A Bayesian framework with implementation error to improve the management of the red octopus (Octopus maya) fishery off the Yucatán Peninsula
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Redalyc · Ciencias Marinas
The red octopus (Octopus maya) is an endemic species of the Yucatán Peninsula and its fishery is one of the most important along the Atlantic coast of Mexico. Commercial exploitation started in 1949. Since 2002 an index of abundance has been estimated, and this index was used to perform a stock asse...
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| Publié dans: | Ciencias Marinas |
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| Format: | Artículo científico |
| Langue: | en |
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Universidad Autónoma de Baja California
2010
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| _version_ | 1878325299694796800 |
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| author | J Jurado-Molina |
| author_facet | J Jurado-Molina |
| collection | Ciencias Marinas |
| container_title | Ciencias Marinas |
| contents | A Bayesian framework with implementation error to improve the management of the red octopus (Octopus maya) fishery off the Yucatán Peninsula J Jurado-Molina Ciencias de la Tierra depletion implementation error Constant harvest rate posterior distribution Markov Chain Monte Carlo simulations The red octopus (Octopus maya) is an endemic species of the Yucatán Peninsula and its fishery is one of the most important along the Atlantic coast of Mexico. Commercial exploitation started in 1949. Since 2002 an index of abundance has been estimated, and this index was used to perform a stock assessment and decision analysis using the Schaefer model. A Bayesian approach was applied to estimate the model parameters and to project the species population under two management scenarios with a constant harvest rate and a positive implementation error. Results suggest that in 1995 the biomass corresponded to 23% of the population carrying capacity (K) and that the current stock is only 14% of K. The population may be depleted and a rebuilding plan might be necessary. In the decision analysis, when the implementation error was included, the Markov Chain Monte Carlo simulations suggested that the current level of exploitation (50% harvest rate) could produce a decreasing trend with the most probable biomass of 9679 t and an expected catch of 7920 t in 2018, and an expected probability of 0.82 of the population being less than 40% of K. On the contrary, a 30% harvest rate would raise the expected catch in 2018 (12,058 t), also reducing the probability of the population being smaller than 40% of K. The inclusion of the implementation error provides a more realistic scenario and represents a more conservative option; therefore, using this type of auxiliary data within a Bayesian framework is recommended for the decision making process. If adopted by Mexican fisheries managers, the approach used in this study could help improve the management of this resource and keep exploitation at sustainable levels. 2010 artículo científico 0185-3880 https://www.redalyc.org/articulo.oa?id=48013190002 en http://www.redalyc.org/revista.oa?id=480 Ciencias Marinas application/pdf Universidad Autónoma de Baja California Ciencias Marinas (México) Num.1 Vol.36 |
| ctrlnum | oai:cienciasmarinas.com.mx:article/1627 10.7773/cm.v36i1.1627 |
| description | The red octopus (Octopus maya) is an endemic species of the Yucatán Peninsula and its fishery is one of the most important along the Atlantic coast of Mexico. Commercial exploitation started in 1949. Since 2002 an index of abundance has been estimated, and this index was used to perform a stock assessment and decision analysis using the Schaefer model. A Bayesian approach was applied to estimate the model parameters and to project the species population under two management scenarios with a constant harvest rate and a positive implementation error. Results suggest that in 1995 the biomass corresponded to 23% of the population carrying capacity (K) and that the current stock is only 14% of K. The population may be depleted and a rebuilding plan might be necessary. In the decision analysis, when the implementation error was included, the Markov Chain Monte Carlo simulations suggested that the current level of exploitation (50% harvest rate) could produce a decreasing trend with the most probable biomass of 9679 t and an expected catch of 7920 t in 2018, and an expected probability of 0.82 of the population being less than 40% of K. On the contrary, a 30% harvest rate would raise the expected catch in 2018 (12,058 t), also reducing the probability of the population being smaller than 40% of K. The inclusion of the implementation error provides a more realistic scenario and represents a more conservative option; therefore, using this type of auxiliary data within a Bayesian framework is recommended for the decision making process. If adopted by Mexican fisheries managers, the approach used in this study could help improve the management of this resource and keep exploitation at sustainable levels.
El pulpo rojo (Octopus maya) es una especie endémica de la Península de Yucatán. La suya es una de las pesquerías más importantes de la costa Atlántica de México; su explotación comercial inicio en 1949, y desde 2002 se ha estimado su índice de abundancia. Este índice se usó para llevar a cabo una valoración del stock y un análisis de decisión usando el modelo de Schaefer. Se usó un enfoque Bayesiano para la estimación de los parámetros del modelo y para proyectar la población de esta especie bajo dos escenarios de manejo con una tasa de explotación constante y un error de implementación positivo. Los resultados sugieren que en 1995 la biomasa correspondía al 23% de la capacidad de carga de la población K y que el stock actual es sólo 14% de K. Los resultados sugieren que la población podría estar sobreexplotada y que podría ser necesario un plan de reconstrucción del recurso. Cuando se usó error de implementación en el análisis de decisión, las simulaciones de cadenas de Markov-Monte Carlo sugieren que el nivel actual de explotación (tasa de explotación del 50%) podría producir una tendencia decreciente en la biomasa, con un valor de biomasa más probable de 9679 t, una esperanza de captura de 7920 t en 2018 y una probabilidad de 0.82 de que la población sea menor que 40% de K. Por el contrario, una tasa de explotación del 30% incrementaría la esperanza de la captura en 2018 (12,058 t), también reduciendo la probabilidad de que la población sea menor al 40% de K. La inclusión del error de implementación proporciona un escenario más realista y representa una opción más conservadora; por consiguiente se recomienda usar este tipo de datos auxiliares en un marco Bayesiano para la toma de decisión. Si las autoridades pesqueras mexicanas adoptan el método usado en este trabajo se puede mejorar el manejo de este recurso, manteniendo su explotación en niveles sustentables. |
| format | Artículo científico |
| id | redalyc_48013190002 |
| institution | Redalyc Ciencias Marinas |
| language | en |
| publishDate | 2010 |
| publisher | Universidad Autónoma de Baja California |
| spelling | A Bayesian framework with implementation error to improve the management of the red octopus (Octopus maya) fishery off the Yucatán Peninsula Enfoque bayesiano con error de implementación para mejorar el manejo de la pesquería de pulpo rojo (Octopus maya) en la Península de Yucatán Jurado-Molina, J constant harvest rate posterior distribution Markov Chain Monte Carlo simulations implementation error depletion tasa de explotación constante distribución posterior, Cadenas de Markov Monte Carlo error de implementación sobreexplotación The red octopus (Octopus maya) is an endemic species of the Yucatán Peninsula and its fishery is one of the most important along the Atlantic coast of Mexico. Commercial exploitation started in 1949. Since 2002 an index of abundance has been estimated, and this index was used to perform a stock assessment and decision analysis using the Schaefer model. A Bayesian approach was applied to estimate the model parameters and to project the species population under two management scenarios with a constant harvest rate and a positive implementation error. Results suggest that in 1995 the biomass corresponded to 23% of the population carrying capacity (K) and that the current stock is only 14% of K. The population may be depleted and a rebuilding plan might be necessary. In the decision analysis, when the implementation error was included, the Markov Chain Monte Carlo simulations suggested that the current level of exploitation (50% harvest rate) could produce a decreasing trend with the most probable biomass of 9679 t and an expected catch of 7920 t in 2018, and an expected probability of 0.82 of the population being less than 40% of K. On the contrary, a 30% harvest rate would raise the expected catch in 2018 (12,058 t), also reducing the probability of the population being smaller than 40% of K. The inclusion of the implementation error provides a more realistic scenario and represents a more conservative option; therefore, using this type of auxiliary data within a Bayesian framework is recommended for the decision making process. If adopted by Mexican fisheries managers, the approach used in this study could help improve the management of this resource and keep exploitation at sustainable levels. El pulpo rojo (Octopus maya) es una especie endémica de la Península de Yucatán. La suya es una de las pesquerías más importantes de la costa Atlántica de México; su explotación comercial inicio en 1949, y desde 2002 se ha estimado su índice de abundancia. Este índice se usó para llevar a cabo una valoración del stock y un análisis de decisión usando el modelo de Schaefer. Se usó un enfoque Bayesiano para la estimación de los parámetros del modelo y para proyectar la población de esta especie bajo dos escenarios de manejo con una tasa de explotación constante y un error de implementación positivo. Los resultados sugieren que en 1995 la biomasa correspondía al 23% de la capacidad de carga de la población K y que el stock actual es sólo 14% de K. Los resultados sugieren que la población podría estar sobreexplotada y que podría ser necesario un plan de reconstrucción del recurso. Cuando se usó error de implementación en el análisis de decisión, las simulaciones de cadenas de Markov-Monte Carlo sugieren que el nivel actual de explotación (tasa de explotación del 50%) podría producir una tendencia decreciente en la biomasa, con un valor de biomasa más probable de 9679 t, una esperanza de captura de 7920 t en 2018 y una probabilidad de 0.82 de que la población sea menor que 40% de K. Por el contrario, una tasa de explotación del 30% incrementaría la esperanza de la captura en 2018 (12,058 t), también reduciendo la probabilidad de que la población sea menor al 40% de K. La inclusión del error de implementación proporciona un escenario más realista y representa una opción más conservadora; por consiguiente se recomienda usar este tipo de datos auxiliares en un marco Bayesiano para la toma de decisión. Si las autoridades pesqueras mexicanas adoptan el método usado en este trabajo se puede mejorar el manejo de este recurso, manteniendo su explotación en niveles sustentables. Universidad Autónoma de Baja California, Instituto de Investigaciones Oceanológicas 2010-03-16 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article Artículo Arbitrado application/pdf https://cienciasmarinas.com.mx/index.php/cmarinas/article/view/1627 10.7773/cm.v36i1.1627 Ciencias Marinas; Vol. 36 No. 1 (2010); 1–14 Ciencias Marinas; Vol. 36 Núm. 1 (2010); 1–14 2395-9053 0185-3880 eng https://cienciasmarinas.com.mx/index.php/cmarinas/article/view/1627/1769 ['Redalyc'] redalyc_48013190002 A Bayesian framework with implementation error to improve the management of the red octopus (Octopus maya) fishery off the Yucatán Peninsula ['A Bayesian framework with implementation error to improve the management of the red octopus (Octopus maya) fishery off the Yucatán Peninsula', 'J Jurado-Molina', 'Ciencias de la Tierra', 'depletion', 'implementation error', 'Constant harvest rate', 'posterior distribution', 'Markov Chain Monte Carlo simulations', 'A Bayesian framework with implementation error to improve the management of the red octopus (Octopus maya) fishery off the Yucatán Peninsula J Jurado-Molina Ciencias de la Tierra depletion implementation error Constant harvest rate posterior distribution Markov Chain Monte Carlo simulations The red octopus (Octopus maya) is an endemic species of the Yucatán Peninsula and its fishery is one of the most important along the Atlantic coast of Mexico. Commercial exploitation started in 1949. Since 2002 an index of abundance has been estimated, and this index was used to perform a stock assessment and decision analysis using the Schaefer model. A Bayesian approach was applied to estimate the model parameters and to project the species population under two management scenarios with a constant harvest rate and a positive implementation error. Results suggest that in 1995 the biomass corresponded to 23% of the population carrying capacity (K) and that the current stock is only 14% of K. The population may be depleted and a rebuilding plan might be necessary. In the decision analysis, when the implementation error was included, the Markov Chain Monte Carlo simulations suggested that the current level of exploitation (50% harvest rate) could produce a decreasing trend with the most probable biomass of 9679 t and an expected catch of 7920 t in 2018, and an expected probability of 0.82 of the population being less than 40% of K. On the contrary, a 30% harvest rate would raise the expected catch in 2018 (12,058 t), also reducing the probability of the population being smaller than 40% of K. The inclusion of the implementation error provides a more realistic scenario and represents a more conservative option; therefore, using this type of auxiliary data within a Bayesian framework is recommended for the decision making process. If adopted by Mexican fisheries managers, the approach used in this study could help improve the management of this resource and keep exploitation at sustainable levels. 2010 artículo científico 0185-3880 https://www.redalyc.org/articulo.oa?id=48013190002 en http://www.redalyc.org/revista.oa?id=480 Ciencias Marinas application/pdf Universidad Autónoma de Baja California Ciencias Marinas (México) Num.1 Vol.36'] ['J Jurado-Molina'] ['J Jurado-Molina'] ['Artículo científico'] ['2010'] ['Universidad Autónoma de Baja California'] ['en'] ['Ciencias de la Tierra', 'depletion', 'implementation error', 'Constant harvest rate', 'posterior distribution', 'Markov Chain Monte Carlo simulations'] ['https://www.redalyc.org/articulo.oa?id=48013190002'] ['A Bayesian framework with implementation error to improve the management of the red octopus (Octopus maya) fishery off the Yucatán Peninsula J Jurado-Molina Ciencias de la Tierra depletion implementation error Constant harvest rate posterior distribution Markov Chain Monte Carlo simulations The red octopus (Octopus maya) is an endemic species of the Yucatán Peninsula and its fishery is one of the most important along the Atlantic coast of Mexico. Commercial exploitation started in 1949. Since 2002 an index of abundance has been estimated, and this index was used to perform a stock assessment and decision analysis using the Schaefer model. A Bayesian approach was applied to estimate the model parameters and to project the species population under two management scenarios with a constant harvest rate and a positive implementation error. Results suggest that in 1995 the biomass corresponded to 23% of the population carrying capacity (K) and that the current stock is only 14% of K. The population may be depleted and a rebuilding plan might be necessary. In the decision analysis, when the implementation error was included, the Markov Chain Monte Carlo simulations suggested that the current level of exploitation (50% harvest rate) could produce a decreasing trend with the most probable biomass of 9679 t and an expected catch of 7920 t in 2018, and an expected probability of 0.82 of the population being less than 40% of K. On the contrary, a 30% harvest rate would raise the expected catch in 2018 (12,058 t), also reducing the probability of the population being smaller than 40% of K. The inclusion of the implementation error provides a more realistic scenario and represents a more conservative option; therefore, using this type of auxiliary data within a Bayesian framework is recommended for the decision making process. If adopted by Mexican fisheries managers, the approach used in this study could help improve the management of this resource and keep exploitation at sustainable levels. 2010 artículo científico 0185-3880 https://www.redalyc.org/articulo.oa?id=48013190002 en http://www.redalyc.org/revista.oa?id=480 Ciencias Marinas application/pdf Universidad Autónoma de Baja California Ciencias Marinas (México) Num.1 Vol.36'] |
| spellingShingle | A Bayesian framework with implementation error to improve the management of the red octopus (Octopus maya) fishery off the Yucatán Peninsula J Jurado-Molina Ciencias de la Tierra depletion implementation error Constant harvest rate posterior distribution Markov Chain Monte Carlo simulations A Bayesian framework with implementation error to improve the management of the red octopus (Octopus maya) fishery off the Yucatán Peninsula J Jurado-Molina Ciencias de la Tierra depletion implementation error Constant harvest rate posterior distribution Markov Chain Monte Carlo simulations The red octopus (Octopus maya) is an endemic species of the Yucatán Peninsula and its fishery is one of the most important along the Atlantic coast of Mexico. Commercial exploitation started in 1949. Since 2002 an index of abundance has been estimated, and this index was used to perform a stock assessment and decision analysis using the Schaefer model. A Bayesian approach was applied to estimate the model parameters and to project the species population under two management scenarios with a constant harvest rate and a positive implementation error. Results suggest that in 1995 the biomass corresponded to 23% of the population carrying capacity (K) and that the current stock is only 14% of K. The population may be depleted and a rebuilding plan might be necessary. In the decision analysis, when the implementation error was included, the Markov Chain Monte Carlo simulations suggested that the current level of exploitation (50% harvest rate) could produce a decreasing trend with the most probable biomass of 9679 t and an expected catch of 7920 t in 2018, and an expected probability of 0.82 of the population being less than 40% of K. On the contrary, a 30% harvest rate would raise the expected catch in 2018 (12,058 t), also reducing the probability of the population being smaller than 40% of K. The inclusion of the implementation error provides a more realistic scenario and represents a more conservative option; therefore, using this type of auxiliary data within a Bayesian framework is recommended for the decision making process. If adopted by Mexican fisheries managers, the approach used in this study could help improve the management of this resource and keep exploitation at sustainable levels. 2010 artículo científico 0185-3880 https://www.redalyc.org/articulo.oa?id=48013190002 en http://www.redalyc.org/revista.oa?id=480 Ciencias Marinas application/pdf Universidad Autónoma de Baja California Ciencias Marinas (México) Num.1 Vol.36 |
| title | A Bayesian framework with implementation error to improve the management of the red octopus (Octopus maya) fishery off the Yucatán Peninsula |
| title_alt | Enfoque bayesiano con error de implementación para mejorar el manejo de la pesquería de pulpo rojo (Octopus maya) en la Península de Yucatán |
| topic | Ciencias de la Tierra depletion implementation error Constant harvest rate posterior distribution Markov Chain Monte Carlo simulations |
| topic_facet | constant harvest rate posterior distribution Markov Chain Monte Carlo simulations implementation error depletion tasa de explotación constante distribución posterior, Cadenas de Markov Monte Carlo error de implementación sobreexplotación |
| url | https://www.redalyc.org/articulo.oa?id=48013190002 https://cienciasmarinas.com.mx/index.php/cmarinas/article/view/1627 |