Multi-level stock–recruit analysis: Beyond steepness and into model uncertainty

Richard M. Hillary, Polina Levontin, Sakari Kuikka, Samu Manteniemi, Iago Mosqueira and Laurie Kell

Ecological Modelling, 2012, vol. 242, issue C, 69-80

Abstract: Meta-analytic and multi-level stock–recruit analyses have traditionally focussed on the similar stock approach, but for a specific stock–recruit model. For six European herring stocks we embed both the stock and model levels within a fully Bayesian hierarchical framework, thus permitting the consideration of a wider class of models, specifically those that do not admit parameterisation via the steepness and unfished spawning potential. Model and parametric uncertainty is jointly characterised and the challenge of addressing model selection when the model itself is part of the hierarchy is addressed using the deviance information criterion (DIC) and posterior predictive analysis. For the six herring stocks the across-stock posterior evidence in favour of over-compensatory dynamics is fairly strong, with the Ricker and Shepherd models performing the best across the model-selection criteria. For a specific model form we perform a 20 year retrospective analysis (hierarchical and non-hierarchical) to see how temporal information flow occurs in a hierarchical framework, how this can improve our estimates of key parameters, and how this might influence management paradigms (such as Maximum Sustainable Yield) that are based on such estimates.

Keywords: Stock–recruit analysis; Hierarchical modelling; Model selection (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304380012002542
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:242:y:2012:i:c:p:69-80

DOI: 10.1016/j.ecolmodel.2012.05.024

Access Statistics for this article

Ecological Modelling is currently edited by Brian D. Fath

More articles in Ecological Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().