Non-linearity in stock–recruitment relationships of Atlantic cod: insights from a multi-model approach. (24th June 2019)

• Record Type: Journal Article
• Authors: Sguotti, Camilla; Otto, Saskia A; Cormon, Xochitl; Werner, Karl M; Deyle, Ethan; Sugihara, George; Möllmann, Christian
• Editors: Griffith, Gary
• Is Part Of: ICES journal of marine science. Volume 77:Number 4(2020)
• Page Start: 1492
• Page End: 1502
• Publication Date: 2019-06-24
• Subjects: Atlantic cod -- empirical dynamic modelling -- non-linear dynamics -- Ricker model -- stochastic cusp model -- stock–recruitment models; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsz113
• Languages: English
• ISSNs: 1054-3139

Abstract

Abstract: The stock–recruitment relationship is the basis of any stock prediction and thus fundamental for fishery management. Traditional parametric stock–recruitment models often poorly fit empirical data, nevertheless they are still the rule in fish stock assessment procedures. We here apply a multi-model approach to predict recruitment of 20 Atlantic cod ( Gadus morhua ) stocks as a function of adult biomass and environmental variables. We compare the traditional Ricker model with two non-parametric approaches: (i) the stochastic cusp model from catastrophe theory and (ii) multivariate simplex projections, based on attractor state-space reconstruction. We show that the performance of each model is contingent on the historical dynamics of individual stocks, and that stocks which experienced abrupt and state-dependent dynamics are best modelled using non-parametric approaches. These dynamics are pervasive in Western stocks highlighting a geographical distinction between cod stocks, which have implications for their recovery potential. Furthermore, the addition of environmental variables always improved the models' predictive power indicating that they should be considered in stock assessment and management routines. Using our multi-model approach, we demonstrate that we should be more flexible when modelling recruitment and tailor our approaches to the dynamical properties of each individual stock.