Adaptive Harvest Management

The Need for a New Approach

Managing commercially harvested resources such as game and timber species, water, and other renewable resources traditionally relied on quantitative data analysis and predicting the optimal harvest level. These analyses necessitated over-harvesting a species before managers could determine an optimal take. Reducing harvest effort after that optimal take had been regulated was difficult to implement because the authorities had to enforce a harvest reduction or buy back licenses issued for harvest.

Even after calculating and enforcing an optimal harvest level, managers noticed dramatic fluctuations in biological populations and needed objective decisions about their exploitation, especially where species crossed multiple jurisdictional boundaries. Continuous species monitoring showed that population demographics remained dependent on factors such as stochastic events, unknown habitat features, or underlying landscape processes. Managing commercially harvested resources was more complicated than estimating the populations of nonharvested species because the behaviors of people, political decisions, market fluctuations, and the values of stakeholders, including those allocated some of the resource, affected management efficacy.

Adaptive Management as a Learning Experiment

Adaptive management forefronts the complexity and interdependence of natural and social systems, their constant adaptation to change, and the inherent risk and uncertainty characteristic of management processes. In complex systems, a positive feedback loop perpetuates change, and a negative feedback loop retards change. Some responses to change reduce the natural variability and diversity of a system, and thus its resilience and capacity to create alternative responses.

Adaptive management relies on a feedback learning loop after managers implement alternative policies as field “experiments” that inform [Page 13]progressive decisions made at regular intervals. Information about the state of a system is collected and interpreted; alternative management scenarios are considered; decisions are implemented, evaluated, and reinterpreted; and responses are altered accordingly. The cycle starts again as the manager or researcher observes the state of the new system.

Harvested Populations

Adaptive harvest management incorporates ecological data about the abundance and reproductive parameters of a species, as well as information about the impacts of various harvest policies (e.g., the costs and benefits of annual quotas, of daily bag limits or sizes and equipment limits in fish and waterfowl hunting, or of retaining brood stock to increase populations).

Dominant approaches to adaptive harvest management apply a recursive computer-generated algorithm to estimate the expected utility of future harvests based on present information. Over a sequence of time intervals, alternative harvest policies are implemented, and their probability as the optimal harvest strategy for the current system is estimated. These estimates are combined with updated ecological data (e.g., the new population size after restricting hunting to a particular area or season) to inform a subsequent iteration of the algorithm. Each iteration recursively adjusts the model probabilities using the impacts of the previous decision and the calculated change in population size. The optimal harvest strategy maximizes the expected cumulative harvest value at any particular point in time.

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