From extracting nutrients to releasing energy, biological metabolism plays an integral role in determining evolutionary patterns of organisms through geologic time. A previous study depicted a positive relationship between metabolic rate and extinction probability for Mollusca within the Neogene period. We hypothesized that this relationship extends to other metazoan phyla during the Cenozoic Era. Using specific respiration rates measured from living organisms and body size data for fossil taxa, we estimated metabolic rates of animals across different phyla: Arthropoda, Brachiopoda, Echinodermata, and Mollusca. This analysis was performed at the class level by using the classes with the most data available to represent each phylum: Malacostraca, Ostracoda, Cirripedia, Rhynchonellata, Echinoidea, Bivalvia, Cephalopoda, and Gastropoda. We then used logistic regression to estimate the relationship between the calculated metabolic rates and extinction probability during each epoch of the Cenozoic Era. Results indicate that while each individual phylum has a different extinction probability across each epoch, the regression coefficients for the combination of all studied phyla illustrate no relationship since there is not enough evidence to reject the null hypothesis of no relationship between metabolic rate and extinction probability. Although this means that there is no significant correlation for most of the phyla, there are some exceptions where metabolism does affect extinction probability. During the Oligocene epoch, animals within the Mollusca phylum portray a clear negative correlation between metabolic rate and extinction probability. A negative relationship is also observed for Echinoderms during the Eocene epoch. Despite the crucial role that metabolism plays in species survival, our results indicate that more information is needed regarding specific environmental conditions in order to accurately predict the factors that ultimately affect species survival across marine animals within the Cenozoic Era.

Extinction within the Paleozoic era has been studied in the past, but there still lacks a comprehensive understanding of how extinction risk changed throughout it. Our research project aims to bridge this gap by exploring extinction risk in relation to major Paleozoic phyla and ecological characteristics. Using R, we analyzed the Stanford Earth Body Size dataset, which includes extensive data (n=8816) on Paleozoic marine animals. In Step 1, regression coefficients were formed, indicating whether being in one of the 6 phyla in each period of the Paleozoic era conferred greater or less extinction risk. In Step 2, the examined ecological characteristics included ocean acidification resilience, feeding patterns, body volume, length, surface area, motility, tiering, circulatory systems, and respiratory organ type. In Step 3, 6 binomial machine learning models were created using the traits from Step 2 to determine whether an individual genus went extinct in a particular period. Our Step 1 results confirm that within these timeframes, while certain phyla have greater extinction risk, extinction risk was not uniform across these groups. Our Step 2 results show certain traits provided advantages and disadvantages for an organism’s extinction risk. One interesting pattern was that the only consistently non-significant traits were body length, area, and volume. Likewise with Step 1, extinction risk for each ecological characteristic varied across the Paleozoic. Finally, in Step 3, the results were largely successful. Most of the six models had an accuracy above 80% with the highest being 92% in the Cambrian. The areas under the Precision-Recall and the Receiver Operating Characteristic Curves were all in the acceptable (<0.6) range, demonstrating that the model has low false positive/ negative rates and is able to distinguish between what trait indicates extinction or survival for each period. Our research project identified phyla at risk of extinction in each period of the Paleozoic, determined which natural traits incited greater extinction risk, and demonstrated machine learning models trained on fossil descriptors can predict when an individual genus became extinct. Our results confirmed that extinction risk is not consistently dependent on a singular factor nor is it constant across every period of the Paleozoic era.