Arboriculture & Urban Forestry 44(3): May 2018 [1] Sunlight Hours = (Total Hours of Sunlight Modelled for the Given Day) – (Hours of Shadow) The number of sunlight hours for each plant- ing location was determined by taking the average of a circle with a radius of 1.5 m circle centered on each planting location, where this zone repre- sented the approximate area of the tree canopy. Sunlight range (maximum value of either March 20 or June 21, minus minimum value of either March 20 or June 21) and average sunlight (light in March 20 minus light in June 21, divided by two) were later calculated from the values of light avail- ability for the two dates to accurately describe the variation of light availability. The four measures of light availability, including sunlight for March 20, sunlight for June 21, sunlight range, and sun- light average, were used in subsequent analyses. Finally, the intention of this study was to account for the effect of climatic influences on tree condi- tion, such as drought and prolonged heat/cold events, or weather influences, such as storm events. And while researchers sourced temperature and precipitation records from the closest Environment Canada weather station, and analyzed these with the yearly tree-condition ratings, this analysis was inconclusive. For this reason, analytical procedures or results from these analyses are not included. Subsampling & Laboratory Analysis In-depth soil analyses were conducted on a sub- sample (n = 57) of all collected soil at the site; this sample represented 43% of all tree growing loca- tions. Since the sample collection procedure did not cover all bed planters, the subsample included all available soil samples from bed planters (17 total) and a representative, randomly selected subsample (40 total) of soil from pit planters. The subsample of pit planters was determined by stratifying trees at the site into five groups of approximately equal number of pit plantings (mean = 17, SD = 2.6) in close proximity to one another, and using the RAND function in Microsoſt® Excel™ to randomly select eight samples from each respective group. Frozen soil samples of the selected subsample were sent to Agri-Food Laboratories Inc. (Guelph, Ontario, Canada), which meets all requirements of the Standards Council of Canada, for a full soil analysis, including texture (% of sand, clay, and silt), 137 organic matter (% of total), pH, electrical conduc- tivity (EC) (in dS/m) (done with the solid paste method at a standard solubility ratio of 1:1; see Rhoades 1996; Thomas 1996), and concentration of Calcium (Ca), Magnesium (Mg), and Sodium (Na) (in ppm) (done with mass spectrometry). Data Analysis The main purpose of the analysis was to inves- tigate the effect of abiotic influences on the pat- terns of tree mortality and condition, measured by two factors: 1) tree condition ratings (dead/ poor/fair/good), and 2) mortality status in 2015 (dead/alive). To achieve a more comprehensive analysis, the role of additional factors, including planter type (beds/pits), planting location (north/ south side of the street), proximity to and type of road intersections (major/minor), and presence of tree damage (yes/no for all measures) were also examined. Although historical tree condition rat- ings were collected for every year from 2011 to 2014, using the sources of data described above, only the City of Toronto data set from 2014, which provided the most recent assessment of tree condition, was useful in the analysis, given the cross-sectional nature of most other variables. As previously mentioned, although temperature and precipitation records were collected, these data could not be analyzed effectively in a way that could be paired with the other temporal data, the yearly tree-condition ratings. As such, there is no report on these analytical procedures. A combination of multivariate statistical tech- niques was used to identify the variables that were related to the patterns of tree mortality and condi- tion along Bloor Street. Correlation analysis, one- way multivariate analysis of variance (MANOVA), and contingency analysis were used, as they are useful to exploring patterns in multivariate data sets (Hair et al. 2010). Correlation analysis was used to explore the association between continu- ous variable pairs (e.g., Na and Ca concentra- tions) and correlation coefficients (e.g., Pearson’s r, non-parametric rank-order Spearman’s Rho) were calculated to evaluate the magnitude of the association. MANOVA was used to explore the differences of the variance among groupings of data (e.g., variability of Ca concentrations between bed and pit planters) (Huberty and Olejnik 2006). ©2018 International Society of Arboriculture
May 2018
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