Data from: Combined Support for Wholesale Taxic Atavism in Gavialine Crocodylians

Morphological and molecular data sets favor robustly supported, contradictory interpretations of crocodylian phylogeny. A longstanding perception in the field of systematics is that such significantly conflicting data sets should be analyzed separately. Here we utilize a combined approach, simultaneous analysis of all relevant character data, to summarize common support and to reconcile discrepancies among data sets. By conjoining rather than separating incongruent classes of data, secondary phylogenetic signals emerge from both molecular and morphological character sets and provide solid evidence for a unified hypothesis of crocodylian phylogeny. Simultaneous analysis of four gene sequences and paleontological data suggest that putative adaptive convergences in the jaws of gavialines (gavials) and tomistomines (false gavials) offer character support for a grouping of these taxa, making Gavialinae an atavistic taxon. Simple new methods for measuring the influence of extinct taxa on topological support indicate that in this vertebrate order fossils generally stabilize relationships and accentuate hidden phylogenetic signals. Remaining inconsistencies in our most well-supported tree, including concentrated hierarchical patterns of homoplasy and extensive gaps in the fossil record, indicate where future work in crocodylian systematics should be directed.

Identifier
DOI https://doi.org/10.5061/dryad.841
PID https://nbn-resolving.org/urn:nbn:nl:ui:13-rm-oli1
Metadata Access https://easy.dans.knaw.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:easy.dans.knaw.nl:easy-dataset:80298
Provenance
Creator Gatesy, John; Amato, George; Norell, Mark; DeSalle, Rob; Hayashi, Cheryl
Publisher Data Archiving and Networked Services (DANS)
Publication Year 2009
Rights info:eu-repo/semantics/openAccess; License: http://creativecommons.org/publicdomain/zero/1.0; http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Representation
Resource Type Dataset
Discipline Life Sciences; Medicine