UNCERTAINTY AND THE MANAGEMENT OF MALLARD HARVESTS

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. S tochastic dynamic programming can be used effectively to account for t hese uncertainties if the probabilities associated with uncertain outc omes can be estimated. To use this approach managers must have clearly -stated objectives, a set of regulatory options, and a mathematical de scription 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 cumulati ve harvest and achieving a specified population goal. Model-specific h arvest strategies, which account for random variation in wetland condi tions on the breeding grounds and for uncertainty about the relation b etween hunting regulations and harvest rates, are provided and compare d. We also account for uncertainty in population dynamics with model p robabilities, which express the relative confidence that alternative m odels adequately describe population responses to harvest and environm ental conditions. Finally, we demonstrate hew the harvest strategy thu s derived can ''evolve'' as model probabilities are updated periodical ly using comparisons of model predictions and estimates of population size.