32 Method for use in walk-by inspection and drive-by inspections (Pokorny 2003). The ISA BMP method is the most recent method commonly used in North America (Smiley et al. 2011). The method first focuses on two main components: the likelihood of failure (ranging from improbable, possible, probable, to imminent) and the likelihood of impacting a target (ranging from very low, low, medium, to high). These are assessed individually and combined in a risk matrix to determine the likeli- hood that a tree will fail and impact a target (ranging from unlikely, somewhat likely, likely, to very likely). This value is then combined in a second matrix with the user’s assessment of consequences of failure (ranging from negligible, minor, significant, to severe) to determine an overall risk rating. The use of risk matrices is not without its criticisms. Cox et al. (2005) notes that matrix-based systems can assign higher risk ratings to situations that realisti- cally present a low level of risk (reversed rankings) and can label situations where there is a low level of quantitative risk with extreme qualitative descriptors, such as high (uninformative ratings). Additionally, it is a frequent occurrence that these same ratings are also assigned to various situations where the actual present risk can vary many magnitudes from another risk that is assigned a similar rating (range compres- sion). Despite these potential pitfalls, risk matrices do have their place in risk assessment and management (Cox et al. 2005). With regard to design, the ISA BMP method meets the authors’ three core axioms of: 1) weak consistency (i.e., not going directly from low to high risk without an intermediary), 2) “between- ness” (i.e., not being overly sensitive), and 3) consis- tent coloring of categories (Cox et al. 2005). Moreover, Cox was asked to review the ISA BMP Method given his critique of matrix based systems (Lilly, personal communication). In reviewing the system, he expressed that the ISA BMP Method appeared to be an appropriate application of matrix-based risk assessment (Lilly, personal communication). Regardless of whether numbers or terms are used to categorize risk inputs in the systems noted above, they all represent qualitative assessment methods. In 1990, Helliwell proposed the need for a quantified risk assessment of trees, an idea that was later expanded by Ellison in managing risk from amenity trees (Helliwell 1990; Ellison 2005a). The QTRA system was created to achieve this goal and it remains commonly used by practitioners in the United ©2019 International Society of Arboriculture Klein et al: Risk Assessment and Risk Perception of Trees Kingdom, Australia, New Zealand, and elsewhere. QTRA users assign individual probabilities to occu- pancy, likelihood of failure, and consequence of fail- ure to calculate the risk of harm (RoH) associated with a tree. This RoH value is then compared to a modified tolerability of risk framework to gauge whether the assessed risk is as low as reasonably prac- tical (ALARP, HSE 2001). In this system, a RoH of 1/10,000 is considered tolerable when imposed on others. Although this method is labeled as quantita- tive, it is worth pointing out that currently there are no true quantitative approaches. All risk assessments require personal judgment to some extent, especially with regard to likelihood of failure. Despite some notable differences, all common assessment methods include: an assessment of the tree structure, identification of defects and subse- quent evaluation of tree failure probability, an assess- ment of targets, and an appraisal of the potential damage caused by target impact (Matheny and Clark 1994; Mattheck and Breloer 1994; Ellison 2005a; Meilleur 2006; Matheny and Clark 2009; van Wasse- naer and Richardson 2009). Beyond these similari- ties, methods vary in how they weight each underlying risk factor; how different defects are rated; and how the various components are combined into a final, comprehensive risk determination (Norris 2007; Matheny and Clark 2009). Rating systems for each of the risk assessment methods assign different numbers or categories in working toward a final, overall risk evaluation. For example, the International Society of Arboriculture Tree Hazard Evaluation (Matheny and Clark 1991) uses a 12-point rating system with four points associ- ated with each of the three main inputs (i.e., failure potential, size of part, target rating). In contrast, the United States Department of Agriculture Forest Ser- vices Community Tree Risk Evaluation Method (Pokorny 2003) is based on a 10 to 12 point rating system. The size of the defective part and the proba- bility of target impact are both 1 to 3 points, the prob- ability of failure is 1 to 4 points, and other risk factors total 0 to 2 points. In an earlier assessment of risk assessment methods, Matheny and Clark (2009) noted that there were no peer-reviewed studies that test and evaluate different risk assessment methods. They also noted that there was uncertainty among professionals about the importance and accuracy of assessment methods. In his thesis work, Norris (2007) compared a number of risk assessment methods in a
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