Cooperative Fish and Wildlife Research Units Program: Georgia
Education, Research and Technical Assistance for Managing Our Natural Resources

Those charged with regulating waterfowl harvests must cope with random environmental variations, incomplete control over harvest rates, and uncertainty about biological mechanisms operative in the population. Stochastic dynamic programming can be used effectively to account for these uncertainties if the probabilities associated with uncertain outcomes can be estimated. To use this approach managers must have clearly-stated objectives, a set of regulatory options, and a mathematical description of the managed system. We used dynamic programming to derive optimal harvest strategies for mallards (Anas platyrhynchos) in which we balanced the competing objectives of maximizing long-term cumulative harvest and achieving a specified population goal. Model-specific harvest strategies, which account for random variation in wetland conditions on the breeding grounds and for uncertainty about the relation between hunting regulations and harvest rates, are provided and compared. We also account for uncertainty in population dynamics with model probabilities, which express the relative confidence that alternative models adequately describe population responses to harvest and environmental conditions. Finally, we demonstrate hew the harvest strategy thus derived can ''evolve'' as model probabilities are updated periodically using comparisons of model predictions and estimates of population size.Those charged with regulating waterfowl harvests must cope with random environmental variations, incomplete control over harvest rates, and uncertainty about biological mechanisms operative in the population. Stochastic dynamic programming can be used effectively to account for these uncertainties if the probabilities associated with uncertain outcomes can be estimated. To use this approach managers must have clearly-stated objectives, a set of regulatory options, and a mathematical description of the managed system. We used dynamic programming to derive optimal harvest strategies for mallards (Anas platyrhynchos) in which we balanced the competing objectives of maximizing long-term cumulative harvest and achieving a specified population goal. Model-specific harvest strategies, which account for random variation in wetland conditions on the breeding grounds and for uncertainty about the relation between hunting regulations and harvest rates, are provided and compared. We also account for uncertainty in population dynamics with model probabilities, which express the relative confidence that alternative models adequately describe population responses to harvest and environmental conditions. Finally, we demonstrate hew the harvest strategy thus derived can ''evolve'' as model probabilities are updated periodically using comparisons of model predictions and estimates of population size.