Arboriculture & Urban Forestry 38(5): September 2012 In 1987, Dr. Henry Gerhold of Pennsylvania State Univer- sity partnered with several electric utilities to begin the Munici- pal Tree Restoration Program. Test trees were planted in plots along streets and under electric conductors to compare perfor- mance in 11 Pennsylvania communities (Gerhold 2007). Twelve years of standardized performance data are helping utilities to select the most appropriate cultivars, and the plantings serve as living demonstrations. The primary tree performance met- ric for this program was tree height because utilities wanted trees that did not exceed 8 m height to plant under conductors. In 2005, the National Elm Trial began producing standard- ized information on the performance of 20 Dutch elm disease (Ceratocystis ulmi) resistant cultivars in 18 plots across the country. Reports from this research include information on survival and growth, as well as damage from pests, disease, abiotic disorders, and pruning requirements (McPherson et al. 2009). High-performing cultivars require minimal treatment for pest infestations or pruning to develop strong structure. MODELING TREE GROWTH Forecasting urban tree growth is challenging because of the long time scales, transient dynamics of tree sites, multitude of management options and large number of tree species to model (Reineking et al. 2004). Estimates of tree size underpin modeling of ecosystem services. Accurate estimates of leaf surface area are particularly important because of its role mediating atmospheric fluxes like air pollutant and rainfall interception, photosynthe- sis, evapotranspiration, respiration, and shading. Two general approaches have evolved to model tree growth. First, empirical models predict tree dimensions, such as diameter at breast height (DBH) and crown volume based on measurements and statisti- cal relationships found to exist among measured variables. Em- pirical models focus on tree morphology. Second, process-based models focus on tree physiology by translating rates of assimila- tion and allocation of carbon and other constituents into growth rates of tree diameters, heights, and other attributes. Examples of each modeling approach are discussed in the next section. Empirical Models Empirical models use field measurements of tree dimensions and statistical methods to predict diameter, height, and crown spread. When information on site conditions is included, separate models can be developed for the same species. There are hundreds of em- pirical models of forest growth, and the variety of models is both a strength and weakness. Empirical growth models can be very accurate for specific sites where extensive measurements were made, but the absence of grounding in a robust theory limits their application to other regions (Valentine and Mäkelä 2005). The advantages of empirical models are that they are quick to devel- op, require few inputs, and uncertainty can be quantified. Because empirical models do not explicitly incorporate causal dynamics, it is difficult to extrapolate how different management interventions will influence future growth. Empirical approaches are used to model urban tree growth in both i-Tree and Lindenmayer-Systems. i-Tree i-Tree is public domain software developed by the USDA For- est Service and cooperators for urban forestry analysis and 173 benefits assessment. Within i-Tree, entire urban forests are as- sessed using Eco (formerly UFORE), and discrete street tree populations are assessed using Streets (formerly STRATUM). i-Tree quantifies urban forest structure, environmental effects, and value to communities from field data and local hourly air pollution and meteorological data (Nowak et al. 2008). Eco estimates standardized tree growth (DBH) based on the number of frost free days and adjusts this base value with tree condition and crown light exposure data (Nowak 1994; Nowak et al. 2008). For example, for Sacramento, California, frost free days are assumed to be 305, the annual base DBH growth rate is 0.83 cm, and it ranges from 0.8 to 1.0 cm across all DBH classes. The base tree growth rate comes from urban street tree (Flem- ing 1988; Frelich 1992; and Nowak 1994), park tree (DeVries 1987), and forest growth (Smith and Shifley 1984) estimates that were standardized to growth rates for Minnesota, U.S. Aver- age height growth is calculated based on formulas from Flem- ing (1988) and the specific DBH growth factor used for the tree. Eco’s growth rates are adjusted based on tree condition: fair to excellent condition, multiplied by 1 (no adjustment), poor con- dition - 0.76, critical condition - 0.42, dying - 0.15, dead - 0. Growth adjustment factors are based on percent crown dieback and the assumption that less than 25% crown dieback had a lim- ited effect on DBH growth rates (Nowak et al. 2008). Crown light exposure provides information on the number of sides of the tree receiving sunlight and ranges from 0 (no full light) to 5 (full light from top and four sides). Leaf area and biomass of trees are calculated using regression equations derived from measurements of 54 open-grown park trees in Chicago, Illinois, U.S., representing five species, and 34 smaller trees of 12 spe- cies from Poland. If required shading coefficients are unavail- able for individual species, genus or hardwood averages are used. Unfortunately, leaf area estimates generated with these regression equations were found to have little correla- tion with actual leaf area harvested for 50 Platanus trees in California (Peper and McPherson 2003). In a compari- son of predicted and measured growth rates (DBH) for com- mon species in Gainesville, Florida, Eco’s growth rates were very different than measured ones (Lawrence et al. 2012). Calculations of ecosystem services produced by i-Tree Streets are based on tree size data collected from a reference city within each of the 16 United States climate regions (Peper and McPherson in preparation). The reference cities used were selected because they had updated computer inventories of at least 20,000 trees, good historic information on planting dates for aging trees, and large, old trees present in the community. Between 18 and 22 predominant species were selected for sampling from each reference city tree inventory. Typically, the predominant species represent over 65% of the municipality’s urban forest. Up to 10 trees were randomly sampled and mea- sured in each of up to nine DBH classes. As few as 30 trees may have been measured for smaller-growing species and as many as 70 for larger-growing species, with individuals present in every size class. Measurements included DBH (to nearest 0.1 cm), tree height, height to crown base, crown height, and crown diameter (all measured to nearest 0.5 m). Digital images were captured to estimate leaf area based on a method developed by the authors (McPherson et al. 2003). Tree age, or the number of years after planting, was determined differently depending on the data avail- able from cities and city foresters, but generally from a combina- ©2012 International Society of Arboriculture
September 2012
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