Inferring past migration: a novel model-based approach to integrating data from genes, fossils, specimens, and environments
Dr Sean Hoban1, Dr Andria Dawson2, Dr Adam Smith3, Dr John Robinson4, Dr Allan Strand5, Ellie Weise4, Dr Jeanne Romero-Severson6
1The Morton Arboretum, Lisle, United States, 2Mount Royal University, Calgary, Canada, 3Missouri Botanic Garden, St Louis, USA, 4Michigan State University, East Lansing, USA, 5College of Charleston, Charleston, USA, 6University of Notre Dame, South Bend, USA
A major goal of ecology and evolutionary biology is to document and understand biogeographic history: where species existed, when, with what abundance, and why. Towards this goal, different scientific communities have used disparate approaches based on different data types (e.g., DNA data, contemporary specimen records, fossil remnants, etc.). Each data type reflects different historical processes, has different limitations, and varies in resolution–none alone captures the entire biogeographic history. As a consequence, despite continued improvements in data quality and quantity, questions about species past range shifts remain hotly debated. There is community consensus on a need to quantitatively integrate information from disparate data and models. In this talk we will present an ongoing effort to develop novel, comprehensive, statistically robust informatic methods to estimate species genetic, demographic, and biogeographic history. The specific objective is to leverage information from multiple sources spanning space and time to estimate: (a) key demographic and genetic parameters, (b) post-glaciation (Holocene) species distributions, and (c) observation and process uncertainty. A modified Approximate Bayesian Computation (ABC) approach, to be developed in R, will link and expand the state-of-the-art in spatially explicit demographic inference. Here we demonstrate the use of this approach to provide new estimates of Holocene range expansion in green ash based upon rangewide SNP and microsatellite data, a rich dataset of fossil pollen, and a large database of contemporary occurrence points. We infer the speed of migration as well as the number of refugia. This work represents a new integrative direction for historical biogeography
As Tree Conservation Biologist, Dr. Sean Hoban works to understand, document, and conserve trees species, both rare and common. Dr. Hoban’s goal is to to provide knowledge and best practice advice for ensuring that species can not only survive into the future, but also thrive, adapt, and provide ecosystem functions in natural and human systems, in an era of rapid changes. His expertise is in demographic inference, gene flow, simulations, and ex situ conservation. He is a member of the IUCN Global Tree Specialist Group and the Conservation Genetics Specialist Group, the IUCN’s SSC Post-2020 Biodiversity Targets Task Force, and GEO BON (the Group on Earth Observation Biodiversity Observation Network). He is also an Editor for the journal Conservation Genetics. He has contributed technical expertise to several government agencies, including helping develop conservation policy. Before coming to the Arboretum, Dr. Hoban received a PhD in Biology from The University of Notre Dame, under the advising of forest genetics researcher Dr. Jeanne Romero-Severson. He then worked as a postdoctoral researcher in France and Italy, and then received a prestigious, competitive postdoctoral fellowship to work at the National Institute for Mathematical and Biological Synthesis in Knoxville, Tennessee. He has published 46 scientific articles and several software tools. Dr. Hoban has worked on a variety of plants, mammals, amphibians, and fish, but specializes in trees (especially oaks and ash).