Authors : Daniel J. Fonseca , Daniel Zavala and Eduardo Del Angel Rincon
Abstract: This paper presents the application of a statistical model for predicting yellowfin tuna fish abundance in the Gulf of Mexico. Data for twelve different independent variables was collected through sensors installed in various space satellites, capable of photographing, and computing factors such as ocean temperatures, sea levels, and concentrations of photosynthetic pigments, among others. In addition, specialized measuring devices were installed on a fishing ship to collect further information on yellowfin tuna feeding behavior, and barometric pressure readings during twenty fishing trips (randomly distributed) across the Mexican waters of the Gulf of Mexico. The results from the study show that barometric pressure is a more significant factor for predicting the distribution of yellowfin tuna than water temperature, a fact not fully addressed by documented research efforts in the field. Moreover, it was concluded that a multiple-degree polynomial model, probably a third-degree model, best represents the relationship between the identified independent variables and the dependent one (i.e., the expected number of yellowfin tuna caught per fishing trip).
Daniel J. Fonseca , Daniel Zavala and Eduardo Del Angel Rincon , 2005. Assessment of the Abundance of Yellowfin Tuna in the Gulf of Mexico . Journal of Animal and Veterinary Advances, 4: 240-246.