20 Steenberg et al: A Social-Ecological Analysis of Urban Tree Vulnerability Table 5. Classification accuracy of the multilayer perceptron neural network for predicting tree mortality (0/1) in the testing and training samples using the vulnerability indicators. Predicted no mortality (0) Training sample Observed no mortality (0) Observed mortality (1) Overall accuracy Testing sample Observed no mortality (0) Observed mortality (1) Overall accuracy 183 17 15 26 92.4 60.5 86.7 454 41 20 50 95.8 54.9 89.2 Predicted mortality (1) Percent correct though adjacency to institutional buildings was also a strong predictor and explained slower growth rates. This unexpected finding can be explained, in part, by the fact that land use was assessed in the field at the parcel level while building type was assessed for the building with the shortest distance to a given tree. Poor tree condition explained slower tree growth rates, as did tree species (i.e., Norway maple and sil- ver maple). Conflicts with infrastructure again were associated with faster growth rates. Lastly, the multi- layer perceptron neural network used to analyze tree mortality performed well using the selected vulnera- bility indicators, which reinforces the utility of these indicators in future vulnerability assessment. The net- work had an accuracy of 89.2% with the training sample and 86.7% with the testing sample, and was more effective in predicting living trees than dead trees (Table 5). DISCUSSION AND CONCLUSIONS The findings of this study suggest that the highest exposure and corresponding levels of urban tree decline and mortality were most influenced by the intensity of land use and the conditions encountered in the built environment. Trees growing in land clas- sified as commercial land uses, and circumstances in which commercial buildings were adjacent to trees, consistently explained higher mortality rates and poor tree conditions. While studies have found vary- ing effects of commercial land uses on urban trees (e.g., Lawrence et al. 2012), it is generally established that these influences are among the most detrimental for tree health (Nowak et al. 2004; Jutras et al. 2010). However, at finer spatial scales it is important to ©2019 International Society of Arboriculture differentiate between different causes and correlates of urban forest decline for trees growing within com- mercial land uses. For example, street width (i.e., wider streets) can be a positive correlate of tree stress (Nagendra and Gopal 2010). Current findings also suggest that distance from streets and buildings are important indicators of urban tree vulnerability. While land use is a fairly established mechanism for stratifying urban landscapes and conducting urban forest research (Nowak et al. 1996; Steenberg et al. 2015), the results of this study suggest that at the household scale, differentiated indicators (e.g., building type, impervious cover, street geometry) are necessary components of urban forest vulnerability assessment. There are myriad physical, biological, and social stressors and disturbances that afflict urban trees and forests (Trowbridge and Bassuk 2004; Steenberg et al. 2017a). Consequently, there are many opportuni- ties to improve upon frameworks of urban forest vul- nerability assessment. In this study, exposure indicators were mainly limited in scope to those stressors associated with the built environment and urban form. However, the intent was that the sensitivity indicators would, in part, address these other dimen- sions of exposure for which quantification and/or data availability were limiting factors for measurement. For example, vulnerability to biological threats (e.g., EAB) or storm events can be captured in the sensitivity metrics of species composition (e.g., ash abundance and distribution; Laćan and McBride 2008; Vander Vecth and Conway 2015) and age struc- ture (e.g., structural diversity and over-mature cano- pies; Staudhammer and LeMay 2001; Lopes et al. 2009). Additionally, it is possible that the widespread
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