Modelling the catch and development phases of the blue crab fishery (Callinectes sapidus) in the Laguna Madre, Tamaulipas, Mexico

Authors

  • Jorge Homero Rodríguez-Castro Instituto Tecnológico de Ciudad Victoria, Laboratorio de Zoología, bulevard Emilio Portes Gil núm. 1301 Poniente, Apartado Postal 175, Ciudad Victoria, Tamaulipas, México, C. P. 87010.
  • Alfonso Correa-Sandoval Instituto Tecnológico de Ciudad Victoria, Laboratorio de Zoología, bulevard Emilio Portes Gil núm. 1301 Poniente, Apartado Postal 175, Ciudad Victoria, Tamaulipas, México, C. P. 87010.
  • José Alberto Ramírez-de-León Universidad Autónoma de Tamaulipas, Unidad Académica de Trabajo Social y Ciencias para el Desarrollo Humano. Centro Universitario Ciudad Victoria, Tamaulipas, México, C.P. 87149.
  • Jorge Alejandro Adame-Garza 3Universidad Autónoma de Tamaulipas, Facultad de Medicina Veterinaria y Zootecnia, carretera Ciudad Victoria a carretera Ciudad Mante km 5, Ciudad Victoria, Tamaulipas, México

DOI:

https://doi.org/10.29059/cienciauat.v12i1.775

Keywords:

Akaike information criterion, Bayesian information criterion, multimodel-inference, fishing catch, Callinectes sapidus.

Abstract

The average annual catch of the blue crab (Callinectes sapidus) (BC) fishery in Tamaulipas, Mexico is estimated at 2 733 T, of which the 82 % is caught in the Laguna Madre, which is considered to be exploited to the maximum of its capacity. The objective of the present investigation was to model the annual catch of the BC in the Laguna Madre, Tamaulipas, by adjusting mathematical functions of the linear and nonlinear (or curvilinear) type, to the time series from 1998 to 2012. In addition, the phases of development of the fishery, according to several generalized models, were identified. We used the information theory approach and multimodel inference procedure (MMI). Eleven linear and nonlinear regression models were fitted. For the selection of models, the corrected Akaike corrected (AICc) and Bayesian (BIC) information criteria were used. For the MMI, the level ∆i < 2 of plausibility of AICc and BIC was considered. The models chosen for the MMI were compound, growth, exponential, logistic, potential and the sigmoid, with the first four models being considered the most suitable of all those cited. The average models of the MMI presented values of β0 and β1: 0.939 and 0.377 respectively, according to CIAc; and 0.952 and 0.344 respectively, according to the CIB. Only the composite and logistic models showed statistical significance in their two regression pa-rameters (β0 and β1 ). The fishery sustainability index revealed six catch periods and a decrease in magnitude of catch changes. The data series analyzed includes two life cycles according to the Csirke and Caddy models. The results showed that at the end of the studied period the fishery was in collapse  and  decay.

References

Akaike, H. (1973). Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika. 60(2): 255-265.

Aragón-Noriega, E. A. (2013). Modelación del crecimiento individual del callo de hacha Atrina maura (Bivalvia: Pinnidae) a partir de la inferencia multimodelo. Revista de Biología Tropical. 61(3): 1167-1174.

Arreguín-Sánchez, F. y Arcos-Huitrón, E. (2011). La pesca en México: estado de la explotación y uso de los ecosistemas. Hidrobiológica. 21(3): 431-462.

Bello, L. D. y Martínez, S. (2007). Una metodología de series de tiempo para el área de la salud; caso práctico. Revista Facultad Nacional de Salud Pública. 25(2): 118-122.

Burnham, K. P. and Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information Theoretic Approach. New York: Springer. 488 Pp.

Burnham, K. P. and Anderson, D. R. (2004). Multimodel inference understanding AIC and BIC in model selection. Sociological methods & research. 33(2): 261-304.

Caddy, J. F. (1984). An alternative to equilibrium theory for management of fisheries. Food and Agricultural Organization (FAO), in Papers presented at the Expert Consultation on the regulation of Fishing Effort (fishing mortality). [En línea]. Disponible en: http://www.fao.org/3/a-ac749e/AC749E15.htm.Fecha de consulta: 25 de mayo de 2014.

Cadima, E. L. (2003). Manual de evaluación de recursos pesqueros. FAO Documento Técnico de Pesca. No. 393. Roma: FAO.162 Pp.

Cailliet, G. M., Smith, W. D., Mollet, H. F., and Goldman, K. J. (2006). Age and growth studies of chondrichthyan fishes: the need for consistency in terminology, verification, validation, and growth function fitting. Environmental Biology of Fishes. 77: 211-228.

Csirke, J. (1984). Report of the working Group on fisheries management, implications and interactions, in FAO Fisheries Report, Food and Agriculture Organization of the United Nations. [En línea]. Disponible en: http://www.fao.org/docrep/005/x6849e/X6849E06. htm#ch5. Fecha de consulta: 9 de septiembre de 2015.

Domínguez-Viveros, J., Rodríguez-Almeida, F. A., Ortega-Gutiérrez, J. A. y Flores-Mariñelarena, A. (2009). Selección de modelos, parámetros genéticos y tendencias genéticas en las evaluaciones genéticas nacionales de bovinos Brangus y Salers. Agrociencia. 43(2): 107-117.

FAO, Food and Agriculture Organization of the United Nations (1999). La ordenación pesquera. FAO orientaciones Técnicas para la Pesca Responsable. 4(1): 1-12.

FAO, Food and Agriculture Organization of the United Nations (2010). The State of World Fisheries and Aquaculture 2010. A Report of the Food and Agriculture Organization of the United Nations. Food and Agriculture Organization of the United Nations, Roma. FAO Fisheries and Aquaculture Department. 197 Pp.

Froese, R. and Kesner-Reyes, K. (2002). Impact of fishing on the abundance of marine species. Council Meetings Report. International Council for the Exploration of the Sea. Conpenhagen, Denmark. [En línea]. Disponible en: http://www.ices.dk/sites/pub/CM%20Doccuments/2002/L/L1202.pdf. Fecha de consulta: 10 de septiembre de 2015.

Froese, R. and Kesner-Reyes, K. (2009). Out of new stocks in 2020: a comment on “Not all fisheries will be collapsed in 2048”. Marine Policy. 33(1): 180–181.

Froese, R. and Pauly, D. (2003). Dynamik der Überfischung. In J. I. Lozan, E. Rachor, K. Reise, J. Sündermann, and H. V. Westernhagen (Eds.), Warnsignale aus Nordsee and Wattenmeer – Eine aktuelle Umweltbilanz (pp. 288–295). Auswertungen, Hamburg: Geo, Wissenschaftliche.

Garibaldi, L. (2012). The FAO global capture production database: a six-decade effort to catch the trend. Marine Policy. 36(3): 760–768.

Gelfand, A. E. and Dey, D. K. (1994). Bayesian model choice: asymptotic and exact calculations. Journal of the Royal Statistical Society. 56(3): 501–514.

Grainger, R. J. R. and Garcia, S. M. (1996). Chronicles of marine fishery landings (1950-1994): trend analysis and fisheries potential Rome: FAO. 51 Pp.

Griffiths, S. P., Fry, G. C., Manson, F. J., and Loü, D. C. (2010). Age and growth of long tail tuna (Thunnus tonggol) in tropical and temperate waters of the central Indo-Pacific. ICES Journal of Marine Science. 67(1): 125-134.

Gulland, J. A. (1971). Manual de métodos para la evaluación de las poblaciones de peces. Zaragoza, España: Acribia. 271 Pp.

Guzmán-Castellanos, A. B., Morales-Bojórquez, E. y Balart, E. F. (2014). Estimación del crecimiento individual en elasmobranquios: la inferencia con modelos múltiples. Hidrobiológica. 24(2): 137-150.

Katsanevakis, S. (2006). Modelling fish growth: Model selection, multi-model inference and model selection uncertainty. Fisheries Research. 81(2): 229-235.

Katsanevakis, S. and Maravelias, C. D. (2008). Modelling fish growth: multi-model inference as a better alternative to a priori using von Bertalanffy equation. Fish and Fisheries. 9(2): 178-187.

Katsanevakis, S., Thessalou-Legaki, M., Karlou-Riga, S., Lefkaditou, E. Dimitriou, E., and Verriopoülos, G. (2007). Information-theory approach to allometric growth of marine organisms. Marine biology. 151(3): 949-959.

Kleisner, K. and Pauly, D. (2011). Stock-catch status plots of fisheries for Regional Seas. In The state of biodiversity and fisheries in Regional Seas. Fisheries Centre Research Report. 19(3): 37-40.

Pauly, D., Alder, J., Booth, S., Cheung, W. W. L., Christensen, V., Close, A., and Wood, L. (2008). Fisheries in large marine ecosystems: descriptions and diagnoses. The UNEP Large Marine Ecosystem Report: a Perspective on Changing Conditions in LMEs of the World’s Regional Seas. UNEP Regional Seas Reports and Studies.(182): 23-40.

Ponce, D. G., Arreguín, F., Beltrán, L. F., Beltrán, M. L. F., Urciaga, J. y Ortega, A. (2006). Indicadores de sustentabilidad y pesca: casos en Baja California Sur, México. En L. F. Beltrán-Morales, J. Urciaga-García y A. Ortega-Rubio (Eds.), Desarrollo sustentable: ¿Mito o realidad?. (pp. 183-272). La Paz. B.C.S., México: Centro de Investigaciones Biológicas del Noroeste, S.C.

Rodríguez-Castro, J. H., Adame-Garza, J. A. y Olmeda-de-la-Fuente, S. E. (2010). La actividad pesquera en Tamaulipas: Ejemplo Nacional. CienciaUAT. 4(4): 28-35.

Romine, J. G., Grübbs, R. D., and Müsick, J. A. (2006). Age and growth of the sandbar shark, Carcharhinusplumbeus, in Hawaiian waters through vertebralanalysis. Environmental Biology of Fishes. 77(3-4): 229-239.

SAGARPA, Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación (2012). Acuerdo por el que se da a conocer la Actualización de la Carta Nacional Pesquera, en Diario Oficial de la Federación. [En línea]. Disponible en: http://www.inapesca.gob.mx/portal/documentos/publicaciones/CARTA%20NACIONAL%20PESQUERA/24082012%20SAGARPA.pdf. Fecha de consulta: 22 de mayo de 2014.

Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics. 6(2): 461-464.

Velásquez, J. D. y Franco, C. J. (2012). Pronóstico de series de tiempo con tendencia y ciclo estacional usando el modelo airline y redes neuronales artificiales. Ingeniería y Ciencia. 8(15): 171-189.

Wagenmakers, E. J. and Farrell, S. (2004). AIC model selection using Akaike weights. Psychonomic bulletin & review. 11(1): 192-196.

Worm, B., Barbier, E. B., Beaumont, N., Duffy, J. E., Folke, C., Halpern, B. S., …, and Watson, R. (2006). Impacts of biodiversity loss on ocean ecosystem services. Science. 314(5800): 787-790.

Worm, B., Barbier, E. B., Beaumont, N., Duffy, J. E., Folke, C., Halpern, B. S., ..., and Watson, R. (2007). Response to comments on ‘‘Impacts of biodiversity loss on ocean ecosystem services’’. Science. 316(5829): 1285d-1285d.

Zeller, D., Cheung, W., Close, C., and Pauly, D. (2009). Trends in global marine fisheriesa critical view. In Fisheries, trade and development. Royal Swedish Academy of Agriculture and Forestry, Stockholm. 87–107 Pp.

Zucchini, W. (2000). An introduction to model selection. Journal of mathematical psychology. 44(1): 41–61.

Zhu, L., Li, L., and Liang, Z. (2009). Comparison of six statistical approaches in the selection of appropiate fish growth models. Chinese Journal of Oceanology and Limnology. 27(3): 457-467.

Published

2017-07-14

How to Cite

Rodríguez-Castro, J. H., Correa-Sandoval, A., Ramírez-de-León, J. A., & Adame-Garza, J. A. (2017). Modelling the catch and development phases of the blue crab fishery (Callinectes sapidus) in the Laguna Madre, Tamaulipas, Mexico. CienciaUAT, 12(1), 96–113. https://doi.org/10.29059/cienciauat.v12i1.775

Issue

Section

Biotechnology and Agricultural Sciences

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