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Pilot Results of the Teens Lifting Teens: A Statewide Youth Development Program
No full textYouth mental health and teen suicide prevention are among the highest needs in Utah communities. The Teens Lifting Teens (TLT) pilot program seeks to equip Youth Councils with resources and education through training, near-peer mentoring, and service-learning opportunities to build protective factors among teens. Evaluation results showed that TLT youth exposure to near-peer mentoring and service-learning opportunities positively impacted their lives
Outcomes and Impact of an E-commerce Extension Program in Rural Utah
No full textUtah State University Extension’s E-commerce Accelerator (ECA), a mentoring service of the Rural Online Initiative, provides hands-on e-commerce training to small rural businesses in Utah. From January 2024 to July 2025, thirty-five (35) businesses have successfully created online sales websites, with 27 (68%) reporting increased sales revenue
Harnessing blueberry flower chemistry and morphology to boost pollination and bee health
No full textData for Large projected increases in area burned and wildfire frequency by 2050 in Utah, USA
No full textChanges in wildfire regimes may disrupt ecosystem processes as wildfires burn larger areas or burn more frequently than the recent natural range of variability. The climatic drivers of wildfire behavior may change in strength but these effects are not likely to be uniform across space and between different vegetation types. Increased understanding of how weather and climate influence patterns of burn area and frequency across vegetation types may assist in better predicting and managing future wildfire regimes. We examined a dataset of all 1469 wildfires ≥40 ha from 1984 – 2021 in Utah, USA and used antecedent daily weather data to analyze how temperature and aridity influenced fire area and frequency across forested and non-forested vegetation types. The number of days in a year where air temperature was ≥ 26.6 °C (80 °F) was the best predictor for area burned for forest (R2 = 0.31) and non-forest ecosystems (R2 = 0.31) in Utah. However, model skill was variable across vegetation types and performed best for high-elevation forest ecosystems (R2 = 0.27 to 0.32) relative to low-elevation, non-forest ecosystems (R2 = 0.11 to 0.31). By 2050, warming trends local to Utah may result in a 60% increase in area burned for forests and a 232% increase for non-forests. These results highlight a simple metric – temperature – that explains large portions of variability in burned area and correlates with fire season length. A simple temperature metric is a good match for the vegetation and fire season climate of Utah, which is generally dry. Our results suggests that a warmer future may bring widespread and vegetation-specific changes in wildfire regimes with large increases in area burned across most vegetation types
Decision Framing Overview and Performance of Management Alternatives for Bison and Elk Feedground Management at the National Elk Refuge in Jackson, Wyoming
No full textThis report was developed to evaluate the performance of a set of proposed alternatives for Cervus elaphus canadensis (elk) and Bison bison (bison) management at the National Elk Refuge in Wyoming, U.S.A., and to inform a National Environmental Policy Act Environmental Impact Statement focused on developing the next “Bison and Elk Management Plan” (BEMP). The U.S. Geological Survey facilitated a structured decision-making process for the U.S. Fish and Wildlife Service to develop the alternatives and the criteria (performance metrics) for evaluating the alternatives. Chapter A (this chapter) provides scoping details of the “BEMP,” a summary of the 19 metrics used to evaluate the performance of each of the 6 alternatives, and methodological details of 2 performance metrics that were not covered in other technical chapters. Additional technical details, results, and interpretations are briefly covered in this chapter but are mostly contained in chapters B–F
Developing Standardized Testing Datasets for Benchmarking Automated Quality Control Algorithm Performance With Aquatic Sensor Data
No full textAdvances in water monitoring technologies have led to a large increase in the amount of data collected from rivers, lakes, and other water systems. However, ensuring that these data are accurate and reliable remains a major challenge. Traditional data quality checks are done manually by a technician, which can be slow, inconsistent, and not practical for real-time monitoring. This research addresses these challenges by developing standardized datasets for testing computer-based methods that automatically detect and correct errors in water data. Using information from the Logan River Observatory in northern Utah, we created a step-by-step process to identify, categorize, and label errors in sensor data. This process uses computer programming to make the work faster, more consistent, and easy to repeat. The standardized datasets developed through this work will help scientists compare different automated quality control methods under a variety of water conditions. Ultimately, this will improve the accuracy and reliability of water data, leading to better water management decisions and greater public confidence in water information systems