Grants

NSF

This project aims to serve the national interest by organizing coordinated multi-site replication studies on open computing education research questions. Research relies on the ability to replicate studies to understand the limits and applicability of research findings and to build confidence in results. Replication studies are especially important in computing education research where results are drawn from human subjects studying computing. The goal of this IUSE:EDU level two Institutional and Community Transformation project is to create cohorts of researchers to design replication packages for rigorous research that will support cross-study meta-analysis, build fundamental knowledge on key topics, and provide the foundation for computing education research theory building. The project will involve approximately ten core replication designers and forty replication participants impacting thousands of students across the United States. A replication design team will be convened to create replication packages that address research goals and questions of interest to the computing education research community. A project team will then be formed to recruit a series of replication participants who will run the study in their context using the replication packages produced by the replication design team. Members of the computing education research community will be invited to run the replications in their classrooms, contributing to the overall investigation into the topic. Results of these studies will address common research questions and lay the foundation for computing-specific educational theory. The results of the coordinated, multi-site replication studies will provide deeper insights into important computing education research topics and will provide a better understanding of the impacts of various contextual factors on improving student learning and success in computing. The project evaluation will consist of formative and summative assessments of the replication process and objective measures such as publications and citations. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities.  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

This project aims to serve the national interest by expanding access to high-quality, adaptable STEM learning materials through the development and large-scale deployment of Open Educational Resources (OER) and integrated social annotation technologies. This Level 2 Institutional and Community Transformation project addresses the ongoing challenge of limited access to affordable and pedagogically effective instructional materials, particularly in large educational systems. At the core of the project is the enhancement and deployment of Nota Bene (NB), a web-based annotation platform that enables students and instructors to engage in contextual conversations directly alongside the textbook content - the digital version of marking up physical textbooks. By analyzing these in-line comments, instructors can identify confusing, outdated, or incomplete material, and students can contribute insights or suggest improvements that reflect their learning needs. This project will integrate Nota Bene into the LibreTexts platform, one of the most widely used OER repositories, with the aim of transforming passive reading into an interactive and collaborative STEM learning experience. The project's goals are to establish social annotation as a core practice in STEM education, to create a scalable infrastructure for collaborative OER development, and to generate actionable knowledge on how learner-instructor interactions through annotation drive content improvement and learning outcomes. Specifically, the project will (1) redesign and expand the NB platform to enhance usability for different types of STEM courses and institutions nationwide, (2) develop and implement workflows for peer review and open pedagogy that embed continuous feedback loops into OER lifecycle management, and (3) build and operationalize CalOPEN, a statewide hub supporting OER creation, adoption, and innovation within California's higher education systems, serving as a replicable national model. The project employs design-based research to iteratively improve the Nota Bene platform and its pedagogical integration, coupled with large-scale learning analytics to examine patterns in annotation use, feedback quality, and their correlation with student learning gains. Assessment and evaluation will be multi-faceted, involving external evaluators who will conduct surveys, interviews, system usage analysis, and performance assessments to measure impact on student engagement, instructor practices, and content quality. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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

Much can be learned about how an organism functions by understanding how its cells and tissues are organized. Cells are very small and so they can best be observed with specialized techniques like electron microscopy. Samples must be preserved carefully for study by electron microscopy and care must be taken not to introduce changes that could mislead interpretation of observations. The best method for preparing tissue for electron microscopy is to rapidly freeze samples and observe them under very low temperatures, a process called cryogenic preservation. Cryopreservation is challenging in plants, and the goal of the proposed research is to develop procedures that overcome these challenges. If successful, this proposal will allow plant cells and tissues to be observed after cryopreservation and give unprecedented information about how parts of plant cells and tissue work together. Understanding how plant cells work will help develop plants that are better able to meet human needs and ensure food security. The methods developed will be shared with others through public databases to allow other scientists to use the methods in their own studies. The research will include several early career scientists, and they will receive training in techniques and develop skills which are in high demand in science labs and in industry. Cryogenic preservation is the gold standard for determining cell ultrastructure in its “native state” by virtue of physical fixation (freezing) to immobilize cell constituents in milliseconds. Volume electron microscopy (vEM) has gained significant traction as a tool for discovering the structure and spatial organization of cell and tissue components. In combination with cryo-electron tomography (cryoET), cryo-vEM has the potential to yield a paradigm shift in multi-scale in situ structural biology. However, due to their physical and chemical properties, plant cells are uniquely recalcitrant to fixation. Thus, plant science is strikingly lagging other fields in the revolution of understanding of how macromolecular complexes and cell compartments coordinate to create life. Determining the structure of cells and organelles in fully frozen hydrated chemically untreated and unstained would allow, for the first time, the observation of these entities in their native states. This proposal will develop cryopreservation and nanoscale imaging procedures for vEM of plant tissues in a focused effort to address this challenge efficiently for the plant community. Whole cell structures will be elucidated in 3D using cryo-vEM, while nanoscale cellular structure and organelles will be determined using cryo-vEM and cryo-ET. The datasets, protocols, and analytical pipelines generated through this research will be made available to the scientific community and the public through public databases. The proposed work will also provide unique training opportunities for several students and postdocs, training them in state-of-the plant structural biology, much desired expertise in the scientific workforce. 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

Much can be learned about how an organism functions by understanding how its cells and tissues are organized. Cells are very small and so they can best be observed with specialized techniques like electron microscopy. Samples must be preserved carefully for study by electron microscopy and care must be taken not to introduce changes that could mislead interpretation of observations. The best method for preparing tissue for electron microscopy is to rapidly freeze samples and observe them under very low temperatures, a process called cryogenic preservation. Cryopreservation is challenging in plants, and the goal of the proposed research is to develop procedures that overcome these challenges. If successful, this proposal will allow plant cells and tissues to be observed after cryopreservation and give unprecedented information about how parts of plant cells and tissue work together. Understanding how plant cells work will help develop plants that are better able to meet human needs and ensure food security. The methods developed will be shared with others through public databases to allow other scientists to use the methods in their own studies. The research will include several early career scientists, and they will receive training in techniques and develop skills which are in high demand in science labs and in industry. Cryogenic preservation is the gold standard for determining cell ultrastructure in its “native state” by virtue of physical fixation (freezing) to immobilize cell constituents in milliseconds. Volume electron microscopy (vEM) has gained significant traction as a tool for discovering the structure and spatial organization of cell and tissue components. In combination with cryo-electron tomography (cryoET), cryo-vEM has the potential to yield a paradigm shift in multi-scale in situ structural biology. However, due to their physical and chemical properties, plant cells are uniquely recalcitrant to fixation. Thus, plant science is strikingly lagging other fields in the revolution of understanding of how macromolecular complexes and cell compartments coordinate to create life. Determining the structure of cells and organelles in fully frozen hydrated chemically untreated and unstained would allow, for the first time, the observation of these entities in their native states. This proposal will develop cryopreservation and nanoscale imaging procedures for vEM of plant tissues in a focused effort to address this challenge efficiently for the plant community. Whole cell structures will be elucidated in 3D using cryo-vEM, while nanoscale cellular structure and organelles will be determined using cryo-vEM and cryo-ET. The datasets, protocols, and analytical pipelines generated through this research will be made available to the scientific community and the public through public databases. The proposed work will also provide unique training opportunities for several students and postdocs, training them in state-of-the plant structural biology, much desired expertise in the scientific workforce. 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

This project will develop novel filters called ring-resonators, imprinted on silicon wafers, to suppress the atmospheric airglow at near-infrared (NIR) wavelengths where cool stars, proto-planets and galaxies glow most brightly. The airglow, coming from narrow emission-lines produced by OH molecules in the upper atmosphere, prevent astronomers from taking full advantage of this window from ground-based observatories. The ring resonators create narrow-band notch filters (like an uneven picket fence with very narrow pickets), thermally tuned to block the air-glow lines but let most of the light from astronomical objects through. This program focuses on solving the engineering problems associated with coupling light from telescopes efficiently into the ring-resonators, and then back out to feed conventional instruments, such as spectrographs. By the conclusion of this project, leveraged by US industrial partners, the technology will be sufficiently advanced for on-sky demonstration of ring resonator-based air-glow suppression. Working with Lowell Observatory’s Marley Foundation Astronomy Discovery Center, this program also will develop a portable, interactive exhibit focused on the critical thinking and the problem-solving tools used by scientists and engineers in the development of astronomical instrumentation. Ring resonator filters currently offer the only viable alternative (potentially in a more compact package) to fiber Bragg gratings (not fabricated in the US) for reducing the pernicious airglow that limits ground-based NIR observations. The ring resonators developed here aim to suppress (by 20-40 dB) the most prominent OH lines, yielding an estimated signal to noise (S/N) improvement in the NIR of a factor of 5 or more in a broad-band sense, and dramatically reducing scattered light in astronomical instruments. Laboratory efforts focus on the integration of commercial off-the-shelf photonic lanterns into the source-device-sensor pathway; coupling both TE and TM input modes to photonic devices simultaneously; and leveraging the design, fabrication, and packaging expertise of industry leaders in silicon photonics, all for the purpose of mitigating the coupling losses that have thus far limited device throughput. The wavelength range over which OH suppression can be accomplished will also be expanded. The outreach exhibit will be centered upon existing instruments, some previously flown on SOFIA, and will be portable, allowing Lowell’s Instrument Transport Vehicle to take it to other sites, such as the schools with which Lowell partners. 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

This project will develop novel filters called ring-resonators, imprinted on silicon wafers, to suppress the atmospheric airglow at near-infrared (NIR) wavelengths where cool stars, proto-planets and galaxies glow most brightly. The airglow, coming from narrow emission-lines produced by OH molecules in the upper atmosphere, prevent astronomers from taking full advantage of this window from ground-based observatories. The ring resonators create narrow-band notch filters (like an uneven picket fence with very narrow pickets), thermally tuned to block the air-glow lines but let most of the light from astronomical objects through. This program focuses on solving the engineering problems associated with coupling light from telescopes efficiently into the ring-resonators, and then back out to feed conventional instruments, such as spectrographs. By the conclusion of this project, leveraged by US industrial partners, the technology will be sufficiently advanced for on-sky demonstration of ring resonator-based air-glow suppression. Working with Lowell Observatory’s Marley Foundation Astronomy Discovery Center, this program also will develop a portable, interactive exhibit focused on the critical thinking and the problem-solving tools used by scientists and engineers in the development of astronomical instrumentation. Ring resonator filters currently offer the only viable alternative (potentially in a more compact package) to fiber Bragg gratings (not fabricated in the US) for reducing the pernicious airglow that limits ground-based NIR observations. The ring resonators developed here aim to suppress (by 20-40 dB) the most prominent OH lines, yielding an estimated signal to noise (S/N) improvement in the NIR of a factor of 5 or more in a broad-band sense, and dramatically reducing scattered light in astronomical instruments. Laboratory efforts focus on the integration of commercial off-the-shelf photonic lanterns into the source-device-sensor pathway; coupling both TE and TM input modes to photonic devices simultaneously; and leveraging the design, fabrication, and packaging expertise of industry leaders in silicon photonics, all for the purpose of mitigating the coupling losses that have thus far limited device throughput. The wavelength range over which OH suppression can be accomplished will also be expanded. The outreach exhibit will be centered upon existing instruments, some previously flown on SOFIA, and will be portable, allowing Lowell’s Instrument Transport Vehicle to take it to other sites, such as the schools with which Lowell partners. 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

Integrated Sensing and Communication (ISAC) is a transformative technology that unifies sensing and communication functions within a single system, significantly enhancing efficiency, cost-effectiveness, and performance. By sharing key resources such as spectrum, power, and hardware, ISAC not only reduces infrastructure costs but also improves spectrum utilization, minimizes interference, and alleviates congestion in increasingly crowded wireless environments. This integration enables real-time environmental awareness and faster decision-making, which are essential for applications such as autonomous vehicles, smart cities, Internet of Things networks, and industrial automation. Despite its promise, ISAC systems face major challenges due to the dynamic and complex nature of the wireless medium, particularly in multi-user scenarios, and the limitations of Radio-Frequency (RF) circuitry, which impact both sensing accuracy and communication reliability. This project introduces a novel ISAC system, Integrated Sensing and Telecommunications for Intelligent Connection and Transmission (INSTINCT), to address key challenges in joint communication and sensing. It advances multi-dimensional signal processing (MSP) across the delay, Doppler, and wavenumber domains, while ensuring compatibility with standard wireless protocols. A central innovation is the use of wavenumber-delay-Doppler domain signal processing, where range and velocity information naturally reside. INSTINCT further enables continuous-aperture phased multiple-input multiple-output (CAP)-MIMO, a reconfigurable sub-aperture architecture that enables dynamic, simultaneous communication and sensing capabilities. Key research contributions of this project include: (1) Developing reconfigurable RF hardware that seamlessly integrates communication and sensing via spatially adaptive apertures; (2) Creating electromagnetic-informed channel models and optimal signaling strategies tailored for CAP-MIMO systems; (3) Designing multi-dimensional waveforms and analyzing the impact of synchronization errors, RF impairments, and multi-user interference in the wavenumber-delay-Doppler domain; (4) Developing domain-informed progressive neural network architectures for joint beamforming and phase shift design in communication and radar sensing using reconfigurable hardware; (5) Designing a practical dynamic spectrum sharing framework using learning-based spectrum activity sensing for INSTINCT. 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

Integrated Sensing and Communication (ISAC) is a transformative technology that unifies sensing and communication functions within a single system, significantly enhancing efficiency, cost-effectiveness, and performance. By sharing key resources such as spectrum, power, and hardware, ISAC not only reduces infrastructure costs but also improves spectrum utilization, minimizes interference, and alleviates congestion in increasingly crowded wireless environments. This integration enables real-time environmental awareness and faster decision-making, which are essential for applications such as autonomous vehicles, smart cities, Internet of Things networks, and industrial automation. Despite its promise, ISAC systems face major challenges due to the dynamic and complex nature of the wireless medium, particularly in multi-user scenarios, and the limitations of Radio-Frequency (RF) circuitry, which impact both sensing accuracy and communication reliability. This project introduces a novel ISAC system, Integrated Sensing and Telecommunications for Intelligent Connection and Transmission (INSTINCT), to address key challenges in joint communication and sensing. It advances multi-dimensional signal processing (MSP) across the delay, Doppler, and wavenumber domains, while ensuring compatibility with standard wireless protocols. A central innovation is the use of wavenumber-delay-Doppler domain signal processing, where range and velocity information naturally reside. INSTINCT further enables continuous-aperture phased multiple-input multiple-output (CAP)-MIMO, a reconfigurable sub-aperture architecture that enables dynamic, simultaneous communication and sensing capabilities. Key research contributions of this project include: (1) Developing reconfigurable RF hardware that seamlessly integrates communication and sensing via spatially adaptive apertures; (2) Creating electromagnetic-informed channel models and optimal signaling strategies tailored for CAP-MIMO systems; (3) Designing multi-dimensional waveforms and analyzing the impact of synchronization errors, RF impairments, and multi-user interference in the wavenumber-delay-Doppler domain; (4) Developing domain-informed progressive neural network architectures for joint beamforming and phase shift design in communication and radar sensing using reconfigurable hardware; (5) Designing a practical dynamic spectrum sharing framework using learning-based spectrum activity sensing for INSTINCT. 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

Rivers are vital to life, providing drinking water and supporting agriculture, recreation, transportation, and fisheries. However, changes on land can alter runoff in ways that negatively impact river ecosystems and water quality. The goal of this research is to better understand and predict changes in river chemistry at the global scale. The investigators will (re)use publicly available water chemistry and flow data from more than 450 rivers across all seven continents. Novel machine learning and other data analysis techniques will be used to determine how and why substances in rivers, including nutrients, metals, salts, and trace minerals, vary throughout the year and across the landscape. The project includes training workshops for early career researchers and public outreach through the Science Museum of Minnesota. Project outcomes will provide insights into how we can better manage rivers and protect water resources for the future. This data-intensive project will examine the similarities and differences in the seasonal distribution of chemicals across rivers with varying flow regimes and watershed characteristics. Researchers will harmonize large datasets from Long-Term Ecological Research, National Ecological Observatory Network, and other federal science investments. They will use deep learning and statistical approaches to identify different drivers and seasonal regimes for a broad range of chemical solutes. The investigators hypothesize less synchrony amongst biologically active substances and across disturbed watersheds. The resulting dataset and analyses will enable prediction of water quality in unmonitored and remote river systems that are difficult and expensive to monitor. The project also supports workforce development in water resources and “big data” analysis. The outcome of this research will be new global frameworks for understanding and predicting how river chemistry responds to environmental changes over long timeframes and broad spatial scales. 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

Rivers are vital to life, providing drinking water and supporting agriculture, recreation, transportation, and fisheries. However, changes on land can alter runoff in ways that negatively impact river ecosystems and water quality. The goal of this research is to better understand and predict changes in river chemistry at the global scale. The investigators will (re)use publicly available water chemistry and flow data from more than 450 rivers across all seven continents. Novel machine learning and other data analysis techniques will be used to determine how and why substances in rivers, including nutrients, metals, salts, and trace minerals, vary throughout the year and across the landscape. The project includes training workshops for early career researchers and public outreach through the Science Museum of Minnesota. Project outcomes will provide insights into how we can better manage rivers and protect water resources for the future. This data-intensive project will examine the similarities and differences in the seasonal distribution of chemicals across rivers with varying flow regimes and watershed characteristics. Researchers will harmonize large datasets from Long-Term Ecological Research, National Ecological Observatory Network, and other federal science investments. They will use deep learning and statistical approaches to identify different drivers and seasonal regimes for a broad range of chemical solutes. The investigators hypothesize less synchrony amongst biologically active substances and across disturbed watersheds. The resulting dataset and analyses will enable prediction of water quality in unmonitored and remote river systems that are difficult and expensive to monitor. The project also supports workforce development in water resources and “big data” analysis. The outcome of this research will be new global frameworks for understanding and predicting how river chemistry responds to environmental changes over long timeframes and broad spatial scales. 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

Use and reuse of long-term ecological data is needed for understanding how biological communities are responding to a changing world. In marine environments, key data includes large-scale patterns such as El Niño, ocean conditions such as sea surface temperature and winds, and biological data such as the distribution and abundance of food resources and marine wildlife. These core data are often collected in non-standardized ways, which makes it a challenge to compare patterns of biological response across different regions or marine ecosystems. The Long-Term Ecological Research (LTER) network provides an opportunity to make comparisons between sites as they share similarities in conceptual design and data collection procedures. In this ULTRA-Data project, a team of scientists is harmonizing ecological data from three different LTER sites representing temperate (California Current), subpolar (Northern Gulf of Alaska) and polar (Antarctic Peninsula) marine ecosystems. These three sites are influenced by global-scale processes and each provides comparable local data on ocean conditions, lower trophic level planktonic food resources (euphausiid crustaceans, also known as “krill”), and upper trophic level consumers (seabirds). The investigators are testing the idea that seabird populations and community structure are affected by local ocean conditions (habitat quality) and food resource availability, affected by larger-scale processes as observed during El Niño. Results from this study are helping scientists and marine stakeholders understand how changing ocean conditions and food availability affect marine biological communities. This study is revealing whether large-scale environmental variability is affecting disparate marine ecosystems similarly or if response mechanisms differ between regions. This research is supplying cross-ecosystem knowledge to help inform management and conservation. The scientists are also training younger researchers, including early-career scientists, graduate students, and an undergraduate intern. This project addresses a gap in our understanding of how marine biological communities respond to environmental change by conducting cross-ecosystem syntheses on the climate responses of geographically disparate but functionally analogous prey and predator communities. Seabird communities are an ideal metric for regional comparisons, as their local distribution and abundance can reflect both short-term and long-term ecosystem dynamics, and geographically unrelated seabird communities retain similar functional compositions (e.g. divers vs. fliers, planktivores vs. piscivores, etc.). By leveraging data available from three different Long-Term Ecological Research (LTER) sites, this project is testing how local ocean conditions affect seabird abundance, diversity, and community composition across the California Current Ecosystem (CCE), Northern Gulf of Alaska (NGA), and Antarctic Peninsula (PAL). These three regions represent temperate, subpolar and polar ecosystems, yet are structurally linked by large-scale Pacific climate modes including the El Niño Southern Oscillation, Pacific Decadal Oscillation, and Southern Annular Mode. The three LTER sites have collected similar long-term datasets on oceanography (hydrographic casts), prey (net-sampled euphausiids), and seabirds (at-sea visual observation surveys). Data are thus being harmonized into 30+ year datasets to investigate bottom-up linkages between climate modes, local oceanographic patterns, and local prey/predator variability. Generalized Additive Mixed Models (GAMMs) and Hierarchical Modeling of Species Communities (HMSC) are being used to test biophysical relationships, including the evaluation of temporal effects such as direct and lagged effects of climate mode variability. The integrative and cross-ecosystem framework utilizes valuable data provided by LTER sites, identifies unknown dynamics underlying ecosystem synchrony and divergence, and provides mechanistic perspectives on how regional biophysical processes contribute to productive and globally important ecosystems. 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

Use and reuse of long-term ecological data is needed for understanding how biological communities are responding to a changing world. In marine environments, key data includes large-scale patterns such as El Niño, ocean conditions such as sea surface temperature and winds, and biological data such as the distribution and abundance of food resources and marine wildlife. These core data are often collected in non-standardized ways, which makes it a challenge to compare patterns of biological response across different regions or marine ecosystems. The Long-Term Ecological Research (LTER) network provides an opportunity to make comparisons between sites as they share similarities in conceptual design and data collection procedures. In this ULTRA-Data project, a team of scientists is harmonizing ecological data from three different LTER sites representing temperate (California Current), subpolar (Northern Gulf of Alaska) and polar (Antarctic Peninsula) marine ecosystems. These three sites are influenced by global-scale processes and each provides comparable local data on ocean conditions, lower trophic level planktonic food resources (euphausiid crustaceans, also known as “krill”), and upper trophic level consumers (seabirds). The investigators are testing the idea that seabird populations and community structure are affected by local ocean conditions (habitat quality) and food resource availability, affected by larger-scale processes as observed during El Niño. Results from this study are helping scientists and marine stakeholders understand how changing ocean conditions and food availability affect marine biological communities. This study is revealing whether large-scale environmental variability is affecting disparate marine ecosystems similarly or if response mechanisms differ between regions. This research is supplying cross-ecosystem knowledge to help inform management and conservation. The scientists are also training younger researchers, including early-career scientists, graduate students, and an undergraduate intern. This project addresses a gap in our understanding of how marine biological communities respond to environmental change by conducting cross-ecosystem syntheses on the climate responses of geographically disparate but functionally analogous prey and predator communities. Seabird communities are an ideal metric for regional comparisons, as their local distribution and abundance can reflect both short-term and long-term ecosystem dynamics, and geographically unrelated seabird communities retain similar functional compositions (e.g. divers vs. fliers, planktivores vs. piscivores, etc.). By leveraging data available from three different Long-Term Ecological Research (LTER) sites, this project is testing how local ocean conditions affect seabird abundance, diversity, and community composition across the California Current Ecosystem (CCE), Northern Gulf of Alaska (NGA), and Antarctic Peninsula (PAL). These three regions represent temperate, subpolar and polar ecosystems, yet are structurally linked by large-scale Pacific climate modes including the El Niño Southern Oscillation, Pacific Decadal Oscillation, and Southern Annular Mode. The three LTER sites have collected similar long-term datasets on oceanography (hydrographic casts), prey (net-sampled euphausiids), and seabirds (at-sea visual observation surveys). Data are thus being harmonized into 30+ year datasets to investigate bottom-up linkages between climate modes, local oceanographic patterns, and local prey/predator variability. Generalized Additive Mixed Models (GAMMs) and Hierarchical Modeling of Species Communities (HMSC) are being used to test biophysical relationships, including the evaluation of temporal effects such as direct and lagged effects of climate mode variability. The integrative and cross-ecosystem framework utilizes valuable data provided by LTER sites, identifies unknown dynamics underlying ecosystem synchrony and divergence, and provides mechanistic perspectives on how regional biophysical processes contribute to productive and globally important ecosystems. 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

Coastal ecosystems provide numerous benefits to people. While much research has focused on the coastal water column, less attention has been given to the importance of the seafloor or sediments of coastal systems. The sediments, however, can recycle nutrients to the water column and support the growth of primary producers such as phytoplankton. In turn these phytoplankton provide food for nutritionally and commercially important shellfish and finfish. The sediments also store carbon and filter excess nutrients from the water column, thereby improving water quality. This project is focused on bringing together a variety of data sources - from observations to modeling results for the Northeast United States. This region was chosen because it is relatively data rich compared to other areas and plays an important role in the US blue economy. A literature review and synthesis will be conducted to gather relevant data, and these data will be evaluated to find patterns of variation. Ocean models will then be used to assess changes across this region that could happen on the short and long-term. Sediment data will also be compared to water column data to see how they are connected, and if their connection is changing over time. This study will engage the scientific community to develop best practice guidelines for sediment data collection and develop community driven priorities for future sediment research studies. The project will provide training for a graduate student and a postdoctoral researcher and support workshops for both scientists and stakeholders. The seafloor plays a major role in influencing atmospheric carbon dioxide and oxygen concentrations, low-oxygen zones in the ocean, and ocean acidity, and represents the only geologic-scale storage of oceanic carbon. The sediments in coastal areas are particularly important as it is estimated that they account for ~70% of ocean carbon burial. Coastal sediments also recycle nutrients to the water column, fueling future water column primary production, and they can improve water quality via nutrient removal. Despite their importance, coastal sediments are poorly sampled relative to the water column, with large spatiotemporal gaps in datasets of nutrients and biogenic gas fluxes. The paucity of coastal sediment flux data leads to incomplete estimates for carbon, oxygen, and nutrient budgets in the ocean. Synthesizing disparate datasets of benthic variables therefore addresses an urgent need to improve the understanding of the role of sediments in the carbon and nutrient cycling in coastal regions at multiple timescales. This effort would further the understanding of mechanisms and environmental conditions influencing benthic dynamics, and consequently the role they play in driving pelagic biogeochemical cycles. The project seeks to do this by focusing on the Northeast US (NE-US), which is a relatively data-rich region. Specifically, existing long-term sediment and water column datasets from the NE-US, model output, and re-analyses will be combined to evaluate the role of different environmental conditions on benthic fluxes of carbon, oxygen, and nutrients. The project will characterize flux patterns in this region, help interpret the larger context these fluxes were observed in, co-locate different benthic fluxes and related variables in space and time to evaluate their relationship to changing environmental conditions, and develop community driven guidelines for data use and future observations. This project will support an early career scientist, one Ph.D. student, and one postdoctoral researcher. Results from this study will be disseminated through publications, presentations at scientific conferences, workshops with stakeholders and public outreach events. 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

Coastal ecosystems provide numerous benefits to people. While much research has focused on the coastal water column, less attention has been given to the importance of the seafloor or sediments of coastal systems. The sediments, however, can recycle nutrients to the water column and support the growth of primary producers such as phytoplankton. In turn these phytoplankton provide food for nutritionally and commercially important shellfish and finfish. The sediments also store carbon and filter excess nutrients from the water column, thereby improving water quality. This project is focused on bringing together a variety of data sources - from observations to modeling results for the Northeast United States. This region was chosen because it is relatively data rich compared to other areas and plays an important role in the US blue economy. A literature review and synthesis will be conducted to gather relevant data, and these data will be evaluated to find patterns of variation. Ocean models will then be used to assess changes across this region that could happen on the short and long-term. Sediment data will also be compared to water column data to see how they are connected, and if their connection is changing over time. This study will engage the scientific community to develop best practice guidelines for sediment data collection and develop community driven priorities for future sediment research studies. The project will provide training for a graduate student and a postdoctoral researcher and support workshops for both scientists and stakeholders. The seafloor plays a major role in influencing atmospheric carbon dioxide and oxygen concentrations, low-oxygen zones in the ocean, and ocean acidity, and represents the only geologic-scale storage of oceanic carbon. The sediments in coastal areas are particularly important as it is estimated that they account for ~70% of ocean carbon burial. Coastal sediments also recycle nutrients to the water column, fueling future water column primary production, and they can improve water quality via nutrient removal. Despite their importance, coastal sediments are poorly sampled relative to the water column, with large spatiotemporal gaps in datasets of nutrients and biogenic gas fluxes. The paucity of coastal sediment flux data leads to incomplete estimates for carbon, oxygen, and nutrient budgets in the ocean. Synthesizing disparate datasets of benthic variables therefore addresses an urgent need to improve the understanding of the role of sediments in the carbon and nutrient cycling in coastal regions at multiple timescales. This effort would further the understanding of mechanisms and environmental conditions influencing benthic dynamics, and consequently the role they play in driving pelagic biogeochemical cycles. The project seeks to do this by focusing on the Northeast US (NE-US), which is a relatively data-rich region. Specifically, existing long-term sediment and water column datasets from the NE-US, model output, and re-analyses will be combined to evaluate the role of different environmental conditions on benthic fluxes of carbon, oxygen, and nutrients. The project will characterize flux patterns in this region, help interpret the larger context these fluxes were observed in, co-locate different benthic fluxes and related variables in space and time to evaluate their relationship to changing environmental conditions, and develop community driven guidelines for data use and future observations. This project will support an early career scientist, one Ph.D. student, and one postdoctoral researcher. Results from this study will be disseminated through publications, presentations at scientific conferences, workshops with stakeholders and public outreach events. 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

Imaging inside the human body can reveal important information for diagnosis and treatment of disease. To obtain a high-quality image, light is usually projected from outside the body using external lenses and microscopes. The imaging depth is limited since light cannot be delivered deep into the tissue from outside. Lens and endoscopes can be inserted into the tissue to mitigate this issue. However, this is invasive and can cause damage. This project will use non-invasive ultrasound waves to guide and focus light through the tissue without the need for inserting a physical lens. This approach will allow for high-resolution imaging in parts of the body, like the brain, that cannot be achieved with MRI, while imaging over a larger volume than possible with a traditional microscope. The project will also help develop the next generation of young scientists and engineers with an interest in ultrasound and optical technologies. This project will develop the first ultrasonically-enhanced swept source optical coherence tomography (ueSS-OCT) system for label-free optical imaging of tissue structure and function with near beam waist limited lateral resolution over an extended depth range. Ultrasound waves can locally change the refractive index profile in tissue to sculpt in situ virtual optical waveguides that can focus and steer light. The project has three main objectives: (1) to utilize the unique interplay between these in situ lenses and SS-OCT detection for beam waist limited resolution over 2 mm of sub-surface structural imaging depth and (2) up to 2 mm of functional phase-resolved imaging with shot noise limited phase resolution with an extended flow velocity range. These advances will be validated by (3) detection of functional activation in the well-established animal model of somatosensory activation in response to whisker stimulation. This project will demonstrate a novel imaging paradigm in which external focusing optics can be replaced by in situ optical waveguides formed within the tissue itself. 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

Imaging inside the human body can reveal important information for diagnosis and treatment of disease. To obtain a high-quality image, light is usually projected from outside the body using external lenses and microscopes. The imaging depth is limited since light cannot be delivered deep into the tissue from outside. Lens and endoscopes can be inserted into the tissue to mitigate this issue. However, this is invasive and can cause damage. This project will use non-invasive ultrasound waves to guide and focus light through the tissue without the need for inserting a physical lens. This approach will allow for high-resolution imaging in parts of the body, like the brain, that cannot be achieved with MRI, while imaging over a larger volume than possible with a traditional microscope. The project will also help develop the next generation of young scientists and engineers with an interest in ultrasound and optical technologies. This project will develop the first ultrasonically-enhanced swept source optical coherence tomography (ueSS-OCT) system for label-free optical imaging of tissue structure and function with near beam waist limited lateral resolution over an extended depth range. Ultrasound waves can locally change the refractive index profile in tissue to sculpt in situ virtual optical waveguides that can focus and steer light. The project has three main objectives: (1) to utilize the unique interplay between these in situ lenses and SS-OCT detection for beam waist limited resolution over 2 mm of sub-surface structural imaging depth and (2) up to 2 mm of functional phase-resolved imaging with shot noise limited phase resolution with an extended flow velocity range. These advances will be validated by (3) detection of functional activation in the well-established animal model of somatosensory activation in response to whisker stimulation. This project will demonstrate a novel imaging paradigm in which external focusing optics can be replaced by in situ optical waveguides formed within the tissue itself. 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

Elastomeric polymers are widely used in various industries, ranging from biomedical devices to automotive safety systems, due to their flexibility, durability, and ability to absorb energy. However, current understanding holds that these materials respond primarily in a highly elastic manner and tend to fail in a brittle fashion under extreme conditions. Research supported by this project aims to transform that understanding by uncovering how elastomers behave when subjected to extremely high deformation rates, such as those experienced in ballistic or shock-loading environments. By doing so, this research looks to provide insight into the ability of elastomeric polymers to undergo permanent, plastic deformation, contrary to conventional belief. These findings could lead to the design of new polymer-based structures with improved performance in impact mitigation, flexible electronics, and rapid manufacturing. Additionally, the project will contribute to training students in cutting-edge experimental and computational techniques, integrating research into engineering education, and promoting an exchange program between the two research laboratories. This research investigates the mechanisms of plastic deformation and failure in elastomeric polymers under ultra-high strain-rate loading using a novel laser-based shock technique coupled with bulk spectroscopy that can strategically shape and tune the rate of deformation. The experimental approach enables repeated shock loading of the same location to simulate and study shock fatigue and damage accumulation. Through advanced characterization methods, including spectroscopy, microscopy, and thermal and mechanical testing, the project seeks to reveal how molecular networks evolve under extreme conditions. These observations look to inform the development of a new multiscale computational model that couples molecular-scale deformation, damage accumulation, and rate-dependent mechanical response. The model intends to incorporate thermodynamic principles and micromechanical behavior to predict how different classes of elastomeric polymers behave under shock loading. The combination of experimental discovery and theoretical modeling is expected to produce a fundamental shift in the understanding of polymer plasticity and damage evolution, with broad applications across manufacturing, defense, and biomedical fields. 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

Elastomeric polymers are widely used in various industries, ranging from biomedical devices to automotive safety systems, due to their flexibility, durability, and ability to absorb energy. However, current understanding holds that these materials respond primarily in a highly elastic manner and tend to fail in a brittle fashion under extreme conditions. Research supported by this project aims to transform that understanding by uncovering how elastomers behave when subjected to extremely high deformation rates, such as those experienced in ballistic or shock-loading environments. By doing so, this research looks to provide insight into the ability of elastomeric polymers to undergo permanent, plastic deformation, contrary to conventional belief. These findings could lead to the design of new polymer-based structures with improved performance in impact mitigation, flexible electronics, and rapid manufacturing. Additionally, the project will contribute to training students in cutting-edge experimental and computational techniques, integrating research into engineering education, and promoting an exchange program between the two research laboratories. This research investigates the mechanisms of plastic deformation and failure in elastomeric polymers under ultra-high strain-rate loading using a novel laser-based shock technique coupled with bulk spectroscopy that can strategically shape and tune the rate of deformation. The experimental approach enables repeated shock loading of the same location to simulate and study shock fatigue and damage accumulation. Through advanced characterization methods, including spectroscopy, microscopy, and thermal and mechanical testing, the project seeks to reveal how molecular networks evolve under extreme conditions. These observations look to inform the development of a new multiscale computational model that couples molecular-scale deformation, damage accumulation, and rate-dependent mechanical response. The model intends to incorporate thermodynamic principles and micromechanical behavior to predict how different classes of elastomeric polymers behave under shock loading. The combination of experimental discovery and theoretical modeling is expected to produce a fundamental shift in the understanding of polymer plasticity and damage evolution, with broad applications across manufacturing, defense, and biomedical fields. 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

Many types of disease can be treated with ablation, a medical procedure which applies energy to destroy small regions of tissue that do not behave normally. Ablation therapy can be used to treat conditions like arthritis, uterine fibroids, and cancer. It can also treat disruptions of the heart’s regular rhythm, such as atrial fibrillation. Ablation procedures can be difficult to perform, and sometimes multiple treatments may be necessary. A deeper understanding of exactly how the settings associated with the ablation procedure affect the biological tissue could lead to better results. This project aims to improve the understanding of radiofrequency ablation’s interactions with heart tissue through a combination of theory, multi-physics and machine-learning models, and experiments. To ensure the experiments reflect the differences in tissue structures and properties of real patients, tissue from human hearts no longer needed after being replaced by transplants will be used when possible. Medical doctors will help assess the practical significance of the project’s results. This study has the potential to lead to improved ablation treatments and patient outcomes, and the new methodology can be extended, with minor adaptations, to other types of diseases. Educational components include training of graduate and undergraduate students, contributions to undergraduate and graduate courses, and engagement of the general public with interactive programs available through a website. Radiofrequency ablation (RFA), used for a wide variety of physiological systems, faces limitations from an imprecise understanding of ablation and tissue interactions, along with challenges in optimizing the procedure given the many parameters associated with ablation and patient variability. This project aims to develop and validate a detailed multi-physics mathematical RFA model with an unprecedented level of accuracy and analysis. It will focus on cardiac tissue, but the tools can be adapted for other biological tissues and ablation therapies. First, the novel computational model will include advanced methods of domain decomposition and model reduction to address the multi-physics nature of the problem and will incorporate important physiological parameters of ablation-tissue interactions. Second, the model will be enhanced by rigorously integrating the sizes, thicknesses and thermal profiles of ablation lesions in cardiac tissue from varying thermal doses, contact angles, and pressures and by comparing with experiments. This project will be enhanced by using optical-mapping methods during ablation in live hearts, including live human explanted hearts from patients undergoing heart transplants, to simultaneously quantify the extent and sensitivity of the ablation at different tissue depths in real time as a function of ablation parameters. This information will enable continuous refinement of the computational model and accurate sensitivity analysis. Finally, simulations and experiments will be integrated to assess how ablation lesions will effectively terminate disorganized electrical wave propagation during fibrillation. The mechanistic RFA model will provide highly accurate predictions of ablation parameter effects on the success rate of terminating cardiac fibrillation. 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

Many types of disease can be treated with ablation, a medical procedure which applies energy to destroy small regions of tissue that do not behave normally. Ablation therapy can be used to treat conditions like arthritis, uterine fibroids, and cancer. It can also treat disruptions of the heart’s regular rhythm, such as atrial fibrillation. Ablation procedures can be difficult to perform, and sometimes multiple treatments may be necessary. A deeper understanding of exactly how the settings associated with the ablation procedure affect the biological tissue could lead to better results. This project aims to improve the understanding of radiofrequency ablation’s interactions with heart tissue through a combination of theory, multi-physics and machine-learning models, and experiments. To ensure the experiments reflect the differences in tissue structures and properties of real patients, tissue from human hearts no longer needed after being replaced by transplants will be used when possible. Medical doctors will help assess the practical significance of the project’s results. This study has the potential to lead to improved ablation treatments and patient outcomes, and the new methodology can be extended, with minor adaptations, to other types of diseases. Educational components include training of graduate and undergraduate students, contributions to undergraduate and graduate courses, and engagement of the general public with interactive programs available through a website. Radiofrequency ablation (RFA), used for a wide variety of physiological systems, faces limitations from an imprecise understanding of ablation and tissue interactions, along with challenges in optimizing the procedure given the many parameters associated with ablation and patient variability. This project aims to develop and validate a detailed multi-physics mathematical RFA model with an unprecedented level of accuracy and analysis. It will focus on cardiac tissue, but the tools can be adapted for other biological tissues and ablation therapies. First, the novel computational model will include advanced methods of domain decomposition and model reduction to address the multi-physics nature of the problem and will incorporate important physiological parameters of ablation-tissue interactions. Second, the model will be enhanced by rigorously integrating the sizes, thicknesses and thermal profiles of ablation lesions in cardiac tissue from varying thermal doses, contact angles, and pressures and by comparing with experiments. This project will be enhanced by using optical-mapping methods during ablation in live hearts, including live human explanted hearts from patients undergoing heart transplants, to simultaneously quantify the extent and sensitivity of the ablation at different tissue depths in real time as a function of ablation parameters. This information will enable continuous refinement of the computational model and accurate sensitivity analysis. Finally, simulations and experiments will be integrated to assess how ablation lesions will effectively terminate disorganized electrical wave propagation during fibrillation. The mechanistic RFA model will provide highly accurate predictions of ablation parameter effects on the success rate of terminating cardiac fibrillation. 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

Ultimately, people want to feel connected to people on their block, in their neighborhood, and in their city or town. That connection can in large part depend on interpersonal interactions, whether experienced as pleasant or uncomfortable, and these interactions are often shaped by people’s positive or negative attitudes. What is known about attitude formation is largely based on individual experiences and how those individual experiences influence attitude formation. This project advances what is known about attitude formation and attitude changes, by examining how geographic contexts shape the formation of attitudes and influence the quality of interpersonal interactions. This project tests three questions derived from an interdisciplinary mathematical model of attitude formation. This model draws inspiration from social psychology and urban science to explicitly account for interactions at different spatial scales. Specifically, the project tests: (1) How much do attitudes vary across place and spatial scale? (2) How much can the nested contexts of cities and neighborhoods explain about attitude formation? (3) How much variation in attitudes across spatial scales and places is related to the quality of local interpersonal interactions? To support training for students and researchers, the project also includes the development of an online course and a set of example data analysis pipelines demonstrating the application of these methods. This research develops novel integrative methods in attitude research, offering new theoretical frameworks that promote national prosperity and welfare. This project is jointly funded by Social Psychology 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

Ultimately, people want to feel connected to people on their block, in their neighborhood, and in their city or town. That connection can in large part depend on interpersonal interactions, whether experienced as pleasant or uncomfortable, and these interactions are often shaped by people’s positive or negative attitudes. What is known about attitude formation is largely based on individual experiences and how those individual experiences influence attitude formation. This project advances what is known about attitude formation and attitude changes, by examining how geographic contexts shape the formation of attitudes and influence the quality of interpersonal interactions. This project tests three questions derived from an interdisciplinary mathematical model of attitude formation. This model draws inspiration from social psychology and urban science to explicitly account for interactions at different spatial scales. Specifically, the project tests: (1) How much do attitudes vary across place and spatial scale? (2) How much can the nested contexts of cities and neighborhoods explain about attitude formation? (3) How much variation in attitudes across spatial scales and places is related to the quality of local interpersonal interactions? To support training for students and researchers, the project also includes the development of an online course and a set of example data analysis pipelines demonstrating the application of these methods. This research develops novel integrative methods in attitude research, offering new theoretical frameworks that promote national prosperity and welfare. This project is jointly funded by Social Psychology 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

Methane is second only to carbon dioxide in its contribution to human-induced climate change due to its global warming potential, which is 34 times greater than that of CO2. Microorganisms in wet landscapes tend to release methane, whereas those in dry ones tend to take up the gas from Earth's atmosphere. Researchers at the Howland Research Forest in Maine have been measuring methane fluctuations across this sub-boreal forest since 2012. Their studies have found that the forest usually serves as a methane "sink" due to microbial consumption, although occasionally, under extremely wet conditions, the reverse can be true. This research site provides an ideal opportunity to study the conditions under which a forest would switch from a net sink to become a source of atmospheric methane. Under future climate change scenarios, the region is expected to become warmer and wetter, conditions that may induce a shift from methane sink to source, with the potential to have an impact on atmospheric methane concentrations at regional to global scale. This project will examine how forest soil microbial communities will change in response to climate warming, to identify the conditions that may lead forests to switch from being a methane sink to more of a source. The project will also support the cross-disciplinary training of graduate and undergraduate students and postdoctoral research scholars, including those from underrepresented groups in science. A series of public talks will be convened, and short videos and StoryMaps focused on science outreach will be paired with “scientist in the classroom” visits to local high schools. The project will host an open house for students and the public at the Howland Research Forest to learn about this important research. This study aims to identify - through the integration of field observations, laboratory analyses, and modeling - the conditions and mechanisms driving methane sink vs source activity in forests, using the Howland Research Forest in Maine as a case study. The project's novel approach focuses on three key areas to improve understanding of methane in such habitats: 1) identify the roles and response of soil microbial communities, specifically, methanogens and methanotrophs (and their functional guilds), in driving methane flux across environmental gradients; 2) understand and quantify how wet vs dry landscape microsites, and belowground vs. aboveground components within a forest contribute to seasonal and annual methane fluxes; and 3) integrate knowledge gained from field and laboratory analyses to inform and improve ecosystem process models. A suite of in-situ and lab-based experimental measures of methane production and oxidation, stable isotopes, and profiles of microbial community composition and function will be used to understand the mechanisms, processes, and feedbacks driving methane sink/source activity from site to landscape levels. At the site level, multi-scale observations of soil and aboveground methane fluxes, microbial traits, and associated in-situ environmental conditions will be obtained. To further understand and quantify methane response, in-situ and laboratory manipulation experiments to identify the role of functional guild activity, under changing environmental conditions, in regulating methane production/oxidation and ultimately net methane flux to and from the atmosphere will be employed. Finally, these data, integrated with project data-enhanced Microbial Model for Methane Dynamics-Dual Arrhenius Michaels Menten (M3D-DAMM) and Community Land Model-Microbe (CLM-Microbe) process models, will allow researchers to identify seasonal and annual methane sink/source activity at the landscape level within Howland Forest from the present to 2100. The research will include training at the undergraduate, graduate and postdoctoral levels, as well as a variety of outreach activities to engage high school students and the public. 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

Rivers and deltas of the world have been extensively engineered for centuries. Past engineering projects have, collectively, enabled the prosperous economies of deltaic regions that are enjoyed today, but have also created conditions that limit the regions in the future. In many places, including along the United States Gulf Coast, planners and governments are implementing new projects that aim to restore coastal changes, enhance economic and environmental support, and mitigate risk to human lives and livelihoods. However, there is not a robust understanding of how past delta management has influenced human decisions and outcomes. This project uses novel numerical and computational modeling approaches to study how human engineering decisions cascade through space and time over centuries of landscape change to create system conditions that limit or enhance the portfolio of management decisions available at future times. This work develops tools with coastal planners to determine the portfolio of projects that maximize coastal survival. Delta engineering projects induce geomorphic change across space and time scales that impacts human lives and society. This project integrates cascading human decision making into landscape evolution models with three modeling approaches that inform one another and have complementary strengths: agent-based modeling, dynamical system modeling, and participatory modeling. Project research focuses on two testable hypotheses towards the tools and quantitative understanding of cascading decision making that are needed for delta planning: (1) local-scale engineering interventions can lead to less geomorphologically stable delta landscapes, when compared to a few larger system-scale interventions, and (2) human decisions can push the coupled system across tipping points, to both system benefit and detriment. Finally, research and development across all modeling approaches in this project is guided by community engagement. 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.