SEARCH

SEARCH BY CITATION

Keywords:

  • biological invasion;
  • leafminer;
  • long-distance dispersal;
  • spatial model;
  • stratified dispersal

Summary

  • 1
    Biological invasions have an anthropogenic origin, and although many species are able to spread on their own within the newly invaded area, long-distance dispersal events shown to accelerate rates of spread are frequently associated with human activities. In a previous study, the performances of several invasion models of the spread of the horse chestnut leafminer Cameraria ohridella in Germany were compared, demonstrating that the best model in qualitative and quantitative terms was a stratified dispersal model taking into account the effect of human population density on the probability of long-distance dispersal events.
  • 2
    Similar data were collected in France over 4 years (2000–2004, 5274 observation points). These data were used to assess the performance of the best-fit models from Germany using the original parameters and to model the spread of the leafminer in France.
  • 3
    The stratified dispersal model accounting for variations in human population density developed in Germany, predicted the invasion of France with a similar level of predictive power as in the area where it was developed. This suggests that an equivalent level of predictability can be expected in a newly invaded country with similar environmental conditions.
  • 4
    We applied the model to forecast the future invasion dynamics in the UK from 2005 to 2008, based on the first observations of Cameraria in the country in 2002–2004. Predictions are discussed in the light of different prevailing environmental conditions.
  • 5
    Synthesis and application. The model and predictions developed in this study provide one of the few examples of an a priori model of invasion in a newly invaded country, and provide a simple modelling framework that can be used to explore the spread of other invading organisms. In the case of Cameraria, little can be done to prevent or slow its spread but our model, by predicting changes in distribution and rates of spread, provides fore-warning of where and when damaging pest populations are likely to appear.