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

Scientific management for American black ducks (Anas rubripes) has been hampered by a lack of understanding regarding factors affecting the dynamics of black duck populations. This has resulted in a lack of agreement among stakeholders as to the potential for arresting the decline of black duck stocks through management intervention, especially through harvest regulations. This work builds on previous work for the Principal Investigator, entitled, "Development of an Integrated, Adaptive Management Protocol for American Black Ducks" (RWO 50). In that work, the PI developed and evaluated several single-and multiple-population adaptive harvest management (AHM) models. Initially, a single-population model was developed based on the monograph by Conroy et.al. (2002), in which abundance of black ducks and mallards (Anas platyrhynchos) were parameterized by midwinter surveys. The AHM project extended these models to breeding surveys implemented by Canadian Wildlife Service starting in 1990, and developed both spatially-stratified and non-stratified models. Fish and Wildlife Service and Canadian Wildlife Service have now agreed to move forward with implementation of AHM for black ducks. Additional technical work is needed to support this implementation, and to address technical issues not completely resolved by the recently completed work order (RWO 85). Specific tasks to be addressed in the proposed project are: The objective of this work is to produce models that can be used to support AHM as an international harvest management strategy for American black ducks. Each of the tasks and deliverables below is essential to meet this objective. 1. Incorporate new integrated fixed-wing/helicopter BPOP estimates into Python models of black duck dynamics. 2. Incorporate historical, longer-term data from MWS surveys and US harvest surveys via Bayesian analyses, in which the latter would be used to build prior distributions. 3. Incorporate estimates from 1) and 2) into dynamic optimization tools (ASDP). 4. Investigate approaches using MCMC or other methods for updating model weights through monitoring.