Colby College

NSF Awards · FY 2026 · 2026-09

The Great Salt Lake (GSL) is vital to Utah’s economy, contributing over $1 billion annually through mineral extraction, brine shrimp harvesting, and recreation. However, the lake has reached historically low water levels due to upstream water diversions for agriculture, industry, and municipalities. As the lake shrinks, the newly exposed lakebed is emitting wind-blown dust containing harmful heavy metals like arsenic and lead—byproducts of past industrial activity. This toxic dust threatens public health, agriculture, and ecosystems, with risks that extend far beyond the lake itself. This project will shed light on the role that dust plays in depositing heavy metals into ecosystems and onto important crops including corn and alfalfa. As metals accumulate in plants, they may ascend the food chain into livestock, predators, and ultimately humans, with a variety of negative health outcomes. Therefore, the results of this study will have direct implications for the health of ecosystems and communities both within the Great Basin and around the world. The results of this research be shared with communities that may be directly impacted by increased dust emission, by leveraging partnerships with local and state agencies and non-profit organizations in outreach through their regular programming such as fact sheets, newsletters, and community presentations. The research will be integrated with education activities by building and distributing soil test kits for students to use within their local communities, and by engaging local K-12 teachers in hands-on research through teacher internships. As the Great Salt Lake continues to shrink and emit more dust, native and agricultural plants may act as vectors of metal contamination, locally and regionally. As such, this study will utilize a combination of greenhouse and field-based sampling and atmospheric modeling to evaluate the risk to humans and ecosystems posed by GSL dust deposited on key native plants and agricultural crops through: 1) Assessing the extent to which plants take up these heavy metals through root and foliar (leaf) uptake, 2)Evaluating differing plant bioaccumulation among taxa key to the Utah economy, 3) Determining the impact of GSL-sourced dust on plants in the Great Basin region, and 4) Identifying potential source regions of dust that are impacting plants. The project will also analyze strontium, neodymium, and lead isotope ratios in plant tissues to determine the ability of these isotopic signals to determine soil and/or foliar dust compositions or “fingerprints”. Through geochemistry and atmospheric modeling, the research will assess the sources and transport pathways of dust from the GSL lakebed and other regional dust emissions areas and quantify the dust contribution to regional soils. The data generated by this project will contribute to environmental and health-related planning and serve as a new tool for understanding heavy metal (re)distribution during natural processes expedited by environmental change and human activity. These insights are essential for addressing the immediate consequences of the lake’s decline and for predicting more severe impacts in the future, as continued drought and future water management practices could expose more lakebed and increase the risk of toxic dust emissions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Atolls, low-lying coral islands surrounding inner lagoons, are critical ecosystems that support biodiversity and human communities, but face escalating threats from sea-level rise, ocean acidification, intensified storms, and human activities. This research project seeks to better understand how atolls respond to these challenges by studying changes in their size and shape over time. By analyzing satellite imagery and modeling the potential response of these islands to changing climates, the project aims to identify atolls most at risk of land loss and provide insights for conservation and community adaptation strategies. This project will provide a detailed and reproducible approach to studying atoll island change and resilience. An essential component of the project is its commitment to education and diversity, offering hands-on research, training, and mentoring opportunities for undergraduate students, particularly those from underrepresented backgrounds. These efforts will not only generate actionable knowledge for policymakers and conservationists but also train a new generation of scientists dedicated to tackling environmental challenges. The outcomes will contribute to safeguarding these fragile islands and the communities and species that rely on them. By leveraging modern remote sensing technologies and numerical modeling, it will enhance our understanding of how these unique landforms respond to both natural and human driven impacts. This research will not only advance scientific knowledge but also foster a new generation of diverse and skilled Earth and Environmental scientists equipped to tackle future environmental challenges. This project integrates remote sensing, machine learning, and numerical modeling to study atoll systems at both global and local scales, with the goal of understanding their resilience to climate change and human impacts. Utilizing Landsat's extensive satellite imagery archive and high-resolution Planet Labs imagery, the research will enhance remote sensing accuracy and expand the spatial coverage of a global database of atoll morphometrics while expanding the datasets to include wave and storm climate data per atoll. By employing machine learning techniques, such as convolutional neural networks, the project will automate landcover classification and atoll mapping of satellite imagery. Fieldwork at Glover’s Reef Atoll in Belize will ground-truth these methods, ensuring robust modeling and accurate predictions. The study addresses key questions about the drivers of atoll evolution—natural processes like coral growth and sediment dynamics versus external influences like wave climate and human activity—and will inform predictive models of atoll responses to environmental challenges. In parallel, the project advances environmental science education by embedding computational thinking, data science, and computational proficiency into a revamped Environmental Computation curriculum at Colby College. By designing courses that blend computer science and environmental studies, including data science, AI, and field-based methods, the project fosters diversity and accessibility in the earth sciences, empowering students from all backgrounds to tackle complex environmental problems. This initiative aligns with the NSF’s mission by addressing pressing environmental challenges while cultivating a new generation of interdisciplinary scientists equipped with computational and analytical skills critical for advancing research and conservation. This project is jointly funded by the Geomorphology and Land-use Dynamics Program (GLD), the Earth Sciences Division (EAR), and the Established Program to Stimulate Competitive Research (EPSCoR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Glacier ice in polar and alpine regions acts as a natural environmental sampler, preserving atmospheric dust and other particulates through time. To learn about the sources of dust to the polar ice sheets, the project team will use a technique called isotopic fingerprinting. They will apply this approach to dust samples isolated from ice cores, which are cylinders drilled through a glacier or ice sheet, and which represent an important archive of Earth’s climate system. They will also measure the grain size distribution and concentration of the dust particles. Together with the isotopic fingerprints, these analyses will allow them to infer the source and transport pathway of the dust at different times in the past to improve understanding of past atmospheric circulation. They will apply this approach to ice core samples from several sites in Greenland and Antarctica. Determination of dust source, or provenance, through compositional analysis is a powerful approach for understanding Earth surface processes and changes in the Earth’s climate system. Previous work has shown that paired measurements of dust particle concentration and grain size distribution represent an important complement to geochemical analyses, allowing simultaneous assessment of changing source inputs and transport intensity to remote ice core locations. Development of provenance and grain size datasets on ice core dust from Greenland and Antarctica will enable the project team to test a range of hypotheses about past atmospheric circulation. For instance, they will evaluate evidence for a potential West Antarctic Ice Sheet collapse during the last interglacial period, known as marine isotope stage 5e, using ice samples from Taylor Glacier. They will test a new hypothesis that summertime warmth during the Younger Dryas interval led to widespread glacial recession in the Northern Hemisphere, activating high-latitude dust sources such as those in southern Alaska. They will compare samples from the Last Glacial Maximum (LGM) and Younger Dryas in the GISP2 ice core to assess whether the Greenland Summit was impacted by the emergence of these high-latitude dust sources. Finally, they will apply a novel method to combine potassium/argon (K/Ar) geochronology with strontium, neodymium, and lead (Sr-Nd-Pb) radiogenic isotope analysis, developed through a previous grant, to a suite of ice core samples from a range of sites in Greenland and Antarctica, including interglacial and LGM-age ice. This will allow them to better constrain past atmospheric circulation patterns and clarify interpretations about dust transport during the past. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Researchers at Northeastern University, the University of Maine, and Colby College are conducting research focused on developing and testing a technology-based solution that improves the accessibility of spreadsheet data tasks for blind and low vision (BLV) STEM employees. Data tasks include data exploration, data manipulation, and data analysis, which are often inaccessible to BLV employees due to the visual attributes of commonly used data tools and the limitations of current keyboard-based functions. Through this research, keyboard functions will be enhanced with the addition of natural language-based functions, resulting in an intuitive and accessible approach to performing complex data tasks. Proficiency in the use of data is foundational for many STEM jobs. This project addresses a longstanding accessibility issue faced by BLV employees in STEM fields Employing a user-centered approach, this project has three phases: 1) establish design considerations and guidelines for developing BLV-focused natural language (NL) interfaces that can facilitate data analytics tasks, 2) develop a prototype NL interface to support BLV users to perform data analytics tasks on STEM-specific datasets, and 3) empirically measure the practical utility of the prototype system for its ability to support BLV users in performing these data tasks and assess its acceptability and usability among target end users. With BLV STEM professionals and researchers involved across all aspects of the research, the methods include interviews, co-design workshops, Wizard of Oz prototyping, and experimental evaluation. The results of this research will be a prototype of an intelligent natural language-based system, called Auxel, that will support BLV STEM professionals with spreadsheet data tasks. This award has been made in response to the NSF solicitation “Workplace Equity for Persons with Disabilities in STEM and STEM Education” (NSF 23-593). The Directorate funds this project for Social, Behavioral and Economic Sciences’ Office of Multidisciplinary Activities and the Division of Graduate Education’s EDU CORE Research program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This collaborative project, Maine-FOREST, supports incubating research themes, teams, and products in a scientific topical area that links to research priorities identified in the state’s approved Science and Technology (S&T) Plan. Maine-FOREST will deliver a diverse, sustainable, and statewide research, education, and innovation incubator that builds strategic research and development (R&D) capacity. This will fuel the dramatic growth of the state’s forest-based economy and the rural communities it supports. Four interconnected research themes in the project reflect strengths and opportunities within the jurisdiction that align with the state’s science and technology, climate action, and economic development strategic plans. The project also will leverage prior investments in Maine’s research infrastructure, including federal awards and philanthropic gifts, and relationships with national laboratories and industry. Novel participatory and inclusive approaches will nurture community resilience and strengthen the capacity of diverse groups of natural resource-dependent rural and Indigenous communities to respond to current and future socio-ecological threats and opportunities. This new, partnership-based infrastructure will help to create a far more inclusive approach to promoting sustainable improvements in Maine’s forest-based research infrastructure, R&D capacity, and national competitiveness. Innovative approaches to STEM education and workforce development will reinforce the research themes in the project while also connecting with project activities in a companion EPSCoR Collaborations for Optimizing Research Ecosystems RII award (OIA-2412130). Maine-FOREST’s four convergent incubator themes include Environmental AI & Informatics, Cellulosic Nanofiber (CNF) Bioproducts, Rural & Tribal Resilience, and Smart Rural Development. Statewide incubator teams associated with each theme aim to close key knowledge gaps. Maine-FOREST will advance one's ability to characterize and utilize CNF within an advanced manufacturing context. New AI/ML-driven technologies will be applied to assess core ecosystem attributes across broad spatial-temporal scales. Innovative, culturally inclusive approaches will be applied to participatory systems dynamics modeling to better leverage stakeholder networks. Research on the program’s collaborative culture and processes, including a cohort-based approach to student engagement, will yield new information regarding convergent science. Deliverables from Maine-FOREST include, in addition to typical scientific outputs, increased research capacity with support for 20 early-career faculty, a new forest sector business development faculty position, and an actionable dashboard of metrics related to forest, economy, and workforce capacity. Maine-FOREST is led by the University of Maine. Project partners include an emerging research institution (the University of Southern Maine), two public, primarily undergraduate institutions, two private colleges (Colby and Bates), and strategic non-profits across the state (the Maine Development Foundation, Maine Mathematics and Science Alliance, Maine TREE, the Rural Aspirations). Maine-FOREST will directly support at least 45 faculty (45% early-career), 85 undergraduate students, 10 graduate students, and 4 postdocs. The project will potentially benefit 200 K-12 educators and nearly 2,000 students in 15 schools will directly benefit. Ten diverse rural and economically distressed Maine communities, and a Tribal Nation, will also be directly engaged. This project is funded by the NSF EPSCoR Research Incubators for STEM Excellence (E-RISE) Research Infrastructure Improvement Program. The E-RISE RII Program supports the development and implementation of sustainable broad networks of individuals, institutions, and organizations that will transform the science, technology, engineering and mathematics (STEM) research capacity and competitiveness in a jurisdiction within a field of research aligned with the jurisdiction's science and technology priorities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.