Analysis and management of migratory bird populations in North America

This dissertation concerns application of decision analysis to problems in population management of migratory birds. Such application involves three operational components: (1) a monitoring program to assess population status; (2) an assessment or modeling program to predict changes in bird abundance as a consequence of management; and (3) a means to identify the best management action. This dissertation provides examples of these components and how they must be integrated to make optimal management decisions.;Counting birds at a fixed point is a popular monitoring method, but a failure to detect all birds present produces biased estimates of abundance. I developed a Bayesian hierarchical model that can be used to account for multiple components of detection probability, provided that observers record the distances at which birds are sighted. The method was applied successfully in Ocala National Forest to estimate abundance of Florida scrub-jays.;As an example of assessment methods, I used a matrix-modeling approach for mottled ducks in Texas and Louisiana to estimate population growth rates and to determine how those growth rates might respond to variation in survivorship and reproduction. My analyses suggest a declining mottled duck population, and population growth rates that are particularly sensitive to changes in adult survival.;I provide two examples of optimal decision making to: (a) prescribe habitat manipulations for Florida scrub-jays, and (b) set hunting regulations for mallards. In managing scrub-jay habitat, the decision to restore degraded scrub, conduct a prescribed burn, or do nothing depended on the proportion of scrub of various heights. My analyses suggest that managers will need to taken frequent action to maintain optimal habitat conditions for scrub-jays. In the mallard example, I explored an adaptive approach to decision making that explicitly accounts for uncertainty concerning the effects of hunting. Since 1995 the United States Fish and Wildlife Service has used a passively adaptive approach in which learning is an unplanned by-product of management. Nonetheless, I show how this approach has reduced uncertainty and led to changes in regulatory policy. I also explored actively adaptive policies, which explicitly recognize learning as a legitimate objective in the pursuit of sustainability.