218 Scharenbroch and Catania: Soil Quality Attributes as Indicators of Urban Tree Performance ther screening. Eigenvalues are the amount of variance explained by each factor. Factors with eigenvalues greater than one were retained for interpretation, because factors with eigenvalues less than one explained less variance than individual soil attributes (Kaiser 1960). The retained factors were subjected to varimax rotation, which redistributes the variance of significant factors to maximize the relationship between interdependent soil variables. All meaningful loadings (i.e., >0.40) were included in the inter- pretation of the PCA. Principal components that explained more than 5% of the total variance were considered significant. PCA was also used to identify single values for an USQI and also to synthe- size tree attributes. Multivariate regression was used to identify relationships in the data sets. Step-wise regression modeling with mixed direction and probability to enter or leave at P ≤ 0.05 was used to develop predictive models among soil and tree properties. RESULTS AND DISCUSSION Establishing the MDS for Assessing Urban Soil Quality Using ANOVA, PCA, and regression analyses the data were screened to identify soil parameters to include in the MDS for assessing urban soil quality. The ANOVA revealed the gen- eral location effects on individual soil parameters. The PCA identified which variables differed most and which were most informative and unique. Regression analyses detected redundancy among the soil properties, and were used to se- lect the practical and informative measures in the final MDS. The results from the ANOVA analyses are summarized measures of SOM. The seven parameters with significant posi- tive loading on PC1were: SOM, total N, total C, fine POM, to- tal POM, K, and WAS (Table 3). Bulk density was a significant negative loading on PC1. Higher PC1 scores appear to relate increases in soil quality. Principal component 2 explained 16% of the total variance and included five positive significant vari- ables: Ca, pH, eCEC, EC, and C/N (Table 3). Higher PC2 scores show relative decreases in soil quality. Significant PC3, PC4, and PC5 loadings explained 10%, 7%, and 6% of the total variance (Table 2). Higher PC3, PC4, and PC5 scores were interpreted as increases in soil quality. The PC3 scores were positively load- ed with root restriction depth, and negatively loaded with wa- ter content, penetration resistance, and chroma. The PC4 scores were positively loaded with microbial biomass N, MBC/TOC, and silt and negatively loaded with sand. Microbial respiration was a positive, and the qCO2 was a negative loading on PC5. moisture, volumetric water content, penetration resistance, root ©2012 International Society of Arboriculture the ANOVA and PCA screening included: SOM, N, C, fine POM, total POM, K, ρb The 24 soil properties (listed in order of importance) passing , WAS, Ca, pH, eCEC, EC, C/N, gravimetric soil in Table 2. Location effects were evident for many factors. Sixteen parameters were excluded from further consider- ation because they failed to meet the screening criteria (P < 0.05 for location effect and CV < 60%). Thirty-two param- eters met the criteria and were retained for further screening. The relative significance of the data set parameters was as- sessed using PCA of the 32 retained variables from the ANOVA (Table 3). There were five significant principal components (PC) that explained 65% of the variance. The first PC explained 26% of the total variance, contrasted ρb , and was positively related to restriction depth, microbial biomass N, MBC/TOC, silt, sand, clay, respiration, and qCO2 , WAS, pH, EC, SOM, and POM. . Further screening with regression analyses were used to identify redundancy among the 24 remain- ing soil parameters (Table 4). In the following section, justifica- tion is provided for reducing these 24 parameters to the nine MDS parameters of: sand, silt, clay, ρb Measures of organic matter (SOM, C, N, and C/N) were heav- ily weighted in the PCA. These responses had relatively low CV values and high R2 values for the ANOVA site differences. Higher SOM, C, and N contents indicate increases in soil quality (Doran and Parkin 1994; Knoepp et al. 2000). Increased C/N ratios in- dicate lower decomposition rates and relatively lower N mineral- ization potentials (Bengtsson et al. 2003). Loss on ignition is the least costly of these analyses and was significantly correlated with the other responses (C = 1.57 + 0.0329 * SOM, R2 = 0.43, P < 0.0001), (N = -0.0458 + 0.00479 * SOM, R2 = 0.86, P < 0.0001), (C/N = 24.0 – 0.147 * SOM + 24.0, R2 = 0.42, P < 0.0001), and color (SOM = 114 – 14.1 * dry value, R2 = 0.24, P < 0.0001) (Table 4). Overestimation errors may occur with loss on ignition for soils with high clay contents and carbonate materials (Nelson and Sommers 1996). Soil C, N, or C/N ratio are more costly, but often preferred to loss on ignition due to greater accuracy. Mea- surements of C, N, C/N ratio, and SOM relate similar informa- tion, and any one of these measurements may be suitable. Loss on ignition was chosen in the MDS due to its lower cost of analysis. Particulate organic matter appears to be a sensitive indicator for assessing urban soil quality. Particulate organic matter is posi- tively related to nutrient supply and soil physical condition (Wan- der et al. 1994; Six et al. 2000; Scharenbroch and Lloyd 2006). Particulate organic matter was identified as the primary indicator of soil quality for assessing the impact of tillage in Illinois, U.S. (Wander and Bollero 1999). The ANOVA showed strong location effects for POM, and POM was a highly weighted variable in the PCA. In this data set, POM was significantly correlated with 27 of 48 total parameters (7 of 17 physical responses, 15 of 17 chemi- cal responses, and 5 of 14 biological responses) (data not shown). Indices of microbial respiration, microbial biomass, and N min- eralization are good estimates of potential nutrient availability, gross microbial functioning, and soil quality (Knoepp et al. 2000). Particulate organic matter was significantly correlated with these measures (microbial biomass N = 87.4 + 14.1 * POM, R2 P < 0.0001), (soil respiration = 53.0 + 6.85 * POM, R2 P = 0.0328), and (N mineralization = 1.18 + 0.184 * POM, R2 = 0.26, = 0.05, = 0.13, P = 0.0010) (Table 4). The POM assessment requires sub- stantially less time, money, and expertise to measure compared to those microbial assessments. These findings suggest POM as a necessary inclusion in a MDS to assess urban soil quality. Soil pH influences many soil properties and is often in- cluded in assessments of soil quality (Schoenholtz et al. 2000). Higher soil pH values (>8.0) are associated with decreases in soil quality (Gale et al. 1991). Acidity is also known to in- hibit biological activity, so the relationship with soil reac- tion and tree growth is likely not linear. Soil pH was heavily weighted in the PCA. Location effects were largely significant and variation was low for pH. Soil pH is relatively easy to mea- sure and cost of analysis is cheap. For all of these reasons soil pH should be included in a MDS to assess urban soil quality. Increased Na and EC indicate greater salinity and are inter- preted as deleterious to soil quality (Doran and Parkin 1994; Karlen and Stott 1994). Increases in exchangeable bases can
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