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Soil microbial community profiles and functional diversity in limestone cedar glades
Jennifer Cartwrighta'*, E. Kudjo Dzantor b, Bahram Momenc
a US. Geological Survey, Lower Mississippi-Gulf Water Science Center, 640 Grassmere Park, Suite 100, Nashville,TN 37211, USA b Tennessee State University, 3500 John A Merritt Blvd., Nashville, TN, 37209, USA
c University of Maryland, College Park, 1425 Animal Sciences/Agricultural Engineering Building, College Park, MD 20742, USA
ABSTRACT
Rock outcrop ecosystems, such as limestone cedar glades (LCGs), are known for their rare and endemic plant species adapted to high levels of abiotic stress. Soils in LCGs are thin (< 25 cm), soil-moisture conditions fluctuate seasonally between xeric and saturated, and summer soil temperatures commonly exceed 48 °C. The effects of these stressors on soil microbial communities (SMC) remain largely unstudied, despite the importance of SMC-plant interactions in regulating the structure and function of terrestrial ecosystems. SMC profiles and functional diversity were characterized in LCGs using community level physiological profiling (CLPP) and plate-dilution frequency assays (PDFA). Most-probable number (MPN) estimates and microbial substrate-utilization diversity (H) were positively related to soil thickness, soil organic matter (OM), soil water content, and vegetation density, and were diminished in alkaline soil relative to circumneutral soil. Soil nitrate showed no relationship to SMCs, suggesting lack of N-limitation. Canonical correlation analysis indicated strong correlations between microbial CLPP patterns and several physical and chemical properties of soil, primarily temperature at the ground surface and at 4-cm depth, and secondarily soil-water content, enabling differentiation by season. Thus, it was demonstrated that several well-described abiotic determinants of plant community structure in this ecosystem are also reflected in SMC profiles.
Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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ARTICLE INFO
Article history:
Received 11 November 2015 Received in revised form 5 May 2016 Accepted 6 July 2016 Available online xxxx
Keywords:
Limestone cedar glades Rock outcrop ecosystem Environmental microbiology Soil microbial community Community level physiological profiles
1. Introduction
Rock-outcrop ecosystems in the USA contribute to regional and global plant biodiversity at levels vastly disproportionate to their small geographic footprints (Quarterman etal., 1993). For example, limestone cedar glades (LCGs) of the southeastern USA support unique assemblages of plant species including rare endemics listed under the U.S. Endangered Species Act (Baskin and Baskin, 1999,1989). Despite their botanical importance, many LCGs face ongoing degradation (Baskin et al., 1995; Quarterman et al., 1993). Indeed, roughly 50% of total LCG area and 90% of ecologically intact LCGs have been lost from Middle Tennessee, rendering LCGs an "endangered ecosystem" (Noss et al., 1995).
Within their geographic range, LCGs provide a unique physical environment, characterized by very thin soils that experience seasonally-
Abbreviations: CCA, Canonical correlation analysis; CF, Canonical function; CLPP, Community level physiological profiling; H, Shannon-Weaver Index representing microbial substrate-utilization diversity; LCG, Limestone cedar glade; LOI, Loss on ignition; MPN, Most probable number; OM, Organic matter; PDFA, Plate-dilution frequency assay; SMC, Soil microbial community.
* Corresponding author.
E-mail addresses: jmcart@usgs.gov (J. Cartwright), edzantor@tnstate.edu (E.K. Dzantor), bmomen@umd.edu (B. Momen).
extreme temperatures at the soil surface combined with widely-fluctuating hydrologic conditions. Soil surface conditions are characteristically hot and xeric in summer and experience prolonged saturation in winter (Baskin and Baskin, 1989). These stress factors in LCGs effectively exclude most vegetation of the surrounding mesophytic forest and thus help maintain habitat for endemic plants (Baskin and Baskin, 2003, 1989,1999). LCGs function as edaphic climax ecosystems where soils are too thin to support the growth of trees. Instead, the vegetation is dominated by C3 forbs and C4 summer annual grasses, accompanied by mosses, lichens, and cyanobacteria in microhabitats of extremely thin soil or exposed limestone bedrock (Baskin and Baskin, 1999; Baskin et al., 2007).
Plant communities in LCGs have been systematically described (e.g. Baskin and Baskin, 2003; Somers et al., 1986; Taylor and Estes, 2012) but their associated soil microbial communities (SMCs) are largely unstudied. Indeed, SMCs have received little scientific attention in rock outcrop ecosystems throughout eastern North America, despite the recognized importance of these ecosystems to global plant biodiversity (Quarterman et al., 1993). This is a key knowledge gap, because plant-SMC interactions influence a host of ecological processes such as bio-geochemical cycling, plant-pathogen dynamics, succession, and soil disturbance (de Vries et al., 2012; Reynolds et al., 2003; van der Heijden et al., 2008).
http://dx.doi.org/10.1016/j.catena.2016.07.010
0341-8162/Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Ongoing efforts to restore glade environments and to understand and predict ecological changes, such as from climate change or exotic species invasions (Cofer et al., 2008; Molano-Flores and Bell, 2012), could be strengthened by studies of SMC dynamics that interact with these processes (Harris, 2009; Van der Putten, 2010). A number of investigators have indicated an urgent need for development of sustainable management practices for fragile ecosystems such as LCGs (Baskin et al., 2007; Nordman, 2004; Noss et al., 1995; Quarterman et al., 1993). Improved understanding of SMCs and their relationships to abiotic stressors is thus vitally important to the long-term conservation of plant biodiversity in LCGs and related ecosystems under changing environmental conditions.
In service of these goals, the objectives of this study were (1) to characterize spatial and temporal patterns of soil microbial numbers and functional diversity in LCGs, and (2) to relate these patterns to key soil physical and chemical properties that define the abiotic stress regime in this ecosystem. This study provides baseline information useful for monitoring abiotic and biotic changes in LCG soils in the face of conservation challenges, such as projected land-use and climate changes. This study also contributes to a holistic ecological understanding of this ecosystem to support scientifically-sound management of LCGs.
2. Materials and methods
2.1. Study area
Soil microbial investigations1 were performed in LCGs at Stones River National Battlefield (Fig. 1) in Rutherford County, Tennessee, USA (35°52'35"N, 86°25'58"W). This park is within a karst sinkhole plain in the Nashville Basin section of the Interior Low Plateaus physiographic province (Fenneman and Johnson, 1946). Bedrock formations within the park are Middle Ordovician limestones (Thornberry-Ehrlich, 2012). Two soil associations are present: the Gladeville-Rock outcrop-Talbott and the Talbott-Barfield-Rock outcrop complex (U.S. Department of Agriculture Soil Conservation Service, 1977). Several dozen LCGs are present within the park, ranging from <70 m2 to approximately 2800 m2. These glades form a roughly ring-shaped configuration surrounding a topographic high underlain by a structural basin (Cofer et al., 2008; Thornberry-Ehrlich, 2012). LCGs consist of outcrops of thinly-bedded limestone surrounded by Gladeville soil, a thin, silty clay loam with limestone flags throughout the soil and forming a layer at the soil surface (U.S. Department of Agriculture Soil Conservation Service, 1977). Areas between glades are predominantly Talbott and Barfield soils, both of which have surface layers of silty clay loam.
Within LCGs at this site, zones of exposed bedrock surrounded by thin soil support mosses, cyanobacteria, lichens, and sparsely-distributed herbaceous plants (Fig. S1, supplementary material). In areas with greater soil thickness, C4 summer annual grasses and C3 forbs are predominant, with woody species generally confined to pockets of deep soil in bedrock cracks and to ecotones with the surrounding oak-red cedar forest (Cofer et al., 2008; Nordman, 2004).
2.2. Sampling design
Twelve LCGs were selected to study spatial and temporal patterns of SMCs (Fig. 1). Thirty six 0.5-m x 0.5-m quadrats were established, with approximately 80% (28 quadrats) located in areas dominated by moss, graminoids, and forbs and the remainder within a 3-m buffer of predominantly Juniperus virginiana forest surrounding glades. Quadrat position was randomly assigned using ArcGIS v. 9.3 (Esri, Redlands, CA, USA). Quadrats were sampled according to a rotational schedule over 16 months (February 2012 to May 2013), with each quadrat sampled 4 times at approximately 4-month intervals. For certain soil properties,
1 Data used in the analysis for this study are available from http://dx.doi.org/10.5066/
F7NV9G9C
multiple subsamples were averaged to obtain a single measurement for each quadrat at each sampling event (see Section 2.4).
2.3. Vegetation characterization
The vegetation cover at each quadrat was characterized based on visual examination. Vascular plants were broadly categorized as graminoid, forb, vine, or shrub, following Cofer et al. (2008). The percentage of each category was estimated for each quadrat and categorized as "none," "< 30%," "30% to 70%," or "> 70% covered" with scores assigned from 0 to 3, respectively. An overall vegetation score was calculated for each quadrat as the sum of individual scores for each vegetation category.
2.4. Soil physical and chemical analysis
Depth to bedrock (soil thickness) was measured by inserting a 1-cm-diameter metal probe into the soil until it contacted bedrock (4 sub-samples per quadrat). Soil temperature at 4-cm soil depth (3 subsam-ples per quadrat) and ground-surface temperature were measured using a Taylor 9842N digital thermometer and a Taylor 1523 digital thermometer/hygrometer, respectively (Taylor Inc., Oak Brook, IL, USA).
At each sampling event, soil samples were collected at a depth of 34 cm using sterile techniques. Soil samples were sealed in autoclaved glass jars and transported on ice to the laboratory for analysis. Gravimetric soil water content (3 subsamples per soil sample) was measured using a Mettler Toledo HB43 halogen moisture analyzer (Mettler Toledo, Columbus, OH, USA). For chemical analysis, a portion of each soil sample was air-dried and sieved using a 2-mm screen. Soils were then analyzed for organic matter (OM), pH, and nitrate concentration. OM was estimated using the loss-on-ignition (LOI) method (Davies, 1973). Soil pHH2O (1:1) was measured using a calibrated FieldScout SoilStik pH Meter (Spectrum Technologies, Inc., Plainfield, IL, USA). Soil nitrate levels were estimated using a Hach Platinum Series combination nitrate electrode (Hach Company, Loveland, CO, USA).
2.5. Soil microbiological characterizations
A separate portion of each soil sample was transferred to a refrigerator (4 °C) within 6 h of leaving the field and was processed for micro-bial characterization within 24 h. Soil microbial populations were characterized using a plate-dilution frequency assay (PDFA), which is a most probable number (MPN) method. SMC metabolic profiles were assessed using community-level physiological profiling (CLPP).
2.5.1. Plate-dilution frequency assay of microbial populations
PDFA (Harris and Sommers, 1968) was used to estimate an MPN of culturable microbes in soil. Six levels of serial dilutions from each soil sample (10-2, 10-3, 10-4, 10-5, 10-6, and 10-7) were inoculated onto plate count agar (Difco™, Benton, Dickinson, and Company, Franklin Lakes, NJ, USA), in petri dishes. The petri dishes were prepared from dehydrated agar, checked for contamination after 48 h, and pre-marked with eight circles per dilution level. Ten microliters (10 ^L) from each dilution were inoculated into the center of each circle, going from the least to the highest concentration in a dilution series. Following incubation in the dark at 25 °C for 7 days, the total number of marked circles with positive growth (colony development) was referenced to statistical tables to estimate MPN of culturable microbes per mL of soil suspension at the 10-2 dilution level, along with a 95% confidence interval on MPN (Cochran, 1950; Harris and Sommers, 1968). PDFA was performed on soil samples collected from May 2012 to May 2013.
2.52. Microbial community level physiological profiling (CLPP)
CLPP based on sole-carbon substrate utilization (Garland and Mills, 1991; Garland, 1996; Preston-Mafham et al., 2002) was performed using 96-well Biolog® EcoPlates (Biolog Inc., Hayward, CA). For each
Fig. 1. Map of sampling locations in 12 selected limestone cedar glades (LCGs) at Stones River National Battlefield. Polygons representing LCGs are courtesy of the National Park Service. Physiographic divisions are from USDA (2015).
soil sample, 150 |L of the 10-2 dilution level suspension from the PDFA procedure (Section 2.5.1) were inoculated into an EcoPlate. EcoPlates contained 31 carbon substrates replicated 3 times on each plate. Three wells contained sterilized water to serve as controls. Immediately following inoculation, EcoPlates were covered and incubated in the dark at 25 °C. Color development of a respiration indicator, tetrazolium chloride, provided a measure of substrate utilization. EcoPlates were read at intervals of 24, 48, 72, 96, and 120 h after inoculation using a Biolog MicroStation plate reader (Biolog, Inc., Hayward, CA, USA) at an absor-bance of 590 nm to monitor changes in respiration over time. CLPP was performed on soil samples collected from February 2012 to March 2013.
2.6. Statistical analyses
All statistical analyses were performed using SAS 9.3 (SAS Institute, Cary, NC, USA). Relationships between vegetation, soil physical and chemical properties, and MPN and CLPP were analyzed using analyses of variance (ANOVA), quantile regression analysis, and canonical correlation analysis (CCA). At each sampling event, multiple subsamples taken per quadrat or soil sample were averaged prior to statistical analysis to avoid pseudoreplication.
2.6.1. Soil sample categorization based on MPN
Based on PDFA analysis, 95% confidence intervals on MPN estimates were used to assign soil samples to low-, medium-, and high-MPN groups, such that between-group MPN estimates were significantly different (P < 0.05), but within-group MPN estimates were not. In this way, 94 out of 99 soil samples were categorized as follows: low: 2.3 x 105 to 7.8 x 105; medium: 1.0 x 106 to 3.1 x 106; high: 4.3 x 106 to 13.7 x 106 organisms per mLof suspension of the 10-2 dilution level. Estimates for 5 samples were significantly lower (1) or higher (4) than any of the MPN groups so these samples were not retained for further analysis. To determine the effects of various soil physical and chemical properties on MPN, one-way ANOVA and Tukey's post-hoc tests were performed using a general linear model with the three MPN groups as categories (PROC ANOVA and PROC GLM; SAS Institute, 2011).
2.6.2. Transformations for CLPP data
To correct for inoculum-density effects, plate-level transformations for carbon substrate data followed Garland and Mills (1991):
T = (R-C)/AWCD
awcd = [£n=1(r-c)l /n
where T represents transformed substrate-level response values, R is the mean absorbance of the response wells (3 wells per carbon substrate), C is the mean absorbance value of the 3 control wells, AWCD is average well color development for the EcoPlate, and n is the number of carbon substrates (31 for EcoPlates). To integrate time-series data from multiple EcoPlate readings (for AWCD and for individual substrates, T), the area under the incubation curve—from 48 h to 120 h of incubation—was calculated and used in subsequent analysis (Preston-Mafham et al., 2002).
2.6.3. Microbial substrate-utilization diversity
To assess community-level microbial substrate-utilization diversity from CLPP data, the Shannon-Weaver index (H) was calculated as:
h = -£ "i=1P> (ln Pi)
Because plots ofH against soil physical and chemical properties indicated highly heteroscedastic, wedge-shaped distributions (Section 3.2), quantile regression was used to investigate relationships between these soil properties and H (PROC QUANTREG; SAS Institute, 2011). Heteroscedasticity implies that multiple rates of change (slopes) may describe the relationships between the response variable and a limiting factor (Cade and Noon, 2003). Quantile regression is well-suited to such distributions when ecological responses are constrained by multiple measured and unmeasured limiting factors (Schmidt et al., 2012). Quantile process plots show the slope parameter and associated 95% confidence interval at all quantile levels for the quantile regressions. This approach has advantages over the use of quantile regression at only one or a few quantile levels because limitations on both the maximum and minimum values of the biological response variable can be identified, as can portions of the response variable distribution most limited by the limiting factor(s) under consideration (Cade and Noon, 2003; Schmidt et al., 2012).
2.6.4. CLPP patterns for soil microbial communities
To investigate relationships between CLPP patterns of SMCs and various soil physical and chemical properties, CCA was performed on CLPP data from soils (PROC CANCORR; SAS Institute, 2011). Pairs of linear combinations of the original variables within each set (Canonical Functions; CFs) were calculated to maximize the correlation among the constructed functions (Lattin et al., 2003; Ye and Wright, 2010).
3. Results
3.1. Soil physical and chemical properties in relation to MPN
The soils of the LCGs in this study were generally thin (all <23 cm depth to bedrock), neutral to slightly alkaline, with variable levels of OM (Table 1). Gravimetric soil water content ranged from xeric (below 5%) to saturated (above 45%). Soil and ground-surface temperatures were often very hot in summer, exceeding 38 °C and 48 °C respectively.
MPN estimates of culturable microbes in 94 soil samples ranged between 2.3 x 105 and 13.7 x 106. Higher MPN was significantly associated with thicker soils, higher soil OM, lower (more neutral) pH, higher water content, and more dense vegetation (Fig. 2). Neither soil nitrate nor either of the temperature variables (ground-surface temperature and soil temperature at 4-cm depth) were significantly related to MPN.
Table 1
Soil physical and chemical properties across 36 quadrats in 12 limestone cedar glades sampled from February 2012 to May 2013.
N R Min Max Mean Standard deviation
Depth to bedrock (cm) Soil organic matter
(fraction, LOI) Soil pH
Gravimetric soil water
content (percent) Soil nitrate (|g g-1 NO3-N) Vegetation score Ground-surface
temperature (°C) Soil temperature at 4-cm depth (°C)
36 144
144 144
144 36 144
2.40 0.04
7.04 3.46
1.07 1
22.62 0.27
8.28 49.46
29.84 8
8.40 0.12
7.80 22.63
5.59 2.78 22.61
5.43 0.05
0.26 8.50
4.32 1.67 11.92
144 3 0.53 38.67 16.20 8.94
where pi is the ratio of the microbial activity on each substrate (T values, area under the incubation curve) to the sum of the microbial activities on all substrates for a given EcoPlate.
N = number of measurements; depth to bedrock and vegetation were measured once per quadrat (36 quadrats); all other properties were measured 4 times per quadrat over 16 months. For certain properties, each measurement represents an average of R subsamples; see Section 2.4.
Fig. 2. Distribution by most probable number (MPN) category of A, depth to bedrock; B, soil organic matter; C, soil pH; D, gravimetric soil water content; E, soil nitrate; F, vegetation score; G, ground-surface temperature and H, soil temperature at 4-cm depth. Differing letters above each boxplot represent significant differences (P < 0.05).
32. Microbial substrate-utilization diversity
Values of the Shannon-Weaver Index (H) for microbial substrate-utilization diversity ranged from 2.00 to 3.38, with 115 samples out of 125 (92%) above 3.0. H was uncorrelated with MPN (r = 0.21; P > 0.05) and values of H were not different across MPN groups (F = 2.74; P > 0.05), indicating that microbial substrate-utilization diversity was not merely a function of the numbers of culturable microbes in soils.
Plots of H versus soil physical and chemical properties indicated highly heteroscedastic distributions (Fig. S2, supplementary material). Consequently, these relationships were investigated by means of quantile process plots based on quantile regression across all quantile levels, t (Fig. 3), with inferences following Schmidt et al. (2012). For any quantile level t, t% of H values are < the t quantile regression function of the given soil property (Cade and Noon, 2003). Thus, at any given
t value (horizontal axes in Fig. 3), the slope parameter estimate of the regression function can be plotted (vertical axes in Fig. 3) and its 95% confidence interval (shaded area) can be compared to zero to test if the soil property under consideration affects H. For quantile ranges, t, where the entire shaded area is above zero on the vertical axis, the effect of the soil property on H is considered positive (P < 0.05). Conversely, t values for which the entire shaded area is below zero indicate that the soil property effect on H is negative. Values of t for which the shaded area includes zero indicate no effect of the soil property on H (P > 0.05).
Several soil properties showed significant associations with H across most of the intermediate (not extremely high or low) quantile ranges. The association between soil thickness (depth to bedrock) and H was positive for 0.15 < t < 0.75 (Fig. 3A). The association between soil pH and H was negative for 0.25 < t, indicating that H was higher in circumneutral soils and decreased with increasing alkalinity (Fig. 3C).
Fig. 3. Quantile process plots showing slope parameter estimates and 95% confidence intervals (shaded areas) for the Shannon-Weaver Index (H) of microbial substrate utilization for A, depth to bedrock; B, soil organic matter; C, soil pH; D, gravimetric soil water content; E, soil nitrate; F, vegetation score; G, ground-surface temparature and H, soil temperature at 4-cm depth. All horizontal axes are quantile levels (t), meaning that t% of H values are < the quantile regression function of the given soil property.
Gravimetric soil water content had a positive association with H for 0.15 < t < 0.85 (Fig. 3D). The association with vegetation score was positive for 0.10 < t < 0.65 (Fig. 3F).
Other soil properties showed no significant associations with H, or their associations were limited to relatively narrow quantile ranges. Soil nitrate was not associated with H at any quantile
level (Fig. 3E), whereas soil OM had a significant positive association with H only for 0.30 < t < 0.50 and 0.55 < t < 0.80 (Fig. 3B). Both ground-surface temperature and soil temperature at 4-cm depth had significant negative associations with H across limited quantile ranges: 0.25 < t < 0.60 and 0.30 < t < 0.65, respectively (Fig. 3G and H, respectively).
Table 2
Correlation coefficients between the first two sets of canonical functions (CFs) and soil physical and chemical properties. CFsoin and CFsoU2 were constructed from soil properties; CFsubsi and CFsubs2 were constructed from CLPP carbon substrate data.
Variable CFsoil1 CFsoil2 CFsubs1 CFsubs2
Soil temperature at 4-cm depth (°C) 0.98* 0.18 0.72* 0.12
Ground-surface temperature (°C) 0.93* 0.26* 0.69* 0.17
Gravimetric soil water content (percent) -0.48* 0.45* - 0.36* 0.30*
Soil organic matter (LOI) -0.37* 0.75* - 0.29* 0.49*
Soil nitrate (ppm NO3-N) -0.35* 0.40* - 0.27* 0.25*
Depth to bedrock (cm) -0.30* 0.44* - 0.22* 0.28*
Vegetation score -0.21* 0.44* - 0.18 0.35*
Soil pH 0.11 - 0.39* 0.11 - 0.29*
* indicates significant correlation at P < 0.05. 3.3. CLPP patterns for soil microbial communities
Of the CFs constructed, the first and second sets correlated significantly (r = 0.83 and 0.72, respectively, P < 0.01) and together accounted for 57% of the total variability of the multivariate data. Based on the absolute values of the correlations between original variables and the canonical functions related to soil properties, the first function (CFsoil1) was most strongly related to soil temperature at 4-cm depth and to ground-surface temperature, and secondarily to soil water content (Table 2). The second function (CFsoil2) primarily reflected soil OM content. The carbon substrates that were correlated with CFsubs1 and CFsubs2 (Table S1, supplementary material) were spread fairly evenly across all six of the substrate guilds present on the EcoPlates: carbohydrates, amino acids, esters, carboxylic acids, amines, and polymers (Zak et al., 1994). All carbon substrates on the EcoPlates that are known components of root exudates (Campbell et al., 1997) were correlated with either CFsub1 or CFsub2. Seasonal differences in substrate utilization profiles were apparent when warm-season (May through August) samples were plotted along with cool-season (November through February) samples in an ordination plot of CFsubs1 and CFsubs2 (Fig. 4).
4. Discussion
4.1. influence of soil physical and chemical properties on soil microbial communities
Findings from this study indicate that microbial substrate-utilization diversity in LCG soils is not simply a reflection of relative numbers of
• • • • • At» • •
* . — 4 * „ a 'l'A A&
• aa
a a Ai
• May through Aug a Nov through Feb
-4 -2 0 2 4
CFsubsi
Fig. 4. Ordination plot of soil samples collected from May through August versus from November through February based on canonical correlation analysis of microbial substrate utilization profiles; CFsubs1 and CFsubs2 are the first two canonical functions constructed from CLPP carbon substrate data.
culturable microbes, and that soil physical and chemical properties appear to influence these microbial indicators in different ways. However, certain soil properties were prominently related both to microbial abundance and to substrate utilization diversity, suggesting their ecological relevance to SMCs.
In particular, thin and relatively alkaline soils were associated both with reduced MPN and with lower H values. It is noteworthy that the effects of soil thickness and pH were so clearly discernible, given that all soils in this study were very thin and soil pH values were within a relatively narrow range (by comparison, soil pH ranged from <4.5 to >8.5 across terrestrial ecosystems in North and South America; Lauber et al., 2009). In thin-soil areas of LCGs, soil chemistry is influenced by limestone parent material and by relative scarcity of leaf-litter inputs. Thin-soil areas support distinct vegetation communities (Quarterman et al., 1993; Somers et al., 1986). Our findings suggest that these thin-soil, relatively alkaline zones support lower numbers of culturable microbes and that SMCs in these zones display reduced substrate-utilization diversity compared to zones of thicker, circumneutral soil. This pattern may account in part for previous findings of reduced soil respiration in thin-soil areas of LCGs (Cartwright and Hui, 2014).
Not surprisingly, vegetation score was positively associated with both MPN and H. Plant community composition influences SMCs through microenvironmental effects (e.g. temperature effects from shading, soil moisture effects from transpiration) and nutrient inputs from litter and root exudates (Thomson et al., 2010; Zak et al., 2003). Differences in quantity and quality of these inputs are reflections of plant community composition that directly affect soil microbial responses (Cong et al., 2015; Wardle et al., 2004). SMCs also help determine plant community composition (Wardle et al., 2004), for example via detrital food webs in which different SMCs generate different nutrient pools for plant use (Reynolds et al., 2003).
If the primary mechanism by which vegetation cover influences MPN and H is through organic inputs and food-web interactions, one might expect that soil OM would also be positively associated with these SMC indicators. We found mixed support for this conclusion. On the one hand, soil OM was strongly related to MPN, suggesting that higher rates of carbon additions to soil in more densely-vegetated zones of LCGs supported greater numbers of culturable microbes. Spatial variability in soil OM was reflected in CLPP data, in that: (1) soil OM was the most strongly-correlated soil property with the second CF, and (2) all six of the EcoPlate substrates that are components of root exudates were significantly correlated with either the first or second CF.
On the other hand, the association of OM with H was relatively weak, i.e. significantly positive only for limited ranges of t based on quantile regression. This is perhaps not surprising given the complexity of factors regulating microbial metabolism in soil environments (Torsvik and 0vreas, 2002). Indeed, the full suite of carbon sources available for microbial metabolism in LCG soils is likely to be vastly more complex than, and could in fact be qualitatively different from, that represented by the 31 substrates present on the EcoPlates in this study (Konopka et al., 1998). Future investigations to quantify expression levels of genes involved in specific microbial metabolic pathways could help elucidate the role played by various forms of OM in soil food webs of LCGs.
The absence of a relationship between soil nitrate and any of the SMC indicators in this study may indicate that N was not a limiting nutrient in soil at this study site. N-fixing cyanobacteria (e.g. Nostoc species) are generally prominent constituents of LCG ecosystems (Baskin and Baskin, 2003; Martin and Sharp, 1983; Quarterman, 1950a), including the glades at this study site (Dubois, 1993; Nordman, 2004).
Findings from this study highlight the importance of seasonally-variable soil conditions to SMCs in LCGs. In particular, soil water content was positively related both to MPN and to H, suggesting that numbers of culturable microbes and microbial substrate-utilization diversity were suppressed by seasonally dry soil conditions. Seasonally dry soils in LCGs result from intense evaporation during summer and early
autumn due to soil thinness combined with full insolation from lack of canopy cover (Baskin and Baskin, 1999; Quarterman, 1950b). Botanists have long noted the prevalence of drought-tolerance adaptations among LCG vegetation, which includes succulents, species using the crassulacean acid metabolism photosynthetic pathway, and winter-annuals that survive dry summer conditions using dormancy (Baskin et al., 1995; Quarterman et al., 1993).
This study indicates that patterns of soil water availability in LCGs are also relevant to soil microbial ecology. Soil moisture limitation can inhibit microbial function by constraining rates of substrate diffusion and by cytoplasmic dehydration that reduces enzymatic efficiency (Stark and Firestone, 1995). In a variety of arid and semi-arid ecosystems, soil moisture availability is a strong control on microbe-mediated processes such as heterotrophic soil respiration (Munson et al., 2009; Talmon et al., 2011). Martin and Sharp (1983) found no clear relationship between soil moisture availability and species richness for protozoa in LCGs, possibly because of relatively limited sampling (5 sites each sampled twice) that did not include xeric conditions (average available soil moisture always >37%). Because seasonal moisture limitation is so ecologically important to plant communities in LCGs (Quarterman, 1950a; Quarterman et al., 1993), further research is warranted concerning its effects on microbial community structure and function, especially in light of predicted increases in summer drought severity in the southeastern United States (Mearns et al., 2003).
In contrast to soil-moisture effects, this study found mixed results for temperature effects on SMCs. Temperatures at the soil surface and at a depth of 4 cm were both unrelated to MPN estimates and were weakly related to H, i.e. significantly negative only for limited ranges of t. A likely explanation is that temperature regimes in the field were not represented in the laboratory protocols for this study. Specifically, inoculated agar plates for PDFA analysis and EcoPlates for CLPP were incubated at ~25 °C, allowing proliferation of culturable microbes that may have been dormant under extremely cold or hot temperatures in the field. However, it is noteworthy that temperatures at the soil surface and at a depth of 4 cm were both strongly correlated to the first pair of CFs based on CCA analysis of CLPP data. Separation of soil samples in ordination space based on substrate-utilization profiles suggests that functionally-different SMCs were present in LCG soils on a seasonal basis (Fig. 4). Future studies could seek to clarify soil-temperature effects on SMCs in this ecosystem by employing a range of seasonally-relevant incubation temperatures (e.g. ranging from 0 °C to 40 °C; Table 1) or by applying culture-independent methods to characterize soil micro-bial community structure across seasons. Such methods, e.g. phospho-lipid fatty acid (PLFA) profiles and fatty acid methyl esters (FAME) analysis, have demonstrated temperature-related shifts in soil microbial community structure (Frey et al., 2008; Zogg et al., 1997).
4.2. Methodological considerations
This study of SMCs in LCG soils employed culture-dependent methods, the limitations of which are thoroughly documented (Degens and Harris, 1997; Konopka et al., 1998; Rastogi and Sani, 2011). However, culture-dependent methods continue to generate useful information for characterizing SMCs in a variety of environmental contexts (Fr^c et al., 2012; Garland etal., 2010; Le et al., 2011; Sutton, 2010). Although a variety of culture-independent, molecular methods can circumvent many of the limitations associated with culture dependency, no single molecular technique can comprehensively describe microbial diversity at a particular site (Dahllof, 2002; Malik et al., 2008). Because culture-dependent and culture-independent approaches involve multiple, non-overlapping sets of biases, concurrent use of the two approaches may allow better representation of true microbial diversity than either approach alone (Al-Awadhi et al., 2013).
In this study, clear and statistically significant patterns were discerned between SMCs and soil properties in LCGs using culture-dependent methods. Although CLPP necessarily restricted our analysis to
organisms capable of rapid metabolism of a limited number of carbon sources under laboratory (not field) conditions, it nonetheless indicated clear differences in SMCs between soil samples (Fakruddin and Mannan, 2013; Preston-Mafham et al., 2002). Furthermore, these differences were associated with a number of environmental factors (e.g. soil thickness, pH, and moisture availability) known to be of primary importance to plant community structure in LCGs (Baskin and Baskin, 2003, 1999; Quarterman et al., 1993; Somers et al., 1986). In future studies, hybrid approaches could be employed—coupling the methods described here with culture-independent procedures—to further elucidate the role of environmental factors in shaping SMCs in this threatened and biodiversity-rich ecosystem.
5. Conclusions
Several soil properties that are key components of abiotic stress regimes in limestone cedar glades showed clear relationships with soil microbial indicators. Most-probable number estimates along with mi-crobial substrate-utilization diversity were positively related to soil thickness, soil water content, and vegetation score, and were higher in circumneutral soil than in alkaline soil. Soil nitrate showed no relationship with SMC indicators, suggesting that N may have been non-limiting in these soils. Soil temperature, at the surface and at 4-cm depth, was a prominent variable based on canonical correlation analysis of community-level physiological profiles, suggesting possible functional differences in microbial communities based on season. Because soil microbial analysis has been lacking for rock outcrop ecosystems, this study provides useful information to begin investigating plant-SMC relationships in these ecosystems. In future studies, a combination of culture-dependent methods with molecular approaches could enable a more comprehensive assessment of microbial diversity and function in this endangered and biodiversity-rich ecosystem.
Acknowledgements
This research was supported by the National Park Service, (Study# STRI-00025; Wolfe, PI), the U.S. Geological Survey, and Tennessee State University (USDA/NIFA award 2010-38821-21594; Dzantor, PI). The authors thank William Wolfe (U.S. Geological Survey) and Gib Backlund and Troy Morris (National Park Service) for their assistance with field logistics. This manuscript was improved based on reviews by Thomas Byl (U.S. Geological Survey) and anonymous reviewers. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Appendix A. Supplementary data
Supplementary data associated with this article can be found in online version, at doi:http://dx.doi.org/10.1016/j.catena.2016.07.010. These data include point locations representing the sampling quadrats described in this article.
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