Adaptive Management for the Northern Bobwhite on the Babcock-Webb Wildlife Management Area Completed
Based on field research conducted during 2002-2009, the bobwhite population on the Babcock-Webb Wildlife Management Area (WMA) in Southwest Florida is incapable of supporting desired levels of sport harvest.
PROJECT COMPLETED
The Science Issue and Relevance: Based on field research conducted during 2002-2009, the bobwhite population on the Babcock-Webb Wildlife Management Area (WMA) in Southwest Florida is incapable of supporting desired levels of sport harvest. Research findings also suggest that increasing the size of the population and its ability to provide harvest opportunity will likely involve changing both harvest and habitat management practices. A key challenge, however, is to determine the precise nature of those changes. Only minimal variation in management practices occurred during the period of field research, thereby limiting inference about the effects of alternative management treatments. We suggest that decision science may assist managers in determining the best course of action to improve the status of bobwhites on the WMA.
Methodology for Addressing the Issue: The suite of potential management practices is large and includes burning (frequency and size), grazing, roller-chopping of palmetto, planting of food strips, and regulating sport harvest. Based on a review of research conducted at the WMA and elsewhere in South Florida we will focus initially on prescribed burning because it has potentially large, yet uncertain, effects. The primary goal of this study is to determine how to allocate management units among three different burning treatments for some pre-defined period (e.g., 5 years). Moreover, we will determine how that allocation might change over time in response to what is learned about the response of quail abundance to the different treatments. The desire to maximize management performance while learning so as to improve future management decisions is known as a dual-control problem. The optimal solution to such problems can be derived with stochastic dynamic programming.
Future Steps: We intend to develop a Bayesian Belief Network (BBN) to summarize what is known about effects of habitat variation on bobwhites and to characterize attendant sources of uncertainty. After review by WMA managers, this BBN will be used to parameterize an optimization algorithm to prescribe optimal treatment allocations.
Based on field research conducted during 2002-2009, the bobwhite population on the Babcock-Webb Wildlife Management Area (WMA) in Southwest Florida is incapable of supporting desired levels of sport harvest.
PROJECT COMPLETED
The Science Issue and Relevance: Based on field research conducted during 2002-2009, the bobwhite population on the Babcock-Webb Wildlife Management Area (WMA) in Southwest Florida is incapable of supporting desired levels of sport harvest. Research findings also suggest that increasing the size of the population and its ability to provide harvest opportunity will likely involve changing both harvest and habitat management practices. A key challenge, however, is to determine the precise nature of those changes. Only minimal variation in management practices occurred during the period of field research, thereby limiting inference about the effects of alternative management treatments. We suggest that decision science may assist managers in determining the best course of action to improve the status of bobwhites on the WMA.
Methodology for Addressing the Issue: The suite of potential management practices is large and includes burning (frequency and size), grazing, roller-chopping of palmetto, planting of food strips, and regulating sport harvest. Based on a review of research conducted at the WMA and elsewhere in South Florida we will focus initially on prescribed burning because it has potentially large, yet uncertain, effects. The primary goal of this study is to determine how to allocate management units among three different burning treatments for some pre-defined period (e.g., 5 years). Moreover, we will determine how that allocation might change over time in response to what is learned about the response of quail abundance to the different treatments. The desire to maximize management performance while learning so as to improve future management decisions is known as a dual-control problem. The optimal solution to such problems can be derived with stochastic dynamic programming.
Future Steps: We intend to develop a Bayesian Belief Network (BBN) to summarize what is known about effects of habitat variation on bobwhites and to characterize attendant sources of uncertainty. After review by WMA managers, this BBN will be used to parameterize an optimization algorithm to prescribe optimal treatment allocations.