Modelling the population dynamics of Philaenus spumarius: a fully mechanistic approach

The meadow spittlebug Philaenus spumarius has been identified as spreading the bacterium Xylella fastidiosa causing olive quick decline syndrome in Apulia in Southern Italy. Prevention, containment, and local eradication of Xylella fastidiosa outbreaks can benefit from vector control to prevent both a pathway for disease spread and the secondary spreading within a crop. Vector monitoring activities and the implementation of mechanical (against vector juveniles) or chemical control (against vector adults) require information on vector population phenology, abundance and activity that are dependent on environmental conditions. In this work we propose a fully mechanistic model simulating the phenology, age structure and population abundance of P. spumarius according to environmental conditions. Local population dynamics is described in terms of physiological responses of the individual life-history strategies (development, survival and reproduction) to local environmental conditions. The non-linear rate functions describing individual physiological responses are estimated based on experiments performed in microcosms at ambient temperature and in climatic chambers at different constant temperatures. The estimated model has been calibrated with data on population dynamics of P. spumarius collected in four olive orchards in Northern (Liguria) and Southern (Apulia) Italy from 2016 to 2018. Model simulations can be used to support vector and disease management. The model can predict the phenology and the abundance of a P. spumarius populations at high spatial and temporal resolution. This information can support precision targeting control strategies against vector nymphs and adults. The model can also generate maps of vector phenology and abundance. These maps can represent a source of information for spatial-explicit epidemiological models describing the disease dynamics and supporting X. fastidiosa quantitative risk assessment and management. Knowledge on model parameters variability offers the possibility to explore the implication of uncertainties in designing vector and disease monitoring and management strategies.