Marginal increment analysis: a new statistical approach of testing for temporal periodicity in fish age verification

Abstract

This paper proposes a new and flexible statistical method for marginal increment analysis that directly accounts for periodicity in circular data using a circular-linear regression model with random effects. The method is applied to vertebral marginal increment data for Alaska skate Bathyraja parmifera. The best fit model selected using the AIC indicates that growth bands are formed annually. Simulation, where the underlying characteristics of the data are known, shows that the method performs satisfactorily when uncertainty is not extremely high.