Annual Variation in Bee Community Structure in the Context of Disturbance (Niagara Region, South-Western Ontario)

This study examined annual variation in phenology, abundance and diversity of a bee community during 2003, 2004, 2006, and 2008 in recovered landscapes at the southern end of St. Catharines, Ontario, Canada. Overall, 8139 individuals were collected from 26 genera and sub-genera and at least 57 species. These individuals belonged to the 5 families found in eastern North America (Andrenidae, Apidae, Colletidae, Halictidae and Megachilidae). The bee community was characterized by three distinct periods of flight activity over the four years studied (early spring, late spring/early summer, and late summer). The number of bees collected in spring was significantly higher than those collected in summer. In 2003 and 2006 abundance was higher, seasons started earlier and lasted longer than in 2004 and 2008, as a result of annual rainfall fluctuations. Differences in abundance for low and high disturbance sites decreased with years. Annual trends of generic richness resembled those detected for species. Likewise, similarity in genus and species composition decreased with time. Abundant and common taxa (13 genera and 18 species) were more persistent than rarer taxa being largely responsible for the annual fluctuations of the overall community. Numerous species were sporadic or newly introduced. The invasive species Anthidium oblongatum was first recorded in Niagara in 2006 and 2008. Previously detected seasonal variation patterns were confirmed. Furthermore, this study contributed to improve our knowledge of temporal dynamics of bee communities. Understanding temporal variation in bee communities is relevant to assessing impacts caused on their habitats by diverse disturbances.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.