Arboriculture & Urban Forestry 46(6): November 2020 (Figure 36). The distribution clearly shows the impact of the impossibility of obtaining risk ratings of 5, 7, 10, 11, 13, 14, or 15 in the method. The Best Manage- ment Guide that accompanies the TRAQ program presented and explained the various risk inputs and the meaning of the outputs well. DISCUSSION Sensitivity analyses demonstrated that most methods placed too great an emphasis on limited aspects of a risk assessment, and in most instances, tree risk assess- ment methods strongly focus on the likelihood of fail- ure or defect aspect of a risk assessment. This is not surprising, given that much of the literature focuses strongly on identification of tree defects, in many cases downplaying the importance or relevance of target usage and particularly consequences in the assessment of tree risk. Both methods of sensitivity analysis iden- tified significant differences between the methods tri- alled. Failure to understand the influence of inputs on the subsequent risk rating can make it easy to unfairly question the validity of a method. Some methods exhibited very large output changes with little move- ment of an input, and in many cases this was different for each of a method’s input categories. This was due to different scaling and/or ranges used for the inputs and the combination mathematics. It was surprising to find that 2 methods did not measure consequences, therefore failing to measure risk as it is commonly defined. Most methods placed more emphasis on the likelihood inputs than those influencing consequences, having in most instances 2 likelihood inputs but only 1 measure of consequences. What the ratio should be has not been defined, but in 13 of the methods, likelihood outweighed conse- quences by a factor of at least 2. If equal likelihood and consequences factor ratios were used, then both components would be reflected in the risk score, which would more accurately represent the real level of risk as it is defined. Having a better balance between likelihood and consequences in tree risk assessment would result in lower risk ratings in most cases, which would be more in line with the very low actual tree-related injury and death rates. The empha- sis on likelihood and the associated higher risk rating is likely to result in unnecessary tree removals. Most tree risk assessment methods focus more on the tree than on the target and the real risk that the tree might pose to the target. Such weightings could result in 425 different methods, creating quite different risk ratings for the same scenario. The output distributions created from the Monte Carlo simulation highlighted that significant differ- ences existed between the tree risk assessments and indicated that different methods created dissimilar risk values due to the differing input ranges, scales, and methods of mathematically combining the inputs. Modelled distributions were used to indicate how a method was likely to operate in the real world and where various strengths and weaknesses existed. For simple, linear methods with constant scaling and ranges for each of the input categories, such as MandC, Private 2, and TRE QL, the regression model proportionally identified the same changes to outputs and influence of variables. The full probabilistic methods, such as QTRA and TRE QT, displayed identical changes and percentage variation. Methods with differing inter- or intra-category ranges or scales yielded differing percentage variations from that of the 1 rank or 25% sensitivity analysis. This was gen- erally due to the multivariate stepwise regression using the full range of available input values, which more accurately represented the overall performance of each method. The Monte Carlo simulation produced theoretical or predicted distribution profiles and tended to be more conservative, because a uniform distribution was used for the modelling (all inputs were equally likely), whereas for most managed tree populations, the number of high-risk trees would be lower than low-risk trees. The Monte Carlo simulation identified that some methods tended to produce larger frequen- cies of particular output values; few methods pro- duced flat or even output distributions; and several methods, including TRAQ, could not produce a full range of the assigned output values (representing word combinations) due to the scales, ranges, and mathematics used. Ten methods had input categories with differing input values, ranges, or scaling. With these methods, the designers had apportioned differential weightings to each input category, so that each influenced the output differently. Some methods appeared to apply a disproportionate weighting to a single input variable. In Bartlett, the “Failure Potential/Defect Severity” category accounted for 80% of the method’s variance, whilst the USDAFS 2 “Defects” category accounted for 77% of the variance. In contrast, the Threats input ©2020 International Society of Arboriculture
November 2020
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