ss3sim: An R Package for Fisheries Stock Assessment Simulation with Stock Synthesis. Anderson, S. C., Monnahan, C. C., Johnson, K. F., Ono, K., & Valero, J. L. PLOS ONE, PUBLIC LIBRARY SCIENCE, 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA, APR 3, 2014.
doi  abstract   bibtex   
Simulation testing is an important approach to evaluating fishery stock assessment methods. In the last decade, the fisheries stock assessment modeling framework Stock Synthesis (SS3) has become widely used around the world. However, there lacks a generalized and scriptable framework for SS3 simulation testing. Here, we introduce ss3sim, an R package that facilitates reproducible, flexible, and rapid end-to-end simulation testing with SS3. ss3sim requires an existing SS3 model configuration along with plain-text control files describing alternative population dynamics, fishery properties, sampling scenarios, and assessment approaches. ss3sim then generates an underlying `truth' from a specified operating model, samples from that truth, modifies and runs an estimation model, and synthesizes the results. The simulations can be run in parallel, reducing runtime, and the source code is free to be modified under an open-source MIT license. ss3sim is designed to explore structural differences between the underlying truth and assumptions of an estimation model, or between multiple estimation model configurations. For example, ss3sim can be used to answer questions about model misspecification, retrospective patterns, and the relative importance of different types of fisheries data. We demonstrate the software with an example, discuss how ss3sim complements other simulation software, and outline specific research questions that ss3sim could address.
@article{ ISI:000334105000033,
Author = {Anderson, Sean C. and Monnahan, Cole C. and Johnson, Kelli F. and Ono,
   Kotaro and Valero, Juan L.},
Title = {{ss3sim: An R Package for Fisheries Stock Assessment Simulation with
   Stock Synthesis}},
Journal = {{PLOS ONE}},
Year = {{2014}},
Volume = {{9}},
Number = {{4}},
Month = {{APR 3}},
Abstract = {{Simulation testing is an important approach to evaluating fishery stock
   assessment methods. In the last decade, the fisheries stock assessment
   modeling framework Stock Synthesis (SS3) has become widely used around
   the world. However, there lacks a generalized and scriptable framework
   for SS3 simulation testing. Here, we introduce ss3sim, an R package that
   facilitates reproducible, flexible, and rapid end-to-end simulation
   testing with SS3. ss3sim requires an existing SS3 model configuration
   along with plain-text control files describing alternative population
   dynamics, fishery properties, sampling scenarios, and assessment
   approaches. ss3sim then generates an underlying `truth' from a specified
   operating model, samples from that truth, modifies and runs an
   estimation model, and synthesizes the results. The simulations can be
   run in parallel, reducing runtime, and the source code is free to be
   modified under an open-source MIT license. ss3sim is designed to explore
   structural differences between the underlying truth and assumptions of
   an estimation model, or between multiple estimation model
   configurations. For example, ss3sim can be used to answer questions
   about model misspecification, retrospective patterns, and the relative
   importance of different types of fisheries data. We demonstrate the
   software with an example, discuss how ss3sim complements other
   simulation software, and outline specific research questions that ss3sim
   could address.}},
Publisher = {{PUBLIC LIBRARY SCIENCE}},
Address = {{1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA}},
Type = {{Article}},
Language = {{English}},
Affiliation = {{Anderson, SC (Reprint Author), Simon Fraser Univ, Dept Biol Sci, Earth Ocean Res Grp, Burnaby, BC V5A 1S6, Canada.
   Anderson, Sean C., Simon Fraser Univ, Dept Biol Sci, Earth Ocean Res Grp, Burnaby, BC V5A 1S6, Canada.
   Monnahan, Cole C., Univ Washington, Seattle, WA 98195 USA.
   Johnson, Kelli F.; Ono, Kotaro, Univ Washington, Sch Aquat \& Fishery Sci, Seattle, WA 98195 USA.
   Valero, Juan L., Ctr Adv Populat Assessment Methodol, La Jolla, CA USA.}},
DOI = {{10.1371/journal.pone.0092725}},
Article-Number = {{e92725}},
ISSN = {{1932-6203}},
Keywords-Plus = {{DATA-LIMITED SITUATIONS; AD MODEL BUILDER; NATURAL MORTALITY; ASSESSMENT
   PROGRAM; AGE; PERFORMANCE; STRATEGIES; MANAGEMENT; FRAMEWORK; DYNAMICS}},
Research-Areas = {{Science \& Technology - Other Topics}},
Web-of-Science-Categories  = {{Multidisciplinary Sciences}},
Author-Email = {{sean@seananderson.ca}},
Funding-Acknowledgement = {{Fulbright Canada; NSERC; Garfield Weston Foundation/B.C. Packers Ltd.;
   Joint Institute for the Study of the Atmosphere and Ocean (JISAO) under
   NOAA {[}NA10OAR4320148, 2218]}},
Funding-Text = {{SCA was supported by Fulbright Canada (hosted by Trevor A. Branch),
   NSERC, and a Garfield Weston Foundation/B.C. Packers Ltd. Graduate
   Fellowship in Marine Sciences. This publication was partially funded by
   the Joint Institute for the Study of the Atmosphere and Ocean (JISAO)
   under NOAA Cooperative Agreement NA10OAR4320148, Contribution No. 2218.
   This research addresses the methods component of the good practices
   guide to stock assessment program of the Center for the Advancement of
   Population Assessment Methodology (CAPAM). The funders had no role in
   study design, data collection and analysis, decision to publish, or
   preparation of the manuscript.}},
Number-of-Cited-References = {{44}},
Times-Cited = {{8}},
Usage-Count-Last-180-days = {{1}},
Usage-Count-Since-2013 = {{22}},
Journal-ISO = {{PLoS One}},
Doc-Delivery-Number = {{AE6LR}},
Unique-ID = {{ISI:000334105000033}},
OA = {{gold}},
DA = {{2017-08-17}},
}

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