Investigating Whitebark Pine Mortality on the Pacific Crest Trail

Map showing Landsat-based detection of trends in disturbance and recovery (LandTrendr) magnitude of disturbance map from 1984-2011 including Inyo and Sierra National Forests.

Landsat-based detection of trends in disturbance and recovery (LandTrendr) magnitude of disturbance map from 1984-2011 including Inyo and Sierra National Forests.



Team Location: NASA Ames Research Center, Moffett Field, California

Authors: Ryan Anderson, University of Wyoming; Andrew Nguyen, San Jose State University; Nathan Gill, Brigham Young University; Soumya Kannan, California Institute of Technology; Neeshi Patadia, St. Francis High School.

Advisors/Mentors: Marc Meyer, U.S. Forest Service; Cindy Schmidt, Bay Area Environmental Research Institute/NASA Ames Research Center.

Other Acknowledgements: Dr. Robert Kennedy, Oregon State University; Dr. Nicholas Coops, University of British Columbia; Dr. Jeffrey Evans, The Nature Conservancy; Peter J. Sands.

Abstract: The Pacific Crest Trail (PCT), one of eight National Scenic Trails, stretches 2,650 miles from Mexico to the Canadian border. At high elevations within Inyo and Sierra National Forests, populations of Whitebark Pine (Pinus albicaulis) have been diminishing primarily due to infestation of the mountain pine beetle (Dendroctonus ponderosae). This species is now a candidate for protection under the Endangered Species Act (ESA). Since Whitebark Pine is an indicator species of climate change effects, understanding the key variables associated with these changes may help predict future changes. For example, higher minimum temperatures accelerate the population growth of the mountain pine beetle and recent droughts have weakened the Whitebark Pine, leaving more trees susceptible to attack. Using remote sensing, we analyzed the rate and spatial extent of Whitebark Pine tree mortality from 1984-2011, using the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) program. Climate data, soil properties, and biological features of the Whitebark Pine were incorporated in the Physiological Principles to Predict Growth (3-PG) model to predict future rates of growth and assess its applicability in modeling natural Whitebark Pine processes. Finally, the Random Forest algorithm was used with topographic data, current climate data, and future climate forecasts for the years 2030, 2060, and 2090 to predict distribution of mountain pine beetle within Whitebark Pine stands. Landtrendr results indicate presence of beetle infestations within 14,940 square kilometers of forest over a 27-year period. The strongest predictor variables of mountain pine beetle and Whitebark Pine habitat suitability are length of time for degree days to exceed 5 degrees Celsius, degree days below freezing, elevation, and degree days greater than 5 degrees Celsius.



Summer VPS > Ecological Forecasting


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