The availability of high-quality morphometric data has exploded in the past few years thanks to the increased development and availability of 3D imaging techniques (such as CT-scans). These new datasets offer a unique opportunity to better characterize and understand how phenotypic diversity has evolved in responses to changes in climates, environments and species ecology through time. However, the high dimensionality of these datasets (they are often described by a larger number of features/traits than species), prevents the use of state-of-the-art modelling and statistical phylogenetic techniques such as maximum likelihood traditionally used.
The development of methods that allow to model and to handle these bigdata in a robust statistical framework is extremely important to understand how a major part of the biodiversity linked to the functioning of species in ecosystems (their morphology) has evolved. For instance, how species have evolved during past climatic changes? How their responses were related to their ecology? How subtle variations in morphology can be linked to various biological factors (ecology, development, genetic diversity)?
The overall objectives of EVOTOOLS were then to (i) develop new phylogenetic comparative methods capable of handling high-dimensional datasets such as from 3D morphometrics, (ii) disseminate these new developments to the scientific community through implementations in free and open-source software, and (iii) apply them on comprehensive empirical datasets across tetrapods that are housed by the host institution. All together, these research objectives have the potential to shed light on major and still open questions in Evolutionary Biology.
