Accurate assessment of body condition is critical for monitoring the health and conservation status of endangered species such as African savanna elephants. However, existing methods rely on subjective visual scoring or logistically challenging field measurements. This project is conducted in collaboration with Save the Elephants, a Kenya-based elephant conservation organization, and Dr. Giacomo D’Ammando, Research Manager with over ten years of experience studying African mammals. This study develops a quantitative, noninvasive, and scalable framework for estimating elephant body condition using three-dimensional (3D) modeling. A dataset of 221 adult male savanna elephants (≥15 years) was used to generate a statistically significant linear regression relating shoulder height to body mass (Y = 34.62X − 5842, p < 0.05), providing a predictive model for estimating mass from morphometric measurements.

A high-resolution 3D scan of an adult male elephant served as the anatomical foundation for a flexible model constructed in Blender (v4.5.3 LTS). Using shape keys, the model simulated body conditions ranging from emaciated to obese. For each state, dorsal and lateral surface area and total volume were calculated to establish standardized metrics. 

I hypothesize that 3D-derived volume and surface area measurements will strongly correlate with estimated body mass, providing an objective proxy for body condition that reduces observer bias in traditional scoring systems. By creating reproducible morphological benchmarks, this approach offers a scalable tool for conservation and wildlife health monitoring. More broadly, this framework illustrates how digital modeling can improve condition assessment in large, free-ranging species where direct measurement is impractical.