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 of Vermont; unpublished data) -- that focused on 29% of the Duriscoe plots; and (3) 2015-2017 re-measure surveys -- performed as part of the current collaboration -- which focuses on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).

Invasive white pine blister rust (Cronartium ribicola, WPBR) threatens white pine populations throughout North America. Sugar pine (Pinus lambertiana), for example, has been declining in Sequoia and King Canyon National Parks (SEKI) due to WPBR, as well as other mortality agents, including fire, drought, and mountain pine beetle (Dendroctonus ponderosae, MPB). Whether these declines reflect population trends farther north in Yosemite National Park, however, is largely unknown. To this end, we surveyed 56 plots between June and October, 2023 in Yosemite. To test whether fire impacted WPBR or sugar pine recruitment, 23 plots were established in the Rim Fire footprint. Average extent (% of plots with ≥ 1 infection) and infection rate (% of all live stems with ≥ 1 infection) was 48.2% and 3.7%, respectively, though the extent was much lower in the Rim Fire plots (26.1%) compared to the primary plots (63.6%). Our generalized linear mixed models of infection rate suggested that fire and the presence of alternate hosts were important correlates of WPBR. As fire severity increased, WPBR infections declined, suggesting that high severity fire may dampen infection rates. Additionally, MPB was typically found in larger diameter stems, and the extent was higher in primary plots (54.6%) compared to the Rim Fire plots (30.4%). Recent surveys from SEKI show that sugar pine and WPBR infection rates are declining, likely due to complex interactions with drought, MPB, and fire. Comparable infection rates in Yosemite suggest that both sugar pine and WPBR may be following a similar trajectory. Though long-term monitoring is needed to quantify trends, our results underscore that restoration will help ensure the persistence of this ecologically important species.

Vegetation data files from the Cerro Seco Unit 5 forest restoration project (thinning, prescribed slash burning) during 2016-2022.

Vegetation data files from the Cerro Seco Unit 5 forest restoration project (thinning, prescribed slash burning) during 2016-2022.

This data consists of presence/absence observations for post-fire conifer regeneration. The data also includes estimates of plot-level topography (slope, aspect), relativized differenced normalized burn ratio (RdNBR), post-fire climate, live basal area, and seed rain.

This data consists of presence/absence observations for post-fire conifer regeneration. The data also includes estimates of plot-level topography (slope, aspect), relativized differenced normalized burn ratio (RdNBR), post-fire climate, live basal area, and seed rain.

This data consists of presence/absence observations for post-fire conifer regeneration. The data also includes estimates of plot-level topography (slope, aspect), relativized differenced normalized burn ratio (RdNBR), post-fire climate, live basal area, and seed rain.

This data consists of presence/absence observations for post-fire conifer regeneration. The data also includes estimates of plot-level topography (slope, aspect), relativized differenced normalized burn ratio (RdNBR), post-fire climate, live basal area, and seed rain.

These datasets contain the attributes of individual trees located in and around Sequoia and Kings Canyon national parks. Attributes include remote sensing indices, terrain characteristics, and-- for the calibration data-- tree size and growth rates. Calibration data (mixedconifervulnerability_calibrationdataset.csv) were collected in long term research plots where trees are visited annually to check for mortality and periodically re-measured for diameter to capture growth. Validation data (mixedconifervulnerability_validationdataset.csv) were collected as part of a randomized sample located within a remote sensing 'flight box'. Remote sensing indices and terrain variables for both datasets were extracted from National Park Service data layers. This dataset also includes model coefficients for the top ranked survival models developed as part of the analysis of the calibration data.

These datasets contain the attributes of individual trees located in and around Sequoia and Kings Canyon national parks. Attributes include remote sensing indices, terrain characteristics, and-- for the calibration data-- tree size and growth rates. Calibration data (mixedconifervulnerability_calibrationdataset.csv) were collected in long term research plots where trees are visited annually to check for mortality and periodically re-measured for diameter to capture growth. Validation data (mixedconifervulnerability_validationdataset.csv) were collected as part of a randomized sample located within a remote sensing 'flight box'. Remote sensing indices and terrain variables for both datasets were extracted from National Park Service data layers. This dataset also includes model coefficients for the top ranked survival models developed as part of the analysis of the calibration data.