Aphids, because of their short generation time and low developmental threshold temperatures, are an insect group expected to respond particularly strongly to environmental changes. Forty years of standardized, daily data on the abundance of flying aphids have been brought together from countries throughout Europe, through the EU Thematic Network 'EXAMINE'. Relationships between phenology, represented by date of first appearance in a year in a suction trap, of 29 aphid species and environmental data have been quantified using the residual maximum likelihood (REML) methodology. These relationships have been used with climate change scenario data to suggest plausible changes in aphid phenology. In general, the date of first record of aphid species in suction traps is expected to advance, the rate of advance varying with location and species, but averaging 8 days over the next 50 years. Strong relationships between aphid phenology and environmental variables have been found for many species, but they are notably weaker in species living all year on trees. Canonical variate analysis and principal coordinate analysis were used to determine ordinations of the 29 species on the basis of the presence/absence of explanatory variables in the REML models. There was strong discrimination between species with different life cycle strategies and between species feeding on herbs and trees, suggesting the possible value of trait-based groupings in predicting responses to environmental changes.
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. (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. ResultsLand 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.
1 A spatial autocorrelation analysis was undertaken to investigate the spatial structure of annual abundance for the pest aphid Myzus persicae collected in suction traps distributed across north-west Europe. 2 The analysis was applied at two different scales. The Moran index was used to estimate the degree of spatial autocorrelation at all sites within the study area (global level). The contributions of each site to the global index were identified by the use of a local indicator of spatial autocorrelation (LISA). A hierarchical cluster analysis was undertaken to highlight differences between groups of resulting correlograms. 3 Similarity between traps was shown to occur over large geographical distances, suggesting an impact of phenomena such as climatic gradients or land use types. 4 The presence of outliers and zones of similarity (hot-spots) and of dissimilarity (cold-spots) were identified indicating a strong impact of local effects. 5 Several groups of traps characterized by similarities in their local spatial structure (correlograms, value of Moran's I i ) also had similar values for land use variables (the area occupied by agricultural zones, forest and sea). 6 It is concluded that trap data can provide information about Myzus persicae that is representative of large geographical areas. Thus, trap data can be used to estimate the aerial abundance of this species, even if the suction traps are not regularly and densely distributed.
The spatial analysis by distance indices (SADIE) technique was developed to evaluate the spatial pattern of point-referenced count data as well as the spatial association between two sets of data sharing the same point locations. This paper presents an analysis of spatial patterns in aphid count data and the association of these data with climate across north-west Europe. The paper tests the applicability of the technique to large geographical areas. Aggregation and cluster indices were calculated for the total annual abundance of the peach-potato aphid Myzus persicae (Sulzer) and for the annual mean rainfall and temperature at aphid monitoring sites. Association indices demonstrated the stability in time of aphid spatial structures and the correlation between aphid density and climate patterns. Groups of relatively large numbers of aphids, termed patches, and groups of relatively small numbers of aphids, termed gaps, were located and their mean size estimated. The aphid patterns were quite stable in time and the spatial patterns of temperature and rainfall were weakly associated with M. persicae annual abundance. Similarities were observed between the results of SADIE and those from the more widely used technique of spatial autocorrelation (SAC). However, the SADIE association index has the advantage of quantifying the possible associations between aphid data and the factors that determine population distribution. Thus, high temperature and low rainfall were identified as environmental factors that were positively associated with aphid abundance across north-west Europe.
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