Predicting insect distributions from climate and habitat data

Knowing the effects of climate and habitat on the distributions of insect pests and their natural enemy would help target the search for natural enemies, increase establishment of intentional introductions, improve risk assessment for accidental introductions and the effects of climate change. Most existing methods used to predict geographical distributions of insects either involve subjective comparisons of climate or require data concerning insect responses to climate. Here we have used geographical distributions of insects to develop statistical models for the effects of climate and habitat on these distributions. We tested this approach using six insect pests found in the United States: Ostrinia nubilalis (European corn borer), Diuraphis noxia (Russian wheat aphid), Helicoverpa zea (Corn earworm), Leptinotarsa decemlineata (Colorado potato beetle), Solenopsis invicta (Red imported fire ant), and Conotrachelus nenuphar (Plum curculio). By randomly separating the data into model-building and test sets, we were able to estimate prediction accuracy. For each species, a unique combination of predictor variables was identified. The models correctly predicted presence for more than 92% of the data on each insect species. The models correctly predicted absence for 59% to 77% of the data on five of six species. Absence predictions were poor for H. zea (21% correct), because distribution data were limited and inaccurate. Predictions of insect absence were more difficult because absence data were less abundant and perhaps less reliable. This approach offers potential for the analysis of existing data to produce predictions about insect establishment. However, accurate prediction depends heavily on data quality, and in particular, more data are needed from locations where insects are sampled but not found.