The rate of invasive species spread and adaptive human behavior

Background/Question/Methods

The spread of invasive species may be greatly affected by human responses to prior species spread, but models and estimation methods seldom explicitly consider human responses. We investigate the effects of management responses and data collection designs on estimates of invasive species spread rates. To do this, we create an agent-based simulation model of an insect invasion across a county-level citrus landscape. Our model provides an approximation of a complex spatial environment while allowing the “truth” to be known. 

The modeled environment consists of citrus orchards with insect pests dispersing among them. Insects move across the simulation environment infesting orchards, while orchard managers respond by administering insecticide according to analyst-selected behavior profiles and management responses may depend on prior invasion states. Dispersal data are generated in each simulation and used to calculate spread rate via a set of estimators chosen based on their predominance in the empirical literature. We test the effectiveness of orchard behavior profiles on invasion suppression and evaluate the robustness of the estimators given orchard responses.

Results/Conclusions

We find that less tolerant orchard behavior profiles generally lead to reduced spread rates, with heterogeneous spray profiles affecting spread rates. Orchards tolerating higher insect populations before management provide havens for insect populations that can disperse back into the system. These results stress the importance of human behavioral responses to pest suppression and observed phenomena. The ability of spread rate estimators to predict future spread varies with orchard behavioral profile and invader biological parameters. The model suggests that understanding human behavioral responses to species invasions is important for selecting phenomenological empirical models that allow forecasting of future insect spread. This indicates a need for caution when evaluating spread. Although standard practice, current empirical estimators may both over- or under- estimate spread in our simulation.