Aim The purpose of this study was to improve understanding of the relationship between the spatial patterns of an important insect pest, the aphid Myzus persicae, and aspects of its environment. The main objectives were to determine the predominant geographical, climatic and land use factors that are linked with the aphid's distribution, to quantify their role in determining that distribution, including their interacting effects and to explore the ability of artificial neural networks (ANNs) to provide predictive models.
Location The study focused on four spatial scales to account for the aphid data base characteristics and available land use data sets: Europe; a broad zone over Europe covering Belgium, Denmark, France, Ireland, Italy, The Netherlands, Scotland, Sweden and Wales (Regio data base coverage); North-West Europe (i.e. Belgium, France and the United Kingdom); and England with Wales.
Methods Multiple linear regression (MLR) was used to identify the variables in the Geographic location, Climate and Land use groups, that explained significant proportions of the variance in M. persicae total annual numbers and Julian date of first capture. A variance partitioning procedure was used to measure the fraction of the variation that can be explained by each environmental factor and of shared variation between the different factors. Finally, ANNs were employed as an alternative modelling approach for the two largest study areas, i.e. Europe and the Regio data base coverage, to determine whether the relationship between aphid and environmental variables was better described by more complex functions as well as their ability to generalize to new data.
Results Land use variables are shown to play a significant role in explaining aphid numbers. The area of agricultural crops, in particular oilseed rape, is positively correlated with M. persicae annual numbers. Among the climatic variables, rainfall is negatively correlated with aphid numbers and temperature is positively correlated. The geographical components also explain a significant part of aphid annual numbers. However, the variance partitioning procedure indicates that while each group has an effect, none is dominant. Aphid first capture is mainly explained by climate where rainfall tends to delay migration and warmer conditions tend to advance it. Climate accounts for the greatest part of the variance when considered separately from the other factors. The geographical and land use components also have a significant effect on first capture at each scale, but their direct contribution is negligible. The ability of the ANN models to generalize to new total numbers and phenological data compared with MLR models was less for Europe (9 and 6% increase in the variance accounted for, respectively) than for the Regio data coverage where an increase of 44% in the variance accounted for was observed.
Main conclusions This research supports the hypothesis that climate, land use and geographical location play a role in determining patterns of aphid annual numbers and phenology. The ability of ANN models to predict aphid distribution is improved by the inclusion of temporal land use data. However, identification of the processes involved in such relationships is difficult due to numerous interactions between the environmental factors.