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Water is the main driver of net primary productivity (NPP) in arid ecosystems, followed by nitrogen and phosphorous. Precipitation is the primary factor in determining water availability to plants, but other factors such as surface rocks could also have an impact. Surface rocks may positively affect water availability by preventing evaporation from soil, but at higher densities, surface rocks may also have a negative impact on water availability by limiting water infiltration or light availability. However, the direct relationship between rock cover and aboveground net primary productivity (ANPP), a proxy for NPP, is not well understood. In this research we explore the relationship between rock cover, ANPP, and soil nutrient availability. We conducted a rock cover survey on long-term fertilized plots at fifteen sites in the Sonoran Desert and used 4 years of data from annual plant biomass surveys to determine the relationship between peak plant biomass and surface rock cover. We performed factorial ANCOVA to assess the relationship among annual plant biomass, surface rocks, precipitation, and fertilization treatment. Overall we found that precipitation, nutrients, and rock cover influence growth of Sonoran Desert annual plants. Rock cover had an overall negative relationship with annual plant biomass, but did not show a consistent pattern of significance over four years of study and with varying average winter precipitation.
ContributorsShaw, Julea Anne (Author) / Hall, Sharon (Thesis director) / Sala, Osvaldo (Committee member) / Cook, Elizabeth (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2015-05
Description
We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.
ContributorsBarolli, Adeiron (Author) / Jimenez Arista, Laura (Thesis director) / Wilson, Jeffrey (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05