Effects of Biotic and Abiotic Factors on Grape Root Borer (Lepidoptera: Sesiidae) Infestations in Commercial Vineyards in Virginia

Abstract

Larval grape root borer, Vitacea polistiformis (Harris) (Lepidoptera: Sesiidae), feed on roots of wild Vitis and commercially important Vitis species and rootstocks in portions of the eastern United States. Grape root borer pupal exuviae sampling in Virginia vineyards from 2008 to 2012 revealed that infestation levels varied substantially among 48 vineyard blocks. Data on horticultural (cultivar, rootstock, vine age, and planting area), cultural (insecticide use, ground cover, weed control, and irrigation), and environmental variables (proximity to forest, soil composition, soil moisture holding capacity, pH, organic matter, bulk density, and cation exchange capacity) from each block were subjected to optimal quantification using categorical principal component analysis (CATPCA). Variables with component loading values ≥0.70 from the CATPCA were used as predictors and pupal exuviae density as the dependent variable in binary logistic regression. A prediction model was developed by including statistically significant variables in the logistic regression. CATPCA showed that seven vineyard factors (ground cover, soil texture, soil mass moisture, soil pH, clay/sand ratio, clay/silt ratio, and sand/silt ratio) based on three selected principal components were significant for subsequent regression analysis. Binary logistic regression showed that soil mass moisture and clay/sand ratio were statistically significant factors contributing to differences in infestation among vineyard blocks. Based on these two factors, a risk prediction model for calculating the probability of grape root borer infestation in vineyards was developed and validated using receiver operating characteristic curve. Results are discussed in relation to the practical implications of a predictive, risk assessment model for grape root borer management.

First Report on Establishment of Laricobius osakensis (Coleoptera: Derodontidae), a Biological Control Agent for Hemlock Woolly Adelgid, Adelges tsugae (Hemiptera: Adelgidae), in the Eastern U.S.

“…Nominal logistic regression was used to develop a predictive model of Laricobius beetle presence in relation to each of the previously defined study site variables. The statistical significance of the predictor variable in the derived model was evaluated by the Wald χ 2 statistic [23]. The Odds ratio was calculated to quantify the odds of the beetle being present for each unit change in the predictor variable [23][24][25].…”

“…The statistical significance of the predictor variable in the derived model was evaluated by the Wald χ 2 statistic [23]. The Odds ratio was calculated to quantify the odds of the beetle being present for each unit change in the predictor variable [23][24][25]. In addition, we used the area under the receiver operating characteristic (ROC) curve, AUC, to assess the predictive ability of the model [23].…”

“…The Odds ratio was calculated to quantify the odds of the beetle being present for each unit change in the predictor variable [23][24][25]. In addition, we used the area under the receiver operating characteristic (ROC) curve, AUC, to assess the predictive ability of the model [23]. AUC values of 0.5-0.7, 0.7-0.9, >0.9 indicate poor, good, and excellent discrimination ability of the model, respectively [23].…”

“…In addition, we used the area under the receiver operating characteristic (ROC) curve, AUC, to assess the predictive ability of the model [23]. AUC values of 0.5-0.7, 0.7-0.9, >0.9 indicate poor, good, and excellent discrimination ability of the model, respectively [23]. All analyses were carried out using JMP Pro version 12.1 (SAS, Cary, NC, USA).…”

Subterranean Acoustic Activity Patterns of Vitacea polistiformis (Lepidoptera: Sesiidae) in Relation to Abiotic and Biotic Factors

“…Indeed, a nonrandom distribution of acoustically monitored vineyard sites with high infestation likelihood was not unexpected in this study. Previous investigations frequently have found distributions of multiple species of insects to be spatially aggregated rather than random or uniform [27,29,54,55,56].…”