Bayesian Dynamic Modeling of Stock-Recruitment Relationships

• Main Authors: Renate Meyer, Russell B. Millar
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Published: 2000
• Subjects: Fisheries stock assessment; Ricker curve; state-space methodology; timeseries bias; Markov Chain Monte Carlo; Gibbs sampler; BUGS. 2; Fraser River; Hastings; Pink salmon
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.20.2590; http://www.stat.auckland.ac.nz/~meyer/ricker.ps

Assessing the relationship between spawning stock size and the resulting number of adult ospring (recruitment) is one of the most fundamental issues in sheries stock assessment and an important cornerstone for management decisions on harvest policies in many sheries. This paper proposes a Bayesian state-space model for tting stockrecruitment curves. This approach eliminates at least two of the four major problems encountered in traditional stock-recruitment analyses, that of "errors-in-variables bias" and "time-series bias". The state-space model takes the temporal dependencies of the observations into account through a conditional modeling of the observations, given unknown states, and speci cation of Markovian transition of states. Both process and observation errors are explicitly captured in the state-space model and quanti ed through posterior distributions of the parameters via the Bayesian paradigm. Beyond bias elimination, this approach is capable of quantifying fundamental uncertainties in parameter estimates and risks of management policies. Problems with posterior computations are overcome using Metropolis-Hastings-within-Gibbs sampling. This novel Bayesian state-space approach to stock-recruitment analysis is illustrated using a dataset on Fraser River pink salmon. The Ricker curve is employed to describe the dependence of recruitment on the spawning stock size. The state-space model is implemented using the readily available software package BUGS.