BGBiogeosciencesBGBiogeosciences1726-4189Copernicus GmbHGöttingen, Germany10.5194/bg-12-2585-2015Soil moisture and land use are major determinants of soil microbial
community composition and biomass at a regional scale in northeastern ChinaMaL.GuoC.LüX.YuanS.WangR.wangrz@ibcas.ac.cnState Key Laboratory of Vegetation and Environmental
Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093,
ChinaUniversity of Chinese Academy of Sciences, Beijing,
100049, ChinaState Key Laboratory of Forest and Soil Ecology, Institute
of Applied Ecology, Chinese Academy of Sciences, Shenyang, 110164, ChinaR. Wang (wangrz@ibcas.ac.cn)2585259612November201418December20148April20159April2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://bg.copernicus.org/articles/12/2585/2015/bg-12-2585-2015.htmlThe full text article is available as a PDF file from https://bg.copernicus.org/articles/12/2585/2015/bg-12-2585-2015.pdf
Global environmental factors impact soil microbial communities and further
affect organic matter decomposition, nutrient cycling and vegetation
dynamic. However, little is known about the relative contributions of
climate factors, soil properties, vegetation types, land management
practices and spatial structure (which serves as a proxy for underlying effects of
temperature and precipitation for spatial variation) on soil microbial
community composition and biomass at large spatial scales. Here, we
compared soil microbial communities using phospholipid fatty acid method
across 7 land use types from 23 locations at a regional scale in
northeastern China (850 × 50 km). The results showed that soil
moisture and land use changes were most closely related to microbial
community composition and biomass at the regional scale, while soil total C
content and climate effects were weaker but still significant. Factors such
as spatial structure, soil texture, nutrient availability and vegetation
types were not important. Higher contributions of gram-positive bacteria
were found in wetter soils, whereas higher contributions of gram-negative
bacteria and fungi were observed in drier soils. The contributions of
gram-negative bacteria and fungi were lower in heavily disturbed soils than
historically disturbed and undisturbed soils. The lowest microbial biomass
appeared in the wettest and driest soils. In conclusion, dominant climate
and soil properties were not the most important drivers governing microbial
community composition and biomass because of inclusion of irrigated and
managed practices, and thus soil moisture and land use appear to be primary
determinants of microbial community composition and biomass at the regional
scale in northeastern China.
Introduction
The soil microbial community plays important roles in regulating organic matter
decomposition, nutrient cycling, soil structural formation and even plant
interactions (Wardle et al., 2004; Harris et al., 2009). At the same time, it is
subjected to the influences of environmental conditions, land use and
spatial structure (Yang et al., 2013). Although there is a growing body of
evidence indicating that climate, soil property, vegetation, spatial
structure and land use are the most important determinants of the global and
regional patterns in soil microbial communities (Kreft and Jetz, 2007;
Nielsen et al., 2010; Zinger et al., 2011; Pasternak et al., 2013; Tsiknia
et al., 2014), how to disentangle the contributions of multiple drivers to
microbial community composition and biomass remains unclear.
Regional climate factors exert major influences on distributions of
microbial communities by determining temperature and soil water availability
along topographic gradients (Hackl et al., 2005; Carletti et al., 2009;
Brockett et al., 2012). Drenovsky et al. (2010) and Brockett et
al. (2012)
found that soil water availability was an important determinant of microbial
community composition, and fungal : bacterial biomass ratios decreased with
increased soil water saturation at regional scales. In different types of
natural Austrian forests, Hackl et al. (2005) showed that mean annual
temperature was the major factor influencing microbial community composition
in zonal forest but that soil water availability was most closely correlated
with microbial community in azonal forests.
Soil property has been found to strongly correlate with soil microbial
community structure and abundance at regional scales. Previous studies
have reported that soil texture, organic matter content, N availability and
pH exhibited dominant effects on soil microbial community composition,
while climatic effects were weaker but still significant at regional scales
(Šantrucková et al., 2003; Brockett et al., 2012; Yang et al., 2013;
Tsiknia et al., 2014). For example, Tsiknia et al. (2014) identified soil
total organic C, pH and geographic distance as the most
important determinants of microbial community abundance at the watershed
scale in Greece. Moreover, plant communities differing in species
composition are likely to produce litter and vary in their chemical
composition, which may subsequently influence soil microbial community
composition (Zhang et al., 2005a; Eskelinen et al., 2009). As a biotic
driver, plants may also exert great effects on soil microbial communities by
controlling allocation of belowground photosynthates (Kaiser et al., 2011).
Spatial structure (which serves as a proxy for underlying effects of temperature
and precipitation for spatial variation) influences the organization of
community as a functional variable in a different way to which the background in which
biological and environmental factors act on community and ecosystem (Borcard
et al., 1992). Recent research has shown there to be strong
autocorrelations between microbial groups, and geographic distance could explain a
high proportion of microbial community variation (Tsiknia et al., 2014).
However, Fierer and Jackson (2006) claimed that soils with similar
environmental characteristics have similar bacterial communities regardless
of geographic distance at continental scales. Using spatial trend surface
analysis, Drenovsky et al. (2010) also found that spatial structure did not
influence microbial community composition across three biogeographical
provinces in California.
At regional scales, land use change is the major reason for spatial
heterogeneity. It has been shown that land use changes lead to great
variation in soil microbial community composition in diverse ecosystems
(Drenovsky et al., 2010), though their impacts depend on many factors,
including the original vegetation that is being replaced and associated
land management practices such as tillage and fallow periods, as well as related water
and nutrient applications such as irrigation and fertilization (Scanlon et
al., 2007; Ma et al., 2013; Yang et al., 2013; Chen et al., 2014). In one
study, Drenovsky et al. (2010) reported that distinct microbial communities
were associated with land use types and disturbance at the regional scale in
California. Tillage influences multiple soil physical and chemical
properties, disrupts soil fungal hyphae (Evans and Miller, 1990), and alters
microbial community composition (Ingram et al., 2008; Drenovsky et al.,
2010). Recently, changes in land use have occurred in temperate areas of
northeastern China as a result of expansion of farmlands and grazed rangelands
at the expense of natural habitats; however, little is known about soil
microbial community composition to land use changes at regional scales.
Sample locations (1–23; see Table 1) at a regional scale in
northeastern China.
In this study we compare microbial community composition and biomass from 23
locations across 7 land use types (i.e., rangeland, artificial grassland,
grazed rangeland, farmland, returned cropland, woodland and rice field) at a
regional scale in the Northeast China Transect (NECT). The NECT is identified as
a midlatitude semiarid terrestrial transect and is sensitive to climate
change and disturbance, and thus provides an ideal setting to investigate
distribution patterns of soil microbial community. Our work specifically aimed
at disentangling the contributions of climate, soil properties, vegetation
types, spatial structure and land use on microbial community composition and
biomass at the regional scale. We hypothesize that climate and soil
properties are the primary drivers affecting soil microbial community
composition and biomass, because the climatic gradient, especially precipitation,
is one of the most notable features in this region (Wang et al., 2003).
Sample locations (1–23), coordinates of the sample location, land
use types, vegetation types and number of replicates (n).
LocationNo.CoordinatesLand use typeVegetation typenBaogedawula143∘56′ N, 114∘34′ ERangelandDesert steppe8Dabuxiletu243∘55′ N, 115∘44′ ERangelandDesert steppe82Grazed rangelandDesert steppe8Aqiwula343∘33′ N, 116∘40′ ERangelandSteppe103WoodlandWood and shrub8Dalainuori443∘16′ N, 117∘09′ ERangelandSteppe8Sanyi543∘12′ N, 117∘18′ EWoodlandWood and shrub8Xinchengzi643∘27′ N, 118∘04′ ERangelandSteppe146Returned croplandAlfalfa8Xinfuzhilu743∘43′ N, 119∘04′ EGrazed rangelandSteppe (site 1)47Steppe (site 2)4Tianshan843∘50′ N, 119∘55′ ERangelandSteppe88Returned croplandAlmond16Tianshan943∘50′ N, 120∘15′ ERangelandSteppe99Returned croplandAlmond9Shaogen1043∘38′ N, 120∘47′ ERangelandSteppe (site 1)810Steppe (site 2)810FarmlandCorn8Molimiao1143∘34′ N, 121∘55′ ERangelandSteppe (site 1)811Steppe (site 2)811FarmlandCorn8Yuxin1243∘34′ N, 121∘59′ ERice fieldRice14Baixingtu1343∘52′ N, 122∘41′ EWoodlandWood and shrub8Baolongshan1443∘56′ N, 122∘42′ ERangelandMeadow (site 1)714Meadow (site 2)614FarmlandCorn8Jiamatu1544∘01′ N, 122∘56′ ERangelandMeadow (site 1)815Meadow (site 2)815FarmlandCorn815Red bean7Taipingchuan1644∘21′ N, 123∘14′ ERangelandMeadow916Rice fieldRice9Yaojingzinan1744∘21′ N, 123∘14′ EWoodlandWood and shrub (site 1)1117WoodlandWood and shrub (site 2)1017FarmlandPeanut8Yaojingzi1844∘34′ N, 123∘29′ ERangelandMeadow (site 1)818Meadow (site 2)718FarmlandPeanut818Mung bean818Corn8Yaojingzi1944∘35′ N, 123∘30′ ERangelandMeadow14Yaojingzi2044∘34′ N, 123∘31′ EArtificial grasslandMeadow (site 1)720Meadow (site 2)820FarmlandCorn8Wulanaodu2144∘36′ N, 123∘48′ ERangelandMeadow (site 1)821Meadow (site 2)821FarmlandCorn721WoodlandWood and shrub9Chaganhua2244∘35′ N, 124∘16′ ERangelandMeadow (site 1)8Wulantuga2244∘28′ N, 124∘18′ ERangelandMeadow (site 2) Meadow823823FarmlandCorn623Peanut623WoodlandWood and shrub8Total451Materials and methodsStudy locations
The field study was conducted on a regional scale (43∘12′–44∘36′ N, 114∘34′–124∘18′ E) across Jilin province and Inner Mongolia (about 850 km from east to
west, and 50 km from north to south) with 23 locations in the Northeast China
Transect (NECT) (Table 1, Fig. 1). The NECT was identified as a core project
of International Geosphere-Biosphere Programme (IGBP), which represents an
array of regional-scale gradients on all continents that vary in major
environmental variables (Koch et al., 1995). This area has a continental
monsoon climate, with large seasonal temperature and precipitation gradients.
Long-term (1950–2000) mean annual temperature, precipitation and radiative
dry index at this spatial scale range from approximately
1.3 to 6.8 ∘C, 237 to 472 mm and 0.91 to 1.44, respectively. The
elevation gradients range from 140 to 1309 m
(http://www.worldclim.com; Zhang et al., 1997; Appendix A1). Mean soil
total C, N and C : N varied 3.3-fold, 2.4-fold and 2.7-fold, respectively, across the region.
Overall, there were 7.4-fold and 2.8-fold differences in soil water content
and water holding capacity, respectively, whereas soil origin and pH differed only slightly
(Appendix A1).
Spatial climatic variability, especially precipitation, is one of the most
notable features of the transect. Due to the large decrease in precipitation
from the east (Jilin province) to the west (Inner Mongolia), vegetation vary
gradually from moist meadows in the east to typical steppes and desert
steppes in the west, with farmlands, returned croplands and woodlands spread
evenly across the gradient (Wang et al., 2003, 2011; Appendix A1). All
farmlands were irrigated only a few times (2–3 times) during the growing
season, and rice field was flood-irrigated. The regions have
remarkable variations in climate, land use types and vegetation types, which
make it an ideal region for studying the primary factors driving soil
microbial community composition and biomass. Detailed descriptions of land
use types, vegetation types and soil properties can be found here in Table 1
and Appendix A1 as well as in Zhang et al. (1997) and Ni and Zhang (2000).
Soil samplings
Four hundred fifty-one soil samples from 23 locations including 7 land use types were collected
along the NECT in 12–18 July 2012. Between 6 and 16 soil core samples were collected
randomly per site (100 × 100 m) for determination of soil microbial
communities (Table 1).
The samples were taken with a cylindrical soil sampler (5 cm inner diameter,
15 cm length) for the 0–15 cm layer, and then immediately preserved at 4
∘C in a cooler for transport to the laboratory within 1 week of
collection. The fresh samples were processed using a 2 mm sieve and manually
cleaned of any visible plant tissues. Two subsamples of each sample were
obtained: one was air-dried for routine soil analyses and the other was
stored at -70 ∘C for phospholipid fatty acids analysis.
Soil microbial community analysis
Phospholipid fatty acids (PLFAs) were extracted and quantified from 8.0 g
(dry weight equivalent) of soils using a procedure described by Bossio and
Scow (1998). The separation and identification of extracted PLFAs were
carried out according to the standard protocol of the Sherlock Microbial
Identification System V4.5 (MIDI) and a gas chromatograph (Agilent 6850,
USA). “A : BωC” represents the number of carbons in the compound:
the number of double bonds in the carbon chain, followed by double-bond
location from the methyl (ω) end of the molecule (Bossio and Scow,
1998). Cis and trans conformations are indicated by the suffixes c and t. The
prefixes a and i indicate anteiso and iso branching, 10Me specifies a methyl
group on the 10th carbon from the carboxyl end of the molecule, OH indicates
a hydroxyl group, and cy indicates cyclopropane fatty acids. In addition, the
fatty acids “sum” indicates that imperfect peak separation occurs and
refers to two or more fatty acids that have the same retention time (Drenovsky et al.,
2004).
Thirty-one fatty acids were included in the analyses. (1) Branched fatty
acids indicative of gram-positive bacteria: a13:0, i14:0, i15:0, i16:0,
i17:0 and a17:0; (2) monounsaturated fatty acids indicative of
gram-negative bacteria: 16:1ω7c, 17:1ω8c, 18:1ω5c,
18:1ω9t, 17:0cy and 19:0cy (Frostegård et al., 1993, 1996);
(3) saturated fatty acid (common in soil microorganisms): 14:0, 15:0, 16:0,
17:0, 18:0 and 20:0; (4) two fatty acids (18:2ω6c, 18:1ω9c) were chosen to represent the fungi (Frostegård et al., 2011); and (5)
actinomycetes was represented by 10Me 17:0 fatty acid. The fatty acids 14:2ω6c and 14:1ω8c were found in only three samples and
were thus excluded from the data set. The ratio of 17:0cy (17cy) to 16:1ω7c
(precursor) was used as an indicator of physiological stress (Knivett and
Cullen, 1965). The viable microbial biomass was calculated by summing
concentration of all fatty acids detected in each soil samples (White et al.,
1979). Total percentages of fatty acid identified for each microbial group
were calculated to represent their relative contributions to the total
microbial biomass. The fungal : bacterial fatty acid
(gram-positive + gram-negative bacteria) ratio was also included in the data
analysis (Frostegård et al., 1996).
Soil property analyses
Soil inorganic N (NH4+-N + NO3--N) was extracted with 2 M
KCl solution, and the extractant was determined using a flow injection
autoanalyzer (FIAstar 5000, Denmark). Soil pH was measured at a
soil : water ratio of 1:2.5 with a pH electrode (PHS 29, China). Soil
total C and N content were measured using an elemental analyzer (Elemetaranalysator
vario Max CN, Germany). Soil texture was determined by the optical size
analyzer (Mastersizer 2000, England). Gravimetric soil water content was
measured by oven-drying samples at 105 ∘C for 24 h. Soil water
holding capacity was measured by means of the Wilcox method (Wilcox, 1962).
Statistical analyses
Unconstrained ordination (correspondence analysis: CA) was used to compare
soil microbial communities among samples (n=451) using the Canoco for
Windows 4.5 package (Ithaca, NY, USA). CA is an indirect gradient analysis
method which can provide the basic overview of soil samples and maximize the
correlation between fatty acids and samples (Lepš and Smilauer, 2003).
Constrained ordination (canonical correspondence analysis: CCA) was used to
represent the relationships among environmental factors (habitat, land
management, spatial structure), sample patterns and fatty acids
distributions (Lepš and Smilauer, 2003). Qualitative factors were coded
for the program using a set of “dummy factors”. That is, if a sample has a
particular value of the factor, then the corresponding dummy factor has the
value 1.0 and the other dummy factors have a value of 0.0 for the same
sample.
In order to separate the effects of environmental factors on microbial
communities, the variation partitioning procedure with CCA was used in the
analysis (Borcard et al., 1992). The environmental factors were divided into
three groups: (1) habitat (mean annual temperature, mean annual
precipitation, radiative dry index, elevation, soil texture, pH class, soil N
availability, soil C and N content, soil C : N, water holding capacity),
(2) land management (tillage, grazing, historical tillage, flooding), and (3)
spatial structure (x, y, xy, x2, y2, x2y, xy2,
x3, y3). The third group consisted of nine terms, in which
latitudinal (x) and longitudinal (y) coordinates were used to calculate a
cubic trend surface. Spatial trend surface analysis is one of the
quantitative ecological methods used to study the relation between spatial
structure and species abundance distribution in community (Legendre, 1990).
The variation partitioning procedure decomposed the total variability into
eight parts: individual effect of habitat (X1), land management
(X2), spatial structure (X3), combined effects of habitat and land
management (X4), combined effects of land management and spatial
structure (X5), combined effects of habitat and spatial structure
(X6), combined effects of the three groups of environmental factors
(X7), and residual variation (X8). A complete explanation of these
partitioning analyses can be found in Lepš and Smilauer (2003).
Stepwise multiple linear analyses were used to determine the relationships
of soil microbial community composition and biomass or the contribution of each
microbial group with environmental factors. Differences among the sites in
soil microbial biomass and contribution of each microbial group were tested
using one-way ANOVA. Data management and statistical analyses were
performed using SPSS 17.0 software (SPSS, Chicago, IL, USA).
ResultsVariation in soil microbial communities
The first axis of CA ordination explained 27.5 % of the variation in
microbial community composition, mainly reflecting soil moisture gradients and
land disturbance intensity (Fig. 2a, b). Wetter soils (e.g., rice field,
moisture rangeland) and heavily disturbed soils (e.g., farmland) with more
branched fatty acids (gram-positive bacteria: a13:0, i14:0, i15:0, i16:0,
i17:0) and saturated fatty acids (14:0, 15:0, 16:0, 17:0, 18:0, 20:0) were
positioned along the right side of the first axis. Drier soils and lightly and
historically disturbed soils (e.g., dry rangeland, grazed rangeland, returned
cropland) with more fungal (18:2ω6c, 18:1ω9c) and
monounsaturated fatty acids (gram-negative bacteria: 16:1ω7c,
16:1ω9c, 17:1ω8c, 18:1ω5c, 18:1ω9t) were
plotted along the left side of the first axis.
The second axis of CA ordination described 20 % of the variation in the
composition, mainly associated with management practices and spatial
variation. In heavily disturbed habitats, the positions of flood-irrigated
rice field and farmland were separated along the second axis (Fig. 2a).
Ordination plots of correspondence analysis (CA) of all samples
and fatty acids. (a) Ordination plot of 451 samples scores across 7 land use
types (rangeland, artificial grassland, grazed rangeland, farmland, returned
cropland, woodland and rice field). (b) Ordination plot of 31 fatty acids
scores. The fatty acids scores are near the points for samples in which they
occur with the highest relative contributions.
Ordination plots of canonical correspondence analysis (CCA) of
all samples and environmental factors. (a) Ordination plot of 451 samples
scores across 7 land use types. (b) Ordination plot of habitat and management
factors scores in which spatial structure was run as a covariate. Mean
annual temperature (MAT), mean annual precipitation (MAP), radiative dry
index (RDI), elevation, soil water content (SWC, including natural
precipitation and managed inputs), soil inorganic N (IN), soil total C and N
(C, N), soil C : N, total (T) PLFAs, water holding capacity (WHC) and soil pH
were quantitative environmental factors, and soil texture (loamy sand, LS;
sandy loam, SL), land management practices (tilled, historically tilled,
grazed) were qualitative (nominal) environmental factors. Quantitative
factors were plotted as vectors, and qualitative factors were plotted as
centroids.
Variation partitioning procedure of microbial community
composition, explained by habitat (mean annual temperature and precipitation,
radiative dry index, elevation, soil texture, pH, soil C and N content, soil
C : N, inorganic N, total PLFAs, water holding capacity), land management
(tilled, historically tilled, grazed, flooded) and spatial
structure (x, y, xy, x2, y2, x2y, xy2, x3,
y3: the nine terms in which latitudinal (x) and longitudinal (y) coordinates were
used to calculate a cubic trend surface) factors.
Relationship between microbial communities and environmental
factors
Soil microbial community composition across seven land use types at the regional
scale was distinguished by environmental factors with the CCA ordination
(Fig. 3a, b). The first axis explained 22 % of the variation in microbial
community composition, mainly associated with water regime (i.e., soil water
availability) and water holding capacity. The second axis described
15.2 % of the variation, primarily related to management intensity
(tillage > historical tillage or grazing). Climate factors (mean annual
precipitation and temperature, radiative dry index, elevation) did not show
strong relationships with distribution of microbial communities. Factors such
as soil texture (sandy loam), soil inorganic N content and pH plotted near
the origin, and thus would not be the major drivers of microbial community
composition (Fig. 3b).
Results of stepwise multiple regression analyses. Independent
variables: soil moisture (%), soil total carbon content (C, %), mean
annual precipitation (MAP), radiative dry index (RDI), soil water holding
capacity (WHC). Dependent variables: soil microbial community composition
(SMCC), soil total PLFAs (i.e., microbial biomass, TPLFAs; nmol g-1),
percentages of branched PLFAs (gram-positive bacteria) (BP, %),
monounsaturated PLFAs (gram-negative bacteria) (MP, %), saturated PLFAs
(common in microorganism) (SP, %), fungal PLFAs (F, %), bacterial
PLFAs (B, %) and fungal : bacterial PLFAs (F : B). Negative values of
parameter estimates refer to negative relationships between the examined
dependent variables and the independent variables.
VariableParameterPartial r2ProbabilityenteredestimateSMCCSoil moisture–0.310.000Tillage–0.230.000TPLFAsSoil moisture6.7940.110.000Soil total C0.6070.110.000RDI-26.8930.100.000BPSoil moisture0.2620.570.000Tillage1.7830.060.000MPSoil moisture-0.1050.570.000Tillage-3.8000.170.000SPSoil moisture0.3290.490.000RDI-3.7960.090.000FRDI7.0740.570.000Tillage-1.5800.140.000Soil moisture-0.0420.060.000BMAP-0.0440.200.000Soil total C1.2180.070.000WHC0.1580.060.000Tillage1.5140.050.001F : BRDI0.1420.420.000Tillage-0.0330.120.000Soil moisture-0.0020.110.000Variation partitioning
Forward selection of the three groups of environmental factors with CCA
suggested that the soil microbial community composition was significantly
related to the habitat (X1) (mean annual precipitation and temperature,
radiative dry index, elevation, soil texture, pH, soil nutrient content,
water holding capacity) and land management (X2) (tillage, grazing,
historical tillage, flooding). The variation partitioning procedure showed
that total explained variation in microbial community composition was 69.9 % (X1+X2+X3+X4+X5+X6+X7) and
undetermined variation in it was 30.1 % (X8) (Fig. 4). The largest
unique fraction in the explained variation was the effect of habitat
(X1 : 27 %), which had a strong overlap with land management
(X4 : 15 %). In addition, the land management effect was also
considerable (X2 : 13.4 %), whereas the unique effect of spatial
structure (X3 : 2.8 %) was very small and statistically not
significant.
Soil microbial biomass (i.e., total PLFAs), percentages of
branched PLFAs (gram-positive bacteria), monounsaturated PLFAs (gram-negative
bacteria), actinomycetes (10Me), saturated PLFAs (i.e., common in
microorganism), fungi (F), fungal : bacterial PLFAs (F : B) and 17cy : precursor
across seven land use types at a regional scale in northeastern China.
Soil microbial biomass and contributions of microbial group
Soil microbial biomass (i.e., total PLFAs) varied 2.4-fold across all the land
use types in this region (P< 0.05, one-way ANOVA; Fig. 5a). The
highest value appeared in one of the rangelands (ca. 35 nmol g-1), and
the lowest value appeared in rice field (ca. 16 nmol g-1). Total PLFAs
in artificial grassland, grazed rangeland, farmland and returned cropland had
intermediate values.
The contributions of each microbial group across seven land use types varied
significantly, except that of actinomycetes. Higher contributions of
gram-positive bacteria were found in wetter soils, whereas higher
contributions of gram-negative bacteria and fungi were observed in drier
soils. The contributions of gram-negative bacteria and fungi were lower in
heavily disturbed soils than historically disturbed and undisturbed soils
(P< 0.05, one-way ANOVA; Fig. 5a–f). Similar to the variation in
fungi, the highest fungal : bacterial PLFAs (ca. 0.35) appeared in one
of the rangelands, and the lowest value occurred in rice field (ca. 0.15)
(Fig. 5g). Surprisingly, the 17cy : precursor ratio (used as an indicator of the
anaerobic stress) across seven land use types fluctuated randomly at this
regional scale (Fig. 5h).
Stepwise multiple regression analysis demonstrated that 54 % of the
variation in microbial community composition could be explained by soil
moisture and tillage. Soil moisture, soil total C content and radiative dry
index together accounted for 32 % of the spatial variation in total
microbial biomass. Soil moisture alone contributed to 57 % of the
variation in the contributions of both branched and monounsaturated PLFAs. In this region, radiative dry index, soil moisture and tillage
together accounted for 77 and 65 % of the variation in the contribution of
fungal PLFAs and fungal : bacterial PLFAs. Of the spatial variability
in the contribution of bacterial PLFAs, 38 % could be attributed to the combination
of precipitation, soil total C content, water holding capacity and tillage
(Table 2).
Discussion
In the exploration of the primary drivers regulating distributions of soil microbial
communities and disentangling relative contributions of multiple
environmental factors (e.g., climate, soil texture, pH, soil organic matter
content, vegetation type), land management practices and spatial structure on
microbial community composition and biomass are important challenges in
microbial ecology. In this study, soil moisture is one of the main controls on
microbial communities across seven land use types at the regional scale, explaining 31 % of the variation in microbial community composition
(Fig. 4; Table 2). Drier soils were more enriched in gram-negative bacteria
and fungi, whereas wetter soils were more enriched in gram-positive bacteria
(Fig. 5). These findings are in agreement with previous observations
(Rinklebe and Langer, 2006; Entry et al., 2008; Clark et al., 2009; Drenovsky
et al., 2010; Ma et al., 2014). Drought stress likely facilitates increased
survival of fungi, because soil fungi rely on more aerobic conditions
and are more tolerant of drought due to their filamentous nature (Zhang et
al., 2005a). Aerobic filamentous fungi have variable hyphal networks that
can relocate water and nutrient resource by cytoplasm translocation (Klein
and Paschke, 2004). As soils become water-saturated, soil oxygen
levels are reduced, creating an environment favorable for facultative and obligate
anaerobic bacteria (Drenovsky et al., 2004).
It has been proposed that the ratio of cyclopropane fatty acids to its
precursor can be used to indicate the levels of anaerobic and nutritional
stress, because oxygen depletion could trigger conversion of monoenoic fatty
acids to cyclopropane fatty acid products (Kieft et al., 1997; Drenovsky et
al., 2010). For instance, Drenovsky et al. (2010) reported that
cyclopropane fatty acid : precursor ratios (17cy : (16:1ω7c);
19cy : (18:1ω7c)) were significantly high under conditions of low
O2 concentration and high temperature. However, whether cyclopropane
fatty acid is representative of aerobic conditions is debatable. Bossio and
Scow (1998) found that the cyclopropane fatty acids were insensitive to water
availability across a large-scale precipitation gradient in California.
Similarly, our result also show that the 17cy : precursor responded modestly to high
water availability in this region (Fig. 5h), whereas we do not know
for sure what limits the formation of cyclopropane. This insensitivity to
anaerobic conditions in the soils contrasts with its widespread use as an
anaerobic marker. These findings suggest that ratios of cyclopropane fatty acids to its
precursor are not generally useful as taxonomic indicators of respiratory
type at regional scales.
Distinct microbial community composition and biomass are associated with land
disturbance levels and management practices at the regional scale in
northeastern China. Continuously farming of land is widely occurring in
various biomes across the world. Repeated tillage heavily disturbs soil
physical properties and decreases soil bulk density and water-retaining
capacity (Bescansa et al., 2006). This frequent disturbance in soil
properties during tillage (and associated fertilization) could rapidly alter
microbial community composition due to the different competitive ability of
specific microbial groups. The groups with the capacity of rapid adaptation
to the frequently changing soil environment (e.g., bacteria) could take
advantage of new resources in disturbed habitats (Cookson et al., 2008; Sun
et al., 2011). Consistent with other large-scale studies, conventional
tillage soils had higher proportions of gram-positive bacteria and lower
proportions of fungi in this study (Fig. 2b) (Galvez et al., 2001; Zhang et
al., 2005a). The ability of gram-positive bacteria to sporulate may allow
them to withstand tillage or other anthropogenic disturbance. In contrast,
fungi are sensitive to disturbance and their hyphae density would decrease
significantly in response to tillage (Drenovsky et al., 2010).
Given the strong effects of heavy disturbance on soil microbial communities,
it is interesting to find that microbial community composition in lightly and
historically disturbed soils (i.e., grazed rangelands, returned croplands)
was similar to that in undisturbed soils. These results are supported by
observations in other studies (Bardgett and McAlister, 1999; Ingram et al.,
2008; Sun et al. 2011). Ingram et al. (2008) reported that long-term light
grazing showed no effect on soil organic C content and microbial community
composition based on concentrations of PLFA biomarkers in a mixed-grass
ecosystem. As the disturbance ceased, microbial biomass increased, probably
because more time and resources were available for specific microbial groups
that have a slower growth rate (e.g., fungi) (Zhang et al., 2005b). However,
Buckley and Schmidt (2003) reported that microbial community composition did
not differ significantly between conventionally cultivated fields and fields
that had been abandoned from cultivation for 9 years. A possible
explanation for this result is that long-term sustainable tillage decreases
soil C content; thus the recovery of soil organic matter to pre-agricultural
levels may require decades or even centuries.
Many previous studies have demonstrated that vegetation types, soil
properties and spatial structure can influence soil microbial community
function and abundance through providing suitable habitats and food sources
(Kourtev et al., 2003; Šantrucková et al., 2003; Han et al., 2007;
Chen et al., 2014), whereas our findings of microbial community composition
were not related to these factors across this region. In the current study,
soils were sampled in different vegetation types and soil nutrient content,
but the microbial community composition was very similar at the same
geographical location in natural habitats (e.g., meadow vs. wood and shrub;
data not shown) (Fig. 5). Similar trends were observed in heavily disturbed
habitats: the distributions of microbial communities depended on land
disturbance levels and practices rather than agricultural plant species. For
example, the farmland land use types (e.g., corn, peanut, mung bean, red bean) in the
same location clustered together in CCA ordination despite the different
plant species that they represented (Figs. 2, 3, 5). These results are
consistent with the study of Drenovsky et al. (2010), who reported that
microbial community composition was more strongly influenced by disturbance
than by agricultural plant species in California.
Habitat factors and land management triggered complex interactive effects on
soil microbial community composition at the regional scale in northeastern
China, as the value of shared variance fraction was 15 % without
considering the variation explained by all three components (Fig. 4). This
was similar to the findings of Drenovsky et al. (2010) that environmental
factors caused significant interactions on microbial community composition
at large spatial and temporal scales. The significant shared effects in our
study could be attributed to the strong effects of land disturbance (e.g.,
flooding, irrigation, tillage) on soil properties that then affect microbial
communities. The findings suggest that land management could partly control
soil environmental effects on microbial community composition and biomass at
regional scales.
Inconsistent with the hypothesis, soil moisture and land use were the most
important factors driving microbial community composition and biomass at the
regional scale in northeastern China. In this study, soil moisture was
determined not only by natural precipitation but also by managed inputs;
thus the effect of precipitation was weaker but still significant. In
addition, factors such as spatial structure, soil texture, pH and vegetation
types did not have significant relationships with microbial community
composition and biomass. These findings will improve predictions of the
ecological processes and consequences of ecosystems under global changes.
Sample locations (1–23; see Fig. 1), land use types, land
management practices, vegetation types, climatic indices and soil properties.
MAP, mean annual precipitation (mm); MAT, mean annual temperature
(∘C); RDI, radiative dry index; ELE, elevation (m); TC, soil total C
(%); TN, soil total N (%); SWC, soil water content (%); WHC,
water holding capacity; IN, soil inorganic N content (mgkg-1);
SL, sandy loam; LS, loamy sand.
LocationNo.Land use typeManagementVegetationMAPMATRDIELEpHSoilCNC : NSWCWHCINpracticestypetextureBaogedawula1RangelandUndisturbedDesert steppe2371.71.4410927.7LS0.670.125.323122.05Dabuxiletu2RangelandUndisturbedDesert steppe2761.41.3711587.8LS0.790.17.945152.472Grazed rangelandGrazedDesert steppe2761.41.3711587.9LS0.810.117.315173.30Aqiwula3RangelandUndisturbedSteppe3401.31.3312398.8SL1.450.159.787173.463WoodlandUndisturbedWood and shrub3401.31.3312397.8SL0.70.154.489203.32Dalainuori4RangelandUndisturbedSteppe3851.31.2113098.1LS0.840.147.678183.77Sanyi5WoodlandUndisturbedWood and shrub3802.31.2111738SL1.110.147.949227.70Xinchengzi6RangelandUndisturbedSteppe3973.51.239197.7LS1.520.1510.0710224.086Returned croplandHistorically tilledAlfalfa3973.51.239197.7SL0.90.19.969237.79Xinfuzhilu7Grazed rangelandGrazedSteppe (site 1)3865.81.187358.4LS0.970.118.958255.877GrazedSteppe (site 2)3865.81.187358.3LS0.990.128.058254.84Tianshan8RangelandUndisturbedSteppe3865.81.185138.3LS1.660.198.488236.148Returned croplandHistorically tilledAlmond3865.81.185138.2SL0.90.18.71102513.08Tianshan9RangelandUndisturbedSteppe3885.81.184138.2LS1.630.198.369225.249Returned croplandHistorically tilledAlmond3885.81.184138.2SL1.810.1710.7810247.34Shaogen10RangelandUndisturbedSteppe (site 1)3856.81.122708LS0.850.117.6612255.1410UndisturbedSteppe (site 2)3856.81.122708.2LS10.119.3611254.5810FarmlandTilledCorn3856.81.122708.6LS0.90.118.08112420.80Molimiao11RangelandUndisturbedSteppe (site 1)3996.31.051798.4SL1.050.128.8512257.5211UndisturbedSteppe (site 2)3996.31.051798.4SL1.10.157.3013256.6511FarmlandTilledCorn3996.31.051798.4SL10.119.1310256.34Yuxin12Rice fieldPeriodically floodedRice3976.31.022117.8SL1.230.158.2332325.23Baixingtu13WoodlandUndisturbedWood and shrub41461.021597.7SL0.970.128.0813288.85Baolongshan14RangelandUndisturbedMeadow (site 1)415611567.9SL1.30.139.0213268.4514UndisturbedMeadow (site 2)415611567.8SL1.340.158.4313277.6214FarmlandTilledCorn415611567.7SL1.30.1111.9212276.24Jiamatu15RangelandUndisturbedMeadow (site 1)422611498.2SL1.730.1710.2014276.0815UndisturbedMeadow (site 2)422611498.3SL1.770.1810.0713286.2215FarmlandTilledCorn422611498.2SL1.220.177.19112510.3415TilledRed bean422611498.4SL10.175.56102518.35Taipingchuan16RangelandUndisturbedMeadow4285.60.971508.6LS1.020.138.0718317.3716Rice fieldPeriodically floodedRice4285.60.971508.3SL1.180.129.8335358.93Yaojingzinan17WoodlandUndisturbedWood and shrub (site 1)4355.40.971507.9SL0.980.137.2714295.7817WoodlandUndisturbedWood and shrub (site 2)4355.40.971507.9SL1.160.167.2713285.7817FarmlandTilledPeanut4355.40.971507.5LS0.90.155.9710303.23Yaojingzi18RangelandUndisturbedMeadow (site 1)4355.40.971597.8SL1.160.167.1917304.4718UndisturbedMeadow (site 2)4355.40.971597.7SL0.820.119.4318305.2518FarmlandTilledPeanut4355.40.971597.5LS1.030.137.9617304.7518TilledMung bean4355.40.971597.6SL1.170.157.7317315.7518TilledCorn4355.40.971597.8SL10.128.6920325.95Yaojingzi19RangelandUndisturbedMeadow4345.40.971658.4SL2.210.239.6623348.38Yaojingzi20Artificial grasslandTilledMeadow (site 1)4335.40.971408.1SL1.850.199.9114336.4420TilledMeadow (site 2)4335.40.971408.1SL1.90.199.9812335.6220FarmlandTilledCorn4335.40.971408.1SL0.920.19.2318328.23Wulanaodu21RangelandUndisturbedMeadow (site 1)4425.30.931528.1SL1.250.167.8922334.2321UndisturbedMeadow (site 2)4425.30.931528.1SL1.30.168.0319344.8721FarmlandTilledCorn4425.30.931528.2SL1.740.247.0220324.1221WoodlandUndisturbedWood and shrub4425.30.931527.5SL1.870.238.1120346.55Chaganhua22RangelandUndisturbedMeadow (site 1)4675.10.932028.5LS1.540.27.6724364.3222UndisturbedMeadow (site 2)4675.10.932028.4LS1.420.197.4422365.01Wulantuga23RangelandUndisturbedMeadow4725.10.912918.5SL2.160.210.6323344.8523FarmlandTilledCorn4725.10.912918.2SL1.730.247.3622337.7523TilledPeanut4725.10.912917.9SL1.720.237.7622323.5223WoodlandUndisturbedWood and shrub4725.10.912917.8SL1.630.198.7518357.39Acknowledgements
We are grateful for the funding provided by the Natural Science Foundation of
China (nos. 31170304, 31070228 and 31300440). We also wish to thank the four anonymous reviewers for
their constructive comments, which helped in improving the manuscript.
Edited by: S. Fontaine
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