Towards a Knowledge-Based Decision Support System for Integrated Control of Woolly Apple Aphid, Eriosoma lanigerum, with Maximal Biological Suppression by the Parasitoid Aphelinus mali.

Abstract

Simple SummaryThe woolly apple aphid Eriosoma lanigerum is an important pest in apple orchards worldwide. At present, effective limitation of woolly aphid populations relies on a good synergy between chemical control treatments and biological suppression by beneficial insects, especially by its main specific natural enemy, the parasitic wasp (parasitoid) Aphelinus mali. In order to reach maximum control levels on woolly apple aphids and avoid negative side effects on A. mali, decision support for the optimal timing and positioning of control treatments is needed. In this study, we developed prediction models that based on the weather conditions (temperature data) can reasonably accurately predict crucial development/activity phases of both insects in the orchard. These prediction models can be utilized to target insecticide sprayings at the most sensitive stage of the pest (woolly apple aphids) and/or to avoid insecticide sprayings with detrimental side effects at the vulnerable stage of the beneficial insect (parasitoid A. mali), as was demonstrated by the outcomes of a field trial in this study.The woolly apple aphid Eriosoma lanigerum (Homoptera: Aphidiae) is an important pest in apple orchards worldwide. Since the withdrawal or restricted use of certain broad-spectrum insecticides, E. lanigerum has become one of the most severe pests in apple growing areas across Western Europe. At present, effective limitation of woolly aphid populations relies on a good synergy between chemical control treatments and biological suppression by beneficial arthropods, especially by its main specific natural enemy, the parasitoid Aphelinus mali (Hymenoptera: Aphelinidae). To develop a knowledge-based decision support system, detailed monitoring data of both species were collected in the field (region of Sint-Truiden, Belgium) for a period of ten years (2010–2020). Aphelinus mali flights were monitored in the field, starting before flowering until the end of the second-generation flight at minimum. The seasonal occurrence of the most important management stages of E. lanigerum, e.g., start of wool production or activity on aerial parts in spring and migration of crawlers from colonies towards flower clusters or shoots, were thoroughly monitored. All obtained data were compared with historical and literature data and analysed in a population dynamics phenological model. Our outcomes showed that the emergence of first-generation A. mali adults (critical for the first parasitation activity and the basis for following A. mali generations in the continuation of the season) can be accurately predicted by the developed model. Hence, this information can be utilized to avoid insecticide sprayings with detrimental side effects at this particular moment as demonstrated by the outcomes of a field trial. In addition, the start of migration of E. lanigerum crawlers towards flower clusters or shoots is accurately predicted by the model. In conclusion, our results demonstrate that the model can be used as decision support system for the optimal timing of control treatments in order to achieve effective control of E. lanigerum with maximal biological suppression by its main natural enemy.