DOI
https://doi.org/10.25772/S95R-PE37
Author ORCID Identifier
0000-0002-9338-5485
Defense Date
2021
Document Type
Dissertation
Degree Name
Doctor of Philosophy
Department
Systems Modeling and Analysis
First Advisor
Dr. Edward L. Boone
Second Advisor
Dr. Ryad Ghanam
Abstract
Ecologists are interested in the composition of species in various ecosystems. Studying population dynamics can assist environmental managers in making better decisions for the environment. Traditionally, the sampling of species has been recorded on a regular time frequency. However, sampling can be an expensive process due to financial and physical constraints. In some cases the environments are threatening, and ecologists prefer to limit their time collecting data in the field. Rather than convenience sampling, a statistical approach is introduced to improve data collection methods for ecologists by studying the dynamics associated with populations of interest. Population models including the logistic equation and the Lotka-Volterra differential equations are employed to simulate species composition. This research focuses on sequentially learning about the behavior of dynamical systems to better inform ecologists of when to sample. The developed algorithm of sequential optimality designs sampling regimes to assist ecologists with resource allocation while providing maximum information from the data. This research in its entirety constructs a method for designing sampling schedules for ecologists based on the dynamics associated with temporal ecological models.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
4-7-2021