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

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. This dataset includes data required to run and test code. See published article for full methodology.
, , # Integrating multiple field measurements in a Bayesian parallel regression framework to estimate Tasmanian devil age

Dataset DOI: [10.5061/dryad.05qfttfh8](https://doi.org/10.5061/dryad.05qfttfh8)

## Description of the data and file structure

This dataset constituted 2,448 capture events of 1,119 unique Tasmanian devils.  Six were caught more than ten times, while 93 individuals were caught more than five times, and 653 individuals were captured only once.  On each capture, a range of morphometric and dental records were collected, providing a suite of measurements that relate to growth and age.

Dataset is for use in the R Language and Environment for Statistical Computing.

Variables are described below:

dN - number of data records (2,448 captures)

dCount - number of individuals (1,119 devils)

dIndex - links each individual to their capture records for multiple captures

dCaptDate - capture dates in continuous time (days)

dMass - devil mass (kgs)

dSex - devil gender (coded as...,