Undergraduate Honors Theses

Thesis Defended

Spring 2016

Document Type

Thesis

Type of Thesis

Departmental Honors

Department

Geography

First Advisor

Jennifer Balch

Second Advisor

William Travis

Third Advisor

Rhonda Hoenigman

Abstract

Humans have altered their natural surroundings since their appearance on Earth. More recently, humans have increased their impact on natural systems causing changes in wildfire behavior, climate and species distribution to occur at a higher rate post Industrial Revolution. Unfortunately, research into wildfire, climate and species distribution tends to be narrowly focused. When investigating the effects that wildfire, climate and species distribution have on each other, it becomes clear that each variable affects the other, but the outcomes of those interactions remain unknown. In order to begin to understand these interactions, this study focuses on a single, tree species, the rocky mountain white fir (A. concolor var. concolor), in Colorado and New Mexico. This study sets the foundation for future work on forecasting the future distribution of rocky mountain white fir due to these interactions through the creation of a binary model to produce an updated species distribution map of rocky mountain white fir in the study area. The results of the binary model, specifically in Colorado, exposed weaknesses in current research regarding environmental factors that affect the growth and regeneration of rocky mountain white fir. Further research into controlling environmental variables for the rocky mountain white fir are imperative to forecasting the future distribution of the species. After analysis of current research as well as the results of the binary model, it becomes evident that with the different ways rocky mountain white fir could respond to changes in wildfire regimes, climate and the distribution of other species in Colorado and New Mexico, future research must focus on the creation of a model to forecast possible changes. A comprehensive, multi-system forecast model would give new insights into how humans have affected the state of different ecosystem goods and services, and what can be done to adapt to changes that have already been set into motion that cannot be undone.

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