A Review of the Dodo and its Ecosystem: Insights from a Vertebrate Concentration Lagerstätte in Mauritius

The dodo Raphus cucullatus Linnaeus, 1758, an extinct and flightless, giant pigeon endemic to Mauritius, has fascinated people since its discovery, yet has remained surprisingly poorly known. Until the mid-19th century, almost all that was known about the dodo was based on illustrations and written accounts by 17th century mariners, often of questionable accuracy. Furthermore, only a few fragmentary remains of dodos collected prior to the bird’s extinction exist. Our understanding of the dodo’s anatomy was substantially enhanced by the discovery in 1865 of subfossil bones in a marsh called the Mare aux Songes, situated in southeastern Mauritius. However, no contextual information was recorded during early excavation efforts, and the majority of excavated material comprised larger dodo bones, almost all of which were unassociated. Here we present a modern interdisciplinary analysis of the Mare aux Songes, a 4200-year-old multitaxic vertebrate concentration Lagerst€atte. Our analysis of the deposits at this site provides the first detailed overview of the ecosystem inhabited by the dodo. The interplay of climatic and geological conditions led to the exceptional preservation of the animal and associated plant remains at the Mare aux Songes and provides a window into the past ecosystem of Mauritius. This interdisciplinary research approach provides an ecological framework for the dodo, complementing insights on its anatomy derived from the only associated dodo skeletons known, both of which were collected by Etienne Thirioux and are the primary subject of this memoir.

Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line

Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.