A refined guide for aging muskoxen (Ovibos moschatus) based on mandibular examination.

Accurately aging wildlife is essential for effective management and conservation efforts because it supports the estimation of demographic parameters used to model population dynamics and determine harvest quotas. Currently, accurately aging muskoxen is limited by the lack of validated and standardized protocols specific to this species. We investigated three methods for aging muskoxen: tooth eruption pattern, mandible morphometrics, and cementum annuli analysis (CAA). We examined 260 mandibles from community-harvested muskoxen with known harvest dates from Nunavut and the Northwest Territories and radiographed 89 of these mandibles with erupting teeth to track eruption stages. From these data, we developed a key to estimate age of muskoxen from newborn until all permanent teeth have completely erupted (60 months). Next, we assessed the relationship between muskox mandible morphometrics and age using 178 archived mandibles from Banks Island, Northwest Territories. Caudal mandible length was the strongest predictor of age in months (Adjusted R2 = 0.917) up to 5 years old (60 months), after which growth was negligible. This regression model included linear, quadratic, and interaction terms for caudal mandible length with sex. To evaluate accuracy of CAA for aging, we compared cementum results from incisors of 14 captive muskoxen to their known age using linear regression (Adjusted R2 = 0.847). We applied this model, fitted to captive muskox data, to predict the age of 32 community harvested adult muskoxen over 60 months old (5 yo+) using cementum age results. While there was a tendency to underestimate age, this method provided a more informative estimate than classifying all animals as adults once all teeth have erupted. Integrating these methods, we developed a decision tree to guide aging based on the putative age class and available sample type. This framework improves age estimation accuracy for harvested muskoxen, supporting population models and enabling more effective management and conservation.