*4.2.4. Habit suitability: field sparrow and fox squirrel*

#### *4.2.4.1. Field sparrow*

The variables that influence field sparrow are V1 – percent shrub crown cover, V2 – percent of total shrubs of height less than 1.5 m, V3 – percent canopy cover of grasses, and V4 – average height of herbaceous canopy. The cover types field sparrow needs for breeding are (1) evergreen shrub land, (2) deciduous shrub land, (3) evergreen shrub savanna, (4) deciduous shrub savanna, (5) grassland, and (6) forbland.

$$\text{Habit sustainability index} = \text{[Min.(V1, V2)} \times \text{Min.(V3, V4)]} \tag{4}$$

The study used Eq. (4) to calculate field sparrow HSI for different scenarios: predevelopment, postdevelopment, and postdevelopment employing LID controls. The results are shown in **Table 4**.


**Table 4.** Habit sustainability index (HSI) of field sparrow.

#### *4.2.4.2. Fox squirrel*

The variables that influence field sparrow are V1 – percent canopy closure of trees that produce hard mast, V2 – distance to available grain, V3 – average dbh of overstory trees (height≥80% of trees), V4 – percent tree canopy closure, and V5 – percent shrub crown cover. The cover types field sparrow needs for breeding are (1) deciduous forest, (2) deciduous tree savanna, and (3) deciduous forested wetland.

$$\text{Winter food} = \frac{\text{3V}1 + \text{V2}}{\text{3}} \tag{5}$$

$$\text{Habitat cover or breadth} = \left(\text{V3} \times \text{V4} \times \text{V5}\right)^{\text{(1\%)}}\tag{6}$$

The study used Eqs. (5) and (6) to calculate field sparrow HSI for different scenarios: prede‐ velopment, postdevelopment, and postdevelopment employing LID controls. The results are shown in **Table 5**.


**Table 5.** Habit sustainability indexes (HSI) of fox squirrel.

The scores of indexes of both field sparrow and fox squirrel are the highest for LID scenario, which reveals that the postdevelopment with LID controls provides the best habitats for them.

#### *4.2.5. Visual quality*

*4.2.4. Habit suitability: field sparrow and fox squirrel*

shrub savanna, (5) grassland, and (6) forbland.

V1 0.00 0.26 0.49 V2 0.20 1.00 1.00 V3 0.29 0.15 0.21 V4 0.00 0.80 0.80 HSI 0.00 0.04 0.12

**Table 4.** Habit sustainability index (HSI) of field sparrow.

and (3) deciduous forested wetland.

The variables that influence field sparrow are V1 – percent shrub crown cover, V2 – percent of total shrubs of height less than 1.5 m, V3 – percent canopy cover of grasses, and V4 – average height of herbaceous canopy. The cover types field sparrow needs for breeding are (1) evergreen shrub land, (2) deciduous shrub land, (3) evergreen shrub savanna, (4) deciduous

The study used Eq. (4) to calculate field sparrow HSI for different scenarios: predevelopment, postdevelopment, and postdevelopment employing LID controls. The results are shown in

The variables that influence field sparrow are V1 – percent canopy closure of trees that produce hard mast, V2 – distance to available grain, V3 – average dbh of overstory trees (height≥80% of trees), V4 – percent tree canopy closure, and V5 – percent shrub crown cover. The cover types field sparrow needs for breeding are (1) deciduous forest, (2) deciduous tree savanna,

3V1+V2 Winter food

The study used Eqs. (5) and (6) to calculate field sparrow HSI for different scenarios: prede‐ velopment, postdevelopment, and postdevelopment employing LID controls. The results are

<sup>3</sup> <sup>=</sup> (5)

(1/3) Habitat cover or breeding = (V3 × V4 × V5) (6)

**Variable Existing Traditional Low impact development**

Habit sustainability index = [Min.(V1,V2) × Min.(V3,V4)] (4)

*4.2.4.1. Field sparrow*

78 Sustainable Urbanization

**Table 4**.

*4.2.4.2. Fox squirrel*

shown in **Table 5**.

The measurement of visual quality for the non-LID treatments revealed scores typical of urban environments for the existing and traditional conditions (mid to low 70s). The LID design contained more green spaces and vegetation, generating scores usually in the 50s. Selected areas within the LID proposal possessing numerous flowers and abundant wildlife scores even better (mid 40s) and portions of the LID proposal with less vegetation within the view scored higher (mid 60s).



**Table 6.** The significant regressors and coefficients forming an equation of the first dimension.

#### *4.2.6. Soil productivity*

Most of the metrics in the study were developed by others and simply applied by the study team, with the exception of the soil productivity equation. Therefore, the results of the soil productivity equation are presented first, before presenting the comparison results. The results of the soil equation development indicate that there are two primary dimensions to soil productivity for the area, forming an annual plant/ woody plant preference cluster forming (**Table 6**), where the preferences for annuals and woody plants negatively covary along the same dimension and wetland plants negatively covary. The second equation represents an annual plant (positive)/wood plant (negative) preference cluster forming (**Table 7**). In other words, the vegetation studied in the investigation did not covary across all types of vegetation and separated themselves into three groups, similar to results discovered in Florida by Burley and Bauer in 1993, where they discovered two groups, a wetland ground and an upland group. Each of the regressors is where each soil setting required a different soil profile [26]. The equations explain between 90 and 97% of the variance and are definitive (*p*-value < .001) and all of the regressors are significant (*p*≤0.05), with no significant multi-collinearity. Depending upon whether the planting area is intended for woody plants, annual plants, or wetland plants, the soil preferences are different (**Figure 9**).


**Table 7.** The significant regressors and coefficients forming an equation of the second dimension.

**Figure 9.** A plot of areas for soil preference for various plant clusters.

**Regressor Coefficient** TP (Topographic Position \* % Organic Matter) −0.51303 SLCL (% Slope \* % Clay) −0.00565 SLBD (% Slope \* Bulk Density) 0.11043 FRBD (% Rock Fragments \* Bulk Density) 2.0781 FRHC (% Rock Fragments \* Hydraulic Conductivity) 0.0303 FRPH (% Rock Fragments \* Soil Reaction pH) −0.262 CLHC (% Clay \* Hydraulic Conductivity) −0.06456 CLPH (% Clay \* Soil Reaction pH) −0.06796 CLOM (% Clay \* % Organic Matter) 0.05429 BDHC (Bulk Density \* Hydraulic Conductivity) −2.73812 HCAW (Hydraulic Conductivity \* Available Water Holding Capacity) 1.33765 HCOM (Hydraulic Conductivity \* % Organic Matter) −0.03302 AWOM (Available Water Holding Capacity \* % Organic Matter) −13.63024 PHOM (Soil Reaction pH \* % Organic Matter) −2.52367 HWSL (High Water Table \* % Slope) −0.02655 HWCL (High Water Table \* % Clay) −0.04633 HWAW (High Water Table \* Available Water Holding Capacity) 9.67109 HWOM (High Water Table \* % Organic Matter) 0.49451

**Table 6.** The significant regressors and coefficients forming an equation of the first dimension.

Most of the metrics in the study were developed by others and simply applied by the study team, with the exception of the soil productivity equation. Therefore, the results of the soil productivity equation are presented first, before presenting the comparison results. The results of the soil equation development indicate that there are two primary dimensions to soil productivity for the area, forming an annual plant/ woody plant preference cluster forming (**Table 6**), where the preferences for annuals and woody plants negatively covary along the same dimension and wetland plants negatively covary. The second equation represents an annual plant (positive)/wood plant (negative) preference cluster forming (**Table 7**). In other words, the vegetation studied in the investigation did not covary across all types of vegetation and separated themselves into three groups, similar to results discovered in Florida by Burley and Bauer in 1993, where they discovered two groups, a wetland ground and an upland group. Each of the regressors is where each soil setting required a different soil profile [26]. The equations explain between 90 and 97% of the variance and are definitive (*p*-value < .001) and all of the regressors are significant (*p*≤0.05), with no significant multi-collinearity. Depending upon whether the planting area is intended for woody plants, annual plants, or wetland plants,

*4.2.6. Soil productivity*

80 Sustainable Urbanization

the soil preferences are different (**Figure 9**).

The results of the soil equation development indicate that there are two primary dimensions to soil productivity for the area, forming an annual plant preference cluster, a woody plant preference cluster, and a wetland plant preference cluster, where each soil setting required a different soil profile. The equations explain between 90 and 97% of the variance and are definitive (*p*-value < .001).

When the Friedman's analysis of variance is applied to the ranks of **Table 8**, at least one of the treatments is significantly different that at least one of the other treatments at a *p*-value of <0.005, where 22.54 is greater than a Chi-square distribution of 10.597. When the Friedman's multiple comparison test is applied, the low impact development treatment is significantly different from the other two treatments at a *p*-value of 0.05. The existing and traditional treatments are also significantly different (*p*-value 0.05).


**Table 8.** Numerical results.
