Quail hunting consists of a complex set of behaviors that involve humans, pointing dogs, and wild birds. The recent development of models known as Hunter–Covey Interface (HCI) theory provides an opportunity to analyze how we perceive, understand, and manage quail hunting. There are 2 groups of HCI models: static and dynamic. The static HCI model predicts daily hunting mortality based on the velocity of the hunt and area covered during a hunt. The dynamic HCI models estimate the probability of flushing a covey given a set of circumstances that revolve around the potential rate of which quail learn to avoid hunters. We quantified the variables required to test whether the static and dynamic models of HCI theory provide meaningful results. We used spatial data on hunting velocity and area covered during >100 quail hunts, along with estimates of quail population density, mortality, and movements from 2 areas in south Texas, to evaluate output from HCI models. The static model predicted average daily harvest rates that ranged from 5 to 50 birds. This average is within the range of average daily bag of south Texas quail hunters in groups of 2–4. Output from the dynamic models suggested that quail populations on our study areas were subjected to relatively low hunting intensities with a corresponding low avoidance behavior and learning rate, which is a scenario that matched 1 of 4 predictions by Guthery (2002). We found it difficult to meet basic assumptions of HCI theory. For example, hunting pressure was potentially redundant, coveys were not always randomly distributed in space, and the extent to which quail are naive at the beginning of a hunting season was unknown. However, both the static and dynamic HCI models appeared robust to violation of these assumptions. Application of HCI theory may provide meaningful results that can be used to manage quail hunting pressure, optimize harvest, and sustain populations.
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Vol. 69 • No. 2