2
Analytical, methodological, and spatial variability
in BiOLOG™ substrate utilization profiles of soil
microbial communities
“Many microbiologists feel the metabolic types and activities of bacteria
are of much greater significance than their taxonomic affiliation.”
❇❇❇
O. Meyer 1994
Balser, T.C. 2000. Linking Soil Microbial Communities and Ecosystem
Functioning. Doctoral Dissertation,
University of California, Berkeley.
Summary
In this chapter I present results from three studies designed to assess the analytical, methodological and
spatial variability of the BiOLOG™ assay of community substrate utilization. I found that condensing the
95-variate observations from the assay to a single dimension using diversity indices or guilds results in a
loss of sensitivity in data analysis. I found that the majority of the methodological variability in the assay
comes from soil replicates rather than plate replicates: incubating replicate plates of a single soil sample
can waste time and resources. Also, microbial communities that are adhered to soil particles differ in
biomass and carbon utilization profile from those that are aqueous. Finally, I found that BiOLOG
community profiles have a spatial dependence that varies in scale across three ecosystems. I use this
information to design an optimal ecosystem sampling scheme.
Introduction
The composition and function of microbial communities in soil are intrinsically linked to
ecosystem properties such nutrient cycling and carbon storage. Because of the importance
of microbial community parameters, scientists have developed a number of methods to
describe and quantify properties of soil communities such as biomass, nitrogen content,
activity, and measures of functional, taxonomic and genetic diversity (Zak et al. 1994;
Tiedje et al. 1999). The BiOLOG™ assay is a recently developed technique for
characterizing communities, based on a pattern of substrate utilization in 96-well
microtiter plate. It is inexpensive, fast, reliable, easy to use and can provide insight into
the physiological ecology of the soil microbial community (Haack et al. 1995; Garland
1996; this volume, Chapter 5).
-5-
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
Because of its potential value as a microbial community functional assay and its
ease of use, the BiOLOG assay has become widely used in recent years (Konopka et al.
1998; Insam and Rangger 1997). However, BiOLOG-based studies have not always
carefully evaluated the variability associated with the assay. For example, is it better to
use replicate soil samples, or replicate plates? What is the best way to analyze 95-variate
data? The result has been a profusion and confusion of methodologies. The lack of
standardization makes it hard to compare results across studies. Adding to the confusion
is the fact that BiOLOG results must be interpreted very carefully. It is uncertain what the
results of a controlled lab assay biased toward a small fraction of the community mean
for soil communities in situ. My work is a step towards gaining a clear understanding of
the analytical and methodological variability of the assay being used.
In this chapter I present the results from several methodological explorations I
performed with the BiOLOG assay from 1993 to 1997. There are three parts to the
chapter: 1) Analytical variability: dealing with 95-variate observations; 2) Exploration of
methodological variability and optimal sampling; and 3) Spatial heterogeneity and
ecosystem sampling.
1. Analytical variability: dealing with 95-variate observations
It is an inescapable fact most microbial community assays generate multivariate
observations. To understand and interpret the results of these assays we need to reduce
the dimensionality of the data. There have been three main ways researchers have
approached this with BiOLOG data: principal components analysis, reduction of the 95
variate data to 5 substrate guilds, and calculation of diversity indices.
Principal components analysis. Principal components analysis (PCA) is a mathematical
technique that allows multivariate data to be characterized by a smaller number of
variables. With PCA, the original component axes are transformed into an orthogonal
(uncorrelated) set of 'principal' axes. The 'first principal component' (PC1) is the linear
combination of the original variables that best represents the spread observed in the data,
and can be used as a summary of the multivariate observations. Univariate statistical
procedures such as plots, correlations, t-tests, and analysis of variance techniques can
then be applied to the principal component values to describe the various relationships
under study (Selvin, 1995).
Substrate guilds. Another technique employed to reduce the number of variables for
analysis is to sum the absorbance from wells containing chemically similar substrates.
The compounds on BiOLOG Gm- plates can be classified into 5 such substrate guilds,
thus producing 5, rather than 95-variate observations. Principal components analysis is
often thought to require more observations than variables, and thus a common complaint
about using PCA on BiOLOG data is that there are rarely, if ever, more observations than
variables. One suggestion has been to first reduce the number of variables by summation
-6--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
into guilds, then perform PCA on the resulting 5-variate observations. In this study I
compare the utility of PCA on guilds versus all 95 variables.
Diversity indices. The third analytical method commonly applied reduces the 95
variables to a single number by calculating a weighted sum for activity in each well. The
most commonly used index is the Shannon Biodiversity Index. This index represents an
entire plate with one number. The index (H) is:
95
H = - Â pi (ln pi ) , where p is the absorbance data for a given well (Zak et al. 1994).
i =1
Each of these three methods reduces 95-variate to univariate observations. In this section
I use data from a nested analysis of variance study (described in detail below) to assess
whether analytical detail is lost by summing the activity from individual wells in the
latter two techniques, compared to PCA on all 95 variables.
2. Exploration of methodological variability and optimal sampling
The BiOLOG assay has become popular as a method for characterizing soil microbial
communities. It is simple and relatively quick and inexpensive to perform. The assay is
based on the ability of microorganisms to readily utilize single carbon sources. This type
of 'carbon source utilization profile' has traditionally been a common way to characterize
the functional ability of bacterial isolates. However, whole soil presents several problems
that we don't encounter with isolate work: (i) soil must be diluted to reduce the particulate
load prior to plating. Dilution reduces the number of bacterial cells per milliliter of
solution, and may result in uneven distribution of rare bacteria from well to well; (ii)
bacteria are not evenly distributed within soil; they tend to be clumped, adhering to
particles; and (iii) within a well, dominant bacteria may outgrow rare organisms, resulting
in a biased community profile (Smalla et al. 1998).
Each of these problems represents a source of methodological variability that will
alter community fingerprints generated using BiOLOG plates, independent of real
differences between communities. In this chapter I present the results from two
experiments that addressed issues i-iii above. In the first experiment, I employed a
hierarchical sampling scheme in order to quantify the impact of soil dilution and to
design a sampling scheme that minimizes the impact of primary sources of variability in
the BiOLOG method. I used a nested analysis of variance (Figure 1), beginning with a
single composited sample from a grassland soil. Then I sub-sampled, made a dilution
series and inoculated BiOLOG microtiter plates in triplicate. If there were no variability
in the soil, I would get 27 identical BiOLOG plates. I used the ANOVA model described
below (in Materials and Methods) to estimate the components of variance, identifying
where the observed results deviate from the overall mean of the soil (the theoretical
"identical" answer for all 27 plates, or the "true mean"). In this way I discovered whether
-7--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
the deviation was caused by differences between initial soil sub-samples, or by
differences between dilution series or between plates. I then used observed variances at
each level of replication to design optimal sampling schemes for the assay. The results
from the variability study can thus be used to determine how best to sample soil to
minimize methodological variance and accurately detect ‘true’ differences between soils
using the BiOLOG assay.
The second experiment was designed to quantify the importance of very small scale
spatial variability in the soil. I asked whether easily extractable bacteria differed
functionally from those adhered to soil particles (problem ii above). I also assessed the
importance of dominant bacteria biasing the community profile (problem iii above).
Using the sequential extraction procedure diagrammed in Figure 2 to isolate easily
extractable, intermediate, and adhered bacteria for analysis with the BiOLOG assay, I
determined if degree of adhesion affected the BiOLOG profile for a community, and if
certain parts of this ‘within-soil’ community tend to dominate BiOLOG results.
3. Spatial heterogeneity and ecosystem sampling
In addition to variability within a soil sample, microbial communities also vary across a
landscape (Paul and Clark 1996). Recent efforts in understanding the dynamics and
structure of microbial communities have focused on quantifying such things as microbial
taxonomic diversity, carbon utilization profiles, and microbial biomass. Equally
important is understanding the spatial and temporal distribution of these parameters.
There have been studies looking at the spatial variability of microbial biomass (Morris
1999; Smith et al. 1994; Winter and Beese 1995; Tessier et al. 1998), and nitrogen
availability (Hook et al. 1991; Jackson and Caldwell 1993). However, biomass and Navailability have little to do with microbial community composition. Because the degree
of spatial dependence differs among soil parameters, we cannot assume that the
dependence quantified for microbial biomass is the same for microbial community
composition (Trangmar et al. 1985; Smith et al. 1994). Thus assessing spatial patterns of
additional microbial community parameters may provide new and potentially useful
information for soil studies.
In the final section of this chapter I report the results from a study to determine the
spatial heterogeneity of microbial community BiOLOG profiles in three ecosystems. I
asked: what is the scale of spatial dependence of BiOLOG profiles? How many samples
do we need to characterize the system, and at what scale should samples be taken? I
addressed these questions by analyzing the variability in BiOLOG profiles of soil
bacterial communities in three ecosystems (grassland, mixed conifer, and subalpine
conifer), along transects at two scales: 100 m and 1 m. I employed semivariogram
analysis at both scales to determine spatial patterning in each system. To calculate the
smallest number of samples necessary to adequately represent each ecosystem, I used the
variability of samples at the 100 m scale.
-8--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
Materials and Methods
Field sites
The studies were conducted at three sites along a climosequence of soils spanning a range of elevations in
the western slope of the Sierra Nevada National Forest. The soils along the sequence are similar to each
other in soil forming properties such as parent material, soil age, and topography (relief, slope, sun angle)
but experience a different annual climate. Figure 1 in Chapter 4 shows the location of the climosequence,
first described by Hans Jenny et al. (1949). Temperature and precipitation data and other soil properties are
summarized in Table 1.
Table 1. Summary of climate and soil properties at study sites
Soil Series
Fallbrook
Musick
Chiquito
(annual grassland)
(mixed conifer)
(Subalpine conifer)
Elevation (m)a
470
1240
2890
MAT (°C)
17.8 a
8.9 b
3.9 a
a
MAP (cm)
31
~95
127
soil properties (~0-18 cm)
B.D. (gcm-3)d
1.4
0.98
1.0
pHc
5.48
5.27
~4.75b
%C
1.01c
5.73 c
3.08a
%N a
0.10
0.22
.
C:N
9.84
25.82
.
%clay
10 a
15-22 a,b
4-6 a,b
WHC (gg-1)c
0.200
0.378
0.243
Microbial
Biomassc
0.597
0.292
.
(nMol gsoil–1)
Fungal:Bacterialc
1.98
3.09
.
a
From Trumbore et al. 1996
b
From Dahlgren et al. 1997
c
Soil properties measured by Balser: pH was measured in 1:2 soil:0.1M CaCl2 after a
30 minute equilibration period; %C and N were measured using a Carlo-Erba
analyzer; water holding capacity (WHC) was measured gravimetrically; and
microbial biomass was quantified from PLFA analysis as described in Chapter 3.
d
Wang and Amundson, unpublished data
The lowest elevation ecosystem is classified as a blue oak annual grassland savanna, composed primarily of a
Quercus douglasii overstory (~60% cover), and an annual grassland understory (Trumbore et al. 1996). The
most abundant species in the open grassland areas are Bromus molli, Hordeum hystrix, and Avena barbata
(Balser, unpublished data). The climate is Mediterranean, with rainfall concentrated from November to
February. In the California annual grassland, soil maximum daily temperatures often exceed 45° C during the
summer, and are usually below 10° C during the winter (Huenneke and Mooney 1989). The soil is from the
Fallbrook soil series, of the subgroup Mollic Haploxeralfs, and is formed on weathered granodiorite material
(Trumbore et al. 1996). A surface organic horizon overlying the mineral soil persists throughout the year,
ranging in depth form 1-2 cm. In the California annual grassland, most plant roots and microbial biomass
occur in the upper 10 cm of the mineral soil (Huenneke and Mooney 1989; Jackson et al. 1987).
-9--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
The mid-elevation forested site is located within the Sierra Nevada National Forest near Shaver
Lake, CA. The forest is a mature mixed-conifer stand comprised of: incense cedar (Calocedrus decurrens),
ponderosa pine (Pinus ponderosa), Manzanita (Chamaebatia foliolosa), and California black oak (Quercus
kelloggii) (Dahlgren et al. 1997). There is a well developed organic horizon, approximately 22 cm in depth
(Dahlgren et al. 1997). The climate is Mediterranean, with sporadic snowfall between October and March.
The soil is the Musick soil series, of the subgroup Ultic Haploxeralfs, and is also formed from weathered
granodioritic parent material (Dahlgren et al. 1997).
The highest elevation site is sparsely forested subalpine mixed-conifer, near the top of Kaiser Pass
above Huntington Lake in the Sierra Nevada National Forest. The forest consists of dominant canopy trees
of Lodgepole and Western White Pine (Pinus contortata murrayana and Pinus monticola), and Sierra
Juniper (Juniperus occidentalis) (Trumbore et al. 1996). The sparse understory is dominated by Lupinus
species. The soil is the Chiquito series of the subgroup Entic Cryoumbrepts, formed over weathered
granodiorite (Dahlgren et al. 1997).
Sampling and Laboratory Methods
Analytical and methodological variability (Questions 1 and 2)
A. Nested ANOVA experiment
To assess the analytical and methodological variability in the BiOLOG method, I used a nested analysis of
variance design with replication at the level of the soil, dilution series, and plate. I used the Fallbrook series
California annual grassland soil described above. I homogenized the soil by thorough mixing, removing
coarse roots and fragments. From an approximately 500 g soil sample, I took 5 g subsamples, then
replicated the dilution series and plates for each subsample. This resulted in 27 replicates of a single soil
sample (Figure 1).
Inoculation of BiOLOG plates. Each 5 g subsample was suspended in 50 ml of 50 µM phosphate buffer
(pH 7.1). This initial 1:10 dilution was shaken vigorously for 30 minutes on a reciprocal shaker. I used 2 ml
aliquots of the initial dilution to create replicate dilution series from each initial 1:10 sample. I plated each
10-3 dilution into triplicate BiOLOG plates, adding 150 µL per well and then measured the absorbance of
each well, using a 570 nm filter, every 12 hours. The 10-3 dilution had approximately 1.5x106 cells/ml (by
acridine orange direct counts). A higher dilution would contain too few cells, and a lower dilution would
contain too many particles.
The final experimental design was random, balanced, and nested by soil subsample, dilution series
and plate. Each level had three replicates, for a total of 27 plates (Figure 1). During incubation, one branch
of the design (subsample A, dilution series b, all 3 plates) failed to develop. As a result, the final statistical
analysis is for a random, nested, unbalanced design.
I subtracted color development in the control well (due to utilization of background dissolved organic
carbon) from absorbance readings in all other wells. Negative values were set to zero. I chose a time point to
analyze for each plate based on its average well color development (AWCD) (as per Garland 1996). Time
points chosen had AWCD values between 0.75-1.0. Prior to statistical analysis, I normalized individual well
absorbance by total plate color to account for possible differences in inoculation density between samples. I
used these processed data for factor analysis and other calculations to generate variables as described below.
For analysis by substrate ‘guilds’ I used the same groups as Zak et al. (1994) (Table 3, Chapter 3).
Statistical tests
Analytical variability. I performed 1) principal components analysis (PCA) on all 95 variables; 2) PCA on
data that were first condensed to substrate guilds; and 3) calculated the Shannon Diversity Index to
-10--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
examine the impact on results and interpretation of different ways of treating the data. I compared impact
of data treatment on resolution using box and whisker plots.
Methodological variability. Using PC1 as a summary variable in place of the 95-variate observations, I
performed a nested analysis of variance for a random, unbalanced design. The statistical model for the
BiOLOG profile per plate was: Yijk = m + SSi + Di j + eijk , where Y is the length of principal
component 1 (PC1) for each observation (a univariate representation of the entire BiOLOG profile), µ is
the overall sample mean, SSi is PC1 for each soil subsample, Dij is PC1 for each dilution series, and eijk is
the random error due to PC1 from each replicate plate. The model for the variance ( s ) was:
2
s 2y = s S2S + s D2 + s 2 . I used the components of variance to estimate an optimal sampling design to
2
simultaneously minimize both sample replication and overall variance ( s y ).
B. Sequential extraction experiment
I separated subcommunities from within soil subsamples by sequential extraction with phosphate buffer
followed by low speed centrifugation (Figure 2). Initially, I added phosphate buffer (pH 7.1) in a 10:1 ratio to
triplicate soil subsamples (approximately 25 g oven dry equivalent). I shook the samples on a rotary shaker for
30 minutes at approximately 100 rpm. I sedimented the bulk of the soil particle phase with centrifugation
(approximately 4,000 rpm for 10 minutes) and decanted the aqueous phase. This aqueous-extractable
microbial community became the ‘aqueous’ or ‘planktonic’ community. Next I obtained an ‘intermediate’
community by repeating the above steps, with the addition of a surfactant during extraction to remove cells
adhering weakly to soil surfaces (Bakken 1985). As diagrammed in Figure 2, I resuspended the pellet in
phosphate buffer containing 0.1% of the surfactant Triton x-100 (Fisher Scientific). Finally, the cells
remaining in the pellet that were not extracted became my ‘particulate’ or ‘adhered’ microbial community.
Inoculation of BiOLOG™ plates. I used BiOLOG™ Gram-negative microtiter plates (BiOLOG, Inc.), as
described above to assess the functional potential, or ‘fingerprint’ of each within-soil community. I ran
three replicates of each community type, from each soil. All plates were incubated at 28° C, and read on a
BiOLOG™ Microplate Reader approximately every 12 hours. The plates were considered ‘finished’ or
fully developed when the average color development in the wells was between 0.75 and 1.0. In faster
developing plates this occurred between 36 and 48 hours. Plates inoculated with less active communities
took up to 100 hours to develop. I analyzed the raw BiOLOG data as described above.
Acridine orange direct counts. I quantified the bacterial biomass present in each of the ‘within-soil’
grassland communities using epifluorescence microscopy. I made direct counts of the initial 1:10 dilution,
as well as of the aqueous, intermediate and particulate preparations using acridine orange stain. Filters were
stained for five minutes each, and were mounted on glass slides with paraffin oil under cover slips. I
counted 48 fields per slide, and three slides per community sample. There were three soil replicates per
community, for a total of nine slides counted per community. All materials were prepared using cell-free
glassware and filtered distilled water. I counted several prepared blank slides and subtracted the results
from the sample slide cell counts.
Final estimates of the cell count per gram of soil were obtained by back-calculation from microscope
fields to oven dry soil basis.
-11--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
Statistical tests. I performed principal components analysis on the raw data set. I used one way ANOVA
followed by Tukey’s HSD test, with community ‘type’ as the independent variable, and PC1 and PC2 as
dependent. For all analyses I used JMPin statistical software (SAS Inc.).
3. Spatial heterogeneity and ecosystem sampling
I addressed large scale variability within ecosystems by analyzing the variability in BiOLOG profiles of
soil bacterial communities from all three sites along the climosequence.
Within ecosystem types I analyzed semivariance/spatial dependence in soil and community
parameters at two scales: 100 m and 1 m. In the summer of 1995, I sampled the 1-10 cm depth at 15
locations along a 110 m transect for each site. The following summer I sampled every 10 cm along a 1 m
transect in the same ecosystems. In addition I measured soil temperature at 10 cm depth, and determined
gravimetric water content for each sample. BiOLOG carbon utilization profiles were determined by plating
the 10-3 dilution of soil from single 20 g subsamples at each point along the transect. Plates were inoculated
and incubated as in section 2B. above. PCA analysis was performed on absorbance data (normalized for
total plate development) from plates showing average well color development between 0.8-1.0. I used
standard deviation of the mean of 15 samples from the 110 m transect to calculate the smallest number of
samples necessary to adequately represent each ecosystem. I used the data at both 1 and 100 m scales to
determine spatial patterning in each system, via semi-variogram calculations.
Results and Discussion
1. Analytical variability in the BiOLOG assay: dealing with 95-variate observations
To analyze data from the BiOLOG assay, the data must be condensed to fewer
dimensions. I compared three different methods: principal components analysis (PCA) on
all data, PCA after summing into guilds, and calculation of a diversity index. These three
treatments allowed substantially different degrees of resolution between soils (Figure 3).
In the guild analysis and diversity index data, detail appears to be lost by summing the
activity in individual wells. PCA analysis, which utilizes all 95 data, gives the most
resolution and information about the differences between the three soil samples analyzed
(maximum separation of the boxes). Thus the use of substrate guilds followed by PCA, or
diversity indices may result in different interpretations of BiOLOG data sets as compared
to results obtained from PCA of all 95 variables. In subsequent analyses and studies, I use
PCA on all variables to summarize multivariate data. In Chapter 3, I test the resolving
power of PCA on all variables versus on guilds for an additional microbial community
assay, phospholipid fatty acid analysis (PLFA).
2. Methodological variability and optimal sampling
A. Nested ANOVA
In addition to variability in data analysis, there is methodological variability. I quantified
sources of this methodological variance with a nested ANOVA. As shown in Table 2,
almost all of the variability in analysis of soil samples A-C could be accounted for at the
level of the soil sub-sample. Replicate dilution series were reproducible and highly
-12--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
homogeneous accounting for only 4.1% of the overall variability. Replicate plates from a
given dilution series were also somewhat reproducible (17.9% of the total variance).
Table 2. Nested analysis of variance results, using PC1 from all data as the summary variable.
d.f.
Mean Square
F-statistic
p
% variance
from given source
Soil Subsample
2
4.899
56.25
<0.005
78%
Dilution Series
Plate
6
15
0.0871
0.0556
1.567
<0.25
4.10%
17.90%
Source
I used this information about soil variability to predict possible reductions in
analytical variance due to replication and subsampling. The total variance estimate is
given by:
2
s2plate
s 2soil sdilution
s =
+
+
i
ij
ijk
2
where s2 is total variance, i, j and k are numbers of replicates at each level, and s2soil etc.
are the individual components of variance.
Using this equation it is possible to substitute different values for i, j, and k
(different numbers of replicates at the different levels). In this experiment I had 3
replicates at each level (i, j and k each = 3; I call it a 3,3,3 sampling scheme). Setting i, j,
and k each equal to 1, I can calculate a baseline estimate of variance for the minimum
level of replication (the 1,1,1 sampling scheme). Two soil subsamples, with one dilution
and one plate replicate would be a 2,1,1 scheme, and so on. Comparing the variance from
a 1,1,1 scheme to that from schemes utilizing replication, I generated a figure showing
the reduction in variance that can be obtained from replication at various levels in the
assay (Figure 4). An additional way to decrease variance is to increase the initial size of
the soil sample. In Figure 4, I show the effect of an increase in initial soil sample size, as
well as the effect of replication.
Relative to a 1,1,1 sampling scheme (one soil aliquot, one dilution series and one
subsequent BiOLOG plate), replication at any level of the method reduces the variance,
improving the sensitivity of the method by reducing methodological noise. Replication of
the initial soil subsamples has the biggest impact on the method (Figure 4). Replication at
any other level has little effect.
This implies that there is little advantage in having more than 3 replicate BiOLOG
plates; even 27 plates in a 3,3,3 sampling scheme only reduce the variance by an
additional 6% beyond the reduction due to 3 soil subsamples.
-13--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
The results of this study are important, and not obvious. My results are in fact the
opposite of those found for assays such as direct cell counts using epifluorescence
microscopy (Jones and Simon 1975; Montagna 1982). In a nested analysis of variance
experiment, Montagna (1982) found that the majority of the variability in direct counts lies
at the level of replicate slides. Thus it is necessary to run many replicate slides in order to
accurately represent a soil sample. Following this model, most researchers working with
the BiOLOG assay incubate replicate plates from a single soil sample, often using as little
as 1 g mineral soil. However, there are fundamental differences in the BiOLOG method
and other community methods: in contrast to direct counting, BiOLOG is highly
homogeneous after the initial steps (subsampling). Simple tests of methodological
variability allow us to increase our ability to accurately represent the soil microbial
community, and possibly save time and money by eliminating unnecessary replication.
B. Sequential extraction
In this experiment I measured the BiOLOG profiles of aqueous and adhered microbial
communities. I used principal components analysis on the raw data set to summarize the
BiOLOG data and to visualize general differences in substrate utilization among aqueous,
intermediate, particulate, and intact soil communities (Figure 5). The separation of the
soil communities along the PC1 axis was significant by analysis of variance with PC1 as
the dependent variable, and community type as independent (ANOVA, a=0.05). ‘Intact’
and ‘particulate’ communities were similar, and differed from aqueous and intermediate
communities (Tukey’s HSD test). This indicates two things: 1) the communities within
soil differ functionally on the microscale; and 2) the particulate community dominates
and may ‘mask’ the profile from the aqueous community.
Stotsky (1986) discusses the soil as the ‘most complex of habitats’. The aqueous
community, or those organisms that are ‘planktonic’ in the soil are likely to experience
different conditions than those that adhere to soil particles (Stotsky 1986). The results from
this experiment support this; the aqueous community had a different carbon utilization
profile than the particulate or adhered community. In a study similar to this one, Kreitz and
Anderson (1997) also found that BiOLOG was able to distinguish between ‘extractable’ and
‘non-extractable’ soil communities in acid and neutral European soils.
In addition to differences in patterns of substrate utilization, there were also
differences in the number of cells found in each of the extractable communities (Table 3).
The particulate accounted for the majority of the cells, followed by the intermediate and
aqueous communities.
-14--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
Table 3. Cell counts in temperate grassland withinsoil communities
Community type
Intact
Particulate
Intermediate
Aqueous
cells/g soil
1.11E+08
1.08E+08
9.60E+05
6.63E+05
s.e.
2.28E+07
1.04E+07
5.70E+04
5.64E+04
The difference in cell counts between aqueous and particulate communities is not
unexpected. It has long been recognized that the majority of cells in the soil are attached
to surfaces, and are not easily removed from the soil (Stotsky 1986).
Using BiOLOG in this way allowed an alternative picture of the microbial
community, one that would be obscured by running only intact soil. The combined
profiles from aqueous and particulate communities may give a more ‘complete’ picture of
the soil microbial community. This has important implications for utilization of the
BiOLOG assay. Many research groups currently using the BiOLOG assay allow the soil
in their dilutions to settle prior to plating BiOLOG plates (e.g. Knight et al. 1997;
Staddon et al. 1997; 1998; Wünsche et al. 1995). The results from this experiment
indicate that the aqueous community thus obtained can differ substantially in carbon
utilization from the adhered community that settles out.
3. Spatial variability and ecosystem sampling
I assessed large scale variability in community BiOLOG patterns using a study of the scale
of spatial dependence in three ecosystems. Independent of scale (all samples averaged
across 110 m transect), the BiOLOG profile from the three systems differed: an ordinate plot
from principal components analysis of the soil communities from the southern Sierra
transect shows significant differences among the communities from the three sites (Figure
6). Clearly the BiOLOG method has the sensitivity to distinguish among geographically
separated soil microbial communities. This has been seen in many studies using the
BiOLOG assay (Palojärvi et al. 1997; Winding 1994; Goodfriend 1998; Zak et al. 1994).
Table 4. Geostatistics: semivariogram parameters with PC1 on all data as the summary variable.
PC1
Mean±standard deviation
100 m
1m
Range (scale of
maximum variance)
100 m
1m
R2
100 m
1m
Grassland
2.57±0.58
3.38±1.2
n.s.
0.37 m
0.968
Mixed Conifer
1.63±0.84
1.5±0.83
n.s.
0.62 m
0.761
Subalpine Conifer
2.24±0.86
1.89±1.1
7.96 m
n.s.
-15--
0.958
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
When I applied semivariogram analysis using the first principal component from
the BiOLOG profile (shown in part 1 of this chapter to be the best summary variable for
BiOLOG data) to the two transects in each ecosystem (1 m and 110 m), I found that the
scale of spatial dependence of BiOLOG data varied from ecosystem to ecosystem (Table
4). The grassland displayed maximum variability on a scale less than 1m, as did the
mixed conifer site. The subalpine conifer site had maximum variability on a scale of 8 m.
The scale of spatial dependence appears to increase as the dominant vegetation at a site
increases in size or separation. Thus in the grassland, the scale is 0.37 m, in the mixed
conifer site where there is a closed canopy, and the understory vegetation is dense, the
range is 0.67 m, and in the subalpine conifer forest where there is little to no understory,
and the trees are on the order of 10 m apart, the range is 7.96 m (Table 4). Other factors
such as total cell counts (with acridine orange), soil temperature, and soil water content
do not explain the semivariogram results (Balser, unpublished data). This implies that the
variability of BiOLOG profiles in these three ecosystems is determined by the
distribution of dominant and understory vegetation. This is often the case with soil
biological characteristics: for example, Morris (1999) found that the spatial variability of
fungal and bacterial biomass in southern Ohio is related to the pattern of litter distribution
around red oak trees. Likewise, Smith et al. (1994) showed that microbial biomass, and
carbon mineralization have a spatial dependence on the order of 0.5-1 m in a shrubsteppe ecosystem; biomass and activity were related to the influence of individual
sagebrush plants. My study shows that microbial activity measured by BiOLOG assay
appears to have spatial dependence similar to the results reported by Smith et al. (1994).
Lastly, I used the samples from the 110 m transect as 15 ecosystem replicates to
provide information about the overall variability of BiOLOG profiles in the ecosystem
in order to determine an optimal sampling scheme for each ecosystem: what number of
samples, and at what scale they must be taken, to adequately characterize an
ecosystem? After having chosen a level of resolution between means (desired
detectable difference), I calculated the number of samples required to be confident that
two samples are truly different using
Ê t * Sx ˆ
˜
n = Á a ,n
Ë x - m0 ¯
2
where ‘t’ is the critical value from the students ‘t’ distribution (a significance level, and v
degrees of freedom), the denominator is the desired detectable difference between
means, and Sx is the standard deviation about the mean.
Using this standard statistical calculation, I determined the number of samples
required to detect differences between mean PC1 values from BiOLOG data (Figure 7).
Figure 7 shows that as the desired difference between means gets smaller and smaller
(approaches zero, x-axis), more samples must be taken. The variability in the ecosystem
determines how many samples are necessary: a highly variable ecosystem will require a
very large sample size. In this study, the mixed conifer site appears to have the highest
-16--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
overall variability, with its curve in Figure 7 beginning to rise sooner than those of
grassland or subalpine conifer. However, all three ecosystems show a similar trend: 10
samples from the ecosystem will allow reliable detection of differences between means of
15-25%. For each sample taken beyond 10, the increase in ability to detect differences
between means increases only slightly. In Figure 8, I plot the decreasing returns in terms
of sensitivity gained for each added sample. Again, all three ecosystems behave similarly.
Beyond a sample size of 6 or 7, each additional sample taken from the ecosystem
increases the ability to detect differences between means by a very small percentage.
Thus it could be considered poor use of time and resources to increase the number of
samples taken from these ecosystems beyond 6 or 7. Coupled with the information about
the scale of spatial dependence, I conclude that an optimal sampling scheme would be 5-7
samples per ecosystem taken at least a meter apart in the grassland and mixed conifer sites,
and 8 m apart in the subalpine site. My results are in accordance with a study by Johnson et
al. (1990), that used bootstrap techniques to estimate ecosystem variability: those authors
show a substantial decrease in information gained for sample numbers larger than 10.
Conclusions
1. Analytical variability: how to deal with 95-variate observations.
Of the three most common methods for reducing 95-variate- to univariate-observations
(PCA on all data, PCA on guilds, diversity indices), detail is lost by summing the activity
from individual wells into guilds, or by calculating a diversity index. The first principal
component obtained from the PCA of all 95 variables retains maximal useful information
within a single variable.
2. Methodological variability in the BiOLOG assay
A. Nested ANOVA. In order to streamline a method, a simple nested analysis of
variance can provide information about methodological variability. In this case, the
BiOLOG assay is the most variable at the level of the soil subsample. Variance can be
reduced dramatically by increasing the number of subsamples, incubating one plate
per sample, or it can be reduced similarly by increasing the size of the initial
subsample. It is an inefficient use of time and money to replicate this assay at the
level of plates or dilution series.
B. Sequential extraction. The BiOLOG profile differed for the planktonic and adhered
microbial communities in the grassland soil. The adhered community was the closest
in profile to the intact soil, and accounted for the largest biomass. The aqueous
community profile in intact soil is ‘masked’ by the adhered community profile. Thus
studies that allow soil to settle prior to plating on BiOLOG plates may not be
accurately representing the soil community.
-17--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
3. Large scale spatial heterogeneity of BiOLOG profiles: ecosystem sampling
The scale of spatial dependence of BiOLOG data varied from ecosystem to ecosystem. As
might be expected, the number of samples required to accurately detect differences
between sample means (at p<0.05) increases markedly as the desired detectable difference
between means decreases (Figure 7). While the mixed conifer site appeared to be the most
variable, the optimal sampling strategy was similar for all three ecosystems (Figure 8).
-18--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
Figure 1. A) Experimental design for analytical variance study. For replicate A-b all
plates failed to develop, resulting in an unbalanced ANOVA.
-19--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
1. Initial Extraction
∑ 20 g soil (removed fine roots/debris, at field wetness)
∑ +200 ml P-buffer (pH 7.1)
∑ 0.5 h shaking
∑ Low-speed centrifugation, 10 min, decant
Soil Fraction (Sediment)
Supernatent (Aqueous community)
∑
Count cells
∑
Inoculate BiOLOG plates
2. Extraction #2
∑
∑
∑
Resuspend sediment in 200 ml P-buffer
containing surfactant (Triton X)
Shake vigorously 0.5 h
Low speed centrifuge, decant
Supernatent (Intermediate community)
Soil Fraction (Particulate community)
∑
Count cells
∑
Resuspend particulate community in
∑
Count cells
∑
Inoculate BiOLOG plates
physiological saline to 10-1
∑
Dilute to 10-3
∑
Inoculate BiOLOG plates
Figure 2. Schematic diagram for extraction procedure used in this study.
-20--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
7.28
Mean of PC1
A.
-5.42
A
B
C
3.16
Mean of PC1
(5 guilds)
B.
-2.44
A
C.
B
C
Shannon Index
4.92
4.21
A
B
C
Figure 3. Dealing with 95-variate data: box and whisker plots of three data condensations. A) Results from
95-variate PCA. B) Summation to guilds followed by 6-variate PCA. C) Diversity. A,B,C are tablespoons
of Fallbrook soil.
-21--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
%decrease in variance relative to no replication
0
-25
Soil replicates
Dilution replicates
Replicate plates
-50
Soil sample size
-75
-100
0
5
10
15
20
25
30
35
40
45
50
replicates at each level (n), or size of bulked sample (#g/5)
Figure 4. Decrease in variance associated with replication and subsample size for
BiOLOG assay. All curves show the decrease in methodological variance that occurs
with replication, relative to a single replicate of a 5 g sample. Replication of dilutions or
plates (upper two lines) decreases the variance very little, whereas increasing initial
subsample size, or replicating at the soil level decrease the variance markedly (lower two
lines).
-22--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
2
PC2 PC2
(16%)
1.5
Particulate
1
Intact
0.5
Aqueous
Intermediate
0
-0.5
-4
-3.5
-3
-2.5
-2
-1.5
PC1PC1
(19%)
-1
-0.5
0
Figure 5. Ordinate plot from PCA on BiOLOG data from temperate grassland, showing
the profile from intact soil compared to that from the ‘particulate’, ‘intermediate’ and
‘aqueous’ microbial communities in the soil. ANOVA with PC1 as the dependent
variable and community type as independent has an R2 = 0.413 (p<0.025). The
community types do not separate out significantly along PC2 by community type.
-23--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
3
2.5
PC2
PC2
PC2
2
Subalpine forest
1.5
Mid-elevation
mixed conifer
1
Grassland
0.5
0
-10
-8
-6
-4
-2
0
PC1
Figure 6. Large scale spatial variability in BiOLOG community profiles. Ordinate plots
of PC1 and PC2.
-24--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
100
90
Grassland
Samplesnecessary
necessary (n)(n)
samples
80
Mixed Conifer
70
Subalpine Conifer
60
50
40
30
20
10
0
70
60
50
40
30
20
10
difference between means as a % of observed means
Figure 7. Increase in the number of samples necessary to take in order to detect smaller
and smaller differences between sample means. Once the desired sensitivity (detectable
difference) between the two means decreases below 20% of the mean value, the number
of samples necessary to detect the difference increases dramatically.
-25--
0
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
Change in sensitivity due to addition of 1 sample
Change in sensitivity due to adding 1 sample (%)
35
Grassland
30
Mixed Conifer
25
Subalpine Conifer
20
15
10
5
0
0
5
10
15
20
25
30
35
number of samples
Figure 8. Diminishing return for increasing number of samples. The ability to detect a difference between
two sample means decreases as the number of samples increases: there is a diminishing return for dollars
and time invested in sampling. For all three ecosystems, the return decreases substantially after 5 samples,
and becomes insignificant after 10.
-26--
40
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
References
Bakken, L., 1985. Separation and purification of bacteria from soil. Applied and Environmental
Microbiology, 49(6):1482-1487.
Dahlgren, R. A., J. L. Boettinger, G. L. Huntington and R. G. Amundson, 1997. Soil development along an
elevational transect in the western Sierra Neveda, California. Geoderma, 78:207-236.
Garland, J. L., 1996. Patterns of potential C source utilization by rhizosphere communities. Soil Biology
and Biochemistry, 28(2):223-230.
Goodfriend, W. L., 1998. Microbial community patterns of potential substrate utilization: a comparison of
salt marsh, sand dune, and seawater-irrigated agronomic systems. Soil Biology and Biochemistry,
30(8-9):1169-1176.
Hook, P., I. Burke and W. Lauenroth, 1991. Heterogeneity of soil and plant N and C associated with
individual plants and openings in North American shortgrass steppe. Plant and Soil 138:247-256.
Huenneke, L. and H. Mooney, 1989. Grassland Structure and Function: California Annual Grassland. In
Kluwer Academic Publishers;
Insam, H., K. Amor, M. Renner and C. Crepaz, 1996. Changes in functional abilities of the microbial
community during composting of manure. Microbial Ecology, 31:77-87.
Jackson, L. E., J. P. Schimel and M. K. Firestone, 1989. Short-term partitioning of ammonium and nitrate
between plants and microbes in an annual grassland. Soil Biology and Biochemistry, 21(3):409-415.
Jackson, R. B. and M. M. Caldwell, 1993. Geostatistical patterns of soil heterogeneity around individual
perennial plants. Journal of Ecology, 81:683-692.
Jenny, H., S. P. Gessel and F. T. Bingham, 1949. Comparative study of decomposition rates of organic
matter in temperate and tropical regions. Soil Science, 68:419-432.
Johnson, C. E., A. H. Johnson and T. G. Huntington, 1990. Sample size requirements for the determination
of changes in soil nutrient pools. Soil Science, 150(3):637-644.
Jones, J. G. and B. M. Simon, 1975. An investigation of errors in direct counts of aquatic bacteria by
epifluorescence microscopy, with reference to a new method for dyeing membrane filters. Journal of
Applied Bacteriology, 39:317-329.
Knight, B. P., S. P. McGrath and A. M. Chaudri, 1997. Biomass carbon measurements and substrate
utilization patterns of microbial populations from soils amended with cadmium, copper, or zinc.
Applied and Environmental Microbiology, 63(1):39-43.
Konopka, A., L. Oliver and R. F. Turco, 1998. The use of carbon substrate utilization patterns in
environmental and ecological microbiology. Microbial Ecology, 35:103-115.
Kreitz, S. and T.-H. Anderson. 1997. Substrate utilization patterns of extractable and non-extractable
bacterial fractions in neutral and acidic beech forest soils. In Microbial Communities: Functional
Versus Structural Approaches H. Insam and A. Rangger, Eds. Springer: pp. 149-160
Montagna, P. A., 1982. Sampling design and enumeration statistics for bacteria extracted from marine
sediments. Applied and Environmental Microbiology, 43(6):1366-1372.
Morris, S. J., 1999. Spatial distribution of fungal and bacterial biomass in southern Ohio hardwood forest
soils: fine-scale variability and microscale patterns. Soil Biology and Biochemistry, 31:1375-1386.
Palojärvi, A., S. Sharma, A. Rangger, M. vonLützow and H. Insam. 1997. Comparison of Biolog and
phospholipid fatty acid patterns to detect changes in microbial community. In Microbial Communities:
Functional Versus Structural Approaches H. Insam and A. Rangger, Eds. Springer: pp. 37-48
-27--
CHAPTER 2: METHODOLOGICAL EXPLORATIONS
Selvin, S. Practical Biostatistical Methods. Duxbury Press, 1995
Smalla, K., U. Watchendorf, H. Heuer, W.-T. Liu and L. Forney, 1998. Analysis of BiOLOG GN substrate
ultilization patterns by microbial communities. Applied and Environmental Microbiology, 64(4):12201225.
Smith, J. L., J. J. Halvorson and H. B. Jr, 1994. Spatial relationships of soil microbial biomass and C and N
mineralization in a semi-arid shrub-steppe ecosystem. Soil Biology and Biochemistry, 26(9):11511159.
Staddon, W. J., L. C. Duchesne and J. T. Trevors, 1997. Microbial diversity and community structure of
postdisturbance forest soils as determined by sole-carbon-source utilization patterns. Microbial
Ecology, 34:125-130.
Staddon, W. J., L. C. Duchesne and J. T. Trevors, 1998. Impact of clear-cutting and prescribed burning on
microbial diversity and community structure in a Jack pine (Pinus banksiana Lamb.) clear-cut using
BiOLOG gram-negative microplates. World Journal of Microbiology and Technology, 14:119-123.
Stotsky, G. 1986. Influence of soil mineral colloids on metabolic processes, growth, adhesion, and ecology
of microbes and viruses. In Interactions of soil minerals with natural organics and microbes P. M.
Huang and M. Schnitzer, Eds. Madison, WI: Soil Science Society of America: pp. 305-428
Tessier, L., E. G. Gregorich and E. Topp, 1998. Spatial variability of soil microbial biomass measured by
the fumigation extraction method, and Kec as affected by depth and manure application. Soil Biology
and Biochemistry, 30(10/11):1369-1377.
Tiedje, J. M., S. Amsung-Brempong, K. Nusslein, T. L. Marsh and S. J. Flynn, 1999. Opening the black
box of microbial diversity. Applied Soil Ecology, 13:109-122.
Trangmar, B. B., R. S. Yost and G. Uehara. 1985. Application of geostatistics to spatial studies of soil
properties. In Advances in Agronomy N. C. Brady, Eds. Academic Press: pp. 45-94
Trumbore, S. E., O. A. Chadwick and R. Amundson, 1996. Rapid exchange between soil carbon and
atmospheric carbon dioxide driven by temperature change. Science, 272(19 April):393-396.
Winding, A. 1994. Fingerprinting bacterial soil communities using BiOLOG microtiter plates. In Beyond
the Biomass K. Ritz, J. Dighton and K. E. Giller, Eds. British Society of Soil Science: pp. 85-94
Winter, K. and F. Beese, 1995. The spatial distribution of soil microbial biomass in a permanent row crop.
Biology and Fertility of Soils, 19:322-326.
Wünsche, L., L. Brüggemann and W. Babel, 1995. Determination of substrate utilization patterns of soil
microbial communities: an approach to assess population changes after hydrocarbon pollution. FEMS
Microbiology Ecology, 17:295-306.
Zak, J. C., M. R. Willig, D. L. Moorehead and H. G. Wildman, 1994. Functional diversity of communities:
a quantitative approach. Soil Biology and Biochemistry, 26(9):1101-1108.
-28--