Geography and Genetics of Ecological Speciation in Pea Aphids

During ecological speciation, divergent natural selection drives evolution of ecological specialization and genetic differentiation of populations on alternate environments. Populations diverging onto the same alternate environments may be geographically widespread, so that divergence may occur at an array of locations simultaneously. Spatial variation in the process of divergence may produce a pattern of differences in divergence among locations called the Geographic Mosaic of Divergence. Diverging populations may vary in their degree of genetic differentiation and ecological specialization among locations. My dissertation examines the pattern and evolutionary processes of divergence in pea aphids (Acyrthosiphon pisum) on alfalfa (Medicago sativa) and clover (Trifolium pretense). In Chapter One, I examined differences among North American aphid populations in genetic differentiation at nuclear, sequence-based markers and in ecological specialization, measured as aphid fecundity on each host plant. In the East, aphids showed high host-plant associated ecological specialization and high genetic differentiation. In the West, aphids from clover were genetically indistinguishable from aphids on alfalfa, and aphids from clover were less specialized. Thus, the pattern of divergence differed among locations, suggesting a Geographic Mosaic of Divergence. In Chapter Two, I examined genomic heterogeneity in divergence in aphids on alfalfa and clover across North America using amplified fragment length polymorphisms (AFLPs). The degree of genetic differentiation varied greatly among markers, suggesting that divergent natural selection drives aphid divergence in all geographic locations. Three of the same genetic markers were identified as evolving under divergent selection in the eastern and western regions, and additional divergent markers were identified in the East. In Chapter Three, I investigated population structure of aphids in North America, France, and Sweden using AFLPs. Aphids on the same host plant were genetically similar across many parts of their range, so the evolution of host plant specialization does not appear to have occurred independently in every location. While aphids on alfalfa and clover were genetically differentiated in most locations, aphids from alfalfa and clover were genetically similar in both western North America and Sweden. High gene flow from alfalfa onto clover may constrain divergence in these locations.

Straddling The Gap: A MIddle School Teacher's Journey Towards Good Geography

Research points to a gap between academic or disciplinary based geography and what is taught in secondary classes across the nation. This study documents a teacher’s journey and efforts to bring a more disciplinary approach to two suburban heterogeneous sixth grade geography classrooms. The researcher traces student perspectives on geography and facility with geographic reasoning as well as his own perspectives and pedagogy with respect to student data. The study attempts to map the space where school geography meets and interacts with disciplinary oriented geography based upon the Geography for Life National Geography Standards. Participants completed two sets of baseline assessments and two sets of end of year assessments as well as an initial intake survey. The seven primary participants were interviewed five times each throughout the academic school year and data were openly coded. The data suggest that students can learn geography and geographic reasoning from a disciplinary perspective. Students sharpened their geographic skills through deeper subject matter knowledge and developing spatial and ecological perspectives. The data also indicate that the teacher researcher faced considerable challenges in implementing a disciplinary approach to teaching geography. The coverage demands of a crowded history-centric curriculum together with ill-fitting resources required a labor-intensive effort to put together and execute this study. Study findings indicate that the path to good geography pedagogy can be impeded by a host of external and internal challenges. However, to forward thinking practitioners, the effort to straddle the gap between school geography and disciplinary-based geography may be well worth it.

Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.