What shapes the invasion of non-native plants?

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	• Department of Botany   • Department of Geography

A 2007 grant from the U.S. Department of Agriculture's National Research Initiative Competitive Grants Program will enable three scientists and their students to study how landscape structure influences the invasion of forests by one of the bush honeysuckle species that threaten forests in the eastern and Midwestern U.S.

David L. Gorchov (botany) and Mary C. Henry (geography) are collaborating with Oscar J. Rocha of Kent State University on this project.

Invasive non-native species often have negative effects on the ecology of forests and other wildlands. Determining how invasive species spread and what sites are at risk of invasion are important for early detection and control. The researchers will determine how landscape patterns shape the invasion of Amur honeysuckle, Lonicera maackii, a shrub native to northeast Asia.

Dr. Henry will take the lead in documenting the historical expansion of Amur honeysuckle into forested stands over the last 20 years in an agricultural area in southwestern Ohio, using her expertise in analyzing satellite images. Because this shrub expands leaves earlier and retains them later than native trees and shrubs, invaded stands can be distinguished using such "remote sensing" approaches. The researchers will then develop a predictive model for how landscape characteristics, including fragmentation and corridors, shape the pattern and rate of invasion.

Dr. Gorchov (left), who has worked with several graduate students to understand the ecology of Amur honeysuckle, will take the lead in testing this model by predicting the likelihood of new invasions in stands at the edge of the current range, and inspecting these stands for new shrubs. Dr. Rocha will test whether seed dispersal processes implicated in the model are supported by genetic data, specifically identifying the most likely source population of the first shrubs to grow in each stand, using molecular markers.

The model the team develops will enable land managers to predict which stands are most likely to be invaded, and therefore focus monitoring, detection, and early eradication efforts at the highest risk sites.