the use of soil parameters as indicators of quality in ...

WETLANDS, Vol. 29, No. 2, June 2009, pp. 666–677 ’ 2009, The Society of Wetland Scientists

THE USE OF SOIL PARAMETERS AS INDICATORS OF QUALITY IN FORESTED DEPRESSIONAL WETLANDS Abby E. Rokosch1,3, Virginie Bouchard1, Siobhan Fennessy2, and Richard Dick1 1 School of Environment and Natural Resources Ohio State University 2021 Coffey Road Columbus, Ohio, USA 43210 E-mail: [email protected] 2

Department of Biology Kenyon College Gambier, Ohio, USA 43022 3

Present address: Brown and Caldwell 5410 Trinity Road, Palisades II, Suite 320 Raleigh, NC, USA 27607 Abstract: Current methods of wetland assessment rely on the use of ecological indicators such as vegetation and amphibians, but often lack an in-depth analysis of soil parameters. The objective of this study was to determine whether the Ohio Rapid Assessment Method (ORAM) can be used to predict soil quality in forested wetlands. Soil cores were taken from six wetlands ranging in ORAM scores. The soil samples were analyzed for key soil parameters (aggregate stability, bulk density, organic matter, C, N, S, P, microbial biomass, and enzyme activity). Some of these soil parameters (i.e., microbial biomass, soil C, N and S, bulk density, soil moisture) were correlated with the ORAM scores, while others (i.e., P, pH, aggregate stability) showed no correlation. Enzyme activity was correlated with the ORAM score for one of the four sampling events. When analyzed together by a principal component analysis, the soil parameters did not separate the wetland sites along a gradient of ORAM scores. Our results indicate that the ORAM reflects some of the key soil quality conditions, but not all. We further discuss whether some of the soil parameters we selected are appropriate indicators of the quality of wetland soils. Key Words: aggregate stability, enzyme activity, microbial biomass, Ohio Rapid Assessment Method, soil quality, wetland assessment

Method (Mack 2001a), the Penn State Stressor Check List (Brooks et al. 2002), and the Massachusetts Coastal Zone Management Rapid Assessment Method (Hicks and Carlisle 1998) are examples of level 2 assessment. These methods require the user to identify indicators within the wetland, such as hydrological modifications (e.g., ditches and dikes), vegetation alteration (e.g., invasive species and logging activities), sedimentation, and eutrophication to evaluate wetland condition. Examples of level 3 wetland assessment include the Index of Biological Integrity (IBI) and the Hydrogeomorphic (HGM) approach. The IBI, originally applied to river and stream ecosystems to measure biological condition (Karr 1981), has been adapted to wetlands by using multiple indicators including vegetation (Gernes and Helgen 1999, Mack 2001b, 2004, Fennessy et al. 2001), invertebrates (Gernes and Helgen 1999), and amphibians (Micacchion 2004). The HGM approach evaluates the functional capacity of the wetland and uses indicators such as plant species composition, tree density, and the presence of hydrological disturbances (Brinson 1993). While these methods, and the majority of

INTRODUCTION The pressure of human activities on wetlands has driven the development of many assessment methods designed to evaluate the impact of human disturbance on these ecosystems (Fennessy et al. 2007). To standardize methods of wetland assessment and meet the requirements of the Clean Water Act, the United States Environmental Protection Agency (USEPA) has proposed a multilevel assessment and monitoring approach. Level 1 consists of the collection of land-use data, level 2 requires the use of rapid field assessment methods, and level 3 involves intensive collection of biological and physio-chemical data. Common to each level of assessment is the use of ecological indicators or metrics that estimate the condition and magnitude of stress impacting the ecosystem in question and indirectly reflect specific wetland functions. The Landscape Development Index (Brown and Vivas 2004, Mack 2006) is an example of level 1 assessment. A remote landscape analysis is performed and land use indicators (e.g., road density, commercial development) are used to predict wetland condition. The Ohio Rapid Assessment 666

Rokosch et al., SOIL PARAMETERS AS INDICATORS other wetland assessment methods, are comprised of a suite of variables describing multiple wetland components, they lack an in-depth analysis of soil parameters. Hydric soils, observed in wetlands, are formed under condition of saturation and are generally anaerobic. These anaerobic conditions lead to the accumulation of organic matter and nutrients in the soil. When included in wetland assessment, soil parameters often consist of relatively simple field measurements such as color and texture, the depth of the organic horizon, and the presence or absence of sedimentation processes. In contrast, soil-based parameters are frequently used in terrestrial ecosystems as indicators of quality because they are known to reflect land use disturbance (e.g., Bending et al. 2004, Knight and Dick 2004) and ecosystem recovery (e.g., Kuperman and Carreiro 1997, Villar et al. 2004, Izquierdo et al. 2005). In agroecosystems, soil quality is a broad concept that describes the soils’ ability to sustain biological productivity and maintain desired functions including net primary productivity, nutrient recycling and retention, and the accumulation of organic matter (Doran and Safley 1997). Multiple indicators are often used to assess quality because of the complexity of biological and biochemical transformations that occur in the soil (Schloter et al. 2003). High quality wetland sites, for reasons outlined above, have generally a greater accumulation of organic matter and nutrients, greater microbial biomass and enzyme activities, lower bulk density and greater proportion of large soil aggregates. Indeed, even if microbial decomposition (and therefore microbial biomass and enzyme activities) is limited because of anaerobic conditions, the abundance of organic matter in high quality sites would still enhance microbial activity compared to degraded sites with limited organic matter input. The objective of this study was to determine whether the Ohio Rapid Assessment Method (ORAM) can be used to predict soil quality in forested wetlands. The ORAM was established to assess the quality of wetlands for regulatory purposes, and was never intended to provide a measurement of soil quality. However, our study also assessed whether soil parameters reflect human disturbance as measured by ORAM and thus further test ORAM’s robustness as an indicator of ecological integrity. We measured biological (enzyme activity and microbial biomass), chemical (organic matter, carbon, nitrogen, phosphorus, sulfur, and soil pH), and physical (water-stable aggregate and bulk density) properties as indicators in six forested wetlands that ranged from high to low quality

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according to the ORAM rating system. The soil parameters were selected because they have been successfully used in terrestrial soil (e.g., enzyme activity and microbial biomass) and/or are known to be critical in the establishment of hydric soils (e.g., bulk density, organic matter). We hypothesized that a wetland with a high ORAM score would sustain soils with greater enzyme activity and microbial biomass, as well as greater amount of organic matter and nutrients. We also hypothesized that ORAM score would correlate positively with the presence of water-stable aggregate and negatively with bulk density. METHODS Site Selection We selected six forested depressional wetlands in Central Ohio, ranging from low to high quality according to the Ohio Rapid Assessment method (ORAM) (Table 1). The ORAM is scored using six principal metrics (i.e., size, buffer, habitat, hydrology, vegetation, and the presence of special wetland communities) (Mack 2001a). The ORAM v5.0 was performed at each of the wetlands in July 2005. In addition to the ORAM score, wetlands were chosen based on common characteristics including size, vegetation community, and soil class (Table 1). The forest community was dominated by Acer rubrum L., Quercus spp., and Salix spp., with very sparse understory vegetation. Two wetlands, Lawrence Woods and Gahanna Woods, had large shrub communities, composed primarily of Cephalanthus occidentalis L. Experimental Design and Sampling Procedures Each wetland was delineated by locating the mean-high water level and by other physical evidence including hydric soils, stained leaves, and tree high water marks. Soil was collected four times throughout the duration of the study for biological analysis (June 2005, August 2005, November 2005, and May 2006) and once to measure physical and chemical parameters (November 2005). Soil samples were collected from eight randomly located sampling plots (1 m2) within each wetland. Sampling plots were only established in the forested community to avoid confounding factors with the shrub community found at two sites. Large woody and leaf debris were removed from the soil surface prior to coring. At each of the eight plots, four soil cores were taken to a depth of 10 cm using a 6 cm diameter stainless steel soil corer.

68.5 67.5 73 36 26 21 Crosby silt loam Carlile muck Pewamo silty clay loam Pewamo silty clay loam Benninston silt loam Kokomo silty loam 0.99 1.73 3.07 0.63 0.16 0.48 Bigwoods Gahanna Woods Lawrence Woods Orange Road Graham Road Johnson Road

40.12889 40.01157 40.56610 40.18106 40.04389 39.89250

284.14972 282.83795 283.62306 283.00329 282.76222 283.18449

Forested (oak, maple ash) Forested /Shrub (oak, maple, ash) Forested/Shrub (oak, maple, buttonbush) Forested (oak, maple, ash) Forested (maple) Forested (maple, cottonwood, willow)

ORAM Plant Community Longitude Latitude Size (ha) Site

Table 1.

Soil Series

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Name, relative size, location, vegetation community, soil series, and ORAM v5.0 score of the six wetlands selected for this study.

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Samples were homogenized and combined into one composite sample per plot; they were stored in sterile plastic bags until analysis. For biological analysis, the soil was sieved through a 2 mm mesh and stored at 4uC until analysis. For chemical analysis, the soil was sieved through a 2 mm mesh, air-dried, and finely ground using a mortar and pestle. For physical analysis, the soil was air-dried for approximately two weeks before being processed. Biological Parameters ß-glucosidase activity was assayed following the method of Tabatabai (1994) which, in brief, utilized the substrate r-nitrophenyl-ß-D-glucoside (PNG) buffered at pH 6.0 and incubated with soil for 1 hr at 37uC. The intensity of the yellow filtrate was measured on a spectrophotometer at 420 nm. Arylsulfatase activity was determined by a similar method (Tabatabai 1994) except the substrate was r-nitrophenyl sulfate solution buffered at pH 5.8. Three analytical replicates and one control were used per sample for all enzyme assays. The results for both assays were expressed as the activity in units of mg r-nitrophenol (PNP) g21 soil hr21. Microbial biomass-carbon (thereafter noted as microbial biomass) was estimated using a modified version of the chloroform fumigation extraction method of Vance et al. (1987). For each plot (n 5 8), two fumigated samples and two controls (unfumigated) were analyzed. In brief, fumigated samples exposed to chloroform under vacuum for 24 hours in the dark; control samples were incubated under the same conditions minus the addition of chloroform. The chloroform-treated replicates and the controls were immediately extracted with 0.5 M K2SO4 and filtered. The filtrate was analyzed on a DC-190 Rosemount/ Dohrman Total Organic Carbon Analyzer. Microbial biomass was calculated by subtracting the extractable total organic carbon (TOC) in the controls from the TOC in the chloroform-treated replicates. An extraction efficiency (kec) factor of 0.37 was used to convert TOC contents to microbial biomass; this value has been calibrated for organic soils (Sparling 1997). Chemical Parameters Total carbon (C) and nitrogen (N) contents were analyzed by dry combustion using a NC 2100 Soil Analyzer (CE Elantech, Inc. Lakewood, NJ). Total sulfur (S) and phosphorus (P) contents were

Rokosch et al., SOIL PARAMETERS AS INDICATORS measured using the EPA 3051 microwave digestion method. Soil pH was measured on a 1:4 soil:water slurry with a Corning 430 pH meter. Percent organic matter was determined through loss on ignition at 550uC for 6 h. Physical Parameters Soil moisture was determined by drying a sub sample of soil in the oven at 105uC until constant weight. This measurement was made at each sampling season, as it was needed for measuring enzyme and microbial activities. Bulk density was determined using the core method (Blake 1965). One core (8 cm diameter 3 10 cm length) constructed of PVC piping was extracted from each plot (n 5 8). Three fractions of water-stable aggregates (WSA fractions; 2–8 mm, 1–2 mm, and 0.25–1 mm) were determined for each plot using a wet-sieving machine. Air-dried samples were sieved through an 8 mm sieve and retained on a 3.35 mm sieve; approximately 25– 35 g of the retained soil were placed on a stack of nested sieves (2000, 1000, and 250 mm) (Kemper and Chepil 1965). Samples were capillary wetted for 30 minutes and then wet sieved for 30 minutes. The remaining fractions on each sieve were oven-dried at 105uC for 24 hours and weighed. To correct for coarse particles, approximately 40 mL of 0.2% sodium hexametaphosphate (HMP) was added to each fraction and shaken for 24 hours on a Lab line Orbit Environ-Shaker (Nimmo and Perkins 2002). After dispersion with HMP, fractions were sieved and oven-dried at 105uC for 24 hours and weighed. The % WSA for each fraction (2–8 mm, 1–2 mm, and 0.25– 1 mm) was calculated by subtracting the corrected coarse particle weight of each fraction from the uncorrected weight; this value was divided by the initial sample weight and multiplied by 100.

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formed to determine the strength of the association between soil biological, chemical, and physical parameters measured from soil samples collected in November. In these analyses, the entire data set was used (n 5 48), and the significance of the relationship was indicated at p # 0.05. The data met the assumptions of the analysis (normality and homoscedasticity). All the above analyses were performed using Statistix 9 (Analytical Software, Tallahassee). A principle components analysis (PCA) was performed to identify patterns in the soil parameters as they related to wetland quality (PCOrd v.4). The purpose of the PCA in this study was to condense the variability contained in the 12 original soil parameters measured into a smaller set of composite dimensions (i.e., principle components); the percent of variability explained by the PCA was indicated on each axis. The position of the sites on the newly defined gradient was used to determine the relationship between the individual wetlands and the different soil parameters. RESULTS Biological Parameters In July, activity of the two enzymes was negatively correlated with ORAM scores (F1,6 5 11.23, r2 5 0.74, p 5 0.028 and F1,6 5 13.06, r2 5 0.76, p 5 0.022 for b-glucosidase and arylsulfatase, respectively), while no correlation was observed during the three other sampling periods (Figure 1). Microbial biomass was not measured in July, but was measured during the three other sampling periods. Microbial biomass increased with the ORAM scores during two of the sampling periods (F1,6 5 4.97, r2 5 0.55, p 5 0.090; and F1,6 5 7.14, r2 5 0.64, p 5 0.056, respectively for August and November), while no correlation was observed in May (Figure 2).

Statistical Analyses

Chemical Parameters

Linear regression was used to identify the degree of relationship between the ORAM scores and individual mean soil parameters (n 5 6). Considering the small number of replicate and the degree of noise associate with such ecosystem-level study, the significance of the relationship was indicated at p # 0.1. The mean score for each soil parameter at each site was calculated as the average of the eight sampling plots. Prior to calculating the means, outliers were identified using the boxplot technique. Two sampling plots located at Bigwood wetland had frequent outliers, reducing the number of plots used to calculate the mean from eight to six for a number of soil parameters. Pearson correlation was per-

Soil C, N, and S increased with the ORAM score (r2 5 0.66, F 5 7.74, p 5 0.050; r2 5 0.55, F 5 4.98, p 5 0.090; r2 5 0.55, F 5 4.90, p 5 0.091, respectively), while soil P and pH showed no relationship with the ORAM score (Figure 3). Organic matter content also increased with an increase in the ORAM score (r2 5 0.57, F 5 5.38, p 5 0.081). Physical Parameters Bulk density was higher in the low quality wetlands and decreased with the ORAM score (r2 5 0.61, F 5 6.17, p 5 0.068) (Figure 4a). Three different water stable aggregate (WSA) size fractions

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Figure 1. Relationship between ORAM scores and A) bglucosidase activity and B) arylsulfatase activity, for each of the four sampling periods. Regression lines indicate a significant relationship in July. BG5b-glucosidase and AS5 arylsulfatase.

were measured (large: 2–8 mm, medium: 1–2 mm, and small: 0.25–1 mm). The abundance of the WSA fractions in soil was not related to wetland quality as measured with the ORAM score (Figure 4b, c and d). Soil moisture increased with the ORAM score (r2 5 0.72, F 5 10.76, p 5 0.030) (Figure 4e). Soil moisture changed over time, with July being the wetter sampling period (Figure 5).

WETLANDS, Volume 29, No. 2, 2009 and biological parameters (Table 2). Soil moisture was positively correlated with C and nutrients concentrations, soil organic matter and microbial biomass. In contrast, soil moisture was negatively correlated with b-glucosidase and arylsulfatase enzyme activities. The relationships observed between moisture and the biological soil parameters in November were also present during the other sampling period (data not shown). Bulk density was inversely correlated with soil moisture, and logically showed inverse relationship with all the parameters mentioned above. Soil C, N, P, S, and organic matter were positively correlated with microbial biomass, while they were negatively correlated with the two enzyme activities measured in this study. There were positive correlations between bulk density and bglucosidase and arylsulfatase enzyme activities. To measure the degree of similarity among the six wetland sites based on soil characteristics, we conducted a principle components analysis using soil physical (the three classes of WSA, bulk density, and soil moisture), chemical (OM, pH, C, N, P, and S), and biological (b-glucosidase, arylsulfatase, and microbial biomass) parameters (Figure 6). Only two principle components out of the six computed were considered for interpretation because their observed eigenvalues, 8.1 (Axis 1) and 3.7 (Axis 2) exceeded those of the broken stick eigenvalues, 3.4 (Axis 1) and 2.4 (Axis 2). The first axis explained approximately 47% of the total variation in the multivariate dataset, while the second axis explained 22% of the variation. One of the highest quality wetlands, Bigwoods (ORAM score of 68.5), was distinctly separated along the first axis from the other five wetlands. Microbial biomass, soil moisture, organic matter, and C, N, and S content were positively associated with the first axis, while bulk density was negatively associated with this axis. The five other wetlands showed some separation along the second axis but this distribution was not associated with ORAM scores. One high quality site, Gahanna Wood (ORAM score of 67.5), was located in close proximity to Johnson Road (ORAM score of 21) and Orange Road (ORAM score of 36), while Graham Road (ORAM score of 26) and Lawrence Wood (ORAM score of 73) were next to each other. The larger water stable aggregate fraction was weakly positively associated to Axis 2, while the two other classes of aggregate (not shown on Figure 6) were even more weakly negatively associated with Axis 2. DISCUSSION

Relationships between Soil Parameters A correlation analysis identified significant relationships between several soil physical, chemical,

The Ohio Rapid Assessment Method (ORAM) is used to categorize natural wetlands for regulatory purposes (Mack 2001a), and was not designed to

Rokosch et al., SOIL PARAMETERS AS INDICATORS

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Figure 2. Relationship between ORAM scores and microbial biomass-carbon, for each of the three sampling periods. Regression lines indicate significant relationships in August and May.

predict soil quality. A number of the soil indicators we measured (i.e., microbial biomass; soil C, N and S; bulk density; soil moisture) were correlated with the ORAM scores, while others (i.e., enzyme activities, P, pH and water stable aggregates) did not correlate with ORAM scores. Therefore, the ORAM is a good predictor of some soil characteristics in forested wetland ecosystems in Ohio, but not all. Alternatively, it is possible that some of the soil parameters we selected are not reliable indicators of the quality of wetland soils. When analyzed on multi-variate basis using principal component analysis, the soil parameters did not separate the wetland sites along a gradient of ORAM scores. Thus, based on our data set, it is preferable to look at individual soil properties to categorize wetlands rather than consider the aggregation of multiple soil properties. In terrestrial ecosystems, some biological soil parameters have proven to be reliable indicators of soil quality and can provide an early indication of changes in soil properties due to land management (Dick 1994, Filip 2002, Gil-Sotres et al. 2005). In these terrestrial systems, soil microbial biomass and enzyme activity often decrease as the result of anthropogenic disturbances (Kuperman and Carreiro 1997, Barbhuiya et al. 2004, Sicardi et al. 2004). In mangroves, microbial biomass and activities of the two enzymes we assessed were also higher in undisturbed sites than in disturbed sites (Dinesh et al. 2004). Microbial biomass and enzyme activity are generally positively correlated with soil organic matter. In our study, we observed a positive

relationship between microbial biomass and soil OM, C, N, and P. Furthermore — with the exception of soil P and microbial biomass measured in May — all of these parameters were positively correlated to ORAM scores, providing a strong indication that the ORAM does capture elements related to microbial biomass and accumulation of materials in the soil. Surprisingly, however, we observed the opposite relationship with enzyme activity: b-glucosidase and arylsulfatase activities were negatively correlated with soil OM, C, and nutrients, and for the most part (i.e., three sampling periods out of four) were not correlated with the ORAM scores. In our study sites, b-glucosidase and arylsulfatase activities seemed to be inhibited by soil moisture. This was particularly pronounced in July when we observed a negative relationship between enzyme activities and ORAM scores: the sites with high ORAM scores were saturated with water while the sites with low ORAM scores were dry. Throughout the year, we observed that wetlands with high ORAM scores were flooded more frequently, retained water for a longer period of time, and had saturated soils even during drawdown periods. Kang and Freeman (1999) found that waterlogging restricts both arylsulfatase and phosphatase enzyme activity in two different wetland types, swamps (freshwater forested wetlands) and fens (nutrient rich peatlands). Similarly, lower b-glucosidase, b-Nacetylglucosaminidase, phosphatase, and arylsulfatase activity was lower in wetland sediment as compared to surrounding upland sites (Kang et al.

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Figure 3. Relationship between ORAM scores and A) soil carbon, B) soil nitrogen, C) soil sulfur, D) soil phosphorus, E) soil organic matter, and F) soil pH. Regressions lines indicate a significant relationship. The parameters were measured from soil samples collected at each wetland site in November.

1998). Reduced enzyme activity has been attributed to the increased solubility of inhibitory metal ions, particularly Fe+2 and Mn+2, that are mobilized in reduced conditions (Freeman et al. 1996). If the

particular enzymes we selected do not respond to site quality as defined by the ORAM method — but ORAM scores are reflected by microbial biomass and organic matter content — other enzymes that


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Figure 4. Relationship between ORAM scores and A) bulk density, B) 2–8 mm water stable aggregates (large WSA), C) 0.25–1 mm (small WSA) water stable aggregates, D) 1–2 mm (medium WSA) water stable aggregates, and E) soil moisture. Regressions lines indicate a significant relationship. The parameters were measured from soil samples collected at each wetland site in November.

are specifically expressed in anaerobic conditions (e.g., denitrification enzyme activity) might have responded (Jordan et al. 2007). Even if bglucosidase and arylsulfatase are widely used in

terrestrial ecosystems to assess soil quality because they are central to organic matter degradation, they seem to be a poor indicator in saturated conditions.

0.64** 20.37** 20.36** 0.30 0.28 0.67** 0.66** 0.43** 0.66** 0.07 0.72** 20.13 20.22 20.46** 0.35 0.02 ,0.01 0.23 20.24 20.28 20.35 20.26 0.17 20.35** 0.67** 20.51** 0.34 20.26 0.24 0.41 20.27 20.30 20.09 20.28 0.35 20.35** 0.89** 20.71** 20.29 20.10 20.03 0.74** 0.71** 0.20 0.75** 0.29 0.21 20.16 0.25 20.31** 20.33** 0.09 0.08 20.14 0.10 0.81** 0.37** 0.81** 20.63** 20.37 20.72** 20.32** 20.31 20.32 0.25 0.26 0.14 0.19 0.25 0.17 0.98** 0.47** 0.97** 0.53** 0.97** 0.47** 0.80** 20.60** 20.35** 0.17 0.21 20.05 20.05 0.78** 0.85** 20.02 20.06 0.80** 20.19 0.26 20.70** SM BD WSA L WSA M WSA S C N P S pH OM BG AS

MB-C AS BG OM pH S P N C WSA S WSA M WSA L

It is not surprising that soil moisture is correlated with many of the soil parameters we measured because of influence on the accumulation and cycling of soil organic matter. Anaerobic conditions occur under saturated conditions, and by limiting microbial decomposition, enhance organic matter and nutrient accumulation. Higher levels of OM, C, and P were found in both undisturbed mangrove forests compared to disturbed mangrove forests (Dinesh et al. 2004) and prairie potholes un-impacted by cultivation activities compared to impacted prairie potholes (Freeland et al. 1999). Organic C was among the variables measured to determine wetland condition in the development of a rapid assessment wetland index in Australia (Spencer et al. 1998) and in Florida (Reiss 2006) where higher levels of organic C reflected a less disturbed wetland. Soil physical properties are included in the assessment of soil quality because they are influenced by biological, chemical, and anthropogenic processes. Bulk density is a common physical property that has been included in the development of wetland assessment methods (Spencer et al. 1998, van Dam et al. 1998, Innis et al. 2000, Pennings et al. 2002, Reiss 2006) and similar to this study, it is generally lower in less disturbed or reference standard wetlands. In this study, percent water stable aggregates did not appear to be sensitive to wetland quality. Aggregate stability has demonstrated higher sensitivity between agricultural management practices, specifically till versus no-till (e.g., Balota et al. 2003, Roldan et al. 2005a,b). The main driver of aggregate

BD

Figure 5. Change in soil moisture at each site over the four sampling period. The six sites (BW 5 Bigwood; GW 5 Gahanna Woods; GR 5 Graham Road; JR 5 Johnson Road; LW 5 Lawrence Woods; OR 5 Orange Road) are arranged from low to high ORAM scores.

WETLANDS, Volume 29, No. 2, 2009 Table 2. Pearson’s correlation (r) of soil physical, chemical, and biological parameters measured in the study during the November sampling period. BD 5 bulk density; WSA L 5 water stable aggregates 2–8 mm; WSA M5 water stable aggregates 1–2 mm; WSA S 5 water stable aggregates 0.25–1 mm; C 5 carbon, N 5 nitrogen, P 5 phosphorus, S 5 sulfur, OM 5 organic matter, BG 5 b-glucosidase; AS 5 arylsulfatase, MB-C 5 microbial biomass-carbon, SM 5 soil moisture. **p # 0.05.

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Figure 6. Principle components analysis (PCA) showing the pattern of distribution of the six wetland sites (BW 5 Bigwood; GW 5 Gahanna Woods; GR 5 Graham Road; JR 5 Johnson Road; LW 5 Lawrence Woods; OR 5 Orange Road) using soil physical (three classes of macro-aggregates, bulk density and soil moisture), chemical (OM, pH, C, N, P and S) and biological (b-glucosidase, arylsulfatase, and microbial biomass-carbon) parameters. Ohio Rapid Assessment Method (ORAM) scores are shown in parenthesis by each site. Only the soil parameters (SM 5 soil moisture; OM 5 organic matter; N 5 nitrogen; S 5 sulfur; C 5 carbon; MB 5 microbial biomass-C; BD 5 bulk density; WSAL 5 large fraction of water stable aggregate). Note that only one arrow was used to represent N, S and C as these parameters fell very closely on the PCA.

stabilization is biological activity through physical enmeshment or cementing of soil particles. Furthermore, during decomposition, the resulting organic materials, including microorganisms and the decomposition products of plant and animal, play a role in forming aggregates. These are of course important components of a wetland’s soil composition. It was expected that because of the higher organic carbon content in the high quality wetlands, there would be a greater percentage of macroaggregates (2–8 mm and 1–2 mm). However, the opposite relationship was observed; organic C was negatively correlated with macroaggregate formation (2–8 mm). This inconsistent relationship could have been due to one of the high quality wetlands, Big Woods, having a high level of organic matter and low percentage of 2–8 mm aggregates. It is worth noting that very few wetland soils studies have actually quantified aggregate formation and thus little is known about the aggregates and their distribution in saturated

organic soils. Hossler (2005) found that macroaggregate stability in the soil of natural wetlands was greater than in the soil of created wetlands, suggesting that macroaggregates accumulate over time in wetland soils. CONCLUSION Our results indicate that the Ohio Rapid Assessment Method (ORAM) reflects some of the key soil metrics we measured, but not all. Those soil indicators that should be considered for assessment of wetland quality in the intensive or level 3 assessment of wetlands recommended by USEPA are bulk density, microbial biomass, and soil C and N. Other researchers have reported certain indicators can be reflective of the quality of both aquatic and soil systems. In summary these reports suggest an ideal indicator should : a) be easy and inexpensive to measure; b) be sensitive to human-induced stress or disturbance; c) be consistent in its response to

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disturbance or degradation practices; d) relate to specific ecosystem function and correlate well with ecosystem processes; e) be relevant to all habitat types; and f) have a minimum amount of seasonal and spatial variability (Breckenridge et al. 1995, Doran and Safley 1997, van Dam et al. 1998, Schloter et al. 2003, Gil-Sotres et al. 2005). Bulk density warrants more research as an indicator of wetland condition because: it was highly correlated with other soil parameters, relates to plant root growth and productivity, and is relatively easy to collect in the field and process in the laboratory. Microbial biomass and soil C and soil N content have potential as indicators of wetland condition because of their close link to C cycling and storage. Soil enzymes are often included in the suite of soil parameters used to assess soil quality in terrestrial ecosystems and have been shown to be sensitive to human-induced disturbance in mangrove ecosystems (Dinesh et al. 2004). The results from this study suggest that because of their dependence on soil moisture, the observed seasonal variability across sampling periods, the cost of enzyme substrate, and the sensitivity of enzyme activity to soil storage and processing procedures, the measurement of soil enzyme activity is not suited for the discrimination of the forested wetlands used in this study. However, the use of other enzymes more specific to saturated conditions (such as DEA; Jordan et al. 2007) is an interesting alternative that should yet be considered. ACKNOWLEDGMENTS This research was supported by the School of Environment and Natural Resources at the Ohio State University. We appreciate the help of Evelyn Anamaet, Amy Barrett, Gwen Dubelko, Jarod Duquette, Becky Fauver, Dan Gillenwater, Kyle Herrman, Katie Hossler, Chad Kettlewell, Nicola Lorenz, Nickla Louisy, Amanda Nahlik, and Erin Rothman for their help with sample collection and laboratory analysis. We are grateful for useful comments provided by the Associate Editor and two anonymous reviewers on an earlier version of this work. LITERATURE CITED Balota, E. L., A. Colozzi-Filho, D. S. Andrade, and R. P. Dick. 2003. Microbial biomass in soils under different tillage and crop rotation systems. Biology and Fertility of Soils 38:15–20. Barbhuiya, A. R., A. Arunachalan, H. N. Pandey, K. Arunachalan, M. L. Khan, and P. C. Nath. 2004. Dynamics of soil microbial biomass C, N, and P in disturbed and undisturbed stands of a tropical wet-evergreen forest. European Journal of Soil Science 40:113–21.

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