Date of Award
2012
Document Type
Dissertation
Degree Name
Ph.D. in Biological Science
Department
Biology
First Advisor
Clifford A. Ochs
Second Advisor
Yixin Chen
Third Advisor
Jason Hoeksema
Relational Format
dissertation/thesis
Abstract
Two stochastic models are designed for answering evolutionary genetic problems. The first study shows that inbreeding-environment interactions increase extinction risk. The second research demonstrates that 1) the carrying capacity and initial population growth rate is critical to determine the population persistence time; 2) increasing the advantageous mutation rate reduces the extinction risk although its effects are usually shadoby population size and fitness; 3) the new mutation correlation among environments rises during the evolution process, but it has compromised effects on population fate. Being able to accurately estimate the persistence time of populations of endangered plants and animals is central to conservation biology and is of considerable importance in forming land-use decisions. Genetic deterioration (due to inbreeding and random genetic drift) and environmental deterioration (e.g. climate change, pollution and introduced species) clearly contributes to population extinction. However, considerable recent evidence suggests that interactions between genetic deterioration and environmental stress are ubiquitous. The importance of these interactions for potentially reducing persistence times has not been quantified and has not been taken into account by major conservation organizations. Using a computer simulation, we have determined that including reasonable estimates of the inbreeding-environment interaction reduces persistence times by 17.5–28.5% (mean=23%) for a wide range of carrying capacities, assumptions concerning the number of lethal equivalents and different regimes for the frequency and magnitude of the stressful environment. We note that the proportional decrease in persistence time with inclusion of the interactions becomes larger (i.e. the interaction becomes more important) as absolute time to extinction gets larger. Thus, inclusion of the interaction is important and surprisingly may be most needed when populations are of intermediate size and considered relatively safe from environmental and genetic stresses acting independently. Mutation is one of the basic and important forces to drive evolution. Due to the small rate per generation of spontaneous mutations and limitation of the experimental method and technology, it is still difficult for scientists to study individual mutation effects and temporal fitness variation under mutational constraints. We use an individual-based computer model to simulate a number of scenarios by combining various variables: carrying capacity—K, initial population fitness/growth rate –λ init, beneficial mutation rate –Ub, the mean selection coefficient of beneficial mutation – sb, and initial correlation structure – rinit. The results show that K and λ init are two critical factors that affect the population persistence time. Mutational parameters, Ub, sb and rinit, definitely affect the population fitness, although they do not show statistical significance for population dynamics. In the long-term view, mutational constraints also indirectly adjust/influence population fate. This study can help conservation organizations develop better breeding strategies to protect endangered species.
Recommended Citation
Liao, Wei, "Stochastic Models For Evolutionary Genetic Problems" (2012). Electronic Theses and Dissertations. 386.
https://egrove.olemiss.edu/etd/386