222 Scharenbroch and Catania: Soil Quality Attributes as Indicators of Urban Tree Performance physical responses, 10 of 17 chemical responses, and 6 of 14 biological responses) (data not shown). It is a low-cost measure that can be performed in the field with minimal equipment require- ments, thus WAS is included in the MDS for urban soil quality. Both gravimetric and volumetric soil moisture contents re- vealed significant location effects with the ANOVA, but soil moisture was weakly loaded in the PCA. Soil water changes rap- idly, and repeated measurements are often needed to make in- ference on soil quality. Furthermore, information on both water content and tension is needed to provide accurate assessments of plant available water status. Soil water content appears too elusive and not practical enough to be included in the MDS. Compared to other studies (Wander and Bollero 1999; Brej- da et al. 2000a; Brejda et al. 2000b), researchers did not detect just one soil property with the greatest potential for relating soil quality. Similar to other studies, the MDS included physical, chemical, and biological soil properties (Shukla et al. 2006; Ro- drigues et al. 2008). The MDS in this research included sev- en soil properties: three physical (texture, ρb , and WAS), two chemical (pH and EC), and two biological (SOM and POM). Establishing an Urban Soil Quality Index A PCA was performed with the MDS soil properties to create an urban soil quality index (USQI) (Table 5). The first principal component explained 44% of the MDS soil parameters and was mostly highly loaded by SOM and pH, followed by WAS, POM, ρb , and EC. The second and third components explained an addi- tional 16% and 13% of the MDS variance and were loaded by soil texture (clay, silt, and sand). The first PC explained most vari- ance, and these values were selected for use as the USQI scores. Evaluating the MDS Parameters and USQI for Predicting Urban Tree Performance A PCA was performed on the nine tree response parameters to identify which variables were most important in explaining vari- ance of the measured tree responses (Table 6). The first princi- pal component explained 50% of the tree performance variation and was positively loaded by tree size parameters (age, trunk diameter, tree height, and crown area), and to a lesser degree, leaf N content. The second principal component explained (6 of 9), and POM (5 of 9) with the individual tree response parameters. Soil pH (5 of 9), EC (5 of 9), clay (4 of 9), SOM (4 of 9), POM (4 of 9), and silt (3 of 9) were highly corre- lated (r-values > 0.4 and P < 0.0001) with many of the tree response parameters. Soil pH, clay, EC, and SOM were well correlated with PC1 ≈ tree size variable and explained 54, 32, 32, and 29% of its variance, respectively. The second prin- cipal component relating to tree growth was only correlated with WAS. The USQI values (loaded by SOM and pH) were significantly correlated with all tree responses aside from the tree condition index. Step-wise regression produced signifi- cant models for all tree response parameters, including PC1 ≈ tree size and PC2 ≈ tree growth (Table 7). Soil parameters that appeared most often in the step-wise models were SOM, pH, and texture (clay and silt). Relationships between the tree responses and the USQI and also the step-wise models were tighter than for the individual MDS parameters, sug- gesting multiple parameters are better predictors of urban tree performance compared to any single soil measurement. These analyses suggest that SOM, pH, and texture are ca- pable and most useful in explaining urban tree performance attributes. To date, no available studies have assessed urban soil quality in relation to tree performance, so it is not possible to relate these findings to an existing knowledge base. Simi- lar approaches to this study have examined soil quality in re- lation to land use or agricultural plant performance. Most of these studies report SOM or C (Brejda et al. 2000a; Brejda et al. 2000b; Shukla et al. 2006; Rodrigues de Lima et al. 2008; Bautista-Cruz et al. 2011) as primary indicators of soil qual- ity. Some of these studies also report pH and texture (Shukla et al. 2006; Bautista-Cruz et al. 2011) along with other prop- erties (e.g., available water, porosity, bulk density, aggregate Table 5. Principal component scores from nine soil MDS parameterszyx Principal component PC1 ≈ OM, pH Eigenvalue Proportion Cumulative proportion SOM (%) WAS (%) POM (%) Silt (%) Sand (%) Clay (%) EC (dS m-1 pH ρb (Mg m-3 ) ) 3.97 44.18 44.18 Scores of three rotated eigenvectors 0.86y 0.76y 0.56y 0.16 -0.03 -0.18 -0.57y -0.68y -0.78y . Data from 84 plots in western suburban Chicago, IL. PC2 ≈ texture 1.42 15.78 59.96 -0.24 0.07 -0.42 -0.94y 0.03 0.96y 0.28 0.28 0.30 z Only principal components (PC) with eigenvalues >1 and that explain >5% of the total variance were retained. y Parameters with significant loadings on the within column principal component. x PCA performed on only nine MDS parameters to establish urban soil quality gradient. PC3 ≈ texture 1.15 12.83 72.79 -0.16 -0.01 -0.37 -0.18 0.83y -0.12 0.36 -0.37 -0.26 an additional 20% and was related to tree growth parameters (trunk diameter growth rate and height growth rate). The prin- cipal components derived from tree responses (PC1 ≈ tree size) and (PC2 ≈ tree growth) were assessed in relation to the soil properties identified in the MDS and the USQI (Table 7). The majority of the individual soil MDS parameters were well correlated to the tree response parameters (Table 7). Sig- nificant correlations were detected for silt (7 of 9), clay (8 of 9), WAS (7 of 9), ρb (6 of 9), pH (9 of 9), EC (6 of 9), SOM ©2012 International Society of Arboriculture
September 2012
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