Artificial intelligence based decision support for trumpeter swan management

• Main Authors: Sojda, Richard S., author, Dean, Denis J., advisor, Fredrickson, Leigh H., committee member, Howe, Adele E., committee member, Loomis, John B., committee member
• Format: Text
• Language: English
• Published: Colorado State University. Libraries 2007
• Subjects: Northwest Territories; Yukon
• Online Access: http://hdl.handle.net/10217/954

Department Head: Susan G. Stafford. 2002 Spring. Includes bibliographical references (pages 108-114). The number of trumpeter swans (Cygnus buccinator) breeding in the Tri-State area where Montana, Idaho, and Wyoming come together has declined to just a few hundred pairs. However, these birds are part of the Rocky Mountain Population which additionally has over 3,500 birds breeding in Alberta, British Columbia, Northwest Territories, and Yukon Territory. To a large degree, these birds seem to have abandoned traditional migratory pathways in the flyway. Waterfowl managers have been interested in decision support tools that would help them explore simulated management scenarios in their quest towards reaching population recovery and the reestablishment of traditional migratory pathways. I have developed a decision support system to assist biologists with such management, especially related to wetland ecology. Decision support systems use a combination of models, analytical techniques, and information retrieval to help develop and evaluate appropriate alternatives. Swan management is a domain that is ecologically complex, and this complexity is compounded by spatial and temporal issues. The Distributed Environment Centered Agent Framework (DECAF) was successful at integrating communications among agents, integrating ecological knowledge, and simulating swan distributions through implementation of a queuing system. The work I have conducted indicates a need for determining what other factors might allow a deeper understanding of the effects of management actions on the flyway distribution of waterfowl. Knowing those would allow the more refined development of algorithms for effective decision support systems via collaboration by intelligent agents. Additional, specific conclusions and ideas for future research related both to waterfowl ecology and to the use of multiagent systems have been triggered by the validation work.