Stock synthesis: A biological and statistical framework for fish stock assessment and fishery management

Stock synthesis (SS) is a statistical age-structured population modeling framework that has been applied in a wide variety of fish assessments globally. The framework is highly scalable from data-weak situations where it operates as an age-structured production model, to complex situations where it can flexibly incorporate multiple data sources and account for biological and environmental processes. SS implements compensatory population dynamics through use of a function relating mean recruitment to spawner reproductive output. This function enhances the ability of SS to operate in data-weak situations and enables it to estimate fishery management quantities such as fishing rates that would provide for maximum sustainable yield and to employ these rates in forecasts of potential yield and future stock status. Complex model configurations such as multiple areas and multiple growth morphs are possible, tag-recapture data can be used to aid estimation of movement rates among areas, and most parameters can change over time in response to environmental and ecosystem factors. SS is coded using Auto-Differentiation Model Builder, so inherits its powerful capability to efficiently estimate hundreds of parameters using either maximum likelihood or Bayesian inference. Output processing, principally through a package developed in R, enables rapid model diagnosis. Details of the underlying population dynamics and the statistical framework used within SS are provided.

[1]  Richard Donald Methot,et al.  Technical description of the stock synthesis assessment program , 2000 .

[2]  R. Parrish,et al.  AGE DEPENDENT FECUNDITY, NUMBER OF SPAWNINGS PER YEAR, SEX RATIO, AND MATURATION STAGES IN NORTHERN ANCHOVY, ENGRAULIS MORDAX , 1986 .

[3]  André E. Punt,et al.  The promises and pitfalls of including decadal- scale climate forcing of recruitment in groundfish stock assessment , 2011 .

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

[5]  David B. Sampson,et al.  An exploration of the shapes and stability of population–selection curves , 2012 .

[6]  C. Goodyear,et al.  Spawning stock biomass per recruit in fisheries management: foundation and current use , 1993 .

[7]  Grant G. Thompson,et al.  ASSESSMENT OF THE PACIFIC COD STOCK IN THE GULF OF ALASKA , 2001 .

[8]  J. Pope,et al.  An investigation of the accuracy of virtual population analysis using cohort analysis , 1972 .

[9]  Michael H. Prager,et al.  Deriving Acceptable Biological Catch from the Overfishing Limit: Implications for Assessment Models , 2010 .

[10]  Mark N. Maunder,et al.  Including parameter uncertainty in forward projections of computationally intensive statistical population dynamic models , 2006 .

[11]  Mark N. Maunder,et al.  Review and evaluation of likelihood functions for composition data in stock-assessment models: Estimating the effective sample size , 2011 .

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

[13]  Ian G. Taylor,et al.  A comparison of stock assessment uncertainty estimates using maximum likelihood and Bayesian methods implemented with the same model framework , 2013 .

[14]  Michael H. Prager,et al.  Probabilistic Approaches to Setting Acceptable Biological Catch and Annual Catch Targets for Multiple Years: Reconciling Methodology with National Standards Guidelines , 2010 .

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

[16]  Richard D. Methot,et al.  Stock Assessment: Operational Models in Support of Fisheries Management , 2009 .

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

[18]  Yong Chen,et al.  Impacts of outliers and mis-specification of priors on Bayesian fisheries-stock assessment , 2000 .

[19]  David A. Fournier,et al.  MULTIFAN-CL: a length-based, age-structured model for fisheries stock assessment, with application to South Pacific albacore, Thunnus alalunga , 1998 .

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

[21]  M. Maunder,et al.  A general framework for integrating environmental time series into stock assessment models: model description, simulation testing, and example , 2003 .

[22]  André E. Punt,et al.  Admitting ageing error when fitting growth curves: an example using the von Bertalanffy growth function with random effects , 2007 .

[23]  James N. Ianelli,et al.  A Statistical Model for Stock Assessment of Southern Bluefin Tuna with Temporal Changes in Selectivity , 2003 .

[24]  John Shepherd,et al.  On the analysis of catch and effort data , 1983 .

[25]  James N. Ianelli,et al.  Bayesian stock assessment using catch-age data and the sampling - importance resampling algorithm , 1997 .

[26]  Richard D. Methot,et al.  The daily fecundity reduction method: a new procedure for estimating adult fish biomass , 1992 .

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

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

[29]  J. J. Hunt,et al.  Workshop on risk evaluation and biological reference points for fisheries management , 2004, Reviews in Fish Biology and Fisheries.

[30]  Trevor A. Branch,et al.  Differences in predicted catch composition between two widely used catch equation formulations , 2009 .

[31]  P. Livingston,et al.  Incorporation of Predation into a Population Assessment Model of Eastern Bering Sea Walleye Pollock , 1998 .

[32]  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 .

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

[34]  P. Mace,et al.  How much spawning per recruit is enough , 1993 .

[35]  J. Hooker.  In This Volume , 2012 .

[36]  Michael H. Prager,et al.  A suite of extensions to a nonequilibrium surplus-production model , 1994 .

[37]  Milner B. Schaefer.  Some aspects of the dynamics of populations important to the management of the commercial marine fisheries , 1991 .

[38]  S HamelOwen,et al.  Can data collected from marine protected areas improve estimates of life-history parameters? , 2011 .

[39]  Richard D. Methot,et al.  Hiding or dead? A computationally efficient model of selective fisheries mortality , 2013 .

[40]  Laurence T. Kell,et al.  A COMPARISON OF STATISTICAL AGE ESTIMATION AND AGE SLICING FOR ATLANTIC BLUEFIN TUNA (THUNNUS THYNNUS) , 2011 .

[41]  Steven J. D. Martell,et al.  A stochastic approach to stock reduction analysis , 2006 .

[42]  Richard D. Methot,et al.  A stock–recruitment relationship based on pre-recruit survival, illustrated with application to spiny dogfish shark , 2013 .

[43]  Scott Nichols.  Updated Yield-per-Recruit Information on the Tortugas Pink Shrimp Fishery , 1986 .

[44]  Jon T. Schnute,et al.  A Versatile Growth Model with Statistically Stable Parameters , 1981 .

[45]  David B. Sampson,et al.  Evaluation of Assumed Error Structure in Stock Assessment Models That Use Sample Estimates of Age Composition , 1998 .

[46]  Alec D. MacCall,et al.  Interactions of Age-Dependent Mortality and Selectivity Functions in Age-Based Stock Assessment Models , 2013 .

[47]  David A. Fournier,et al.  A General Theory for Analyzing Catch at Age Data , 1982 .

[48]  André E. Punt,et al.  Experiences in the evaluation and implementation of management procedures , 1999 .

[49]  Terrance J. Quinn,et al.  Fishery Stock Assessment Models , 1998 .

[50]  Alec D. MacCall,et al.  Depletion-Based Stock Reduction Analysis: A catch-based method for determining sustainable yields for data-poor fish stocks , 2011 .