©2023 International Society of Arboriculture Arboriculture & Urban Forestry 49(6): November 2023 273 USA and QTRA in the UK and Australia). The study compared system ratings, consistency, and mitigation measures for each method. To do so, 3 unique groups were used: arborists without the TRAQ credential (abbreviated as ISA BMP throughout this paper), those with the TRAQ credential, and those with QTRA training. Potential study participants were identified using publicly available information from ISA’s “Find an Arborist” search directory (Trees Are Good 2023) for ISA BMP and TRAQ respondents, along with the “Directory of Registered Users” (Quantified Tree Risk Assessment 2023) for QTRA respondents. To do so, user contact information was collected from the respective sites and entered in an excel database. A target sample of 150 individuals randomly selected for the ISA BMP and 150 individ- uals for the TRAQ group were selected from a list of approximately 2,500 arborists located in the USA. Similarly, the QTRA sample (150) from Australia (49 selected) and the UK (101 selected) was from a list of approximately 500 arborists. In excel, the respective arborist lists were listed on separate excel spread- sheets, and the random number generator function =RAND() was used to assign each arborist a number. The numbers were then sorted from the lowest to highest and the first 150 people were selected for the study. Participant Recruitment Before inviting participants into the study, appropri- ate Institutional Review Board (IRB) protocols were followed. Formal approval was granted through the University of Florida (IRB201702109) for working with human subjects domestically. Additionally, eth- ics boards in both the UK and Australia were con- sulted to make sure of compliance with all human subject research regulations. On 2021 January 22, participants were sent a prenotice email informing them that they were selected for the study and that if they wished to participate, they would receive the survey packet in the coming weeks. Over the next several weeks, response emails were received from individuals confirming their participation in the study, asking additional questions about the project, and providing updated mailing addresses. In the few cases when selected participants asked to be removed from the study, another randomly selected participant from their respective group (i.e., QTRA, TRAQ, or ISA BMP) was chosen. If a selected participant did not financial value of the property (Ellison 2019). Tree part size ranges of greater than 18 in (450 mm) to 1 in (25 mm) are grouped into categories 1 through 4 with impact potential ranges from 1:1 to 1:2,500. Proba- bility of failure ranges from 1:1 to 1:10,000,000 are grouped into categories 1 through 7 with an associ- ated timeframe of 1 year. Once each risk factor has been quantified, the assessor inputs them into a QTRA manual calculator or software program to calculate the overall risk of harm (Ellison 2019). Comparing these systems has the potential to gauge their reproducibility and illuminate assessor biases that affect assessment outcomes and mitiga- tion recommendations. Verifying the validity and analyzing the effect of common assumptions, mental shortcuts, and standard operating procedures of tree risk assessors is crucial for decision-making based on actual rather than perceived risk, preventing unrealis- tic recommendations and undesirable mitigation such as unwarranted tree removals or the retention of com- promised trees (Ellison 2005; Koeser 2009; Hauer and Peterson 2016; Smiley et al. 2017; Coelho-Duarte et al. 2021; Judice et al. 2021). As such, this research had 3 main objectives. First, trees were rated with both the TRAQ and QTRA methodologies to evaluate differences in ratings and statistically compare variability among assessors. Next, as there is no requirement to take the TRAQ training to use the TRAQ system as informed in the ISA BMP, we compared ratings derived from the TRAQ approach for respondents possessing or lack- ing the associated credential. Beyond these 2 primary objectives, we evaluated the impact of timeframe on rating consistency. As such, our final objective was to compare variability for likelihood of failure ratings using both a 1-year and 3-year timeframe. The insight gained from the study will contribute to furthering our understanding of tree risk assessment, risk per- ception, and the decision-making process. This has the potential to benefit not only tree management but also overall public health and safety. METHODS Study Rationale and Participant Selection This study was designed to test the outcomes of 2 common tree risk evaluation methods. The study occurred in 3 countries where they are the most often used tree risk assessment methods (TRAQ in the
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