abstract: Isolation-by-distance is a specific type of spatial genetic structure that arises when parent-offspring dispersal is limited. Many natural populations exhibit localized dispersal, and as a result, individuals that are geographically near each other will tend to have greater genetic similarity than individuals that are further apart. It is important to identify isolation-by-distance because it can impact the statistical analysis of population samples and it can help us better understand evolutionary dynamics. For this dissertation I investigated several aspects of isolation-by-distance. First, I looked at how the shape of the dispersal distribution affects the observed pattern of isolation-by-distance. If, as theory predicts, the shape of the distribution has little effect, then it would be more practical to model isolation-by-distance using a simple dispersal distribution rather than replicating the complexities of more realistic distributions. Therefore, I developed an efficient algorithm to simulate dispersal based on a simple triangular distribution, and using a simulation, I confirmed that the pattern of isolation-by-distance was similar to other more realistic distributions. Second, I developed a Bayesian method to quantify isolation-by-distance using genetic data by estimating Wright’s neighborhood size parameter. I analyzed the performance of this method using simulated data and a microsatellite data set from two populations of Maritime pine, and I found that the neighborhood size estimates had good coverage and low error. Finally, one of the major consequences of isolation-by-distance is an increase in inbreeding. Plants are often particularly susceptible to inbreeding, and as a result, they have evolved many inbreeding avoidance mechanisms. Using a simulation, I determined which mechanisms are more successful at preventing inbreeding associated with isolation-by-distance. / Dissertation/Thesis / Doctoral Dissertation Molecular and Cellular Biology 2015

No description available.

The Neighborhood Size Effect (NSE), characterized as the effect in word determination based on changing one orthographic aspect of that word. The amount of words that can be created through such manipulation is called a neighborhood size (NS). Number of other factors such as frequency, how often a word appears and morphology, the combination of meaningful word units, have been suggested to have an overriding effect on NSE. In addition, there is a lack of research on NSE with non-alphabetical languages such as Chinese, which uses characters comprising of a multitude of semantic or phonetic markers. In this experiment, participants coming from mainland China were presented with 60 individual characters and 59 characters with Chinese morphology made up of two characters which form single words. Both conditions, were manipulated with NS by adjusting the semantic or phonetical radical within a character and with frequency by using a website that measures how frequent a character appeared within the language. Both character conditions were found to have a significant effect with frequency and neighborhood size (NS) with characters with higher frequency and lower NS found to have higher accuracy and lower reaction times. With low frequency single characters, it was that those with higher neighborhood size had greater delay in reaction time and lower accuracy. With low frequency morphologically constructed characters, it was found that lower neighborhood size had higher accuracy, but no significant result with regards to reaction time. Due to differing accuracy results with NS and character condition, it is suggested that further factors such as morphological processing in single characters and bigram frequency in morphologically constructed characters might have an effect on word determination in conjunction with neighborhood size. Thus, it is a possibility that Chinese morphological may depend more on other factors than neighborhood size.