A winter snowpack is not runoff capable until it is warmed to 0 degree Celsius and saturated in liquid water, the achievement of this state is called ripening Whether a snowpack is ripened or not is a critical information of the snowpack state and behavior regarding water availability and floods. Before a snowpack is ripened, it is a buffer to rain and intense melt. Once a snowpack ripens, rain and high temperature will cause water releases that may be available for soil and ecosystems, but can also cause floods. In addition, hydroelectric dams rely on gradual release of water caused by gradual ripening of the snowpack in the spring in order to use and store water resources efficiently. Yet, the variability of the snowpack ripening is poorly constrained, especially within a watershed with complex topography and considering inter-annual variability. UsingK-Means clustering on the output of SNODAS, a widely used operational model, we identify a tipping point in the behavior of the snowpack. Warm and low-snow years experience chaotic behavior and mid-winter releases of water instead of ordered progressive spring stream and soil recharge. Using a Long Short-Term Memory Neural Network and K-Means clustering, we develop a methodology to predict the spring ripening of a snowpack using information on the snowpack during the first half of winter, with an error of 14 days, corresponding to the natural variability of weather.

Suppose everyone at Gradcon listed their top 5 favorite movies of all time. To save everyone the trouble of ranking, we'll assume the lists are unordered. Here's the question: how many lists do we need to guarantee we have three lists, any two of which share the exact same titles? For example, three lists that share nothing in common would satisfy our condition. It turns out that such an event is guaranteed if we have 3840 lists. This seems rather large, and is probably far too many, but trying to find a better upper bound has been a project of extremal set theory for over fifty years!

To generalize this problem, we consider sets (unordered collections of objects) and families, which are sets of sets. Extremal set theory is concerned with how large (or small) a family of sets can be if they must satisfy certain restrictions. A relatively young discipline of mathematics, its earliest problems continue to remain unsolved to this day. The sunflower conjecture is one such problem.

A family of sets is called a sunflower if each possible pairwise intersection (the set induced by including only objects included in a pair of sets) produces the same subset. Given the size of the sets in a family, the sunflower lemma establishes sufficient conditions for finding a sunflower subfamily of any predetermined size. Unfortunately, the bound determined by the lemma is quite large. Without getting too technical, the sunflower conjecture posits that the true upper bound is, for the most part, much smaller. As the existence of a sunflower can imply further structural properties, such a result would have far-reaching effects.

This presentation serves as an introduction to the sunflower lemma (and its associated conjecture) for a general audience. A willingness to learn is the only prerequisite.

Missoula and Ravalli Counties in Montana, renowned for their prime bear habitats encompassing mountains, rivers, creeks, and forests, have witnessed a notable increase in human-bear conflicts over the past two decades. This escalation is attributed to concurrent growth in both human and bear populations, coupled with climate-induced constraints on natural food sources. Human-bear conflicts arise primarily when bears access human-associated foods (attractants), including fruit trees, garbage, and bird feeders. Evaluation of conflicts relies predominantly on voluntary reports, revealing a significant reporting gap between Ravalli and Missoula Counties. Despite this, conflict reports and prevention strategies remain unexamined based on attractant types and geographical features at a county scale.

This study aims to comprehend the spatial and temporal shifts in conflicts and prevention priorities over five years, considering alterations in land cover and escalating human development in both counties. By analyzing a comprehensive database of conflicts reported to Montana Fish, Wildlife, and Parks, along with implemented conflict prevention strategies, the research examines the spatial dynamics and social demographics of human-black bear conflicts in Missoula and Ravalli Counties from 2017 to 2022. The objectives include: (1) comparing geographical features strongly correlated with conflicts and conflict prevention strategies in both counties and (2) contrasting demographic profiles in conflict-prone areas between Ravalli and Missoula Counties to reveal potential social determinants contributing to reporter bias in Ravalli County.

Preliminary findings reveal garbage as the primary attractant driving conflicts in both counties, particularly along riparian corridors, emphasizing the need for comprehensive conflict prevention strategies in housing development zones near water features.

This study guides bear managers in adapting prevention strategies to target areas highly correlated with conflicts in both counties. Furthermore, the results will inform the establishment of bear buffer zones through community planning in Ravalli County and inform areas to prioritize the Missoula Bear Smart Community Plan implementation.

Rangelands cover approximately 47% of the Earth’s land surface and are often dominated by perennial grasslands. In recent decades many of these perennial grass populations have been declining due to land use and climate shifts. With this decline of many perennial grass populations, and the vulnerable state of many rangelands, several varieties of perennial grass have been developed for consideration for rangeland restoration in the Southwestern United States. Using three perennial grass restoration “workhorse” species (Bouteoula gracilis, Bouteloua curtipendula, and Sporobolus cryptandrus), several varieties of these species have been developed and are commonly used. Three different types are Cultivars, Wild-collected seeds, and Novel-Composites. Cultivars have been artificially selected for specific traits and often have improved performance compared to Wild-collected seeds that are usually locally sourced but expensive and difficult to collect in sufficient quantities for restoration. Finally, Novel-Composites, a recently emerging seed variety, are ideally a blend of multiple wild-populations mixed with the goal of maintaining genetic diversity. However, to fully understand their efficacy in restoration, we must first assess their early life stages, including germination characteristics. Germination is one of the first steps a seed takes toward establishment and is critical to a population’s success. Utilizing germination chambers and petri dishes, seeds of these different varieties were exposed to warming and drought stress to assess their relative performance. Across environmental treatments, Cultivars out-performed both Wild-collected and Novel-Composite individuals while the different species exhibited variable germination characteristics. As expected, Cultivars are a likely strong contender for restoring rangelands, though could perform with more variability compared to Novel-Composites and Wild-collected seeds. With an increasing need for rangeland restoration, careful selection of different seed varieties could play a vital role in supporting and protecting perennial grasslands.

Being one of the leading causes of maternal and neonatal morbidity and mortality worldwide, hypertensive disorders of pregnancy (HDPs), including eclampsia, preeclampsia, and gestational hypertension, are a major public health burden. Research links fine particulate matter air pollution (PM_2.5­) to increased risk of HDPs, but such studies are lacking in wildfire-prone rural areas. While PM_2.5 concentrations have been decreasing around most of the US in the last few decades, Montana and other western states have experienced an increase in PM_2.5 concentrations due to increasing wildfire activity.

Moreover, a growing body of research into the health effects of air pollution has grown methodologic literature to improve how such research is conducted. As these research methods advance, preserving individual confidentiality is crucial. Geomasking is a class of methods that conceals individuals’ locations while attempting to preserve the location-health outcome relationship. However, in rural areas like much of Montana, adequately concealing an individual’s location may introduce substantial error into their air pollution exposure estimate. By extension, this could introduce bias into conclusions about the true association between air pollution and the health outcome of interest.

The purpose of this study was twofold: (1) to determine the association between PM_2.5 and HDPs and (2) to use simulation to explore conditions under which geomasking may introduce bias into the results of research on the health effects of air pollution.

Using Montana birth certificate records from 2008-2019, we used multiple logistic regression and found a 10% increase in risk of HDP associated with each 3 µg/m³ increase in mean pregnancy PM_2.5 exposure and used distributed lag nonlinear models to identify two sensitive time periods of exposure: (1) the week of conception and three weeks before and after conception; and (2) gestational weeks 19 through 33.

The geomasking simulation is in progress and results will be presented.

Purpose:

The purpose of our study was to collect information on patients’ experiences interacting with the maternal healthcare system before, during, and after pregnancy.

Methods:

The Maternal Healthcare Experiences Study included a statewide survey. We used two patient-informed, valid, and reliable scales, the Mothers on Respect Index and the Mothers Autonomy in Decision Making Scale. We added additional items to collect information on patient sociodemographic attributes (race/ethnicity, education level, income), social risk, prepregnancy wellness visits, and birth location (home, hospital, birth center). We measured social risk with the Health Leads Social Screening Tool. The UM IRB approved this study under protocol # 120-22. Data collection occurred from July 26, 2022 – October 15, 2022. The study included 484 Montanans who had been pregnant in the last five years. We used convenience and purposive sampling methods and recruited participants through social media platforms, Facebook and Instagram.

Originality:

Historically, maternal healthcare has focused primarily on clinical outcomes with little emphasis on patient experiences. Interactions with care providers significantly impact a patient’s pregnancy and childbirth experiences. Negative experiences can influence future health-seeking behaviors and have long-term adverse impacts. Gathering information on patient experiences can drive quality improvement initiatives at healthcare organizations aimed at respectful patient-provider interactions and broader health-system reform.

Significance:

The United States has high rates of maternal morbidity and mortality, with significant disparities across socially marginalized populations. Pregnancy marks a critical period for a person’s health, and disrespectful treatment can cause mental and emotional harm that extends beyond birth. Little information exists on patient maternal healthcare experiences in Montana. This study aims to fill this knowledge gap and inform the provision of respectful maternal care in the state.