Short-term Visitor
Convergent evolution is recognized as a major component of the history of life. However, there are currently no widely-recognized, standard metrics for quantifying either the magnitude or the significance of convergent patterns. This gap prevents the synthesis of information derived from existing convergence studies and prevents future large-scale quantitative explorations of convergence throughout the tree of life. At NESCent I will develop a suite of metrics for quantifying the magnitude of convergence observed in a given phylogeny. I will also use evolutionary simulations to explore the expected amounts of convergence in data simulated on trees of varying shapes and sizes under a variety of evolutionary scenarios (e.g., random drift, adaptation towards one or more adaptive peaks). The metrics will be implemented in a series of routines written in R and freely distributed online; R routines for assessing the significance of convergent patterns using simulations will also be freely distributed. Results from the simulation studies will be published in open-access journals.
Tools for measuring the magnitude and significance of convergent evolution
| PI(s): | Charles Stayton (Bucknell University) |
| Start Date: | 27-May-2013 |
| End Date: | 14-Jun-2013 |
| Keywords: | computational modeling, evolutionary computation, evolutionary theory, macroevolution, software |
Convergent evolution is recognized as a major component of the history of life. However, there are currently no widely-recognized, standard metrics for quantifying either the magnitude or the significance of convergent patterns. This gap prevents the synthesis of information derived from existing convergence studies and prevents future large-scale quantitative explorations of convergence throughout the tree of life. At NESCent I will develop a suite of metrics for quantifying the magnitude of convergence observed in a given phylogeny. I will also use evolutionary simulations to explore the expected amounts of convergence in data simulated on trees of varying shapes and sizes under a variety of evolutionary scenarios (e.g., random drift, adaptation towards one or more adaptive peaks). The metrics will be implemented in a series of routines written in R and freely distributed online; R routines for assessing the significance of convergent patterns using simulations will also be freely distributed. Results from the simulation studies will be published in open-access journals.
