AUTHOR=Schachat Sandra R. TITLE=Examining paleobotanical databases: Revisiting trends in angiosperm folivory and unlocking the paleoecological promise of propensity score matching and specification curve analysis JOURNAL=Frontiers in Ecology and Evolution VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2022.951547 DOI=10.3389/fevo.2022.951547 ISSN=2296-701X ABSTRACT=

Paleobotany is at a crossroads. Long-term trends in the fossil record of plants, encompassing their interactions with herbivores and with the environment, are of the utmost relevance for predicting global change as pCO2 continues to rise. Large data compilations with the potential to elucidate those trends are increasingly easy to assemble and access. However, in contrast to modern ecology and unlike various other paleontological disciplines, paleobotany has a limited history of “big data” meta-analyses. Debates about how much data are needed to address particular questions, and about how to control for potential confounding variables, have not examined paleobotanical data. Here I demonstrate the importance of analytical best practices by applying them to a recent meta-analysis of fossil angiosperms. Two notable analytical methods discussed here are propensity score matching and specification curve analysis. The former has been used in the biomedical and behavioral sciences for decades; the latter is a more recent method of examining relationships between, and inherent biases among, models. Propensity score matching allows one to account for potential confounding variables in observational studies, and more fundamentally, provides a way to quantify whether it is possible to account for them. Specification curve analysis provides the opportunity to examine patterns across a variety of schemes for partitioning data—for example, whether fossil assemblages are binned temporally by stage, epoch, or period. To my knowledge, neither of these methods has been used previously in paleontology, however, their use permits more robust analysis of paleoecological datasets. In the example provided here, propensity score matching is used to separate latitudinal trends from differences in age, climate, and plant community composition. Specification curve analysis is used to examine the robustness of apparent latitudinal trends to the schema used for assigning fossil assemblages to latitudinal bins. These analytical methods have the potential to further unlock the promise of the plant fossil record for elucidating long-term ecological and evolutionary change.