The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 5290002, IsraelLife Sciences International Postgraduate Educational Center, 0040 Yerevan, Armenia
r t i c l e i n f o
rticle history:eceived 5 May 2013eceived in revised form 25 July 2013ccepted 16 August 2013
A fieldwork was carried out in Caesarea sand dunes, Israel, to determine the influence of fine-scalelandscape-patch abiotic-factor heterogeneity on microbial activity in a Mediterranean region. Soil organ-isms in terrestrial systems are unevenly distributed in time and space, and are often aggregated.Spatio-temporal patchiness in the soil environment is thought to be crucial for the maintenance ofsoil biodiversity, providing diverse microhabitats that are tightly interwoven with resource partitioning.Determination of a ‘scale unit’ to help understand ecological processes has become one of the importantand most debatable problems in recent years. To better understand the distribution of soil microbial com-munities at multiple spatial scales, a survey was conducted to examine the spatial organization of thecommunity structure in two sandy soil ecosystems. One-hundred forty-four soil samples were collectedfrom two patches 4000 m apart from each other. Basal respiration (CO2 evolution without the additionof any external substrate), microbial biomass, functional diversity, and community-level physiologicalprofile (CLPP) in soil were measured with a MicroRespTM system. Soil abiotic analysis was performedby soil standard analytical methods. The results demonstrated that bacterial distributions can be highlystructured, even within a habitat that appears to be relatively homogeneous at the plot and field scale.Different subsets of the microbial community were distributed differently across the plot. This is due tospatial heterogeneity associated with soil physical, chemical, and biological properties. Although spatialvariability in the distribution of soil microorganisms is generally regarded as random, this variability
often has a predictable spatial structure. This study provided evidence that a spatially explicit approachto soil ecology can enable the identification of factors that drive the spatial heterogeneity of populationsand activities of soil organisms, at scales ranging from meters to hundreds of meters. Furthermore, thereis increasing evidence that spatial soil ecology can yield new insights into the factors that maintain andregulate soil biodiversity, as well as on how the spatial distribution of soil organisms influences plant
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Recent studies have emphasized the importance and role ofnvironmental factors in the erratic distribution of microbial com-unities in terrestrial ecosystems (Berg and Steinberger 2008;
indstrom and Langenheder 2012). Microorganisms are not dis-ributed uniformly in the environment, rather their abundancend activity change along environmental gradients. Even within a
Please cite this article in press as: Martirosyan, V., et al., Spatial hetePedobiologia - Int. J. Soil Biol. (2013), http://dx.doi.org/10.1016/j.pedo
omogeneous system, biological processes (e.g., growth or colonyormation, functional and structural diversity) can produce aggre-ations of organisms at various spatial scales. Since microorganisms
play vital roles in surface and subsurface soil geology, hydrol-ogy, and ecology, knowledge concerning the microbial-communitystructure and composition is very important for improving our con-ceptual and projective understanding of surface and subsurfacesoil-ecosystem processes, functions, and management. However,studies of soil microbial-communities’ vertical distribution areknown to be complex due to changes in soil physical and chemicalcharacteristics, yielding high levels of spatial and temporal vari-ability in hydrological and biogeochemical processes (Zhang et al.1998; Tobin et al. 1999). Soils are considered the most microbiallydiverse environments on earth due to their immense physical,chemical, and biological heterogeneity (Daniel 2005), e.g., up to
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109 microbial cells, representing more than 10,000 genomes, caninhabit one gram of soil (Torsvik and Ovreas 2002).
The abundance, composition, and diversity of microbial commu-nities within soils were found to be strongly depth-dependent, as
hown by Fierer et al. (2003a), LaMontagne et al. (2003), Agnellit al. (2004), and Kemnitz et al. (2007). In their study, Kemnitzt al. (2007) they showed that the bacterial biomass concentra-ion (bacterial 16S rRNA genes), number of terminal restrictionragment-length polymorphism peaks, denaturing gradient gellectrophoresis bands (representative of richness), and the pro-ortion of Gram-negative to Gram-positive bacteria are lower inubsurface than in surface soils.
The changes in the microbial community structure with soilepth are attributed to the response of microbes to the contrastinghysical and chemical conditions associated with surface, vadoseone, and saturated soils (Holden and Fierer 2005). Environmen-al factors that influence microbial-community composition andiversity include (but are not limited to) pH (Eichorst et al. 2007),article size (Sessitsch et al. 2001), organic-carbon content (Zhout al. 2002), nutrient availability (Fierer et al. 2003b), water con-ent (Treves et al. 2003), and oxygen concentration (Ludemannt al. 2000). The magnitude and variation of these parameters dif-er between surface and subsurface soils. Holden and Fierer (2005),n their study, elucidated that water availability, plant-derivedesources (carbon, nitrogen, and other nutrients), mineralizablearbon and nitrogen, and the oxygen concentration – all declineteeply with depth. Thus, the physiology and metabolic potentialf a microbial community will vary greatly with location along aoil profile.
Given that environmental factors do not necessarily operatendependently or at distinct spatial scales, studying microbial sys-ems using a single analytical scale cannot provide a completenderstanding of community dynamics. Multi-scale comparisons,
n which patterns are analyzed at several different spatial scales,an be more useful when trying to identify the factors that con-rol community development. Conclusions about the organizationf microbial communities, the effect of disturbance, or the rolesf various limiting factors, are likely to differ at different spa-ial scales (Wiens et al. 1986). Moreover, the characterization of
icrobial communities on several different scales can help explainaradoxes that arise when different investigators, studying similarommunities but on different scales, arrive at different conclu-ions about the factors that structure these communities. Accordingo Rahel (1990), these disagreements may reflect viewpoints ofifferent scales, and not differences in the way communities arerganized. Robertson and Gross (1994) and Ettema and Wardle2002) have recently begun to focus on multi-scale comparisons,nd have found evidence of nested scales of spatial structure. Theresence of nested scales of variation suggests that the variousactors that regulate the development of microbial communi-ies in the soil ecosystems may operate on different scales and
simultaneous analysis of the multi-scale spatial variability oficrobial-community structure and soil microenvironment can
elp identify these factors and determine their relative influenceRobertson and Gross 1994; Robertson et al. 1997; Nunan et al.002; Smith et al. 2002).
The present study was designed to address the general need forncreased research into multi-scale patterns of spatial organizationn two sandy soil ecosystems. In particular, the research focusedn quantifying the spatial patterns associated with the microbial-ommunity structure within-patch and in different patches. Nestedevels of spatial distribution were quantified in areas ranging from
m to 10 m units, and between two scales of similar sites located km from each other. The aim of the present study was to revealhe influence of fine-scale landscape-patch moisture and organic-
atter heterogeneity on microbial-activity linkage in coastal sandy
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cosystems.Soil moisture availability is one of the most important triggers
nown to determine primary production (Saleska et al. 1999), nitro-en and carbon mineralization (Leiros et al. 1999; Savin et al. 2001),
PRESSogia xxx (2013) xxx–xxx
and soil organism distribution and activity (Steinberger and Sarig1993), while the soil upper and lower layers are recognized as beingenvironmentally, chemically and physically heterogeneous. Thisresulted in two questions: (1) how do such heterogeneous envi-ronments affect microbial distribution and (2) how will microbialfunctional diversity be altered spatially.
One of the factors that has been studied in a multi-scale soilenvironment is microbial respiration, which is a general and sim-ple tool for evaluating biological activity. We hypothesized that: (1)since soil respiration increases in terms of CO2 evolution as micro-bial activity increases and is related to organic-matter availability(Wong and Wong 1986), any change in C levels will affect the car-bon exchange between terrestrial ecosystems and the atmosphere,altering net ecosystem productivity; and (2) during the same periodof time, microbial biomass (MB), microbial functional diversity,community-level physiological profile (CLPP), and the biomass-specific respiration rate or metabolic quotient (qCO2) in surface andsubsurface soil layers, will follow the changes in soil-moisture andorganic-matter patterns.
Materials and methods
In order to undertake the present study, two sites were chosen:west and east, 100 and 4000 m from the Mediterranean Sea shore,respectively, with similar plant cover and topography in a coastalsandy ecosystem.
Study area
The western (32◦28′ N, 34◦53′ E) and eastern (32◦28′ N, 34◦55′ E)study sites were located in the northern Sharon region of Israelsouth of Caesarea (Danin 2005). The distance between the twosites is about 4000 m. A randomly selected 10 m × 10 m area at eachsite was subdivided into 2 m × 2 m plots. The climate in the regionis sub-humid Mediterranean. The average annual temperature is19.7 ◦C.
The mean temperature of the coldest month (January) is 13.5 ◦Cand that of the warmest month (August) 25.9 ◦C. The mean multi-annual amount of rainfall is 580 mm, falling on 50 rainy days duringthe months of October to April. The sandy soils are well drained.However, an impervious claypan is present. The sandy soils at theeastern study site are more reddish brown (rich with iron) than atthe west study site (dark grayish brown).
The study sites are dominated by shrub associations (Koller et al.1964; Danin and Yaalon 1982; Danin 2005). The schematic distri-butions of the shrubs within the sites are presented in Fig. 1. Thedominant shrubs in the study areas are Retama raetam, Ammophilaarenaria, Artemisia arenaria, Helianthemum stipulatum, Dittrichiaviscosa, and Heterotheca. Associations dominated by R. raetam havesoil richer in silt, clay, and humus, especially in the shade of theshrubs. The entire area supports an increasing number and diver-sity of annual plants. There are interesting developments in theRetama-shade microhabitat, which belongs to the Papilionaceaeand possibly has nitrogen-rich litter (Danin 1997).
Sampling
Soil samples were collected from the 0 to 10 cm and the 10to 20 cm layers at each point of the 2 m × 2 m intersections in the10/10 m plots, from each of the sites. A total of 72 soil samples ateach site, a total of 144 soil cores from both study sites, during wetseasons (December), were collected from two depths using a 7-cm
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diameter soil auger. Each soil sample was placed in an individualplastic bag and then transported in an insulated container to thelaboratory, where it was stored at 4 ◦C until biological and chemi-cal analyses were conducted. The soil samples were sieved (2-mm
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ig. 1. Schematic diagram of the study areas with different plant locations: (A) easite – (� – Helianthemum stipulatum; � – Ammophila arenaria; � – Retama raetam; �
esh size) before analyses in order to remove root particles andther organic debris.
aboratory analyses
All chemical and biological analyses were conducted on each ofhe replicates collected in the field from each treatment.
biotic parameters
Soil moisture (SM) was determined gravimetrically by dryinghe soil samples at 105 ◦C for 24 h.
The total organic carbon (TOC) content in soil samples wasetermined by muffling soil at 400 ◦C and waiting before and afteruffling.
iotic parameters
Basal respiration (CO2 evolution without the addition of anyxternal substrate) of the microbial community in soils was mea-ured with a MicroRespTM system (Campbell et al. 2003). Activeicrobial biomass (MB) was estimated by converting micro-
ial glucose-induced respiration rates to biomass (Anderson andomsch 1978). Then, the biomass-specific respiration rate oretabolic quotient (qCO2), expressing the ratio between basal res-
iration and microbial biomass, was calculated. All respiration-rateeasurements were expressed on a soil dry-weight basis. qCO2 of
he microbial communities was suggested by Fliessbach and Mader2000) and Dilly et al. (2003) as correlating positively with energyemand and, hence, negatively with carbon-use efficiency of soilicrobial communities. It is widely accepted that qCO2 is elevatedhen a microbial community operates inefficiently and – as a result
f an exogenous disturbance – diverts a higher proportion of car-on to maintenance requirements than to biosynthesis (Andersonnd Domsch 1993).
Microbial functional diversity and community-level physiolog-cal profile (CLPP) in soil were also detected using the MicroRespTM
late (Campbell et al. 2003). Fourteen different carbon sources ofarbohydrates, carboxylic acids, amino acids, and aromatic car-oxylic acids (Table 1) were added to whole, ground, straw soilamples in deep well plates (about 0.47 g in each well). Carbon diox-
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de evolution was measured by dye plates – a colorimetric reactionsing absorbent alkali with the ability to measure carbon diox-
de evolution. The plates were read twice in a spectrophotometert 590 nm: just before the plates were placed on the deep wells
ite – (� – Retama raetam; – Dittrichia viscosa; � – Heterotheca); and (B) westernts of Heterotheca).
containing the ground straw samples (time 0) and after 1 h ofmicrobial respiration (time 1). During that time, the plates wereincubated in the dark at 27 ◦C. The result for each well was calcu-lated and compared to the 16th well (which contained the samestraw sample and water), measuring the basal respiration with noadded carbon source.
Microbial functional diversity was determined using theShannon–Weaver index (H′):
H′ = −∑
pi ln pi
where pi is the ratio of the activity of a particular substrate and thesum of activities of all substrates (Zak et al. 1994).
Statistical analysis
Statistical analysis was conducted using JMP software (JMPversion 10; SAS Institute, Inc., Cary, NC). Multivariate analysis(pairwise correlation) was used to determine differences betweenvariables within and between layers and between two patches:east and west. Data from all sampling locations were analyzed toobtain a picture of the spatial relationships in the patches. Differ-ences obtained at levels of p < 0.05 were considerate significant. Thefigures were created using MATLAB software (version 7).
Results
Abiotic parameters
Results of abiotic data are presented in Figs. 2 and 3. Fig. 2A–Dexemplifies the spatial and patchy distribution of soil moistureat each of the sampling sites at each of the two depths. The soilmoisture mosaic patchiness – roughness shows the dissimilaritybetween layers with a mean moisture level of 2.14% (Fig. 2A) and2.22% (Fig. 2C) in the 0–10 cm layer and with a mean moisture levelof 2.96% (Fig. 2B) and 2.73% (Fig. 3D) in the 10–20 cm layer for theeast and west patches, respectively. Multivariate analysis of theeast patch showed no significant (NS) (p > 0.05) differences in thespatial distribution of SM between the two layers (Fig. 2A and B). Asimilar trend of soil moisture levels was obtained for the west sam-pling sites, with no significant (p > 0.05) difference between the two
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soil layers (Fig. 3C and D). No significant (p > 0.5) differences werefound between the two sampling sites (east and west).
The mean soil organic-matter levels were lower in the deepersoil layer than in the upper layer (0.21% and 0.17% for the 0–10 cm
Please cite this article in press as: Martirosyan, V., et al., Spatial heterogeneity of a microbial community in a sandy soil ecosystem.Pedobiologia - Int. J. Soil Biol. (2013), http://dx.doi.org/10.1016/j.pedobi.2013.08.001
ARTICLE IN PRESSG ModelPEDOBI 50369 1–9
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Table 1The different carbon sources added to straw samples and divided into chemical groups using the MicroRespTM technique.
Fig. 2. Spatial distribution of soil moisture contents in the 10 m × 10 m grid on a dry weight basis: (A) 0–10 cm soil layer in the eastern site; (B) 10–20 cm soil layer in theeastern site; (C) 0–10 cm soil layer in the western site; and (D) 10–20 cm soil layer in the western site.
Fig. 3. Spatial distribution of organic content in the 10 m × 10 m grid on a dry weight basis: (A) 0–10 cm soil layer in the eastern site; (B) 10–20 cm soil layer in the easternsite; (C) 0–10 cm soil layer in the western site; and (D) 10–20 cm soil layer in the western site.
ayer and 0.36% and 0.35% for the 10–20 cm layer for the easternnd western sites, respectively). Multivariate analysis of the spatialistribution of OM for the east patch study site showed significantifferences (p < 0.02) only for the B and F columns in the 0–10 cmoil layer (Fig. 3A) and for the D and B columns (p < 0.0001) in the0–20 cm soil layer (Fig. 3B). However, no significant (p > 0.05) dif-erence was found between the two soil layers for OM within theatch.
At the west site, the distribution of OM was relatively homoge-eous, with no significant difference between the two soil layersFig. 3C and D). Based on the above data, no difference was foundetween the sites and depths, raising the possibility that we have
relatively homogeneous distribution of OM across the sites.
iotic parameters
O2 evolutionMultivariate analysis of CO2 evolution in the soil samples col-
ected from the upper 0–10 cm soil layer at the eastern sitehowed a significant (p < 0.03) difference between the C and Folumns (Fig. 4A), while no significant difference was obtainedetween the 32 samples in the 10–20 cm soil layer of this siteFig. 4B). Moreover, due to the relative variation in CO2 evo-ution, no significant difference was obtained between the twooil layers at the eastern site, although the mean CO2 evolutionas 0.94 �g CO2-C g dry soil−1 h−1 for the 0–10 cm soil layer and
.30 �g CO2-C g dry soil−1 h−1for the 10–20 cm soil layer.The spatial distribution of CO2 evolution in soils collected from
he western site showed a trend similar to that of the easternite, with significant differences in the 0–10 cm soil layer for the
and D columns (p < 0.02) and for the F and B columns (p < 0.007)Fig. 4A), while significant differences were observed in the deeperoil layer (10–20 cm) for the D and B columns (p < 0.02) and the
and B columns (p < 0.007) (Fig. 4B). At the western site, a trendimilar to that at the eastern site was obtained – with no signifi-ant difference between the two soil layers, although the mean CO2volution was 0.74 �g CO2-C g dry soil−1 h−1 in the upper layer and.42 �g CO2-C g dry soil−1 h−1 in the deeper soil layer (Fig. 4C and).
icrobial biomass (MB)The mean microbial biomass distributed in the 2 lay-
rs showed a configuration similar to that of CO2 evolution:igh biomass in the upper soil layer (0–10 cm) and lowiomass in the deeper soil layer (10–20 cm) at both sites.t the eastern and western sites, the mean (n = 32) MB was4.04 �g C g dry soil−1 and 45.99 �g C g dry soil−1 in the 0–10 cmoil layer, and 72.52 �g C g dry soil−1 and 43.76 �g C g dry soil−1 inhe 10–20 cm soil layer, respectively.
Multivariate analysis of the spatial distribution of MB at the east-rn site showed a significant (p < 0.02) difference only for the D, B, F,nd E columns in the 0–10 cm (Fig. 5A) soil layer and no significantifferences between the samples collected from the 10 to 20 cmoil layer (Fig. 5B). Moreover, no significant differences were foundetween the two soil layers within the patch.
The spatial distribution of MB in soil samples collected fromhe western site showed a significant (p < 0.01) difference in the–10 cm soil layer only for the A and D columns (Fig. 5C) and inhe E and B columns (p < 0.01) for the 10–20 cm soil layer (Fig. 5D).he results indicate that the non-significance can reflect a homo-eneous spatial distribution of MB in the two soil layers at theites.
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he spatial distribution of the metabolic quotient (qCO2)The spatial distribution of the metabolic quotient (qCO2) at the
astern site exhibited no significant differences for the 0–10 cm soil
PRESSogia xxx (2013) xxx–xxx 5
layer (Fig. 6A) and significant differences for the C and B (p < 0.007)and F and E (p < 0.001) columns in the 10–20 cm soil layer (Fig. 6B). Atendency for homogeneous distribution of qCO2 was found withinthe site and between the layers. The mean qCO2 for the 0–10 cm and10–20 cm soil layers was similar, i.e., 0.30 �g CO2-C g−1 biomass-C h−1.
Multivariate analysis of the spatial distribution of qCO2 at thewestern site showed a significant (p < 0.04) difference only in the0–10 cm soil layer for the C and B columns (Fig. 6C) and in the10–20 cm soil layer for the D and A columns (p < 0.03) (Fig. 6D). Nosignificant differences were found between the two layers withinthe western site. The mean qCO2 was 0.78 �g CO2-C g−1 biomass-C h−1 and 0.29 �g CO2-C g−1 biomass-C h−1 in the 0–10 cm and10–20 cm soil layers, respectively. The spatial distribution of theqCO2 between the two layers showed a tendency toward homo-geneity, similar to the eastern site.
Changes in the community-level physiological profile (CLPP)In the east-site patch, the spatial distribution of CLPP was rela-
tively homogeneous between and within soil layers (Fig. 7A and B).The mean CLPP for the upper soil layer (0–10 cm) and the deepersoil layer (10–20 cm) was 20.97 �g CO2-C g dry soil−1 h−1 and18.27 �g CO2-C g dry soil−1 h−1, respectively. Multivariate analysisof the spatial distribution of the CLPP at the western site showeda significant difference only for the F and B columns (p < 0.04) inthe 0–10 cm soil layer (Fig. 7C), no significant difference in thedeeper (10–20 cm) soil layer (Fig. 7D), and no significant differ-ence between the two layers within the site. The mean CLPP in theupper and deeper soil layers at the western site was 17.11 �g CO2-C g dry soil−1 h−1 and 15.76 �g CO2-C g dry soil−1 h−1, respectively.A homogeneous spatial distribution of CLPP was observed betweenthe patches. The spatial distribution of the microbial functionaldiversity or Shannon–Weaver index (H′) exhibited patterns similarto the CLPP profiles.
Changes in the community-level physiological profile (CLPP inpercentage) of the four detected carbon groups (aromatic acids,carboxylic acids, amino acids, and carbohydrates) represented by14 different substrates are presented in Fig. 8A–D.
Based on the data, the percentage of the four groups at theeastern site varied between the two soil layers as follows: aro-matic carboxylic acids – 35.00% (0–10 cm), 26.53% (10–20 cm);carboxylic acids – 56.05% (0–10 cm), 66.15% (10–20 cm); carbo-hydrates – 3.30% (0–10 cm), 3.29% (10–20 cm); and amino acids– 5.65% (0–10 cm), 4.03% (10–20 cm).
The means of the four carbon groups in the west-site soil sam-ples were as follows: aromatic carboxylic acids – 29.94% (0–10 cm),23.92.53% (10–20 cm); carboxylic acids – 58.44% (0–10 cm), 67.43%(10–20 cm); carbohydrates – 4.29% (0–10 cm), 3.46% (10–20 cm);and amino acids – 7.33% (0–10 cm), 5.23% (10–20 cm). It shouldbe mentioned that the changes in the patterns of the four groupsbetween the two soil layers at both sites were similar: carboxylicacids > aromatic carboxylic acids > amino acids > carbohydrates.
It is also interesting to mention that at the eastern site, all 3carbon groups (aromatic, amino acids, and carbohydrates) had ahigh mean value in the 0–10 cm soil layer, except carboxylic acids.
Multivariate analysis of the spatial distribution of the fourcarbon groups at the eastern site showed significant (p < 0.001)differences between the groups in the 0–10 cm (Fig. 8A) and the10–20 cm (p < 0.001) soil layers (Fig. 8B). No significant differencewas found within each group between the two layers at the site.
The spatial distribution of the four carbon groups at the western
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site showed significant (p < 0.001) differences between the groupsin the 0–10 cm (Fig. 8C) and the 10–20 cm (p < 0.001) (Fig. 8D) soillayers. No significant difference was found between the two lay-ers within each group. The only significant (p < 0.03) difference
6 V. Martirosyan et al. / Pedobiologia xxx (2013) xxx–xxx
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ig. 4. Spatial distribution of CO2 evolution (�g CO2-C g dry soil−1 h−1) in the 10 m ×ite; (C) 0–10 cm soil layer in the western site; and (D) 10–20 cm soil layer in the w
etween the two sites was for the amino acids group, which wasbserved only in the 10–20 cm layer.
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nteraction between abiotic and biotic parametersThe obtained data elucidated the important and significant
ffect of SM on CO2 evolution (p < 0.01) and MB (p = 0.05), and
ig. 5. Spatial distribution of microbial biomass (�g C g dry soil−1) in the 10 m × 10 m griite; (C) 0–10 cm soil layer in the western site; and (D) 10–20 cm soil layer in the western
grid: (A) 0–10 cm soil layer in the eastern site; (B) 10–20 cm soil layer in the eastern site.
were not correlated with qCO2, CLPP, or functional diversity forthe upper soil layer (0–10 cm) at the eastern site. No correlation
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between the biotic variable and SM was found in the deeper soillayer at the same site. Moreover, organic matter at this site wasfound to be an ineffectual factor in the relation with the bioticvariable.
d: (A) 0–10 cm soil layer in the eastern site; (B) 10–20 cm soil layer in the eastern site.
V. Martirosyan et al. / Pedobiologia xxx (2013) xxx–xxx 7
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ig. 6. Spatial distribution of the metabolic quotient (qCO2) (�g CO2-C g−1 biomassoil layer in the eastern site; (C) 0–10 cm soil layer in the western site; and (D) 10–
The data acquired from the western site yielded different inter-ctions between the abiotic and biotic variables than from theastern site. The interaction between the biotic and abiotic param-ters was significant (p < 0.03) only between SM and CLPP in
Please cite this article in press as: Martirosyan, V., et al., Spatial hetePedobiologia - Int. J. Soil Biol. (2013), http://dx.doi.org/10.1016/j.pedo
he upper soil layer (0–10 cm), whereas no significant interactionetween the biotic and abiotic variables was found in the deeperoil layer (10–20 cm).
ig. 7. Spatial distribution of the community-level physiological profile (CLPP) (�g CO2-CB) 10–20 cm soil layer in the eastern site; (C) 0–10 cm soil layer in the western site; and
) in the 10 m × 10 m grid: (A) 0–10 cm soil layer in the eastern site; (B) 10–20 cm soil layer in the western site.
Discussion
In order to more fully characterize the spatial variability ofmicrobial systems, studies that use several different scales of mea-
rogeneity of a microbial community in a sandy soil ecosystem.bi.2013.08.001
surement are necessary. In this research, multi-scale analysis ofthe spatial distribution of a soil microbial community revealedseveral different scales of organization, ranging from 2 m to 10 m
g dry soil−1 h−1) in the 10 m × 10 m grid: (A) 0–10 cm soil layer in the eastern site;(D) 10–20 cm soil layer in the western site.
8 V. Martirosyan et al. / Pedobiologia xxx (2013) xxx–xxx
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ig. 8. CLPP (%) in the eastern and western sites: (A) 0–10 cm soil layer in the eastite; and (D) 10–20 cm soil layer in the western site. � – aromatic carboxylic acids;
ithin the patch, with a distance of about 4 km between theatches.
In the present study, we focused on vertical as well as horizon-al patterns of soil microbial communities. Horizontal patterns areess well-documented than vertical gradients. Parkin (1993), in histudy, referred to four main scales of interest: microscale, plot scale,eld or landscape scale, and regional scale. Many ecological theo-ies and models acknowledge that elements that are close to onenother in space or time are more likely to be influenced by theame generating processes.
In our study, abiotic parameters exhibit similar patternsetween the patches. SM was found to be higher in the upper soil
ayer than in the lower layer at both sites, although the mean valuesere higher at the western patch site, which can be due to it beingearer to the sea. A similar trend was found for OM patterns – theestern patch exhibited relatively higher organic matter than the
astern patch in both layers.Biotic parameters related to the microbial community, such as
icrobial biomass, CO2 evolution, CLPP, and functional diversity,ad nearly similar patterns in both site-patches, where these bioticarameters had a negative correlation with the abiotic parameters.he mean values of biotic variables obtained for the eastern-siteatch were higher than those in the western-site patch, exceptor CO2 evolution in the 10–20 cm soil layer of the western patch,
Please cite this article in press as: Martirosyan, V., et al., Spatial hetePedobiologia - Int. J. Soil Biol. (2013), http://dx.doi.org/10.1016/j.pedo
hich demonstrated an opposite trend. Moreover, a negative corre-ation was found between abiotic and biotic parameters in both siteatches: the upper soil-surface layer exhibited higher biotic activ-
ty than the deeper soil layer. Based on the above, we can assume
e; (B) 10–20 cm soil layer in the eastern site; (C) 0–10 cm soil layer in the westernarboxylic acids; � – carbohydrates; – amino acids.
that these biotic factors were triggered by the patchiness and thedifferences in vegetation cover.
Moreover, although there were differences in microbial biomassand CO2 evolution, the qCO2 was found to be similar for both soillayers in the eastern-site patch, and about 2-fold higher in the0–10 cm layer of the western-site patch.
The microbial metabolic quotient (respiration-to-biomass ratio)or qCO2 is conceptually based on Odum’s theory of ecosystemsuccession, and is increasingly used as an index of ecosystem devel-opment (during which it supposedly declines) and disturbance(due to which it supposedly increases). Thus, qCO2 is a bioindi-cator of disturbance and ecosystem development (Anderson andDomsch 1978; Wardle and Ghani 1995), where an increase indi-cates a reduction in microbial efficiency that appears to be relatedto stress (independent of disturbance) resulting from steady-stateconditions (Wardle and Ghani 1995). Computation of the qCO2 fordisturbance and ecosystem development indicated that this indexresponds unpredictably and does not necessarily decline duringsuccession. At the same time, it was demonstrated that qCO2 oftendeclines with increasing pH, clay content, and amounts of micro-bial biomass. These three soil properties are all indicative of varyingstress rather than disturbance levels (Wardle and Ghani 1995).Thus, qCO2 undoubtedly provides a useful measure of microbialefficiency. Our data are, on the one hand, in agreement with the
rogeneity of a microbial community in a sandy soil ecosystem.bi.2013.08.001
literature data: an increase in qCO2 brings a reduction in micro-bial efficiency. High qCO2 and low microbial efficiency (microbialbiomass and CO2 evolution) were obtained for the westernpatch.
On the other hand, it is worth mentioning that the distributionf the four carbon groups does not have a similar pattern as thether biotic parameters: 3 carbon groups have a relatively loweralue in the eastern patch in both soil layers than in the westernatch, except for the aromatic carboxylic-acid group: the mean ofhis group is higher in eastern patch than in the western patch. Ithould be mentioned that between the layers in both patches, thehanges in the patterns of the four groups are the same: carboxyliccids > aromatic carboxylic acids > amino acids > carbohydrates. Its also interesting that in the eastern patch, all three groups have
high mean value in the 0–10 cm soil layer except the carboxyliccids, and the same was observed for the western patch, with a highean value in the deeper soil (10–20 cm) layer.Based on all obtained data, we assume that the richness of
he microbial communities in both layers within the site patches,s well as the spatial distribution of the microbial communitiesetween the patches, can be due to the distribution of abiotic fac-ors, as well as the relative effect of the vegetation cover.
Based on the assumption that soil microorganisms are usu-lly not randomly distributed and are spatially predictable, databtained in the present study revealed that the overall micro-ial community structures on horizontal patterns are more similarmong the samples within a site than among those taken betweenites, since the geochemical and physical environments appear toe more similar in the former than in the latter case. It was alsohown that there is variability in vertical patterns for the micro-ial community in these sandy soil ecosystems. Our study focusedn comparing the spatial structure of microbial communities withhat of environmental properties and might yield new insights intoow communities develop in soil systems, and what factors may be
mportant in maintaining and regulating soil-ecosystem function.
cknowledgments
We wish to thank Ms. Sharon Victor for her useful comments.pecial thanks to Dr. Stanislav Pen-Mouratov and Ms. Nathaliaitoussi for their kind assistance with soil sampling, and to Dr.duard Aleksanyan for his generous help with the preparation ofhe figures using MatLab software.
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