BIOdiversity over Microevolutionary and Macroevolu.. (BIOMME)
BIOdiversity over Microevolutionary and Macroevolutionary scales: Evolutionary and ecological determinants revisited
Start date: Feb 1, 2015,
End date: Jan 31, 2018
Evolutionary biologists have long sought to understand the relationship between microevolution (processes within species), as observed in nature and the laboratory, and macroevolution (processes at and above the species), which occurs over intervals that far exceed a human lifespan. They have also long endeavoured to determine which factors – whether abiotic like climatic or geological changes (Court Jester), and/or biotic like species interactions, traits, or niche space (Red Queen) – govern biodiversity dynamics. The connection between these factors and processes within and among species has become a major source of dichotomy between population genetics and phylogenetics. This project constitutes a unique opportunity to address this gap and to assess whether similar evolutionary and ecological processes govern tempo and mode of evolution in swallowtail butterflies. The research will make four relevant contributions: (1) building a unique dataset for swallowtail butterflies that includes genomic, morphological, ecological, geographic and paleontological data to further investigate processes at each evolutionary scale; (2) using genomic data and phylogeographic analyses to test speciation theories by investigating the determinants of species formation within selected species-groups; (3) using the ecomorphological dataset and time-calibrated phylogenies to understand macroevolutionary determinants of large-scale patterns of biodiversity, and (4) using all the data to explore the link between micro and macroevolution in a novel framework. The University of Alberta and Real Jardín Botánico have a strong tradition of using large datasets to answer questions pertaining to patterns of evolution and ecology. Thus, the integration of many layers into a conceptual framework, at two institutions with a history of using advanced bioinformatic and statistical methods to address evolutionary questions, will provide an ideal environment to achieve these objectives.
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