©2023 International Society of Arboriculture Arboriculture & Urban Forestry 49(6): November 2023 279 assessment, and willingness to accept risk is a key component with developing the time interval. In addi- tion to risk, this interval might include the resources available to periodically evaluate trees, the structural condition of the tree (especially in the case of trees with an imminent likelihood of failure), and the local climate, as the latter can influence tree growth and decay rates. Our findings indicate there is no need to add another consideration (i.e., minimizing variability) to this list. As one would expect, likelihood of failure ratings increased as one increased the timeframe. Although we believe that the results of the study are in line with past work on the topic of tree risk assess- ment ratings and the influence of the assessor, it’s worth noting several potential limitations to such studies. These may include accounting for nonresponse error and ensuring the target population is represented in the sample; a relatively small sample size; the use of videos rather than assessing trees in person; differences in age, knowledge, and experience among respon- dents; and an unfamiliarity of the species assessed in the study. Such limitations have the potential to lead to variability of results. CONCLUSION As in other past works, this study shows that risk assessment training as indicated through earning a credential can have a significant impact on prescribed risk mitigation activities, leading to suggested man- agement decisions that favor continued monitoring over tree removal. Past research has documented this with the ISA TRAQ training program. This effort shows the same pattern with risk assessments derived using the QTRA approach. Moreover, while risk assessments can vary from evaluator to evaluator given a whole host of factors, the timeframe selected (1 versus 3 years) for the inspection does not appear to influence overall reproducibility of assessments. Future work should continue to look at both the repro- ducibility of assessments and accuracy of all com- monly used tree risk assessment methods. LITERATURE CITED Ball DJ, Watt J. 2013. The risk to the public of tree fall. Journal of Risk Research. 16(2):261-269. https://doi.org/10.1080/136 69877.2012.737827 Blais AR, Weber EU. 2006. A domain-specific risk-taking (DOSPERT) scale for adult populations. Judgment and Deci- sion Making. 1(1):33-47. https://doi.org/10.1017/ S1930297500000334 One of the challenges of comparing risk assess- ment methods is gathering a sample of trained arbor- ists who are knowledgeable in the methods of interest and having them evaluate the same group of trees. While some risk assessment methods are used con- currently within a region or country (Koeser et al. 2016c), others are very much tied to specific loca- tions around the world. To overcome this challenge, we mailed videos of trees to our survey group. While this approach allowed us to access a greater number of fully trained arborists than has previously been seen in the literature (Norris 2007; Reyes de la Barra et al. 2018; Coelho-Duarte et al. 2021), the use of vid- eos has its own limitations as it is somewhat of an abstraction compared to a true site visit. Moreover, 3 of the respondents (5%) stated that they were unfa- miliar with the species selected. This study found the perceptions of financial or health risk were relatively static across our respon- dents from the USA, UK, and Australia. While the countries have many similarities with regard to polit- ical origin and language, their legal systems do vary, which may influence tree risk ratings and perceptions of risk. Concerning the methods assessed and their influ- ence of risk mitigation, the pattern observed in Figure 2 was as expected. Our past work (Koeser and Smiley 2017) showed that arborists with risk assessment training were more apt to recommend more passive mitigation measures like monitoring and less likely to recommend tree removal. Though not a significant difference, the tendency for the QTRA group was to recommend removal less often than the TRAQ group. This result is consistent with the authors’ experiences evaluating trees with both systems (unpublished data). When noting differences between the 3 groups, it should be noted that the actual risk posed by the trees is unknown. As such, there is no way to identify the “most accurate” group. Additionally, we did look at the variability among the 3 inputs (i.e., likelihood of impact, likelihood of failure, and consequences of failure) for the TRAQ and ISA BMP groups. Con- trary to Koeser and Smiley (2017), none of the inputs were any more or less variable than the others (min P-value = 0.091). Finally, it was interesting to see that increasing the timeframe of risk assessments from 1 year to 3 years did not impact reproducibility. A timeframe for the likelihood for failure is a core concept of tree risk
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