Data integration for inferring biogeographic history
The ability of species to move to suitable regions is critical to their capacity to cope with climate change. Understanding species’ responses to past environmental changes is needed to forecast their responses to ongoing climate change. Our lab is developing methods for integrative post-glacial tree range dynamics modeling. This research project is a collaboration with John Robinson (Michigan State University), Andria Dawson (Mount Royal University), Sean Hoban (The Morton Arboretum), Adam Smith (Missouri Botanical Garden), and Allan Strand (College of Charleston).
Our TIMBER group. On the top, from left to right, Dr. Andria Dawson (Mount Royal University), Dr. John Robinson (Michigan State University), Dr. Allan Strand (College of Charleston), Lauren Jenkins (Duke University), Dr. Adam Smith (Missouri Botanical Garden). A the bottom, Dr. Sean Hoban (The Morton Arboretum), Dr. Antonio R. Castilla (Oklahoma State University) and our team friend Lainey.
Our research integrates fossil pollen, occurrence data via species distribution models, and population genomics to quantify historical range shifts. Using Approximate Bayesian Computation, we are developing an integrative analytical framework to combine these three sources of information to infer demographic parameters, the location of refugia, and the pace of range movement (more information in Hoban et al. 2019). We are implementing our integrative approach in the R package holoSimCell.
Conceptual diagram showing our framework to integrate multiple data types via Approximate Bayesian Computation. Modified from Hoban et al. (2019): Hoban, S., Dawson, A., Robinson, J. D., Smith, A. B., & Strand, A. E. (2019). Inference of biogeographic history by formally integrating distinct lines of evidence: genetic, environmental niche and fossil. Ecography 42: 1991-2011.
Population genomics in the oak syngameon
We are establishing a research line focused on the population genomics of oak species across southern North America. This initiative integrates landscape genomics, fieldwork, and greenhouse experiments to address key questions in oak evolution. Our research explores evidence of local adaptation in oak populations inhabiting arid environments, the role of hybridization in enhancing genetic diversity, and how landscape heterogeneity shapes the genetic structure of oak populations.
Leaves of shinnery oak in a population located in Spur, Texas. (Photo credit: Castilla Lab).
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Our collaborator Dr. Sean Hoban (The Morton Arboretum) with a specimen of shinnery oak in the Arizona desert (Photo credit: Hoban Lab).
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Our team collecting botanical vouchers from the individuals genotyped in our project. From left to right, Adam Nemeth (MSU Texas), Mercy Dahunsi (MSU Texas), Jacob Martin (Mercer Botanical Garden and Arboretum) and Dr. William Cook (MSU Texas)
Next-Generation Monitoring Satellite Remote Sensing and eDNA Integration for the Early Detection and Management of Aquatic Invasive Plants in the Upper Mississippi Basin
Our team is developing cost-effective tools for the early detection and management of invasive aquatic plant species through an interdisciplinary collaboration with the U.S. Geological Survey, the University of Montana, and Oklahoma State University. By integrating remote sensing with environmental DNA (eDNA) analysis, we aim to enhance early detection capabilities for invasive plant species in the Upper Mississippi River and improve the efficiency of field resources. Ultimately, our integrative approach will enable to survey large areas over time, optimizing detection probabilities while reducing costs associated with large-scale eDNA samplings.
