Ticks (Ixodidae) are important vectors of pathogens that affect humans, and methods to reduce impacts of vector-borne pathogens usually involve tick control. Improving the efficacy of such methods requires an understanding of climate-host-parasite-landscape interactions which is difficult to obtain from empirical observations alone. We developed a spatially-explicit, individual-/agent-based, stochastic model that can simulate the spatial-temporal dynamics of ticks in response to changes in climatic conditions, landscape structure, and host community composition. As a case study, we parameterized the model to represent the lone star tick (Amblyomma americanum(L.)) under ecological conditions typical of the south-central United States. We evaluated general model performance and examined model sensitivity to broad changes in climatic conditions, landscape structure, and host community composition, and then demonstrated application of the model by simulating the effect of a hypothetical greenbelt placed within a real landscape near Houston, Texas, U. S. A. on the exposure of humans to ticks.
Results/Conclusions
The model generated appropriate trends in densities of ticks in both off-host and on-host life stages in response to typical seasonal changes in densities of small-, medium-, and large-sized hosts, and mean numbers of ticks in each life stage on each size of host were appropriate given the seasonal progression of the tick life cycle. Simulated tick populations also responded appropriately to broad changes in climate, landscape structure, and host community composition. Simulated tick densities increased within the greenbelt soon after its establishment, and densities of off-host larvae, nymphs, and adults all were significantly higher in a small meadow (the "park") with versus without the greenbelt; without greenbelt, the park was completely surrounded by urban development. Our model should prove useful in exploring the role of host community characteristics in the maintenance of tick populations, and associated vector-borne diseases, in nature and in evaluating the potential efficacy of novel methods of tick control.