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Sequoia and Kings Canyon National Parks White Pine Blister Rust Project Dataset (1993-2017)

Published by National Park Service | Department of the Interior | Metadata Last Checked: January 25, 2026 | Last Modified: 2020-10-01T00:00:00Z
This dataset stores white pine blister rust field data collected as part of a project to inventory and monitor white pine blister rust (Cronartium ribicola) in Sequoia and Kings Canyon National Parks. It is related to the Sierra Nevada Network (SIEN) White Pine (Pinus albicaulis, P.balfouriana, P. flexilis) Community Dynamics Monitoring Protocol. The project is entitled: Assessing the severity and rate of spread of an invasive forest pathogen: a foundation for management response in the Sierra Nevada national parks (2015-2017). It focuses on White pine blister rust (WPBR) – an exotic fungal pathogen first introduced into western North America in 1910 – which has contributed to dramatic population declines in several species of Western five-needled pines (the “white pines”). The current project results from a collaboration between SIEN, U.S. Geological Survey, Sequoia and Kings Canyon Field Station (N. Stephenson, A. Das), and academic partners at the University of California Berkeley (J. Battles, J. Dudley). Full contact information is below. This dataset is an Excel spreadsheet created to store and manipulate data associated with specific publications related to the White Pine Blister Rust project. This dataset compiles data from several sources: (1) surveys performed in late 1990’s by Duriscoe and Duriscoe (2002), which established Sequoia and Kings Canyon National Parks' (SEKI) first formal baseline assessment of the extent and severity of WPBR infection; (2) 2013 re-measure surveys - performed by Matt Cahill (Univ. of Vermont; unpublished data) -- that focused on 29% of the Duriscoe plots; and (3) 2015-2017 re-measure surveys -- performed as part of the current collaboration -- which focuses on the remaining 71% of the plots that were not surveyed by Cahill. Data underwent additional quality assurance and quality control procedures in 2018 and 2019. The spreadsheet also includes comparison between the two survey periods (1993-1995 and 2013-2017).

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