Meeting Abstract
Recent advances in phylogenomics and next-generation sequencing technologies have made phylogenetic inference of large radiations of organisms possible. These comprehensive genomes and large phylogenies have been successfully used in conjunction with existing datasets to answer key questions about species diversification and morphological evolution. However, collecting large amounts of new phenotypic data has typically been bottlenecked by researcher availability and effort. Here we present a method and toolkit to efficiently collect two-dimensional geometric morphometric phenotypic data at a “phenomic” scale using workers recruited through Amazon Mechanical Turk. We examine inter-and intra-observer accuracy by assigning identical image sets and digitization protocols to experienced fish morphologists and Amazon workers, and use statistical methods to determine the accuracy of remote workers compared to morphologists. We show that the quality of Amazon workers’ data are not significantly different from results collected via traditional sources. Furthermore, the ability to easily parallelize this pipeline allows data to be collected in hours, as opposed to days or weeks. We demonstrate this workflow by collecting body shape data across approximately 7,000 species of ray-finned fishes. We combine this data with a phylogeny of approximately 12,000 fishes and estimate the rates of phenotypic evolution and lineage origination. Our results suggest that the morphological diversification of ray-finned fishes predates the Cretaceous-Paleogene mass extinction event, and highlight the use of crowdsourced “phenomics” as a competitive method to bring phenotype data into the genomic era.
