28 an event occurring and the severity of its potential consequences. Thus, risk has three elements: 1) an adverse event or consequence, 2) some probability or likelihood that the adverse event could occur, and 3) a specified period. Risk assessment is a formalized method of identi- fying, analyzing, and evaluating risk (Dunster et al. 2013). In assessing trees, all commonly used risk assessment methods consider: 1) the likelihood that all or part of the tree will fail (i.e., failure potential); 2) the likelihood of the target being present/struck (i.e., target occupancy/likelihood of impact); and 3) the consequences of failure (i.e., personal injury, damage to property, or disruption of services/activi- ties) (Matheny and Clark 1994; Mattheck and Breloer 1994; Pokorny 2003; Ellison 2005a; Smiley et al. 2011; Dunster et al. 2013). Going back to the defini- tion offered by Dunster et al. (2013) above, the first two considerations (i.e., likelihood of impact and likelihood of failure) directly relate to the likelihood that an adverse event will occur. While it is possible to quantitatively measure some factors that directly influence tree risk during a risk assessment (i.e., target occupancy of the site or the size of the tree/tree part of concern), in practice the inputs of a tree risk assessment are typically left to the judgment of the assessor (Pokorny 2003; Ellison 2005a). As such, management decisions are influ- enced by the actual, assessed, and perceived risk sur- rounding trees. Recommendations based on the findings of the assessment are then passed on to the person or people who ultimately make the final deci- sions—typically a homeowner, property manager, or urban forester (Dunster et al. 2013). As such, both the assessor’s and the decision maker’s perceptions and tolerances of risk affect what, if any, mitigation efforts are taken to reduce potential harm to people and dam- age to property (Pokorny 2003). An arborist’s assessment of risk is influenced by both the reality of the situation and his or her percep- tion of the threat posed. Risk perception is influenced by personal experiences (Spangler 1984; Gavin 2001; Botterill and Mazur 2004) and one’s personal fears (Slovic 1999; Botterill and Mazur 2004). Additionally, individuals sharing social networks within a commu- nity tend to have similar perceptions of risk (Scherer and Cho 2003). For example, the authors have noticed arborists from different nations, whom are trained to use different risk assessment methods, appear to have differing underlying perceptions of tree risk. ©2019 International Society of Arboriculture Klein et al: Risk Assessment and Risk Perception of Trees Depending on an individual’s background, their perception of risk may or may not correspond with the reality of the situation (Renn 2004). Predicting the failure of a living structure with heterogeneous mate- rial properties is in itself an imprecise undertaking. The addition of personal biases can contribute addi- tional variability to risk ratings (or artificial precision among assessors, if a strong bias is shared by a group). Risk reality, the arborist’s assessment of that risk, and the property manager’s or homeowner’s percep- tion of risk are interconnected and each influence which risk management strategy is adopted. In a study on tree risk management and arboriculture in Australia, Davison and Kirkpatrick (2014) interviewed several arborists who expressed their frustration in dealing with clients that seemed to possess an illogical fear of trees. The authors noted that disconnects in real and per- ceived risk could negatively affect management efforts to maximize the benefits trees provide, or to limit the effectiveness of efforts to minimize the related risks. In the most extreme cases, disconnects between risk reality and risk perception can lead to unnecessary tree removal or ill-advised tree retention (Smiley et al. 2011). Identifying and documenting the underlying beliefs or biases that influence perceived risk may ultimately make risk assessments and management strategies less variable—potentially limiting cases where practitioners suggest mitigation options that appear at odds with one another (Stewart et al. 2013). While all commonly used risk assessment methods consider the same three standard inputs (i.e., likeli- hood of failure, likelihood of impact, and conse- quences of failure), imprecise definitions and vague decision criteria give users the flexibility to draw on their knowledge and expertise. Research is needed to identify whether this (often intentional) imprecision reflects the current uncertainty surrounding tree risk assessment or if it simply adds an unnecessary layer of variability to assessments (Koeser and Smiley 2017). For example, the ISA BMP method does not clearly define how many hours a day constitute a given level of occupancy, or what large, medium, or small mean when describing a tree part. This lack of a common starting point may explain why, when assessments for nearly 300 arborists using the ISA BMP were analyzed, likelihood of impact and conse- quences of failure were the two most significant sources of variation (Koeser and Smiley 2017).
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