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.