Recital Program Notes

This program includes: O Magnum Mysterium (2003) Morten Lauridsen (1943 - ), transcribed for band by H. Robert Reynolds (1934 - ) Outdoor Overture (1948) Aaron Copland (1900 – 1990) Ammerland (2001) Jacob de Haan (1959 - ) El Capitan (1896) John Philip Sousa (1854 – 1952) arranged by Keith Brion (1933 - ) and Loras Schissel (1964 - ) October (2000) Eric Whitacre (1970 - ) Sleigh Ride (1948) Leroy Anderson (1908 – 1975) Windscape (2011) David Marlatt (1973 - ) Sailor’s Delight (2018) Frank McKinney (1953 - ) Spring: A New Beginning (2009) David Marlatt (1973 - ) Elegy for a Young American (1967) Ronald Lo Presti (1933 – 1985) Chester Overture (1957) William Schuman (1910 – 1992

Advancing Digital Soil Mapping and Assessment in Arid Landscapes

There is a need to understand the spatial distribution of soil taxonomic classes, the spatial distribution of potential biological soil crust, and soil properties related to wind erosion to address land use and management decisions in arid and semi-arid areas of the western USA. Digital soil mapping (DSM) can provide this information. Chapter 2 compared multiple DSM functions and environmental covariate sets at three geographically distinct semi-arid study areas to identify combinations that would best predict soil taxonomic classes. No single model or type of model was consistently the most accurate classifier for all three areas. The use of the “most important” variables consistently resulted in the highest model accuracies for all study areas. Overall classification accuracy was largely dependent upon the number of taxonomic classes and the distribution of pedons between taxonomic classes. Individual class accuracy was dependent upon the distribution of pedons in each class. Model accuracy could be increased by increasing the number of pedon observations or decreasing the number of taxonomic classes. Potential biological soil crust level of development (LOD) classes were predicted over a large area surrounding Canyonlands National Park in Chapter 3. The moderate LOD class was modeled with reasonable accuracy. The low and high LOD classes were modeled with poor accuracy. Prediction accuracy could likely be improved through the use of additional covariates. Spatial predictions of LOD classes may be useful for assessing the impact of past land uses on biological soil crusts. Threshold friction velocity (TFV) was measured and then correlated with other, easier-to-measure soil properties in Chapter 4. Only soils with alluvial surficial rocks or weak physical crusts reached TFV in undisturbed conditions. All soil surfaces reached TFV after disturbance. Soils with weak physical crusts produced the most sediment. Future work on wind erosion in the eastern Great Basin should focus on non-crusted/weakly crusted soils and soils formed in alluvium overlying lacustrine materials. Soils with other crust types are likely not susceptible to wind erosion. Threshold friction velocity in undisturbed soils with weak physical crusts and undisturbed soils with surficial rocks was predicted using a combination of penetrometer, rock cover, and silt measurements

What’s the VALUE of Information Literacy? Comparing Learning Community and Non-Learning Community Student Learning Outcomes

Using the Information Literacy VALUE Rubric provided by the AAC&U, this study compares thirty final capstone assignments in a research course in a learning community with thirty final assignments in from students not in learning communities. Results indicated higher performance of the non-learning community students; however, transfer skills were higher with the learning community students. Reasons for the findings are discussed, along with suggestions for future research. This article contributes to the growing literature about the role of librarians and information literacy in learning communities

Establishing Big Sagebrush Seedlings on the Colorado Plateau

Factors such as soil type and precipitation vary across rangeland landscapes, and these factors affect restoration outcomes and ultimately mean that “one size fits all” management strategies are not effective across large, complex landscapes. Big sagebrush (Artemisia tridentata) is a foundational rangeland species that is important to wildlife habitat across the western U.S. On the Colorado Plateau, sagebrush is important browse for ungulates, such as mule deer and pronghorn, which motivates a great deal of restoration effort. However, most scientific knowledge of big sagebrush comes from the Great Basin, and we know much less about how to restore sagebrush on the Colorado Plateau, where soils and precipitation patterns are different and conditions are warmer and drier. This fact sheet describes research about establishing and restoring sagebrush seedlings on the Colorado Plateau

Some methods to improve the utility of conditioned Latin hypercube sampling

The conditioned Latin hypercube sampling (cLHS) algorithm is popularly used for planning field sampling surveys in order to understand the spatial behavior of natural phenomena such as soils. This technical note collates, summarizes, and extends existing solutions to problems that field scientists face when using cLHS. These problems include optimizing the sample size, re-locating sites when an original site is deemed inaccessible, and how to account for existing sample data, so that under-sampled areas can be prioritized for sampling. These solutions, which we also share as individual R scripts, will facilitate much wider application of what has been a very useful sampling algorithm for scientific investigation of soil spatial variation

Soil Property and Class Maps of the Conterminous US at 100 meter Spatial Resolution based on a Compilation of National Soil Point Observations and Machine Learning

With growing concern for the depletion of soil resources, conventional soil data must be updated to support spatially explicit human-landscape models. Three US soil point datasetswere combined with a stack of over 200 environmental datasets to generate complete coverage gridded predictions at 100 m spatial resolution of soil properties (percent organic C, total N, bulk density, pH, and percent sand and clay) and US soil taxonomic classes (291 great groups and 78 modified particle size classes) for the conterminous US. Models were built using parallelized random forest and gradient boosting algorithms. Soil property predictions were generated at seven standard soil depths (0, 5, 15, 30, 60, 100 and 200 cm). Prediction probability maps for US soil taxonomic classifications were also generated. Model validation results indicate an out-of-bag classification accuracy of 60 percent for great groups, and 66 percent for modified particle size classes; for soil properties cross-validated R-square ranged from 62 percent for total N to 87 percent for pH. Nine independent validation datasets were used to assess prediction accuracies for soil class models and results ranged between 24-58 percent and 24-93 percent for great group and modified particle size class prediction accuracies, respectively. The hybrid "SoilGrids+" modeling system that incorporates remote sensing data, local predictions of soil properties, conventional soil polygon maps, and machine learning opens the possibility for updating conventional soil survey data with machine learning technology to make soil information easier to integrate with spatially explicit models, compared to multi-component map units.Comment: Submitted to Soil Science Society of America Journal, 40 pages, 12 figures, 3 table

POLARIS: A 30-meter probabilistic soil series map of the contiguous United States

A newcomplete map of soil series probabilities has been produced for the contiguous United States at a 30mspatial resolution. This innovative database, named POLARIS, is constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm (DSMART-HPC) to remap the Soil Survey Geographic (SSURGO) database. This 9 billion grid cell database is possible using available high performance computing resources. POLARIS provides a spatially continuous, internally consistent, quantitative prediction of soil series. It offers potential solutions to the primary weaknesses in SSURGO: 1) unmapped areas are gap-filled using survey data from the surrounding regions, 2) the artificial discontinuities at political boundaries are removed, and 3) the use of high resolution environmental covariate data leads to a spatial disaggregation of the coarse polygons. The geospatial environmental covariates that have the largest role in assembling POLARIS over the contiguous United States (CONUS) are fine-scale (30 m) elevation data and coarse-scale (~2 km) estimates of the geographic distribution of uranium, thorium, and potassium. A preliminary validation of POLARIS using the NRCS National Soil Information System (NASIS) database shows variable performance over CONUS. In general, the best performance is obtained at grid cells where DSMART-HPC is most able to reduce the chance of misclassification. The important role of environmental covariates in limiting prediction uncertainty suggests including additional covariates is pivotal to improving POLARIS\u27 accuracy. This database has the potential to improve the modeling of biogeochemical, water, and energy cycles in environmental models; enhance availability of data for precision agriculture; and assist hydrologic monitoring and forecasting to ensure food and water security

Deciphering the past to inform the future: preparing for the next (“really big”) extreme event

Climate change will bring more extremes in temperature and precipitation that will impact productivity and ecosystem resilience throughout agroecosystems worldwide. Historical events can be used to identify drivers that impact future events. A catastrophic drought in the US in the 1930s resulted in an abrupt boundary between areas severely impacted by the Dust Bowl and areas that were less severely affected. Historical primary production data confirmed the location of this boundary at the border between two states (Nebraska and Iowa). Local drivers of weather and soils explained production responses across the boundary before and after the drought (1926–1948). During the drought, however, features at the landscape scale (soil properties and wind velocities) and regional scale (the Missouri River, its floodplain, and the nearby Loess Hills) explained most of the observed variance in primary production. The impact of future extreme events may be affected by land surface properties that either accentuate or ameliorate the effects of these events. Consideration of large-scale geomorphic processes may be necessary to interpret and manage for catastrophic events

Machine learning for predicting soil classes in three semi-arid landscapes

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes. Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination. Overall, complex models were consistently more accurate than simple or moderately complex models.Random forests (RF) using covariates selected via recursive feature elimination was consistently most accurate, or was among the most accurate, classifiers sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used. Overall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. 43 Individual subgroup class accuracy was generally dependent upon the number of soil pedon 44 observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil-landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area