Electronic Theses and Dissertations

Date

2024

Document Type

Thesis

Degree Name

Master of Science

Department

Earth Sciences

Committee Chair

Youngsang Kwon

Committee Member

Dorian J Burnette

Committee Member

Hsiang-te Kung

Abstract

This thesis presents the development of a novel Tree Interaction Model (TIM), which investigates the interplay of biotic and abiotic factors influencing tree species growth and abundance in Eastern US forests. The TIM merges species importance values, growth metrics, beta diversity components, and environmental variables to address the limitations of previous species distribution models (SDMs), which have concentrated on abiotic drivers of niche. With tree data from the Forest Inventory and Analysis (FIA) program, the model applies a Random Forest machine-learning approach, augmented by SHAP (SHapley Additive exPlanations) analysis, to provide interpretable insights into the interactions of various variables and variable types. Results indicate the species-specific nature of key predictors of growth and abundance, while revealing the significant role of biotic factors and beta diversity and highlighting the need for a comprehensive understanding of species interactions and landscape compositional variation. Future model iterations should incorporate multi-scale analyses to capture hierarchical processes and focus on finer spatial resolutions to discern mechanisms driving interactions. Additionally, integrating mortality metrics and climate projections will refine the TIM, enhancing its future utility.

Comments

Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to ProQuest.

Notes

Open Access

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