Arboriculture & Urban Forestry 34(1): January 2008 Table 6. Regression analysis showing standardized coefficients among the six models. Variables Models 1 Structural/demographic Education Income State resources Municipal organization Mayor characteristics Valued by mayor Years served by mayor Gender of mayor Importance of tree maintenance Challenges of maintenance Costs of planting Costs of maintenance Increased liability/insurance Cleanup after storms Impeding progress Consensus on tree value Social capital Intercept R2 zP < 0.10, y P < 0.05, x P < 0.01, w P < 0.001. best overall predictor of tree maintenance. This model included structural demographic, state resources, municipal organiza- tional structure, and characteristics of the mayor. Models 5 and 6, which included challenges communities face, and social capi- tal, were not found to have any significant additional explanatory power. Using model 4, the most statistically significant predictor, controlling for all other variables, was municipal organizational structure; having a person, department, and budget designated for UCF is positively related to the mayor’s reports of routine tree maintenance. Second and third statistically significant pre- dictors were characteristics of the mayor: whether he or she saw value in the urban forest and the gender of the mayor. In this case, mayors who saw value in the urban forest were more likely to report routine tree maintenance. Additionally, female mayors were more likely to support routine tree maintenance. Also sta- tistically significant was state resources (P < 0.05). The model indicated that increased mayoral knowledge of state urban forest resources was also significantly associated with tree mainte- nance. Other variables that approach significance and would have probably reached significance, had the sample size been larger, included the extent to which mayors saw benefits in the UCF and the number of years he or she had served as mayor. Mayors who cited more benefits of UCF and who served longer reported more tree maintenance activities. The findings in this study are interesting in many ways. First, having a person, department, and budget designated for UCF is the most important predictor of routine tree maintenance. The linkage of state resources and their encouragement of commu- nities to set up those organizational structures give evidence for the importance of the role that has been played by U.S. Depart- ment of Agriculture Forest Service and their support of state agencies. Also important is to note which factors were not significant. First, the demographic factors of communities, notably the edu- cational and income levels, were not important. Communities that are poor and wealthy appear to be equally able to practice routine maintenance. Furthermore, although mayors report vari- ous challenges such as costs of planting, maintenance, liability/ insurance, cleanup, and other challenges, these proved to be insignificant as well. Lastly, social capital, as we measured it, was not statistically significant. Social capital was measured as the amount of influence of various constituencies had in promot- ing planting and maintenance. Examples were newspapers, gar- den clubs, utility companies, city councils, and general public, to name a few. Although individually, these groups may be impor- tant, grouped together as a measure of social capital, other fac- tors were more important. CONCLUDING REMARKS The findings in this study are important in several ways. Al- though many studies have described urban forest practices, no studies, to our knowledge, have attempted to measure the rela- tive effects of various factors that might predict how much small towns and cities perform basic tree maintenance tasks. In doing so, we have identified what is not only important, but also what is not important. First, although it may seem logical that towns with greater community wealth and resources (higher educa- tional levels, higher median incomes) might be better able to support their UCFs, this idea did not prove to be supported. Also not important are the unique challenges faced by communities. What is most significant for successful routine tree maintenance is for towns to have the basic organizational structure in place, including a specific department, person, and budget dedicated to tree maintenance, and having a tree ordinance on record. This is exactly what the UCF literature has been recommending for many years, but now it has been established as the most signifi- cant predictor. Although state resources were less important, they, in many cases, are the persons and organizations that have promoted these practices. Lastly, it is now statistically docu- mented that the support of the mayor, with attitudes valuing UCF, and with an understanding of its benefits, result in suc- ©2008 International Society of Arboriculture 1.730 0.013 1.592 0.079 1.220 0.263 1.052 0.320 0.099 0.019 2 0.048 0.051 0.258w 3 0.009 0.001 0.159x 0.450w 4 –0.030 0.036 0.111y 0.397w 0.084z 0.081z –0.128x 0.172w 5 –0.027 0.018 0.107y 0.408w 0.084 0.078 –0.139x 0.167w 0.061 –0.083 –0.052 0.010 –0.015 –0.015 1.328 0.331 6 –0.027 0.018 0.108y 0.409w 0.085 0.078 –0.139x 0.170x 0.061 –0.083 –0.052 0.011 –0.015 –0.016 –0.008 1.361 0.331 45
January 2008
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