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MESA: Modeling

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Fishery models vary in complexity
Fishery models vary in complexity.

Models have played an integral part of moving fisheries biology from a qualitative art to a quantitative science. They have allowed biologists to represent systems through simple theoretical models and assess statistically whether models perform when confronted with data.

In the MESA program we use models for a wide range of applications. Many models are simple regressions to examine trends, or several parameter growth or stock-recruitment models. These types of models are relatively easy to derive and use, and can easily be implemented in a spreadsheet package. In general, evaluation of species that are below Tier 3 in the management system rely on these type of models and simple summary statistics for harvest recommendations.

As data and model complexity increases, spreadsheet packages are not powerful enough to handle the computational requirements for estimating many parameters over a large data set. MESA staff prefer two packages for statistical applications. For relatively simple models, but with large data sets, and for graphics, the open-source R package is excellent.

Our most complex models are the species that are managed under Tier 3 in the Council structure and are called statistical catch-at-age models. These are the species where we have the most data including biological parameters by age. We also are often integrating data from multiple surveys, fishery observer data and possibly environmental data. Models for these species such as Pacific ocean perch and sablefish have hundreds of parameters. For these models analytical solutions are impossible, so AD Model Builder is the package of choice because of its efficient minimization routines. AD Model Builder also provides multiple ways to incorporate and convey uncertainty in our fishery models.

Dana Hanselman
Auke Bay Laboratories
Alaska Fisheries Science Center, NOAA Fisheries

Ted Stevens Marine Research Institute
17109 Pt Lena Loop Rd
Juneau AK 99801

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