Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub
    Author(s): Louis R. IversonJoanne RebbeckMatthew P. PetersTodd HutchinsonTimothy Fox
    Date: 2019
    Source: Forest Ecosystems
    Publication Series: Scientific Journal (JRNL)
    Station: Northern Research Station
    PDF: View PDF  (39.0 MB)

    Description

    Background: The negative impacts of the exotic tree, Ailanthus altissima (tree-of-heaven, stink tree), is spreading throughout much of the Eastern United States. When forests are disturbed, it can invade and expand quickly if seed sources are nearby. Methods: We conducted studies at the highly dissected Tar Hollow State Forest (THSF) in southeastern Ohio USA, where Ailanthus is widely distributed within the forest, harvests have been ongoing for decades, and prescribed fire had been applied to about a quarter of the study area. Our intention was to develop models to evaluate the relationship of Ailanthus presence to prescribed fire, harvesting activity, and other landscape characteristics, using this Ohio location as a case study. Field assessments of the demography of Ailanthus and other stand attributes (e.g., fire, harvesting, stand structure) were conducted on 267 sample plots on a 400-m grid throughout THSF, supplemented by identification of Ailanthus seed-sources via digital aerial sketch mapping during the dormant season. Statistical modeling tools Random Forest (RF), Classification and Regression Trees (CART), and Maxent were used to assess relationships among attributes, then model habitats suitable for Ailanthus presence. Results: In all, 41 variables were considered in the models, including variables related to management activities, soil characteristics, topography, and vegetation structure (derived from LiDAR). The most important predictor of Ailanthus presence was some measure of recent timber harvest, either mapped harvest history (CART) or LiDARderived canopy height (Maxent). Importantly, neither prescribed fire or soil variables appeared as important predictors of Ailanthus presence or absence in any of the models of the THSF. Conclusions: These modeling techniques provide tools and methodologies for assessing landscapes for Ailanthus invasion, as well as those areas with higher potentials for invasion should seed sources become available. Though a case study on an Ohio forest, these tools can be modified for use anywhere Ailanthus is invading.

    Publication Notes

    • Check the Northern Research Station web site to request a printed copy of this publication.
    • Our on-line publications are scanned and captured using Adobe Acrobat.
    • During the capture process some typographical errors may occur.
    • Please contact Sharon Hobrla, shobrla@fs.fed.us if you notice any errors which make this publication unusable.
    • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

    Citation

    Iverson, Louis R.; Rebbeck, Joanne; Peters, Matthew P.; Hutchinson, Todd; Fox, Timothy. 2019. Predicting Ailanthus altissima presence across a managed forest landscape in southeast Ohio. Forest Ecosystems. 6(1): 87-. 13 p. https://doi.org/10.1186/s40663-019-0198-7.

    Cited

    Google Scholar

    Keywords

    Ohio, Random Forest, CART, Maxent, Landscape model, Non-native invasive species

    Related Search


    XML: View XML
Show More
Show Fewer
Jump to Top of Page
https://www.fs.usda.gov/treesearch/pubs/59018