Arboriculture & Urban Forestry 41(5): September 2015 control may have wide-reaching impacts, including contamination of terrestrial and aquatic ecosystems (Kreutzweiser et al. 2007; Szczepaniec et al. 2013). Decision models are useful in incorporating con- tinuous numerical and categorical data, handling non-linear relationships effectively, and allowing for missing values (Fayyad and Irani 1992; Friedl and Brodley 1997). To avoid confusion in the data and interpretation, researchers of the current study omit the term ‘tree’ from the model technique, although it is the commonly used term. Use of the terms ‘branching’, ‘pruning’, and ‘rooting’ are specific to the model type, and should not be confused with for- estry techniques. Within areas of emerald ash borer infestation, signs and symptoms of attack (e.g., bark splits, exit holes, woodpecker activity, epicormic shoots) provide rapid, categorical assessment of individual tree infestation (de Groot et al. 2006). Couple these signs with ash health characteristics and the framework for decision model development is in place, rapidly assessed, and effectively deploy- able. The objectives of this study were to 1) develop management decision models based on multiyear health and mortality assessments, 2) assess those models using distinct data sets, and 3) test the hypothesis that rapid health and infestation assess- ments can provide predictability to ash mortality. MATERIALS AND METHODS symptoms of emerald ash borer infestation (bark splits, exit holes, woodpecker activity, and epicor- mic sprouting). Bark roughness was assessed using digital images as described by Marshall et al. (2013). Roughness values were a count of black pixels aſter images were converted to binary, black-white images. Roughness groups were then categorized into five equal interval groups based on mini- mum and maximum roughness values for pooled Tree Assessments Mature ash were identified at five Huron-Clinton Metroparks in metro Detroit, Michigan, U.S., ini- tially via helicopter survey and then through ground verification (Marshall et al. 2013). Trees were iden- tified to species, diameter at breast height (DBH) was measured, and assessments of percent crown dieback (5%–100%) and vigor rating (1–6, Millers et al. 1991) were performed for June–August 2009 (Year0 , n = 203). Researchers also noted signs and 271 trees. For all trees, mortality was assessed each year in July 2010–2012 (Year1 DBH, Fort Wayne, Indiana, U.S., via ground surveys during July 2011 (Year0 dieback, vigor, and signs and For = 56) and Indiana (n = 18) were used to calculate ten-year and five-year growth rates prior to Year0 each tree, two cores were A subsample of trees from both Michigan (n . , Year2 , and Year3 collected per- pendicular to each other with a 4.5 mm diam- eter increment borer at breast height. Ring widths were measured to the nearest 0.01 mm and ring basal area (mm2 DBH. Mean annual growth rates were calcu- lated for the five years and 10 years prior to Year0 ) was calculated based on Year0 . Decision Model Development Trees from Michigan and Indiana were pooled and randomly placed into model development (n = 146) and model assessment categories (n = 148). Trees used in model development and assessment were unique individuals and overlap between the two categories did not occur. T-tests were used to com- pare DBH and percent crown dieback for trees that survived and died within the study period. Mood’s median test was used to compare bark roughness group and vigor rating between trees that survived and died within the study period. Decision models were developed through recursive partitioning using rpart and rpart.plot packages in R (version 3.1.1, The R Foundation for Statistical Computing) with inputs of species, bark roughness group (1–5), DBH (cm), bark splits (0,1 absence, presence), exit holes (0,1), woodpecker activity (0,1), epicormic sprouting (0,1), vigor (1–5), percent crown die- back (5%–100%), and growth rate (average per year increase in DBH) as independent variables and mortality [alive (0), dead (1) during the three- year period] as the dependent variable. The recur- sive partitioning approach to model development allows for the input of numerous categorical and continuous variables with the final product of the approach being truncated (i.e., pruned) to a set complexity parameter, which limits the ‘cost’ of ©2015 International Society of Arboriculture Mature ash were identified at three city parks in , n = 91). Methods for species, , Year2 , and Year3 symp- toms were similar to above. Roughness groups defined from images were used to categorize trees from the Indiana parks. Mortality was assessed in July 2012–2014 (Year1 ). ).
September 2015
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