Chapter 17
Classification of Fish Stock-Recruitment
Relationships in Different Environmental
Regimes by Fuzzy Logic with Bootstrap
Re-sampling Approach
D.G.Chen
17.1
Introduction
The analysis of stock-recruitment (SR) relationships is a basic step in developing
and evaluating fishery policies, such as establishing optimal escapement goals for
salmon or optimal size of spawning stocks at maximum sustainable yield (MSY).
Traditional SR analyses assume that there is a functional relationship between the
size of the stock spawning biomass and the biomass of fish that recruit in the
future. Numerous models have been discussed for this functional relationship. A
comprehensive summary can be found from Ricker (1975), Hilborn and Walters
(1992) and Quinn and Deriso (1999).
In the search for better tools for fish stock assessment, there has recently been a
growing interest in the use of machine learning models (such as neural network
models, fuzzy logic models and genetic algorithms) for research and management
of natural resources (Lek et al. 1995; Mackinson et al. 1999 and Tang et al. 2000).
It has been demonstrated that these models offer substantial advantages over
traditional SR methods in model fit and forecast (Saila 1996; Chen and Ware 1999
and Chen et. al. 2000).
In this paper, the utility of fuzzy logic model with a hybrid global learning
algorithm is explored to classify the SR relationships under different regimes for
environmental and fishery management interventions. A bootstrap re-sampling
scheme is also proposed to address the lack of uncertainty estimation in the
machine-learning methods. The scheme produces a sampling probability
distribution for the SR parameters related to fishery management policies so that
the associated uncertainty measures (such as, variance, standard error, or
confidence interval) can be obtained. Two SR applications: 1) southeast Alaska
(SEAK), USA, pink salmon, and 2) west coast Vancouver Island (WCVI), BC,
Canada, herring, are examined to demonstrate the advantages of this new model to
Classification of Fish Stock-Recruitment
Relationships in Different Environmental
Regimes by Fuzzy Logic with Bootstrap
Re-sampling Approach
D.G.Chen
17.1
Introduction
The analysis of stock-recruitment (SR) relationships is a basic step in developing
and evaluating fishery policies, such as establishing optimal escapement goals for
salmon or optimal size of spawning stocks at maximum sustainable yield (MSY).
Traditional SR analyses assume that there is a functional relationship between the
size of the stock spawning biomass and the biomass of fish that recruit in the
future. Numerous models have been discussed for this functional relationship. A
comprehensive summary can be found from Ricker (1975), Hilborn and Walters
(1992) and Quinn and Deriso (1999).
In the search for better tools for fish stock assessment, there has recently been a
growing interest in the use of machine learning models (such as neural network
models, fuzzy logic models and genetic algorithms) for research and management
of natural resources (Lek et al. 1995; Mackinson et al. 1999 and Tang et al. 2000).
It has been demonstrated that these models offer substantial advantages over
traditional SR methods in model fit and forecast (Saila 1996; Chen and Ware 1999
and Chen et. al. 2000).
In this paper, the utility of fuzzy logic model with a hybrid global learning
algorithm is explored to classify the SR relationships under different regimes for
environmental and fishery management interventions. A bootstrap re-sampling
scheme is also proposed to address the lack of uncertainty estimation in the
machine-learning methods. The scheme produces a sampling probability
distribution for the SR parameters related to fishery management policies so that
the associated uncertainty measures (such as, variance, standard error, or
confidence interval) can be obtained. Two SR applications: 1) southeast Alaska
(SEAK), USA, pink salmon, and 2) west coast Vancouver Island (WCVI), BC,
Canada, herring, are examined to demonstrate the advantages of this new model to