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 how the harvest strategy thus derived can "evolve" as model probabilities are updated periodically using comparisons of model predictions and estimates of population size.

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The Singing‐Ground Survey (SGS) is a primary source of information on population change for American woodcock (Scolopax minor). We analyzed the SGS using a hierarchical log‐linear model and compared the estimates of change and annual indices of abundance to a route regression analysis of SGS data. We also grouped SGS routes into Bird Conservation Regions (BCRs) and estimated population change and annual indices using BCRs within states and provinces as strata. Based on the hierarchical model–based estimates, we concluded that woodcock populations were declining in North America between 1968 and 2006 (trend = −0.9%/yr, 95% credible interval: −1.2, −0.5). Singing‐Ground Survey results are generally similar between analytical approaches, but the hierarchical model has several important advantages over the route regression. Hierarchical models better accommodate changes in survey efficiency over time and space by treating strata, years, and observers as random effects in the context of a log‐linear model, providing trend estimates that are derived directly from the annual indices. We also conducted a hierarchical model analysis of woodcock data from the Christmas Bird Count and the North American Breeding Bird Survey. All surveys showed general consistency in patterns of population change, but the SGS had the shortest credible intervals. We suggest that population management and conservation planning for woodcock involving interpretation of the SGS use estimates provided by the hierarchical model.

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