As a case study, this thesis develops methods for the analysis of large amounts of data generated from a simulated ecosystem designed to understand how mammalian biomechanics interact with environmental complexity to modulate the outcomes of predator–prey interactions. These simulations investigate how other biomechanical parameters relating to the agility of animals in predator–prey pairs are better predictors of pursuit outcomes. Traditional modelling techniques such as forward, backward, and stepwise variable selection are initially used to study these data, but the number of parameters and potentially relevant interaction effects render these methods impractical. Consequently, new modelling techniques such as LASSO regularization are used and compared to the traditional techniques in terms of accuracy and computational complexity. Finally, the splitting rules and instances in the leaves of classification trees provide the basis for future simulation with an economical number of additional runs. In general, this thesis shows the increased utility of these sophisticated statistical techniques with simulated ecological data compared to the approaches traditionally used in these fields. These techniques combined with methods from industrial Design of Experiments will help ecologists extract novel insights from simulations that combine habitat complexity, population structure, and biomechanics.
Microsatellite analyses of additional locations within the M. mendax range suggest that polygyny is also present in some other populations, especially in central-northern Arizona, albeit at lower frequencies than that in the Sierra Anchas. In addition, analyses of multiple types of genetic data, including microsatellites, the mitochondrial barcoding region, and over 2000 nuclear ultra-conserved elements indicate that M. mendax populations within the southwestern U.S. and northwestern Mexico are geographically structured, with strong support for the existence of two or more divergent clades as well as isolation-by-distance within clades. This structure is further shown to correlate with variation in queen number and hair length, a diagnostic taxonomic feature used to distinguish honey ant species.
Together, these findings suggest that regional ecological pressures (e.g. colony density , climate) may have acted on colony founding and social strategy to select for increasing workforce size and, along with genetic drift, have driven geographically isolated M. mendax populations to differentiate genetically and morphologically. The presence of colony fusion in the laboratory and life history traits in honey ant that are influenced by colony size, including repletism, brood raiding, and tournament, support this evolutionary scenario.
Ultimate Frisbee or "Ultimate," is a fast growing field sport that is being played competitively at universities across the country. Many mid-tier college teams have the goal of winning as many games as possible, however they also need to grow their program by training and retaining new players. The purpose of this project was to create a prototype statistical tool that maximizes a player line-up's probability of scoring the next point, while having as equal playing time across all experienced and novice players as possible. Game, player, and team data was collected for 25 different games played over the course of 4 tournaments during Fall 2017 and early Spring 2018 using the UltiAnalytics iPad application. "Amount of Top 1/3 Players" was the measure of equal playing time, and "Line Efficiency" and "Line Interaction" represented a line's probability of scoring. After running a logistic regression, Line Efficiency was found to be the more accurate predictor of scoring outcome than Line Interaction. An "Equal PT Measure vs. Line Efficiency" graph was then created and the plot showed what the optimal lines were depending on what the user's preferences were at that point in time. Possible next steps include testing the model and refining it as needed.