Date of Award

2013

Degree Type

Thesis

Degree Name

Master of Science in Environmental Sciences

Department

Natural Resources Science

First Advisor

Yeqiao Wang

Abstract

The Appalachian National Scenic Trail (A.T.) is a footpath stretching from Springer Mountain in Georgia to Mount Katahdin in Maine and spanning over 3,500 km of peaks, valleys, and ridges. The A.T.'s gradients in elevation, latitude, and moisture and north-south alignment represent a continental scale cross-section, or "MEGA-Transect," of eastern U.S. forest and alpine areas and offer a setting for collecting scientific data on the health of ecosystems and species that inhabit them.

The Appalachian Trail Decision Support System, or A.T.-DSS, is an Internet-based implementation and dissemination toolset directed at enhancing the decision-making process for managing natural resources. The A.T.-DSS provides a coherent framework for monitoring, reporting, and forecasting ecological conditions by integrating NASA multi-platform sensor data, NASA Terrestrial Observation and Prediction System (TOPS) models, and in situ measurements from A.T. MEGA-Transect partners.

The purpose of this research is to develop a prototype habitat suitability model for the invasive species tree-of-heaven (Ailanthus altissima (Mill.) Swingle), an exotic tree species pervasive throughout the United States due to its rapid growth, high fecundity, hardy tolerance, and strong competitive ability. This prototype model demonstrates the capabilities of the A.T.-DSS by leveraging seamless geospatial data and climate models from TOPS along with ground based Forest Inventory and Analysis data from the USDA Forest Service to model the current and potential future distributions of suitable Ailanthus habitats within the A.T. landscape.

Analysis of the FIA records revealed that Ailanthus was most abundant in the Mid-Atlantic States and tended to occur at lower elevations, closer to roadways, and in younger forest stands. Maximum entropy modeling (Maxent) was used to relate the observed distribution of Ailanthus to an array of geospatial data layers representing environmental conditions, termed environmental variables. Significant relationships were detected for land cover (developed areas, canopy cover) and topographic (elevation, slope) variables. However, climatic variables were consistently the highest performing predictors, and revealed a preference for warmer and drier regions.

Projected precipitation and temperature data based on scenarios from the Intergovernmental Panel on Climate Change for the period 2095-2099 were substituted for current climate variables to examine potential trends in the distribution of suitable Ailanthus habitats. The resulting models indicate that total suitable area will increase from 56% to 82% of the study area. Additionally, the mean elevation of suitable habitats will increase by 59 m and the mean latitude will shift north by 49 km. The predicted changes were most dramatic along the New England section of the A.T.

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