This thesis is submitted in the form of two chapters. The first chapter encompasses background research, a literature review, important information, as well as motivation for our study. This first chapter provides insight into fire ecology, functional traits, and context about Quercus. The second chapter is written as a standalone paper for submission to the journal of Global Ecology and Biogeography, thus this second chapter can be read and understood independently of the first chapter. The study of biogeography is a discipline encompassing a species distribution across varying ecosystems in space and time. Biogeography allows for the synthesis of large-scale patterns of a species including its evolutionary history, as well as varying biotic and abiotic factors. Here, we create a framework to assess functional traits in their fire resistance using a quantitative approach, utilizing the study system Quercus. In our study we spatially analyzed oak species adaptation patterns to fire utilizing functional traits and large-scale forest inventory analysis (community) datasets. We employ herbarium and field-collected functional trait data (plant height, bark thickness, self-pruning, specific leaf area, leaf habit, flame height and flame duration) to map oak species fire resistance across the US. We created a community-weighted mean fire resistance score of the US oaks and were able to identify areas of mismatch between a species assigned fire resistance score (FRS) and historical fire return interval (FRI) of an area. The FRS traits were analyzed using model selection Akaike Information Criterion. The FRS and FRI were analyzed for evolutionary trends using Phylogenetic Generalized Least Squares, and Ancestral Character State Reconstruction. We provide evidence that our assigned FRS did vary depending on the fire regime group (FRG) they occurred within. Our findings indicate that oak distribution across FRGs is not uniform, with a majority of oaks existing in FRG 1. Species in our FRS index that possessed a suite of functional traits, rather than scoring very high on just one singular trait resulted in higher FRS. We provide a framework of integrating functional traits into spatial analysis with implications for future research.


Matthew Kaproth

Committee Member

Phillip Larson

Committee Member

Christopher Ruhland

Date of Degree




Document Type



Master of Science (MS)

Program of Study



Biological Sciences


Science, Engineering and Technology

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

Available for download on Tuesday, June 04, 2024

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