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Accurate age determination is a fundamental prerequisite for demographic studies as well as population monitoring efforts that provide information for management and conservation. Yet, common age determination methods suffer from low accuracy rates, impose additional handling and time costs on animals and biologists, or rely on invasive techniques such as tooth extraction. We introduce an alternative, mixture modeling approach for age determination that exploits mammalian growth patterns to classify newly encountered animals as juveniles or adults, and present an example analysis that classifies Allegheny woodrats based solely on their capture dates and mass at capture, in combination with data from known adults. We also introduce and validate a simulation-based heuristic to evaluate potential classification accuracy when no known-age test cases are available. In the Allegheny woodrat example, the mixture model achieved a 90-92% accuracy rate (heuristic range: 89–94%), far better than the 36–43% achieved with a fixed mass criterion, and comparable to accuracies reported for other species using more data-intensive, multivariate classification techniques. The model can be extended to classify multiple age groups, estimate chronological age, or further improve accuracy by including additional morphometric measures.

Bayesian model-based age classification using small mammal body mass and capture dates