Spatio-temporal heterogeneity of soil microbial properties in a conventionally managed arable field

Abstract

Purpose
Knowledge about the spatio-temporal variability of soil microbial properties is crucial in evaluating their structure-function relationship and their impact on ecosystem functions. The aim of the study was to determine the spatio-temporal variation of the selected microbial properties at the surface horizon in a conventionally managed arable field.

Materials and methods
The area selected for the research, which was mainly covered with typical Luvisols, was a uniformly managed system that was considered to be homogenous in respect to texture (mostly loamy fine sand). Winter wheat was cultivated after winter rape as the forecrop. A grid soil sampling (10 m × 10 m) was used to assess the spatial heterogeneity of soil properties across a 0.5-ha field. Soil samples were collected at 50 points from the upper 20 cm of soil in April and August 2007. Colony-forming units (CFUs) of bacteria, fungi and actinomycetes, and basal respiration (BR) were analyzed. Data were evaluated using classical statistical and geostatistical methods.

Results and discussion
Fungal CFUs were significantly lower than the bacterial ones with a B/F (bacteria/fungi) ratio of 80.0 in April and 45.1 in August. Bacterial CFUs, B/F ratio, and BR level revealed significantly higher values in April than in August, while fungi showed the opposite trend. Other studied properties did not show significant differences between sampling months. Only some of the properties, such as the bacterial community in August, the number of actinomycetes in April, and qCO2 on both sampling dates, revealed significant spatial autocorrelation (Moran’s I) and were spatially dependent at the scale of sampling grid, whereas the qCO2 revealed a higher differentiation in the spatial pattern between April and August than the other studied properties. Most of the spatially correlated properties were in the weak variability class (a nugget effect > 75%), while only the qCO2 (August) ratio was in the moderate variability class (a nugget effect between 25 and 75%).

Conclusions
Most of the microbial-related properties did not exhibit a spatial structure at the examined scale, thus suggesting that changes in these properties would be detectable at a distance shorter than 10 m. More frequent seasonal sampling must be included in the sampling strategy in order to better understand whether studied properties show any permanent spatial patterns in soil over time or whether they are more randomized.

Full text

SOILS, SEC 5 •SOIL AND LANDSCAPE ECOLOGY •RESEARCH ARTICLE
Spatio-temporal heterogeneity of soil microbial properties
in a conventionally managed arable field
Anna Piotrowska-Długosz
1
&Barbara Breza-Boruta
2
&Jacek Długosz
1
Received: 24 January 2016 /Accepted: 3 May 2018 /Published online: 11 May 2018
#
Abstract
Purpose Knowledge about the spatio-temporal variability of soil microbial properties is crucial in evaluating their structure-
function relationship and their impact on ecosystem functions. The aim of the study was to determine the spatio-temporal
variation of the selected microbial properties at the surface horizon in a conventionally managed arable field.
Materials and methods The area selected for the research, which was mainly covered with typical Luvisols, was a uniformly
managed system that was considered to be homogenous in respect to texture (mostly loamy fine sand). Winter wheat was
cultivated after winter rape as the forecrop. A grid soil sampling (10 m × 10 m) was used to assess the spatial heterogeneity of
soil properties across a 0.5-ha field. Soil samples were collected at 50 points from the upper 20 cm of soil in April and August
2007. Colony-forming units (CFUs) of bacteria, fungi and actinomycetes, and basal respiration (BR) were analyzed. Data were
evaluated using classical statistical and geostatistical methods.
Results and discussion Fungal CFUs were significantly lower than the bacterial ones with a B/F (bacteria/fungi) ratio of 80.0 in
April and 45.1 in August. Bacterial CFUs, B/F ratio, and BR level revealed significantly higher values in April than in August,
while fungi showed the opposite trend. Other studied properties did not show significant differences between sampling months.
Only some of the properties, such as the bacterial community in August, the number of actinomycetes in April, and qCO
2
on both
sampling dates, revealed significant spatial autocorrelation (Moran’sI) and were spatially dependent at the scale of sampling grid,
whereas the qCO
2
revealed a higher differentiation in the spatial pattern between April and August than the other studied
properties. Most of the spatially correlated properties were in the weak variability class (a nugget effect > 75%), while only
the qCO
2
(August) ratio was in the moderate variability class (a nugget effect between 25 and 75%).
Conclusions Most of the microbial-related properties did not exhibit a spatial structure at the examined scale, thus suggesting that
changes in these properties would be detectable at a distance shorter than 10 m. More frequent seasonal sampling must be
included in the sampling strategy in order to better understand whether studied properties show any permanent spatial patterns in
soil over time or whether they are more randomized.
Keywords Geostatistics .Microbial colony-forming units .Microorganisms .Soil respiration .Spatial variability
1 Introduction
Soil microorganisms are the living components of soil or-
ganic matter, and despite comprising only a small percent-
age of the total mass of organic matter, they are considered
to have a crucial impact on numerous important biochem-
ical processes such as the release of plant-available nutri-
ents, organic matter dynamics, and soil structure formation
(Gangneux et al. 2011).
Soil is a very heterogeneous and complex system in which
neither microorganisms nor other soil components are irregu-
larly distributed (Černohlávková 2009). Even within uniform-
ly managed systems, which are typically assumed to be more
Responsible editor: Richard K. Shaw
*Anna Piotrowska-Długosz
ap03@wp.pl
1
Department of Biogeochemistry and Soil Science, Laboratory of Soil
Science and Biochemistry, Faculty of Agriculture and
Biotechnology, UTP University of Science and Technology, 6
Bernardyńska St., 85-029 Bydgoszcz, Poland
2
Department of Microbiology and Food Technology, Faculty of
Agriculture and Biotechnology, UTP University of Science and
Technology, 6 Bernardyńska St., 85-029 Bydgoszcz, Poland
Journal of Soils and Sediments (2019) 19:345–355
https://doi.org/10.1007/s11368-018-2022-3
The Author(s) 2018
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

homogenous than natural systems, biological processes (e.g.,
growth and colony formation) may produce aggregations of
microorganisms at various spatial scales (Franklin and Mills
2003). Soil microorganisms and microbial processes are con-
trolled by factors such as organic matter content, soil moisture,
texture, and pH, all of which exhibit spatial heterogeneity as
well (Guox et al. 2012). Studies of the spatial patterns in
microbial communities are still relatively rare (Martiny et al.
2011), and determining their spatial patterns is extremely
complicated due to the large number of factors that affect
microbial activity (Yergeau et al. 2010) as well as the fact that
microbial properties have considerable temporal variability
(Lauber et al. 2013). Soil microbiota can exhibit patchy dis-
tributions at a scale from several millimeters to several meters
(Nunan et al. 2003; Tecon and Or 2017). The spatial variabil-
ity is often determined for one scale (e.g., Peigné et al. 2009),
but multi-scaled comparisons have been considered as well
(e.g., Franklin and Mills 2009). Franklin and Mills (2003)
had previously explored the multi-scale spatial distribution
of microbial community structure in an agricultural field and
found that several scales of spatial autocorrelation can exist
within the centimeter to 10-m scale.
Most studies of microbial and other soil properties are fo-
cused on the differences in their spatial structure that are
caused by natural and anthropogenic factors. It was shown
that the spatial distribution of soil microorganisms depends
on the position in landscape (Du et al. 2015), type of land
use (e.g., forest vs. cultivated field) (Francioli et al. 2014),
time and methods of soil sampling, and soil treatment (e.g.,
different types of tillage, fertilization, or crop rotation)
(Garcia-Orenes et al. 2013).
Less attention has been paid to the spatial variation of
soil properties in a uniformly managed arable field, where
no experimental factors were tested (Peigné et al. 2009;
Franklin and Mills 2009). Very often, the soils within
uniformly managed sites are considered to be homoge-
nous, and thus, it is assumed that the soil properties are
similar. Accordingly, the number of analyses of soil prop-
erties is limited to a few measured points in given area,
and a composite sample is often assessed to represent the
average population in the area sampled. However, the av-
erage values of homogenized soil samples often do not
accurately reflect the real state of soil ecosystems.
High spatial heterogeneity of soil properties, especially mi-
crobial ones, can mask the effects of different soil manage-
ment treatments (Peigné et al. 2009). It is, therefore, important
to detect, and ultimately estimate and map the spatial patterns
of soil properties. Spatial variability and the dependence of
soil properties are controlled by inherent variations in soil
characteristics (e.g., parentalmaterial, vegetation, and climate)
or are affected by exogenous factors such as crop production
practices (tillage, fertilization, and crop rotation) (Gülser et al.
2016). Such practices may alter the local patchiness of soil
nutrients and contribute to the additional heterogeneity of soil
properties and thus affect soil microbial communities
(Cavigelli et al. 2005).
Conventional statistical analyses are not appropriate to
identify spatial patterns as these analyses require an assump-
tion of independence among samples, which is violated when
autocorrelated (spatially dependent) data are considered
(Deblauwe et al. 2012). Thus, since the 1950s, spatial statistics
have been developed to deal with the problem of spatial auto-
correlation (Cobo et al. 2010). One of the most widely used
approaches for evaluating the spatial variability of natural re-
sources such as soils is geostatistics (Jafari et al. 2011). The
geostatistical methods primarily consider the spatial variabil-
ity of soil properties as a random process that is dependent on
space but there has been an increasing need to estimate tem-
poral changes in the spatial patterns of such properties as well
(Shahandeh et al. 2005; Liu et al. 2009). The application of
geostatistical methods by soil scientists focuses on predicting
the spatial variability of soil properties using different kriging
methods over small to large spatial scales (Peigné et al. 2009;
Wang et al. 2009; Baldrian et al. 2010a).
Current studies on the temporal variability in microbial
properties have shown contradictory trends with various
studies reporting peaks in different seasons as well as both
positive and negative responses to temporal patterns in soil
temperature and moisture (Borowik and Wyszkowska
2016; Bao et al. 2016). The composition of bacterial and
fungal communities can vary on the scale of days (Zhang
et al. 2011), seasons (Kennedy et al. 2006;Lipson2007),
and years (DeBruyn et al. 2011). In some cases, the chang-
es in these communities can be linked to changes in the soil
environmental conditions (Rasche et al. 2011). Studies
from temperate ecosystems found temperature to be impor-
tant in determining temporal variability (Wardle 1998),
while in a tropical forest, the highest microbial biomass
was usually observed when soil moisture was favorable
(Pandey et al. 2007).
Taking into account the significant temporal variability
of the soil biological properties, we hypothesized that these
properties differed in spatial distribution over time in the
same area, which may be helpful for addressing basic sci-
entific questions, such as what is the nature of the spatial
distribution of microorganisms and whether the spatial pat-
tern of soil microbial properties is time dependent and if so
to what extent. Therefore, the aims of this study were to (1)
investigate the spatial variation of microbial properties in
the surface horizons of Luvisols and the contribution of
random variability (nugget) to total variability (sill)of
studied properties, (2) determine whether temporal vari-
ability affects the spatial pattern variation of microbial
properties, and (3) assess the relationship between the set
of soil microbial features and some physicochemical prop-
erties across the studied area.
346 J Soils Sediments (2019) 19:345–355
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2 Material and methods
2.1 Site description and sample collection
The study was carried out in a grid patterned 40 × 90 m
area in 80 ha of an agricultural field located near the
village of Orlinek (near Mrocza) in the Pomerania and
Cuiavia region (53° 15′31″N, 17° 32′43″E), northwest
Poland. The field has been continuously managed for
more than 20 years. The soil that was studied was clas-
sified as a typical Luvisol (IUSS Working Group WRB
2007), which was composed of 79.3% sand, 14.6% silt,
and 6.1% clay. The landform of the study area is flat.
Winter wheat (Triticum aestivum L.) was cultivated after
winter rape (Brassica napus L.) as the forecrop. Mineral
fertilization was applied in accordance with winter wheat
requirements and presented in details earlier (Piotrowska-
Długosz et al. 2017). The area was characterized by an
average monthly air temperature of 12.9 °C and the total
rainfall of 447.4 mm during the growing season (March–
October) in 2007 (Table 1). Soil samples (50) were col-
lected from the 0–20-cm top layer of soil at regular in-
tervals (10 m × 10 m) during the stage of winter wheat
spreading on April 12, 2007 and directly after the harvest
on August 6, 2007. Each composite sample comprised
ten sub-samples that were taken randomly from a circular
area with a radius of 2 m from the node point (ISO
10381-2: 2002), mixed to account for short-range (<
10 m) spatial variability.
The soil samples were transported to the laboratory in
sterile plastic bags and were stored at 4 °C (up to 24 h) to
estimate the number of aerobic heterotrophic bacteria, fil-
amentous fungi, and actinomycetes as well as the soil
respiration rate.
2.2 Soil biological properties
The plate count method was employed to estimate the number
of aerobic heterotrophic bacteria, filamentous fungi, and acti-
nomycetes. Microbiological inoculation was done during 24 h
after sample collection. Ten grams of each soil sample was
added to 90 ml of Ringer’s solution. After homogenization for
30 min, tenfold serial dilutions were made (10
−1
to 10
−6
).
Then, inoculations of the prepared soil solutions were made
on proper culture media. In order to determine the total num-
ber of bacteria, a yeast extract-peptone-soil extract medium
was used (YPS) (Atlas 1997). Actinomycetes were isolated
on yeast extract glucose agar (YGA) with 100 μgml
−1
nysta-
tin and filamentous fungi were isolated on Rose-Bengal agar
containing 30 μgml
-1
streptomycin (Atlas 1997). The num-
ber of colony-forming units (CFUs) that was obtained was
determined per 1 g of soil dry matter (CFUs g
−1
d.m. of soil).
Basal respiration (BR) was determined according to
Stotzky (Stotzky 1965) with some modification (Piotrowska
et al. 2006). The rate of BR was expressed in milligrams/CO
2
–
C/kilogram of soil per hour within 15 days of incubation (sum
of five individual measurements). The specific respiration of
the microbial biomass or the microbial metabolic quotient was
calculated as the ratio of BR to microbial biomass: qCO
2
(μCO
2
–C/mg MBC per hour) (Anderson and Domsch
1990). The data about soil microbial properties are reported
as averages of fourfold laboratory determinations.
2.3 Statistical and geostatistical approaches
The dataset was analyzed independently twice—first in a clas-
sical way in order to investigate the general status of the soil
microbiological properties at two sampling times and their
links with the physicochemical soil properties and then using
a geostatistics approach in order to investigate the spatial var-
iation of soil properties. The basic statistical parameters were
evaluated using STATISTICAv. 9.0 Software. Normality was
the general observation for only some variables (Shapiro-Wilk
test). Most of the properties did not show a normal distribution
and therefore were transformed accordingly (Table 2). Since
the transformation improved the normality of most of the
properties, further analyses were performed with the corrected
data. Differences in the average values of the soil properties
collected in April and August were determined using Tukey’s
test (significant level = p< 0.05). Linear correlation analysis
based on Pearson’scoefficients(p< 0.05) was performed to
determine the relationships between the variables. A classifi-
cation scheme based on their CV [%] was used to identify the
extent of variability for the soil properties (Wilding 1985).
The spatial relationship among neighboring observations
of the variables were assessed using the global Moran’sI
autocorrelation coefficient (Liu et al. 2013). The spatial auto-
correlation is a measure of the similarity (correlation) between
Table 1 Distribution of monthly averaged air temperature and monthly
sum of rainfall in 2007
Months Temperature (°C) Rainfall (mm)
January 3.4 73
February −0.8 33
March 5.5 55
April 9.0 17
May 14.3 84
June 18.2 112
July 18.0 89
August 18.1 29
September 12.7 40
October 7.2 23
November 1.7 27
December 0.8 36
J Soils Sediments (2019) 19:345–355 347
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nearby observations of a single variable on a two-dimensional
surface. The pairs of observation of a single variable that are
close to each other are more similar, and pairs of observations
of variables that are far apart from each other are more likely
to be less similar (Griffith and Chun 2016). The values of
Moran’sIrange from + 1.0 (strong positive spatial autocorre-
lation) to 0 (no autocorrelation, a random pattern) and to −1.0
(strong negative autocorrelation). Moran’sIwas calculated
using a 50-m active lag distance and a 10-m lag interval
(ArcGIS 9.3).
A semivariograms were determined for soil properties that
were studied in order to characterize the degree of spatial
variability between neighboring samples, and the appropriate
model function was fitted to the semivariogram (Webser and
Oliver 2008). Depending on the spatial characteristics de-
scribed by the data, spherical, linear, and Bassel-K models
were fitted to different variables. The parameters of the model
describing the spatial structure (γ(h)=C
o
+C)suchasnugget
semivariance, range, and sill or total semivariance were cal-
culated. C
o
represents the nugget effect, which is the variance
at zero distance and represents field and experimental variabil-
ity or random variability, which is undetectable at the sam-
pling scale. The sill (C
o
+ C) is the lag distance between the
measurements at which one value for a variable does not in-
fluence the neighboring values. The range is the separation
distance at which the values of one variable become spatially
independent of another. At separation distances greater than
the range, the sampled points are no longer spatially correlated
(Shahandeh et al. 2005). Furthermore, the ratio of the nugget
to sill [Co/(Co + C)]·100 indicates the degree of randomness
in the data’s spatial variability (Cambardella et al. 1994). This
ratio was used in this study to define three classes of spatial
dependencefor the soil variables. When the ratio was less than
25%, the variable was considered to have a strong
dependence; when it was between 25 and 75%, the soil vari-
able had a moderate spatial dependence; and if the ratio was
higher than 75% or the slope of the semivariogram was = 0,
the variable was considered to be random or a non-spatially
correlated (pure nugget).
To choose the best models to adjust the empirical
variograms, a cross-validation procedure was applied. The
criterion for selecting the best fitting models was the mean
squared deviation ratio (MSDR), which was calculated from
the squared errors and kriging variances (Bishop and Lark
2006). If the model for the variogram is accurate, the MSDR
should be close to one (Kerry and Oliver 2007). The punctual
kriging was the procedure by which the values of the soil
properties were estimated at unsampled locations (Webser
and Oliver 2008). The maps illustrating the spatial variance
of the parameters that were determined were drawn based on
the semivariograms. The geostatistical calculations were done
using Isatis software (Geovariance Co.).
3 Results
3.1 Descriptive statistics and correlation analysis
of soil properties
The dataset of the basic properties of the soil under study (e.g.,
pH, soil moisture, total C and N, clay, silt, and sand fractions)
was presented earlier (Piotrowska and Długosz 2012).
Only fungal CFUs in April and qCO
2
ratio in August were
normally distributed in the studied soil. The other properties
were not distributed normally and an appropriate transforma-
tion was done before further geostatistical analysis (Table 2).
Although a logarithmic transformation of B/F ratio values
reduced its skewness, the values were still not normally dis-
tributed. A normal distribution of soil properties was con-
firmed by the similar values of means and medians as well
as by skewness coefficients that were close to 0 (between 0.13
and 0.59) (Table 3). A left-tailed skewness resulted in the
mean being lower than the median and more results were
higher than the mean, which was mainly true for the total
bacteria count and the B/F ratio. A positive kurtosis indicates
a relatively peaked distribution whereas a negative one indi-
cates a flat distribution compared to the normal distribution.
Fungal CFUs showed kurtosis values not much higher than
zero, while the total count of bacteria and the B/F ratio data
both in April and August as well as the qCO
2
results in April
indicated a high leptokurtic distribution.
The coefficient of variation (CV%) was calculated as an
index for assessing the overall variability of the dataset
(Wilding 1985). Most of the studied variables revealed a high
variability (CV > 35%), which indicated that the results were
quite differentiated on the studied area. Among these proper-
ties, the total bacteria count and the B/F ratio from both
Table 2 pvalues and statistical transformations of soil properties
Property Sampling month pvalue Transformation
Bacterial CFUs (× 10
6
) April 0.0000 Log
August 0.0000 Log
Fungal CFUs (× 10
5
) April 0.4017 –
August 0.8354 –
Actinomycetes CFUs
(× 10
5
)
April 0.0003 Log
August 0.0019 Log
B/F April 0.0000
August 0.0000
BR (mg CO
2
·kg
−1
h
−1
) April 0.0090 Log
August 0.0077 Sqrt
qCO
2
April 0.0002 Log
August 0.0944 –
B/F bacteria to fungi ratio, BR basal respiration, qCO
2
metabolic quotient,
Log logarithmic transformation, Sqrt square root transformation
348 J Soils Sediments (2019) 19:345–355
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sampling dates tended to be the most variable with a CV value
that ranged from 62.8 to 110% (Table 3). Only soil respiration
showed a moderate variability as was indicated by CV values
of between 24.9 and 25.8%.
The significant correlation coefficients obtained among the
studied properties and chemical properties showed earlier
(Piotrowska and Długosz 2012) are presented in Table 4
(p< 0.05). A significant but relatively weak relationship was
found between bacterial CTUs and pH H
2
OinbothApriland
August. A significant but negative relationship was found
between fungal CTUs and pH H
2
Oonbothsamplingdates.
The organic carbon content was positively correlated with BR,
and fungal CTUs in both April and August.
3.2 Geostatistical analysis of properties studied
Spatial autocorrelation of soil properties was calculated from
grid points at 10-m lag intervals. Significant spatial autocor-
relation was only found for bacterial CFUs in August, actino-
mycetes count in April, and qCO
2
ratio on both sampling
dates. (Table 5). The spatial autocorrelation was low ranging
from 0.158 to 0.097. The highest value of Moran’sIwas noted
for the qCO
2
ratio determined in August (0.158).
To characterize the spatial variability of the properties, spatial-
ly autocorrelated linear (L) or spherical (Sph) models with the
nugget effect (NE) were fitted to the calculated semivariograms
(Table 6,Fig.1a–c). The results indicated that most of the het-
erogeneity of soil properties was due to the random variability.
The spatial heterogeneity of the variables studied was categorized
into two classes based on the percentage of total variance (sill)
present as a random variance [(Co/Co + C),%]. Most of the var-
iables showed a weak spatial dependence and had a nugget/sill
ratio over 75%, and only soil moisture and qCO
2
in August
revealed a moderate spatial structure. The qCO
2
ratio showed a
higher differentiation in the spatial pattern than the other proper-
ties studied between April and August (Table 6). Variables such
as total fungi count and BR level showed a pure nugget effect (a
nugget/sill ratio = 100%) (Fig. 1c).
The ranges of influence, which ranged between 40 and 50 m
(Table 6), were only calculated for the qCO
2
ratio since it was
characterized by a spherical model of the semivariogram. The
range values were not, however, calculated for the total bacteria
and the actinomycetes count despite the fact that they were
spatially dependent at the scale of our sampling grid, because
the linear model was fitted to the calculated semivariograms of
these properties.
3.3 Temporal changes of data and their spatial
patterns
Significant differences between the properties that were stud-
ied in April and August were observed only for the bacterial
and fungal CFUs, the B/F ratio, and soil respiration (Tukey’s
Table 3 Statistics of soil microbial properties (n=50)
Property Sampling month Min Max Mean Geometric mean Median SD Skewness Kurtosis CV (%)
Bacterial CFUs (× 10
6
) April 11.0 141.0 36.0a 30.0 28.0 26.0 2.34 6.54 72.0
August 6.0 77.0 23.9b 20.5 19.2 15.0 1.88 3.63 62.8
Fungal CFUs (× 10
5
) April 1.4 9.4 4.5b 4.2 4.4 1.7 0.44 0.29 38.7
August 1.0 9.3 5.3a 5.0 5.0 1.8 0.13 0.12 33.3
Actinomycetes CFUs (× 10
5
) April 2.5 28.6 11.4a 10.2 10.5 5.7 1.27 1.61 49.6
August 3.2 33.8 13.7a 12.2 12.7 6.7 1.15 1.44 49.2
B/F ratio April 78.6 150 80.0a 71.4 63.6 152.9 2.84 9.02 110
August 60.0 82,8 45.1b 41.0 38.4 83.3 3.21 12.2 86.7
BR (mg CO
2
·kg
−1
h
−1
) April 0.53 1.58 0.93a 0.91 0.92 0.23 0.69 0.82 24.9
August 0.28 1.17 0.69b 0.67 0.66 0.18 0.72 1.01 25.8
qCO
2
ratio April 0.17 1.01 0.38a 0.35 0.35 0.18 1.32 2.14 45.9
August 0.05 0.75 0.32a 0.27 0.29 0.16 0.59 −0.17 50.8
Different small letters between April and August indicate significant differences (Tukey’s test, p< 0.05); The abbreviations of each variable are those
given in Table 2
SD standard deviation; CV (%) coefficient of variation
Table 4 Correlation matrix
Relationship Coefficient of correlation
April August
Bacterial CFUs/pH H
2
O
Fungal CFUs/pH H
2
O
C
ORG
/BR
C
ORG
/fungal CFUs
0.483
−0.424
0.328
0.326
0.423
−0.465
0.452
0.415
Correlations presented are significant at p<0.05
C
ORG
organic carbon, BR basal respiration
J Soils Sediments (2019) 19:345–355 349
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test, p<0.05, Table3). Based on the mean values, both bac-
terial CFUs and the B/F ratio and soil basal respiration were
higher in April than in August (43, 44, and 26%, respectively),
while fungal CFUs were significantly higher in August com-
pared to April (15%).
The main application of geostatistics to soil science has
been to estimate and map the soil property values that could
occur in unsampled areas. The spatial pattern of the spatially
autocorrelated properties are shown in Fig. 2a–d. The kriged
maps of bacterial CFUs differed on both sampling dates (Fig.
2a, b). The highest bacterial CFUs in April were observed
along the western part of the transect at a length of 0 to
10 m and over the entire width of the area. The lowest count
of bacteria was distributed in the eastern part of the field (65 to
90 m long) and over the entire width of the area). The distri-
bution of bacterial CFUs in August was different. The lowest
count of bacteria in August was noted in the northwest quad-
rant of the field (0 to 20 m long and 20 to 40 m wide), while
the highest was observed in an area 50 to 90 m long and over
the entire width of the area. The spatial distribution of the
actinomycetes CFUs was different than those of the bacteria
and did not share any similarities at both sampling times. In
April, a higher count of actinomycetes tended to be located at
lengths of 0 to 60 m and 15 to 40 m wide. A band of relatively
lower actinomycetes CFUs ran vertically from the north to the
south of the field at a length of 60 to 90 m (Fig. 2c). In August,
the actinomycete count was uniformly distributed within the
area, and only small area between 30 and 70 m long and 0 to
15 m wide had lower values of this property (Fig. 2d).
Similarly, other properties showed different spatial structures
depending on the sampling time (data not presented).
4 Discussion
4.1 Basic statistics of studied variables and their
seasonal changes
The data of the properties that were measured appeared to be
variable within one sampling time as well as between both
Table 5 Moran’sIfor properties
studied Property Sampling month Moran’sIZScore pvalue
Bacterial CFUs (× 10
6
) April 0.046 1.472 0.142
August 0.106 2.731 0.006
Fungal CFUs (× 10
5
)April −0.018 0.044 0.965
August 0.026 0.967 0.334
Actinomycetes CFUs (× 10
5
) April 0.103 2.602 0.009
August 0.038 1.226 0.221
B/F ratio April 0.050 1.607 0.108
August 0.032 1.233 0.218
BR (mg CO
2
·kg
−1
h
−1
) April 0.011 0.651 0.515
August −0.097 −1.613 0.107
qCO2 April 0.098 2.521 0.012
August 0.158 3.709 0.0002
The abbreviations of each variable are those given in Table 2
Table 6 Parameters of variogram models
Property Sampling month Model Nugget
(Co)
Sill
(Co + C)
Co/(Co + C)
(%)
Range
(m)
SE MSDR SD
Bacterial CFUs April
August
L, NE
L, NE
0.044
0.030
0.0485
0.0334
90.7
90.0
–−0.040
0.005
1.076
1.277
W
W
Fungal CFUs August NE 3.63·10
8
–100 –0.090 0.978 W
Actinomycetes CFUs April
August
L, NE
L, NE
0.036
0.036
0.0384
0.0390
93.7
92.3
–−0.029
0.021
0.992
1.077
W
W
BR (mg CO
2
·kg
−1
h
−1
)April
August
NE
NE
0.011
0.011
0.011
0.011
100
100
–0.0457
0.0589
0.987
1.079
W
W
qCO2 April
August
Sph, NE
Sph, NE
0.0336
0.0284
0.028
0.012
83.3
42.2
40
50
0.0303
−0.0260
0.992
1.041
W
M
The abbreviations of each variable are those given in Table 2
Sph spherical, Llinear, NE nugget effect, SE standard error, MSDR mean squared deviation ratio, SD spatial dependence, Mmoderate, Wweak
350 J Soils Sediments (2019) 19:345–355
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

sampling periods and a moderate variability was observed.
The results from the present study (Table 3) showed a higher
variability of microbial rather than physicochemical properties
(Piotrowska and Długosz 2012) as described by the coeffi-
cient of variation (CV%) as well as the minimum, maximum,
and standard deviation. Our results are in agreement with sev-
eral studies that were carried out at the field scale on arable
soils (Sebai et al. 2007;Štursová et al. 2016). In the study of
Sebai et al. (2007), the microbial properties and processes
were significantly influenced by properties such as organic
matter content, soil moisture, pH values or texture. In this
study, however, the variability of these properties was not a
source of variability for the microbiological properties since
all of them exhibited low CV values (less than 15%)
(Piotrowska and Długosz 2012).
In our conventionally managed soil, more bacterial than
fungal CFUs were noted, which confirmed studies that had
been done earlier (Hassink et al. 1993; Velvis 1997). Some
studies have shown that soil management on arable land af-
fects the B/F ratio. In most cases, bacteria dominate under
conventional tillage, whereas fungi dominate under no-
tillage (de Vries et al. 2006). This has been attributed to the
direct contact between bacteria and the substrate under
conventional tillage, which encourages bacterial growth
(Jiang et al. 2011) and could be ascribed to the suppression
of fungal hyphae, and hence fungal growth, by intensive till-
age (de Vries et al. 2006).
The soil microbial biomass and activity usually shows
strong seasonal variation, although the trends in this variation
are differentiated due to many various climatic (temperature,
moisture), environmental, and anthropogenic factors that in-
fluence soil microbial communities across wide geographical
regions (Gourmelon et al. 2016). Therefore, the data for the
seasonal changes of soil microorganisms that is found in the
literature are contradictory (e.g., Baldrian et al. 2010b), sug-
gesting that both bacteria and fungi react to various seasonal
natural and anthropogenic factors in different ways. We found
significantly higher fungal CFUs in August than in April and
an opposite trend for bacterial community, which could be
explained by a higher temperature and soil moisture, since
fungi are more sensitive to changes in climatic condition than
bacteria (Kaisermann et al. 2015). In this study, a higher air
temperature in August than in April (about 10 °C, Table 1)
may also reflect the higher soil temperature. This could have
an impact on the soil microbial properties as was stated earlier
by Khalid (2012). Additionally, the nitrogen fertilizer applied
Fig. 1 Experimental
semivariograms of (a)qCO
2
in
April, (b)qCO
2
in August, (c)BR
in April
J Soils Sediments (2019) 19:345–355 351
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in spring (March) could have stimulated the bacterial commu-
nity and decreased the fungi, which is partially in line with de
Vries et al. (2006), who noted that an increasing N input in a
grassland caused a decrease of the fungi biomass, while the
bacterial biomass remained constant. The lack of significant
differences in the actinomycetes CFUs between April and
August may be due to the fact that this community has been
shown to be less sensitive to stressful conditions than either
bacteria or fungi (Pourreza et al. 2014).
4.2 Spatial variability of microbial properties
Most of the properties studied did not exhibit spatial structure
at the examined scale, thus suggesting that changes in these
properties would be probably detectable at a distance shorter
than 10 m. This was confirmed by the lack of an autocorrela-
tion and a high or pure nugget, which represents the measure-
ment error or non-measurable variation at distances other than
those sampled (Mummey et al. 2010) and is used to calculate
the nugget/sill ratio, which indicates the spatial structure at the
sampling scale and allows the relative size of the nugget effect
among different soil properties to be compared (Webser and
Oliver 2008). The absence of spatial structure or weak spatial
dependence that was obtained for most of the microbial prop-
erties in this study was probably due to several different ef-
fects of (1) the content of clay, silt, and sand, which also was
not spatially structured (pure nugget effect) (Piotrowska and
Długosz 2012); (2) the nested occurrence of microorganisms;
(3) soil structure, e.g., different type, quantity, and shape of
soil aggregates; and (4) measurement error. In fact, it has been
stated earlier that a weak spatial dependence of a soil property
(high variability) is controlled by exogenous factors such as
climatic factors (moisture and temperature) vegetation cover,
or human activity and is related to experimental error
(Cambardella et al. 1994; Webser and Oliver 2008). Some
authors have suggested that soil microorganisms are particu-
larly variable compared to other properties, and that their
abundance and activity change along an environmental gradi-
ent (Franklin and Mills 2003; Philippot et al. 2009). Even
within a homogenous system, biological processes may pro-
duce aggregations of organisms at various spatial scales
(Franklin and Mills 2003). The level of spatial variability of
microbial communities varied in different ranges and is related
to various biotic and abiotic factors (e.g., temperature, pH,
C
ORG
content, and substrate availability) (Guox et al. 2012).
In fact, soil properties do not vary independently and rather
variably measured at a certain point in space in the outcome of
several physical, chemical, and biological processes, all of
which are more or less spatially variable (Guox et al. 2012).
In this study, however, soil pH and C
ORG
content could not be
the source ofvariability for soil microbial properties since they
revealed strong and moderate spatial dependence
(Piotrowska-Długosz et al. 2016). Based on the earlier studies,
we suppose that high contribution of nugget effect in sill or
pure nugget effect in this study may be partially related to the
applied tillage and fertilizers that was applied in autumn in the
previous year. In fact, the weak spatial dependence of soil
properties or a pure nugget effect indicates an extrinsic vari-
ability, i.e., due to management practices such as tillage,
Fig. 2 Spatial distribution of (a) bacterial CFUs in April, (b)bacterial
CFUs in August, (c) actinomycetes CFUs in April, (d) actinomycetes
CFUs in August. In each figure, darker shading represents the highest
values while light shading represents the lowest values
352 J Soils Sediments (2019) 19:345–355
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

mineral and organic fertilization, and pesticide application,
(Nayak et al. 2007; García -Ruiz et al. 2008) or it is the result
of an inappropriate sampling scheme.
4.3 Correlations among the properties studied
Previous studies have shown that the size and activity of soil
microbial communities are sensitive to variations in the soil
physical and chemical properties (Aciego Pietri and Brookes
2009), mainly to soil moisture, soil reaction, and organic car-
bon content. Some authors indicated contrasting soil pH ef-
fects on fungal and bacterial growth (Rousk et al. 2009), what
is in agreement with our data (Table 4). In this study, we found
a significant positive relationship between pH and the bacte-
rial colony-forming units, while negative relationship between
pH and fungal CFUs, for which the values of the relationship
were however low (Table 4). It is possible that the small pH
gradient (2 units) (Piotrowska and Długosz 2012) did not alter
strongly the spatial distribution of microorganisms in soil,
which was confirmed by other studies (Drenovsky et al.
2010), and did not reveal the negative relationship between
bacterial and fungal CFUs. Several recent studies have shown
however that soil pH is a strong driver of the abundance of
microbial communities (Hallin et al. 2009;Kaiseretal.2016).
Thus, Bru et al. (2011) reported that soil pH alone explained
17.1 and 21.4% of the spatial variability in the abundance of
the total bacterial community and the functional microbial
community that are involved in N-cycling, respectively.
The content of C
ORG
was significantly correlated with the
rate of soil respiration and fungal colony-forming units, al-
though values of the correlation coefficients were low as
was in the case with the soil reaction. Other studies indicated
a closer relationship between C
ORG
content and microbial
properties and showed the importance of C
ORG
as a microbial
substrate and as a habitat for microbial growth and mainte-
nance (Wallenius et al. 2011; Garcia-Orenes et al. 2013).
5 Conclusions
Most of the microbiological properties did not exhibit spatial
structure at the examined scale, thus suggesting that changes
in these properties would probably be detectable at a distance
shorter than the one used in the study. Therefore, an evaluation
of their spatial characteristics would require a higher density
of sampling. Only some of the microbial properties, such as
bacterial CFUs in August, actinomycetes CFUs in , and qCO
2
ratio on both sampling dates, were spatially dependent at the
scale of sampling grid, whereas they reveal a high variability.
The spatial structure of most the variables that showed a spa-
tial autocorrelation differed depending on the sampling time.
A variation in time has affected the spatial variability and
consequently has changed the shape of the semivariograms
of these variables. That is why more frequent seasonal sam-
pling should be included in the further experimental planning
in order to find a suitable sampling distance for characterizing
the spatial structure of properties being studied.
Acknowledgments Much gratitude is due to Michele Simmons for proof
reading the article.
Funding information The research was financially supported by the
Polish Ministry of Science and Higher Education (project no. N 310
030 32/1588).
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appro-
priate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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