Cooperative Fish and Wildlife Research Units Program: Headquarters
Education, Research and Technical Assistance for Managing Our Natural Resources

Adaptive management of waterfowl harvests accounts for uncertainties about population responses to harvest, with a focus on the reduction of uncertainties pursuant to harvest objectives and other management goals. Important sources of uncertainty include limited knowledge about underlying biological relationships (structural uncertainty), sampling variation in population monitoring (partial observability), and uncontrolled variation in the setting of harvest rates (partial controllability). We used a model for adaptive harvest management to investigate the use of harvests in reducing structural uncertainties. The model allows for both compensatory and additive relationships between harvest and survival, and also includes predictors incorporating the compensatory and additive hypotheses, along with a procedure for updating predictor probabilities. The model was used to (i) characterize population changes through time, and predict population changes based on simulated data; (ii) compare predicted and observed population sizes in an effort to identify the appropriate predictor for the population; and (iii) examine the effect of harvest rate, monitoring variation, and partial controllability on the rate of reduction in structural uncertainty. Results indicate that harvest can be used to learn about additive and compensatory relationships, whichever is operative for a population. Within limits, learning can occur even with imprecise data about population status, and with substantial imprecision in the setting of harvest rates. However, learning rates are depressed by high levels of monitoring and/or harvest imprecision.Adaptive management of waterfowl harvests accounts for uncertainties about population responses to harvest, with a focus on the reduction of uncertainties pursuant to harvest objectives and other management goals. Important sources of uncertainty include limited knowledge about underlying biological relationships (structural uncertainty), sampling variation in population monitoring (partial observability), and uncontrolled variation in the setting of harvest rates (partial controllability). We used a model for adaptive harvest management to investigate the use of harvests in reducing structural uncertainties. The model allows for both compensatory and additive relationships between harvest and survival, and also includes predictors incorporating the compensatory and additive hypotheses, along with a procedure for updating predictor probabilities. The model was used to (i) characterize population changes through time, and predict population changes based on simulated data; (ii) compare predicted and observed population sizes in an effort to identify the appropriate predictor for the population; and (iii) examine the effect of harvest rate, monitoring variation, and partial controllability on the rate of reduction in structural uncertainty. Results indicate that harvest can be used to learn about additive and compensatory relationships, whichever is operative for a population. Within limits, learning can occur even with imprecise data about population status, and with substantial imprecision in the setting of harvest rates. However, learning rates are depressed by high levels of monitoring and/or harvest imprecision.