Final Report: Informing Management of Waterfowl Harvest in a Changing Climate

Our ability to effectively manage wildlife in North America is founded in an understanding of how our actions and the environment influence wildlife populations. Current practices use population monitoring data from the past to determine key ecological relationships and make predictions about future population status. In most cases, including the regulation of waterfowl hunting in North America, these forecasts assume that the environmental conditions observed in the past will remain the same in the future. However, climate change is influencing wildlife populations in many dynamic and uncertain ways, leading to a situation in which our observations of the past are poor predictors of the future. If we continue to...

Categories: Publication; Types: Citation

Optimal Strategies for Managing Wildlife Harvest Under System Change

Abstract (from The Journal of Wildlife Management): Wildlife populations are experiencing shifting dynamics due to climate and landscape change. Management policies that fail to account for non-stationary dynamics may fail to achieve management objectives. We establish a framework for understanding optimal strategies for managing a theoretical harvested population under non-stationarity. Building from harvest theory, we develop scenarios representing changes in population growth rate () or carrying capacity () and derive time-dependent optimal harvest policies using stochastic dynamic programming. We then evaluate the cost of falsely assuming stationarity by comparing the outcomes of forward projections in which...

Categories: Publication; Types: Citation

Optimal Harvest of a Theoretical Population Under System Change

Wildlife populations are experiencing shifting dynamics due to climate and landscape change. Management policies that fail to account for non-stationary dynamics may fail to achieve management objectives. We establish a framework for understanding optimal strategies for managing a theoretical harvested population under non-stationarity. Building from harvest theory, we develop scenarios representing changes in population growth rate (r) or carrying capacity (K) and derive time-dependent optimal harvest policies using stochastic dynamic programming. We then evaluate the cost of falsely assuming stationarity by comparing the outcomes of forward projections in which either the optimal policy or a stationary policy...