Date of Award:

5-2009

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Wildland Resources

Committee Chair(s)

R. Douglas Ramsey

Committee

R. Douglas Ramsey

Committee

Eugene W. Schupp

Committee

Janis L. Boettinger

Abstract

Georeferenced field data were used to develop logistic regression models of the geographic distribution of 38 frequently common plant species throughout Rich County, Utah, to assist in the future correlation of Natural Resources Conservation Service Ecological Site Descriptions to soil map units. Field data were collected primarily during the summer of 2007, and augmented with previously existing data collected in 2001 and 2006. Several abiotic parameters and Landsat Thematic Mapper imagery were used to stratify the study area into sampling units prior to the 2007 field season.

Models were initially evaluated using an independent dataset extracted from data collected by the Bureau of Land Management and by another research project conducted in Rich County by Utah State University. By using this independent dataset, model accuracy statistics widely varied across individual species, but the average model sensitivity (modeling a species as common where it was common in the independent dataset) was 0.626, and the average overall correct classification rate was 0.683. Because of concerns pertaining to the appropriateness of the independent dataset for evaluation, models were also evaluated using an internal cross-validation procedure. Model accuracy statistics computed by this procedure averaged 0.734 for sensitivity and 0.813 for overall correct classification rate. There was less variability in accuracy statistics across species using the internal cross-validation procedure.

Despite concerns with the independent dataset, we wanted to determine if models would be improved, based on internal cross-validation accuracy statistics, by adding these data to the original training data. Results indicated that the original training data, collected with this modeling effort in mind, were better for choosing model parameters, but sometimes model coefficients were better when computed using the combined dataset.

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121296cdddedf37cfe600d25bd70ad07

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