Morphological Grouping and Body Mass Prediction Models

Abstract

Every facet of an organism’s function is affected by its body size, such as heart rate, metabolism, organ size and function, feeding ecology, and locomotion. Using body size to assess the function and size of organs in extant mammals is relatively easy. However, estimating the body size and organ function of transitional fossil species, such as the Eocene whales, is difficult. The Eocene whales are diverse in body size, structure, and habitat, therefore a single allometric model will not provide the most accurate body size estimation for each fossil whale family and therefore less accurate organ function assessment. The goal of my research is to predict the body size of the Eocene whales using morphological models of extant mammals as guides. The first stage is the morphological grouping and body mass prediction models using modern mammal skeletons.I measured the skull, vertebrae, and appendicular bones of multiple species of modern aquatic, semi‐aquatic, and terrestrial mammals. I calculated an average body mass representative of each species using recorded body weights. Using Principal Component Analysis (PCA), I determined morphological groupings for these mammals from the skeletal measurements. For each PCA analysis completed, I created a linear regression equation to predict body size from the skeletal variables used in each analysis and the calculated average body sizes. I tested these prediction models using the measurements from 12 representative skeletons that were not included in the data set used to create the model, specifically Odocoileus virginianus, Tragulus napu, Hippopotamus amphibus, Tapirus indicus, Sus scrofa, Canis lupus lycaon, Nyctereutes procyonid, Ehydra lutris, Gulo gulo, Neophoca cinerea, Lobodon carinophagus, and Ursus maritimus.From the PCA, I identified five morphological groups for the mammals selected: Aquatic, Semi‐aquatic, Semi‐terrestrial, Large Terrestrial and Small Terrestrial. For the prediction model using all skeletal variables, the standard error of the estimate (SEE) for all 12 test individuals ranged from 0.3359 – 0.7496. For the skull variables, the SEE ranged from 0.1260 – 0.3197. For the limb variables, the SEE ranged from 0.1716 – 0.3964. For the vertebral variables, the SEE ranged from 0.1511 – 0.2851. These small SEE values and the large sample size substantiate the accuracy of these predictive models and the reliability of their respective confidence intervals (CI) and prediction intervals (PI) for the models. The results confirm that these linear regression models will be appropriate to predict the body mass of the Eocene whales in the second stage of this project.Support or Funding InformationKent State University; East Tennessee State University; Sigma Xi