UVicSpace | Institutional Repository
UVicSpace is the University of Victoriaās open access scholarship and learning repository. It preserves and provides access to the digital scholarly works of UVic faculty, students, staff, and partners. Items in UVicSpace are organized into collections, each belonging to a community.
For more information about depositing items, see the Submission Guidelines.
Recent Submissions
Offshore carbon sequestration using renewable ocean energy as a means to meet the Paris Agreement
(2026) Moffat, Leslie; Weaver, Andrew J.; Eby, Michael
The Intergovernmental Panel on Climate Change has identified the need for negative emission technologies to limit the rise in Global Mean Surface Air Temperature (GMSAT) to 2.0Ā°C above pre-industrial temperatures. Coupled Ocean Thermal Energy Conversion (OTEC) and Direct Air Carbon Capture and Storage (DACCS) in marine environments could create a renewable energy powered method of sequestering atmospheric CO2 to prevent surpassing, or limit overshoot, of this threshold. This research identifies the magnitude of coupled deployment required to prevent surpassing the 1.5Ā°C and 2.0Ā°C Paris Agreement GMSAT thresholds. Through a series of sensitivity experiments, using the University of Victoria Earth System Climate Model, the effects of the initial climate state, OTEC cold water intake depth, and deployment timeline are explored to maximize the efficiency of coupled OTEC and DACCS. Potential locations for coupled deployment are identified and used to estimate the magnitude of GMSAT reductions. Using estimates of the transient climate response to emissions and carbon emissions diagnosed from representative concentration pathways, target OTEC power production estimates were determined. By current DACCS technology standards, it was found that under low emissions scenarios, no action is required to remain below either GMSAT threshold. The potential to satisfy required power production under moderate emission scenarios varied depending on the timeline. At no point could either GMSAT threshold goal be achieved under high emission scenarios. Finally, the likelihood of remaining below the 1.5Ā°C and 2.0Ā°C GMSAT thresholds was significantly improved assuming an increase in the energy efficiency of DACCS technology.
Transcriptional impact of glucose enrichment on Caenorhabditis elegans oocytes
(2026) Karmani, Muskan; Templeman, Nicole M.
Reproductive aging in biological females is driven by a decline in oocyte quality, a process accelerated by metabolic stress such as high dietary glucose. The insulin/insulin-like growth factor-1 (IIS) pathway is a key nutrient-sensing regulator, and in C. elegans, reduced IIS via the daf-2(e1370) reduction-of-function mutation protects against glucose-induced reproductive decline. To uncover the molecular basis of this protection, I performed RNA-sequencing on oocytes from wild-type (N2) and daf-2(e1370) mutants after 48 hours of glucose exposure.
Glucose caused distinct transcriptional responses in each genotype, suppressing 30 transcripts in wild-type and 34 different genes in daf-2(e1370) oocytes, prior to any morphological deterioration. I identified 14 transcripts that form a candidate protective signature. These genes were downregulated by glucose in wild-type oocytes, yet under the same glucose condition, they had significantly higher transcript abundance in daf-2(e1370) oocytes compared to wild-type oocytes. The most dramatic change was in icmt-1, a regulator of cell survival signaling, which was suppressed 10-fold in response to glucose in oocytes of wild-type hermaphrodites but maintained at high levels in the oocytes of daf-2(e1370) mutants despite glucose exposure.
Mechanistically, glucose-suppressed genes in both genotypes were enriched for targets of intestinal transcription factors (PQM-1, CEH-60, ELT-2), suggesting glucose disrupts somatic support of the developing germline of the P1 generation. Conversely, compared to glucose-exposed daf-2(e1370) oocytes, glucose-exposed wild-type oocytes uniquely upregulated 1044 gene transcripts enriched for energetically costly membrane and transport functions, most likely related to a maladaptive response. These changes in transcript abundance predicted physiological outcomes with glucose-exposed wild-type hermaphrodites showing severely reduced late-life fertility (30.98% vs. 53.87% in controls), while daf-2(e1370) mutants maintained high reproductive success regardless of glucose enrichment.
My findings demonstrate that glucose may impair oocyte quality through the suppression of critical stress defense genes. Resilience in daf-2(e1370) mutants is likely conferred by the sustained expression of this protective program, which is sufficient to maintain oocyte quality and reproductive function with age.
Bayesian methods of integrating multiple sources of data to estimate wild population abundance
(2026) Jahid, Mehnaz; Cowen, Laura
Estimating abundance of wildlife species is a fundamental problem in ecological statistics. Ongoing research continues to develop methods that produce more precise and accurate estimates. Data collected from wild populations are often sparse, noisy, or incomplete. This limits the effectiveness of likelihood based methods which require sufficient data to produce reliable estimates. Bayesian methods provides a robust and reliable framework in such settings by incorporating uncertainty and prior information into the model. As a result, ecological statistics, especially hierarchical modeling has increasingly shifted towards Bayesian approaches. In addition, integrating data from different sources has been shown to improve estimation by reducing bias and increasing both accuracy and precision. Although promising, these type of methods still have some shortcomings to the applicability for some specific type of data.
Application of integrated models to capture-recapture and presence-absence data is dependent on the sampling scheme. We explored the applicability of such datasets collected at the same sampling sites and occasions. In chapter 2, we reviewed two integrated models that combine presence-absence data from camera traps and capture-recapture data from hair snares to compare bias and precision to estimate the population abundance of grizzly bears of the central Rocky Mountains of Alberta, Canada. Unlike many other studies, we found that integrating presence-absence data with capture-recapture data does not improve the precision of the density estimates. The potential reasons for such results are discussed in detail. A possible reason is the violation of one of the integrated models assumptions: independence among multiple datasets. To address this, in chapter 3, we proposed an open population integrated population model that explicitly models the dependence of presence-absence data to capture-recapture data. We performed a simulation study to evaluate the model performance and also investigate the effect of different sampling scenarios on model performance. We compared the integrated population model with the spatially explicit capture-recapture (SECR) model as a single dataset model. Later, we apply the model to the grizzly bear data. From both the simulation study and the case study, we found that the proposed integrated model improved the estimates compared to SECR in terms of accuracy, precision, and bias. This model is more effective than
SECR in the various sampling scenarios with budget and logistical constraints.
In chapter 4, we explored the applicability of integrating remote sensing data (chlorophyll-a (chl-a) and sea surface temperature (SST) anomaly) as covariates for salmon stock recruitment models. We used spawner-recruitment data of sockeye salmon from Pitt River, British Columbia, Canada. Remote sensing data were extracted from the central and northern region of the Strait of Georgia (SoG), British Columbia, which represents the ocean entry point for the Pitt River stock when juvenile sockeye salmon migrate towards the ocean. For comparison, we also used in-situ SST data from two of the lighthouses in SoG. The spawner-recruitment relationship was modeled using Ricker and Larkin models. To account for potential temporal autocorrelation, an autoregressive lag 1 (AR(1)) component was also considered. We found that remote sensing chl-a data in the Larkin model were useful to predict Fraser River sockeye salmon stock recruitment; however, in-situ sea surface temperature data outperformed remote sensing sea surface temperature data. We concluded that integrating remote sensing data could improve the stock recruitment forecasting, although longer time series might produce better results.
Analyzing the effects of high autistic traits on neural markers of learning and memory: An EEG approach analysis
(Brain and Cognition, 2025) Parsons, Ellis M.; Hammerstrom, Mathew R.; Nazaroff, Anya; Kemp, Mckinley; Montgomery, Patrick; Macoun, Sarah; Krigolson, Olave E.
Objective
A body of electroencephalographic (EEG) research demonstrates that executive functioning (EF) differences exist in autistic people. Here, we aimed to investigate how and to what extent these EF differences appear in people with high autistic traits in contrast to a low autistic traits comparison.
Methods
The present study used a series of EEG markers (frontal theta power, frontal beta power, the reward positivity ERP component, and the P300 ERP component) to examine potential differences in EF over the course of gambling and oddball tasks. Qualitative research measures to include the perspectives of the autistic people who took part in the study were also used.
Results
While frontal theta and beta power differed between groups, we observed no significant component or correlational differences. However, it was found that high autistic traits participants perceived their task performance as worse than low autistic traits participants despite task performance being equal across groups.
Conclusions
EF differences as measured by frontal theta and beta power were observed across groups. Self-perception of task performance may differ in high autistic traits participants when asked to complete tasks under a time constraint.
How low can it go: The Mountain Image Analysis Suite
(University of Victoria, 2026) Friesen, Hannah
The Mountain Image Analysis Suite (MIAS) is an open-source QGIS plugin developed to generate landcover viewsheds from oblique imagery, primarily applied to high elevation alpine landscape images through the Mountain Legacy Project. This study evaluates the efficacy of MIAS on low elevation, urban imagery using historic (1908) and contemporary (2022) repeat photograph pairs taken from Pkaals (Mt. Tolmie) towards PKOLS (formerly, Mt. Douglas) in Victoria, B.C. Four of the five MIAS processing steps were applied: [1] landcover classification, [2] virtual photograph production, [3] image alignment, and [4] viewshed creation. Results indicate that while MIAS shows promise for low elevation applications, further training is required for the deep learning models to accurately identify landcover without extensive manual correction. The existing deep learning model overproduced unclassified pixels while underrepresenting key landcover categories. Large foreground trees, temporal mismatches between imagery and DEM data, and classification scale incompatibilities posed additional challenges. Despite these limitations, MIAS demonstrates potential for quantifying landcover change in low elevations with further development, particularly for analyzing historic images predating aerial photography or on landscapes with variable topography.