Government of Alberta

Abstract. Permafrost soils are one of the largest terrestrial pools of mercury (Hg) in the world, storing an estimated 500–1500 Gg of Hg in the top three meters of soil. Ongoing climate-driven thaw threatens to release this legacy Hg into the environment. Efforts to quantify and model this pool have been hindered by a lack of harmonized, spatially resolved observations. To address this, we compiled a database of 117,802 Hg observations collected between 1988 and 2022 from 59 studies across Arctic, sub-Arctic, and alpine permafrost regions of the Northern Hemisphere, including North America, northern Europe, Eurasian and the Tibetan Plateau. The database includes Hg concentration measurements in solid materials—such as soil, leaves, roots, wood, and litter—as well as in water samples from soil porewater, lakes, and rivers across the northern hemisphere permafrost domain. The database enables cross-site synthesis, model calibration and evaluation, and environmental assessments by standardizing and harmonizing data from diverse sources. Data harmonization steps included unit conversion, categorization of observations by type, and quality control measures to ensure consistency across studies. Analytical uncertainty was preserved where reported in source studies, and qualitative uncertainty indicators and flags were applied where uncertainty information was incomplete or heterogeneous. Mercury concentrations vary widely across observations, with lake sediment showing the highest median values (70 ng g⁻¹, IQR: 45–116), followed by soil (50 ng g⁻¹, IQR: 32–90), and vegetation (15 ng g⁻¹, IQR: 9–33). Water observations had a median of 2 ng L⁻¹ (IQR: 2–6). Statistically significant differences in Hg concentrations among observation types were observed at both global and regional scales, consistently following the pattern: lake sediment > soil > vegetation. These patterns, along with spatial and observation-type biases, highlight the need for improved coverage in underrepresented regions such as Eurasia. The database is freely accessible through Zenodo under the concept DOI 10.5281/zenodo.18300989 (all versions), to support ongoing research and model development in Arctic and sub-Arctic Hg cycle studies.

This study evaluates the performance of artificial intelligence (AI) technologies for wetland classification in the province of Alberta, Canada, using integrated remote sensing inputs, including airborne light detection and ranging (LiDAR), orthophotography, and multi-sensor satellite imagery (Sentinel-1, Sentinel-2, PlanetScope). Our primary objective was to assess whether AI-driven modelling approaches, specifically machine learning (ML) and deep learning (DL), can meet Alberta’s provincial wetland mapping standards. We hypothesized that integrating high-resolution LiDAR with multi-seasonal optical and radar data composites into advanced AI algorithms would achieve the required classification accuracy, detail, and minimum mapping unit targets. We tested several methodologies in four ecologically distinct pilot areas representing Alberta’s Boreal, Grassland, and Parkland Natural Regions. AI models included ensemble ML using Extreme Gradient Boosting (XGBoost) and Random Forest, and a DL U-Net convolutional neural network (CNN). AI models were trained on expert-labelled photoplots and validated using in situ field surveys. Our findings demonstrate that both ML and DL models met and, in several cases, exceeded the provincial mapping standards with validation overall accuracies surpassing >70% (form), >80% (class), and >90% (wetland–upland). U-Net CNN models generally produced the highest overall accuracies and most precise wetland extent delineation, but XGBoost offered finer detail and granularity for detailed mapping of rare wetland forms. Integrating LiDAR data and derivatives further enhanced model performance, improving accuracy by as much as 13%. Based on these outcomes, we provide a set of recommendations for scaling up these approaches, focusing on model selection, LiDAR imagery integration, and the continued value of field surveys to support the operational scaling of AI-driven classification approaches for wetland inventory updates across Alberta’s diverse landscapes. However, key challenges remain in scaling up this approach due to the cost of acquiring high-resolution LiDAR and satellite imagery.

BackgroundHuman papillomavirus (HPV) vaccination programs in Canada primarily target school-aged adolescents (≤ 15 years) through publicly-funded, school-based delivery. In the province of Alberta, publicly-funded HPV vaccination for older youth (age ≤ 26) began with a risk-based program (i.e. availability limited to those at elevated risk for HPV infection and/or progression to disease) in 2018, expanding to an age-based program (i.e. available to all individuals within funded age groups) in July 2020. This study explores HPV vaccination patterns among 16–26-year-olds during this time period, in the context of the COVID-19 pandemic.MethodsWe conducted a retrospective cross-sectional study using population-wide administrative health data (February 2018–December 2023). Rates of HPV vaccination per 10,000 individuals were calculated by sex and age group, comparing the quarterly absolute and percent difference between the pre-pandemic risk-based period and the pandemic age-based period. We also calculated the quarterly proportion of doses delivered by sex (stratified by age group), delivery site, and individual residence (metro/urban/rural, age of arrival to province).ResultsHPV vaccine administration rates were higher among females than male throughout the study period (84–224 doses/10,000 compared to 40–135 doses/10,000). Rates dropped sharply after pandemic onset (March 2020), with the largest and most sustained drops among 16–17-year-olds (156 doses/10,000). Rates for 18–21 and 22–26-year-olds, who were newly eligible under the age-based program, returned to or exceeded pre-pandemic levels by 2022. Delivery site distribution shifted, with increased vaccination at post-secondary and STI health centres. Dose delivery remained consistently highest among metro residents, and individuals who entered Alberta after age 16.ConclusionsExpansion to an age-based HPV vaccination program may have mitigated pandemic-related declines in vaccination rates among older youth, though overall administration remained low. Results highlight persistent inequities by sex and geography, and underscore the importance of targeted promotion, improved service delivery, and expanded provider engagement to optimize HPV vaccine uptake outside school-based settings.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12889-025-25283-7.

Species interactions affect ecosystem productivity. Positive interactions (resource partitioning and facilitation) increase productivity while negative interactions (species interference) decrease productivity relative to the null expectations defined by monoculture yields. Effects of competitive interactions (resource competition) can be either positive or negative. Distinguishing effects of species interactions is therefore difficult, if not impossible, with current biodiversity experiments involving mixtures and full density monocultures. To partition changes in ecosystem productivity by effects of species interactions, we modify null expectations with competitive growth responses, i.e., proportional changes in individual size (biomass or volume) expected in mixture based on species differences in growth and competitive ability. We use partial density (species density in mixture) monocultures and the competitive exclusion principle to determine maximum competitive growth responses and full density monoculture yields to measure species ability to achieve maximum competitive growth responses in mixture. Deviations of observed yields from competitive expectations represent the effects of positive/negative species interactions, while the differences between competitive and null expectations reflect the effects of competitive interactions. We demonstrate the effectiveness of our competitive partitioning model in distinguishing effects of species interactions using both simulated and experimental species mixtures. Our competitive partitioning model enables meaningful assessments of species interactions at both species and community levels and helps disentangle underlying mechanisms of species interactions responsible for changes in ecosystem productivity and identify species mixtures that maximize positive effects.

Reclamation is a requirement of mountaintop coal mines. Many jurisdictions require a return to predevelopment condition or to equivalencies based on unimpacted (i.e., upstream) reaches. Here we summarize decades of government and industry water quality monitoring and research spanning the onset, operation, closure, and reclamation of three Rocky Mountain coal mines in Alberta, Canada. The mines all occur within the McLeod River basin, offering opportunities to examine cumulative inputs and impacts. Selenium concentrations remain elevated and above guidelines for the protection of aquatic life decades after mine closure and reclamation despite regulatory requirements for the opposite. But the water quality impacts extended beyond selenium. Higher concentrations of a broad suite of both solutes and particle-bound heavy metals were noted downstream of all three mines, with impacts most obvious in Luscar Creek and Gregg River - tributaries to the McLeod River. After decades of coal extraction, changing regulatory requirements, and near complete reclamation of one coal mine, water quality remains negatively impacted with higher concentrations of ions, nutrients, and metals downstream of coal mines. These results suggest current reclamation practices and regulatory requirements for water quality and aquatic ecosystems are not meeting the desired objectives.

ABSTRACTStudying an organism's foraging behavior, especially for predator species, provides insight into their ecology, habitat needs, and interspecific relationships. American black bears (Ursus americanus) are generalist omnivores, with a diet primarily composed of vegetation and are known predators of a number of ungulate species, particularly their neonates. In this study, we analyzed the spring diet of black bears occupying the neonatal range of a small, threatened wood bison (Bison bison athabascae) herd in the Ronald Lake area of northeast Alberta to determine the predation risk of neonate bison. To estimate black bear consumption rates of bison, we used scat analysis and DNA metabarcoding to describe the spring diet of bears occupying the Ronald Lake wood bison herd's (RLBH) neonatal range. If black bears occupying the RLBH's neonatal range are consuming bison, either through predation or scavenging, then we would expect bison DNA to be present in black bear scats. We predicted that the increased availability of neonate bison in the spring would provide bears with greater predation and scavenging opportunities. Conversely, if black bear predation risk is low within the RLBH's neonatal range, then we would predict that herbaceous plants would dominate black bear diet early in the spring and berries later in the spring and summer. The spring diet of black bears was dominated by herbaceous and fruiting plants. Bison DNA, without visual evidence of animal remains, was found in only 1 of 79 scat samples (1.3%). Our results suggest that consumption rates of bison are low and that predation risk to neonate wood bison, during the RLBH's occupancy of their neonatal range, from black bears is likely minimal despite the two species' overlap in space and time.

Abstract The COVID-19 pandemic has had a lasting impact on mental health, with lingering effects on the healthcare utilization of developmental disorders, such as autism spectrum disorder, attention-deficit/hyperactivity disorder, and intellectual disabilities. This study explores changes in developmental disorder healthcare utilization in the Alberta healthcare system before and after the pandemic. Results indicate an overall increase in the healthcare utilization of developmental disorders from 2018 to 2022. The ongoing impact of the pandemic on developmental disorders highlights the value of better surveillance, mental health support, and informed policy decisions to ensure individuals with developmental disorders and their families receive the necessary support and resources.

Mental health diagnoses possess unique challenges that often lead to nuanced difficulties in managing an individual’s well-being and daily functioning. Self-report questionnaires are a common practice in clinical settings to help mitigate the challenges involved in mental health disorder screening. However, these questionnaires rely on an individual’s subjective response which can be influenced by various factors. Despite the advancements of Large Language Models (LLMs), quantifying self-reported experiences with natural language processing has resulted in imperfect accuracy. This project aims to demonstrate the effectiveness of zero-shot learning LLMs for screening and assessing item scales for depression using LLMs. The DAIC-WOZ is a publicly available mental health dataset that contains textual data from clinical interviews and self-report questionnaires with relevant mental health disorder labels. The RISEN prompt engineering framework was utilized to evaluate LLMs’ effectiveness in predicting depression symptoms based on individual PHQ-8 items. Various LLMs, including GPT models, Llama3_8B, Cohere, and Gemini were assessed based on performance. The GPT models, especially GPT-4o, were consistently better than other LLMs (Llama3_8B, Cohere, Gemini) across all eight items of the PHQ-8 scale in accuracy (M = 75.9%), and F1 score (0.74). GPT models were able to predict PHQ-8 items related to emotional and cognitive states. Llama 3_8B demonstrated superior detection of anhedonia-related symptoms and the Cohere LLM’s strength was identifying and predicting psychomotor activity symptoms. This study provides a novel outlook on the potential of LLMs for predicting self-reported questionnaire scores from textual interview data. The promising preliminary performance of the various models indicates there is potential that these models could effectively assist in the screening of depression. Further research is needed to establish a framework for which LLM can be used for specific mental health symptoms and other disorders. As well, analysis of additional datasets while fine-tuning models should be explored.