A Production Model Without Arbitrary Constraints and With Process Errors and Environmental Covariates

The current stock assessment models are mainly classified into surplus production and age structured models. For simplifying population dynamics and under limited data, surplus production models are useful. However, the simplicity may incorrectly reflect reality, and we raise a need to allow process errors as well as observation errors. Further we aim to link the population dynamics with environmental covariates. Additionally we raise more issues: an arbitrary constraint in the previous practice for surplus production models, allowing different variances of observation errors, and incorporation of dependence between multiple series of survey data. Often the arbitrary constraint has been used that the initial population size is assumed to be equal to the unexploited population size. Also most previous studies about surplus production models were based on a time-constant variance in survey data, and one time series of survey data. For illustration purposes, we use data on Georges Bank yellowtail flounder (Limanda ferruginea).