Fred A. Johnson

Adaptive harvest management of North American waterfowl populations: a brief history and future prospects

Since 1995, the US Fish and Wildlife Service has used an adaptive approach to the management of sport harvest of mid-continent Mallard ducks (Anas platyrhynchos) in North America. This approach differs from many current approaches to conservation and management in requiring close collaboration between managers and scientists. Key elements of this process are objectives, alternative management actions, models permitting prediction of system responses, and a monitoring program. The iterative process produces optimal management decisions and leads to reduction in uncertainty about response of populations to management. This general approach to management has a number of desirable features and is recommended for use in many other programs of management and conservation.

Bayesian inference and decision theory - A framework for decision making in natural resource management

Bayesian inference and decision theory may be used in the solution of relatively complex problems of natural resource management, owing to recent advances in statistical theory and computing. In particular, Markov chain Monte Carlo algorithms provide a computational framework for fitting models of adequate complexity and for evaluating the expected consequences of alternative management actions. We illustrate these features using an example based on management of waterfowl habitat. n nCorresponding Editor: D. B. Lindenmayer.

THE IMPORTANCE OF FUNCTIONAL FORM IN OPTIMAL CONTROL SOLUTIONS OF PROBLEMS IN POPULATION DYNAMICS

Optimal control theory is finding increased application in both theoretical and applied ecology, and it is a central element of adaptive resource management. One of the steps in an adaptive management process is to develop alternative models of system dynamics, models that are all reasonable in light of available data, but that differ substan- tially in their implications for optimal control of the resource. We explored how the form of the recruitment and survival functions in a general population model for ducks affected the patterns in the optimal harvest strategy, using a combination of analytical, numerical, and simulation techniques. We compared three relationships between recruitment and pop- ulation density (linear, exponential, and hyperbolic) and three relationships between survival during the nonharvest season and population density (constant, logistic, and one related to the compensatory harvest mortality hypothesis). We found that the form of the component functions had a dramatic influence on the optimal harvest strategy and the ultimate equi- librium state of the system. For instance, while it is commonly assumed that a compensatory hypothesis leads to higher optimal harvest rates than an additive hypothesis, we found this to depend on the form of the recruitment function, in part because of differences in the optimal steady-state population density. This work has strong direct consequences for those developing alternative models to describe harvested systems, but it is relevant to a larger class of problems applying optimal control at the population level. Often, different func- tional forms will not be statistically distinguishable in the range of the data. Nevertheless, differences between the functions outside the range of the data can have an important impact on the optimal harvest strategy. Thus, development of alternative models by iden- tifying a single functional form, then choosing different parameter combinations from extremes on the likelihood profile may end up producing alternatives that do not differ as importantly as if different functional forms had been used. We recommend that biological knowledge be used to bracket a range of possible functional forms, and robustness of conclusions be checked over this range.

The Need for Coherence Between Waterfowl Harvest and Habitat Management

Abstract Two of the most significant management efforts affecting waterfowl populations in North America are the North American Waterfowl Management Plan (the Plan) and Federal harvest management programs. Both the Plan and harvest management are continental in scope, involve an extensive group of stakeholders, and rely on adaptive processes of biological planning, implementation, and evaluation. The development of these programs has occurred independently, however, and there has been little explicit recognition that both harvest and habitat effects should be considered for coherent management planning and evaluation. For example, the harvest strategy can affect whether population objectives of the Plan are met, irrespective of the success of the Plans habitat conservation efforts. Conversely, habitat conservation activities under the Plan can influence harvest potential and, therefore, the amount of hunting opportunity provided. It seems increasingly clear that the Plans waterfowl population objectives can only be useful for conservation planning and evaluation if they are accompanied by an explicit specification of the harvest strategy and environmental conditions under which they are to be achieved. This clarification also is necessary to ensure that Plan population objectives are not attained solely through the reduction of hunting opportunity. We believe then that it is imperative that these key waterfowl-management programs work to harmonize their objectives. Harvest management programs and the Plan ought to be working toward the same ends, but that is not possible so long as the mutually reinforcing relationship of these programs is obscured by ambiguities in their management objectives.

Optimization and Resilience in Natural Resources Management

We consider the putative tradeoff between optimization and resilience in the management of natural resources, using a framework that incorporates different sources of uncertainty that are common in natural resources management. We address one-time decisions, and then expand the decision context to the more complex problem of iterative decision making. For both cases we focus on two key sources of uncertainty: partial observability of system state and uncertainty as to system dynamics. Optimal management strategies will vary considerably depending on the timeframe being considered and the amount and quality of information that is available to characterize system features and project the consequences of potential decisions. But in all cases an optimal decision making framework, if properly identified and focused, can be useful in recognizing sound decisions. We argue that under the conditions of deep uncertainty that characterize many resource systems, an optimal decision process that focuses on robustness does not automatically induce a loss of resilience.