This course will highlight the theory, issues, challenges, methods, and approaches for implementing Ecosystem-based Fisheries Management (EBFM). The course will examine the history and background of fisheries management, leading up to why more holistic considerations are being considered. A series of operational approaches, emphasizing a familiarity with technical efforts and models, will be explored as they are being used to implement EBFM. An examination of institutional structures and considerations will be executed to elucidate those situations where EBFM has been successful. The course will emphasize current literature and case studies as main learning elements.

Generalized linear models, inference of parameters in non-linear models, and Bayesian statistics. The course is designed for advanced graduate students in ecology and fisheries who want to study statistical theories in GLM and parameter inference, to infer parameters using the state-of -the art language (ADMB, BUG, R), and to develop his/her own models. Most models used in ecology and fisheries management are non-linear, and often many parameters are involved. The existing models/toolbox software are useful in research and practical management, but researchers often have their own unique problem, which those existing models cannot be used for.

Generalized linear models, inference of parameters in non-linear models, and Bayesian statistics. The course is designed for advanced graduate students in ecology and fisheries who want to study statistical theories in GLM and parameter inference, to infer parameters using the state-of -the art language (ADMB, BUG, R), and to develop his/her own models. Most models used in ecology and fisheries management are non-linear, and often many parameters are involved. The existing models/toolbox software are useful in research and practical management, but researchers often have their own unique problem, which those existing models cannot be used for.