Including unsexed individuals in sex-specific growth models

Sexually dimorphic growth models are typically estimated by fitting growth curves to individuals of known sex. Yet, macroscopically ascribing sex can be difficult, particularly for immature animals. As a result, sex-specific growth curves are often fit to known-sex individuals only, omitting unclassified immature individuals occupying an important region of the age–length space. We propose an alternative whereby the sex of the unclassified individuals is treated as a missing data problem to be estimated simultaneously with the sex-specific growth models. The mixture model that we develop includes the biological processes of growth and sexual dimorphism. Simulations show that where the assumed growth model holds, the method improves precision and bias of all parameters relative to the data omission case. Ability to chose the correct combination of sex-specific and sex-generic parameters is also improved. Application of the method to two shark species, where sex can be ascribed from birth, indicates improvements in the fit but also highlights the importance of the assumed model forms. The proposed method avoids discarding unclassified observations, thus improving our understanding of dimorphic growth.