Integrating multiple field measurements in a Bayesian parallel regression framework to estimate Tasmanian devil age

Abstract Understanding age structure in populations is foundational for ecological study, yet ascertaining the age of individuals in the wild can be problematic. Methods typically rely on the prior determination of a relationship between age and some morphological measurement specific to the species or animal group under investigation. We present a novel method to incorporate multiple measures of animal size and development to estimate age, using Bayesian parallel regression to integrate multiple regression relationships into predictions. We apply the method to Tasmanian devils, a carnivorous marsupial that is threatened by a transmissible cancer. We estimate devil age based on measures of mass, head width, canine over‐eruption and molar characteristics. Practical implication . The method provides a flexible framework with potential application to a range of species; however, the method is best suited to capture–mark–recapture style studies where repeated measures data have been collected for individuals across multiple points in time.