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Abstract

The article discusses the role of the soil aggregate structure in quantifying the marginality and specialisation axes of the ecological niche of the micromollusc Vallonia pulchella (Muller 1774) that inhabits technosols. The experimental polygon consisted of 105 samples located within 7 transects (15 samples each). The distance between rows of sampling points was 3 m. The average density of V. pulchella was 1393 ind.•m-2. The soil aggregate fraction of 1-5 mm was found to be predominant within the technosol. The spatial variation of aggregate fractions was characterised by a moderate level of the spatial dependence. It was impossible to choose an adequate covariance model from among the traditional ones to interpolate the spatial variation of aggregate fractions, and only the Matérn model was best suited. The axis of marginality and specialisation of the V. pulchella ecological niche projected in soil aggregate fractions was significantly different from a random alternative. The ecological niche of the V. pulchella was presented by integral variables, such as the axis of marginality and specialisation, which were the basis to build a map of the spatial variation of the habitat suitability index. The marginality of the V. pulchella ecological niche correlates with soil penetration resistance indicators at depths ranging from 0-5 to 20-25 cm, soil humidity, acidity, and aeration. The specialisation correlates with the soil mechanical impedance at 25-35 cm, nitrogen content, and the soil acidity regime.
The soil aggregate structure as a marker of the ecological niche
of the micromollusc Vallonia pulchella
Аva Umerova , Olexander Zhukov , Nadezhda Yorkina
Bogdan Khmelnitsky Melitopol State Pedagogical University, Faculty of Chemistry and Biology,
Hetmanska st., 20, 72318, Melitopol, Ukraine
RECEIVED 21.07.2020 REVIEWED 01.12.2020 ACCEPTED 28.12.2020
Abstract: The article discusses the role of the soil aggregate structure in quantifying the marginality and specialisation
axes of the ecological niche of the micromollusc Vallonia pulchella (Muller 1774) that inhabits technosols.
The experimental polygon consisted of 105 samples located within 7 transects (15 samples each). The distance between
rows of sampling points was 3 m. The average density of V. pulchella was 1393 ind.∙m
–2
. The soil aggregate fraction of
1–5 mm was found to be predominant within the technosol. The spatial variation of aggregate fractions was
characterised by a moderate level of the spatial dependence. It was impossible to choose an adequate covariance model
from among the traditional ones to interpolate the spatial variation of aggregate fractions, and only the Matérn model
was best suited. The axis of marginality and specialisation of the V. pulchella ecological niche projected in soil aggregate
fractions was significantly different from a random alternative. The ecological niche of the V. pulchella was presented
by integral variables, such as the axis of marginality and specialisation, which were the basis to build a map of the
spatial variation of the habitat suitability index. The marginality of the V. pulchella ecological niche correlates with soil
penetration resistance indicators at depths ranging from 0–5 to 20–25 cm, soil humidity, acidity, and aeration. The
specialisation correlates with the soil mechanical impedance at 25–35 cm, nitrogen content, and the soil acidity regime.
Keywords: ecological niche, geostatic analysis, habitat, Matérn model, phytoindication, soil mechanical impedance
INTRODUCTION
Aggregates are the main component of the soil structure, which
allows to measure its physical state as an environment for living
organisms. Soil structure affects soil moisture content, infiltration
capacity, erodability, circulation of nutrients, stabilisation of
organic matter, root penetration, productivity of natural plant
communities, and crop yields [CHAPLOT COOPER 2015]. The
aggregate stability is used as an indicator of the soil structure
[MUSTAFA et al. 2020]. The structure and stability of soil
aggregates is the most important, since enables to examine
conditions for increasing agronomic productivity and reducing
soil erosion [ZHANG et al. 2016]. Aggregation of soil was studied
mainly in the agricultural context. A study [WILPISZESKI et al.
2019] examined the role of tillage, soil texture, and the presence
of carbon in the agricultural land as factors that influence the
aggregate structure. In the process of land reclamation, it is
important to select optimal management strategies to create not
only the desired vegetation cover, but also to promote the
preservation of the macroaggregate structure in soils to improve
long-term nutrient supply and physical properties of soil [WICK
et al. 2016].
Aggregation processes in soil are the result of interaction of
a number of physical, chemical and biological factors with the
complex feedback mechanisms [RIVERA, BONILLA 2020; SODHI et al.
2009]. The soil aggregation is regulated by the biota [DUCHICELA
et al. 2013]. In soils where organic matter is a major aggregate
binding agent, a link can be established between aggregate size
distribution and soil biological functions [JASTROW, MILLER 1998].
The role of biodiversity in soil aggregation is of particular interest
[DELGADO-BAQUERIZO et al. 2017; WAGG et al. 2014]. There are
different mechanisms of soil biota influence on the aggregation of
soil [LEHMANN, KLEBER 2015; SIX et al. 2004]. Bacteria are known to
be able to synthesise biopolymer that acts as binder [DENG et al.
2015], and the mushroom mycelium can entangle soil particles to
keep them together. Earthworms, insect larvae, and other large
JOURNAL OF WATER AND LAND DEVELOPMENT
e-ISSN 2083-4535
Polish Academy of Sciences (PAN) Institute of Technology and Life Sciences – National Research Institute (ITP – PIB)
JOURNAL OF WATER AND LAND DEVELOPMENT
DOI: 10.24425/jwld.2021.139945
2022, No. 52 (I–III): 66–74
© 2022. The Authors. Published by Polish Academy of Sciences (PAN) and Institute of Technology and Life Sciences – National Research Institute (ITP – PIB).
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/)
soil animals may stabilise the aggregate structure [BERTRAND et al.
2005]. Soil saprophages consume the soil and mix it with their
intestinal content. After digestion, the mixture takes the form of
a highly structured formation, such as casts or coprolites [GORRES,
AMADOR 2010]. The spatial variation of soil aggregate structure
can influence the organisation of the soil macrofauna community.
The soil aggregates also help to form the unique ecological
isolation of the microbial community in the soil. Soil aggregates
can serve as a refuge for microbes from predators [RILLIG et al.
2017]. There are practically no researches into the influence of
soil aggregate structures on functional features of mollusc
populations. It is possible, that organic substances in the soils have
a indirect impact on the molluscs. This requires a deep under-
standing of the structure and formation of aggregates [LEHMANN,
KLEBER 2015].
The concept of the ecological niche plays a central role in
the modern ecology [HOLT 2009; HUTCHINSON 1957]. A set of
biotic and abiotic conditions under which a given organism can
survive and reproduce is considered as its ecological niche
[HUTCHINSON 1957]. The ecological niche may be understood in
the context of the two dimensions: Grinnellian and Eltonian. The
Grinnellian niche takes into account the importance of a given set
of resources for the survival of a species [DEVICTOR et al. 2010;
GRINNELL 1917]. The Grinnellian niche is considered in two ways:
as a complex of the habitat conditions and as behavioural
adaptations allowing organisms to persist and produce offspring
[GRINNELL 1917]. The Eltonian niche is based not only on the
consideration of species response to the environment impact but
on the species impact on the environment. The niche reflects
place of a species in the biotic environment, and its relations to
food and enemies [ELTON 1927]. Hutchinson suggested that an
ecological niche should be considered as a hyper-volume in the
multidimensional space, where species could potentially support
the viability of their populations under the influence of
environmental conditions [HUTCHINSON 1957]. Thus, the use of
the term “niche” is advisable both in the relation to the organism
and in the relation to the population or the species.
The habitat preferences of land molluscs were studied in the
ecosystems that differed in vegetation, soil type, and moisture
level [KUNAKH et al. 2008b; MILLAR, WAITE 1999]. Among climatic
factors, temperature and humidity have the greatest impact on
land molluscs. Other climatic factors much less, or indirectly,
affect molluscs, e.g. changes in humidity and temperature. The
calcium concentration and the pH value correlated with it are the
most significant soil parameters that influence snails [HOTOPP
2002]. The terrestrial molluscs are able to fix calcium due to
intracellular and extracellular biomineralisation, since their shells
are an important source of calcium for other animals. Soil
moisture has also been detected as an essential factor in the
diversity of terrestrial snail fauna [ČEJKA, HAMERLÍK 2009]. The
available soil moisture is an important ecologic factor for the
biota of reclaimed land. The degree of human-induced ecosystem
transformation can be assessed through the diversity of terrestrial
snail communities [DOUGLAS et al. 2013]. It has been shown that
a series of models that best explain the distribution of mollusc
and their abundance are specific to a particular species and type
of technosols, and these tend to be invariant over time [KUNAKH
et al. 2018a].
Ecological factors affecting the species distribution are in
principle spatially structured, so the species community also has
a spatial structure [THUILLER et al. 2004]. Based on Hutchinson’s
concept of an ecological niche, the Factor Analysis of an
Ecological Niche (ENFA) can be used for the modelling of the
species geographical distribution [HIRZEL et al. 2002]. This
approach was useful for the simulation of the ecological niche
of the mollusc in biotopes resulting from reclamation of degraded
lands. Molluscs may also be the cause of the spatial heterogeneity
of environmental regimes. The substantial contribution of snails
to the nitrogen cycle was proved in a nitrogen-limited ecosystem,
which can be a source of spatial heterogeneity of the higher plant
production [JONES, SHACHAK 1994]. Some terrestrial gastropod
communities cause of changes in the content of nitrogen and
phosphorus in soil. This shows that the spatial and temporal
dynamics of plant communities depend on the detritivore food
chain structure [THOMPSON et al. 1993].
The impact of the aggregate soil structure on molluscs has
not been sufficiently studied, which was an incentive to develop
the study described in the article. The study includes the spatial
distribution of the technosols aggregate structure (sod-lithogenic
soils on grey-green clays) as a marker of the ecological niche of
micromolluscs Vallonia pulchella (Muller 1774).
MATERIAL AND METHODS
The research was conducted at the Research Centre of the Dnipro
Agrarian and Economic University in Pokrov (Ukr. Dniprovs’kyy
derzhavnyy ahrarno-ekonomichnyy universytet), Ukraine. This
experimental field for studying the optimal modes of agricultural
reclamation was established in 1968–1970. The territory has
a temperate continental climate with an average annual maximum
temperature of 26.4°C and a minimum of –8.2°C, with an average
annual rainfall of about 511 mm (the average for 20 years
according to the Nikopol meteorological station) – Figure 1.
Sampling was conducted in June 2019. The main goal of our
work was to study the spatial variation of micromolluscs.
Therefore, sampling was carried out during the highest activity
of animals and plants supported by optimal humidity and
temperature during the year. Sampling was carried out on
a variant of artificial soil (technozems) formed in sod-lithogenic
soils on grey-green clays. The study was conducted in June 2019.
According to IUSS WRB 2007 [IUSS… 2007], the soil belongs to
the RSG Technosols. The examined profile also satisfies the
criterion for the Spolic prefix qualifier having 20 percent or more
artefacts (consisting of 35 percent or more of mine spoil) in the
upper 100 cm from the soil surface [YORKINA et al. 2018].
The polygon consisted of 7 transects and each transect
consisted of 15 sampling points. The distance between rows
within the polygon was 3 m. A soil was sampled with a cylinder
5 cm in height and 10 cm in diameter from the centre of each
sampling point. Each sample was divided into 10 sub-samples 10
g each. Each soil sample was examined in the laboratory using
a 10 binocular MBS-9 microscope and the number of V. pulchella
individuals was recorded.
The mechanical resistance of the soil was measured to
a depth of 50 cm at 5 cm intervals in the field using the
Eijkelkamp manual penetrometer. The average error of the
measurement device was ±8%. Measurements were made with
a cone of a 1 cm
2
cross section. Within each measurement point,
the mechanical impedance of the soil was made in one
© 2022. The Authors. Published by Polish Academy of Sciences (PAN) and Institute of Technology and Life Sciences – National Research Institute (ITP – PIB).
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Аva Umerova, Olexander Zhukov, Nadezhda Yorkina 67
replication. The soil electrical conductivity was measured in situ
using HI 76305 meter. The aggregate size distribution was
determined in accordance with the recommended soil sampling
and analysis methods [KROETSCH, WANG 2008].
Based on geobotanical descriptions of vegetation the
phytoindication, the study assessed environmental factors accord-
ing to DIDUKH [2011]. Didukh phytoindication scales include
edaphic and climatic scales. The edaphic phytoindication scales
include the soil water regime (Hd), variability of humidity (fH),
soil aeration (Ae), soil acidity (Rc), total salt regime (Sl),
carbonate content in the soil (Ca), and the nitrogen content in
the soil (Nt). The climatic scales include thermal climate
parameters (thermal regime, Tm), humidity (Om), cryo-climate
(Cr) and the continentality of climate (Kn). In addition to these,
the lighting scale (Lc) is indicated, which is characterised as
a microclimate scale [ZHUKOV et al. 2018].
The geostatic analysis was carried out with the kriging
method. The Matérn variogram model was used [MINASNY,
MCBRATNEY 2005]. This model is flexible and can reflect the
various behaviour of spatial processes. On this basis, the Matérn
variogram can be used to model the soil properties. The main
feature of the Matérn model is the inclusion of the smoothness
parameter, which is directly capable of explaining the spatial
autocorrelation at close distances [GENTON, KLEIBER 2015]. We
used the cross-validation procedure, and the normalised root
mean square error (NRMSE), mean error (ME), and mean
squared deviation ratio (MSDR) were calculated [VASAT et al.
2013]. The root mean square error (RMSE) was calculated as
follows:
RMSE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Pn
i¼1x1;i x2;i
 2
n
sð1Þ
Normalised root mean squared error (NRMSE) was calculated as
follows:
NRM SE ¼RMSE
x1;max x1;min ð2Þ
Mean squared deviation ratio (MSDR) was calculated as follows:
MSDR ¼Pn
i¼1
x1;ix2;i
ð Þ2
vari
 
nð3Þ
where: x
1
= prediction of the variable X, x
2
= measure of that
variable, n = number of the records, var = kriging variance.
Fig. 1. Research centre for the study of optimal regimes of agricultural recultivation
near Pokrov (Ukraine): A) satellite image of the study area; B) experimental polygon
position; C) technosols profile; D) sampling point location within the experimental
polygon; source: own elaboration
© 2022. The Authors. Published by Polish Academy of Sciences (PAN) and Institute of Technology and Life Sciences – National Research Institute (ITP – PIB).
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/)
68 The soil aggregate structure as a marker of the ecological niche of the micromollusc Vallonia pulchella
The lower the NRMSE, the more accurate the map is. The
MSDR indicates whether the variance of measurement data is well
reproduced with the kriging interpolation and ideally it equals 1
[VASAT et al. 2013]. Spatial variations of predictors and regression
models of the soil mechanical impedance were displayed using
the ‘Surfer®12 (www.goldensoftware.com). Statistical calculations
were performed by the Statistica 7.0 software and the project for
statistical computations R [R Core Team 2020] using adehabitat
[CALENGE 2011] and vegan [OKSANEN 2017] libraries. Two-
dimensional mapping, estimation of geostatistics, and creation
of asc-files with spatial variability of the environment indicators
was accomplished with the use of ArcGis 10.0 [YORKINA et al.
2018].
RESULTS
Air-dry soil sample of 10.5 kg was examined within the research
area, in which 266 individuals of Vallonia pulchella (Muller 1774)
were found. Thus, the average density of this species in the sod-
lithogenic soils on gray-green clays was 1393 ind.∙m
–2
. The
aggregate structure analysis showed that the prevailing fraction by
its content are the aggregates of 1.0–2.0 to 3.0–5.0 mm, the
content of which was 18.1 ±0.12 and 23.0 ±0.09% respectively.
The geostatistical analysis was carried out to determine spatial
patterns of aggregate fraction distribution (Tab. 1). The spatial
variation of aggregate fractions is characterised by the moderate
level of spatial dependence, as it is evidenced with the SDL of
24.68–52.55% (nugget to sill ratio as an indicator of spatial
autocorrelation strength). There were two local maxima of spatial
dependence: for aggregate fractions of 0.5–1.0 mm and 5.0–7.0
and 7.0–10.0 mm. Correspondingly, the local minimum of spatial
dependence was characteristic of the 2.0–3.0 mm aggregate
fraction. The practical range of the spatial variation of aggregate
fractions is in the diapason of 5.80–38.85 m.
The practical range for the variogram of the spatial
variability of aggregate fraction <0.25 mm is 6.55 m. For
aggregates of 0.25–0.50 mm the geostatistic was much higher
and reached its maximum (38.85 m) for aggregates of 0.5–10 mm.
Further, with the increasing fraction size, the practical range
monotonically decreased. The Matérn model can be considered as
a generalised series of theoretical variogram models [MINASNY,
MCBRATNEY 2005]. A Gaussian model was the most suitable as
Kappa tends to infinity. The Whittle function [WHITTLE 1954] was
the best for an aggregate fraction of 5.0–7.0 as the Kappa
parameter was very close to 1.0. For aggregates with sizes 0.25–
0.5, 0.5–1.0 and 3.0–5.0 mm, it is impossible to choose an
adequate model from among the traditional ones, so only the
Matérn model is considered the most suitable.
The geostatistical analysis enables to interpolate the value on
points where measurements were not made and, on the basis of
the results, we can build maps of the data spatial variation as
ecogeographical variables in terms of the ecological-niche factor
analysis (Fig. 2).
The content of organic matter in technosol varied within the
range of 0.22–1.48% (Tab. 2). In general, water extraction acidity
was slightly alkaline. The high level of salt content in water was
typical for technosols. The content of carbonates varied from
11.10 to 47.30%. According to Katschinski nomenclature
[KATSCHINSKI 1956], particle size distribution technosols can be
qualified as silty medium clay. Analysis of the spatial variation of
these soil features showed that there was no spatial variation
component, so they were not used to describe the spatial patterns
of micromolluscs.
The water content increased in the profile with depth
(Tab. 3). The soil bulk density did not change significantly in the
profile. The density of the solid phase increased with depth. Soil
porosity was within the range of 49.61–55.91%.
The distribution of the ecogeographical variables within the
site may be considered as the global distribution. The information
Table 1. Descriptive statistic and geostatistic parameters of the aggregate fraction variation
Fraction
(mm)
Mean
±standard
error
Phi Pr_Range
(m) Sill Nugget SDL Kappa NRMSE ME MSDR R2_cross
≥10 11.2 ±0.27 0.57 6.42 4.66 3.19 40.62 →∞ 0.20 –0.0009 0.65 0.36
<7.0–10.0) 7.2 ±0.06 0.59 6.60 0.31 0.15 33.15 →∞ 0.24 0.0014 0.76 0.23
<5.0–7.0) 8.3 ±0.10 3.84 14.68 0.66 0.34 34.19 0.9 0.24 0.0003 0.66 0.33
<3.0–5.0) 18.8 ±0.21 2.44 12.83 2.45 2.18 47.10 1.9 0.17 0.0011 0.63 0.37
<2.0–3.0) 23.0 ±0.09 2.24 25.16 0.49 0.54 52.55 →∞ 0.25 –0.0027 0.71 0.28
<1.0–2.0) 18.1 ±0.12 4.52 28.80 1.12 0.55 32.95 →∞ 0.22 –0.0028 0.62 0.40
<0.5–1.0) 5.2 ±0.06 7.97 38.85 0.38 0.12 24.68 1.6 0.18 0.0009 0.40 0.59
<0.25–0.5) 5.7 ±0.09 8.25 36.84 1.02 0.41 28.60 1.3 0.22 0.0030 0.54 0.46
<0.25 2.6 ±0.04 0.58 6.55 0.09 0.05 38.14 →∞ 0.22 0.0010 0.68 0.31
Explanations: Phi = variogram range (distance at which theoretical variogram curve reaches its maximum as the range); Pr_Range = practicle range
(value at which variogram reaches 95% of the asymptote); Sill = difference between the asymptote and the nugget; nugget = intercept of the variogram
model curve; SDL = nugget to sill ratio as an indicator of spatial autocorrelation strength; Kappa = Matern model smoothing parameter; regression R
adj2
= adjusted R
2
of the regression model with terrain and tree stand variables as predictors; NRMSE = normalised root mean squared error; MSDR = mean
squared deviation ratio.
Source: own study.
© 2022. The Authors. Published by Polish Academy of Sciences (PAN) and Institute of Technology and Life Sciences – National Research Institute (ITP – PIB).
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Аva Umerova, Olexander Zhukov, Nadezhda Yorkina 69
about the spatial distribution of the molluscs allows us to obtain
the partial distribution of ecogeographical variables in places
where molluscs are found. Mollusc microhabitat preferences may
be quantified by comparing the global and partial distributions.
Obviously, the fact that global and partial distributions do not
converge indicates the structural role of the corresponding
variable in determining the form of the ecological niche (Fig. 3).
The assessment of the ecological niche parameters can be
obtained using the ENFA approach. The ENFA approach is
allowed to identify the marginality axis and the specialisation axis
of the ecological niche of V. pulchella using the aggregate
fractions of the soil as the predictors (Fig. 4).
The test for the statistical significance has shown that the
eigenvalues of the marginality axis of the ecological niche of the
Fig. 2. Spatial variation of aggregate fractions (in mm); source: own study
Table 2. Descriptive statistics of the organic matter content, particle size distribution, pH, and the ion composition of the soil water
extract (N = 105)
Property Mean ±standard error Minimum Maximum Standard deviation
Organic matter content
Organic matter (%) 0.60 ±0.015 0.22 1.48 0.20
pH and ion composition of the soil water extract
pH 7.16 ±0.019 6.50 7.80 0.25
Cl
+
(meq·dm
−3
) 0.39 ±0.007 0.19 0.76 0.09
SO
42+
(meq·dm
−3
) 0.42 ±0.008 0.23 0.73 0.10
Ca
2+
(meq·dm
−3
) 0.49 ±0.008 0.34 1.16 0.10
Mg
2+
(meq·dm
−3
) 0.26 ±0.011 0.00 0.72 0.14
HCO
3+
(meq·dm
−3
) 1.36 ±0.019 0.89 2.38 0.25
Ka
+
+Na
+
(meq·dm
−3
) 1.42 ±0.026 0.36 2.52 0.33
Carbonate content an particle size distribution (according Katschinski)
Carbonate content (%) 22.86 ±1.43 11.40 47.30 9.52
Medium and coarse sand (1.00–0.25 mm) 1.68 ±0.25 0.21 6.18 1.67
Fine sand (0.25–0.05 mm) 17.22 ±2.00 0.06 62.84 13.27
Coarse silt (0.05–0.01 mm) 15.54 ±1.62 4.12 37.08 10.74
Medium silt (0.01–0.005 mm) 7.68 ±0.80 0.00 24.72 5.28
Fine silt (0.005–0.001 mm) 4.88 ±0.32 0.21 8.24 2.15
Clay (<0.001 mm) 53.00 ±2.31 18.62 86.52 15.29
Physical clay (<0.01 mm) 65.56 ±2.46 26.86 95.00 16.31
Source: own study.
© 2022. The Authors. Published by Polish Academy of Sciences (PAN) and Institute of Technology and Life Sciences – National Research Institute (ITP – PIB).
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70 The soil aggregate structure as a marker of the ecological niche of the micromollusc Vallonia pulchella
V. pulchella (γ
marg
= 0.98, p < 0.001) and the axis of the
specialisation (γ
spec1
= 1.94, p < 0.03) significantly differ from the
eigenvalues obtained as the result of a similar statistical procedure
for random alternatives. The results of the ENFA approach may
suggest that the marginality of the V. pulchella ecological niche is
closely related to the variability of the aggregate structure:
molluscs prefer areas where the content of aggregates of 5.0–7.0
to 10.0 mm prevails, and they avoid areas where the content of
small aggregates increases.
The ecological niche can be presented by integral variables
such as the axis of marginality and specialisation, which are the
basis to build the spatial variation map for the habitat suitability
index (HSI) (Fig. 5).
The axes of the marginality and the specialisation of the
ecological niche correlate with the edaphic factors and the
phytoindication scales (Tab. 4). The marginality of the V.
pulchella ecological niche correlates with indicators of the soil
impedance at depths from 0–5 to 20–25 cm. It is also worth
noting that the axis of marginality correlates with the variability
scale for variability of damping (fH), acidity (Rc), aeration (Ae),
and humidity (Om). The specialisation correlates with the soil
penetration resistance at the depth of 25–35 cm, nitrogen content
in the soil, and acidity regime of the soil.
Fig. 3. The total distribution of the aggregate fractions (in mm) on the
area (black lines) and the partial distribution of aggregate fractions in
points where Vallonia pulchella were detected (grey lines); source: own
study
Fig. 4. Correlation between aggregate fractions (in mm) and axes selected
as a result of ENFA analysis: X-axis corresponds to marginality and Y-axis
specialisation; source: own study
Fig. 5. Spatial distribution of the habitat suitability index (HSI) for
Vallonia pulchella within the experimental site on green-grey clays based
on ENFA: on the abscissa and ordinate axis local polygon coordinates
(m), scale – habitat suitability index (0–100%); source: own study
Table 3. Profile distribution of physical properties of technosol
Layer (cm)
Water
content
(%)
Bulk density
(g·cm
−3
)
Solid phase
density
(g·cm
−3
)
Porosity
(%)
mean ±standard error
0–10 15.34 ±0.13 1.20 ±0.14 2.53 ±0.15 52.57 ±0.14
10–20 16.51 ±0.16 1.23 ±0.19 2.54 ±0.11 51.57 ±0.19
20–30 17.50 ±0.15 1.24 ±0.07 2.54 ±0.03 51.18 ±0.20
30–40 24.76 ±0.10 1.12 ±0.05 2.54 ±0.10 55.91 ±0.19
40–50 25.97 ±0.15 1.14 ±0.09 2.54 ±0.15 55.12 ±0.23
50–60 25.30 ±0.12 1.15 ±0.10 2.55 ±0.16 54.90 ±0.27
60–70 25.30 ±0.20 1.16 ±0.09 2.55 ±0.09 54.51 ±0.10
70–80 19.04 ±0.19 1.15 ±0.19 2.55 ±0.05 54.90 ±0.17
80–90 21.57 ±0.16 1.27 ±0.16 2.55 ±0.16 50.20 ±0.12
90–100 23.67 ±0.14 1.29 ±0.13 2.56 ±0.13 49.61 ±0.10
Source: own study.
Table 4. The correlation of marginality (Mar) and specialisation
(Spel) with environmental variables
Environmental variables Mar Spel
Electrical conductivity (dS∙m
–1
) 0.34 0.19
Soil penetration resistance in MPa at depth (cm)
0–5 –0.33
5–10 0.34
10–15 0.26
15–20 0.38
20–25 0.34
25–30 0.20
30–35 0.27
35–40
40–45
45–50
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Аva Umerova, Olexander Zhukov, Nadezhda Yorkina 71
DISCUSSION
The aggregate composition corresponds to the relative content of
aggregates of different sizes in soil. The aggregate structure is an
informative indicator denoting the spatial variability of technosols
properties. In various soils, aggregates differ in size and shape.
The stability of aggregates reduces the loss of soil, carbon,
nitrogen, and phosphorus [KASPER et al. 2009], and it increases the
number of macro aggregates. We found that mesoaggregates,
namely the aggregates of 1.0–5.0 mm in size, dominate in the
studied area. This suggests that organic matter plays a consider-
able role in the mesoaggregates formation because the organic
substance promotes soil aggregation [DUCHICELA et al. 2013]. The
increase in the organic matter content promotes an increase in
the proportion of mesoaggragates due to microaggragates [OADES
1993].
In the conditions of the polygon examined, it was found that
aggregate fractions are characterised by their spatial variability
pattern. Thus, spatial patterns should be noted to be significantly
different for aggregate fractions of various sizes. This may
indicate different mechanisms behind the formation of soil
aggregates of different sizes. Experimentally determined values of
practical ranges exceed the distance between sampling points,
which indicates that the selected sampling strategy allows to
assess the spatial variability of the aggregate soil structure.
For the studied aggregate fractions, it is impossible to find
a universal model to describe the spatial variation from among
the traditional ones. Therefore, the Matérn model is the most
suitable model to study features of aggregate fractions spatial
patterns. A significant difference in geostatistics, which describes
the spatial variation of aggregate fractions, indicate a significant
heterogeneity of processes that result in the formation of
aggregates. Accordingly, these processes may lead to a significant
diversity of ecological regimes, which have a direct impact on
micromolluscs. We established that aggregate fractions are
valuable predictors that can explain ecological niche marginality
and specialisation of V. pulchella. This is quite consistent due to
the proportional heterogeneity of the soil which is induced by
aggregate fractions and the size of micromolluscs. The specificity
of water and air regimes is determined by the ratio of aggregate
fractions, which explains the effect on micromolluscs.
Another important result is that the axes of marginality and
specialisation for the micromolluscs ecological niche correlate
with ecosystem properties that are at another spatial scale level.
The mechanical impedance of the soil was measured at the depth
where molluscs are unlikely to be found due to unfavourable
living conditions. Vegetation was described per squares of
3 × 3 m, which significantly exceeded the size of a soil sample
of 10 g, in which micromolluscs were found. However, there is
a link between these ecosystem scales. The reason for this is that
soil properties and features of the vegetation cover affect the
aggregate structure of soil, and the structure determines living
conditions for molluscs.
The distribution of micromolluscs is uneven and it is
explained by the variability of the natural environment char-
acteristics. It contributes to the structural and the functional
diversity [BRIND’AMOUR et al. 2011]. Aggregates of various
sizes have been affected by V. pulchella, i.e. macro-aggregates of
large porosity. This is important for the penetration of water and
air, and for the development of microbes, small animals, and
roots of plants. This is a necessary condition to support molluscs,
their breathing, movement, and consumption of nutrients. Small
aggregates form a small pore system that adversely affects the life
of micromolluscs. Thus, soil aggregates of various sizes cause
dynamic changes in the spatial distribution of V. pulchella. It can
play an important role in the breadth of the soil biodiversity and
its functioning.
Our study focused on the spatial variation of molluscs and
soil properties. Of course, the abundance of molluscs and the
influence of environmental factors on them varies throughout
the season. The studying of spatial and temporal dynamics of
mollusc populations will dealt with in our future studies. It is also
an important aspect of further research into unit resistance and
importance of this property as a factor that affects micromol-
luscs.
CONCLUSIONS
The aggregate structure of soil is one of the main conditions that
determines the temporal dynamics of the terrestrial invertebrate
community. The study revealed that soil aggregates play an
important role in the structuring of the V. pulchella ecological
niche. The sensitivity of micromolluscs to environmental factors
changes in space. The content of agronomically valuable
aggregates has а positive effect on the abundance of micro-
molluscs. The marginality of the ecological micromollusc niche of
is correlated with soil humidity, high soil aeration, and soil acidity
(according Didukh). Moreover, V. pulchella avoids areas of high
electrical conductivity and an increased soil penetration resis-
tance. Soil aggregates have a great ecological importance for soil
biodiversity, but many other aspects of these relationships require
detailed studies.
Environmental variables Mar Spel
Didukh phytoindicator values
Hd
fH 0.30
Rc –0.31 –0.44
Sl
Ca
Nt 0.29
Ae 0.28
Tm
Om 0.23
Kn
Сr
Lc
1)
Only correlation coefficients are shown that are significant for p < 0.05.
Explanations: Hd = soil humidity, fH = variability of damping, Rc = soil
acidity, Sl = total salt regime, Ca = carbonate content in soil, Nt =
nitrogen content in soil, Ae = soil aeration, Tm = thermal climate, Om =
humidity, Kn = continental climate, Cr = cryoclimate, Lc = light regime.
Source: own study.
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cont. Tab. 4
72 The soil aggregate structure as a marker of the ecological niche of the micromollusc Vallonia pulchella
REFERENCES
BERTRAND M., BAROT S., BLOUIN M., WHALEN J., DE OLIVEIRA T., ROGER-
ESTRADE J. 2015. Earthworm services for cropping systems.
A review. Agronomy for Sustainable Development. Vol. 35
p. 553–567. DOI 10.1007/s13593-014-0269-7.
BRIND’AMOUR A., DANIEL B., DRAY S., LEGENDRE P. 2011. Relationships
between species feeding traits and environmental conditions in
fish communities: A three-matrix approach. Ecological Applica-
tions. Vol. 21 p. 363–377. DOI 10.1890/09-2178.1.
CALENGE C. 2011. Exploratory analysis of the habitat selection by the
Wildlife in R: the adehabitatHS Package. R CRAN Project pp. 60.
ČEJKA T., HAMERLÍK L. 2009. Land snails as indicators of soil humidity in
Danubian woodland (SW Slovakia). Polish Journal of Ecology.
Vol. 57 p. 741–747.
CHAPLOT V., COOPER M. 2015. Soil aggregate stability to predict organic
carbon outputs from soils. Geoderma. Vol. 243–244 p. 205–213.
DOI 10.1016/j.geoderma.2014.12.013.
DELGADO-BAQUERIZO M., POWELL J.R., HAMONTS K., REITH F., MELE P.,
BROWN M.V., DENNIS P.G., FERRARI B.C., FITZGERALD A., YOUNG A.,
SINGH B.K., BISSETT A. 2017. Circular linkages between soil
biodiversity, fertility and plant productivity are limited to topsoil
at the continental scale. New Phytologist. Vol. 215 p. 1186–1196.
DOI 10.1111/nph.14634.
DENG J., ORNER E.P., CHAU J.F., ANDERSON E.M., KADILAK A.L.,
RUBINSTEIN R.L., BOUCHILLON G.M., GOODWIN R.A., GAGE D.J.,
SHOR L.M. 2015. Synergistic effects of soil microstructure and
bacterial EPS on drying rate in emulated soil micromodels. Soil
Biology and Biochemistry. Vol. 83 p. 116–124. DOI 10.1016/j.
soilbio.2014.12.006.
DEVICTOR V., CLAVEL J., JULLIARD R., LAVERGNE S., MOUILLOT D., THUILLER
W., VENAIL P., VILLÉGER S., MOUQUET N. 2010. Defining and
measuring ecological specialization. Journal of Applied Ecology.
Vol. 47 p. 15–25. DOI 10.1111/j.1365-2664.2009.01744.x.
DIDUKH Y.P. 2011. The ecological scales for the species of Ukrainian
flora and their use in synphytoindication. Kyiv. Phytosociocentre
pp. 176.
DOUGLAS D.D., BROWN D.R., PEDERSON N. 2013. Land snail diversity can
reflect degrees of anthropogenic disturbance. Ecosphere. Vol. 4
(2), 28. DOI 10.1890/ES12-00361.1.
DUCHICELA J., SULLIVAN T.S., BONTTI E., BEVER J.D. 2013. Soil aggregate
stability increase is strongly related to fungal community
succession along an abandoned agricultural field chronosequence
in the Bolivian Altiplano. Journal of Applied Ecology. Vol. 50
p. 1266–1273. DOI 10.1111/1365-2664.12130.
ELTON C. 1927. Animal ecology. New York. Macmillan Co. pp. 13.
GENTON M.G., KLEIBER W. 2015. Cross-covariance functions for
multivariate geostatistics. Statistical Science. Vol. 30 p. 147–163.
DOI 10.1214/14-STS487.
GORRES J.H., AMADOR J.A. 2010. Partitioning of habitable pore space in
earthworm burrows. Journal of Nematology. Vol. 42 p. 68–72.
GRINNELL J. 1917. The niche-relationships of the California Thrasher.
The Auk. Vol. 34 p. 427–433. DOI 10.2307/4072271.
HIRZEL A.H., HAUSSER J., CHESSEL D., PERRIN N. 2002. Ecological-niche
factor analysis: How to compute habitat–suitability maps without
absence data? Ecology. Vol. 83 p. 2027–2036. DOI 10.1890/0012-
9658(2002)083[2027:ENFAHT]2.0.CO;2.
HOLT R.D. 2009. Bringing the Hutchinsonian niche into the 21st
century: Ecological and evolutionary perspectives. Proceedings of
the National Academy of Sciences of the United States of
America. Vol. 106 p. 19659–19665. DOI 10.1073/pnas
.0905137106.
HOTOPP K. 2002. Land snails and soil calcium in central Appalachian
mountain forest. Southeastern Naturalist. Vol. 1. No. 1 p. 27–44.
HUTCHINSON G.E. 1957. Concluding remarks. Cold Spring Harbor
Symposia on Quantitative Biology. Vol. 22 p. 415–427. DOI
10.1101/sqb.1957.022.01.039.
IUSS Working Group WRB 2007. World Reference Base for Soil
Resources 2006, first update 2007. World Soil Resources Reports.
No. 103. Rome. FAO pp. 115.
JASTROW J.D., MILLER R.M. 1998. Soil aggregate stabilization and carbon
sequestration. In: Soil processes and the carbon cycle. Eds. R. Lal,
J.M. Kimble, R.F. Follett, B.A. Stewart. Boca Raton, FL. CRC
Press p. 207–233.
JONES C.G., SHACHAK M. 1994. Desert snail’s daily grind. Natural
History. Vol. 103 p. 56–61.
KASPER M., BUCHAN G.D., MENTLER A., BLUM W.E.H. 2009. Influence of
soil tillage systems on aggregate stability and the distribution of
C and N in different aggregate fractions. Soil and Tillage
Research. Vol. 105 p. 192–199. DOI 10.1016/j.still.2009.08.002.
KATSCHINSKI N.A. 1956. Die mechanische Bodenanalyse und die
Klassifikation der Boden nach ihrer mechanischen Zusammen-
setzung [The mechanical soil analysis and the classification of the
soil according to its mechanical composition]. Rapports Au
Sixieme Congres International de La Science Du Sol. Paris,
France p. 321–327.
KROETSCH D., WANG C. 2008. Particle size distribution. In: Soil sampling
and methods of analysis. Eds. M.R. Carter, E.G. Gregorich. Boca
Raton. CRC Press p. 713–726.
KUNAKH O.N., KRAMARENKO S.S., ZHUKOV A.V., KRAMARENKO A.S.,
YORKINA N.V. 2018a. Fitting competing models and evaluation of
model parameters of the abundance distribution of the land snail
Vallonia pulchella (Pulmonata, Valloniidae). Regulatory Mechan-
isms in Biosystems. Vol. 9 p. 198–202. DOI 10.15421/021829.
KUNAKH O.N., KRAMARENKO S.S., ZHUKOV A.V., ZADOROZHNAYA G.A.,
KRAMARENKO A.S. 2018b. Intra-population spatial structure of the
land snail Vallonia pulchella (Müller, 1774) (Gastropoda;
Pulmonata; Valloniidae). Ruthenica. Vol. 28 p. 91–99.
LEHMANN J., KLEBER M. 2015. The contentious nature of soil organic
matter. Nature. Vol. 528 p. 60–68. DOI 10.1038/nature16069.
MILLAR A.J., WAITE S. 1999. Mollusks in coppice woodland. Journal of
Conchology. Vol. 36 p. 25–48.
MINASNY B., MCBRATNEY A.B. 2005. The Matérn function as a general
model for soil variograms. Geoderma. Vol. 128(3–4) p. 192–207.
DOI 10.1016/j.geoderma.2005.04.003.
MUSTAFA A., MINGGANG X., ALI SHAH S.A., ABRAR M.M., NAN S., BAOREN
W., ZEJIANG C., SAEED Q., NAVEED M., MEHMOOD K., NÚÑEZ-
DELGADO A. 2020. Soil aggregation and soil aggregate stability
regulate organic carbon and nitrogen storage in a red soil of
southern China. Journal of Environmental Management.
Vol. 270, 110894. DOI 10.1016/j.jenvman.2020.110894.
OADES J.M. 1993. The role of biology in the formation, stabilization and
degradation of soil structure. Geoderma. Vol. 56 p. 377–400. DOI
10.1016/0016-7061(93)90123-3.
OKSANEN F.J., BLANCHET F.G., FRIENDLY M., KINDT R., LEGENDRE P.,
MCGLINN D., ..., WAGNER H. 2017. Vegan: Community Ecology
Package. R package version 2.4-3. [online]. Vol. 1. [Access
25.10.2018]. Available at: http://CRAN.R-project.org/package=-
vegan
R Core Team 2020. A Language and Environment for Statistical
Computing. R: A language and environment for statistical
computing [online]. R Foundation for Statistical Computing,
Vienna, Austria. [Access 15.06.2020]. Available: https://www.R-
project.org/
© 2022. The Authors. Published by Polish Academy of Sciences (PAN) and Institute of Technology and Life Sciences – National Research Institute (ITP – PIB).
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/)
Аva Umerova, Olexander Zhukov, Nadezhda Yorkina 73
RILLIG M.C., MULLER L.A.H., LEHMANN A. 2017. Soil aggregates as
massively concurrent evolutionary incubators. ISME Journal.
Vol. 11 p. 1943–1948. DOI 10.1038/ismej.2017.56.
RIVERA J.I., BONILLA C.A. 2020. Predicting soil aggregate stability using
readily available soil properties and machine learning techniques.
Catena. Vol. 187, 104408. DOI 10.1016/j.catena.2019.104408.
SIX J., BOSSUYT H., DEGRYZE S., DENEF K. 2004. A history of research on
the link between (micro)aggregates, soil biota, and soil organic
matter dynamics. Soil and Tillage Research. Vol. 79(1) p. 7–31.
DOI 10.1016/j.still.2004.03.008.
SODHI G.P.S., BERI V., BENBI D.K. 2009. Soil aggregation and distribution
of carbon and nitrogen in different fractions under long-term
application of compost in rice-wheat system. Soil and Tillage
Research. Vol. 103 p. 412–418. DOI 10.1016/j.still.2008.12.005
THOMPSON L., THOMAS C.D., RADLEY J.M.A., WILLIAMSON S., LAWTON J.H.
1993. The effect of earthworms and snails in a simple plant
community. Oecologia. Vol. 95 p. 171–178. DOI 10.1007/
BF00323487.
THUILLER W., LAVOREL S., MIDGLEY G., LAVERGNE S., REBELO T. 2004.
Relating plant traits and species distributions along bioclimatic
gradients for 88 Leucadendron taxa. Ecology. Vol. 85 p. 1688–
1699. DOI 10.1890/03-0148.
VAŠÁT R., PAVLŮ L., BORŮVKA L., DRÁBEK O., NIKODEM A. 2013. Mapping
the topsoil pH and humus quality of forest soils in the North
Bohemian Jizerské hory Mts. region with ordinary, universal, and
regression kriging: Cross-validation comparison. Soil and Water
Research. Vol. 8 p. 97–104. DOI 10.17221/62/2012-swr.
WAGG C., BENDER S.F., WIDMER F. VAN DER HEIJDEN M.G.A. 2014. Soil
biodiversity and soil community composition determine ecosys-
tem multifunctionality. Proceedings of the National Academy of
Sciences of the United States of America. Vol. 111 p. 5266–5270.
DOI 10.1073/pnas.1320054111.
WHITTLE P. 1954. On stationary processes in the plane. Biometrika.
Vol. 41 p. 434−449. DOI 10.1093/biomet/41.3-4.434.
WICK A.F., DANIELS W.L., NASH W.L., BURGER J.A. 2016. Aggregate
recovery in reclaimed coal mine soils of SW Virginia. Land
Degradation & Development. Vol. 27 p. 965–972. DOI 10.1002/
ldr.2309.
WILPISZESKI R.L., AUFRECHT J.A., RETTERER S.T., SULLIVAN M.B., GRAHAM
D.E., PIERCE E.M., ZABLOCKI O.D., PALUMBO A. V., ELIAS D.A. 2019.
Soil aggregate microbial communities: Towards understanding
microbiome interactions at biologically relevant scales. Applied
and Environmental Microbiology. Vol. 85(14), e00324-19. DOI
10.1128/AEM.00324-19.
YORKINA N., MASLIKOVA K., KUNAH O., ZHUKOV O. 2018. Analysis of the
spatial organization of Vallonia pulchella (Muller, 1774) ecolo-
gical niche in Technosols (Nikopol manganese ore basin,
Ukraine). Ecologica Montenegrina. Vol. 17 p. 29–45. DOI
10.37828/em.2018.17.5.
ZHANG S., WANG R., YANG X., SUN B., LI Q. 2016. Soil aggregation and
aggregating agents as affected by long term contrasting manage-
ment of an Anthrosol. Scientific Reports. Vol. 6 p. 1–11. DOI
10.1038/srep39107.
ZHUKOV O., KUNAH O., DUBININA Y., NOVIKOVA V. 2018. The role of
edaphic, vegetational and spatial factors in structuring soil animal
communities in a floodplain forest of the Dnipro River. Folia
Oecologica. Vol. 45 p. 8–23. DOI 10.2478/foecol-2018-0002.
© 2022. The Authors. Published by Polish Academy of Sciences (PAN) and Institute of Technology and Life Sciences – National Research Institute (ITP – PIB).
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/)
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