We established mathematical models and explored the role of a learned response (avoidance behavior) to understand and manage the hunter-covey interface. Furthermore, we examined the dynamic nature of the probability of flush, given encounter, in a population that learned to avoid hunters as time passed. Learning rate was defined as the proportion of a covey that leaves the naive population and enters the experienced population per unit of hunter-covey contact. The conditional probability of flushing and shooting at a covey, given a covey encounter, declined through the season. This is because the probability of flushing was lower for experienced than for naive coveys and the population of experienced coveys grew with exposure. Thus, quality of hunting declined at a faster rate than quail population; i.e., birds became more wary as the hunting season progresses. The birds\u27 ability to avoid hunters provided an explanation of the sudden reappearance of bobwhites contributing to reproduction in areas where hunters were unsuccessful the previous hunting season. Management can use our models to manipulate the interface and obtain a desired population following the hunting season
