And Management: A Synthesis

2007

Reviews in Fish Biology and Fisheries, 2000

2012

The complexity, ambiguity and various sources of uncertainty related to fisheries systems are increasingly acknowledged. This has led to questioning the conventional practices of producing the knowledge base for fisheries policy. The prevailing practice relies on biological stock assessments, whereas uncertainties stemming from the behavior of humans are usually ignored. Focusing on biological management advice has further led to defining management objectives and related reference points in biological terms only. Currently, ...

In this paper we examined Bayesian update for descriptive statistics for a random sample of fisheries. The Bayesian method is applied to a real sample of 730 Por’s Goat fish’s (Upeneus pori, Ben&Tuvia and Golani, 1989) length weight observation which is collected from Iskenderun Bay, Northeast Mediterranean Sea. Computational approach is to use the Markov Chain Monte Carlo simulation to draw samples from the posterior distributions of model parameters implementing the simulation in Open BUGS software. We assigned past experience as a prior distribution. This information comes from various previous studies that have been conducted in the same area. The priors for length is; ϑ_1~N(11.843,1.714) and for weight is; ϑ_2~N(15.815,27.321). According to the result, the posterior distribution for mean and variance of length were found 11.1cm and 0.003, for weight, 15.7 and 0.026. The 95% credible interval of length and weight are [10.99-11.21] and [15.42-16.05]. One of the key results of this study is that previous studies are part of the estimation and this makes variance and uncertainty low. This makes estimation sufficient and more reliable.

2009

The Bayesian inference and decision making has experienced fast growth over the last thirty years in fisheries modeling. In this period, various Bayesian applications were developed by scientists. One of them, the Bayesian updating for population mean and variance, was developed by Box and Tiao (1992), Gelman et al. (2003), Lee (2004) and McCarthy (2007). In this study we have taken a step further towards fisheries data. Moreover this paper attempts to answer a simple question: "Given my past experience and samples obtained, what should I think about the descriptive statistics of population". The Bayesian method is suitable of answering this question. It takes into account both past knowledge and knowledge comes from sample. Descriptive statistics are the most important topic in statistics. In this paper we examined Bayesian update for descriptive statistics for a random sample of fisheries. Of course, our desire is not to suggest a new method. However, we tried to show ho...

2010

PLoS ONE, 2014

Marine and Freshwater Research, 2008

One of the key features of a Bayesian stock assessment is that the modeller needs to provide knowledge on model parameters. Priors summarise modellers' understanding of model parameters and are often defined by a probability distribution function. Priors are often mis-specified with arbitrary and unrealistic accuracy and precision in perceiving the state of nature for the parameters as a result