Importance of vegetation index in codling moth Cydia pomonella distribution modeling

Codling moth, Cydia pomonella L. (Lepidoptera: Tortricidae) is the key insect pest of apple orchards in Iran. This study was conducted in the main apple-growing regions of East Azarbaijan Province to generate potential habitat suitability maps of C. pomonella using MaxEnt modeling and to determine the importance of vegetation index in improving the accuracy of these models. Field surveys for collecting the occurrence data of codling moth were conducted during three growing seasons, ۲۰۱۷ - ۲۰۱۹. The activity of codling moth adult males was monitored using delta-shaped traps baited with female sex pheromone. Fifteen environmental variables were considered as potential predictors for estimating codling moth distribution. These variables were categorized into topographic, climatic, and remote sensing variables. A MaxEnt modeling algorithm was used to predict the distribution of codling moth. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). By using the topographic, climatic, and topographic+climatic variables, the AUC values were ۰.۸۴۰, ۰.۹۵۱, and ۰.۹۳۸, respectively. The model including normalized difference vegetation index (NDVI) had the highest AUC value (۰.۹۹), which strongly supports model predictive power and indicates the importance of vegetation index in codling moth distribution modeling. NDVI was the most contributed variable in the model followed by precipitation of September, slope, minimum temperature of May, and mean temperature of April. The distribution map obtained in MaxEnt provides an important tool for identifying potential risk zones of codling moth. This map can assist managers in forecasting and planning control measures and therefore, effective management of current infestations of codling moth.

کلیدواژه های Importance of vegetation index in codling moth Cydia pomonella distribution modeling:

Species distribution ، niche modeling ، risk map ، Pest Management ، Forecasting