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    Author(s): Holly S. J. Kearns; William R. Jacobi; Kelly S. Burns; Brian W. Geils
    Date: 2008
    Source: Journal of the Torrey Botanical Society. 135(3): 423-437.
    Publication Series: Miscellaneous Publication
    PDF: View PDF  (765 B)

    Description

    Ribes (currants and gooseberries) are alternate hosts for Cronartium ribicola, the invasive fungus that causes blister rust of white pines (Pinus, subgenus Strobus) in the Rocky Mountain region of Colorado and Wyoming. The location, species, and density of Ribes can affect the spread and impact of this potentially serious forest disease. We assessed the distribution and density of Ribes growing near white pine stands for 15 study areas in Colorado and Wyoming with 1258 survey plots of two types, an intensive white pine/Ribes survey and an extensive Ribes survey. Species present, total numbers of stems and bushes, average number of stems per bush, and average stem length were recorded. Various Ribes species were present in all study areas across a range of elevations. The most frequent and common species were R. cereum, R. inerme, R. lacustre, and R. montigenum. Densities and probabilities of occurrence were related to site variables and varied by Ribes species. The most common predictive variables included type of dominant overstory, elevation, and general plot classification such as riparian area. The predictive information provided can be utilized by tree health specialists for risk rating of forests and in planning white pine blister rust mitigation projects. We expect that other areas in Colorado and Wyoming with similar site characteristics will have similar distributions and densities of Ribes and thus have similar risks to white pine blister rust.

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    Citation

    Kearns, Holly S. J.; Jacobi, William R.; Burns, Kelly S.; Geils, Brian W. 2008. Distribution of Ribes, an alternate host of white pine blister rust, in Colorado and Wyoming. Journal of the Torrey Botanical Society. 135(3): 423-437.

    Keywords

    Cronartium ribicola, currants, gooseberries, invasive species, predictive models

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