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Bayesian methods for spatio-temporal ecological processes using imagery data

Date

2021

Authors

Lu, Xinyi, author
Hooten, Mevin, advisor
Kaplan, Andee, committee member
Fosdick, Bailey, committee member
Koons, David, committee member

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Abstract

In this dissertation, I present novel Bayesian hierarchical models to statistically characterize spatio-temporal ecological processes. I am motivated by the volatility of Alaskan ecosystems in the face of global climate change and I demonstrate methods for emerging imagery data as survey technologies advance. For the nearshore marine ecosystem, I developed a model that combines ecological diffusion and logistic growth to quantify colonization dynamics of a population that establishes long-term equilibrium over a heterogeneous environment. I also unified modeling concepts from entity resolution and capture-recapture to identify unique individuals of the population from overlapping images and infer total abundance. For the terrestrial ecosystem, I developed a stochastic state-space model to quantify the impact of climate change on the structural transformation of land cover types. The methods presented in this dissertation provide interpretable inference and employ statistical computing strategies to achieve scalability.

Description

2021 Summer.
Includes bibliographical references.

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