428 An over-emphasis on an input in a method can raise the risk rating considerably, which again could lead to the unnecessary removal of trees which actu- ally pose a very low risk. Users of methods should be aware of how they work and the extent of their outputs. For example, for methods which do not allow a full range of outputs, ensure that you do not report an output that cannot be obtained, either due to typographical error, or in the mistaken belief that an intermediate value better rep- resents the perceived risk. For wholly qualitative (word based, ordinal) meth- ods, such as MandC, CTC, or TRAQ, the changes made in 1 rank sensitivity analyses matter less to the actual change in risk rating than for fully quantitative methods. For example, a 1 rank increase in TRAQ’s “Likelihood of Failure” or “Likelihood of Impact” changes the risk rating from moderate to high (1 cat- egory), and a decrease changes the risk rating from moderate to low (2 categories); but a 1 rank increase in “Consequences” has no effect on the risk rating, while a decrease changes the risk rating from moder- ate to low (2 categories). This raises questions as to the balance of the method. The inputs of most methods were highly subjec- tive due to a lack of data related to the inputs. Often methods do not include well-considered descriptors, which created uncertainty and variability in the inter- pretation of the meaning of an output, which may affect the robustness, credibility, repeatability, or validity of the methods. Methods such as TRAQ and QTRA, which have a strong training program, are more likely to provide consistent assessments of risk. Risk assessments are often assumed to be a matter of “common sense.” Such an assumption is incorrect. Even well-trained and experienced assessors may have widely differing opinions as to levels of risk. Users of tree risk methods should understand the relative strength and weaknesses of any method that they use. It could be relatively simple to challenge the results of a tree risk assessment in a court of law based on the limitations inherent in the underlying methodology, particularly if the user was not aware of them and had failed to take them into account in inter- preting the outcomes. The sensitivity analyses indi- cated clear and strong differences between the methods, reflecting that the underlying mathematics, input categories, ranges, and scaling influence the way that different tree risk assessment methods ©2020 International Society of Arboriculture Norris and Moore: How Tree Risk Assessment Methods Work process and express risk. Hence, it is not surprising that methods will perform differently in different cir- cumstances and will express risk levels differently. LITERATURE CITED Albers J, Eiber T, Hayes E. 1996. How to recognize hazardous defects in trees. St. Paul (MN, USA): Minnesota Department of Natural Resources and USDA Forest Service. NA-FR-01- 96. 20 p. Ball DJ, Watt J. 2013. The risk to the public of tree fall. Journal of Risk Research. 16:261-269. Beer R, Frank S, Waters G. 2001. Overview of street tree popu- lations in Melbourne—turn of the 21st century. Journal of Arboriculture. 32:155-63. Bloniarz DV. 2004. A community tree risk calculator for pocket PCs. Amherst (MA, USA): USDA Forestry Service, Northeast Center for Urban and Community Forestry. [Accessed 12 April 2014]. http://www.umass.edu/urbantree/hazard/pda.shtml Coder KD. 1996. Tree risk management and hazard assessment: a general overview. Athens (GA, USA): Warnell School of For- estry and Natural Resources, University of Georgia. Extension Publication FOR 96-033. 10 p. Coder KD. 2000. Tree risk management. City Trees. 36(6):26-29. Colorado Tree Coalition. 2004. Tree Hazard Assessment System 2004. Broomfield (CO, USA): Colorado Tree Coalition. 93 p. Cornell Law School. 2020. Sovereign immunity. Ithaca (NY, USA): Wex, Legal Information Institute (LII). [Accessed 20 January 2020]. https://www.law.cornell.edu/wex/sovereign_immunity Department of Planning NSW. 1992. Risk criteria for land use safety planning. Parramatta (NSW, Australia): Department of Planning, New South Wales Government. Hazardous Industry Planning Advisory Paper No. 4. Dockter D. 2001. Tree technical manual. 1st Ed. Palo Alto (CA, USA): The City of Palo Alto Department of Planning and Community Environment. 161 p. Dunster JA. 2003. An overview of legal considerations. In: Wildlife/danger tree assessor’s course workbook—parks and recreation sites course module. Victoria (BC, Canada): Wild- life Tree Committee of British Columbia. Dunster JA, Smiley ET, Matheny NP, Lilly SJ. 2017. Tree risk assessment manual. 2nd Ed. Champaign (IL, USA): Interna- tional Society of Arboriculture. 194 p. Ellison M. 2005a. Quantified tree risk assessment—user manual. Macclesfield (UK): Quantified Tree Risk Assessment Ltd. Ellison M. 2005b. Quantified tree risk assessment used in the man- agement of amenity trees. Journal of Arboriculture. 31:57-65. Fischhoff B. 1994. The psychology of risk characterization. In: Sahlin NE, Brehmer B, editors. Future risks and risk manage- ment. Dordrecht (The Netherlands): Springer. 256 p. Forbes-Laird J. 2003. Threats—tree hazard: risk evaluation and treatment system—a method for recording, identifying, and managing hazard trees. Bedford (UK): Forbes-Laird Arbori- cultural Consultancy Ltd. Forbes-Laird J. 2006. Threats—tree hazard: risk evaluation and treatment system—a method for identifying, recording, and managing hazards from trees. Bedford (UK): Forbes-Laird Arboricultural Consultancy Ltd.
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