CATCH-EFFORT MAXIMUM-LIKELIHOOD-ESTIMATION OF IMPORTANT POPULATION PARAMETERS

The relative ease with which linear regression models are understood e xplains the popularity of such techniques in estimating population siz e with catch-effort data. However, the development and use of the regr ession models require assumptions and approximations that may not accu rately reflect reality. We present the model development necessary for maximum likelihood estimation of parameters from catch-effort data us ing the program SURVIV, the primary intent being to present biologists with a vehicle for producing maximum likelihood estimates in lieu of using the traditional regression techniques. The differences between t he regression approaches and maximum likelihood estimation will be ill ustrated with an example of commercial fishery catch-effort data and t hrough simulation. Our results indicate that maximum likelihood estima tion consistently provides less biased and more precise estimates than the regression methods and allows for greater model flexibility neces sary in many circumstances. We recommend the use of maximum likelihood estimation in future catch-effort studies.