Data from: Estimating age and age class of harvested hog deer from eye lens mass using frequentist and Bayesian methods

Estimation of the age or age class of harvested animals is often necessary to interpret the condition and dynamics of wildlife populations. The mammalian eye lens continues to grow until death and hence the dry mass of the eye lens has commonly been used to estimate the age of mammals. The method requires the relationship between eye lens mass and age to be parameterized using individuals of known age. However, predicting age is complicated by the curvilinear relationship between eye lens mass and age. We used frequentist and Bayesian methods to predict the ages and age classes of harvested hog deer Axis porcinus from eye lens mass. Deer were tagged as calves and harvested 4–177 months later in southeastern Australia. Lenses were extracted, fixed and oven-dried. Of the five growth models evaluated, the Lord model best described the relationship between age and eye lens dry mass (R2 = 95%). The precision of age predictions obtained using the Lord model in a Bayesian mode of inference decreased with increasing eye lens dry mass, with the size of the 95% CI equaling or exceeding predicted age for hog deer > 6 years. However, most predictions of hog deer age will have reasonable precision because few animals > 6 years are harvested. Linear discriminant analysis had high predictive power for classifying hog deer to four widely-used age classes (juvenile, yearling, prime-age and senescent). The Bayesian method is recommended for inverse non-linear prediction of age and the frequentist linear discriminant analysis method is recommended for estimating age class. We provide tables of correspondence between hog deer eye lens dry mass and predicted age and age class. Our statistical methods can be used to estimate age and age class for other mammalian species, including from other ageing techniques such as tooth eruption-wear criteria.

对猎获野生动物的年龄或年龄组进行估测，往往是解析野生种群现状与动态特征的必要环节。哺乳动物的晶状体（eye lens）直至死亡仍会持续生长，因此晶状体干重常被用于估测哺乳动物的年龄。该方法需以已知年龄的个体为样本，对晶状体质量与年龄之间的关联进行参数化建模。然而，晶状体质量与年龄间呈曲线关联，这给年龄预测带来了复杂性。本研究采用频率学派与贝叶斯学派的两种方法，基于晶状体干重对猎获的豚鹿（Axis porcinus）的年龄及年龄组进行预测。研究对象为被标记为幼鹿的个体，在澳大利亚东南部于标记后的4至177个月间被猎获。晶状体被提取、固定后置于烘箱烘干。在评估的5种生长模型中，Lord模型最能拟合年龄与晶状体干重间的关联（决定系数R²=95%）。采用贝叶斯推断框架下的Lord模型进行年龄预测时，预测精度随晶状体干重的增加而下降；对于年龄超过6岁的豚鹿，其95%置信区间（CI）的宽度等于甚至超过其预测年龄值。但由于野外猎获的豚鹿中超过6岁的个体极少，绝大多数年龄预测结果都能具备合理的精度。线性判别分析（linear discriminant analysis）可将豚鹿高效划分为四类通用年龄组：幼体、1龄个体、壮年个体与衰老个体。建议采用贝叶斯方法开展年龄的非线性反演预测，而使用频率学派的线性判别分析方法来估测年龄组。本研究提供了豚鹿晶状体干重与预测年龄、年龄组之间的对应表格。本研究采用的统计方法可推广应用于其他哺乳动物的年龄与年龄组估测，包括基于牙齿萌出与磨损标准等其他年龄鉴定技术的相关研究。