| Summary: | Whitebark pine (Pinus albicaulis) serves as a subalpine keystone species by regulating snowmelt runoff, reducing soil erosion, facilitating the growth of other plants, and providing food for wildlife, particularly grizzly bears (Ursus arctos horribilis). Mountain pine beetle (Dendroctonus ponderosae) is an ideal bio-indicator of climate change, as its life cycle is entirely temperature dependent. Western North America is currently experiencing the largest outbreak of mountain pine beetle on record, and evidence suggests that a changing climate has accelerated the life-cycle of this bark beetle, allowing it to expand into new habitat. This study explored the relationships between climate, mountain pine beetles, and whitebark pine mortality in the Greater Yellowstone Ecosystem (GYE). A time-series of Landsat satellite imagery was used to monitor whitebark pine mortality in the GYE from 1999 to 2008. The patterns of mortality were analyzed with respect to monthly climate (temperature and precipitation) variations over the 9-year period. The impacts of topography and autocorrelation (both spatial and temporal) were also analyzed with respect to whitebark pine mortality. Whitebark pine mortality was assessed using the Enhanced Wetness Difference Index (EWDI), a Landsat-derived measure of canopy moisture. Regression tree models were built to predict yearly changes in EWDI. Thirty-eight percent of the deviance in whitebark pine was explained by a regression tree with 10 predictor variables. The most important predictor variables were autocorrelation terms, indicating a strong host-tree depletion effect, where mountain pine beetles were much less likely to attack recently attacked areas. Topographic variables (elevation, slope, aspect) were not useful in predicting whitebark pine mortality. Climate variables alone were used to construct a regression tree with 14 predictor variables which predicted 15% of the dataset deviance in whitebark pine mortality. Drier climatic conditions favored increased whitebark pine ...
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