Publicação
Streamlined Optimization of a Data-Limited Catch Rule for Raja clavata in Portuguese Ports: Parameter Estimation and Uncertainty Assessment
| Resumo: | Assessing data-limited stocks in fisheries management presents significant challenges, due to the difficulty of collecting important data. Traditional stock assessment methods often require large datasets for biological parameters and stock population numbers, which are not always available for many fisheries. Developing and refining methodological approaches for data-limited stocks is urgent. One of the suggested approaches involves the application of a case specific, tunable catch rule that maintains precautionary principles defined by International Council for the Exploration of the Sea (ICES). This new approach uses simulation through the modeling of the operating model and deploy an optimization process to identify the best set of parameters, that improve the performance of the catch rule within the management procedure. To achieve this, the particle swarm optimization (PSO) is employed to identify a set of parameters that minimize the risk of a stock collapse while maximizing sustainable yield. Various operating models are used to observe how the optimization solutions change when key aspects of the population dynamics are adjusted based on the data from Raja clavata in Portuguese coastal waters. Additionally, the impact of variability in critical life-history parameters is examined to understand its influence on the optimization outcomes. The particle swarm optimization proved to be a better approach for addressing the problem. The study demonstrated that by using the PSO with a more refined search space, the run time of the optimization process used to determine the parameters used for creating a catch rule can be significantly reduced. The use of different operating models showed that the optimization can return specific solutions for the stock in study, but they depend on creating a fishing history that accurately represents the stock, with the estimation of parameters and population dynamics can help to achieve that. |
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| Autores principais: | Chatalov, Erick Barreiro |
| Assunto: | Optimização por enxame de partículas modelagem de estoque de pesca avaliação da estratégia de gestão pesca com dados limitados estimação de selectividade Trabalhos de projeto de mestrado - 2024 |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| Resumo: | Assessing data-limited stocks in fisheries management presents significant challenges, due to the difficulty of collecting important data. Traditional stock assessment methods often require large datasets for biological parameters and stock population numbers, which are not always available for many fisheries. Developing and refining methodological approaches for data-limited stocks is urgent. One of the suggested approaches involves the application of a case specific, tunable catch rule that maintains precautionary principles defined by International Council for the Exploration of the Sea (ICES). This new approach uses simulation through the modeling of the operating model and deploy an optimization process to identify the best set of parameters, that improve the performance of the catch rule within the management procedure. To achieve this, the particle swarm optimization (PSO) is employed to identify a set of parameters that minimize the risk of a stock collapse while maximizing sustainable yield. Various operating models are used to observe how the optimization solutions change when key aspects of the population dynamics are adjusted based on the data from Raja clavata in Portuguese coastal waters. Additionally, the impact of variability in critical life-history parameters is examined to understand its influence on the optimization outcomes. The particle swarm optimization proved to be a better approach for addressing the problem. The study demonstrated that by using the PSO with a more refined search space, the run time of the optimization process used to determine the parameters used for creating a catch rule can be significantly reduced. The use of different operating models showed that the optimization can return specific solutions for the stock in study, but they depend on creating a fishing history that accurately represents the stock, with the estimation of parameters and population dynamics can help to achieve that. |
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