Arboriculture & Urban Forestry 46(6): November 2020 427 Table 7. Grouping of methods with similar characteristics. Group 1 methods produce a normal distribution with most values around the mean; group 2 methods produce outputs at the lower end of the risk scale; and group 3 methods produce outputs evenly across the risk scale. Group 1 Group 2 Group 3 MandC QTRA/QTRA W Private 1 Bartlett Private 2 Private 3 USDAFS 1 Threats USDAFS 2 inputs provided a significantly stronger influence on the output than other inputs. This is demonstrated by comparing the two versions of QTRA. QTRA equally weighted each input category (a probability between 0 and 1), and so each input provided the same influ- ence on the output value; but QTRA W weighted the 3 input categories differently (Target = 45%, Size of Part = 21%, and Failure = 31%). The tendency to more strongly favour likelihood inputs can be partly explained by the mathematics of most methods. Most methods use 3 inputs, 2 of which were typically a likelihood of failure and a probabil- ity of impact, which are likelihood factors, but used only a single measure of consequence (typically size of part). Therefore, for simple, equally scaled sum- mation methods, this resulted in a 2:1 ratio favouring likelihood over consequence. While multiplication complicated the process, the skewed weighting remains. Methods that had the reverse weighting typically have a “damage factor” modifier in the final equation. For QTRA, the weighting changed depending on what was being assessed. CONCLUSION Most of the tree risk assessment methods analysed would be suitable for managing large urban tree pop- ulations, provided the user understands their strengths and weaknesses. The better methods have a balanced set of inputs that consider both likelihood and conse- quence and produce a full and even range of output values. Whether they meet the criteria of complete- ness, credibility, reliability, repeatability, robustness, and validity was not tested by the sensitivity analyses and so remains in question. However, methods that explain the meaning of inputs and outputs and which train users in their procedures are more likely to be reliable and repeatable. Furthermore, in arboriculture, widely recognised, acceptable risk levels do not exist, and so an industry-based approach should form the foundation standard of what is considered to be accept- able tree-related risk. It is important that users of tree risk assessment methods understand the relationship between consequence and likelihood, and the influ- ence that range, weighting, scaling, and number of input variables have on distribution curves and output values. In general it can be concluded: Risk methods varied in how they measured risk and will give different results. The choice of tree risk assessment method will influence assessed risk lev- els, so assessors should be aware of the influence that the method they use may have on risk ratings. If it is accepted that risk is defined as R = Li × Co, then methods that better balance the 2 components, such as TRAQ and QTRA, better express the risk than methods which do not, such as USDAFS 2, QTRA W, or Threats. Methods that do not balance likelihood and conse- quence tend toward higher tree risk outputs, which could lead to unnecessary tree removals. Methods that utilise a full range of risk ratings will be superior to those with gaps in the range of outputs, such as TRAQ, Bartlett, Private 1, Private 3, Threats, and USDAFS 2. Gaps are easily seen in the outputs of numeric methods, but they can occur in methods using ordinal outputs, as the assigning of numeric values for analysis revealed. Methods where there is a clear, balanced, and log- ical relationship between input and output values will be more defensible than those methods with inconsis- tent inputs, ranges, and mathematics. MandC, QTRA, TRAQ, TRE QT, and TRE QL, with balanced rela- tionships between input values, will be more defensi- ble than other methods. Private 2 is similar to MandC upon which it is based. Private 3 has balanced inputs for likelihood, but does not consider consequences, and CTC, HCC, Private 1, and USDAFS 1 have most but not all of their inputs balanced. ©2020 International Society of Arboriculture Risk assessment methods Tre QL Tre QT Kenyon CTC HCC TRAQ
November 2020
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