CAREER: Combining traits, phylogenies, and historical data to predict and quantify rates of plant geographic movements

This project investigates why some plants are able to spread to new places and thrive, while others are not. The answer can help explain large patterns in nature and improve how we manage ecosystems, especially as invasive species become more common. Plants move across landscapes in many ways, such as through seeds carried by wind, water, or animals. Some can quickly establish new populations, even from just a few individuals. Yet scientists still do not fully understand what makes certain plants more successful at moving and establishing new populations than others, or whether the same factors matter over short time scales – like recent invasions – and over long-time scales that shape today’s global biodiversity. To address this, the project will combine knowledge from plant biology and natural history collections with new analytical approaches to identify the traits that help plants spread and succeed in new environments. Part of the work will focus on invasive species in the Great Lakes region, helping improve our ability to anticipate and manage species that can harm crops and ecosystems in this area. The project will also support education and public engagement. It will develop easy to use tools to help educators teach how species distributions change over time, preparing students for careers in science and technology. In collaboration with the University of Michigan Museum of Natural History, the project will create programs and exhibits that show how scientists study plant movements across the globe and why this research matters, helping connect the public with science and environmental challenges that affect society. This project investigates how lineage-specific traits influence the geographic movement of plant populations across ecological and macroevolutionary timescales. It focuses on identifying whether traits related to establishment (e.g., life form, breeding system) or dispersal (e.g., fruits and seeds) better predict short- and long-term range shifts, and whether their effects are consistent across temporal scales. At the macroevolutionary scale, the project will develop and implement new models that incorporate trait evolution into estimates of dispersal among disjunct regions, with particular emphasis on transoceanic long-distance dispersal. It will apply these models across numerous flowering plant clades to test for general patterns linking traits to geographic movement. At the ecological scale, the project will use historical occurrence data from herbarium specimens to quantify the range expansion dynamics of invasive species in the Great Lakes region and evaluate how species traits predict their spatial spread. Through comparative analyses spanning multiple clades and timescales, this project addresses longstanding debates about whether long-distance dispersal is predictable and how lineage-specific traits shape these events. The results will contribute to a more predictive framework for understanding species distributions, speciation, and extinction processes, all of which are closely tied to spatial dynamics. Additionally, the project will produce an R package and accompanying Shiny application to facilitate teaching of biogeographical models and concepts, improving the accessibility of methods used in the field for students and the broader research community. Students will also gain hands-on research and outreach training through a partnership with the University of Michigan Museum of Natural History supporting curriculum development and a public exhibit. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2541068 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Thais Vasconcelos | Institution: Regents of the University of Michigan - Ann Arbor, ANN ARBOR, MI | Award Amount: $881,574 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2541068 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2541068.html