A computer model of attractant-based traps in a landscape

Trap networks are crucial components of many invasive insect detection, delimitation, Integrated Pest Management (IPM) and eradication programs. Where to place traps and the optimal density for cost-effective operation are central questions facing those managing programs that employ trapping. We present a novel probabilistic model of trap attraction that will be useful to addressing both questions. The model focuses on the relationship between insect distance from a trap and probability of the insect being captured and scales this up to a landscape level by considering both the spatial relationship between the insects and traps as well as the ecology of the area. Here we describe the mathematical basis of the model, parametrize it with field data, and use a computational approach to place modeled traps into a landscape and examine the effectiveness of trapping networks.  Our model is useful for optimizing trap placement to minimize program operating costs and comparing the sensitivity of networks with varying densities or placements. It can also be used to rigorously estimate the probability of invasive insects remaining in the area when none are captured, a topic of considerable ecological, social and regulatory importance .