LAUSR

Fossils are the remains organisms from earlier geological periods preserved in sedimentary rock. The global fossil record documents and characterizes the evidence about organisms that existed at different times and places during the Earth’s history. One of the major directions in computational analysis of such data is to reconstruct environmental conditions and track climate changes over millions of years. Distribution of fossil animals in space and time make informative features for such modeling, yet concept drift presents one of the main computational challenges. As species continuously go extinct and new species originate, animal communities today are different from the communities of the past, and the communities at different times in the past are different from each other. The fossil record is continuously increasing as new fossils and localities are being discovered, but it is not possible to observe or measure their environmental contexts directly, because the time is gone. Labeled data linking organisms to climate is available only for the present day, where climatic conditions can be measured. The approach is to train models on the present day and use them to predict climatic conditions over the past. But since species representation is continuously changing, transfer learning approaches are needed to make models applicable and climate estimates to be comparable across geological times. Here we discuss predictive modeling settings for such paleoclimate reconstruction from the fossil record. We compare and experimentally analyze three baseline approaches for predictive paleoclimate reconstruction: (1) averaging over habitats of species, (2) using presence-absence of species as features, and (3) using functional characteristics of species communities as features. Our experiments on the present day African data and a case study on the fossil data from the Turkana Basin over the last 7 million of years suggest that presence-absence approaches are the most accurate over short time horizons, while species community approaches, also known as ecometrics, are the most informative over longer time horizons when, due to ongoing evolution, taxonomic relations between the present day and fossil species become more and more uncertain.