AbstractUnderstanding pest population dynamics and seasonal phenology is a critical component of modern integrated pest-management programs. Accurate forecasting allows timely, cost-effective interventions, including maximum efficacy of, for example, biological control and/or sterile insect technique. Due to the variation in life stage-related sensitivity toward climate, insect pest population abundance models are often not easily interpreted or lack direct relevance to management strategies in the field. Here we apply a process-based (biophysical) model that incorporates climate data with life stage-dependent physiology and life history to attempt to predict Eldana saccharina life stage and generation turnover in sugarcane fields. Fitness traits are modelled at two agricultural locations in South Africa that differ in average temperature (hereafter a cold and a warm site). We test whether the life stage population structures in the field entering winter and local climate during winter directly affect development rates, and therefore interact to determine the population dynamics and phenological responses of E. saccharina in subsequent spring and summer seasons. The model predicts that: (1) E. saccharina can cycle through more generations at the warm site where fewer hours of cold and heat stress are endured, and (2) at the cold site, overwintering as pupae (rather than larvae) confer higher relative fitness and fecundity in the subsequent summer adult moths. The model predictions were compared with a large dataset of field observations from scouting records. Model predictions for larval presence (or absence) generally overlapped well with positive (or negative) scout records. These results are important for integrated pest management strategies by providing a useful foundation for future population dynamics models, and are applicable to a variety of agricultural landscapes, but especially the sugarcane industry of South Africa.
Population dynamics of Eldana saccharina Walker (Lepidoptera: Pyralidae): application of a biophysical model to understand phenological variation in an agricultural pest