Improving estimates of recruitment and catchability of Jumbo squid Dosidicus gigas in the Gulf of California

We analyzed the effect of outliers of the catch-per-unit effort on the catchability coefficient estimated by using a depletion model. When we used catch-per-unit effort in the Delury model, we observed a curve in the regression of depletion against time. When we then solved the model with a normal p...

Descripción completa

Detalles Bibliográficos
Autores: Morales Bojorquez, Enrique, Hernández Herrera, Agustin, Cisneros Mata, M. A., Nevarez Martinez, M. O.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2008
País:México
Institución:Instituto Politécnico Nacional
Repositorio:Repositorio Digital del IPN
OAI Identifier:oai:www.repositoriodigital.ipn.mx:123456789/13309
Acceso en línea:http://www.repositoriodigital.ipn.mx/handle/123456789/13309
Access Level:acceso abierto
Palabra clave:Management
catchability
depletion model
Recruitment
Dosidicus gigas
outliers
squid
Descripción
Sumario:We analyzed the effect of outliers of the catch-per-unit effort on the catchability coefficient estimated by using a depletion model. When we used catch-per-unit effort in the Delury model, we observed a curve in the regression of depletion against time. When we then solved the model with a normal probability distribution, the catchability coefficient was poorly estimated. We improved the estimation of catchability using an algorithm that used a two-component-mixture probability distribution. The estimations for catchability (q) and recruitment (N0) were q = 0.41 X 10–3, N0 = 9.13 X 106, and the estimated likelihood was 2.65 X 104 using an algorithm of the normal probability distribution, whereas the estimations made using the algorithm of a two-component-mixture probability distribution were q = 0.23 X 10–3, N0 = 18.07 X 106, and the estimated likelihood was 4.89 X 106. The maximum likelihood estimated with the mixture-distribution algorithm was greater than the maximum likelihood estimated with the normal-distribution algorithm. We believe the two-component-mixture probability distribution fit the data better than the normal probability distribution. From this we determined the consequences on management when overestimations or underestimations of catchability are estimated.