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ICES Journal of Marine Science: Journal du Conseil Advance Access published online on September 26, 2008
ICES Journal of Marine Science: Journal du Conseil, doi:10.1093/icesjms/fsn162
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© 2008 International Council for the Exploration of the Sea. Published by Oxford Journals. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Hierarchical Bayesian approach for population dynamics modelling of fish complexes without species-specific data

Yan Jiao1, Christopher Hayes1 and Enric Cortés2

1 Department of Fisheries and Wildlife Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0321, USA
2 National Marine Fisheries Service, Panama City Laboratory, Panama City, FL 32408, USA

Correspondence to Y. Jiao: tel: +1 540 2315749; fax: +1 540 2317580; e-mail: yjiao{at}vt.edu

Jiao, Y., Hayes, C., and Cortés, E. 2009. Hierarchical Bayesian approach for population dynamics modelling of fish complexes without species-specific data. – ICES Journal of Marine Science, 66: 000–000.

Modelling the population dynamics of fish complexes is challenging, and many species have been assessed and managed as a complex that was treated as a single species. Two Bayesian state-space surplus production models with multilevel priors (hierarchical models) were developed to simulate variability in population growth rates of species in a complex, using the hammerhead shark complex (Sphyrna spp.) of the Atlantic and Gulf of Mexico coasts of the US as an example. The complex consists of three species: scalloped (Sphyrna lewini), great (Sphyrna mokarran), and smooth hammerhead (Sphyrna zygaena). Bayesian state-space surplus production models with multilevel priors fitted the hammerhead data better than a model based on single-level priors. The hierarchical Bayesian approach represents an intermediate strategy between traditional models that do not include variability among species, and highly parameterized models that assign an estimate of parameters to each species. By ignoring the variability among species, confidence intervals of the estimates of stock status indicators can be unrealistically narrow, possibly leading to high-risk management strategies being adopted. Use of multilevel priors in a hierarchical Bayesian approach is suggested for future hammerhead shark stock assessments and for modelling fish complexes lacking species-specific data.

Keywords: Bayesian hierarchical model, fish complex, hammerhead sharks, multilevel prior, uncertainty

Received 3 September 2007; accepted 2 September 2008.


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