Available at: https://digitalcommons.calpoly.edu/theses/3390
Date of Award
6-2026
Degree Name
MS in Statistics
Department/Program
Statistics
College
College of Science and Mathematics
Advisor
Trevor Ruiz
Advisor Department
Statistics
Advisor College
College of Science and Mathematics
Abstract
This thesis develops a structured dynamic factor analysis (sDFA) framework for decomposing multivariate environmental time series into latent biological and physical components. The methodology is applied to five years of high-resolution passive monitoring data collected from two sites in Morro Bay, California from 2020 through 2024. Relative contribution indices are developed based on the structured DFA that measure how much each latent process contributes to each observed variable at any given time. Structured DFA models fit to the application data suggest site-specific patterns in how biological and physical processes affect water quality variables. At the bay mouth location, physical processes contribute substantially more to pH variability than to dissolved oxygen, indicating decoupling between these variables, while at the bay south location, DO is more physically driven than pH. Site-level dynamic linear models are fit to relate external environmental variables (including temperature, salinity, and chlorophyll) to temporal variation in pH, dissolved oxygen, and decoupling to identify which environmental conditions drive patterns observed across sites. This framework provides a generalizable approach for investigating complex environmental systems.