2006
DOI: 10.1007/s10818-005-5783-x
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Implementing a Stochastic Bioeconomic Model for the North-East Arctic Cod Fishery

Abstract: In this paper, we study how a stochastic model can be used to determine optimal levels of exploitation of the North-East Arctic Cod (NEAC, Gadus morhua). A non-critical depensation growth model is developed for this species in order to examine both deterministic and stochastic cases. Estimation of the biological and the noise term parameters in the stochastic biomass dynamics is based on simulation and use of empirical NEAC data sets for the years 1985–2001. The Kolmogorov– Smirnov criterion-based method is us… Show more

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Cited by 28 publications
(34 citation statements)
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“…Second, if the model is to be relevant for bioeconomic analysis, we have to limit the complexity and dimensionality of the model. We have in mind the type of analysis carried out in Sandal and Steinshamn (2010); see also Kugarajh et al (2006). To limit complexity, we use simple growth functions and interaction terms common in traditional bioeconomic analysis.…”
Section: The Modelmentioning
confidence: 99%
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“…Second, if the model is to be relevant for bioeconomic analysis, we have to limit the complexity and dimensionality of the model. We have in mind the type of analysis carried out in Sandal and Steinshamn (2010); see also Kugarajh et al (2006). To limit complexity, we use simple growth functions and interaction terms common in traditional bioeconomic analysis.…”
Section: The Modelmentioning
confidence: 99%
“…The values were inspired by earlier estimates (Kugarajh et al 2006, p. 75, Table 3) and, when it comes to the capacity parameter c 2 , the range of observed stock levels. For the pelagic stocks capelin and herring, we used the modified logistic growth function; see equations (12) and (13).…”
Section: The Initial Ensemblementioning
confidence: 99%
“…2). MSY is widely used for finding optimal rates of harvest, however and as it was mentioned before, there are problems with MSY approach (Kugarajh et al, 2006;Kulmala et al, 2008). (McDonald et al, 2002) To make the model more realistic one has to take into account different types of uncertainties introduced by diverse events as fires, pests, climate changes, government policies, stock prices etc.…”
Section: T X mentioning
confidence: 99%
“…The model (7) was used in (Filatova & Grzywaczewski, 2009) for named task solution, other production function models can be found, for an example in (Kugarajh et al, 2006) or (Gonzalez-Olivares, 2005)):…”
Section: T X mentioning
confidence: 99%
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