OPUS: Open Ulethbridge Scholarship
Open ULeth Scholarship (OPUS) is the University of Lethbridge's open access research repository. It contains a collection of materials related to research and teaching produced by the academic community.
Self-archiving your research in OPUS is one way to meet Open Access policies of granting agencies. It is important to retain your final, post-peer-reviewed drafts for submission to OPUS, as this is often the only version publishers will allow to be archived. Click here for information on the U of L Open Access Policy.
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Using stacked SDMs with accuracy and rarity weighting to optimize surveys for rare plant species
(Springer, 2020) Rosner-Katz, Hanna; McCune, Jenny L.; Bennett, Joseph R.
Effective conservation of rare species requires reasonable knowledge of population locations. However, surveys for rare species can be time-intensive and therefore expensive. We test a methodology using stacked species distribution models (S-SDMs) to efficiently discover the greatest number of new rare species’ occurrences possible. We used S-SDMs for 22 rare plant species in southern Ontario, Canada to predict the best survey locations among individual 1-ha cells. For each cell, we weighted distribution model outputs by accuracy and species rarity to create an efficiency value. We used these efficiency values as an index to determine the locations of our field surveys. We conducted field surveys in multi-species cells, “MSC” (areas with high predicted efficiency for multiple species) and single species cells, “SSC” (areas with high probability for only one species) to determine the relative efficiency of a multi-species survey approach. MSC were more than twice as likely as SSC to have at least one rare plant species discovered. Efficiency ranks were also useful in directing surveyors toward incidental discoveries of other rare species that were not modeled. Our technique of using S-SDMs can help direct surveys to more efficiently find rare species occurrences.
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Gurmukhi Punjabi (PA) as a low resource-language through the lens of the BLARK model.
(Lethbridge, Alta. : University of Lethbridge, Dept. of English, 2023) Kaur, Kirandeep; O'Donnell, Daniel Paul; Snoek, Conor
We are venturing into the next phase of digital divide (unequal access to digital technology), where the languages which are not ready for Natural Language Processing (NLP) are at the most risk of losing out on the developments in the fields of Speech and Language technologies. This has brought forth a big gap between the readiness of different languages in terms of taking advantage of the recent developments in the field of computational technologies.
Common Language Resources and Technology Infrastructure (CLARIN) - a large-scale pan-European collaborative effort to create, coordinate and make language resources and technology available and readily usable, has developed the Basic Language Resource Kit (BLARK) model to assess the readiness for speech and language technology developments in any language.
Punjabi, despite being a major language with millions of native speakers and a significant diaspora population around the world, has received limited attention in the computational technologies. The thesis aims to provide a comprehensive overview of the existing resources, tools, and techniques for Punjabi NLP, as well as to identify the gaps and opportunities for future research using BLARK model as a framework. The thesis, after giving the current (sorry) state of Punjabi in terms of its readiness for computation technologies, concludes with some suggestions for directions and effort which are needed for making Punjabi ready for development of speech and language technologies. The thesis contributes to the field of Punjabi language processing by proposing a generic model for comparing and enhancing Punjabi linguistic resources.
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Innovations in headwater snow monitoring in the southern Canadian Rockies
(Lethbridge, Alta. : University of Lethbridge, Dept. of Geography and Environment, 2023) Barnes, Celeste C.; Hopkinson, Chris
The Alberta Rocky Mountain region is a large contributor to the water supply for populations, ecosystems, wildlife, and industry. Water resource managers and governmental policy makers require estimates to ensure there is a sufficient supply to meet increasing demands while at the same time responding to potential decreases in the supply from a changing climate. This research was conducted in the Southwestern Alberta Rocky Mountains and explored precipitation patterns and quantified spatially explicit estimates of winter snowpack SWE water yields to address the need for improved headwater resource assessments. There is high spatial and temporal variability of precipitation and the winter snowpack in mountain regions. Precipitation gauges are prone to sensor- and wind-induced measurement errors. Quality Control Corrections were applied to two valley and one alpine gauge. After corrections, the alpine site had up to a 50% increase in precipitation depths while the valley sites had up to a 5% change. A seasonality component was present where the alpine site had up to 80% more precipitation in the winter months and all sites received 50% to 70% lower precipitation in the summer months. This seasonality caused valley to alpine sites to have different monthly elevational precipitation gradients. Six “single point in time” mesoscale snow water equivalent (SWE) estimates were created using a combination of a) airborne lidar derived or predicted snow depths; and b) publicly accessible snowpack monitoring datasets to constrain snow density models for each SWE estimate. The most productive elevation zone was at the mid-mountain treeline between 1900 masl to 2200 masl producing approximately half of the estimated total water yield. Precipitation corrections, elevational precipitation gradients, and SWE water yields created in this research can be used by water managers to calibrate models used to derive real-time Alberta water resource estimates.
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A new record of Stylophorum diphyllum (Michx.) Nutt. in Canada: a case study of the value and limitations of building species distribution models for very rare plants
(BioOne, 2019) McCune, Jenny L.
Stylophorum diphyllum (Michx.) Nutt. is an endangered plant of rich floodplain forests in southern Ontario, Canada. Prior to 2015 there were only four known populations in Ontario. I built a species distribution model (SDM) based on the known occurrences, and tested it by surveying 156 forest sites that varied in their predicted suitability. An indicator species analysis showed that sites predicted to be suitable had significantly higher frequency and abundance of common species usually associated with S. diphyllum, demonstrating the ability of the SDM to pinpoint similar habitat, although none of these sites contained S. diphyllum. The most important predictors used by the SDM to determine habitat suitability were growing season precipitation, surficial geology, and soil texture. I discovered a new population of S. diphyllum more than 50 km north of the known populations, at one of the sites not predicted to be suitable. This demonstrates a clear example of SDM overfitting, which may occur when models are built based on few, spatially limited occurrence records. Nonetheless, the key environmental predictors remained the same in an updated SDM including the new record. Stylophorum diphyllum provides a case study of both the value and the limitations of using SDMs to predict suitable habitat for very rare and geographically restricted plants, and the need for more rare plant surveys even in human-dominated landscapes.