
xi
P
Source
Site
Plot
Site
Plot
Site
Plot
Site
Plot
Site
Plot
Site
Plot
Site
Plot
df
5
22
13
52
11
38
2
12
7
32
10
40
8
36
SS
0.715
0.127
1.790
1.045
0.358
0.141
0.342
0.106
0.219
0.328
1.073
0.440
0.936
0.423
Components of variance
si2
%
0.029
0.006
0.025
0.020
0.007
0.003
0.032
0.009
0.004
0.010
0.021
0.011
0.021
0.011
84
16
55
45
65
35
79
21
29
71
65
35
65
35
2
j nij zij: ÿzi::
100
i1
Ni: ÿ1
Pl P j nij zij: ÿzi:: 2 ,where
i1
j1
Ni: ÿ1
zi:: is a mean ergosterol/ATP value for ecotype i; zij: is a mean ergosterol/ATP value for site j in ecotype i;
nij is the number of observations in site j of ecotype i; Ni: is the total number of observations within ecotype i.
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, generating 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 incubation under standardised conditions. The in situ biomass ``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 ectomycorrhizae contribute to the ergosterol concentration
(after homogenisation of the soil core a sub-sample
was immediately frozen) but not to the ATP concentration 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 Ve
www.elsevier.com/locate/soilbio
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 10 10 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 r 2 0:80), 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
of evar and epop formed 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
* 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.
E-mail addresses: stephan@ruzicka.freeserve.co.uk (S. Ruzicka),
t.c.j.hill@vel.ac.uk (T. Hill).
To date, the measurement of the ergosterol concentration in natural substrates is arguably the most ecient 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
0038-0717/00/$ - see front matter 7 2000 Elsevier Science Ltd. All rights reserved.
PII: S 0 0 3 8 - 0 7 1 7 ( 0 0 ) 0 0 0 0 9 - 2
990
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
error in the relationship between ergosterol concentration 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 contributes 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 methanol R freezing
followed by lyophilisation < lyophilisation R freezing
at ÿ208C 6) being preconditioned with
1 M HCl to remove carbonates.
992
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
Table 2
Estimates of relative fungal biomass in temperate soils obtained using direct microscopy (DM), selective inhibition of respiration (SI) and phospholipid 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 inhibition 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)
Jordan et al. (1995)
Stamatiadis et al. (1990)
Anderson and Domsch (1973)
Anderson and Domsch (1975)
VancÆura and Kunc (1977)
Domsch et al. (1979)
DM
DM
DM
"
"
"
SI
"
SI
SI
SI
SI
Anderson and Domsch (1980)
West et al. (1987b)
Killham et al. (1988)
SI
SI
SI
Wardle and Parkinson (1990a)
SI
Brown chernozem
ZL (0±10 cm)
S (Ap, 0±13 cm)
"
SCL (A, 0±30 cm)
"
S (Ap, 0±13 cm)
SCL (A, 0±30 cm)
Brown podzol (A)
Brown soil & chernozem
Chernozem & brown soil
Limed bog
Chernozem
Parabrown soil
±
SL (0±7.5 cm)
CL, SCL & SL
"
Dark brown chernozem, SL
85±92c
62±88
74 (total)
75 (active)
96 (total)
15 (active)d
53
48
78
65±90
67±82
75
70
80
70±90
81
31±55
62±69
70±80
Wardle and Parkinson (1990b)
Wardle et al. (1993)
SI
SI
Arable
Arable
Arable
"
"
"
"
"
Arable
Arable
± (probably arable)
Arable
"
" (26 y bare fallow)
Arable
Arable (stored for 2±10 w)
Arable (no amendment)
Arable (straw incorporation)
Arable (matric potential ÿ3000 to
ÿ6 kPa)
Arable (soil dosed with herbicides)
Arable (weed control: sawdust
mulch, cultivation, hoeing &
herbicides)
Dark brown chernozem, SL
SL (0±5 cm)
61±84
1 62±92 (median 1 76)
Federle (1986)
PLFA
± (probably arable)
" (5±10 cm)
L, SL, ZL & CL
1 54±86 (median 1 71)
13±33e
Nannipieri et al. (1978)
Jordan et al. (1995)
DM
DM
Domsch et al. (1979)
West (1986)
SI
SI
West et al. (1987b)
SI
Grassland
Pasture
Virgin prairie
Grassland
Pasture (fertilized)
Pasture (unfertilized)
Pasture (unfertilized, stored for 2±
10 weeks)
Pasture (fertilized, stored for 2±10
weeks)
Pasture (fertilized & limed)
Pasture (limed only)
Pasture (no input)
grassland (no input)
ZL (0±7.5 cm)
56±70
Brown earth (0±15 cm)
"
"
"
33
34
44
30
Dark. brown chernozems, SL & C 84
ZL (0±10 cm)
89±91
± (0±10 cm)
82
Brown soil, SL
70
ZL (0±7.5 cm)
55
"
50
ZL (0±7.5 cm)
54±56
Bardgett et al. (1996)
SI
Fñgri et al. (1977)
DM
Subalpine vegetation
Alpine vegetation
Calluna heath
O
A (0±10 cm)
O/A
68
96
95
Alphei et al. (1995)
Anderson and Domsch (1975)
SI
SI
Tate (1991)
SI
Roberts et al. (1980)
Bewley and Parkinson (1985)
SI
SI
Fagus sylvatica forest
Fagus sp. forest
Quercus/Carpinus forest
Quercus spp. forest
"
Pinus rigida forest
"
Pinus sylvestris forest
Pinus sp. forest
"
Ah
Ol&h
Ah (0±10 cm)
O
A
O
A
O/A
Of/h
A (0±5 cm)
53±58
70(l) & 60(h)
80
82
69
82
62
90
82±95
62±70
993
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
Table 2 (continued )
Reference
Method
Vegetation/land use
Soil detailsa
Fungi (%)b
Scheu and Parkinson (1994)
SI
Parkinson (1986)
SI
Flanagan and van Cleve (1977)
Parkinson et al. (1978)
Domsch et al. (1979)
Parkinson et al. (1980)
SI
SI
SI
SI
Pinus contorta forest
Populus tremuloides forest
"
Pinus contorta forest
Populus tremuloides forest
Abies lasiocarpa forest
Picea glauca forest
Picea mariana forest
Picea abies forest
Picea abies forest
Picea abies forest
Of/h
Ol,f&h
Ah
Of/h
"
"
"
O/A
Ol,f&h
Oh
O/A
57
85 (l), 67 (f), 55 (h)
50
82±83
80
87
82±85
85
80 (l), 78 (f), 67 (h)
70 & 75
80
a
Type, texture or horizon sampled if known; S, sand; Z, silt; L, loam; and C, clay (e.g. SZL is silty clay loam).
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 glucose and NH4NO3.
d
Accumulation 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.
e
Obtained from the abundance of the fungal fatty acid 18:2o6 relative to the combined abundance of all other detected PLFAs, and converted
to biomass by assuming that it comprised 43% of total fungal fatty acids based on studies of pure cultures. Neither FrostegaÊrd and BaÊaÊth (1996)
nor Bardgett et al. (1996, 1997) chose to extrapolate to biomass from PLFA ratios. They did, however, also record low fungal to bacterial PLFA
ratios, with medians in the range 0.05±0.08, by using the ratio of abundance of 18:2o6 to the combined abundance of 9±12 fatty acids representative of bacteria.
b
2.1. Ergosterol
The analytical technique used to determine ergosterol evolved during the course of this study, starting
with the assay used by Bentham et al. (1992) and ending 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 forest ¯oor and forest 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 hexane. 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 minÿ1 with 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 hexane:propan-2-ol (98:2, v/v). After steeping for 15 min,
10 ml of a methanol:ethanol mixture (4:1, v/v) was dispensed 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 immediately 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, approximately 2 ml of the top layer (hexane:propan-2-ol)
were transferred into a microfuge tube and centrifuged
at 7000 g for 10 min. HPLC analysis was performed
on the supernatant as above, except that 100 ml instead
of 20 ml was injected. The recovery of added ergosterol
ranged from 70 to 95%.
994
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)
Location
(no. of sites)
Vegetation
Soil type, Avery
(1990) (FAO, 1974)
Depth
(cm)
Texture
pHa
Corgb
(g kgÿ1)
Total N
(g kgÿ1)
Dune slack
Restored grassland/pasture
Ken®g (6)
Erin OCCM (5)
Butterwell OCCM (3)
Lounge OCCM (2)
Derbyshire (3)
Cumbria
Oxford (3)
Hainault (4)
Hainault
Epping
Krusne hory
Whitehouse Norman
Bryngwyn
Dungeness (3)
Hainault (2)
Epping
Epping
Slavkovsky les
Krusne hory
Krusne hory
Krusne hory
Hainault (2)
Hainault
Slavkovsky les (2)
Krusne hory (3)
Sumava
Sumava (2)
Hainault forest
Slavkovsky les
Slavkovsky les
Krusne hory (2)
Krusne hory
Sumava
Sumava (2)
Dune slack sere
Improved pasture
Improved pasture
Improved pasture
Improved pasture
Improved pasture
Floodmeadow sere
Low input meadow
Low input meadow
Low input meadow
Calcifuge montane meadow
Low input pasture
Low input pasture
Gorse scrub/calcifuge grassland sere
Birch woodland/hawthorn shrub
Ancient oak/hornbeam woodland
Birch woodland
Spruce forest
Mature spruce forest
Dead spruce
Birch/mountain ash shrub
Ancient oak/hornbeam woodland
Hawthorn scrub
Spruce forest
Mature/dead/young spruce sere
Ancient spruce forest
Spruce forest
Ancient oak/hornbeam woodland
Spruce forest
Spruce forest
Dead/young spruce
Spruce forest
Ancient spruce forest
spruce forest
Calcaric sandy regosol (Sandy Regosol)
Disturbed
Disturbed
Disturbed
Disturbed
Disturbed
Pelocalcaric alluvial gley (Calcaric Fluvisol)
Pelo-orthic Gley (Eutric Gleysol)
Pelo-orthic Gley (Eutric Gleysol)
Pelo-orthic Gley (Eutric Gleysol)
Colluvial orthic brown soil (Humic Cambisol)
Stagnogley (Humic Gleysol)
Stagnogley (Humic Gleysol)
Non-calcaric rego-alluvial (Dystric Fluvisol)
Pelo-orthic gley (Dystric Gleysol)
Pelo-orthic gley
Pelo-orthic gley
Luvic podzol (Orthic Podzol)
Typ. podzolic brown soil (Podzoluvisol)
Typ. podzolic brown soil (Podzoluvisol)
Gleyic podzolic brown soil (Leptic Podzol)
Mull
Mull/moder
Mor
Mull
Moder
Mor
Pelo-orthic gley (Dystric Gleysol)
Luvic podzol (Orthic Podzol)
Typ. gleyic brown soil (Gleyic Cambisol)
Typ. podzolic brown soil (Podzoluvisol)
Typ. podzolic brown soil (Podzoluvisol)
Typ. orthic brown soil (Dystric Cambisol)
Typ. orthic-brown soil (Dystric Cambisol)
0±30
0±30
0±30
0±30
0±30
0±30
0±30
0±5
5±30
0±30
0±30
0±30
0±30
0±30
0±30
0±30
0±30
0±30
0±30
0±30
0±30
O/Ac
Horizons
"
"
"
"
A/Bd
Horizons
"
"
"
"
"
S
ZL/CL
CL/L
ZCL/CL
nd
nd
ZCL
ZCL
ZCL
ZCL
nd
nd
nd
na
ZCL
ZCL
ZCL
LS
L
SL
SL
na
na
na
na
na
na
ZCL
LS
SL
SL
L
SL
SL
7.8±8.4
6.4±6.8
7.2±7.6
7.3
6.0±7.3
6.9
6.4±7.3
4.8±5.4
5.5
6.0
3.8
7.3
6.2
4.1±6.2
4.0±4.5
3.7
4.1
3.7
3.6
3.6
3.9
3.8
4.5
2.8±3.1
2.5±3.0
2.9
2.7±2.8
4.2
3.7
4.4
3.4±3.7
3.6
3.4
3.5±3.9
1.0±34.3
19±31
20±26
60
nd
nd
56±73
59±nd
26
38
216
nd
nd
272±456
31±32
43
41
60
107
88
71
216
nd
255±257
267±328
302
128±147
27
10
8
21±29
29
88
16±31
0.04±2.0
1.6±1.7
1.8±2.1
2.5
1.3±2.9
2.4
6.2±7.4
4.4±nd
2.3
3.3
15.0
2.6
1.8
13±30
2.1±2.2
2.4
3.0
3.1
4.9
4.7
3.6
11
nd
11±13
12±16
14
6
1.8
0.8
0.6
1.4±2.0
1.4
4.2
0.9±1.4
Meadow
Shingle ridge
Forest full pro®le
Forest ¯oor
Forest mineral
a
Soils marked with asterisk were determined in 1 M KCl.
It should be noted that soils from grasslands restored after opencast coal mining could be contaminated with coal particles, thus biasing the organic C values.
c
Predominantly Of and Oh.
d
Predominantly B.
b
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
Ecotype
995
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
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:disodium hydrogen orthophosphate extractant. Duplicate 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), luminescence 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 x a: 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, respectively. The systematic eects of soil properties and
sampling designs were investigated using multiple regression 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 gÿ1 and ATP from 0.10 to 33.6 mg gÿ1 of 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 1 mg gÿ1 of 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 sieving 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 replicate 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 variance. 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 in mg gÿ1 soil. Each replicate is a mean of three subsamples from 10 10 m quadrat
Ecotype
Dune slack
Restored grassland
Meadow
Shingle ridge
Forest full pro®le
Forest ¯oor
Forest mineral
Variable
ATP
ergosterol
ATP
ergosterol
ATP
ergosterol
ATP
ergosterol
ATP
ergosterol
ATP
ergosterol
ATP
ergosterol
n
28
28
66
66
50
50
15
15
40
40
51
51
45
45
Range
0.12±3.36
0.08±9.71
0.10±1.67
0.54±5.21
0.82±9.61
1.74±18.2
12.3±33.60
67.5±230.00
0.94±2.78
3.57±10.8
3.96±16.5
18.6±175.00
0.24±2.05
0.33±8.04
Geometric mean
0.95
2.17
0.55
1.77
3.94
6.46
17.40
132.00
1.61
5.41
8.83
46.30
0.80
1.82
Standard deviation
Upper tail
Lower tail
1.47
7.67
0.54
1.17
3.89
6.24
5.04
59.20
0.53
1.64
3.75
30.00
0.53
1.92
0.58
1.69
0.27
0.70
1.96
3.17
3.91
40.80
0.40
1.26
2.63
18.20
0.32
0.93
996
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
Fig. 1. Scatterplot of ergosterol and ATP concentrations in temperate soils (n = 295).
total microbial biomass, its ecological signi®cance
must be carefully considered. Soil is a complex, nonlinear (i.e., potentially chaotic) environment containing
a network of strongly and weakly interconnected elements. As a result, an observed correlation between
two variables does not necessarily indicate that a functional 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 regression: the amount of organic substrate Corg ), one
aspect of its quality (C/N ratio), soil moisture status
(moisture content) and soil acidity (pH). Having chosen 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 P 0:053 pH is
likely to be another predictor; as would be expected, it
was negatively correlated with ergosterol concentration.
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 dierences in moisture contents may have introduced a systematic 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 P 0:84), 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 predictor of ergosterol concentration r 2 0:51).
Two other aspects of the regression, presented in
Table 5, contain important information on the ergosTable 5
Multiple regression y Bi x c predicting ergosterol from ATP and
soil physicochemical properties, where i represents the predictors (r 2
= 0.847; F [5, 216] = 239.5, P < 0.001)
Intercept
ATP
Moisture content
pH
Corg
C/N
B
SE of B
t (216)
P
0.898
0.987
0.014
ÿ0.071
0.013
0.002
0.269
0.053
0.006
0.036
0.063
0.007
3.34
18.8
2.41
ÿ1.96
0.21
0.24
0.001
< 0.001
0.017
0.053
0.838
0.812
997
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
terol±ATP relationship. These are, that the B coecient 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 distinctly 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 ergosterol as a measure of fungal biomass. If epop was much
higher than evar , the actual ergosterol-to-fungal biomass 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 relationship, 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 ergosterol±ATP regression would be mostly attributable to
changes in evar , the error in each variable as an index
of biomass, and epop , the error caused by lack of equivalence 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 covariation with general soil properties. This is a gross approximation 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 individual soil systems upon the ergosterol vs. ATP variation, utilising the fact that the crucial parameters
aecting sample variation are the size and the hierarchy of sampling units and an appropriate level of replication. Given that we maintained a uniform level of
replication (10 10 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
Large
Variables
evar )
Failure of ergosterol or ATP to accurately
measure each of their respective populations
Analytical
eanal )
Inadequate sample processing and analysis of
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
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
Incomplete extraction of ergosterol or ATP; losses
of either chemical during analysis
Medium
Small (error remained
constant)
998
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
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 both epop and evar contain a small
random element, we refer only to their systematic
parts.
As the ergosterol±ATP relationship was approximately linear, we can simplify the relationship and isolate its residual error by standardising both ergosterol
and ATP (already log-transformed) to a zero mean
and unit variance, obtaining:
ergosterols ATPs eS eR
1
with
eS 1epop evar
2
where ergosterols and ATPs are standardised variables,
eS is systematic error and eR is random error. Note
that following the standardisation, eS eR , re¯ecting
the 20% uncertainty of the relationship, has a variance
s 2 0:20 (cf. Table 7). As discussed, eS is eectively
the sum of the systematic fractions of epop and evar :
The residual variance of the ergosterol±ATP relationship 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:
2
2
2
ssite
splot
eS eR
s 2 seco
3
with
2
2
ssite
1eS 1epop evar
seco
4
2
,
where s 2 is the total variance of the residuals and seco
2
2
ssite and splot are the components of the variance attributable to ecotype, site and plot sampling levels, respectively.
Two nested regression models were used to progressively reduce, and thereby partition, s 2 : One accounted
for the in¯uence of ecotype (E ) and the other for the
eects of both ecotype and site (within each ecotype)
E S). 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 dierences between amounts of residual variance between
models (Table 7).
The workings of the models are more readily visualised in Fig. 2. Fig. 2(a) shows the scatter of the residuals from the basic regression between ergosterol
and ATP, whereas Fig. 2(b) shows the attenuated pattern 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 diamonds) showed a similarly large movement, but opposite 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 signi®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 original residual of 20% was, thus, itself partioned into an
2
ecotype error seco
of 6.4/20 or 31%, a site component
2
ssite of 9.2/20 or 45% and a residual plot variance
2
of 4.8/20 or 24% (Table 7). Clearly, the soil
splot
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
ergS ATPS
ergS ATPS E
ergS ATPS E S
Residuals
Components of variance
n
Min.
Max.
Variance
Step
si2
295
295
295
ÿ1.290
ÿ1.153
ÿ0.769
1.227
1.053
0.909
0.204
0.141
0.048
Total
Ecotype
Site
Plot
0.204
0.064
0.092
0.048
%
100
31
45
24
a
The ecotype factor (E ) consisted of a suite of seven dummy variables Ei ), one for each ecotype, and used in the regression according to the
P
following, b2 E 7i1 b2i Ei , where b2i is a regression constant for each ecotype and Ei is de®ned as 1 for all cases from ecotype i and 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 the E S interaction.
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
Fig. 2. Eects of sampling level on the ergosterol±ATP relationship;
(a) residuals from the regression of the raw variables (Fig. 1), (b) residuals 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
i ÿx
to a zero mean and unit variance si xSD
). See Table 7 for details
on the individual regression models.
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
999
of the dune slack succession the site means for Corg , 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 succession Corg , C/N and pH changed 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 withinecotype variation in ergosterol/ATP ratio was only 4
and 5%, respectively, of the total contributed by all
ecotypes (Table 8). Within this, the amount attributable 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 variability; 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) horizons of forests and woodlands are considered separately. Note that horizontal heterogeneity of these two
ecotypes, approximated by the plot components of
variance si,2 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 neutralises much of this between-site variation.
The forces that aect ecotype, site and plot variability of both ergosterol and ATP are primarily caused
by the vegetation, via its control over the quality,
amount and distribution (both vertically and horizontally) of organic matter, the size and properties of the
rhizosphere and, associated with that, the mycorrhizal
type, composition and abundance. pH and soil structure, 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 variables, 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
1000
S. Ruzicka et al. / Soil Biology & Biochemistry 32 (2000) 989±1005
Table 8
Relative contribution of the ecotypes to the within-ecotype variance of the ergosterol±ATP relationship (index x). The within-ecotype variance
was further partitioned into site and plot components
x (%)a
Ecotype
Dune slack
19.7
Restored grassland
20.6
Meadow
5.4
Shingle ridge
18.2
Forest full pro®le
4.2
Forest ¯oor
16.0
Forest mineral
15.8
a
xi
P
Source
Site
Plot
Site
Plot
Site
Plot
Site
Plot
Site
Plot
Site
Plot
Site
Plot
df
5
22
13
52
11
38
2
12
7
32
10
40
8
36
SS
0.715
0.127
1.790
1.045
0.358
0.141
0.342
0.106
0.219
0.328
1.073
0.440
0.936
0.423
Components of variance
si2
%
0.029
0.006
0.025
0.020
0.007
0.003
0.032
0.009
0.004
0.010
0.021
0.011
0.021
0.011
84
16
55
45
65
35
79
21
29
71
65
35
65
35
2
j nij zij: ÿzi::
100
i1
Ni: ÿ1
Pl P j nij zij: ÿzi:: 2 ,where
i1
j1
Ni: ÿ1
zi:: is a mean ergosterol/ATP value for ecotype i; zij: is a mean ergosterol/ATP value for site j in ecotype i;
nij is the number of observations in site j of ecotype i; Ni: is the total number of observations within ecotype i.
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, generating 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 incubation under standardised conditions. The in situ biomass ``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 ectomycorrhizae contribute to the ergosterol concentration
(after homogenisation of the soil core a sub-sample
was immediately frozen) but not to the ATP concentration 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 Ve