Skip to main content

Detecting and forecasting complex nonlinear dynamics in spatially structured catch-per-unit-effort time series for North Pacific albacore (Thunnus alalunga)

Buy Article:

$50.00 plus tax (Refund Policy)


The presence of complex, nonlinear dynamics in fish populations, and uncertainty in the structure (functional form) of those dynamics, pose challenges to the accuracy of forecasts produced by traditional stock assessment models. We describe two nonlinear forecasting models that test for the hallmarks of complex behavior, avoid problems of structural uncertainty, and produce good forecasts of catch-per-unit-effort (CPUE) time series in both standardized and nominal (unprocessed) form. We analyze a spatially extensive, 40-year-long data set of annual CPUE time series of North Pacific albacore (Thunnus alalunga) from 1°× 1° cells from the eastern North Pacific Ocean. The use of spatially structured data in compositing techniques improves out-of-sample forecasts of CPUE and overcomes difficulties commonly encountered when using short, incomplete time series. These CPUE series display low-dimensional, nonlinear structure and significant predictability. Such characteristics have important implications for industry efficiency in terms of future planning and can inform formal stock assessments used for the management of fisheries.

La présence d'une dynamique complexe et non linéaire dans les populations de poissons et l'incertitude concernant la structure (forme fonctionnelle) de cette dynamique posent des défis en ce qui à trait à l'exactitude des prédictions faites par les modèles traditionnels d'évaluation des stocks. Nous décrivons deux modèles non linéaires de prédiction qui recherchent les signes d'un comportement complexe, évitent les problèmes de l'incertitude structurale et produisent de bonnes prédictions de séries chronologiques de captures par unité d'effort (CPUE) à la fois de forme standardisée et de forme nominale (non traitée). Nous analysons une banque de données à large représentation spatiale couvrant 40années de séries chronologiques annuelles de CPUE de germons (Thunnus alalunga) du Pacifique Nord provenant de cellules de 1°× 1° de l'est du Pacifique Nord. L'utilisation de données à structure spatiale dans les techniques de composition améliore les prédictions de CPUE au-delà des échantillons et surmonte les difficultés couramment rencontrées à l'utilisation de séries chronologiques courtes et incomplètes. Ces séries de CPUE possèdent une structure à faible dimension et non linéaire et une prédictibilité significative. De telles caractéristiques sont de grande importance pour la planification efficace future par l'industrie et peuvent enrichir les évaluations formelles de stocks utilisées dans la gestion des pêches.

Document Type: Research Article

Publication date: 2011-03-01

More about this publication?
  • Published continuously since 1901 (under various titles), this monthly journal is the primary publishing vehicle for the multidisciplinary field of aquatic sciences. It publishes perspectives (syntheses, critiques, and re-evaluations), discussions (comments and replies), articles, and rapid communications, relating to current research on cells, organisms, populations, ecosystems, or processes that affect aquatic systems. The journal seeks to amplify, modify, question, or redirect accumulated knowledge in the field of fisheries and aquatic science. Occasional supplements are dedicated to single topics or to proceedings of international symposia.
  • Information for Authors
  • Submit a Paper
  • Subscribe to this Title
  • Terms & Conditions
  • Sample Issue
  • Reprints & Permissions
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free ContentFree content
  • Partial Free ContentPartial Free content
  • New ContentNew content
  • Open Access ContentOpen access content
  • Partial Open Access ContentPartial Open access content
  • Subscribed ContentSubscribed content
  • Partial Subscribed ContentPartial Subscribed content
  • Free Trial ContentFree trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more