Ontogenetic niche shifts generally coincide with changes in size, morphology, behavior, and/or feeding preferences during development, resulting in a shift in preferred habitat. In aquatic species, these ontogenetic niche shifts are often associated with habitat-dependent changes in competition and/or predation dynamics, expressed as a size-depth relationship where the larger-bodied adults occupy deeper habitats while the smaller-bodied juveniles primarily reside in shallower regions. While the influence of interspecific interactions on size-structured habitat occupancy has been well studied, few have examined the potential role of intraspecific agonistic interactions between size classes in ontogenetic niche shifts. A simulation model was developed to test whether the size-specific habitat occupancy observed in signal crayfish can be explained by the size-structured individual responses to agonistic interaction, where the smaller-bodied juvenile responds to the interaction with an escape movement, to avoid engagement and the risk of mortality or injury from the adult. The simulated movements of signal crayfish resulted in higher juvenile occupancy of riffles relative to pools reflecting a greater rate of escape from adults into riffles, away from the higher densities of adults in their preferred habitat (i.e. pools). This provides evidence that the juvenile escape response to size-structured, intraspecific, agonistic interactions may contribute to ontogenetic niche shifts.

Online shopping is becoming a popular option for consumers. Currently, the most common product searching method that online shopping websites provide is keyword search. Most shoppers have to carefully select relevant keywords to search for their favorite products. Finding desired products using a query image for online shopping is currently not available. Image has been used for searching similar images in the database but they are usually not well annotated. Research effort has been devoted to developing reliable image-based retrieval systems for applications such as medical image retrieval and trademark search. None of these developments focuses on improving online shopping experiences for consumers. This thesis reports the development of an image retrieval system to provide better online shopping experience for consumers. The system searches products with similar appearance such as shape and textures to the query images the user provides. Turn angle is a contour based shape descriptor. It has many unique properties that make it a perfect shape matching method for image retrieval. The best matching image has the shortest shape distance to the query shape. Turn angle, however, could fail with slightly stretched shapes. Dynamic programming is used to help turn angle match slightly deformed shapes. Another technique called centroid distance is also included as a restriction for shape matching in order to avoid retrieving irrelevant or disparate shapes. With a well-built database, the enhanced turn angle descriptor that includes dynamic programming and centroid distance is able to reach a high accuracy rate.Shape matching alone is usually not sufficient for a powerful retrieval system. Products with similar shape but very different textures will not be distinguished based solely on shape matching. Edge histogram is a robust shape descriptor for texture matching. It can be implemented to construct either global or local histogram for this purpose. Global edge histogram uses only 5 bins, which is simple but ignores detail texture information. Local and semi-global edge histograms are more complex but retains detail texture information. A hierarchical matching system is built to combine the shape and texture descriptors for better retrieval accuracy.Easy access to the shopping system is desired. An Android Application is developed to provide consumers a convenient and friendly tool to use the system. Grab cut is applied to the captured image to segment the object from the background. The segmentation provides the retrieval system the required contour information for shape matching. The Android Application submits the captured image along with the segmented contour to the server. After the retrieval process is completed, the server sends retrieved images of similar products back to the Android App for the user to consider. Using the retrieval system via a handheld device provides a user-friendly online shopping experience.