Developing SSMSE, an R package for Management Strategy Evaluation with Stock Synthesis

Tuesday May 12th, 2020: 2:00 PM PST

Kathryn L. Doering (1), Nathan R. Vaughan (2) , John F. Walter (3), Richard D. Methot (4), Skyler R. Sagarese (3), Matthew Smith (3), Nancie Cummings (3), Nicholas A. Farmer (5), Shannon Calay (3), Kelli Johnson (6), Kristin Marshall (6), Cassidy Peterson (7), Ian Taylor (6), and Chantel Wetzel (6)

1. Caelum Research Corporation in support of Northwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Seattle, WA

2. Vaughan Analytics in support of Southeast Fisheries Science Center, National Oceanic and Atmospheric Administration, Miami, FL

3. Southeast Fisheries Science Center, National Oceanic and Atmospheric Administration, Miami, FL

4. NOAA Senior Scientist for Stock Assessments, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, WA

5. Southeast Regional Office, National Oceanic and Atmospheric Administration, St. Petersburg, FL

6. Northwest Fisheries Science Center, National Oceanic and Atmospheric Administration, Seattle, WA

7. Southeast Fisheries Science Center, National Oceanic and Atmospheric Administration, Beaufort, NC

Management Strategy Evaluation (MSE) is designed to holistically evaluate alternative management strategies, data collection approaches, and modeling structures. While MSE is becoming routine, the task of conducting it is currently limited by the challenge of creating realistic operating models (OMs) for the population and fishery processes. The stock assessment software package Stock Synthesis (SS) represents one of the most complete, generalizable population assessment models available, which is why it provides the necessary architecture for developing complex MSE OMs, while also formalizing the parameterization, fitting, and evaluation of data fit to a model. Pre-existing SS assessment models therefore represent a potential pool of rich OMs, however attempts to use this existing resource for MSE often result in simplified versions of the original SS model itself. We propose an alternative path to MSE functionality of using stock assessments implemented in SS as OMs. The goal of this project is to build the capacity to facilitate converting any SS model into an OM with as much of the OM engine as possible coded within SS, thereby making OM output an innate capacity of SS. Users will be able to access this capability and conduct MSE analyses through an R package we are developing called SSMSE (https://github.com/nmfs-fish-tools/SSMSE). In this presentation, the features of SSMSE will be described and demonstrated. Further, we discuss future directions to expand the functionality of the SSMSE package. We welcome your suggestions and comments on this in-progress project throughout the presentation. This approach should allow the complexity of the MSE OMs to grow with SS, leveraging the future development of SS and innately linking the MSE and stock assessment processes.

Presentation Slides