Left: common tansy; right: leafy spurge
Image credits
Left: "Common Tansy" by Dan Mullen is licensed under CC BY-NC-ND 2.0
Right: "Leafy spurge - bloom" by Tarry Edington is licensed under CC BY 2.0
Background
Computer-generated models make it possible for researchers to predict where invasive species are most likely to spread under future climate conditions. Machine learning techniques coupled with field survey data produce reliable maps that managers can use in early detection efforts. MITPPC scientists have already created predictive model maps of 9 invasive weed species on Minnesota’s Noxious Weed List to make early detection easier and improve the State’s eradication efforts.
A new phase of the project aims to improve the model for more fine-scale prediction of the invasive species common tansy and leafy spurge. Researchers will take an innovative approach to species surveying across the Minnesota landscape by using techniques like remote sensing and hyperspectral satellite imaging.
Research questions
- What is the current and future distribution of common tansy and leafy spurge?
- How will changes in temperature and moisture affect the growth and reproduction of common tansy and leafy spurge?
- How do populations of common tansy and leafy spurge differ across the state and how have both species been spreading?
Practical implications
Maps that describe the future distribution of common tansy and leafy spurge will have direct value to managers and decision makers as they create plans for control and eradication throughout Minnesota. Common tansy and leafy spurge are noxious weeds in Minnesota and must be controlled to prevent the maturation and spread of propagating parts.
Publications
- Deep learning detects invasive plant species across complex landscapes using Worldview-2 and Planetscope satellite imagery (Remote Sensing in Ecology and Conservation, 2022)
- Improving predictions of range expansion for invasive species using joint species distribution models and surrogate co‐occurring species (Journal of Biogeography, 2021)
- Predicting range expansion of invasive species: Pitfalls and best practices for obtaining biologically realistic projections (Diversity and Distributions, 2020)
- Species distribution models throughout the invasion history of Palmer amaranth predict regions at risk of future invasion and reveal challenges with modeling rapidly shifting geographic ranges (Nature Scientific Reports, 2019)
- Climate change and forest herbs of temperate deciduous forests chapter from The Herbaceous Layer in Forests of Eastern North America (Oxford University Press, 2014)
News and media
- Caught on camera: invasive species monitored using satellite imagery and AI (BioTechniques.com, 2022)
- Meet the Researcher: Ryan Briscoe Runquist (MITPPC)
Outreach
- Upper Midwest Invasive Species Conference, 2022
- Ecological Society of America - Montreal, 2022
- UMN American Society of Photogrammetry and Remote Sensing Student Chapter, 2022