Workshop on marine mammal research & monitoring in the National Marine Sanctuaries, Wailea, Maui, Hawaii, 28 November 1999

The Second National Workshop on Marine Mammal Research and Monitoring in the National Marine Sanctuaries was held on 28 November 1999 in Maui, Hawaii. The workshop preceded the Thirteenth Biennial Conference on the Biology of Marine Mammals, and provided an opportunity to review and promote marine mammal research and monitoring in the National Marine Sanctuaries (NMS). The purpose of the workshop was to bring together researchers and sanctuary staff and to improve marine mammal research and monitoring throughout the sanctuaries. Discussion topics included: potential multi-sanctuary projects, sources of funding for multi-sanctuary projects, services and equipment for researchers through the sanctuaries, consolidating small levels of funding, help in funding and support for writing up data, publishing documents in Technical Memoranda, and letters of support. Representatives from the NMS national office and nine sanctuaries provided participants with overviews of marine mammal research within the sanctuaries. Presentations were also given by representatives from the National Marine Fisheries Service’s Permits and Health and Stranding programs. During the breakout working groups, there were several comments and suggestions consistent among each of the groups to improve marine mammal research. Each group emphasized the need to improve communication among researchers and to better share data. These suggestions included web-based information networks, advisory panels, and workshops. Regionally based research projects were also emphasized. In order to best study marine mammal populations, collaborative studies must take place throughout multiple sanctuaries. In order to achieve these large scale studies, funding and staffing must be directed towards these studies and distributed among each of the sanctuaries so that they may all be able to have the staffing, equipment, and vessels necessary to achieve a collaborative, ecosystem-based, regional marine mammal monitoring program. It will take several years to achieve all of the suggestions from the workshop, but thanks to the workshop participants, the National Marine Sanctuary Program has begun to direct marine mammal research and monitoring in order to achieve the goals of the workshop. This document provides a summary of the workshop with a focus on key points/main issues. We have included contact information intended to encourage continued collaboration among the individuals and organizations represented at the 1999 Marine Mammal Research and Monitoring in the National Marine Sanctuaries Workshop. (PDF contains 71 pages.)

Estimating relative abundance from catch and effort data, using neural networks

We develop and test a method to estimate relative abundance from catch and effort data using neural networks. Most stock assessment models use time series of relative abundance as their major source of information on abundance levels. These time series of relative abundance are frequently derived from catch-per-unit-of-effort (CPUE) data, using general linearized models (GLMs). GLMs are used to attempt to remove variation in CPUE that is not related to the abundance of the population. However, GLMs are restricted in the types of relationships between the CPUE and the explanatory variables. An alternative approach is to use structural models based on scientific understanding to develop complex non-linear relationships between CPUE and the explanatory variables. Unfortunately, the scientific understanding required to develop these models may not be available. In contrast to structural models, neural networks uses the data to estimate the structure of the non-linear relationship between CPUE and the explanatory variables. Therefore neural networks may provide a better alternative when the structure of the relationship is uncertain. We use simulated data based on a habitat based-method to test the neural network approach and to compare it to the GLM approach. Cross validation and simulation tests show that the neural network performed better than nominal effort and the GLM approach. However, the improvement over GLMs is not substantial. We applied the neural network model to CPUE data for bigeye tuna (Thunnus obesus) in the Pacific Ocean.