Predicting calyptrate fly populations from the weather, and probable consequences of climate change.
Calyptrate flies include numerous species that are disease vectors and have a high nuisance value, notably Musca domestica. Populations are often associated with livestock farms and domestic waste disposal facilities such as landfill, where the accumulating organic matter provides suitable breeding conditions for a range of species. We examined the relationship between fly numbers and weather conditions using a 4-year data set of weekly fly catches from six sites in southern UK, together with meteorological data. The first 3 years were used to develop predictive models, and these were then used to forecast fly populations in the fourth year. The accuracy of these predictions was assessed by comparison with the actual fly catches for that year. Separate models were developed for M. domestica, Calliphora spp. and all calyptrate flies combined. Predictions based only on humidity, temperature and rainfall were strongly correlated with observed data (r2 values ranged from 0.52 to 0.84), suggesting that fly population changes are largely driven by the weather rather than by biotic factors. We can forecast fly populations so that control measures need only be deployed when weather conditions are suitable for a fly outbreak, reducing the need for prophylactic insecticide use. Climate change was simulated using the most recent predictions of future temperature increases. Our models predicted substantial increases in fly populations up to 244% by 2080 compared with current levels, with the greatest increases occurring in the summer months. Synthesis and applications. Models developed use weather data to predict populations of pestiferous flies such as M. domestica, which may prove valuable in integrated control programmes. These models predict substantial increases in fly populations in the future under likely scenarios of climate change. If this occurs we may expect considerable increases in the incidence of fly-borne disease.