Grants

NSF

This doctoral dissertation project investigates adaptive processes in an early complex cultural contexts. Bioarchaeology, the study of human skeletal remains from archaeological sites, provides a means to uncover specific details about the lives of past populations, including their health, diet, and how ancient communities adapted to change. Beyond the training of a graduate in scientific methods of data collection and analysis, the project contributes bioarchaeological data to an open-access database, and fosters scientific collaboration. This project uses bioarchaeological, isotopic, biodistance, and paleopathological methods to reconstruct profiles, dietary patterns, and health indicators from skeletal remains. Isotopic analyses provide data about mobility and diet, while comparative analyses with collections from other sites provide a robust dataset for contextualizing the findings within regional patterns. The use of photogrammetry during fieldwork ensures the preservation of burial data, supporting viable archival practices and creating long-term digital records for future research. By integrating new datasets with existing collections, this project underscores the importance of preserving archaeological heritage while advancing understanding of culture. 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

Healthy bone growth is essential to attaining adult stature and maintaining good health, including appropriate levels of physical activity, through life. However, bone growth can be compromised by other essential life functions (e.g., immune function), especially among individuals with restricted access to resources (low-resource environments). Accordingly, the purpose of this doctoral dissertation project is to examine how variation in living conditions impact bone growth and immune function in children. The study uses minimally invasive methods, and the data generated aid the identification of factors that affect bone growth during childhood. Participants are informed of their bone growth and immunological status, and research findings are shared with public health agencies. In addition to training the graduate student researcher, the study provides additional student and public training and educational opportunities. This project studies energetic trade-offs in children with varying access to resources to better understand how factors related to living conditions and environmental circumstances relate to childhood bone growth. Quantitative ultrasonometry and accelerometry are used to measure bone growth and physical activity, respectively. Data on food is collected using surveys. Child growth is evaluated through height and skinfolds. Dried blood spots, from fingerpicks, are implemented to measure markers of immune function (e.g., immunoglobulin E and C-reactive protein). In measuring biomarkers of immune function, the project advances NSF investment in biotechnology. 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 doctoral dissertation project documents and analyzes a language with a complex system of clause-linkage. Defying the traditional notion of the “sentence,” languages of this type can combine several predicates together in a single chain. Use of complex clause-linkage raises scientifically important questions regarding how humans package and interpret information. Closer examination of data from the research language, and incorporation of comparative data from some of its sister languages, enriches a scientific understanding of how clause-linking devices work and how this type of linguistic complexity may arise. Broader impacts of the project include workforce development and educational opportunities for individuals who are involved in the language documentation and linguistic analysis aspects of the project. All data are deposited in an established archive and made publicly accessible. This project generates a grammatical description of a language that shows evidence of recent syntactic change in several of its clause-linking devices. This language employs systems of clause-chaining and switch reference, while also making use of serial verb constructions and coordination of fully finite clauses. Along with the synchronic description, this doctoral dissertation project includes diachronic analysis of how these clause-linking devices arose, which advances a better understanding of their full range of functions and supports a better understanding of how syntactic change occurs in clause-chaining systems. 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

Humans inhabit and thrive in almost every corner of the globe thanks to adaptations and complex skills than enhance our species’ ecological flexibility. Contributing to this flexibility are cognitive traits, such as problem-solving capabilities, that allow us to respond to fluctuating conditions. However, the evolution of these cognitive capacities is not fully understood, and comparative behavioral and cognitive studies in non-human primate species can contribute to this understanding. This doctoral dissertation project assesses problem solving and knowledge transmission in a social non-human primate, using experiments in a wild setting. In addition to training a graduate student, the project promotes education through varied outreach activities and training, including the development of educational material. This study assesses how a social non-human primate species solves complex tasks and transmits learned behaviors to others. To attain this goal, the study implements field-friendly experiments that assess: (1) how this species innovates (solves novel foraging tasks), (2) how it adapts (changes behavior when a previously learned solution no longer works), and (3) what factors affect whether individuals learn from others, and who they learn from. Methods include the implementation of a multi-access box paradigm with three experimental phases, and eighteen (n=18) trials per box. Boxes are placed in platforms, and cameras are used to record the behavior. Field observations during testing identify each engaged individual, narrate the engaged individual’s behavior, identify and record individuals located within 5m of the platforms, time that individuals spend observing participants, social interactions, and experimental outcomes (e.g., solutions used). All experimental videos will be edited and coded. 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 doctoral dissertation research investigates the societal impacts of forensic science, particularly of genetic and DNA analysis. As genetic identification and DNA analysis are becoming widely used at the community level, the data collected from this study expands the translational impact of forensic science. The data collection uses a longitudinal research design to track changes over time in how communities engage forensic science and DNA analysis. Using ethnographic and behavioral data collection with families, forensic scientists and community members, and the analyzing of archival sources, the investigators track ongoing forensic investigations. Broader impacts inform educational and citizen science initiatives through wide dissemination of data and findings and will improve public literacy on the relationships between forensic science, technology, and society. The research makes contributions to the science of forensic science and community responses to genetic analysis and DNA testing. The research will expand the participation and training in science and the scientific workforce by supporting the training of a graduate student in anthropological science. The research also provides insights into the science of forensic science. By expanding our understanding of the societal impacts of DNA testing and the new tools and technologies of forensic science, the project responds to NSF priorities in translational science. The project also advances NSF priorities in biotechnology by furthering understanding of the human adoption of biotechnological innovations. 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

Previous research has found that social factors influence linguistic differences among language users. However, individuals can hold numerous social identities, which may intersect with each other to influence linguistic variation. This doctoral dissertation project examines the factors that lead to linguistic similarities and differences between social groups, and how this relates to individuals’ understanding of the place where they live. In doing so, this research advances scientific theories of how linguistic patterns vary among language users and dynamically change over time. Additional benefits to society include educational and workforce development opportunities for undergraduate research assistants who receive training in research methods. In addition, data are publicly shared through community and academic repositories. Finally, the data collected in this project may be used to develop educational materials and promote awareness of dialect variation. This doctoral dissertation project investigates the effect of social meanings on language variation and change. This project advances two related goals. The first goal increases a scientific understanding of the factors that lead to linguistic similarity or differences between social groups. The second goal identifies associations between certain linguistic features and geography and how these associations influence linguistic behavior. Because these language-place associations may or may not be shared across specific social lines, this project asks if they play a role in explaining why speakers of different social groups do or do not share certain linguistic characteristics. These research questions are addressed through long-term observation and immersion in the community of interest, to gain an understanding of how community members understand the place where they live, and interviews with a sample of community members. Linguistic characteristics of interest, consisting of certain vowels and consonants, are acoustically measured and quantified. Statistical models that include both linguistic factors and social factors are used to identify predictors of linguistic behavior. 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 examines the combined effects of stressors on the livelihoods of populations. In settings where instability disrupts infrastructure and transportation, there are potentially detrimental impacts on the food security of rural residents, and these effects can be compounded by extreme weather, such as droughts and intense rainfall. Using an array of methods, including agricultural mapping, survey research, analysis of satellite imagery, and spatial data, the researchers evaluate the effects of weather extremes and other stressors on the food security of rural residents. Also, the researchers document evidence of abandoned agricultural plots and concomitant attempts by farmers to adapt to novel challenges. The project informs understandings of the precursors to migration, which has implications for border security. The project also contributes to the education and training of a graduate student. In addition to contributing to the priority areas of security and public safety, the project's use-inspired approach and pathways for disseminating its findings align with the priority area of translational research. This study combines novel sources of data to elucidate the effects of weather extremes and stressors on the food security and subsistence strategies of rural residents. Data on food security stem from a longitudinal multi-sited survey of geolocated households, which is combined spatially and temporally with measures of temperature, precipitation, and conflicts. Similarly, these predictor variables are combined with remote sensing data that show geolocated processes of agricultural abandonment over time. In addition to the quantitative analyses, the researchers use methods to examine the adaptive strategies that residents employ in response to the livelihood risks. The study expands insights about the multifactorial determinants of food insecurity and land management decisions. In turn, this project enhances theoretical understandings about the proximate determinants of human mobility. 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 doctoral dissertation project examines how young children learning two languages acquire new words and connect them to their existing vocabulary. While bilingual children often know fewer words in each language compared to monolingual children, their combined knowledge across languages can match or surpass that of monolingual peers. This project tests whether the way words are organized in the brain helps bilingual children learn new words, focusing on organization due to shared meanings or related concepts. By studying bilingual toddlers, this research informs how children link their knowledge across languages, providing insights into early language development. The findings may advance strategies for teaching vocabulary to bilingual children, helping parents, educators, and caregivers support language learning in ways that build on children’s existing strengths. This project involves bilingual toddlers from diverse communities. Other benefits to society include providing educational and workforce development opportunities for undergraduate research assistants. This project examines how the connections between words in one language influence learning in the other language. For example, a child who knows many words about animals in one language may find it easier to learn new animal-related words in the other language. This research examines two types of words, completely new words (that require learning both a concept and a label) and translation equivalents (words for the same concept in both languages). Testing is conducted virtually, which increases access for families who might not otherwise participate in research studies. This work (1) advances a scientific understanding of how bilingual children leverage cross-language semantic density to learn new words and translation equivalents, (2) contributes new knowledge to theories and models of bilingual word learning, accounting for cross-language semantic density, and (3) informs models of bilingual instruction, potentially extending their application to an earlier developmental age. 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 doctoral dissertation project investigates the role of craft specialization in shaping social roles and economic relations within village societies. Although the organization and socioeconomic role of craft specialists can vary depending on a society's level of complexity, craft production is integrated into both socioeconomic systems and relationships. Craft specialization can be detected archaeologically through careful examination of household spatial organization, house inventories, and the accumulation of goods. Previous Neolithic research has mainly focused on identifying craft specialization and exchange networks at the site level, whereas, this study focuses on both, as well as the production of items intended for exchange. The project provide scientific training opportunities for students in field methods and artifact analysis. The research team conducts expansive excavations of Neolithic houses, analyze artifacts, and documents house inventories to determine whether one or more households were involved in tool production, trace their exchange with other villages in the area, and look at the accumulation of goods to look if there are any differences related to craft production, house size, and quantity of goods. The team also examines faunal and botanical remains and investigates any differences in economic modes of subsistence, land ownership, and status within the community. The data collected helps to develop a model for understanding household craft specialization and its implications for village societies. 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

Understanding how adversaries make decisions is essential for enhancing security and protecting public spaces. This project develops new tools to help security professionals allocate resources more effectively, minimizing vulnerabilities at soft targets such as schools, public venues, and places of worship. By combining insights from human-subject experiments and surveys with advanced mathematical modeling, this research provides practical strategies for making better defensive decisions. Unlike traditional, theoretical approaches, which assume adversaries act purely rationally, this project incorporates data from human decision-making experiments and surveys to create more realistic and actionable models. The outcomes of this research extend beyond improving security as they also offer valuable insights into adversarial behavior that can inform fields like marketing and risk management. Additionally, the project supports education by involving students in cutting-edge research and developing tools that make complex mathematical models accessible to non-experts. By advancing our understanding of decision-making and optimizing resource allocation, this project promotes national safety and prosperity. This project develops a novel framework for studying adversarial decision-making by combining game-theoretic modeling with data from human-subject experiments and surveys. The primary objective is to model an attacker’s best response function based on actual decisions made in attacker-defender scenarios, rather than assuming purely rational and theoretical strategies. The research investigates key variables—reward, cost, and probability of success—and their influence on adversarial behavior. Using this data, the project derives closed-form Nash equilibria to optimize defensive resource allocation strategies across multi-layered security systems. The study incorporates mixed-effects regression and numerical optimization techniques to analyze decision-making processes and solve for equilibrium strategies. Additionally, by utilizing the research methodology of designing and conducting behavioral experiments that simulate attacker-defender games, collecting empirical data, and using advanced game-theoretic modeling, the project validates and refines prescriptive solutions displayed in Graphical User Interface toolkits for stakeholders. The results contribute to actionable resource allocation strategies while offering a scalable framework for analyzing adversarial behavior in diverse applications, including security and risk management. 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 doctoral dissertation award investigates human settlement and agricultural practices at a site that has largely characterized the region as dominated by hunter-gatherers with low population density, who moved seasonally to different environmental settings, and practiced limited, if any, domestic agriculture. New evidence suggests the possible presence of widespread maize cultivation and nucleated permanent villages. Resolution of questions surrounding past settlement and subsistence practices has broad implications for understanding of the relationships among sedentism, emergent social complexity, and agricultural intensification, as well as the anthropogenic impact on the natural environment prior to population expansion. The study’s use of phytolith analysis advances administrative priorities for investments in understanding the adoption of biotechnological innovations in scientific research. The researcher also makes use of remote sensing methods (LiDAR) and ground-penetrating radar systems (GSST), which enhance the integration of artificial intelligence in scientific research. The project also provides training for graduate students in these analytical and other archaeological methods. This project locates, documents, and analyzes archaeological remains through a multi-scalar research strategy. The region where the research is focused is ideal because of the minimal development that has occurred compared to other parts of New England, coupled with an environment well-suited for agriculture and a rich ethnohistoric tradition. The project integrates regional remote sensing, landscape-scale geophysical prospection using ground penetrating radar and other methods, as well as targeted excavations to secure dating, artifactual, and paleoethnobotanical samples. Soil samples are processed for phytoliths to understand the ecological composition of the region and recover evidence of domesticated crops. Collectively, the results reshape understanding of agriculture, the social organization of the communities who resided in these regions, and their potential impacts on the region’s ecology. Likewise, innovative methods developed by the project form a blueprint for archaeological investigations broadly. 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

Stress, or the location of prominence within a word, may be encoded and used differently across languages. Second language (L2) learners whose native language does not use stress to contrast word meaning often face difficulty in perceiving stress in their L2. Even when both languages do use stress contrastively, stress acquisition may be challenging if the two languages differ in their phonetic cues used for stress, or if stress plays a stronger role in differentiating words in the L2 compared to the first. While phonetic training has been shown to be effective for improving the perception of L2 speech segments and tone acquisition, whether it is effective for improving suprasegmental stress perception in a L2 has not been well established. This doctoral dissertation investigates whether the perception of stress by L2 learners may be improved through phonetic training and whether the benefits of training generalize to untrained syllable positions. Additionally, this dissertation investigates the role of attention in the learning of L2 stress. The importance of attention for the acquisition of unfamiliar speech sounds has been discussed by L2 learning models, but few studies have examined its role during phonetic training for non-segmental features. Understanding whether phonetic training is effective for improving stress acquisition and the role that attention plays during training can help inform linguistic theories for L2 acquisition and aid in developing effective pedagogical methods for teaching second language stress. The doctoral dissertation aims to establish whether the perception and phonological processing of suprasegmental stress by L2 learners may be improved through phonetic training, the role that attentional allocation plays during the acquisition of suprasegmental features, and whether the benefits of training are generalizable to untrained syllable positions. The Automatic Selective Perception model posits that attentional focus is required during the early stages of L2 learning in order to successfully perceive and acquire novel L2 sounds. This predicts that mere exposure to L2 sounds may not result in improving perceptual accuracy if a learner’s attention is not directed towards the feature in question. The present dissertation provides participants with a series of four phonetic training sessions. While all participants are exposed to the same stimuli during training, they are split into two groups with different tasks: a stress group and a segmental group. The stress group receives feedback during training directing their attention to identifying stress placement, while the segmental group has their attention directed to initial consonant voicing. Four pre- and post-tests are given to measure performance differences after training: a sequence-recall task, two discrimination tasks (an AX task and a novel AQX task), and an identification task. In sum, this dissertation aims to contribute to our understanding of the role of attention during L2 stress acquisition and determine whether phonetic training is effective for improving stress perception for first and second language pairs that differ in their instantiation of stress. 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 doctoral dissertation project examines the impact of changes in population pressure and dietary diversification on the long-term sustainability of small-scale, self-sufficient societies. The underlying rationale for this project, grounded in empirical research in ethnobotany and ecology, suggests the implementation of multiple dietary plant staples allows food producing societies to increase and stabilize food yields. Diverse food systems can thus constitute a strategic response to episodes of demographic growth. Archaeology can further substantiate these insights because it provides subsistence and population data on the centennial and millennial scales, highlighting successful strategies that have stood the test of time. At present, the link between subsistence diversity and socio-environmental sustainability increasingly draws interdisciplinary attention, largely due to the vulnerabilities of industrial farming. Contemporary farming is predicated on the continued success of high-yielding monocultures, but the biological simplification of food systems has increased dependency on human inputs and susceptibility to change. Understanding the long-term implications of species variability in farming systems is therefore of value in the United States today. The project addresses these issues by looking at two research questions in the context of an archaeological habitation site where both farming and plant foraging took place. The project first determines the extent of demographic change at the site throughout its occupation: this is accomplished through an analysis of wood charcoal remains that have already been excavated. Based on ethnographic observations of fuelwood collection, charcoal remains are expected to reflect population levels because small groups that do not experience resource stress tend to only select a handful of tree species from easy-to-access locations. The investigator therefore measures changes over time in the number of tree species used for fuel, as well as the accessibility of where the fuel was collected. The latter is assessed through oxygen stable isotope analysis, which represents a methodological innovation in archaeobotany. Data concerning the type of fuelwood that was selected over time serves as a proxy for population pressure. A second question focuses on whether the local food system became more diverse to cope with increased population pressure. 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 doctoral dissertation research investigates the impacts of digital innovations on eldercare in aging societies. Researchers specifically test the extent to which digital tools and an eldercare industry can transform the quality and capacities for elder care. Investigators specifically study the use of a wide range of digital platforms and smart technical devices such as wearable sensors, telecare and telemedicine, big data platforms for health monitoring and prediction, and smart home technology. Broader impacts of this research include training of a graduate student and dissemination of findings at industry conferences, through podcasts, and in public venues that focus on elder-care practices. Research findings will contribute to understanding the intersections between technological innovations and society. The data will inform the future management of eldercare and aging and responds to research priorities in the science of artificial intelligence through the study and usage of computers and software capable of intelligent behavior and their impact on society. The research also contributes to the translation of findings into novel use-inspired technologies to improve elder care management. 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 addresses the documentation and infrastructure developing for a low-resource language. It leverages traditional linguistic fieldwork, digital and computational tools for language documentation and linguistic analysis to document and analyze naturalistic speech data of monolingual and bilingual speakers. Low-resource languages have limited or no digital resources, and they often have few speakers. However, these languages offer valuable insights into language sciences and linguistics, as they serve as robust testing ground for formal linguistic hypotheses, which have traditionally been built on data from major, well-known languages. This research will focus on the documentation of an under-studied language. It will investigate structures found within the speech of monolingual speakers and compare it with that of bilingual speakers. By leveraging digital tools for linguistic annotations, the project will produce an annotated speech corpus as well as language datasets with previously undocumented language structures, such as specialized verb forms and noun markers, which offer valuable insights into language change and language variability. The machine readable infrastructure (database and datasets) that results from this project has the potential to provide new training data for AI systems, which rely on naturalistic language data from a variety of languages. The project advances models in language contact and grammatical restructuring and contributes to debates about language origins. Additionally, it promotes translational research by bridging academic study with real-world impact through language education. All materials will be archived for open access and future use in linguistic and cultural research. 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

Disasters alter the physical environment, destroy homes, and leave survivors with challenging decisions: should they return to their prior community or relocate to a new community? Past studies suggest that factors such as familial ties to an area, history in a community, age, employment opportunities, insurance coverage, and risk perceptions shape these residential decisions. Most of these studies, however, have focused on homeowners exclusively, ignoring renters, which make up 34 percent of U.S. households. Renters also encounter unique challenges in deciding where to live after disasters, since they do not decide whether to rebuild rental units after disasters. Understanding how renters make residential decisions after disasters, however, is paramount to ensuring communities recover after disasters. A wide range of businesses essential to community functioning rely heavily on renters, including restaurants, construction, farming, vegetation management, landscaping, and catering. This study addresses how renters make residential decisions after wildfires in two communities recently affected by major wildfires. Study findings support the development of disaster recovery plans that support the recovery of the housing market, the local economy, and renters after disasters. This project investigates how renters’ make residential decisions after wildfires using a modified Push-Pull Model of Migration. Previous literature, primarily focused on floods, suggests that residential decisions after disasters are shaped by several factors, including functional and emotional place attachments, hazard experiences, and risk perceptions. These factors can push individuals out of communities, pull them into others, or anchor them in place after disasters. This study uses photovoice interviews to examine how place attachment and risk perception shape the residential decisions of renters displaced by wildfires and compare how residential adjustment pathways vary between renters and homeowners. Study findings advance theory by applying a modified version of the Push Pull Model, accounting for the role of anchoring effects in residential adjustment decisions of renters and examining interdependencies between place attachments and risk perceptions among renters. Likewise, the study sheds light on the factors that shape renters’ residential adjustment decisions, supporting the development of recovery programs that account for the unique needs of renters. Study findings are relevant to other hazards as well. 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 doctoral dissertation incorporates ethnoarchaeology, experimental archaeology, and microarchaeology to examine the technological development of early human groups. This study focuses on fire use and habitat arrangement, features that have proven critical importance for understanding ancient human behavior due to their ability to inform important aspects in evolutionary history, such as increasing cognitive complexity, changing social structures, and adaptations necessary for survival. This project offers training to undergraduate and graduate students in analytical methods such as micromorphology, Fourier-transform infrared spectroscopy (FTIR), phytolith analysis, and microcharcoal quantification. This project will disentangle two critical challenges facing Paleolithic archaeology, identifying the function of hearth features and detecting the presence of habitation materials in the archaeological record. Leveraging ethnoarchaeological data, hearth features with known functions will be sampled for microarchaeological analysis, providing critical analogs that can be used to better understand the function of hearth features found in Paleolithic contexts. Experimental archaeology will involve the analysis of a 14-year-old hearth and habitat feature, which will provide important information on how these features are preserved in the archaeological record. These insights will then be applied to the Middle Paleolithic site which features a series of complex hearths and potential habitat material, allowing for a precise understanding of how ancient groups who occupied this site organized and interacted with their domestic living areas. This multi-disciplinary study will allow for a broader understanding of what conditions are necessary for human technological development. 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 dissertation research studies the impacts of wildlife programs on the management of rangelands. The research findings contribute to the scientific understanding of the effectiveness of the governance and economic structures of wildlife management programs. In addition to providing training for a graduate student in anthropological science and collaborative anthropological methods, the research contributes to public knowledge about scientific validated techniques of rangeland management. To investigate the impacts of wildlife programs on communities, the research collects data using qualitative methods including semi-structured interviews, oral history interviews, behavioral observations, and archival analyses. The research contributes to environmental and economic anthropology, and land and resource management studies. 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 doctoral dissertation research studies the dynamics of workforce development. In addition to providing scientific training for a graduate student in anthropology, the project will inform understandings of corporate-community relations and contribute to the knowledge base that addresses the economic and societal variations in the workforce experience. In order to understand workforce dynamics, the investigators utilize multimodal methods that include audio-visual outputs, semi-structured interviews, and behavioral observations. The research expands the anthropological science of workforce development and makes clear contributions to economic anthropology. 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 examines the charging of electric vehicles from a geographical perspective. Using locational data from mobile phones, the researchers implement machine learning algorithms to study how drivers of electric vehicles select locations for charging their vehicles. These methods permit the identification of drivers’ preferences for charging locations, including the times when these charging stations are used. In turn, these findings permit the identification of geographical locations where new charging stations would be valuable. The results of this project can be used by regional planners to deduce promising locations for new infrastructure, and the methods used in this work can be extended to other planning contexts in which the analysis of large datasets is helpful. The project extends the application of artificial intelligence methods in the geographical sciences and provides valuable research training for a graduate student. This study develops a novel methodological approach to studying the decisions made by users of electric vehicles. Contributing to the field of GIScience, the researchers advance geospatial methods and statistical analyses to model the spatiotemporal patterns of charging electric vehicles. The insights from this study contribute to the efficiency, sustainability, and optimization of charging locations for electric vehicles that reduce the need for detours while minimizing wait times. The study advances a new optimization metric that balances the geographical availability of charging stations and the anticipated demand, and this metric can be implemented in analogous infrastructural contexts. 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

Landscape transformation can result in environments that are variable and patchy, potentially limiting an animal’s ability to move and find a mate. While this situation can hinder a species’ capacity to maintain healthy populations, the impact of landscape transformation on a species depends on its response. This doctoral dissertation project examines how different species of non-human primates respond to landscape changes, by identifying the species traits and landscape features that help or hinder their movement and genetic connectivity. The project advances knowledge of the traits that impede or promote gene flow (movement of genetic material from one group to another), diversity, and species survival among non-human primates. The study informs conservation efforts that support the persistence of non-human primates in the wild, highlighting the importance of tailoring conservation strategies. This doctoral dissertation research uses a multispecies landscape genetics approach to investigate how habitat variation affects gene flow and population connectivity among six non-human primate species in the wild. By combining population genetics, remote sensing, and data on species traits, the project identifies environmental features that help or hinder movement in species with diverse biological traits, such as body size, diet, and locomotion. Using novel single-nucleotide polymorphism genotyping techniques on non-invasive samples, this research analyzes genetic diversity and structure of multiple non-human primate species. The results advance the understanding of how species’ traits influence their vulnerability to habitat fragmentation and provide actionable insights for designing corridors and reforestation strategies to support multiple species. 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

Humans live in social groups, but current biocultural changes have led to significant variation in levels of social connectedness or belonging, which may activate the neuro-immune networks that regulate inflammation. Inflammation is a form of stress response that can have negative health effects, but that is also necessary for adaptation and health. To date, studies of social connectedness and inflammation are limited to individuals from populations with similar levels of social belonging, and these studies emphasize negative social conditions, so that the impact of positive social connections remain understudied. This Doctoral Dissertation Research project adopts a biocultural approach to investigate how variations in belonging affect inflammatory regulation dynamics among adults from social contexts with varying levels of belonging. The study integrates iterative ethnography, with surveys and minimally invasive biological sampling to identify the conditions under which varying levels of connectedness may lead to adaptive or maladaptive inflammatory levels. The study builds STEM capacity through student training, and expands its impact through outreach activities. Scientists have demonstrated that the inflammatory system is sensitive to social experiences such as belonging and exclusion. Social threats can activate the same conserved neuro-inflammatory pathways as physical ones, involving the hypothalamic pituitary adrenocortical (HPA) axis and the sympathetic nervous system’s regulation of acute inflammation. Chronic activation of these pathways can lead to a conserved transcriptional response to adversity (CTRA) and promote a pro-inflammatory phenotype by impairing the downregulation of inflammation in innate immune cells. While ex-vivo cell culture models have advanced our understanding of these processes, they remain limited by costly and invasive sampling methods that require sterile laboratory conditions. Moreover, the effects of positive social connections on inflammation regulation remain underexplored. This Doctoral Dissertation Research project contributes to biocultural frameworks of belonging and inflammatory regulation by: 1) developing a culturally and geographically specific measure of belonging among peoples experiencing different cultural conditions, 2) adapting an ex-vivo cell culture protocol with dried blood spots for use in field settings, and 3) synthesizing novel data to elucidate how diverse social contexts influence biological variation and health in increasingly complex social settings. 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

There is an inherent and intimate relationship between people and their environment, which inevitably leaves detectable traces that can be extracted, analyzed and interpreted to build a more comprehensive understanding of human cultural variation throughout time. This doctoral dissertation project investigates the relationship between human land-use activities associated with early horticulture (i.e., farming, gardening, cultivation) and how they manifest in the archaeological record through changes in plant and microbial communities. The project advances the existing understanding of human-environment interactions in the context of increasing social complexity observed through changes in subsistence (i.e., increased reliance on food production) and settlement patterns. This study contributes to the development of two novel techniques for paleoenvironmental reconstruction from sediment that allow researchers to detect discrete but subtle levels of ecological change than current standard methods are capable of. Additionally, it produces an open-access database of high-resolution scanning electron microscope images of fossil plant structures (phytoliths), support laboratory training and professional development opportunities for undergraduate students, and provide educational material to community members. The primary research question evaluated is: What is the detectable ecological response of land-use activity related to food production, specifically horticulture, at a newly occupied site? This question is addressed through 1) the analysis of phytoliths (fossil plant micro-remains) to inform on vegetation history, and 2) an assessment of microbial DNA extracted from archaeological sediment, comparing the periods before and after people are believed to have arrived at the site. The pre- and post-settlement distinction is based on associated cultural materials (e.g., pottery sherds) from previous excavation at the site and associated radiocarbon dates. The data obtained as part of this project is evaluated with previous research completed on the site and in the surrounding area, which includes a complete fire history, organic matter analysis, stable carbon isotope ratios, sediment grain size analysis, preliminary phytolith analysis, the pollen record, and two field seasons of archaeological excavation. 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 doctoral dissertation research studies the impacts of integrated medicine on birth outcomes in hospital settings. The investigators specifically address several variables related to patient needs including emotional, physical and clinical support to test the ways that integrated support from both clinical and non-clinical experts improves maternal health outcomes. The research findings contribute to the understanding of how clinical and non-clinical experts can be integrated into obstetric models of medical care to meet patient needs and to coordinate maternal care. In addition to providing training in anthropological sciences to a graduate student, research findings will inform the expansion of public knowledge regarding access to integrated medical care and its associated benefits for maternal health. In order to study the impacts of integrated medical approaches, the investigators utilize a mixed-methods and community engaged research design including semi-structured interviews, quantitative survey instruments, and focus groups. Validity and reliability of the data will be tested using community-based participation. The research contributes to medical anthropology, participatory research methodologies, and the social scientific study of public health and medicine. 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.