ABSTRACT Epigenetic aging models hold great promise for enhancing many aspects of wildlife research and management. However, their utility is limited by the need to train models using known‐aged animals, which are rare among wildlife species. We present a novel approach to developing methylation‐based age prediction models that enables us to train models using samples from individuals whose chronological age is estimated with uncertainty based on photo‐identification catalogue data. Our approach incorporates this uncertainty into model training by representing the age of each individual with a probability distribution rather than a point estimate. We similarly represent the methylation profiles of individuals as binomial distributions and produce a distribution of predicted age for each sample that reflects the uncertainty in both its age and methylation profile. We compared age models trained using a wide range of parameterisations, training data sets and analytical methods to determine how well they predicted the catalogue‐based age estimates. The resulting model has a median absolute error of 1.70 years, outperforming many published clocks trained with known‐age samples. This approach significantly expands the range of species for which accurate methylation‐based age models can be developed, particularly those of conservation concern where known‐age samples are limited. By producing distributions of predicted age, it also enables researchers to accurately communicate the uncertainty in their age estimates to subsequent data users.
Martien et al. (2026) studied this question.