VISUALIZING ECOLOGICAL DATA IN PENNSYLVANIAN COAL BALLS USING COMPUTER TOMOGRAPHY (CT)

Pennsylvanian coal balls are calcareous concretions of permineralized peat that contain a rich fossil assemblage of plant debris and invertebrate material. Plant tissue from a variety of taxa preserved at the cellular level, arthropod frass (or coprolites), and other arthropod traces are often found preserved in these coal balls, capturing the complex ecological interactions that occurred in Pennsylvanian peat swamps. Coal ball analysis has advanced little since it was first established by Darrah (1936) and Phillips et al. (1977). The cellulose acetate peel method is the primary technique for identifying material in coal balls. It is accomplished by etching the surface of a polished coal ball with 5% HCL to dissolve the matrix releasing a part of the cellular structure; 25-40 μm of material is then transferred to a cellulose acetate sheet using acetone. These would-be thin sections are known as peels. A single peel is limited two-dimensionally to capture the complete contents of plant organs, coprolites, a burrow network, or other arthropod traces. To reconstruct the three-dimensional spatial distribution of plant organs and arthropod traces, multiple peels must be taken in sequence. However, this method is labor and time-intensive and requires cutting a coal ball into two-inch slabs. These cuts are made blind, without a priori knowledge of the location of fossil material. To further increase the quantity and resolution of information that can be extrapolated from coal balls, we use computer tomography (CT) to three-dimensionally visualize a coal ball. Two 6x2.5x1 cm blocks cut from the same coal ball were scanned using an X5000 2D Digital Radiography and 3D Computed Tomography at the Carl Woese Institute for Genomic Biology at the University of Illinois. This method proves to resolve the limitations associated with the traditional method of slicing and peeling coal balls. CT allows for the exploration of a coal ball’s contents without cutting it, increasing the resolution of data that can be extrapolated.