©2023 International Society of Arboriculture Arboriculture & Urban Forestry 49(2): March 2023 91 methods exist for determining labile organic matter including direct measures of organic matter pools (Marriott and Wander 2006) or microbial activity (Zou et al. 2005). Particulate organic matter (POM) is a measure of the low-density, sand-sized organic mat- ter (Cambardella and Elliott 1992). Permanganate oxidizable carbon (POXC) is labile organic matter measured with a chemical reaction (Tirol-Padre and Ladha 2004). Total microbial biomass carbon (MBC) and nitrogen (MBN) are the total carbon (C) or nitro- gen (N) contained in the microbial biomass pool and are measured with fumigation and extraction. Indi- rect measurements of labile organic matter include quantifying microbial respiration defined as the CO2 production of microbial communities within a soil sample that is placed in a sealed container (Alvarez and Alvarez 2000). These CO2 levels are often mea- sured by observing a color change in chemical indica- tors. The inclusion of a more sensitive soil biological indicator may increase accuracy of the RUSI, allow- ing it to be used to assess soil management actions. Objectives This study investigated 3 knowledge gaps in the cur- rent RUSI. First, does the RUSI correlate to urban tree performance in other urban tree populations? Second, can customizing the RUSI with parameter weighting increase its correlation to tree growth and health? Third, is the RUSI sensitive to soil manage- ment actions and does the addition of a labile organic matter parameter increase this sensitivity? To address these knowledge gaps 3 specific hypotheses were developed: (1) the RUSI will significantly correlate to tree performance in 3 Wisconsin cities; (2) adjusting the parameter weighting will improve the correlation between RUSI and tree performance; (3) the addition of a labile organic matter parameter will increase the RUSI correlation to urban tree performance. METHODS AND MATERIALS Description of Study Cities and Plots This research was conducted in Stevens Point, Green Bay, and Milwaukee, WI, USA. These cities were chosen due to funding available for travel to conduct the research, the cities’ willingness to participate, and the presence of accurate planting and tree inventories. Full descriptions and data on human and tree popula- tions, climate, and native soils are provided in the Appendix. Thirty sample plots were randomly selected precipitation (PPT), growing degree days (GDD), and exposure (EXP). Urban parameters include traffic (TRA), infrastructure (INF), and surface (SUR). Soil physical parameters include texture (TEX), structure (STR), and penetration (PEN). Soil chemical param- eters include pH, electrical conductivity (EC), and organic matter (SOM). Soil biological parameters include estimated rooting area (ERA), depth of the A-horizon (HOR), and wet aggregate stability (WAS). Each parameter is measured and scored from 0 to 3 using scoring functions which are described in the Appendix. After development, the RUSI was tested in 7 cities to determine its ability to predict urban tree performance. Initial testing was performed in Boston, MA, USA; Chicago, IL, USA; Cleveland, OH, USA; Springfield, MA, USA; Toledo, OH, USA; Ithaca, NY, USA; and New York City, NY, USA (Scharenbroch et al. 2017). This research showed a significant correla- tion between the RUSI and urban tree performance across all cities and species tested (P < 0.0001; R2 values of 0.18 to 0.40). Initial RUSI testing showed the need for refinement to other urban tree populations, parameter weighting, and inclusion of dynamic parameters that would respond to soil management. To date the RUSI has only been tested in a limited number of cities and with a few urban tree species. Research is needed to test the RUSI model’s applicability in other cities and tree species. The current RUSI assigns equal weights for all 15 parameters, but initial testing identified several param- eters which appeared to be better predictors of urban tree performance. These parameters include those associated with soil volume and compaction, such as estimated rooting area (ERA), structure (STR), and wet aggregate stability (WAS). This importance was expected, as many urban tree health issues are due to limited soil volume and compaction (Jim 1998). Soil quality indices often utilize unequal parameter impor- tance with weighting schemes (Andrews et al. 2002). In this approach, parameter weights are assigned based on available data, literature, and expert knowl- edge (Karlen et al. 2003). Labile organic matter is a portion of total soil organic matter (SOM) that is readily available for decomposition by soil organisms. Consequently, it is proposed as an ideal indicator of dynamic soil proper- ties, such as nutrient availability, and has been found to be responsive to soil management actions (Sharifi et al. 2008; van der Heijden et al. 2008). A variety of
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