William Duke Pauli
- Assistant Professor, Plant Science
- Assistant Professor, Genetics - GIDP
- Member of the Graduate Faculty
- Ph.D. Plant Sciences
- Montana State University, Bozeman, Montana, United States
- Application of genomic-assisted breeding for improvement of barley cultivars
- B.S. Biotechnology
- Montana State University, Bozeman, Montana, United States
- Cornell University, Ithaca, New York (2014 - 2017)
- Montana State University, Bozeman, Montana (2008 - 2011)
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.
Crop Science+ProductionPLS 306 (Fall 2022)
Dept of Plant Sci SmnrPLP 596A (Fall 2022)
Dept of Plant Sci SmnrPLS 596A (Fall 2022)
DissertationPLS 920 (Fall 2022)
ResearchPLS 900 (Fall 2022)
DissertationPLS 920 (Spring 2022)
ResearchPLS 900 (Spring 2022)
Directed ResearchPLS 592 (Fall 2021)
Plant Breeding and GeneticsPLS 415 (Fall 2021)
ResearchPLS 900 (Fall 2021)
ResearchPLS 900 (Spring 2021)
Crop Science+ProductionPLS 306 (Fall 2020)
Directed ResearchPLS 592 (Fall 2020)
Independent StudyPLS 599 (Spring 2020)
Plant Breeding and GeneticsPLS 415 (Spring 2020)
ResearchPLS 900 (Spring 2020)
Plant Breeding and GeneticsPLS 415 (Spring 2019)
- 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.
- Zarei, A., Gonzalez, E., Merchant, N., Pauli, D., Lyons, E., & Barnard, K. (2021). MegaStitch: Robust Large Scale Image Stitching.
- 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 infoSodium (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.
- 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, 1405.More infoMany systems for field-based, high-throughput phenotyping (FB-HTP) quantify and characterize the reflected radiation from the crop canopy to derive phenotypes, as well as infer plant function and health status. However, given the technology's nascent status, it remains unknown how biophysical and physiological properties of the plant canopy impact downstream interpretation and application of canopy reflectance data. In that light, we assessed relationships between leaf thickness and several canopy-associated traits, including normalized difference vegetation index (NDVI), which was collected via active reflectance sensors carried on a mobile FB-HTP system, carbon isotope discrimination (CID), and chlorophyll content. To investigate the relationships among traits, two distinct cotton populations, an upland ( L.) recombinant inbred line (RIL) population of 95 lines and a Pima ( L.) population composed of 25 diverse cultivars, were evaluated under contrasting irrigation regimes, water-limited (WL) and well-watered (WW) conditions, across 3 years. We detected four quantitative trait loci (QTL) and significant variation in both populations for leaf thickness among genotypes as well as high estimates of broad-sense heritability (on average, above 0.7 for both populations), indicating a strong genetic basis for leaf thickness. Strong phenotypic correlations (maximum = -0.73) were observed between leaf thickness and NDVI in the Pima population, but not the RIL population. Additionally, estimated genotypic correlations within the RIL population for leaf thickness with CID, chlorophyll content, and nitrogen discrimination ([Formula: see text] = -0.32, 0.48, and 0.40, respectively) were all significant under WW but not WL conditions. Economically important fiber quality traits did not exhibit significant phenotypic or genotypic correlations with canopy traits. Overall, our results support considering variation in leaf thickness as a potential contributing factor to variation in NDVI or other canopy traits measured via proximal sensing, and as a trait that impacts fundamental physiological responses of plants.
- 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 infoThe 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).
- 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.
- 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). UA Field Scanner: Progress and Update. 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. (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.
- 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.