Research interests
My research program focuses on the genetic and ecological processes enabling plant populations to cope with global change. I am particularly interested in the evolutionary forces shaping genetic and phenotypic variability in plant populations.
Two evolutionary forces lay the foundations of my research program:
My research intersects evolutionary ecology, population genetics, and plant ecology, using an interdisciplinary approach that combines genomics, field experimentation, high-performance computing, and integrative modeling. Specifically, my research program addresses (i) the effects of past climate change and contemporary global change on dispersal processes of plant populations and (ii) the evolutionary potential of plant populations to cope with environmental change.
Two evolutionary forces lay the foundations of my research program:
- Gene flow as one critical evolutionary force enhancing genetic variability in plants.
- Local adaptation as a driver of evolutionary change in plant populations.
My research intersects evolutionary ecology, population genetics, and plant ecology, using an interdisciplinary approach that combines genomics, field experimentation, high-performance computing, and integrative modeling. Specifically, my research program addresses (i) the effects of past climate change and contemporary global change on dispersal processes of plant populations and (ii) the evolutionary potential of plant populations to cope with environmental change.
In my current position at Michigan State University, I am working with Dr. John Robinson (MSU), Dr. Andria Dawson (Mt. Royal University), Dr. Sean Hoban (Morton Arboretum), Dr. Adam Smith (Missouri Botanical Garden), and Dr. Allan Strand (College of Charleston) on the NSF project “Collaborative Research: ABI Innovation: Quantifying biogeographic history: a novel model-based approach to integrating data from genes, fossils, specimens, and environments.” In this project, we are developing an integrative modeling approach based on Approximate Bayesian Computation able to integrate fossil pollen data, occurrence data, and ecological niche models, and population genetic data to infer demographic parameters, the location of glacial refugia, and the pace of post-glacial range movement of tree species.
Contact information
Michigan State University College of Agriculture and Natural Resources Department of Fisheries and Wildlife MSU Natural Resource Building room 13 Email: acastillaalva@gmail.com / casti209@msu.edu Phone: (517) 355-4478 |
