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Darwin famously identified that species interactions in a “tangled bank” could influence how species evolve. Yet community ecology – the study of species interactions – and evolutionary biology – the study of phenotypic evolution – have been primarily studied in isolation; as a result, we still have limited knowledge of how ecological interactions cause phenotypic change in multiple types of traits, and how the evolutionary effects of these interactions may vary across communities. In flowering plant communities, co-occurring plant species often share pollinators, which can lead to indirect beneficial facilitation or detrimental competition between plant species for pollination. I hypothesized that interactions between co-occurring plant species might affect the composition of and patterns of selection in flowering plant communities. In this dissertation, I conducted field and greenhouse common garden studies using species in the genus Clarkia, which are California-native annual plants that commonly co-occur in multi-species communities and are primarily pollinated by solitary bees that specialize on the genus. In the first chapter, I assessed how interactions among plants that share pollinators might affect communities ecologically, by determining what species can co-occur, and evolutionarily, by affecting ongoing, in situ evolution. I conducted a field survey of species co-occurrence patterns and a greenhouse common garden study of trait variation. Two plant species co-occur more frequently than expected by chance alone, and these species have converged in flowering time and diverged in flower size where they co-occur. In the second chapter, I tested if differences in flowering times observed in Clarkia communities minimize competition for pollination by conducting a phenology manipulation experiment with potted plants. Contrary to the general expectation, my results indicated that staggered flowering in these communities does not minimize competition for pollinators and may result from selection from herbivory or water availability. In the third chapter, I conducted the first test for character displacement—the evolution of trait differences where species co-occur relative to where they occur alone—in a trait that mediates many plant- pollinator interactions, floral scent. I developed high-throughput methods for measuring the floral scent of two species of Clarkia to examine whether the emission rates of floral volatiles varied across communities that contain one, two, or four Clarkia species. I found a pattern consistent with character displacement in the emission rates of eight species-specific compounds, and novel evidence that this evolutionary process can be context-dependent and may occur via multiple pathways in plants. In the fourth chapter, I examined whether interactions between co- occurring plant species in natural communities alter patterns of net- and pollinator-mediated phenotypic selection on floral traits. Across two years, I estimated phenotypic selection on four species and experimentally tested for pollinator-mediated selection on two species in >20 communities that differ in species richness and floral density. Intraspecific competition at high densities and interspecific competition at low densities may affect the evolution of floral traits in these communities. Together, my results indicate that patterns of trait evolution in more species- rich communities are not predictable from simpler communities, and that species interactions can affect the evolution of multiple aspects of a species' phenotype. Because plant-plant interactions could be modifying selection exerted by the abiotic or biotic environment, my results illustrate that species interactions can have wide-ranging effects on species’ evolutionary trajectories.

Much of the prior research on white power groups focuses on very rare outcomes – criminal events, especially violent ones – without as much attention devoted to the more common or fundamental activities that often work to start the mobilization process for ethnocentric groups and the individuals associated with them. Broadly, the goal of the current study is to fill this gap in knowledge by integrating prominent criminological theories and themes drawn from the Social movement literature in order to explore the geographic distribution and macro-level correlates of ideologically-motivated white power movement activities. Specifically, I implement content analysis techniques of open source and news media materials that geo-locate a wide variety of different white power events expected to signify support and/or mobilization (e.g., rallies), which I then aggregate and pair with demographic and structural measures drawn from the United States Census’s American Communities Survey. The nation-level results indicate that white power organizations target traditionally conservative and financially well-off medium sized towns that have relatively small foreign-born populations. The nation-level results are tempered by the wide variability found in the regional models, highlighting the secretive, grassroots approach typically employed by white power activists.

The characterization and modeling of the spatial variability of soil moisture is an important problem for various hydrological, ecological, and atmospheric processes. This dissertation proposes a compact representation of interdependencies among soil moisture distribution and environmental factors using two complimentary approaches. In the first approach, a stochastic framework is developed for characterizing the soil moisture distribution. The resulting model provides closed form analytical solutions for (a) the variance of soil moisture distribution; (b) the covariance between soil moisture distribution and soil properties; and (c) the covariance between soil moisture distribution and topography as a function of soil heterogeneity, topography and soil moisture. Series of simulations are performed using various combinations of parameters. Comparisons between simulated results and a number of field observations show qualitative agreement. Application of the proposed stochastic framework requires statistical information of soil characteristics. In the second approach, possibility of inferring soil physical properties from remotely sensed brightness temperature maps is explored. Remotely sensed brightness temperature data from a single drying cycle from Washita '92 Experiment and two different ANN architectures (Feed-Forward Neural Network (FFNN), Self Organizing Map (SOM)) are used to classify soil types into three categories. Results show that FFNN yield better classification accuracy (about 80% accuracy) than SOM (about 70% accuracy). The SOM, however, has an advantage because it requires very little information regarding soil properties. To classify soil into more than three categories, this study suggests the use of multiple-drying-cycle brightness temperature data. Use of multiple-drying-cycle brightness temperature data from the Southern Great Plains suggests that it is possible to classify soil into more than three groups. It appears that the requirement of rapidly changing decision boundary, in the case of space-time evolution of brightness temperature data, will restrict the FFNN model to yield better accuracy. Motivated by these observations, a simple prototype-based classifier, known as 1-NN model, is used which yield 86% classification accuracy for six textural groups. A comparison of classification error regions for both models suggests that, for the given input representation, further improvement in classification accuracy is feasible with different ANN structure.