Doctoral Dissertations

Keywords and Phrases

Phase Transition; Population Dynamics


“This study is an exploration of phase transition behavior in evolutionary and population dynamics, and techniques for predicting population changes, across the disciplines of physics, biology, and computer science. Under the looming threat of climate change, it is imperative to understand the dynamics of populations under environmental stress and to identify early warning signals of population decline. These issues are explored here in (1) a computational model of evolutionary dynamics, (2) an experimental system of decaying populations under environmental stress, and (3) a machine learning approach to predict population changes based on environmental factors. Through the lens of critical phase transition behavior, the non-equilibrium continuous transition of a neutral agent-based model is shown to exhibit power-law-like behavior for two control parameters in the critical regime. The model does not fall within the directed percolation universality class, despite exhibiting some characteristics of directed percolation. The results also compare a system exhibiting quenched randomness with one that does not. Experimentally, the impact of two stressors, temperature and NaCl stress, are examined in S. cerevisiae. Increased levels of NaCl in growth media result in a smooth transition from a survivable to an uninhabitable environment, whereas increased temperature stress results in a transition with signs of critical behavior. Lastly, population data from the Living Planet Index and weather data from NOAA are used to predict population changes based on weather attributes using classification and regression machine learning models. Results indicate that a machine learning approach is viable, but additional data and environmental factors are needed to improve the predictive models”--Abstract, page iii.


Bahar, Sonya
Yamilov, Alexey

Committee Member(s)

Flores, Ricardo
Medvedeva, Julia E.
Olivas, Wendy



Degree Name

Ph. D. in Physics


A dissertation presented to the Graduate Faculty of the Missouri University of Science and Technology and University of Missouri--St. Louis in partial fulfillment of the requirements for the degree Doctor of Philosophy in Physics

The author thanks funding sources for this work, the James S. McDonnell Foundation and the UM system IDIC grant.


Missouri University of Science and Technology

Publication Date

Fall 2021


xii, 117 pages

Note about bibliography

Includes bibliographic references (pages 109-116).


© 2021 Stephen Walter Ordway, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Thesis Number

T 11960