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confusus Responses to Trees and Landscape We applied the individual traits from the 320 trees analyzed, to develop a predictive habitat model of the distribution of I. confusus. The logistic regression model derived for I. confusus response to trees and landscape resulted within the following equationP ps confusus1 1 e63108665 log10 F466It 665moth125elev253slope347windEnvironmental Management (2010) 45239Where F is definitely the estimate with the food availability (volume of the cylinder around the trunk), It indicates no matter whether or not the nearest tree was infested, moth is the presence of moth resistant phenotype, and elev, slope and wind are elevation, slope and wind, respectively. Infestation of the nearest tree and food availability substantially increased the probability that a tree would be infested with I. confusus. On the other hand, moth resistant phenotype and unfavorable wind had a important unfavorable impact around the presence of I. confusus infestation (Table 4). Elevation and slope also had important, but with little constructive effects on the probability of obtaining I. confusus, as their regression coefficients demonstrate. Our model has good overall predictive ability. Internal accuracy assessment (using the data made use of to train the model) showed that this model correctly classified 98 with the absences and 95 on the presences. General model predictability was 96.9 . External accuracy assessment (with an added data set) performed with equally high appropriate classification prices (Table four). As described in the methods,AUC was substantially higher than random (area = 0.998, P ( 0.001), displaying a higher predictive energy with the model.Discussion Throughout a record drought in northern Arizona, I. confusus outbreaks occurred in steep lowland places with favorable winds, in bigger trees, and with stem-boring moth resistant phenotypes that had infested trees nearby. Following, we talk about the extent our findings corroborate or refute the impact of biotic and abiotic factors on the likelihood of I. confusus outbreaks in northern Arizona. Predictors of I. confusus Outbreaks Stand condition impacts bark beetle swarming dates (Amezaga and Rodriguez 1998), population size (Hanula and Franzreb 1998), reproductive success (Reid and Robb 1999), and colonization (Erbilgin and Raffa 2002).Table 3 Pearson correlation coefficients, ANOVA, descriptive statistics (Mean and 95 self-confidence intervals) and percentage of each and every class of tree and landscape variables and I. confusus presence in pinyon pine Variable Easting (x coordinate) Northing (y coordinate) Tree age Tree Actinomycin D price canopy Tree height (cm) Perimeter in the trunk (cm) Tree DBH (cm) Meals availability (cm3) Scale Moth r ANOVA I. confusus presence Mean; (95 CI) 438824; [434554,443094] 3907229; [3895241,3919216] Sapling = 20.8 Mature = 79.two 0.021 405.1; [377.8,432.3] 64.3; [57.five,71.1] 30; [26.8,33.2] 484050; [349959,618141] NA = 81 NA = 20 Susceptible = 12.7 ; Resistant = 68.4 Sub-canopy presence I. confusus in nearest tree Mesic semiarid Mesic subhumid Frigid subhumid Thermic Arid Aspect Slope ( Elevation (m) Wind 0.123 F = four.86 F = four.26 F = 5.83 F = five.83 20; [13.5,26.5] 1733.2; [1674.six,1791.7] 11.03; [7.six,14.4] 1829.6; [1789.five,1869.68] Absent = 46.7 ; Present = 53.three two.85; [2.12,three.59] Distance to nearest tree (m) -0.138 0.0.