Demographic structure is central to understanding the dynamics of animal populations. However, determining the age of free-ranging mammals is difficult, and currently impossible when sampling with noninvasive, genetic-based approaches. We present a method to estimate age class by combining measures of telomere lengths with other biologically meaningful covariates in a Bayesian network. We applied this approach to American and Pacific martens (Martes americana and M. caurina) and compared predicted age with that obtained from counts of cementum annuli. Using telomere length and the covariates sex, species, and estimates of population density obtained from commercial trapping records, we assigned martens to juvenile (<1 year) or adult (≥1 year) classes with 75–88% accuracy. In our analysis for live-captured martens, for which information on body size and whether animals were juveniles or adults would be available, we achieved 90–93% accuracy when assigning individuals to 5 discrete age classes (0–4 years). This general approach could be extended to other species for noninvasive estimation of age class, or in place of invasive aging methods, and enable demographically based population analyses that have heretofore been impossible.
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Vol. 92 • No. 3