A stochastic surplus production model in continuous time

Martin Wæver Pedersen, Casper Willestofte Berg

Research output: Contribution to journalJournal articleResearchpeer-review


Surplus production modelling has a long history as a method for managing data-limited fish stocks. Recent advancements have cast surplus production models as state-space models that separate random variability of stock dynamics from error in observed indices of biomass. We present a stochastic surplus production model in continuous time (SPiCT), which in addition to stock dynamics also models the dynamics of the fisheries. This enables error in the catch process to be reflected in the uncertainty of estimated model parameters and management quantities. Benefits of the continuous-time state-space model formulation include the ability to provide estimates of exploitable biomass and fishing mortality at any point in time from data sampled at arbitrary and possibly irregular intervals. We show in a simulation that the ability to analyse subannual data can increase the effective sample size and improve estimation of reference points relative to discrete-time analysis of aggregated annual data. Finally, subannual data from five North Sea stocks are analysed with particular focus on using residual analysis to diagnose model insufficiencies and identify necessary model extensions such as robust estimation and incorporation of seasonality. We argue that including all known sources of uncertainty, propagation of that uncertainty to reference points and checking of model assumptions using residuals are critical prerequisites to rigorous fish stock management based on surplus production models.
Original languageEnglish
JournalFish and Fisheries
Issue number2
Pages (from-to)226-243
Publication statusPublished - 2017


  • Aquatic Science
  • Oceanography
  • Ecology, Evolution, Behavior and Systematics
  • Management, Monitoring, Policy and Law
  • Data-limited methods
  • Fisheries management
  • Maximum sustainable yield
  • Pella-Tomlinson model
  • Seasonal population dynamics
  • Stock assessment

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