Data and Management Strategies for Recreational Fisheries with Annual Catch Limits [Minkštas viršelis]

Marine recreational fishing is a popular activity enjoyed by more than 9 million Americans annually and is a driver of the American ocean-or blue-economy. To ensure that fish populations are not overexploited, the NOAA Fisheries' Marine Recreational Information Program (MRIP) monitors recreational catch through a variety of in-person, telephone, mail-in, and other surveys. NOAA Fisheries' management of recreational catch also must take into account annual catch limits (ACLs) established to prevent overfishing for all managed species in federal waters.



While MRIP has worked to improve recreational catch surveys over the past decade, the surveys were never designed to meet the demands of in-season management of ACLs. In some cases, estimates of harvest have triggered accountability measures such as early season closures and reductions in future recreational ACLs, which have been a source of contention with the recreational fishing community. This report presents approaches for optimizing MRIP data and complementary data for in-season management and considers alternatives for managing recreational fisheries with ACLs to better serve both social and economic management objectives.

Table of Contents



Front Matter Summary 1 Introduction 2 The U.S. Fisheries Management and Assessment Framework 3 Existing Recreational Fisheries Surveys and ACL-Based Fisheries Management 4 Optimizing Use of MRIP Data and Complementary Data for In-Season Management 5 Alternative Management Strategies for Recreational Fisheries Appendix A: Multiple-Frame Methods Appendix B: Leveraging Covariances and Conditionals Appendix C: Contemporaneous Correlation SUR Model Appendix D: Bayesian Methods Appendix E: Rare-Event Species: Normal or Poisson? Appendix F: Rare-Event Species: Inverse Sampling Appendix G: Rare-Event Species: Uninformative Priors and Bayes' Rule Appendix H: Defining and Managing Outliers in MRIP Output: An Order Statistics Approach