ss3sim: An R Package for Fisheries Stock Assessment Simulation with Stock Synthesis

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.

[1]  Richard D. Methot,et al.  A Simulation-Based Method to Determine Model Misspecification: Examples using Natural Mortality and Population Dynamics Models , 2011 .

[2]  André E. Punt,et al.  Performance of a fisheries catch-at-age model (Stock Synthesis) in data-limited situations , 2011 .

[3]  Richard D. Methot,et al.  Stock synthesis: A biological and statistical framework for fish stock assessment and fishery management , 2013 .

[4]  E. Smith,et al.  Modelling non-stationary natural mortality in catch-at-age models , 2012 .

[5]  Richard D. Methot,et al.  Simulation testing the robustness of stock assessment models to error: some results from the ICES strategic initiative on stock assessment methods , 2015 .

[6]  André E. Punt,et al.  Time-varying natural mortality in fisheries stock assessment models: identifying a default approach , 2015 .

[7]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[8]  Carl J. Walters,et al.  A General Model for Simulation of Stock and Fleet Dynamics in Spatially Heterogeneous Fisheries , 1987 .

[9]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[10]  O. Hoegh‐Guldberg,et al.  Ecological responses to recent climate change , 2002, Nature.

[11]  T. Royama,et al.  Analytical Population Dynamics , 1994, Population and Community Biology Series.

[12]  James R. Bence,et al.  Performance of time-varying catchability estimators in statistical catch-at-age analysis , 2006 .

[13]  R. Peterman,et al.  Comparison of methods to account for autocorrelation in correlation analyses of fish data , 1998 .

[14]  Arni Magnusson,et al.  What makes fisheries data informative , 2007 .

[15]  Randall M. Peterman,et al.  Possible solutions to some challenges facing fisheries scientists and managers , 2004 .

[16]  Richard Hillary,et al.  An introduction to FLR fisheries simulation tools , 2009 .

[17]  David B. Sampson,et al.  Application of statistical model selection criteria to the Stock Synthesis assessment program , 2000 .

[18]  Assessment of the Pacific Sardine Resource in 2011 for U.S. Management in 2012 , 2013 .

[19]  J. M. Elliott,et al.  Fish Stock Assessment. A Manual of Basic Methods , 1984 .

[20]  Mark N. Maunder,et al.  Selectivity : theory , estimation , and application in fishery stock assessment models Workshop Series Report 1 June 2013 , 2013 .

[21]  C. Phillip Goodyear,et al.  Testing different methods of incorporating climate data into the assessment of US West Coast sablefish , 2009 .

[22]  John Sibert,et al.  AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models , 2012, Optim. Methods Softw..

[23]  R. Mohn,et al.  The retrospective problem in sequential population analysis: An investigation using cod fishery and simulated data , 1999 .

[24]  John C. Nash,et al.  Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS , 2013 .

[25]  R. Beamish,et al.  Climate change and northern fish populations , 1995 .

[26]  R. Mohn,et al.  Why the Atlantic cod (Gadus morhua) stock off eastern Nova Scotia has not recovered , 2001 .

[27]  Philippe Grosjean,et al.  FLR: an open-source framework for the evaluation and development of management strategies , 2007 .

[28]  Richard D. Methot,et al.  Can steepness of the stock–recruitment relationship be estimated in fishery stock assessment models? , 2012 .

[30]  Andrew A. Rosenberg,et al.  Uncertainty and Risk Evaluation in Stock Assessment Advice for U.S. Marine Fisheries , 1994 .

[31]  Jason M. Cope.  Implementing a statistical catch-at-age model (Stock Synthesis) as a tool for deriving overfishing limits in data-limited situations , 2013 .

[32]  C. Walters,et al.  Quantitative fisheries stock assessment: Choice, dynamics and uncertainty , 2004, Reviews in Fish Biology and Fisheries.

[33]  R. Hilborn,et al.  Frequency and intensity of productivity regime shifts in marine fish stocks , 2013, Proceedings of the National Academy of Sciences.

[34]  Richard D. Methot,et al.  Estimating natural mortality within a fisheries stock assessment model: An evaluation using simulation analysis based on twelve stock assessments , 2011 .

[35]  André E. Punt,et al.  A review of integrated analysis in fisheries stock assessment , 2013 .

[36]  Ian M. Mitchell,et al.  Best Practices for Scientific Computing , 2012, PLoS biology.

[37]  André E. Punt,et al.  Use of multiple selectivity patterns as a proxy for spatial structure , 2014 .

[38]  Richard D. Methot,et al.  Adjusting for bias due to variability of estimated recruitments in fishery assessment models , 2011 .

[39]  Jonathan J. Deroba,et al.  Performance of stock assessments with misspecified age- and time-varying natural mortality , 2013 .

[40]  C. Legault.  Report of the Retrospective Working Group, January 14-16, 2008, Woods Hole, Massachusetts , 2009 .

[41]  André E. Punt,et al.  The importance of length and age composition data in statistical age-structured models for marine species , 2015 .

[42]  David B. Sampson,et al.  Bias and Precision of Estimates from an Age-Structured Stock Assessment Program in Relation to Stock and Data Characteristics , 2004 .