136 Ordóñez et al.: Influence of Abiotic Factors in a Commercial-Retail Streetscape The city is located in the 7a plant hardiness zone (NRC 2016), and its soils, where not highly altered by anthropogenic processes, are representative of the Lawrence River soil system, characterized by silt clay soils from glacial and fluvial deposits. Data Collection Since tree decline and failure are usually the cu- mulative effect of several stressors over time (Trowbridge and Bassuk 2004), this study con- sidered as many different physical, design, and maintenance factors as possible to understand the causes driving tree mortality and decline along Bloor Street. Abiotic factors were the focus of the study because the trees showed no visual signs of being affected by pests and/or diseases before being removed, and all the trees were the same species. Soil samples, built environment details, and tree performance metrics were collected as trees were replaced throughout the removal op- erations (May–June of 2015), explained hereaſter. Soil samples were collected from the two dif- ferent types of planters (flower bed or “beds,” and ground-level pit or “pits”) at between 30 and 40 cm away from the tree trunk and at a depth of between 15 and 30 cm. Additional soil samples were col- lected from a distance of between 50 to 75 cm away from the tree trunk at a depth of between 15 to 30 cm. Soil samples were frozen and stored before texture and chemical analysis was conducted. Soil compaction was measured on-site using a Field- Scout SC 900 Soil Compaction Meter (Spectrum® Technologies, Inc., Plainfield, Illinois, U.S.) at two distances from the tree trunk, between 20 and 30 cm and between 65 and 75 cm. Compaction mea- surement profiles were taken from the soil surface to 45 cm deep at 2.5 cm intervals. These values were averaged into three groupings: 0–15 cm; 15–30 cm; and 30–45 cm. Compaction was only measured in pit planters, given that ornamental plants in the bed planters prohibited acquisition of accurate compaction meter measurements. Information was recorded on the type of planter (beds/pits), planter location (north or south side of the street), distance of planting site to the near- est street intersection, and the type of intersection (major or minor street intersection, determined through analysis of the City of Toronto Roads Dataset in ArcGIS 2016, v.10.4.1). While the trees ©2018 International Society of Arboriculture were being removed, data were collected on tree mortality (alive/dead before removal), diameter at breast height (DBH), and damage, for which a bino- mial yes/no measure was used to discern whether the tree had appreciable torn limbs, trunk scars, missing canopy, pruning scars, cracks, and peel- ing bark before removal in 2015. Historical data on the condition of each tree were captured using a qualitative assessment based on a scale of 0 to 3, where 0 = dead; 1 = poor; 2 = fair; and 3 = good. These data were collected using three sources: 1) contractual reports based on summer field surveys by a registered arborist covering the pre-removal period 2011–2014; 2) close-range digital images from the Google StreetView™ mapping service, an efficient way to survey street trees and up to 90% agreeable with field survey data (Berland and Lange 2017); and, 3) a 2014 summer street tree survey, available digitally from the City of Toronto. Since light availability in urban canyons (i.e., high-density urban streetscapes) can influence tree growth (Jutras et al. 2010), data were col- lected on the hours of exposure to sunlight received at each tree-planting site. The 3D building data set (i.e., 3D Massing) of the City of Toronto was used in Sun Shadow Volume tool included in the Visibility Toolset ArcGIS 3D Analyst extension (2016, v.10.4.1) to model the shadow patterns for each building in proximity to the planting sites. March 21 (spring equinox) and June 21 (sum- mer solstice) were selected for shadow model- ing. These days represent the lower and upper range (minimum and maximum) of light avail- ability for the growing season for trees. Light availability was modelled differently for each day, including from 9:00AM to 6:00PM for March 20, and from 7:00AM to 7:00PM for June 21. The modelling timeframe was offset by 1.5 hours aſter and before sunrise and sunset times, given that the sun is low in the horizon at these times and may not cast shadow on the planting sites. Daily shadow patterns at the study site were estimated at four meters above the ground sur- face, as this area better approximates the light availability at the canopy level. The site had minimal elevation variability (<1 m). Shadow hours were converted to sunlight hours by using the following equation in the Raster Calcula- tor tool within ArcGIS’s Map Algebra Toolset:
May 2018
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