The impacts of permafrost thaw slumps on Arctic stream ecology

Climate change is intensifying permafrost thaw across terrain of the circumpolar Arctic. Increased temperatures and rainfall at northern latitudes have resulted in the expansion of thermokarst features such as retrogressive thaw slumps, which make previously frozen debris available for transport into nearby river systems. In the Peel Plateau, Northwest Territories, the mobilization of these materials exposes rivers to a large quantity of sediment and solutes that may act as a chronic, press disturbance, potentially altering stream food webs. Here, I examine if: 1) thaw slumps have driven changes to the physical and biological properties of slump impacted streams since an initial sampling campaign during 2010-2014, 2) thaw slump derived carbon is incorporated into stream food webs, and 3) thaw slump presence changes stream food web structure. I found that thaw slumps acted as press disturbances on streams, where slump-driven pressures on invertebrate communities remained consistent between 2010-2014 and 2021. High total suspended solids (TSS) persisted in impacted streams resulting in continually low invertebrate abundance in slump affected watersheds. However, changes to community composition through increased chironomid diversity were evident in the most recent sampling campaign, a trend that was reflected in increased taxonomic diversity in 2021 relative to previous assessments. All invertebrate metrics measured throughout 2010-2014 and 2021 were driven by the number of slumps upstream, where abundance decreased in response to large numbers of slumps, and diversity increased. Thaw slumps also altered food web structure in impacted streams through the addition of previously frozen organic carbon (C) into watercourses. As slump impact increased, consumers relied more on slump-derived C as a dietary resource. Sites with intermediate impact exhibited the broadest range of resource use, as consumers used food sources typical of both unimpacted and impacted food webs. Finally, thaw slumps affected food web structure by reducing trophic redundancy, but increasing trophic diversity, likely reflecting differences in microbial communities between levels of impact. Overall, thaw slumps cause a variety of impacts on the physical and biotic aspects of streams that will continue to affect ecosystems as climate change progresses

The role of the individual in animal collectives

Animals form groups for a variety of reasons, primarily as a way to increase access to food, mates, and safety. An important attribute of these groups is their personality composition. Personality is defined as repeatable traits over time and context, and has been found across multiple taxa. Traits have been found to be heritable, and are thus a unit for natural selection to act upon. Having a heterogeneous group may be beneficial for survival in a changing environment. The question of how the personality composition of the group affects group behaviour has rarely been asked in the field of animal behaviour, especially across different species. This question forms the central part of this dissertation. To attempt to answer it, I first present an agent-based model of cooperation in fruit fly larvae with differing personalities, which shows how individual and environmental effects interact to alter cooperation. I next present an experiment on two species of fish (zebrafish and guppies), in which I artificially created groups with specific personality compositions, and ran them through a battery of tasks, both alone and in groups. I found strong differences across species in how personality affected their collective behaviours. Finally, I asked how groups are formed, when individuals are allowed to form their own groups. Large groups of zebrafish were given 3 days to assort into groups in a large arena, and then I measured their personalities. I present evidence that personality affects how large groups can get, but that zebrafish do not seem to care who they form groups with. This work deepens our understanding of how personalities function in shaping the behaviours of groups, and adds a necessary comparative lens to understanding both the proximate and, indirectly, the ultimate reasons for why variation in personality persists

Political Communication in A Multicultural Metropolis: Chinese Ethnic Media and the 2023 Toronto Mayoral By-Election

Chinese are the second largest visible minority group in Canada and the most frequently reported ethnic or cultural origin in Toronto. Mandarin and Cantonese are, respectively, the largest and third largest non-official languages in Toronto. However, little research exists on the role of the Chinese ethnic community in Toronto municipal politics, especially the role of Chinese ethnic media in Toronto municipal elections. The key argument of this thesis is that “Chinese ethnic media represent complementary public sphere(s) for informing and engaging Chinese Canadians in local political participation and discussion.” Through the 2023 Toronto Mayoral By-Election case study, this thesis examines how Chinese ethnic media represent an essential complementary public sphere for informing and engaging Chinese Canadian residents. The author tries to answer these questions: What role do Chinese ethnic media play in Canadian municipal politics? How do Chinese ethnic media cover municipal election campaigns? Are there any similarities and differences in the news reporting of Chinese ethnic media with different origins? This thesis applies quantitative content analysis to examine the “frequency” and “intensity” of election-focused stories, applies qualitative content analysis to explore the “direction” of television and radio programs, and applies qualitative framing analysis to examine the communicators, texts, receivers, and cultures of commentary articles during the campaign period of the 2023 Toronto Mayoral By-Election (April - July 2023). 11 Chinese ethnic media agencies, including dushi.ca, Ming Pai Daily News, Ming Sheng Bao, info.51.ca, Epoch Times, Toronto News Net, Fairchild Television, Talentvision, A1 Chinese Radio, and The Chaser News, are examined in this thesis. Overall, 196 news articles, 10 interviews, and 9 commentary articles are covered in this thesis. The key argument that “Chinese ethnic media represent complementary public sphere(s) for informing and engaging Chinese Canadians in local political participation and discussion” is proven to be established. Chinese ethnic media play an essential role in political communication. They share many similarities with mainstream media and among themselves, especially in news reporting on policy issues, and contribute to the “civic assimilation” of Chinese communities. Meanwhile, different Chinese ethnic media producers show distinct characteristics in the multi-ethnic public sphere due to their various media activism and advocacy in ethnic media structures. Other main findings include: (1) Although Chinese ethnic media pay attention to community-specific issues, they focus on diasporic politics related to the PRC government rather than identity politics here in Canada. (2) In most cases, Chinese ethnic media reported Olivia Chow significantly more than any other mayoral candidate, indicating that Chinese ethnic media are more inclined to focus on Chinese Canadian politicians. (3) Most Chinese ethnic media depicted Olivia Chow\u27s identity primarily as “Chinese Canadian” rather than a more limited term such as “Hongkonger Canadian” or a more extensive term such as “Asian Canadian.” (4) Hongkonger-oriented Chinese ethnic media paid more attention to Olivia Chow’s Hong Kong origin and her past story. (5) Mainlander-oriented Chinese ethnic media paid more attention to other ethnic Chinese candidates (most of them have mainland Chinese origin). (6) With various formats and exclusive interviews, generally speaking, Hongkonger-oriented Chinese ethnic media performed better than Mainlander-oriented Chinese ethnic media regarding breadth and depth

Applying Machine Learning and Optimization Algorithms to Perform Feature Selection

The objective of feature selection in the realms of machine learning and data mining is integral, serving as an efficient mechanism to eradicate redundant or irrelevant features, and subsequently augmenting the performance of predictive models. In the contemporary landscape of big data, with the escalating dimensionality of datasets, the efficacy of traditional feature selection methodologies is compromised, due to their computational complexity and ineptitude in addressing the curse of dimensionality. This thesis posits a pioneering feature selection framework that amalgamates machine learning with advanced optimization algorithms. The methodology employs a Support Vector Machine (SVM), in conjunction with a cutting-edge metaheuristic algorithm, namely the Black Widow Optimization (BWO) algorithm, as a means to address feature selection (FS) challenges. The SVM, renowned for its robustness and ability in addressing complex classification dilemmas, was strategically amalgamated with the binary form of BWO. Additionally, this study delves into the integration of a recently formulated K-Nearest Neighbor (KNN) algorithm with BWO, utilizing an innovative set of classification metrics. The empirical evaluation of the proposed methodologies was conducted through two distinct experimental sets. The inaugural set of experiments was dedicated to the comparative ii analysis of the binary BWO with SVM (BBWO-SVM) against its counterpart, the binary BWO with KNN (BBWO-KNN). The subsequent set of experiments aimed to place the performance of BBWO-SVM and BBWO-KNN against six globally renowned metaheuristic algorithms. Both experimental sets utilized a comprehensive array of metrics, encompassing the number of features selected, accuracy, recall, precision, and the F1-Score, as the basis for performance comparison. The datasets employed for these experiments comprised 28 public datasets of varying magnitudes, as sourced from the UCI repository. The findings gleaned from the experimental analysis attest to the superior performance of BBWO-SVM, as it transcended the traditional algorithms and manifested exceptional prowess in enhancing classification performance across an array of benchmark datasets. The empirical evidence further substantiates the potential of BBWO-SVM as a versatile tool applicable across diverse domains, inclusive of healthcare, finance, and cybersecurity

Social Behavior & Personality in Corn Snakes

The sociality of snakes, long thought simplistic, has been shown to be governed by a host of different factors such as kinship, experience, sex, weight and personality. There is, however, still a need to untangle how snakes prioritize different kinds of information when interacting. I hypothesized that examining each dyad that composes a group could partly predict how individuals would behave in a larger group context, providing insight into the mechanisms governing aggregation. Six groups of six corn snakes (Pantherophis guttatus) had their personalities tested, then underwent dyadic interactions with each member of their cohort followed by a week aggregating with all five of those snakes. No significant relationships between the snakes’ personalities, dyads or aggregations were found, beyond a consistent weak preference for interacting with the opposite sex, suggesting that corn snakes are more plastic in their behavior than other snake species. In my second experiment, four clutches of corn snake eggs were incubated at different temperatures (hot, medium, cold and variable) to attempt to physiologically manipulate cognitive mechanisms of sociality and personality. These snakes (N = 50) underwent the same personality assays, revealing a trend for boldness to increase with incubation temperature, as well as a strong effect of clutch on boldness and several other measured traits. Altogether, this thesis presents some of the first findings on corn snake sociality and personality, and contributes to our understanding of the diversity of cognition among snake species