Abstract Detail



Biodiversity synthesis: Linking large phylogenies with species traits and ecologies.

Edwards, Robert D. [1], Mandel, Jennifer [2].

The Importance of Large-scale Phylogenomics in Understanding the Evolution of North American Compositae.

The generation of multi-locus phylogenies for large numbers of taxa is critical to an understanding of many fundamental evolutionary processes and patterns. And while the field of genomics continues to be revolutionized by high throughput methods and economies of scale, we are still a long way from a comprehensive and robust understanding of much of the tree of life, especially large and complex groups such as the Compositae. Where Big Data, in the form of global collections databases (eg. GBIF), soil maps (eg. SoilGrids250), and climate layers (eg. WorldClim) as well as phenotypic data from machine learning are now available at continental and even global scales, the phylogenetic framework within which to understand these data now appears to lag behind. Comprising more than 1/10 flowering plants in North America, the Compositae represent an unrivaled opportunity to study large-scale adaptive and evolutionary processes, yet even many relationships at a tribal level are not resolved. Here we present phylogenetic diversity and ecological diversification analyses of North American Compositae with available metaphylogeny data, to illustrate the scale and scope of questions that can be addressed with existing data, and call for an ongoing effort to build a cohesive community and phylogenetic understanding of this group.


1 - Smithsonian Institution and University of Memphis
2 - University Of Memphis, Biological Sciences, 3744 Walker Ave, 339 Ellington Hall, Memphis, TN, 38152, United States

Keywords:
Phylogenetics
Macroecology
Biodiversity.

Presentation Type: Colloquium Presentations
Session: COL01, Biodiversity synthesis: Linking large phylogenies with species traits and ecologies
Location: Virtual/Virtual
Date: Wednesday, July 29th, 2020
Time: 12:30 PM
Number: COL01009
Abstract ID:175
Candidate for Awards:None


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