The development and use of statistical methodology in fisheries research

Limited data and the requirement to make scientific based decisions to manage exploited fish populations has led to the development of statistical methods that combine a variety of information and describe uncertainty. Integrated analysis (including multiple data types in a single analysis), Bayesian analysis (describing uncertainty and including prior information), state-space models (including process variability), and highly parameterized mechanistic models have been standard practice in fisheries stock assessment for over a decade. Increased computational power and modeling environments (e.g. AD Model Builder) have greatly facilitated the application of these analyses and there are now several general stock assessment model programs that allow the integration of many different data types and different model assumptions. However, due to the complexity of these analyses, there are a number of issues that still need to be resolved (e.g. diagnostics, data weighting). Presentations in this session focus on statistical methods used in fisheries research with a particular focus on methods developed within the fisheries scientific community that are generally applicable to any ecological problem. The session will start with a short introductory presentation on "The contribution of fisheries stock assessment to statistical ecology" and conclude with a small panel discussion on "Statistical issues in fisheries stock assessment that still need addressing".

Chair: Mark Maunder, Inter-American Tropical Tuna Commission

Invited talks:

Published Sep. 19, 2011 10:52 AM - Last modified Oct. 25, 2019 10:20 AM