CAREER: Elucidating the invasion histories of two widespread terrestrial slug genera in North America using phylogenetics, population genetics, and environmental data

Invasive species pose increasingly daunting threats to biodiversity, agriculture, and more. Moreover, they serve as compelling natural laboratories for addressing fundamental questions such as how species adapt rapidly to new environments. Nearly 70 species of terrestrial snails and slugs have been introduced into the contiguous US, but they are critically understudied. This project focuses on two genera of invasive slugs that are widespread in North America—Arion and Deroceras. Although they are known crop pests, very little is known about their invasive histories or the traits that make them successful invaders. The proposed work will develop new genomic resources for these groups, infer the history of their invasions into North America, and identify factors that have promoted their rapid spread. To accomplish these goals, the proposed work will take advantage of the flexibility offered by Artificial Intelligence. Furthermore, the project will develop an undergraduate course on invasive species and engage in outreach to local gardeners. By providing students with opportunities to analyze biological datasets and contribute to the proposed outreach, the course will emphasize the hard and soft skills that are critical in today’s workforce. Slugs of the genera Arion and Deroceras have been introduced and become established in North America and are recognized as crop pests both within the US and internationally. Furthermore, both genera are represented by multiple, independent invasions in North America and possess variation in traits likely to impact invasion trajectories (e.g., the propensity to reproduce via selfing), making them ideal natural laboratories for studying the ecological and evolutionary drivers of invasions. Despite this, the identities, distributions, and sources of invasive populations remain unresolved. The overall objectives of the proposed work are to characterize the invasive histories of Arion and Deroceras in North America and to identify factors that determine the invasion trajectories and range limits of introduced populations. The proposed work will attain these overall objectives by pursuing three specific research objectives. First, this project will characterize the invasive history of Arion and Deroceras in North America using phylogenetic approaches. The proposed work will collect genomic data from museum specimens to identify the species affinities, distributions, and putative sources of introduced populations. Second, it will infer the population invasion histories of select widespread and narrowly distributed introduced species in Arion and Deroceras. Using population-level sampling, the proposed work will characterize the direction and rate of spread and the role of introgression between newly introduced and established or native populations. Third, it will identify ecological and life history traits that predict invasion trajectories and ranges in Arion and Deroceras. The proposed work will use species distribution models, trait data, and methods from landscape genetics to identify factors predictive of invasion trajectories. The expected outcomes are basic knowledge of the invasive histories of these taxa and insights into the factors that impact invasion trajectories. This work will also set up these two systems as model taxa for future investigations into the ecological and evolutionary causes and consequences of invasions. 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: 2540653 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Megan Smith | Institution: Mississippi State University, MISSISSIPPI STATE, MS | Award Amount: $703,995 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2540653 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2540653.html