Unveiling human-assisted dispersal mechanisms in invasive alien insects: integration of spatial stochastic simulation and phenology models

Capturing the spread of biological invasions in heterogeneous landscapes is a complex modelling task where information on both dispersal and population dynamics needs to be integrated. Spatial stochastic simulation and phenology models have been rarely combined to facilitate such integration in order to assist on the study of human-assisted long-distance dispersal events. Here we develop a process-based spatially explicit landscape extent simulation model that considers the spread and detection of invasive insects. Natural and human-assisted dispersal mechanisms are modelled with an individual-based approach using negative exponential and negative power law dispersal kernels and gravity models. The model incorporates a phenology submodel that uses daily temperature grids for the prediction and timing of the population dynamics in each habitat patch. The model was applied to the study of the invasion by the important maize pest Western corn rootworm (WCR) Diabrotica virgifera ssp. virgifera in Europe. We parameterised and validated the model using maximum likelihood and simulation methods from the historical invasion of WCR in Austria. WCR was found to follow stratified dispersal where international transport networks in the Danube basin played a key role in the occurrence of long-distance dispersal events. Detection measures were found to be effective and altitude had a significant effect on limiting the spread of WCR. Spatial stochastic simulation combined with phenology models, maximum likelihood methods and predicted versus observed regression showed a high degree of flexibility that allowed capturing the salient features of WCR spread in Austria. This modelling approach is useful because allows to fully exploit the limited and heterogeneous information regarding the population dynamics and dispersal of harmful non-indigenous species.