Logistic Regression Model for Determination of the Age of Brown Hare (Lepus europaeus Pall.) Based on Body Weight

Simple Summary Because the determination of the age of brown hares (Lepus europaeus) is difficult, we developed a method for age determination on the basis of animal body weight. On the basis of the results of postmortem analysis of body weight and eye-lens weight, which provide reliable information on the change in these parameters with age, a logistic regression model was developed to classify brown hares as either juveniles (up to one year old) or adults (more than one year old), with a body weight of 4.227 kg as the limiting interval. With an accuracy of 95%, this noninvasive method can be used in both scientific research and population management. We developed an algorithm to classify brown hares into two age classes, juveniles (up to 1 year old) and adults (over 1 year old), based on body weight, which can be determined by both the examination of live animals and postmortem analysis. Considering the strong correlation between lens weight and carcass weight, we assumed that hares could be classified into one of the two age groups based only on carcass weight, using a logistic regression model. Using logistic regression, a model was constructed to assess the age of hares based on their body weight. For comparison with the current age-assessment method based on the dry lens weight, a logistic regression classifying the hares based on the dry lens weight was performed as well. The results of the study facilitated the development of a method to classify hares into age groups based on body weight. The proposed approach is innovative, as it allows for the determination of the age of not only culled (postmortem) but also live hares. The method is easy and does not require laboratory tests; hence, the results can be used immediately following evaluation. This method allows hares to be categorized into two age groups (juveniles and adults). With an accuracy of 97.52% and 95.45% in the case of juvenile and adult hares, respectively, the proposed approach can be widely used both in population management and scientific research.