High-resolution (centimetre-scale) GPS/GIS-based 3D mapping and spatial analysis of in situ fossils in two horned-dinosaur bonebeds in the Dinosaur Park Formation (Upper Cretaceous) at Dinosaur Provincial Park, Alberta, Canada

Abstract

Mapping of fossil sites represents an important aspect of palaeontology, because the data collected are required for interpreting the taphonomic and depositional history of the site, as well as the palaeoecology and behavior of the organisms. Methods for mapping and documenting certain vertebrate fossil sites, such as trackways, have drastically changed in recent years, with the integrated technologies of photogrammetry, laser scanning, and geographic information systems becoming standard practice, and providing digital, three-dimensional, and georeferenced data for analyses. Contrasting this technological revolution, the methods for mapping vertebrate bone accumulations, such as bonebeds, have changed little in recent decades, and are largely limited to two dimensions, are non-georeferenced, and produce static maps. Here, we present a novel test case in the mapping of two ceratopsid (Dinosauria: Ornithischia) monodominant bonebeds (mass death assemblages) that are documented digitally, fully georeferenced, and in three dimensions, using a combination of high-resolution (at centimetre-scale) global positioning system, photogrammetry, and geographic information systems. Importantly, accompanying spatial data (i.e., size and orientation) are collected in the field in the traditional manner and directly compared with values calculated from the digital map. Parameters describing bone length and orientation exported from the digital map are largely reflective of measured field data, with both size and orientation distributions being statistically indistinguishable, but with disproportionate error for elements smaller than 10 cm. Protocols and methods tested here will hopefully add to the discussion about the future of fossil bonebed mapping, specifically incorporating digital, three-dimensional, and fully georeferenced data into a powerful analytical tool.