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Map showing Utah drought conditions. The Drought Monitor focuses on broad-scale conditions.

Livestock movements between agricultural premises is an important pathway for the spread of infectious disease. Data providing details about the origin and destination of shipments, as well as information about the shipment size is an important component of computer models used to formulate mitigation strategies and design surveillance programs. The United States (U.S.) currently lacks a comprehensive database of farm animal shipments, which hinders such efforts. With the U.S. Animal Movement Model (USAMM), earlier work has successfully scaled up from limited data based on interstate certificates of veterinary inspection (CVI) to comprehensive county-level shipment networks at the national scale. In this work, we present three major improvements to earlier versions of USAMM: (1) increased resolution of the model and simulated networks to the level of individual premises; (2) predictions of shipment sizes; (3) taking into account the types and herd sizes of the premises. We fitted parameters in a Bayesian framework to two sets of CVI data consisting of sub-samples of one years between-state beef and dairy shipments. Through posterior predictive simulation, we then created 1,000 synthetic beef and dairy networks, which we make publicly available to support livestock disease modeling. The simulated networks were validated against summary statistics of the training data as well as out-of-sample CVI data from subsequent years. This new development opens up the possibility of using USAMM in a broader spectrum of applications where information about shipment size and premises identity is necessary and gives novel insights into the U.S. cattle shipment network., Funding Agencies|U.S. Department of Homeland Security Science and Technology Directorate [D15PC00278]; USDA [AP17VSCEAH00C012]

The spread of infectious livestock diseases is a major cause for concern in modern agricultural systems. In the dynamics of the transmission of such diseases, movements of livestock between herds play an important role. When constructing mathematical models used for activities such as forecasting epidemic development, evaluating mitigation strategies, or determining important targets for disease surveillance, including between -premises shipments is often a necessity. In the United States (U.S.), livestock shipment data is not routinely collected, and when it is, it is not readily available and mostly concerned with between-state shipments. To bridge this gap in knowledge and provide insight into the complete livestock shipment network structure, we have developed the U.S. Animal Movement Model (USAMM). Previously, USAMM has only existed for cattle shipments, but here we present a version for domestic swine. This new version of USAMM consists of a Bayesian model fit to premises demography, county-level livestock industry variables, and two limited data sets of between-state swine movements. The model scales up the data to simulate nation-wide networks of both within-and between-state shipments at the level of individual premises. Here we describe this shipment model in detail and subsequently explore its usefulness with a rudimentary predictive model of the prevalence of porcine epidemic diarrhea virus (PEDv) across the U.S. Additionally, in order to promote further research on livestock disease and other topics involving the movements of swine in the U.S., we also make 250 synthetic premises-level swine shipment networks with complete coverage of the entire conterminous U.S. freely available to the research community as a useful surrogate for the absent shipment data., Funding Agencies|USDA National Institute of Food and Agriculture [2018-67015-28289]; USDA Cooperative Agreements [USDA-APHIS-AP20VSCEAH00C049, USDA-APHIS-AP19VSCEAH00C023, USDA-APHIS-AP17VSCEAH 00C012]

Transboundary animal diseases, such as foot and mouth disease (FMD) pose a significant and ongoing threat to global food security. Such diseases can produce large, spatially complex outbreaks. Mathematical models are often used to understand the spatio-temporal dynamics and create response plans for possible disease introductions. Model assumptions regarding transmission behavior of premises and movement patterns of livestock directly impact our understanding of the ecological drivers of outbreaks and how to best control them. Here, we investigate the impact that these assumptions have on model predictions of FMD outbreaks in the U.S. using models of livestock shipment networks and disease spread. We explore the impact of changing assumptions about premises transmission behavior, both by including within-herd dynamics, and by accounting for premises type and increasing the accuracy of shipment predictions. We find that the impact these assumptions have on outbreak predictions is less than the impact of the underlying livestock demography, but that they are important for investigating some response objectives, such as the impact on trade. These results suggest that demography is a key ecological driver of outbreaks and is critical for making robust predictions but that understanding management objectives is also important when making choices about model assumptions., Funding Agencies|U.S. Department of Homeland Security Science and Technology Directorate [HSHQDC-13-B0028, D15PC00278]; USDA National Institute of Food and Agriculture [2022-67015-36923]

Live animal shipments are a potential route for transmitting animal diseases between holdings and are crucial when modelling spread of infectious diseases. Yet, complete contact networks are not available in all countries, including the USA. Here, we considered a 10% sample of Interstate Certificate of Veterinary Inspections from 1 year (2009). We focused on distance dependence in contacts and investigated how different functional forms affect estimates of unobserved intrastate shipments. To further enhance our predictions, we included responses from an expert elicitation survey about the proportion of shipments moving intrastate. We used hierarchical Bayesian modelling to estimate parameters describing the kernel and effects of expert data. We considered three functional forms of spatial kernels and the inclusion or exclusion of expert data. The resulting six models were ranked by widely applicable information criterion (WAIC) and deviance information criterion (DIC) and evaluated through within- and out-of-sample validation. We showed that predictions of intrastate shipments were mildly influenced by the functional form of the spatial kernel but kernel shapes that permitted a fat tail at large distances while maintaining a plateau-shaped behaviour at short distances better were preferred. Furthermore, our study showed that expert data may not guarantee enhanced predictions when expert estimates are disparate., Funding Agencies|US Department of Homeland Security Science and Technology DirectorateUnited States Department of Homeland Security (DHS) [HSHQDC-13-C-B0028]

Interconnected food, energy, and water (FEW) nexus systems face many challenges to support human well-being (HWB) and maintain resilience, especially in arid and semiarid regions like New Mexico (NM), United States (US). Insufficient FEW resources, unstable economic growth due to fluctuations in prices of crude oil and natural gas, inequitable education and employment, and climate change are some of these challenges. Enhancing the resilience of such coupled socio-environmental systems depends on the efficient use of resources, improved understanding of the interlinkages across FEW system components, and adopting adaptable alternative management strategies. The goal of this study was to develop a framework that can be used to enhance the resilience of these systems. An integrated food, energy, water, well-being, and resilience (FEW-WISE) framework was developed and introduced in this study. This framework consists mainly of five steps to qualitatively and quantitatively assess FEW system relationships, identify important external drivers, integrate FEW systems using system dynamics models, develop FEW and HWB performance indices, and develop a resilience monitoring criterion using a threshold-based approach that integrates these indices. The FEW-WISE framework can be used to evaluate and predict the dynamic behavior of FEW systems in response to environmental and socioeconomic changes using resilience indicators. In conclusion, the derived resilience index can be used to inform the decision-making processes to guide the development of alternative scenario-based management strategies to enhance the resilience of ecological and socioeconomic well-being of vulnerable regions like NM.

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Mathematical models are key tools for the development of surveillance, preparedness and response plans for the potential events of emerging and introduced foreign animal diseases. Creating these types of plans requires data; when data are incomplete, mathematical models can help fill in missing information, provided they are informed by the data that are available. In the United States, the most complete national-scale data available on cattle shipments are based on Interstate Certificates of Veterinary Inspection, which track the shipment of cattle between states; data on intrastate cattle shipments are lacking. Here we develop four new datasets on intrastate cattle shipments in the U.S., including an expert elicitation survey covering 19 states and territories and three state-level brand inspection data sets. The expert elicitation survey provides estimates on the proportion of shipments that travel interstate over multiple regions of the U.S. These survey data also identify differences in shipment patterns between regions, cattle commodity types, and sectors of the cattle industry. These survey data cover more states than any other source of intrastate data; however, one limitation of these data is the small number of participating experts in many of the states, only seven of the 19 responding states and territories had a group size of three or larger. The brand data sets include origin and destination information for both intra- and interstate shipments. These data, therefore, also provide detailed information on the proportion of interstate shipments in three Western states, including the temporal and geographic variation in shipments. Because the survey and brand data overlap in the Western U.S., they can be compared. We find that in the Western U.S. the expert estimates of the overall proportion of cattle shipments matched the brand data well. However, the experts estimated that there would be larger differences in beef and dairy shipments than the brand data

Over the past decade, there has been an increased emphasis on evidence-based practice (EBP) and the use of outcome measurement in clinical practice, however, the implementation of evidence into practice remains challenging and irregular. During fieldwork, students often experience a disconnect between the emphasis on EBP in the classroom and lack of use in the clinic. Recognizing the need to develop high-quality, evidence-based and data-driven models of practice for student training, we partnered with local fieldwork educators to develop an innovative program that guides students and simultaneously trains fieldwork educators (FWE) in the use of a systematic data driven decision making (DDDM) process to infuse evidence into practice. Using a pre-post quasi-experimental design, we evaluated the impact of this program on students’ perceived knowledge and skills in use of EBP and DDDM. A focus group with participating fieldwork educators captured their knowledge and attitudes in the use of EBP and DDDM in their clinical sites. Eleven FWEs and twenty four students participated. Results revealed significant change in students’ knowledge and skill in use of EBP and DDDM. FWEs reported the program clarified the role of occupational therapy, enhanced communication, and validated the value of occupational therapy in their clinical site. This program serves as a model for training students to implement evidence and data driven approaches in clinical practice, thus bridging the gap between classroom and clinic.

This dissertation comprises a series of close readings of music written and performed in intimate devotional contexts by nun composers Sulpitia Cesis, Alba Tressina, and Lucrezia Vizzana in seventeenth-century convents in Modena, Vicenza, and Bologna, respectively. I argue that in singing music written by their sisters, nuns were able to use their voices to mediate a space between their own corporeal bodies and an ephemeral Divine presence. In so doing, these nuns were able to engage in practices conducive to the experience of ecstasy for both singers and listeners, cultivate an outlet for creativity and entertainment, and strengthen their relationships with one another and with the Divine. This mediation also functioned as an act of self-empowerment, as nuns derived agency through composing, performing, and listening to this music. Nuns' voices therefore occupied a queer and transgressive space that threatened patriarchal control over women's sexualities, allowing for more autonomous nurturing of their own identities and spiritualities. Through musicking, nuns were able to communicate with each other, with God, and with the outside world through choice of text, musical setting thereof, manipulation of performance space, and subtle relational cues between singers and audience members.I draw from a range of disciplines: historical musicology, sound studies, queer studies, theater and art history, liturgical history, and Renaissance medicine. My methodologies are similarly multivalent and include "thick" description of the real-time experience of musical performance, sonic space, and the role of the listener; close examination of texts and their relationship to the music; hermeneutical analysis of the musical scores; and analysis of the gestures, bodily movements, and non-verbal cues that occur between singers and audience members during performance.

Little is known about the seventeenth-century musician and composer Alba Tressina, and even less is known about her musical career, since only four of her compositions survive. She was a Clarissan nun at the church of Santa Maria in Aracoeli in the city of Vicenza, 60 kilometers west of Venice, and through the years of her stay there she rose in the monastic hierarchy to become abbess of her house. She studied music and composition with Leone Leoni, who published four of her pieces in one of his numerous books of motets. This joint publishing effort is the reason why we can discuss Tressina and her musical abilities today, for the only extant works we have of hers are those that appeared in this book. It is also because of this book of motets that we know of Tressina’s talent for musical rendition, which Leoni references in his dedication. Therein he mentions her “melodious voice” and the “graces of [her] noble compositions.” He finishes the dedication with a reference to how, when she performs these works, she gives them spiritual life, making them “breathe celestial harmony.”