34 world’s population, there is a need for greater focus on the potential risks and the perceptions of tree risk. In a similar vein, there is a need to survey arborists, urban forest managers, and other tree care profession- als to understand how they perceive and communi- cate risk regarding an urban forest. This information could be useful in future design planning, risk assess- ment, and risk mitigation. Currently, there is no one tree risk assessment method that is accepted as the standard for all situa- tions. Standardizing a method would allow for con- sistency among assessments, and potentially lower the liability of individual assessors. However, differ- ences in application (e.g., private versus public trees) and national legal/cultural norms make acceptance of a single risk assessment method unlikely. Current models of tree risk assessment serve their purpose, but could be improved with testing to assess system biases, accuracy, reproducibility, and overall impact on prescribed mitigation measures. Future research needs to identify biases inherent to a given risk assessment system. At least with the ISA BMP Method, more effort is needed to reduce unwar- ranted variability in the assessment of likelihood of impact and consequences of failure. Additionally, research should look at how factors like the time frame of the assessment (e.g., one, three, five, or more years) impact the reproducibility of risk assessments. Ultimately, developing a truly quantitative risk assessment approach might advance our understand- ing of risks from tree failure. LITERATURE CITED Anonymous. 2006. Population Estimates. UK Statistics Direc- tive, Office for National Statistics, Fareham. Arciniegas, A., F. Prieto, L. Brancheriau, and P. Lasaygues. 2014. 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Group-tree hazard analysis. Journal of Arbo- riculture 21(3):150–155. Clark, J.R., N.P. Matheney, G. Cross, and V. Wake. 1997. A model of urban forest sustainability. Journal of Arboriculture 23(1):17–30. Costello, L.R., and S.L. Quarles. 1999. Detection of wood decay in blue gum and elm: An evaluation of the RESISTOGRAPH® and the portable drill. Journal of Arboriculture 25(6):311–318. Cox, L.A.T., D. Babayev, and W. Huber. 2005. Some limitations of qualitative risk rating systems. Risk Analysis 25(3):651–662. Cox, L.A.T. 2008. What’s wrong with risk matrices? Risk Analy- sis 28(2):497–512. Dahle, G., J. Grabosky, B. Kane, J. Miesbauer, W. Peterson, F.W. Telewski, A.K. Koeser, and G.W. Watson. 2014. Tree biome- chanics: A white paper from the 2012 international meeting and research summit at The Morton Arboretum (Lisle, Illi- nois, U.S.). Arboriculture & Urban Forestry 40(6):309–318. Dahle, G.A, K.R. James, B. Kane, J. Grabosky, and A. Detter. 2017. A review of factors that affect the static load-bearing capacity of urban trees. Arboriculture & Urban Forestry 43(3):89–104. Davison, A., and J.B. Kirkpatrick. 2014. Risk and the arborist in the remaking of the Australian urban forest. Geographical Research 52(2):115–122. Dolwin, J.A., D. Lonsdale, and J. Barnett. 1999. Detection of decay in trees. Arboricultural Journal 23(2):139–149. Duryea, M.L., G.M. Blakeslee, W.G. Hubbard, and R.A. Vasquez., 1996. Wind and trees: A survey of homeowners after Hurricane Andrew. Journal of Arboriculture 22(1):44–50. Duryea, M.L., and E. Kampf. 2007. Wind and Trees: Lessons Learned from Hurricanes. University of Florida, IFAS Extension. Dunster, J.A., E.T. Smiley, N. Matheny, and S. Lilly. 2013. Tree risk assessment manual. International Society of Arboriculture. Champaign, Illinois, U.S. 194 pp. Dwyer, J.F., E.G. McPherson, H.W. Schroeder, and R.A. Rown- tree. 1992. Accessing the benefits and the costs of the urban forest. Journal of Arboriculture 18(5):227–234. Ellison, M.J. 2005a. Quantified tree risk assessment used in the management of amenity trees. Journal of Arboriculture 31(2):57–65. Ellison, M.J. 2005b. Quantified Tree Risk Assessment User Man- ual. Quantified Tree Risk Assessment Ltd. Ellison, M.J. 2007. Moving the focus from tree defects to rational risk management—A paradigm shift for tree managers. Arbo- ricultural Journal 30:137–142. Fay, N. 2007. Towards reasonable tree risk decision-making. Arboriculture Journal 30:143–161. Fink, S. 2009. Hazard tree identification by visual tree assessment (VTA): Scientifically solid and practically approved. Arbori- cultural Journal 32:139–155. Gilbert, E.A., and E.T. Smiley. 2004. Picus sonic tomography for the quantification of decay in white oak (Quercus alba) and hick- ory (Carya spp.). Journal of Arboriculture 30(5):277–281. Gilman, E.F. 2003. Branch-to-stem diameter ratio affects strength of attachment. Journal of Arboriculture 29(5):291–294. Gilman, E.F., and F.J. Masters. 2010. Effect of tree size, root pruning, and production method on root growth and lateral
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