©2023 International Society of Arboriculture 272 Klein et al: Evaluating the Reproducibility of Tree Risk Assessment Ratings larger body of data indicating that variability in tree risk assessments is impacted by the assessor (Norris 2007; Koeser et al. 2015; Klein et al. 2016; Koeser et al. 2017; Koeser and Smiley 2017; Coelho-Duarte et al. 2021). The current study compares 2 systems that have relatively balanced risk inputs (Norris and Moore 2020) and are being increasingly used by tree managers (Koeser et al. 2016c), the method taught as part of the ISA TRAQ and the Quantified Tree Risk Assessment (QTRA) method. The TRAQ method is based on ISA’s Best Manage- ment Practices (BMP): Tree Risk Assessment (Smiley et al. 2017). This system uses 2 matrices to determine the overall risk of a tree or tree part: the first to deter- mine a rating for the likelihood of failure and impact, and a second that considers that rating in conjunction with a rating for consequences of failure. Each of these risk factors is expressed with qualitative terms. Likelihood of failure is categorized as improbable, possible, probable, or imminent. Likelihood of impact is described as very low, low, moderate, or high. Con- sequences of failure are qualified as negligible, minor, significant, or severe. The TRAQ system directs users to conduct assessments at 1 of 3 levels of detail (Dun- ster et al. 2017): limited visual (Level 1), basic (Level 2), or advanced (Level 3) assessments. Level 2 assess- ments are 360° inspections of a tree from ground- level, used to identify the species and notable defects, evaluate condition, and determine potential targets that could be impacted, for which the assessor evalu- ates the 3 components of risk (i.e., likelihood of fail- ure, likelihood of impact, and consequences of failure)(Dunster et al. 2017; Smiley et al. 2017). The QTRA system proposes a formula-based approach to maintaining an acceptable level of risk, defined as a 1/10,000 chance of significant harm occurring (Ellison 2005; Ellison 2019). QTRA’s risk factors of a target’s impact potential, the size of the tree or tree part, and the probability of failure are expressed in broad ranges of numerical ratios that are categorized with ordinal numbers. As with TRAQ, the assessor performs a 360° inspection of the tree from ground-level to assess the species and condition of the tree, identifies defects that could lead to failure as well as the size of the affected tree part, identifies potential targets of impact, and gauges the conse- quences of such a failure. Target ranges are catego- rized according to either occupation rates of people or vehicles, ranging from 1:1 to 1:1,000,000, or the et al. 2016a; Klein et al. 2022b). However, previous research has shown that tree risk assessments are inherently subjective, and that the assessor has just as much influence as the method on the overall risk rat- ing (Norris 2007; Koeser and Smiley 2017; Koeser et al. 2017). While experience and professional creden- tialing often influence how risk is rated (Smiley et al. 2017; Klein et al. 2021a; Klein et al. 2022b), different experiences and training may result in divergent views on risk, the need for mitigation, and appropri- ate mitigation measures (Ball and Watt 2013; Klein et al. 2016; Koeser and Smiley 2017; Norris and Moore 2020). In a study involving nearly 300 arborists conduct- ing basic (Level 2) assessments of 3 different trees, Koeser and Smiley (2017) found that likelihood of impact and consequence of failure ratings were vari- able among all participants, and that the assessor was 4 times more likely to impact the overall risk rating than characteristics of the tree being assessed. Like- wise, arborists with experience and professional cre- dentialing were 4 times more likely to recommend retaining and monitoring rather than tree removal compared to those with no such training. Addition- ally, arborists with risk assessment training were sig- nificantly more likely to assign lower likelihood of impact and likelihood of failure ratings, as well as lower overall risk ratings. Klein et al. (2016) compared the perceived occu- pancy rates with actual occupancy rates as measured by traffic counters (Klein et al. 2022a). In this study, likelihood of impact ratings among participants with experience and credentialing were less variable than those without. However, in a study capturing conse- quences of failure ratings, Klein et al. (2021a) found no correlation between an assessor’s professional experience and estimates of branch size. Specifically, participants with no risk assessment experience more accurately estimated branch size than participants with previous experience. However, participants who had the International Society of Arboriculture (ISA) Tree Risk Assessment Qualification (TRAQ) and Board Certified Master Arborist® (BCMA™) creden- tial were more likely to give a higher consequence of failure rating than other participants based on estima- tions of branch size and branch size class (Klein et al. 2021a). The results suggest that something other than part size may be responsible for variability in conse- quence of failure ratings and may contribute to a
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