Development of Urban Tree Growth Models Based on Site and Soil Characteristics
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Trees provide numerous benefits crucial to urban environments, yet poor growing conditions often prevent trees from reaching their genetic potential for growth, longevity, and ecosystem function. To overcome these limitations, greater understanding of tree growth in the urban environment is needed. The goal of this research project was therefore to characterize a broad suite of soil characteristics associated with urban tree plantings and evaluate their suitability for modeling physical dimensions and growth rates of urban trees. A series of observational studies and experiments was conducted on urban soils inhabited by two tree species (Zelkova serrata (Thunb.) Mikano and Quercus phellos L.) in Washington, DC and one tree species (Quercus virginiana Mill.) in Jacksonville, FL – two major metropolitan areas of the eastern United States with contrasting climate and soils. Characterization of urban soil attributes within cities revealed low variability for some properties (soil texture, pH, and certain plant nutrients with coefficients of variation (CV) below 0.5), but high variability (CV>1.0) for others (nitrate, ammonium, copper, and zinc). This is dependent on the location. These findings suggest that tree planting site evaluations may not require measurements for all soil properties and that representative sampling may be sufficient to accurately characterize most soil properties within a city. Field assessment of urban tree soils also revealed that conventional measures of soil compaction are difficult to obtain due to obstructions by roots and other foreign objects. To address the critical need for efficient and reliable assessment of soil compaction around urban trees, an experiment was conducted to develop bulk density estimation models for four common soil texture classes using soil strength and soil moisture as predictor variables. These models provided medium (0.42) to high (0.85) coefficients of determination when volumetric water content (VWC) was log transformed, demonstrating that measurements of soil texture, strength, and moisture can provide rapid, reliable assessment of soil compaction. Tree growth modeling focused on three response variables: canopy projection (CP), canopy volume (CV), and peak-increment-area age (PIA). To calculate PIA, tree-ring analysis was used to determine the age at which maximal trunk diameter growth occurred between transplanting and time of sampling. Because Q. virginiana has difficult-to-distinguish growth rings, an intensive tree-ring analysis of cores collected from these trees was conducted. The analysis revealed interseries correlation coefficients of up to 0.66, demonstrating that Q. virginiana can be aged with fairly high confidence in an urban setting. Empirical models developed for all three tree species using the suite of soil and site variables explained 25% – 83% of the observed variability in tree physical dimensions and growth rates. Soil pH was found to be a significant predictor variable for the majority of growth models along with nutrients such as Fe, B, Mn, and Zn, which are also associated with soil alkalinity. Models for PIA possessed the highest coefficient of determination, suggesting that measurements of soil conditions can be used confidently to predict the age at which growth rate subsides in these species. CV and CP were not predicted as well by soil-related variables, presumably because above-ground constraints such as pruning and building encroachment can affect canopy size without necessarily affecting growth rate. Certain prediction models for all three species included predictor variables with counterintuitive influences on tree growth (e.g., negative influences of soil depth on Q. phellos and soil volume on Q. virginiana), suggesting that either these urban trees are responding to these variables in a novel manner or that variables unaccounted for in these models (perhaps related to urbanization or high vehicular traffic) are concomitantly influencing tree growth.
- Doctoral Dissertations