Assembling the Tree of Life: Deep Scaly

Deep Scaly is a large-scale, collaborative effort by seven main investigators (plus postdocs and students) at six institutions to determine the evolutionary relationships among the major lineages of squamate reptiles (lizards, snakes, and amphisbaenians). Squamates are the second largest group of terrestrial vertebrates after birds, comprising 96% of all non-avian reptile species. They are the subject of many phylogeny-based research programs in ecology and evolution, yet the relationships among major lineages of squamates remain remarkably uncertain.

The aim of this project is to resolve higher-level squamate relationships using data from DNA sequences, morphology, and fossils. The project is highly integrative, making use of genomics, morphological imaging techniques, comparative anatomy, and intensive sampling of both fossil and extant representatives of all recognized squamate subfamilies. The results of the research will provide a rigorous phylogenetic framework for comparative and systematic studies within and between squamate families.

Additional Information

We will focus on 143 extant squamate species (representing all currently recognized families and subfamilies) and 9 extant outgroup species (including a tuatara, turtles, and crocodilians). For these 152 species, we will obtain DNA sequence data from ~50 single-copy nuclear protein coding genes (~400-800 bp each). We will also score ~500 morphological characters for each of these species, with emphasis on characters from osteology (from dry skeletal preparations, cleared-and-stained skeletons, and high resolution X-ray computed tomography) and external morphology. We will also include ~60 fossil taxa based primarily on osteological characters. Data sets will be analyzed separately and together using parsimony and Bayesian methods. The project will also address the consequences of including fossil taxa in phylogenetic analyses that combine large molecular data sets and morphology, using simulations and analyses of our empirical data.