Wild turkey (Meleagris gallopavo; hereafter turkey) management has been in a state of transition since the turn of the 21st century, shifting away from restoration and towards a focus on sustainable harvest management. Several previous studies used mathematical models of population and harvest dynamics to understand performance of turkey management. However, increases to the number of hunters, changes to management objectives, and broad-scale shifts in demography (e.g., declining productivity) imply that many assumptions of previous studies may be untenable under modern conditions. More generally, the science informing sustainable harvest has progressed in recent years. These advancements include a more thorough understanding of uncertainty and increased use of decision-theory to inform management under uncertainty. Importantly, many of these developments are not captured by studies that used models to aide turkey harvest management, and as a consequence the performance of current harvest-management recommendations in the face of relevant uncertainties is only partially understood. In this dissertation I attempt to bridge the gaps between turkey harvest models and more general developments in sustainable harvest management by addressing overlooked sources of uncertainty, and by linking simulation-modeling exercises to decision-theoretic methods that provide a rigorous foundation to decision making. Throughout I focus on the implications of structural uncertainty for modern management and the performance of existing harvest recommendations when structural uncertainty is acknowledged. Structural uncertainty refers to uncertainty about the dynamic processes of populations and harvesting, which creates uncertain responses of populations to management activities. This can be represented as uncertainty about functional forms of system models (model uncertainty), uncertainty about values of specific model parameters (parameter uncertainty), or both. Structural uncertainty is thus directly relevant to modern management due to uncertainty about mechanisms of density regulation, causes of demographic changes, and segment-wise harvest rates resulting from hunting regulations. I used simulation modeling and decision-analytic tools to demonstrate that structural uncertainty has important consequences for modern turkey management. Important findings include: 1) recommendations for maximizing turkey harvest are not robust to uncertainty, and depend on poorly understood aspects of turkey ecology and harvest; 2) currently accepted rules of thumb for fall harvest management are sensitive to uncertainty in turkey demography, and may perform poorly over a broad range of conditions; 3) existing fall harvest recommendations appear to only be optimal over a narrow range of the plausible parameter space for turkey populations; 4) the unknown causes of productivity declines directly affect risks imposed by fall harvest and the magnitude of harvest a population sustain; 5) target fall harvest rates of < 5% are more likely to achieve management objectives across a range of conditions than harvests ≥ 5%; 6) if reliable information about productivity, relative harvest vulnerabilities, and magnitude of spring harvest are not available, then 4% fall harvest of males should be viewed as the upper value likely to achieve modern objectives; and 7) if information about productivity, vulnerability, and magnitude of spring harvest are available, then my simulation results can be used to identify fall harvest targets that are tailored to characteristics of local populations and likely to accomplish objectives in the face of uncertainty. As such, I expect this research to be broadly useful to turkey management, and to facilitate continued advancement and adaptation of turkey harvest management systems into the future.
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