William Duke Pauli

Interests

Research

The long-term goals of my research program are to understand and utilize the genetic and functional phenotypic variation present in plant populations to responsibly address the challenges facing a growing global population including food and fiber security. The research program is composed of three separate but synergistic areas that combine to elucidate the genetic mechanisms responsible for key agronomic, quality, and stress-adaptive traits that are critical to crop production in areas prone to intense abiotic stress pressures. The first area is centered on identifying and characterizing existing genomic variation in plant populations to better understand the dynamics of phenotypic diversity. The second area concentrates on using emerging high-throughput phenotyping (HTP) technologies to quantify and record complex phenotypes that are responsive to environmental fluctuations throughout the plant’s life cycle in order to understand temporal trait expression patterns. The final area is focused on discovering allelic variants and causative genes responsible for observed phenotypic variation through the use of genetic mapping populations and statistical methods. Together, my research program’s findings are used to more efficiently develop improved crop cultivars that are capable of meeting the socioeconomic demands and environmental constraints of the future.

Courses

Scholarly Contributions

Journals/Publications

• Christenson, C. G., Gohardoust, M. R., Calleja, S., Thorp, K. R., Tuller, M., & Pauli, D. (2024). Monitoring cotton water status with microtensiometers. Irrigation Science, 42(5), 995-1011.
• Garcia, C., Pauli, D., Plecki, C., Alnasser, H., Rozzi, B., Calleja, S., & Arnold, A. E. (2024). The Root Endophytic Microbiome Shifts Under Drought in High-Performing Sorghum. Phytobiomes Journal, 8(3), 282-296.
• Gonzalez, E. M., Zarei, A., Calleja, S., Christenson, C., Rozzi, B., Demieville, J., Hu, J., Eveland, A. L., Dilkes, B., Barnard, K., Lyons, E., & Pauli, D. (2024). Quantifying leaf symptoms of sorghum charcoal rot in images of field-grown plants using deep neural networks. The Plant Phenome Journal, 7(1), e20110.
• Sreedasyam, A., Lovell, J. T., Mamidi, S., Khanal, S., Jenkins, J. W., Plott, C., Bryan, K. B., Li, Z., Shu, S., Carlson, J., Goodstein, D., De, S. L., Kirkbride, R. C., Calleja, S., Campbell, T., Koebernick, J. C., Dever, J. K., Scheffler, J. A., Pauli, D., , Jenkins, J. N., et al. (2024). Genome resources for three modern cotton lines guide future breeding efforts. Nature Plants, 10(6), 1039-1051.
• Yu, L., Dittrich, A., Zhang, X., Brock, J. R., Thirumalaikumar, V. P., Melandri, G., Skirycz, A., Edger, P. P., Thorp, K. R., Hinze, L., Pauli, D., & Nelson, A. D. (2024). Regulation of a single inositol 1-phosphate synthase homeologue by HSFA6B contributes to fibre yield maintenance under drought conditions in upland cotton. Plant Biotechnology Journal, 22(10), 2756-2772.
• Zarei, A., Li, B., Schnable, J. C., Lyons, E., Pauli, D., Barnard, K., & Benes, B. (2024). PlantSegNet: 3D point cloud instance segmentation of nearby plant organs with identical semantics. Computers and Electronics in Agriculture, 221, 108922.
• Gonzalez, E. M., Zarei, A., Hendler, N., Simmons, T., Zarei, A., Demieville, J., Strand, R., Rozzi, B., Calleja, S., Ellingson, H., Cosi, M., Davey, S., Lavelle, D. O., Truco, M. J., Swetnam, T. L., Merchant, N., Michelmore, R. W., Lyons, E., & Pauli, D. (2023). PhytoOracle: Scalable, modular phenomics data processing pipelines. Frontiers in plant science, 14, 1112973.
  More info

  As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to () improve data processing efficiency; () provide an extensible, reproducible computing framework; and () enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).
• Thompson, A. L., Thorp, K. R., Conley, M. M., & Pauli, D. (2023). A proximal sensing cart and custom cooling box for improved hyperspectral sensing in a desert environment. Frontiers in Agronomy, 5.
• Alptekin, B., Erfatpour, M., Mangel, D., Pauli, D., Blake, T., Turner, H., Lachowiec, J., Sherman, J., & Fischer, A. (2022). Selection of favorable alleles of genes controlling flowering and senescence improves malt barley quality. Molecular breeding : new strategies in plant improvement, 42(10), 59.
  More info

  Malt barley ( L.) is an important cash crop with stringent grain quality standards. Timing of the switch from vegetative to reproductive growth and timing of whole-plant senescence and nutrient remobilization are critical for cereal grain yield and quality. Understanding the genetic variation in genes associated with these developmental traits can streamline genotypic selection of superior malt barley germplasm. Here, we determined the effects of allelic variation in three genes encoding a glycine-rich RNA-binding protein (GR-RBP1) and two NAC transcription factors (NAM1 and NAM2) on malt barley agronomics and quality using previously developed markers for and and a novel marker for . Based on a single-nucleotide polymorphism (SNP) in the first intron, the utilized marker differentiates alleles of low-grain protein variety 'Karl' and of higher protein variety 'Lewis'. We demonstrate that the selection of favorable alleles for each gene impacts heading date, senescence timing, grain size, grain protein concentration, and malt quality. Specifically, combining 'Karl' alleles for the two genes with the 'Lewis' allele extends grain fill duration, increases the percentage of plump kernels, decreases grain protein, and provides malt quality stability. Molecular markers for these genes are therefore highly useful tools in malt barley breeding.
• Thorp, K. R., Calleja, S., Pauli, D., Thompson, A. L., & Elshikha, D. E. (2022). Agronomic Outcomes of Precision Irrigation Management Technologies with Varying Complexity. Journal of the ASABE, 65(1), 135--150.
• Alptekin, B., Mangel, D., Pauli, D., Blake, T., Lachowiec, J., Hoogland, T., Fischer, A., & Sherman, J. (2021). Combined effects of a glycine-rich RNA-binding protein and a NAC transcription factor extend grain fill duration and improve malt barley agronomic performance. Theoretical and Applied Genetics, 134(1), 351--366.
• Herritt, M. T., Long, J. C., Roybal, M. D., Moller Jr, ,. D., Mockler, T. C., Pauli, D., & Thompson, A. L. (2021). FLIP: FLuorescence Imaging Pipeline for field-based chlorophyll fluorescence images. SoftwareX, 14, 100685.
• Jain, P., Liu, W., Zhu, S., Chang, C. Y., Melkonian, J., Rockwell, F. E., Pauli, D., Sun, Y., Zipfel, W. R., Holbrook, N. M., & others, . (2021). A minimally disruptive method for measuring water potential in planta using hydrogel nanoreporters. Proceedings of the National Academy of Sciences, 118(23).
• Melandri, G., Thorp, K. R., Broeckling, C., Thompson, A. L., Hinze, L., & Pauli, D. (2021). Assessing Drought and Heat Stress-Induced Changes in the Cotton Leaf Metabolome and Their Relationship With Hyperspectral Reflectance. Frontiers in plant science, 12.
• Pugh, N. A., Thorp, K. R., Gonzalez, E. M., Elshikha, D., & Pauli, D. (2021). Comparison of image georeferencing strategies for agricultural applications of small unoccupied aircraft systems. The Plant Phenome Journal, 4(1), e20026.
• Sagan, V., Maimaitijiang, M., Paheding, S., Bhadra, S., Gosselin, N., Burnette, M., Demieville, J., Hartling, S., LeBauer, D., Newcomb, M., & others, . (2021). Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data. IEEE Transactions on Geoscience and Remote Sensing, 60, 1--20.
• Gazave, E., Tassone, E. E., Baseggio, M., Cryder, M., Byriel, K., Oblath, E., Lueschow, S., Poss, D., Hardy, C., Wingerson, M., & others, . (2020). Genome-wide association study identifies acyl-lipid metabolism candidate genes involved in the genetic control of natural variation for seed fatty acid traits in Brassica napus L.. Industrial Crops and Products, 145, 112080.
• Herritt, M. T., Pauli, D., Mockler, T. C., & Thompson, A. L. (2020). Chlorophyll fluorescence imaging captures photochemical efficiency of grain sorghum (Sorghum bicolor) in a field setting. Plant methods, 16(1), 1--13.
• Maimaitijiang, M., Sagan, V., Erkbol, H., Adrian, J., Newcomb, M., LeBauer, D., Pauli, D., Shakoor, N., & Mockler, T. C. (2020). UAV-BASED SORGHUM GROWTH MONITORING: A COMPARATIVE ANALYSIS OF LIDAR AND PHOTOGRAMMETRY.. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 5(3).
• Wang, D. R., Venturas, M. D., Mackay, D. S., Hunsaker, D. J., Thorp, K. R., Gore, M. A., & Pauli, D. (2020). Use of hydraulic traits for modeling genotype-specific acclimation in cotton under drought. New Phytologist, 228(3), 898--909.
• Nelson, A., Ponciano, G., McMahan, C., Ilut, D. C., Pugh, N. A., Elshikha, D. E., Hunsaker, D. J., & Pauli, D. (2019). Transcriptomic and evolutionary analysis of the mechanisms by which P. argentatum, a rubber producing perennial, responds to drought. BMC PLANT BIOLOGY, 19(1).
• Sagan, V., Maimaitijiang, M., Sidike, P., Eblimit, K., Peterson, K. T., Hartling, S., Esposito, F., Khanal, K., Newcomb, M., Pauli, D., Ward, R., Fritschi, F., Shakoor, N., & Mockler, T. (2019). UAV-Based High Resolution Thermal Imaging for Vegetation Monitoring, and Plant Phenotyping Using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap Cameras. REMOTE SENSING, 11(3).
• Thompson, A. L., Thorp, K. R., Conley, M. M., Elshikha, D. M., French, A. N., Andrade-Sanchez, P., & Pauli, D. (2019). Comparing Nadir and Multi-Angle View Sensor Technologies for Measuring in-Field Plant Height of Upland Cotton. REMOTE SENSING, 11(6).
• Clohessy, J. W., Pauli, W. D., Kreher, K. M., Buckler, E. S., Armstrong, P. R., Wu, T., Hoekenga, O. A., Jannink, J., Sorrells, M. E., & Gore, M. A. (2018). A Low-Cost Automated System for High-Throughput Phenotyping of Single Oat Seeds. The Plant Phenome Journal, 1(1). doi:10.2135/tppj2018.07.0005
• Hazzouri, K. M., Khraiwesh, B., Amiri, K. M., Pauli, D., Blake, T., Shahid, M., Mullath, S. K., Nelson, D., Mansour, A. L., Salehi-Ashtiani, K., Purugganan, M., & Masmoudi, K. (2018). Mapping of Gene in Barley Using GWAS Approach and Its Implication in Salt Tolerance Mechanism. Frontiers in plant science, 9, 156.
  More info

  Sodium (Na) accumulation in the cytosol will result in ion homeostasis imbalance and toxicity of transpiring leaves. Studies of salinity tolerance in the diploid wheat ancestor showed that -like gene was a major gene in the QTL for salt tolerance, named . In the present study, we were interested in investigating the molecular mechanisms underpinning the role of the gene in salt tolerance in barley (). A USDA mini-core collection of 2,671 barley lines, part of a field trial was screened for salinity tolerance, and a Genome Wide Association Study (GWAS) was performed. Our results showed important SNPs that are correlated with salt tolerance that mapped to a region where ion transporter located on chromosome four. Furthermore, sodium (Na) and potassium (K) content analysis revealed that tolerant lines accumulate more sodium in roots and leaf sheaths, than in the sensitive ones. In contrast, sodium concentration was reduced in leaf blades of the tolerant lines under salt stress. In the absence of NaCl, the concentration of Na and K were the same in the roots, leaf sheaths and leaf blades between the tolerant and the sensitive lines. In order to study the molecular mechanism behind that, alleles of the gene from five tolerant and five sensitive barley lines were cloned and sequenced. Sequence analysis did not show the presence of any polymorphism that distinguishes between the tolerant and sensitive alleles. Our real-time RT-PCR experiments, showed that the expression of gene in roots of the tolerant line was significantly induced after challenging the plants with salt stress. In contrast, in leaf sheaths the expression was decreased after salt treatment. In sensitive lines, there was no difference in the expression of gene in leaf sheath under control and saline conditions, while a slight increase in the expression was observed in roots after salt treatment. These results provide stronger evidence that gene in barley play a key role in withdrawing Na from the xylem and therefore reducing its transport to leaves. Given all that, these data support the hypothesis that gene is responsible for Na unloading to the xylem and controlling its distribution in the shoots, which provide new insight into the understanding of this QTL for salinity tolerance in barley.
• Pauli, W. D., Ziegler, G., Ren, M., Jenks, M., Hunsacker, D., Zhang, M., Baxter, I., & Gore, M. (2018). Multivariate analysis of the cotton seed ionome reveals a shared genetic architecture. G3: Genes, Genomes, Genetics, 8, 1147-1160. doi:https://doi.org/10.1534/g3.117.300479
• Dabbert, T. A., Pauli, D., Sheetz, R., & Gore, M. A. (2017). Influences of the combination of high temperature and water deficit on the heritabilities and correlations of agronomic and fiber quality traits in upland cotton. EUPHYTICA, 213(1).
• Pauli, D., White, J. W., Andrade-Sanchez, P., Conley, M. M., Heun, J., Thorp, K. R., French, A. N., Hunsaker, D. J., Carmo-Silva, E., Wang, G., & Gore, M. A. (2017). Investigation of the Influence of Leaf Thickness on Canopy Reflectance and Physiological Traits in Upland and Pima Cotton Populations. FRONTIERS IN PLANT SCIENCE, 8.
• Thompson, A. L., Pauli, D., Tomasi, P., Yurchenko, O., Jenks, M. A., Dyer, J. M., & Gore, M. A. (2017). Chemical variation for fiber cuticular wax levels in upland cotton (Gossypium hirsutum L.) evaluated under contrasting irrigation. INDUSTRIAL CROPS AND PRODUCTS, 100, 153-162.
• Pauli, D., Andrade-Sanchez, P., Carmo-Silva, A. E., Gazave, E., French, A. N., Heun, J., Hunsaker, D. J., Lipka, A. E., Setter, T. L., Strand, R. J., Thorp, K. R., Wang, S., White, J. W., & Gore, M. A. (2016). Field-Based High-Throughput Plant Phenotyping Reveals the Temporal Patterns of Quantitative Trait Loci Associated with Stress-Responsive Traits in Cotton. G3 (Bethesda, Md.), 6(4), 865-79.
  More info

  The application of high-throughput plant phenotyping (HTPP) to continuously study plant populations under relevant growing conditions creates the possibility to more efficiently dissect the genetic basis of dynamic adaptive traits. Toward this end, we employed a field-based HTPP system that deployed sets of sensors to simultaneously measure canopy temperature, reflectance, and height on a cotton (Gossypium hirsutum L.) recombinant inbred line mapping population. The evaluation trials were conducted under well-watered and water-limited conditions in a replicated field experiment at a hot, arid location in central Arizona, with trait measurements taken at different times on multiple days across 2010-2012. Canopy temperature, normalized difference vegetation index (NDVI), height, and leaf area index (LAI) displayed moderate-to-high broad-sense heritabilities, as well as varied interactions among genotypes with water regime and time of day. Distinct temporal patterns of quantitative trait loci (QTL) expression were mostly observed for canopy temperature and NDVI, and varied across plant developmental stages. In addition, the strength of correlation between HTPP canopy traits and agronomic traits, such as lint yield, displayed a time-dependent relationship. We also found that the genomic position of some QTL controlling HTPP canopy traits were shared with those of QTL identified for agronomic and physiological traits. This work demonstrates the novel use of a field-based HTPP system to study the genetic basis of stress-adaptive traits in cotton, and these results have the potential to facilitate the development of stress-resilient cotton cultivars.
• Pauli, D., Chapman, S. C., Bart, R., Topp, C. N., Lawrence-Dill, C. J., Poland, J., & Gore, M. A. (2016). The Quest for Understanding Phenotypic Variation via Integrated Approaches in the Field Environment. Plant physiology, 172(2), 622-634.
• Pauli, D., Brown-Guedira, G., & Blake, T. K. (2015). Identification of Malting Quality QTLs in Advanced Generation Breeding Germplasm. JOURNAL OF THE AMERICAN SOCIETY OF BREWING CHEMISTS, 73(1), 29-40.
• Pauli, D., Muehlbauer, G. J., Smith, K. P., Cooper, B., Hole, D., Obert, D. E., Ullrich, S. E., & Blake, T. K. (2014). Association Mapping of Agronomic QTLs in US Spring Barley Breeding Germplasm. PLANT GENOME, 7(3).

Proceedings Publications

• Mockler, T., Lee, S., Shakoor, N., Ozersky, P., Schmutz, J., Healey, A., Morris, G., Hu, Z., Pauli, D., Pless, R., & others, . (2022). Integrating Pan-Genomic Information with High-Resolution Phenotyping in Sorghum. In ASA, CSSA and SSSA International Annual Meetings (2020)| VIRTUAL.
• Krechmer, J., Meredith, L. K., Gil-Loaiza, J., Clark, M., Grigory, J., Demieville, J., Pauli, D., Lunny, E. M., Shorter, J. H., & Roscioli, J. R. (2021). Real-Time in-situ Measurements of Subsurface Volatile Organic Compounds as Tracers for Crop Signaling. In AGU Fall Meeting 2021.
• Lunny, E., Roscioli, J. R., Shorter, J. H., Krechmer, J., Meredith, L. K., Gil-Loaiza, J., Clark, M., Grigory, J., Pauli, D., & Demieville, J. (2021). Subsurface measurements of nitrogen dynamics in agricultural soil. In AGU Fall Meeting 2021.
• Ren, C., Dulay, J., Rolwes, G., Pauli, D., Shakoor, N., & Stylianou, A. (2021). Multi-resolution Outlier Pooling for Sorghum Classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Presentations

• Pauli, W. D. (2019, February). Desert Phenomics: Understanding Crop Hydraulics for the Climate of Future. Phenome. Tucson, AZ.
• Pauli, W. D. (2021, February). The Future of Agriculture. SciTech Institute. Online.
• Pauli, W. D. (2022, November). Genetic Improvemtn of Lettuce Through Phenomics. Desert Agriculture Research Symposium. Yuma, AZ: YCEDA.
• Pauli, W. D. (2022, October). Phenomics Data Processing: A New Bottleneck in Plant Science Research. James Hutton Institute. Online: James Hutton Institute.
• Pauli, W. D. (2023, A[pril). Leading Technologies for Agriculture. Panel presentation for Denice Ross, U.S. Chief Data Scientist - White House. Tucson, AZ: Data Science Institute.
• Pauli, W. D. (2023, April). Plant Phenomics: Turning a Petabyte of Data into Knowledge. Department of BioStats Seminar Series. Tucson, AZ: University of Arizona BioStats Department.
• Pauli, W. D. (2023, December). High-Throughput Plant Phenotyping: Using the World's Largest Phenotyping Platform. Rutgers University. Online: Rutgers University.
• Pauli, W. D. (2023, November). Phenomics Data Processing: Turning Data into Knowledge. Center for Research on Programmable Plant Systems. Online: Cornell.
• Pauli, W. D. (2023, November). Plant Improvement for Climate Change. Central Arizona College Biology Class. Casa Grande, AZ.
• Pauli, W. D. (2023, October). Tepary Bean: Unlocking the Potential of a Desert Adapted Superfood. UA Climate Health Summit. Washington D.C.: UA Health Sciences.
• Pauli, W. D. (2024, April). ARID: Defining the Future and Finding Solutions for Arid Land Agriculture. University and Industry Consortium hosted by University of Arizona. Maricopa, AZ: Dean Burgess, University of Arizona.
• Pauli, W. D. (2024, January). ARID: Finding Solutions for Challenges Facing Arid Land Agriculture. Desert Ag Research Symposium on Soil Health. Yuma, AZ: YCEDA.
• Pauli, W. D. (2024, January). Transcriptome and Gene Expression Network Analyses Reveal an Association of Homoelogous Transcriptional Factors with Fiber Yield Under Drought Conditions. Cotton Beltwide Conference. Fort Worth, TX: National Cotton Council.
• Luo, W., Gonzalez, E., Zarei, A., Li, H., Michelmore, R., Dyer, J., Pauli, W. D., & Jenks, M. (2023, March). Characterization of Leaf Cuticular Wax Composition on a Genetically Diverse Collection of Lettuce (Lactuca sativa L.) using a Linear Mixed Model.. Southwest Data Science Conference. Baylor University.
• Tuller, M., Sagan, V., Pauli, W. D., Rozzi, B., Demieville, J., Calleja, S., Gohardoust, M. R., & Babaeian, E. (2021, December). High Resolution Three-Dimensional Mapping of Vegetation Water Content via Fusion of SWIR Reflectance and Canopy Laser Topography. American Geophysical Union (AGU) Fall Meeting. New Orleans, LA: American Geophysical Union (AGU).
• Tuller, M., Sagan, V., Pauli, W. D., Rozzi, B., Demieville, J., Calleja, S., Gohardoust, M. R., & Babaeian, E. (2021, November). A New Approach to Short-Wave Infrared Remote Sensing of Plant Canopy Water Content. ASA-CSSA-SSSA International Annual Meeting. Salt Lake City, UT: Soil Science Society of America (SSSA).
• Pauli, W. D. (2020, January). Towards better varieties: Advances in plant phenotyping. Arizona Ag100 Meeting. Yuma, AZ: Ag100 and Yuma Center for Excellence in Desert Agriculture.
• Pauli, W. D. (2019, April). TERRA-REF: A Reference Phenotyping Platform. Department of Energy ARPA -E Congresional ShowcaseDepartment of Energy.
• Pauli, W. D. (2019, February). Phenome Tour of UA Field Scanner. Phenome. Maricopa, AZ: American Society of Plant Biologist.
• Pauli, W. D. (2019, January). Leveraging Phenomics and Ecophysiological Models to Unravel Stress-Adaptive Traits in Cotton. Cotton Beltwide Conference.
• Pauli, W. D. (2018, April). Cotton Improvement for Wrangler Jeans. Wrangler Jeans. Greensboro, NC: VF Corporation.
• Pauli, W. D. (2018, February). HTP Imaging: Challenges and Solutions. USDA-ARS Arid Land Agricultural Research Center. Maricopa, AZ: USDA-ARS Arid Land Agricultural Research Center.
• Pauli, W. D. (2018, February). Phenomics: Illuminating the Genetic Basis of Cotton Resiliency. Texas A&M Plant Breeding Symposium. College Station, TX: Texas A&M.
• Pauli, W. D. (2018, February). The Phenomics Data Challenge. USDA-ARS Arid Land Agricultural Research Center. Maricopa, AZ: USDA-ARS Arid Land Agricultural Research Center.
• Pauli, W. D. (2018, January). Deep Learning for Image-Based Detection of Northern Leaf Blight in Maize. Genomes to Fields Conference. Ames, IA: Genomes to Fields.
• Pauli, W. D. (2018, June). Unraveling Stress Adaptive Traits Through the Use of Phenomics. Colorado State University Drought Symposium. Fort Collins, CO: Colorado State University.
• Pauli, W. D. (2018, May). Ionomics Reveals Integrated Genetic Signatures of Abiotic Stress Response. International Cotton Genome Initiative. Edinburgh, Scotland, UK: International Cotton Genome Initiative.
• Pauli, W. D. (2018, May). Manipulation of Flowering in Guayule as a Potential Mechanism to Increase Rubber Biosynthesis. The Sustainable Bioeconomy for Arid Regions Center of Excellence. Tucson, AZ: The Sustainable Bioeconomy for Arid Regions Center of Excellence.
• Pauli, W. D. (2018, November). Phenomics: Bringing the Lab to the Field. Ecosystem Genomics Seminar Series. Tucson, AZ: University of Arizona.

Poster Presentations

• Newcomb, M., Dornbusch, T., Herritt, M., Demieville, J., White, J. W., Ward, R., Huen, J., Strand, R. J., Pauli, W. D., Shakoor, N., & Mockler, T. (2018, November). Automated Chlorophyll Fluorescence Imaging of Dark-Adapted Plants in Field Plots. American Society of Agronomy and Crop Science Society of America Meeting. Baltimore, MD: American Society of Agronomy and Crop Science Society of America.