i MANAGEMENT OF ROOT LESION NEMATODES IN MONTANA WINTER WHEAT by Erika Consoli A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Plant Science MONTANA STATE UNIVERSITY Bozeman, Montana December 2024 ©COPYRIGHT by Erika Consoli 2024 All Rights Reserved ii DEDICATION In the loving memory of my father, Renato Consoli. Thank you for instilling in me a deep love for agriculture and for teaching us to be strong and resilient. iii ACKNOWLEDGEMENTS This work would not have been possible without funding from the United States Department of Agriculture (USDA-NIFA), and the Montana Wheat and Barley Committee. I would like to express my sincere gratitude to the Montana growers, Craig Henke and Terry Turner, for providing us with research plots on their lands. Thanks to Montana State University and the PSPP Department for proving the infrastructure for this project. A special thanks to Irene Decker and Jennifer DeChaine for their prompt support. Many thanks to my amazing committee members, Alan Dyer, Jed Eberly, Horacio Lopez- Nicora, Inga Zasada and Qing Yan for their time, support and guidance. I would like to express my sincere gratitude to my advisor, Alan Dyer, for the opportunity to conduct this project, and for the constant support throughout my years at MSU. To my lab mates, Dipiza, Laura and Tom, I appreciate your friendship. Thanks also to Gary Strobel for his constant kindness and encouragement. A big thanks to Jeff Johnston for all his support with my field trials, and to Asa Hurd at CARC for the technical support provided. To all the undergrad students who helped me at some stage of my research, thank you again! I cannot finish this section without acknowledging all the support and friendship from Dr. C. Marcelo Oliveira, with whom I was very fortunate to debut in my nematology career. I am also deeply grateful to have such a supportive family and friends who always find ways to express their encouragement and love even if from afar. Finally, I would like to express my sincere gratitude to my partner, Lorenzo Bonomi, who stood by my side through the challenges and joys of the past nine and a half years. I am also thankful for his willingness to “volunteer” for numerous weekend fieldwork activities. iv TABLE OF CONTENTS 1. INTRODUCTION .............................................................................................................. 1 Wheat: Importance, Production and Yield Losses Caused by Diseases ............................. 1 Biology of Root Lesion Nematodes .................................................................................... 2 Management of Root Lesion Nematodes ............................................................................ 6 Root Lesion Nematodes in Montana................................................................................... 9 Soil Microbial Communities and Plant Health ................................................................. 13 Goals of This Research ..................................................................................................... 14 Research Hypothesis and Objectives ................................................................................ 15 References ......................................................................................................................... 16 2. HOST SUITABILITY OF WINTER WHEAT BREEDING LINES AND ROTATIONAL CROPS TO THE ROOT LESION NEMATODES PRATYLENCHUS NEGLECTUS AND PRATYLENCHUS THORNEI ....................... 25 Contribution of Authors and Co-Authors ......................................................................... 25 Manuscript Information .................................................................................................... 26 Summary ........................................................................................................................... 27 Abstract ............................................................................................................................. 27 Introduction ....................................................................................................................... 28 Material and Methods ....................................................................................................... 31 Root Lesion Nematode Cultures and Inoculum Preparation ................................ 31 Evaluation of Breeding Lines ............................................................................... 33 Evaluations of Rotational Crops ........................................................................... 34 Nematode Inoculation ........................................................................................... 37 Experiment Evaluations ........................................................................................ 37 Nematode Quantification and Reproductive Factor ............................................. 38 Statistical Analysis ................................................................................................ 39 Results ............................................................................................................................... 39 Multiplication Comparison of P. neglectus and P. thornei in Winter Wheat Lines .......................................................................................................... 39 Multiplication Comparison of Pratylenchu neglectus and Pratylenchus thornei on Rotational Crops ............................................................ 43 Discussion ......................................................................................................................... 45 Conclusion ........................................................................................................................ 48 References ......................................................................................................................... 48 v TABLE OF CONTENTS CONTINUED 3. FIELD ASSESSMENT OF WINTER WHEAT LINES FOR RESISTANCE TO PRATYLENCHUS NEGLECTUS ............................................................................... 53 Contribution of Authors and Co-Authors ......................................................................... 53 Manuscript Information .................................................................................................... 54 Summary ........................................................................................................................... 55 Abstract ............................................................................................................................. 55 Introduction ....................................................................................................................... 56 Materials and Methods ...................................................................................................... 58 Plant Material ........................................................................................................ 58 Experimental Sites ................................................................................................ 59 Experimental Design ............................................................................................. 61 Soil Sampling, Extraction, and Nematode Quantification .................................... 62 Agronomics ........................................................................................................... 63 Statistical Analysis ................................................................................................ 64 Results ............................................................................................................................... 64 Nematode Quantification ...................................................................................... 64 Agronomics ........................................................................................................... 70 Effect of Pratylenchus neglectus Densities on Agronomics ................................. 76 Discussion ......................................................................................................................... 78 Conclusion ........................................................................................................................ 83 Acknowledgements ........................................................................................................... 83 Description of Appendix Material .................................................................................... 83 References ......................................................................................................................... 84 4. SOIL MICROBIOME DRIVEN BY ROOT LESION NEMATODE DENSITY IN WHEAT FIELDS ....................................................................................... 88 Contribution of Authors and Co-Authors ......................................................................... 88 Summary ........................................................................................................................... 89 Abstract ............................................................................................................................. 89 Introduction ....................................................................................................................... 90 Materials and Methods ...................................................................................................... 94 Plant Material ........................................................................................................ 94 Field Experiments ................................................................................................. 95 Site Descriptions ....................................................................................... 95 Experimental Design ................................................................................. 97 Data Collection ..................................................................................................... 98 Nematodes Densities ................................................................................. 98 Agronomic Data ........................................................................................ 99 Microbiome ............................................................................................... 99 vi TABLE OF CONTENTS CONTINUED Bioinformatics and Statistical Analysis .............................................................. 101 Results ............................................................................................................................. 102 Nematode Quantification .................................................................................... 102 Overall Composition of Bacterial and Fungal Communities .............................. 105 Effect of Nematode Density on Bacterial Community ....................................... 109 Discussion ........................................................................................................................ 111 Conclusion .......................................................................................................................115 Description of Appendix Material ...................................................................................115 Acknowledgements ..........................................................................................................116 References ........................................................................................................................116 5. CONCLUSION AND FUTURE DIRECTIONS ............................................................ 124 CUMULATIVE REFERENCE CITED ................................................................................ 128 APPENDICES ...................................................................................................................... 146 ADDITIONAL TABLES FOR CHAPTER 3 ................................................................. 147 ADDITIONAL TABLES FOR CHAPTER 4 ................................................................. 153 vii LIST OF TABLES Table Page 1. Table 2.1. Plant Materials for the breeding lines assay. .................................................... 34 2. Table 2.2. Plant material for the rotational crop multiplication assay............................... 36 3. Table 2.3. Multiplication comparison and nematode density on advanced winter wheat lines for Pratylenchus neglectus and P. thornei. ......................................... 41 4. Table 3.1. Plant material used in the field trials ................................................................ 59 5. Table 3.2. Pratylenchus neglectus reproduction factor (Rf) across field locations and years ............................................................................................................ 69 6. Table 3.3. Yield of winter wheat lines at Bozeman, Chester and Havre locations for the 2023 and 2024 field seasons .................................................................. 72 7. Table 3.4. Protein content of winter wheat lines at Bozeman, Chester and Havre locations for the 2023 and 2024 field season. ........................................................ 73 8. Table 3.5. Test weight of winter wheat lines at Bozeman, Chester and Havre locations for the 2023 and 2024 field season. ................................................................... 74 9. Table 3.6. Thousand kernel weight of winter wheat lines at Bozeman, Chester and Havre locations for the 2023 and 2024 field season ..................................... 75 10. Table 4.1. Plant material used in the field trials. ............................................................... 95 11. Table A3.1. Average monthly temperatures at the research field sites throughout the duration of the field trials ....................................................................... 148 12. Table A3.2. Planting dates and nematode sampling for the 2022-2023 and 2023-2024 cropping season. ........................................................................................... 149 13. Table A3.3. Pre-planting P. neglectus densities across sites and years. .......................... 150 14. Table A3.4. ANOVA results indicating the effect of different variables used in linear models for comparing grain yield and grain quality. ........................................ 151 15. Table B4.1. Pre-planting P. neglectus densities across sites and years. .......................... 154 viii LIST OF FIGURES Figure Page 1. Figure 1.1. Distribution of reproductive factors (Rf) of double haploid lines displaying significant variation in P. neglectus resistance. ................................................11 2. Figure 1.1 Continued ........................................................................................................ 12 3. Figure 2.1. Cone tray used for the multiplication comparison in the winter wheat breeding lines. ........................................................................................................ 36 4. Figure 2.2. Multiplication comparison of P. neglectus and P. thornei on winter wheat lines. ............................................................................................................ 42 5. Figure 3.1. Field locations in Montana selected for the study. ......................................... 60 6. Figure 3.2. Percentage of nematode tropic groups per 400 cc of soil at each field site at pre-planting at the beginning of the trials (2022)........................................... 66 7. Figure 3.3. Percentage of nematode tropic groups per 400 cc of soil at each field site at post-harvest. ................................................................................................... 66 8. Figure 3.4. Percentage of nematode tropic groups per 400 cc of soil at Bozeman and Chester sites at post-harvest in 2024. ......................................................... 66 9. Figure 3.5. Distribution of Pratylenchus neglectus densities at pre-planting in Chester field during the first year of the field trials ...................................................... 67 10. Figure 3.6. Effect of initial nematode densities on agronomics between resistant and susceptible wheat phenotypes. ..................................................................... 77 11. Figure. 4.1. Distribution of reproductive factors (Rf) of double haploid lines displaying significant variation in nematode resistance ................................................... 94 12. Figure 4.2. Field locations in Montana selected for the study. ......................................... 96 13. Figure 4.3. Percentage of nematode tropic groups per 400 cc of soil at each field site at pre-planting. ................................................................................................. 104 14. Figure 4.4. Percentage of nematode tropic groups per 400 cc of soil at each field site at post-harvest in 2023. .................................................................................... 104 ix LIST OF FIGURES CONTINUED 15. Figure 4.5. Percentage of nematode tropic groups per 400 cc of soil at Bozeman and Chester sites post-harvest in 2024. ........................................................... 104 16. Figure 4.6. Principal coordinate analysis (PCoA) based on Bray-Curtis distances comparing the composition of bacteria communities across locations and soil (bulk vs rhizosphere) for the first year of the field trials (2022-2023)..................................................................................................................... 107 17. Figure 4.7. Principal component analysis (PCA) of microbial communities in the rhizosphere samples based on Bray-Curtis dissimilarity matrix ........................... 108 18. Figure 4.8. Principal Component Analysis (PCA) plot of bacterial taxa based on Bray-Curtis dissimilarity metric. ..................................................................... 109 19. Figure 4.9. Non-Dimensional Scaling (MDS) plot of bacterial taxa based on Bray- Curtis dissimilarity metric. ....................................................................................110 20. Figure A3.1. Modified Barmann Tray. Soil is placed in a gause supported by a metal wire. ............................................................................................................... 152 21. Figure B4.1: Saturation curve for 16S sequencing for 2023 (A) and 2024 (B). .................................................................................................................................. 155 22. Figure B4.2. Relative abundance of bacterial family across locations for rhizosphere samples in 2023 (A) and 2024 (B). ............................................................. 156 23. Figure B4.3: Saturation curve for ITS sequencing for 2023 (B) and 2024 (B). .................................................................................................................................. 157 24. Figure B4.4. Relative abundance of fungal family across locations for rhizosphere samples in 2023 (A) and 2024 (B). ............................................................. 158 25. Figure B4.5. Principal component analysis of bacterial and fungal taxa based on Bray-Curtis dissimilarity metric according to sampling year. Circles within the PCA plot are 95% confidence ellipses............................................... 159 26. Figure B4.6. Composition of alpha diversity across wheat phenotypes ......................... 160 27. Figure B4.7. Composition of bacterial alpha diversity across locations. A: Observed; B: Chao1; C: Shannon; D: Simpson. ............................................................. 160 x LIST OF FIGURES CONTINUED 28. Figure B4.8. Alpha diversity of bacterial 16S for all locations according to the sampling time (bare soil/ bulk vs rhizosphere). ........................................................ 161 29. Figure B4.9. Fungal alpha diversity between bulk / bare soil and rhizosphere soil. .................................................................................................................................. 161 30. Figure B4.10. Composition of fungal alpha diversity across wheat phenotypes ...................................................................................................................... 162 31. Figure B4.11. Composition of fungal alpha diversity across location. A: Observed; B: Chao1; C: Shannon; D: Simpson. ............................................................. 162 32. Figure B4.12. Principal Component Analysis of bacterial taxa based on Bray-Curtis dissimilarity metric and fungal according to plant phenotype. A: Bacterial taxa, B: Fungal Taxa. .................................................................................. 163 xi ABSTRACT Wheat is one of the most important crops globally and a key component of Montana’s economy. However, wheat production faces various challenges, including water availability and pests such as root lesion nematodes (RLNs; Pratylenchus spp). These nematodes feed on and migrate through plant roots, causing radicular lesions that impair the plant's ability to absorb water and nutrients, making it more susceptible to drought and high temperatures. In Montana, yield losses due to Pratylenchus neglectus infections are estimated at US$84 million annually, while the spread and extent of damage caused by P. thornei remains unknown. To better manage the nematode infestation, a breeding program was initiated to incorporate P. neglectus resistance into Montana-adapted winter wheat lines, resulting in the selection of resistant lines in greenhouse trials. Recent research suggests that resistant plants support specific microbial communities that may help them cope with both biotic and abiotic stressors, including pest pressure. This dissertation begins by comparing the reproduction rates of P. neglectus and P. thornei on Montana crops and selected double haploid lines under controlled conditions. Field trials were then conducted to assess agronomic performance and nematode suppression in real field scenarios. Microbiome studies were incorporated to compare microbial communities between resistant and susceptible phenotypes, providing insights into the underground interactions between nematode infections and plant resistance. The reproduction rate and host suitability of the nematodes vary by Pratylenchus species, highlighting the need for accurate diagnostics before recommending rotational crops to growers facing RLN issues. Under field conditions, P. neglectus reproduction was found to be dependent on the initial nematode density, with higher reproduction rates observed at lower initial densities. Double haploid lines selected for resistance in greenhouse settings demonstrated better agronomic performance under high nematode pressure, and lower P. neglectus multiplication at low nematode densities. The microbiome composition was more strongly associated with nematode density in the soil than with the plant phenotype. Given their low reproduction rates and improved yield and grain quality, two of the resistant lines hold potential for benefiting Montana growers dealing with high P. neglectus pressures. 1 CHAPTER ONE INTRODUCTION Wheat: Importance, Production and Yield Losses Caused by Diseases Wheat (Triticum aestivum L.) is the most widely grown crop worldwide, with an estimated area of 217 million hectares distributed over 90 countries (Langridge et al., 2022). It ranks second among the most important commodities in terms of both import quantity and value (FAO, 2024). In 2024, the annual production is estimated at approximately 793 million tonnes (FAO, 2024). Looking ahead, total wheat utilization for the 2024/2025 period is projected to reach 794 million metric tonnes (FAO, 2024). This underscores the essential role wheat plays in the global food supply, highlighting its significance for food security and economic stability worldwide. Leading traditional wheat -producing countries, ranked by production, include China (137.7 million tonnes, 5-year average yield 5.8 T/Ha), India (107.7 million tonnes, 5-year average yield 3.6 T/Ha), Russia Federation (104.2 million tonnes, 5-year average yield 2.9 T/Ha), the United States of America (48.9 million tonnes, 5-year average yield 3.2 T/Ha), and Australia ( 29,8 million tonnes, 5-year average yield 2.4 T/Ha) (FAOSTAT, 2022, USDA-FAS, 2024). The broad adaptation of wheat has made it suitable for many production environments worldwide. However, in some of the largest wheat producing countries such as China, the United States, Canada and Turkey, the area dedicated to wheat cultivation has been declining over the past 20 years (Simão et al., 2024; Guarin et al., 2022). This trend can be attributed to several factors, including advancements in agricultural technologies that have made alternative crops 2 more appealing to farmers, as well as fluctuating wheat prices that incentivize producers to pivot toward more profitable options such as corn (Zea mays) and soybeans (Glycine max) Merr (Simão et al, 2024). Consequently, the landscape of global wheat production is evolving, presenting challenges to its historical prominence in the agricultural sector. With the world population expected to reach 9.7 billion within the next 15 years, global wheat demand is projected to increase by 50% by 2050 (Singh et al., 2022). To meet this escalating demand, significant improvements in yield are essential, particularly as competition for productive land from other crops and sectors intensifies. This calls for advancements in genetic, physiological, and agronomic interventions (Simão et al., 2024; Singh et al., 2022). Wheat production faces growing challenges from both biotic and abiotic stresses, further exacerbated by climate change. In addition to losses from water scarcity and soil fertility issues, approximately 20% of wheat yield is lost to diseases caused by fungi, bacteria, viruses, and nematodes (Singh et al., 2022). To mitigate potential food shortages, effective pest and disease management will be crucial, particularly as the proportional production of certain commodities continues to shift. Therefore, it is essential to consider the full range of crop production limitations, including the often-overlooked constraints posed by nematodes. Biology of Root Lesion Nematodes Plant-parasitic nematodes (PPNs) are tiny (< 1 mm in length), unsegmented roundworms. Although only about 4,000 PPN species have been described to date (Decraemer & Hunt, 2024), they pose a significant threat to agricultural systems worldwide, with direct and indirect losses of more than $100 billion every year (Elling, 2013). These microscopic organisms can severely 3 damage the plant root system by inducing specialized feeding sites in vascular radicular tissues, compromising water and nutrient uptake, resulting in stunted growth (Decraemer & Hunt, 2024). Plant-parasitic nematodes also affect the aerial part of the plant, given that enough moisture is available for movement. As obligate biotrophs, PPNs skillfully reprogram host plant cells to fulfil their needs, establishing an intimate host-pathogen relationship in which secretions from the oesophageal glands of PPNs play a crucial role (Decraemer & Hunt, 2024). Furthermore, PPNs can predispose plant roots to secondary infections by fungi and bacteria, and they may act as vectors for certain plant viruses (Jones et al., 2013). However, due to their microscopic size and non-specific symptoms they cause, PPNs infestations are easily mistaken for abiotic stresses and frequently go unnoticed by farmers, who may not recognize them as significant threats (Ansari & Saleem, 2023). This lack of awareness leads to avoidable reductions in crop yield and harvest quality, underscoring the urgent need for greater recognition and proactive management strategies of nematodes in plant protection. Root-lesion nematodes, Pratylenchus spp., (RLNs; Nematoda, Pratylenchidae) are polyphagous, migratory endoparasites, ranking second in order of importance and research commitment (Alake & Nasamu, 2024). Although the genus Pratylenchus comprises approximately 70 nominal species distributed worldwide, most of the crop damage is attributed to a dozen of species, such as: P. penetrans in grasses, forages, fruit trees and strawberries; P. brachyurus in corn, cotton, peanut, pineapple, potato and tabaco; P. coffeae in coffee, citrus, sugarcane and tea; P. neglectus and P. thornei in cereals and legumes; P. scribneri in potato, soybean and strawberries; P. pratensis in cereals, P. goodeyi in banana; P. vulnus in apple and 4 stone fruits, ornamental and roses; and P. zeae in corn, rice, sugarcane and wheat (Castillo and Vovlas, 2007). Root lesion nematodes are among the most significant yield-limiting pests affecting small-grain cereals worldwide. Of the four economically important species - P. thornei, P. neglectus, P. penetrans, and P. crenatus -, P. neglectus and P. thornei are the most widely distributed and have a substantial economic impact on wheat (Mokrini et al., 2019; Smiley, 2010). These two species have been reported in various wheat-growing regions, including Australia (Thompson et al., 2008), Canada (Nicol and Rivoal, 2008), Jordan (Al-Banna et al., 2015), Turkey (Toktay, 2008; Sogut and Devran, 2005), Iran (Ghaderi et al., 2010; Pourjam et al., 1999), North Africa (Sikora et al., 1988), and the United States (Consoli & Dyer, 2024; May et al., 2016; Yan et al., 2016; Smiley et al., 2004; Armstrong et al., 1993). Species may coexist in some fields, but more commonly, only one species is present in a single field (Smiley, 2010). Reported yield losses attributed to P. neglectus and P. thornei range from 12% in Montana (P. neglectus; May et al., 2016), as high as 50% in Oregon (P. neglectus; Smiley et al., 2010), and 65% in Australia (P. thornei; Mokrini et al., 2017; Thompson et al., 2008). Yields are often negatively correlated with pre-plant nematode densities. While estimating damage thresholds — the number of nematodes per unit of soil that reduce plant yield — can be challenging due to various soil and plant factors, the economic threshold for RLN damage is anticipated to be low in low-rainfall dryland areas (Smiley, 2010). Root lesion nematodes can exhibit a remarkable ability to multiply within host tissue, enabling them to thrive in semi-arid wheat-growing regions where free moisture is scarce (Vanstone, 2008). The life cycle of Pratylenchus begins with the development of a first-stage 5 juvenile (J1) inside the egg, which subsequently hatches into a second-stage juvenile (J2). The J2s molt into third (J3) and fourth (J4) stage juveniles before reaching adulthood. Actively seeking plant hosts, these nematodes migrate through the root system, molting as they progress until they mature into reproductive adults. While males have been observed (Al-Khafaji, 2018), P. neglectus and P. thornei reproduce primarily by parthenogenesis (De Waele and Elsen, 2002). All mobile life stages of RLNs are parasitic, capable of entering and exiting plant roots (Castillo & Vovlas, 2007). They can embed themselves within root tissue, migrating from cell to cell, and spend most of their life cycle within host plant roots, although they can also be found on the root surface and in adjacent soil while searching for new hosts. Females can deposit eggs both in the roots and in the soil (Castillo & Vovlas, 2007). Their life cycle depends on food availability, temperature, host species, and moisture typically ranging from 25 to 65 days depending on the species, they can complete three to six generations in a single cropping season (Castillo & Vovlas, 2007; Taylor et al., 2000). Although optimal temperatures of both P. neglectus and P. thornei range from 20°C to 25°C (68°F to 77°F), they can reproduce, at lower rates, when soil temperatures drop to 7°C (Smiley, 2010). Their reproduction is not limited by soil type or moisture, as they can reach damaging population levels even in conditions of very low soil moisture (Smiley, 2021). In the absence of crops, these nematodes can survive in alternative hosts such as weeds, or by entering in an inactive stage known as anhydrobiosis. Pratylenchus spp. move mostly lengthwise through the cortical tissue, and their movement and feeding causes collapse of the cell wall and the development of cavities and root discoloration (Jackson-Ziems, 2016), it is common to find several nematodes aggregated together in the infected root. The primary symptoms of RLNs infestation occur underground and 6 include radicular lesions, sloughing of cortical and epidermal cells, degradation of lateral roots, loss of root hairs, and necrosis (Castillo and Vovlas, 2007). These effects significantly impair the plant's ability to absorb water and nutrients, and because of that, high population densities of RLNs can further reduce the plant's water uptake, even when water is available. Additionally, plants experiencing drought stress are more likely to suffer yield losses when infected by RLNs. In fact, the damage caused by RLN infection may exceed that caused by drought stress alone (Smiley, 2010). While affected plants often show stunted growth, premature yellowing of older leaves, and reduced tillering, these aboveground symptoms are typically non-specific (Castillo & Vovlas, 2007; Mokrini et al., 2019), complicating the timely identification of RLN infestations. Penetration of root tissues by RLNs results in lesions that promote greater colonization by root- rotting fungi, saprophytic bacteria, and non-parasitic nematodes (Moens & Perry, 2009). These secondary organisms can intensify rotting and discoloration beyond what is caused by RLNs alone, and misidentification of the primary disease agent can significantly hinder effective management strategies. Management of Root Lesion Nematodes Management strategies for root lesion nematodes (RLNs) involve several factors, including prompt and accurate identification, field sanitation, crop rotation, planting date adjustment, chemical treatments, and the use of genetically resistant or tolerant varieties (Duncan & Moens, 2013; Smiley, 2021; Castillo and Vovlas, 2007). Once RLNs are established in agricultural land, they are almost impossible to eradicate and become difficult to manage, 7 necessitating an integrated nematode management approach that combines at least two practices for effective control (Vanstone et al., 2008). Crop rotation, alternating between wheat and non-host crops, is sometimes used to mitigate damage caused by RLNs. However, its effectiveness can be limited due to the polyphagous nature of these nematodes (Mokrini et al., 2019), and successful implementation of rotational crops requires a thorough understanding of the specific rotation in use. For example, in the presence of mixed RLN populations, one crop may inadvertently increase the populations of one nematode species while decreasing those of another. Since one Pratylenchus species often predominates when mixed species are present (Thompson et al., 2008), growers must tailor their rotations to effectively target the dominant species. Despite these challenges, several effective rotations have been developed. For instance, in Australia, rotating wheat with barley cv. Clipper was shown to reduce P. thornei population densities (Thompson et al., 2012). In Mexico, rotations that include corn, cotton, or soybean for two consecutive years significantly reduced P. thornei population densities in wheat fields (Mokrini et al., 2019). In Montana, rotating wheat with peas and barley has been effective in lowering P. neglectus densities (May et al., 2016), however, these results varied depending on the crop variety, and later research identified host- specific pathotypes that are more likely to infect rotational resistant crops (Al-Khafagi et al., 2019). The ability of populations within a given nematode species to parasitize specific hosts or cultivars of crop plants is commonly used to classify species variants into races, pathotypes, or biotypes (Castillo & Vovlas, 2007; Triantaphyllou, 1987). The concept of Pratylenchus pathotypes is essential for understanding how different populations of these nematodes vary in 8 their ability to parasitize specific plant hosts, including various cultivars or species. Al-Khafagi et al. (2019) defined the different P. neglectus pathotypes based on differences in virulence or host specificity between P. neglectus populations. Studies have also shown that different Pratylenchus pathotypes exhibit increased resistance to other management strategies, including soil fumigants (Sipes & Bert, 2004) and nematicide resistance (Bridge & Starr, 2007). The rationale for using crop rotation to reduce RLN population densities is that the monoculture of host plants often leads to increased nematode densities and subsequent yield losses (Castillo and Vovlas, 2007). However, some long-term studies on monoculture indicate that sustained cultivation of a single crop can ultimately reduce RLN population densities (Castillo and Vovlas, 2007). For example, Andersen (1975) observed that densities of P. crenatus and P. neglectus peaked during the initial three years of barley monoculture, after which their densities gradually declined and stabilized at lower levels (Mokrini et al., 2019). The use of resistant (i.e., the capacity of a cultivar to suppress nematode multiplication) and tolerant (i.e., the capacity of a cultivar to grow and yield well even under high nematode pressure) cultivars is considered the most economically and environmentally friendly means for controlling PPNs, however, germplasm resistant to RLNs is less available than for sedentary endoparasites (Duncan & Moens, 2013). Some of the challenges faced when breeding wheat for P. neglectus and P. thornei resistance is that resistance and tolerance for these species is genetically independent (Smiley & Nicol, 2010), and a wheat cultivar with resistance to P. thornei is not necessarily resistant to P. neglectus, and vice versa. Still, variations of cultivar resistance to different populations of the same Pratylenchus species also complicates this 9 management strategy (Al-Khafaji, 2018). Also, some resistant cultivars may be very sensitive to the initial RLN invasion, resulting in reduced growth and yield (Smiley, 2010). Resistance to P. neglectus has been investigated less than to P. thornei because the latter species causes higher damage and is frequently found in wheat growing areas where RLN research is more intensive. For instance, in Australia, the bread wheat line GS50a was first identified as resistant to P. thornei, and additional screenings identified several other lines with resistance to P. thornei (Sheedy and Thompson, 2009; Thompson et al., 2009). The resistant gene Rlnn1, located on chromosome 7AL has been identified as providing high resistance to P. neglectus (Williams et al., 2002), but this gene is linked to undesirable yellow flour color, while the gene QRlnn.lrc-2B has been demonstrated to only provide moderate resistance (Sheedy et al., 2022). Root Lesion Nematodes in Montana In Montana, P. neglectus was first identified as a factor reducing wheat yields in 2006 (Johnson, 2007). Higher densities of P. neglectus have been primarily associated with winter wheat, leading to estimated yield losses of 12-14% (May et al., 2016). While there are reports of spring wheat experiencing statewide yield losses of 36% or more due to RLN infestations (Johnson, 2007; Smiley, 2005a), the greater RLN densities linked to winter wheat in Montana were likely due to its longer growing season - approximately nine months - compared to three months for spring wheat. The initial management strategy evaluated to mitigate yield loss from P. neglectus in Montana involved the use of resistant rotational crops, such as peas, lentils, and barley (May et al., 2016). After years of use of peas, barley and lentils to suppress RLNs and 10 other fungal diseases in the state, P. neglectus pathotypes in barley and lentils were identified (Al-Khafaji et al., 2019), highlighting the urgent need for more durable and sustainable management alternatives for P. neglectus. Within a Pratylenchus species, pathotypes may be identified based on the level of damage they inflict on a particular plant species, their differences in reproductive success on different host plants, and their resistance to environmental conditions (Castillo & Vovlas, 2007). In response to this challenge, crosses were initiated in 2008 to incorporate RLN resistance into winter wheat cultivars adapted to Montana. This effort began with backcrossing Persia-20, a landrace known for its resistance to P. neglectus (Smiley et al., 2014; Smiley 2010), with ‘Yellowstone’, a widely cultivated winter wheat variety. In greenhouse screenings, 200 backcross lines were evaluated for resistance to P. neglectus, leading to the identification of six lines with significant resistance (May, 2015). Genetic analyses using single nucleotide polymorphisms confirmed the presence of resistance quantitative trait loci (QTLs) on chromosomes 5BL, 7AL, and 7BL (Williams et al., 2002; Mulki et al., 2013; Thompson, 2013). While these resistant lines outperformed ‘Yellowstone’ in yield, their height was unsuitable for commercial production. Consequently, two of the resistant lines were crossed with ‘Warhorse’, a shorter, solid-stemmed winter wheat cultivar. The F1 generation from this cross was then used to produce 110 double haploid lines (DHLs; May, 2015), which were subsequently screened in the greenhouse for resistance to P. neglectus (Figure 1.1 and Figure 1.2), and fewer selected lines were screened in the field for agronomic performance. This effort yielded 13 advanced winter wheat lines exhibiting considerable P. neglectus resistance (reproductive factor Rf < 1) alongside good agronomic traits, including plant height, yield, and protein content. Additional non-resistant 11 sister lines were also identified. The next step of the breeding program will be the evaluation of these lines under nematode-infested fields across the state. Figure 1.1. Distribution of reproductive factors (Rf) of double haploid lines displaying significant variation in P. neglectus resistance. Average P. neglectus reproductive factor (Rf = final/ initial nematode density) of 1.0 indicates the density at the end of the experiment numbered the same as those at the beginning. Error bars indicate standard error among five replicates. 12 Figure 1.1 Continued 13 Soil Microbial Communities and Plant Health Soils are among the most biodiverse ecosystems on Earth, teeming with various groups of bacteria, fungi, archaea, protists, viruses, and macro-organisms. Plants play a crucial role in this diversity by actively recruiting and nurturing a community of microbes in the soil that inhabit their surroundings, surfaces, and roots. This recruitment takes place in the narrow zone of the root’s influence, known as the rhizosphere, through the exudation of nutrients, rhizodeposition, and other chemical compounds (Dini-Andreote, 2020). The rhizosphere microbiome, a dynamic community comprised of both beneficial and pathogenic microorganisms, has been the subject of extensive research aimed at understanding its role in plant growth and health. This interest stems from the microbiome's influence on enhancing the plant’s genomic and metabolic capabilities (Mendes et al., 2013, Mendes et al., 2018; Carrión et al., 2019; Liu et al., 2020; Yu et al., 2021; Faist et al., 2021). Microorganisms associated with the rhizosphere are integral to key soil processes, including decomposition, mineralization, nutrient acquisition and recycling, as well as the modulation of immune responses (Dini-Andreote, 2020; van der Heijden et al., 2008). Microbial communities significantly impact plant performance by helping the plant cope with both biotic and abiotic stressors (Hubbard et al., 2021; Lazcano et al., 2021). The rhizosphere microbiome serves as a crucial first line of defense against soil-borne plant pathogens (Mendes et al., 2018; Chiaramonte et al., 2021; Yin et al., 2021). Recent studies demonstrated that resistant plant cultivars can enhance microbiome composition to better cope with plant diseases. For instance, resistant strawberry cultivars assemble distinct microbiomes characterized by higher network connectivity and an enrichment of beneficial microbes 14 compared to susceptible varieties (Lazcano et al., 2021). Similarly, Fusarium oxysporum (Fox)- resistant common bean cultivars exhibit greater rhizobacterial abundance and more complex microbial communities with functional traits that bolstered plant defense (Mendes et al., 2017). In cotton, resistant cultivars had increased levels of beneficial Pseudomonas spp. in their endophytic communities, contributing to enhanced resistance to Verticillium wilt (Zeng et al. 2022). These findings underscore the importance of plant genetic traits in assembling beneficial microbiomes and suggest that breeding for disease resistance may also co-select traits that support advantageous microbial communities. Understanding the mechanisms behind microbiome assembly is essential for developing sustainable disease management strategies and enhancing future plant breeding programs (Lazcano et al. 2021). Given the beneficial functions of the rhizosphere microbiome and the potential for manipulation through plant genetics, it is imperative to explore their role as a complementary tool in plant breeding initiatives. Goals of This Research This dissertation consists of four research projects centered on managing RLNs in Montana. Following the recent discovery of P. thornei at one of our field sites, the first project utilized a greenhouse assay to evaluate the resistance of selected winter wheat lines to P. neglectus in comparison to the multiplication of P. thornei. Additionally, this project examined the multiplication rates of both Pratylenchus species on rotational crops commonly used by Montana growers. The second project involved field trials to assess the agronomic performance and nematode suppression potential of selected winter wheat lines and commercial cultivars 15 across three distinct environments in Montana: high-rainfall dryland, irrigated, and low-rainfall dryland. The third project examined the microbial composition and diversity between RLN- resistant and susceptible winter wheat lines under field conditions, considering variable RLN infestation levels and different nematode feeding groups at three locations. Together, these projects provide valuable insights into the impact of RLNs on wheat production, the relationship between RLNs and the surrounding microbiome, and lay a foundation for future research on the complex challenge of managing RLNs in wheat. Research Hypothesis and Objectives Root-lesion nematodes are often asymptomatic pathogens that can significantly reduce wheat yields, thereby impacting growers' profits. Plant resistance is the most effective way to manage RLN in low-input crops such as wheat, however, no wheat cultivar resistant to P. neglectus is available in the United States. After several years of winter wheat breeding and selection screening under controlled conditions, we identified winter wheat lines that exhibit resistance to P. neglectus in greenhouse tests. For field trials, we hypothesized that (i) greenhouse resistance will be maintained under field conditions despite variable biotic and abiotic factors, with resistant lines better able to cope with nematode pressure, thereby maintaining yield and agronomic performance compared to nematode-susceptible cultivars, and (ii) resistant phenotypes will harbor a distinct microbial community compared to susceptible ones, which may help them better withstand nematode attack. Recently, P. thornei, a more aggressive species of root-lesion nematode, was identified in wheat, corn and alfalfa fields in Montana, however, the damage potential of this nematode to Montana crops remains unknown. Given that the initial 16 steps of our breeding program utilized ‘Persia-20’, an Iranian landrace wheat cultivar reported to be resistant to both P. neglectus and P. thornei, as one of the parental lines, we hypothesized that (iii) the multiplication of P. neglectus and P. thornei in Montana crops will differ according to nematode species, and that winter wheat lines resistant to both species are present in the advanced wheat pipeline. 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Nature Plants, 7(4):481-499. doi: 10.1038/s41477-021-00897-y Zeng Q., Man X., Dai Y., & Liu, H. (2022) Pseudomonas spp. enriched in endophytic community of healthy cotton plants inhibit cotton Verticillium wilt. Frontiers in Microbiology, 13:906732. doi: 10.3389/fmicb.2022.906732 https://doi.org/10.1016/B978-0-12-821715-3.00001-0 https://doi.org/10.1186/s40168-020-00997-5 25 CHAPTER TWO HOST SUITABILITY OF WINTER WHEAT BREEDING LINES AND ROTATIONAL CROPS TO THE ROOT LESION NEMATODES PRATYLENCHUS NEGLECTUS AND PRATYLENCHUS THORNEI Contribution of Authors and Co-Authors Manuscript in Chapter 2 Author: Erika Consoli Contributions: conceptualization, methodology, validation, formal analysis, investigation, data curation, writing – original draft, visualization. Co-Author: Erika Emerson Contributions: investigation. Co-Author: Alan Dyer Contributions: resources, writing – review and editing, funding acquisition. 26 Manuscript Information Consoli, E., Emerson, E., & Dyer, A. T. (2024). Comparison of host suitability of winter wheat breeding lines and rotational crops to the root lesion nematodes Pratylenchus neglectus and Pratylenchus thornei Status of Manuscript: ☒Prepared for submission to a peer-reviewed journal ☐Officially submitted to a peer-reviewed journal ☐ Accepted by a peer-reviewed journal ☐ Published in a peer-reviewed journal 27 Summary 1. The resistance of winter wheat lines varied depending on the root-lesion nematode species. 2. Pratylenchus thornei exhibited the highest multiplication rates in both resistance screening and rotational crop trials. 3. The presence of P. thornei in the state requires survey and accurate diagnostics prior to recommending rotational crops to growers. Abstract The root lesion nematodes Pratylenchus neglectus and Pratylenchus thornei, are among the most damaging nematode species affecting wheat production worldwide. A statewide field survey in 2005-2006 found P. neglectus widespread in Montana, while P. thornei was only identified in the state infesting wheat fields in 2024. This study evaluated the multiplication rates of P. neglectus and P. thornei on advanced winter wheat breeding lines (developed for managing P. neglectus) and rotational crops commonly grown in Montana (i.e., barley, pea, lentils, chickpea, spring wheat, and corn). Multiplication ratings were assessed using the ratio of final versus initial nematode densities. For the winter wheat breeding lines, the ‘54DH60’ was the only line that reduced both P. neglectus and P. thornei densities. The line ‘54DH31’, selected for being resistant to P. neglectus, had the highest P. thornei multiplication rate. For the rotational crops, P. thornei had the highest multiplication rate on pea, which is usually recommended as a rotational crop to manage P. neglectus due to its non-host status. Corn was the only non-host to P. thornei, but this crop is not suitable for the Golden Triangle, the traditional winter wheat growing area in Montana. Overall, P. thornei had higher multiplication rates than P. neglectus on 28 the rotational crops tested. These results indicate that line ‘54DH60’ has the potential to be used for managing both Pratylenchus species. Field studies are needed to further explore this application. Verifying the distribution of P. thornei in the state would improve the management of this nematode and improve management practices by identifying appropriate rotational crops for farmers dealing with one or both species of Pratylenchus. Introduction Wheat (Triticum aestivum L.) is a critical crop for global food security, ranking among the most widely cultivated and consumed cereal grains worldwide (FAO, 2024). In 2024, wheat annual production was estimated at 792.9 million tonnes, while total wheat utilization for 2024/2025 period is projected to reach 793.7 million metric tonnes (FAO, 2024). With the global population projected to reach 9.7 billion by 2050, the demand of wheat for both food and feed is expected to rise by 50% (Singh et al., 2024). However, some of the largest traditional wheat-growing regions, including China, Canada, Turkey, and the United States, have seen a decline in cultivated area in recent years, primarily due to fluctuating wheat prices and the increasing economic advantages of soybeans and corn over wheat (Simão, 2024). Recent events, such as the COVID-19 pandemic and ongoing conflicts between countries accounting for about 30% of the decline in global wheat trade (notably Russia and Ukraine), highlight the vulnerability of the global wheat supply chain. This underscores the urgent need to enhance food production and diversify crop production to bolster global food security (Simão, 2024). Meeting the demands of this growing population, alongside rising per capita purchasing power, presents a significant challenge for sustainable agriculture. Increasing food production 29 without expanding agricultural land will require innovative advancements in crop performance, while ensuring that natural resources are not exploited faster than the Earth can replenish them. One of the key challenges and opportunities for increasing wheat yields without expanding the planting area lies in the management of biotic stressors. Wheat losses due to pathogens and pests are estimated to range from 20% to 30% worldwide (Savary et al., 2019). Implementing effective management strategies, such as integrated pest management (IPM), crop rotation, and the use of resistant varieties, is essential for mitigating these losses. One important biotic stressor contributing to wheat yield loss worldwide is plant-parasitic nematodes (Mokrini, 2019). Among them, root lesion nematodes (Pratylenchus spp.), rank second to cyst nematodes (Heterodera spp.) in terms of their economic importance in wheat production systems (Owen, 2022; Smiley, 2021). Pratylenchus spp. are polyphagous, migratory endoparasitic nematodes that feed on and migrate through root cells, and as a result, they impair the plant's ability to absorb water and nutrients efficiently, leading to reduced vigor, lower grain yield, and diminished grain quality in infested crops (Smiley & Nicol, 2009). From the approximately 70 Pratylenchus spp. described (Decraemer & Hunt, 2023), eight species are parasites of cereals, with Pratylenchus neglectus (Rensch) Filipjev Schuurmanns & Stokhoven and Pratylenchus thornei Sher & Allen, being the most widespread and of highest economic impact in wheat growing regions (Savary et al., 2019; Mokrini, 2019; Smiley et al., 2010). The economic impact of root lesion nematodes is significant, with reported yield losses due to P. neglectus ranging from 12% to 15% in winter wheat crops in Montana (May et al., 2016), 30% in Australia (Vanstone et al., 2008), exceeding 5% in the Central Great Plains (Todd et al., 2014), and reaching approximately 37% in the Pacific Northwest (Smiley et al., 2005a). 30 Damage due to P. thornei is often greater than P. neglectus (Taylor et al., 2000). For example, in Australia, yield loss in intolerant varieties can be as high as 70% due to P. thornei (Thompson et al., 2008). In the United States, losses due to this species were estimated at 50% (Smiley et al., 2005b). However, accurately quantifying yield losses caused by migratory nematodes, such as Pratylenchus, remains challenging, and this combined with the lack of nematologists working on cereals, suggests that current estimates of their impact on wheat is potentially underestimated. To minimize damage from plant-parasitic nematodes, a key strategy is to reduce pre-plant nematode densities, as research has shown that high initial nematode densities are correlated with greater yield losses (Fanning et al., 2018; Nicol et al., 1999). In low-input cropping systems like wheat, this is most effectively accomplished by cultivating resistant crops or cultivars, which can be achieved by rotating wheat with non-host crops and using resistant wheat cultivars (Brown, 1987). Crop rotation with non-hosts can disrupt the nematode life cycle while promoting crop diversity and improving soil health parameters (Stirling et al., 2002). However, this management approach can be challenging to implement due to the polyphagous nature of root-lesion nematodes. Despite these challenges, successful results have been reported. In Australia, the integration of tolerant wheat cultivars with crop rotation led to a significant reduction in annual wheat yield losses due to P. thornei, decreasing estimated losses from AU$104 million to AU$38 million (Owen, 2022). Another more long-term approach is to develop resistant wheat cultivars. Resistance is typically assessed by comparing initial and final nematode densities in plants or by calculating the multiplication rate, defined as Pf/Pi (final population density/initial population density; Alcaniz et al., 1996). Non-host plants generally exhibit a multiplication rate 31 of less than 1, while hosts have a multiplication rate greater than 1. By evaluating multiplication rates, we can facilitate comparisons across various experiments and among different nematode species. In Montana, P. neglectus was first identified as a factor reducing wheat yields in 2005, particularly impacting winter wheat production (Johnson, 2007). A field evaluation of rotational crops revealed that peas were non-hosts for P. neglectus, while the suppression potential of lentils and barley varied depending on the plant cultivar (Zuck et al., 2010; May et al., 2016). Additionally, P. thornei, initially detected in an alfalfa and corn fields in eastern Montana (Ozbayrak et al., 2019), was recently detected in a winter wheat field in Gallatin County, Montana (Consoli & Dyer, 2024). Although the distribution and impact of P. thornei on local crops remains poorly understood, this research aimed to compare the multiplication potential of P. neglectus and P. thornei on important rotational crops commonly used by Montana growers. In addition, this research also compared the multiplication potential of these two species on winter wheat lines from our b project focused on breeding for resistance to P. neglectus. The findings will inform decision-making guidelines for local producers in the state, contributing to pest- management strategies. Material and Methods Root Lesion Nematode Cultures and Inoculum Preparation The P. neglectus population used in this study was collected in 2016 from a winter wheat field in Dawson County, Montana. Nematodes were maintained on winter wheat under greenhouse conditions, and later transferred to an in vitro carrot disc culture for mass production 32 to be used in the trials herein described. This in vitro culture allows mass production of inoculum in a standardized way, and loss of Pratylenchus infectivity by this culturing technique have not been reported (De Waele & Elsen, 2002) .The P. thornei population was isolated from wheat roots collected at the Arthur Post Farm MSU Research Station in 2024 (Consoli & Dyer, 2024), and mass produced in carrot cultures to be used in the assays herein described. Nematodes were mass produced in carrots because it guarantees a higher number of nematodes compared to root multiplication. Directly using nematodes contained in infested soil is not recommended, as it can greatly underestimate nematode densities, especially in the case of endoparasitic nematodes such as Pratylenchus spp.. Moreover, the process of mixing and manipulating the infested soil may negatively impact the nematode inoculum (Wesemael, 2022). In vitro carrot cultures of P. neglectus and P. thornei were established three months before the experiment with modifications from the methodology of Esteves et al. (2019). In brief, fresh organic carrots (with leaves attached) were purchased at a local organic grocery store. The carrots were thoroughly washed under running tap water, sprayed with 70% ethanol, rinsed with sterile water, and then transferred to a sterile beaker and placed under a sterile flow hood. Each carrot received a second spray of 70% ethanol, was gently flamed, and then peeled from top to bottom. Using sterile tools, carrots were sliced in rings of 3-4 mm diameter using a sterile knife, and individual carrot discs were placed in a 15 mm x 60 mm Petri dishe (Drosophila Supplies Petri Dishes, Fisher Scientific). The carrots discs were maintained at 25°C for two weeks to monitor for contamination prior to nematode inoculation. Specimens of P. neglectus and P. thornei were extracted from wheat roots using a mist-chamber (Viaene et al., 2020; Crow et al., 2020). The nematodes were surface sterilized overnight in a 2,000-ppm streptomycin solution, 33 followed by triple wash in sterile water. Fifty adult females were placedper carrot discs using a picking needle. Petri discs containing an inoculated carrot disc were sealed with parafilm, and incubated at 25 ± 1°C. Once nematodes were observed emerging from the carrots, they were extracted in a mist chamber. The nematode solution was kept in the refrigerator at 7°C for no longer than five days prior to inoculation. Evaluation of Breeding Lines A comparison of multiplication between P. neglectus and P. thornei was conducted across nine pre-selected winter wheat double haploid (DH) lines from our breeding pipeline, which is part of the project aimed at developing resistance to P. neglectus. The experiment also included two parental lines (P; RLN84 and RLN145), and commercial winter wheat cultivars were used as controls (C), as outlined in Table 2.1. Controls included the P. neglectus susceptible cultivar ‘Judee’, the moderately susceptible cultivar ‘Yellowstone’, the moderately resistant Iranian landrace ‘Persia-20’ (Smiley et al., 2014b; Vanstone et al., 2008), and the widely grown Montana cultivar ‘Warhorse’. The experiment was conducted on SC10 conetainers (164 ml; 3.8 cm x 21 cm long; Figure 2.1). Each cone received one wheat seed. The substrate, herein called soil, consisted of a mixture of silt-loam field-soil: sand: sunshine potting soil mixed at 3: 1: 1ratio (v: v: v). The soil was autoclaved at 134°C for 60 minutes and sieved through a 450 mm sieve to remove larger stones when these were present. Nematodes were inoculated at a density of 3,050 Pratylenchus / Kg soil at two leaf developmental stages (one week after germination). The experiment was kept in the Plant Growth Center greenhouse at 25±3°C and 16h photoperiod for eight weeks. An equal amount of Osmocote Plus fertilizer (Scotts, Ohio) was added to each cone following the company’s recommendation. The plants were observed daily and irrigated as 34 needed. Plant watering was stopped two days before the experiment ended to facilitate separation of roots from soil. Table 2.1. Plant Materials for the breeding lines assay. 1Breeding line or cultivar; 2DHL: Double Haploid Line; 3P: Parental line; 4C: Cultivar. 5Used as the resistant parent in the initial breeding stages. Entry Crop developmental status1 Entry Crop developmental status 34DH35 Double haploid line (DHL2) 54DH31 Double haploid line (DHL) 34DH37 Double haploid line (DHL) 54DH55 Double haploid line (DHL) 34DH41 Double haploid line (DHL) 54DH60 Double haploid line (DHL) 34DH39 Double haploid line (DHL) RLN145 Parental line (P2) 54DH32 Double haploid line (DHL) RLN84 Parental line (P) 54DH38 Double haploid line (DHL) Persia-20 Cultivar (C4); Iranian Landrace5 54DH37 Double haploid line (DHL) Yellowstone Cultivar (C) 54DH55 Double haploid line (DHL) Warhorse Cultivar (C) 54DH31 Double haploid line (DHL) Judee Cultivar (C); Susceptible control Evaluations of Rotational Crops The multiplication rate comparison of P. neglectus and P. thornei was tested on commonly grown Montana crops often recommended as rotational crops to manage P. neglectus (Table 2.2). These included barley cv. ‘Drummond’, pea cv. ‘Amarillo’, lentil cv. ‘Richlea’, chickpea cv. ‘Sierra’ and corn cv. ‘Silver Queen’. In addition, winter wheat cv. ‘Warhorse’ and spring wheat cv. ‘Duclair’ were included as positive winter and spring crop controls, respectively. The number of plants per cone (Table 2.2) was based on recommended sowing rates 35 for each of these crops. The experiment was a randomized complete block design blocked by crops. This design was selected to prevent interference with plant growth, as mixing different species could disrupt the natural establishment of the canopy. Uninoculated controls were included, and the whole trial was repeated once. Nematodes were inoculated one week after germination, when the second leaf was formed for the monocot plants. Nematodes were inoculated at a density of 3,700 Pratylenchus / Kg soil at one week after germination. Plants with longer germination time (i.e., pea and chickpea) were sowed beforehand. The experiment was conducted in D40L deepots (276.5 ml; 6.5 cm wide x 25 cm long). The substrate, herein called soil, consisted of a mixture of silt-loam field-soil: sand mixed at 3: 1 ratio (v: v). The soil was autoclaved at 134°C for 60 minutes and sieved through a 450 mm sieve to remove larger stones when these were present. The experiment was kept in the Plant Growth Center greenhouse at 25±3°C and 16h photoperiod for eight weeks. An equal amount of Osmocote Plus fertilizer (Scotts, Ohio) was added to each cone following the company’s recommendation. The plants were observed daily and irrigated as needed. Plant watering was stopped two days before the experiment ended to facilitate separation of roots from soil. 36 Table 2.2. Plant material for the rotational crop multiplication assay. Crop Cultivar Scientific Name Family Number of Plants per Cone Barley Drummond Hordeum vulgaris Poaceae 2 Chickpea Sierra Cicer arietinum Fabaceae 3 Corn Silver Queen Zea mays Poaceae 1 Lentil Richlea Lens culinaris Fabaceae 3 Pea Amarillo Pisum sativum Fabaceae 3 Spring wheat Duclair Triticum aestivum Poaceae 2 Winter wheat Warhorse Triticum aestivum Poaceae 2 Figure 2.1. Cone tray used for the multiplication comparison in the winter wheat breeding lines. 37 Nematode Inoculation Nematodes were inoculated one week after germination. Before inoculation, the nematode density was estimated by counting three aliquots of 1 ml nematode solution. Aliquots were counted in a multichambered counting slide (Chalex, Utah, U.S.A.) under a stereomicroscope at 10x magnification. The nematode density was then adjusted to ensure a consistent inoculum volume across nematode species. For nematode inoculation, two holes (0.5 cm wide, 5 cm long) were created in the soil of each cone, spaced 0.5 cm from the seedling stem, with each hole receiving half of the total inoculum volume. Holes were closed immediately after nematode inoculation to avoid nematode desiccation. Inoculum numbers varied according to the cone size. For the multiplication comparison assay on wheat breeding lines, approximately 500 nematode specimens were inoculated (3,050 Pratylenchus / kg soil), while cones from the crop rotation assay received approximately 1,000 nematodes (3,700 Pratylenchus/ kg soil). Nematode inoculum consisted of mixed vermiform stages (juveniles and adults). Experiment Evaluations For both trials, nematodes were extracted and quantified from roots and soil separately. Nematodes were extracted from soil using a modified Baermann tray for 48h (Viaene et al., 2022), while roots were cut into in 1 cm segments and placed in the mist chamber for five days (Viaene et al., 2022; Crown et al., 2019). Nematodes extracted from the soil and roots from the same unit were combined in a 50 ml Falcon tube for nematode quantification. Additionally, aboveground and belowground dry plant biomass was measured. Aboveground biomass was obtained by drying plant leaves for four days at 43°C, and 10% moisture, while belowground biomass was dried after nematode extraction under the same conditions. 38 Nematode Quantification and Reproductive Factor After nematode extraction, the tubes containing extracted nematodes were stored in the refrigerator at 7°C and nematodes were counted immediately. If immediate nematode quantification was not possible, the nematodes were fixed using TAF fixative (EPPO, 2021) for later quantification. Nematodes counts were performed from triplicates of 1 ml aliquots under a stereomicroscope at 40X magnification, using an opaque nematode counting slide (Clalex, Utah, USA). The total nematode numbers were extrapolated to the tube volume, representing the final nematode population (Pf). This total number of nematodes was subsequently used to calculate the nematode reproduction factor. Nematode Reproduction Factor (Rf) was calculated by dividing the final nematode density (Pf) by the initial nematode density (Pi), as follow: 𝑅eproduction factor (R𝑓) = Final Population (P𝑓) Initial Population (P𝑖) To determine the host suitability of each plant, the reproduction factor was divided into five groups according to Neupane & Yan (2023). These include non-host (N; Rf < 0.15), poor host (P; 0.15 ≤ Rf < 1.0), population maintenance host (M; 1.0 ≤ Rf < 2), good host (G; 2.0 ≤ Rf ≤ 4.0), and excellent host (Rf ≥ 4). Lastly, the dry root biomass was taken to calculate the number of nematodes per gram of root by estimating the total nematode density in one gram of root, as this technique was found to clearly indicate the reduction in plant biomass due to nematode pressure (Bhuiyan and Garlick, 2021). If accession lines with similar root mass harbor different numbers of nematodes, the discrepancy is likely due to resistance. However, if an accession line has a smaller root system, the lower nematode count may be attributed to the limited root biomass available as a food source (Bhuiyan and Garlick, 2021). 39 Statistical Analysis For both assays, a two-way and one-way analysis of variance (ANOVA) was performed to assess the significances in multiplication among Pratylenchus species between and within wheat lines or rotational crops, respectively. Data were transformed to log-normal to satisfy the normality assumption when required, and later, they were reverse-transformed to the original scale for graphical presentation. Where significant differences were observed among treatments, pairwise comparisons were conducted using Tukey’s Honestly Significant Difference (HSD) test (α = 0.05). All statistical analyses were conducted in R 4.4.1. (R Core Team, 2024). Results Multiplication Comparison of P. neglectus and P. thornei in Winter Wheat Lines The multiplication comparison between P. neglectus and P. thornei on winter wheat breeding lines and commercial cultivars was carried out in a conetray assay under greenhouse conditions using autoclaved soil. Table 2.3 displays the reproduction factor and host suitability classification, and nematodes per gram of roots for P. neglectus and P. thornei on the winter wheat lines. The wheat lines allowed for different levels of nematode multiplication (ANOVA, p < 0.001), and there was an interaction between Pratylenchus species and wheat line (ANOVA, p < 0.001). Comparing the nematode density (i.e., nematodes per gram of root) between Pratylenchus species, eight (40%) of the tested lines displayed a statistical difference in nematode density (Figure 2.2). Only two of the tested lines allowed for higher P. neglectus densities (i.e., ‘Warhorse’ and ‘54DH38’, p < 0.001 and p = 0.002, respectively), while P. thornei 40 densities were higher on RLN145 (p = 0.002); ‘54DH41’ (p = 0.03), ‘54DH31’ (p < 0.001), ‘34DH41’ (p = 0.04), and ‘Yellowstone’ (p = 0.1). There was a significant difference in nematode density per wheat line for both P. neglectus (p < 0.001) and P. thornei (p < 0.001) (Table 2.3 and Figure 2.2). For P. neglectus, densities ranged from 1,587 ± 411 nematodes gram root-1 to 12,538 ± 2,347 nematodes gram of root-1, while for P. thornei, densities ranged from 788 ± 66 nematode gram root-1 to 13,819 ± 2,027 nematodes gram root-1. The highest difference (p < 0.001) in nematodes per gram of root between RLN species was observed for the line ‘54DH31’, with 1,924 ± 592 nematodes gram root for P. neglectus, in contrast to 13,819 ± 2,027 nematodes gram root-1 for P. thornei. Based on the Rf, the host suitability analysis (Table 2.3) indicated that four wheat lines (22%) were categorized as poor-hosts, 13 wheat lines (72%) were categorized as moderate hosts, and one wheat line (6%) was categorized as good host for P. neglectus. For P. thornei, four wheat lines (22%) were categorized as poor hosts, nine wheat lines (17%) were categorized as moderate hosts, three wheat lines (17%) were categorized as good hosts, and two wheat lines (11%) were categorized as excellent hosts. 4 1 Table 2.3. Multiplication comparison and nematode density on advanced winter wheat lines for Pratylenchus neglectus and P. thornei. aDensity of nematodes per gram of dry root biomass (means ± standard error). bReproduction factor (means ± standard error); a line is considered resistant if Rf < 1. cHosting ability ratings: non-host (N; Rf < 0.15), poor host (P; 0.15 ≤ Rf < 1.0), population maintenance host (M; 1.0 ≤ Rf < 2), good host (G; 2.0 ≤ Rf ≤ 4.0), and excellent host (E; Rf ≥ 4). dWithin a column (within nematode species), means followed by the same letter are not statistically different at P < 0.05 according to ANOVA using Least Significant Differences to separate means. Line P. neglectus P. thornei Nematode g root-1 a Rfb Host suitabilityc Nematode g root-1 Rf Host suitability 34DH33 2,946 ± 396 b-ed 1.1 ± 0.2 ab M 1,390 ± 952 cd 1.1 ± 0.3 ab M 34DH35 4,105 ± 779 a-d 1.5 ± 0.3 ab M 3,383 ± 530 a-d 1.9 ± 0.7 ab M 34DH37 3,548 ± 1,073 b-e 1.3 ± 0.2 ab M 2,439 ± 788 b-d 0.8 ± 0.2 ab P 34DH39 11,430 ± 3,286 ab 2.3 ± 0.6 a G 7,586 ± 2,280 a-c 3.2 ± 0.8 ab G 34DH41 1,068 ± 374 e 0.4 ± 0.1 b P 3,018 ± 330 a-d 1.0 ± 0.1 ab P 54DH31 1,924 ± 516 de 1.1 ± 0.2 ab M 13,819 ± 2,027 a 4.3 ± 0.6 a E 54DH32 2,910 ± 608 b-e 1.1 ± 0.2 ab M 4,889 ± 2,060 a-d 1.8 ± 0.7 ab M 54DH37 4,010 ± 1,335 a-d 1.1 ± 0.4 ab M 2,289 ± 721 b-d 1.1 ± 0.4 ab M 54DH38 4,024 ± 593 a-d 1.1 ± 0.1 ab M 788 ± 66 d 0.6 ± 0.1 b P 54DH41 2,626 ± 1,133 de 0.9 ± 0.2 ab P 4,458 ± 966 a-d 2.0 ± 0.1 ab M 54DH55 2,426 ± 682 c-e 1.1 ± 0.2 ab M 2,711 ± 536 a-d 1.2 ± 0.2 ab M 54DH60 1,587 ± 411 de 0.5 ± 0.1 b P 1,566 ± 288 cd 0.6 ± 0.1 b P Judee 4,665 ± 756 a-d 1.4 ± 0.2 ab M 5,295 ± 611 a-c 2.1 ± 0.3 ab G Persia-20 7,673 ± 2,451 a-c 1.4 ± 0.1 a M 6,539 ± 824 a-c 2.1 ± 0.4 ab G RLN145 2,755 ± 702 b-e 0.9 ± 0.2 ab P 10,287 ± 3,083 ab 4.3 ± 1.0 ab E RLN84 3,992 ± 592 a-d 1.1 ± 0.2 ab M 4,081 ± 1,175 a-d 1.2 ± 0.3 ab M Warhorse 12,538 ± 2,347 a 1.0 ± 0.2 ab M 4,723 ± 1,576 a-d 1.7 ± 0.7 ab M Yellowstone 2,888 ± 542 b-e 1.2 ± 0.3 ab M 5,441 ± 1,048 a-c 1.7 ± 0.2 ab M Pr > F < 0.0001 0.01 < 0.0001 0.005 CV% 6.18 18.9 5.39 19.4 4 2 Figure 2.2. Multiplication comparison of P. neglectus and P. thornei on winter wheat lines. Bars indicate standard error among four replicates. Significance levels indicate the differences in nematode number between Pratylenchus species for the same wheat line. p > 0.1 ns, p < 0.1·, p < 0.05 *, p < 0.01 **, p < 0.001 *** 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 34DH41 54DH60 54DH31 54DH55 54DH41 RLN145 Yellowstone 54DH32 34DH33 34DH37 RLN84 54DH37 54DH38 34DH35 Judee Persia-20 34DH39 Warhorse W in te r w h ea t li n e Pratylenchus thornei Pratylenchus neglectus Number of nematodes per gram of dry root biomass *** ** ** · * *** ns ns ns ns ns ns ns * ns ns ns ns 43 Multiplication Comparison of Pratylenchu neglectus and Pratylenchus thornei on Rotational Crops The multiplication rates of P. neglectus and P. thornei were significantly influenced by the type of crop (p < 0.0001), with a notable interaction between nematode species and crop type (p < 0.0001). Between the Pratylenchus species, significant differences in nematode density (number of nematodes per gram of dry root biomass) were observed: P. neglectus had a higher density on corn (p = 0.02), while P. thornei had higher densities on lentil (p = 0.04) and pea (p < 0.001). Pratylenchus thornei had a higher Rf on pea (p < 0.0001), chickpea (p = 0.003), and lentil (p = 0.02), and a relatively higher multiplication rate on spring wheat (p = 0.06). In contrast, P. neglectus had higher multiplication than only on corn (p = 0.01). The highest P. neglectus multiplication factors were observed in spring and winter wheat (Rf = 3.2 and 3.5, respectively), followed by barley (Rf = 2.4) and corn (Rf = 2.3). Peas, lentil, and corn were identified as poor hosts for P. neglectus. Notably, despite corn being classified as a good host, it had the lowest P. neglectus density among the tested crops, with only 803 ± 136 nematodes per gram of root. In contrast, pea was an excellent host for P. thornei, exhibiting the highest reproduction factor (Rf = 13.6) and a density of 48,218 ± 10,161 nematodes per gram of root. Barley (Rf = 2.6 and 4,519 ± 1,370 P. thornei/ g root), chickpea (Rf = 2.7 and 5,162 ± 1,318 P. thornei / g root), spring (Rf = 2.3 and 4,137 ± 1,098 P. thornei/ g root) and winter wheat (Rf = 2.6 and 2,725 ± 408) followed as good hosts. Corn was the only crop that did not facilitate P. thornei multiplication, with an Rf of less than 1 and 301 ± 87 P. thornei/ g root (Table 2.4). 4 4 Table 2.4. Multiplication comparison and nematode density of P. neglectus and P. thornei on rotational crops. aDensity of nematodes (number of nematodes per gram of dry root biomass; means ± standard error). bReproduction factor (means ± standard error); a line is considered resistant if Rf < 1. cHosting ability ratings: non-host (N; Rf < 0.15), poor host (P; 0.15 ≤ Rf < 1.0), population maintenance host (M; 1.0 ≤ Rf < 2), good host (G; 2.0 ≤ Rf ≤ 4.0), and excellent host (Rf ≥ 4). dWithin the same row (between nematode species), means followed by the same capital letter are not statistically different at P < 0.05 according to ANOVA using Least Significant Differences to separate means. eWithin the same column, means followed by the same small letter are not statistically different at P < 0.05 according to ANOVA using Least Significant Differences to separate means. Crop Pratylenchus neglectus Pratylenchus thornei Pr > F (Pratylenchus / g root) Pr > F (Rf) P. neglectus/ g roota Rf ± SEb Hc P. thornei/ g. root Rf ± SE H Barley 3,283 ± 574 Adae 2.4 ± 0.5 Aa G 4,519 ± 1,370 Abc 2.6 ± 0.6 Abc G 0.55 0.83 Chickpea 3,339 ± 315 Aa 0.6 ± 0.1 Ab P 5,162 ± 1,318 Abc 2.7 ± 0.7 Bbc G 0.37 0.003 Corn 803 ± 136 Ab 2.3 ± 0.3 Aa G 301 ± 87 Bd 0.7 ± 0.2 Bc P 0.02 0.01 Lentil 5,394 ± 278 Aa 0.9 ± 0.2 Ab P 10,847 ± 1,578 Bab 1.9 ± 0.3 Bbc M 0.04 0.02 Pea 2,302 ± 231 Aa 0.6 ± 0.1 Ab P 48,860 ± 9,240 Ba 13.6 ± 1.6 Ba E < 0.001 < 0.001 Spring wheat 6,191 ± 992 Aa 3.5 ± 0.2 Aa G 4,137 ± 1,098 Abc 2.3 ± 0.5 b G 0.1 0.06 Winter wheat 3,098 ± 669 Aa 3.2 ± 0.5 Aa G 2,725 ± 408 Ac 2.6 ± 0.3 Abc G 0.5 0.34 Pr > F < 0.001 < 0.001 ̶ < 0.001 < 0.001 ̶ ̶ ̶ CV 5.2 14.2 ̶ 17.6 6.6 ̶ ̶ ̶ 45 Discussion Pathogens and pests present a significant challenge to global crop production, resulting in considerable economic losses which can significantly contribute to reducing food security (Savary et al., 2019). For wheat, one of the most vital cereal crops worldwide, it is estimated that pathogen attacks account for approximately 21.5% of yield loss (Savary et al., 2019). Among these threats, root lesion nematodes, particularly Pratylenchus neglectus and Pratylenchus thornei, are key pests impacting wheat production globally (Owen, 2022; Smiley, 2021; Mokrini et al., 2019). Montana is among the largest wheat grower and exporter in the United States. This crop plays a crucial role in the state’s economy and the global wheat supply. A statewide survey conducted from 2005 to 2006 revealed that P. neglectus significantly impacted winter wheat yields in Montana, resulting in estimated losses of 12% to 15%, which translates to approximately $82 million (NASS, 2023). More recently, Pratylenchus thornei, recognized as a more damaging Pratylenchus species (Mokrini et al., 2019), was identified in a Montana wheat field (Consoli & Dyer, 2024). Its distribution and potential impact on Montana crop yields remain largely unknown. To assist farmers in managing nematode pressure, researchers have investigated rotational crops suited to Montana’s environmental conditions to reduce P. neglectus populations (Zuck, 2010). The study found that pea (Pisum sativum L.) was the most effective crop at reducing P. neglectus densities, while barley (Hordeum vulgare) and lentils (Lens culinaris) also showed promise as rotational crops, although their effectiveness in suppressing nematode 46 pressure varied depending on the specific crop variety (May et al., 2016). Peas are increasingly recognized for their importance as both a cash crop and a rotational crop in Montana (Miller et al., 2015), and as non-host for P. neglectus, this crop can play a crucial role in reducing nematode populations when indicated to farmers dealing with damaging nematode populations. This is particularly beneficial for wheat farmers, as it helps mitigate yield losses associated with nematode infestations (May et al., 2016). In addition to crop rotation, plant resistance is one of the best management practices against pathogens and pests in low-input farming systems, such as those involving wheat. In Montana, the widespread prevalence and significant impact of P. neglectus on winter wheat yields (Johson, 2007; May et al., 2016), prompted the development of a winter wheat breeding program aimed at reducing the damage caused by this nematode pest (May, 2015). The presence of P. thornei in Montana, despite the unknown extent of its distribution in the state, highlights the necessity for investigating a combined management strategy. This study evaluates the multiplication potential of P. neglectus and P. thornei on winter wheat breeding lines, as well as on rotational crops commonly used in Montana, including barley, lentils, peas, and chickpeas, in addition to spring and winter wheat. Corn was also included in the study, despite its uncommon use as a rotational crop in winter wheat fields, primarily due to limited water availability in winter wheat growing areas of Montana. Nevertheless, corn is an important crop in the state, and a COI DNA barcoding survey detected P. thornei in corn and alfalfa fields in eastern Montana (Ozbayrak et al., 2019). Fourteen winter wheat advanced lines from our breeding pipeline were selected to compare the multiplication potential of P. neglectus and P. thornei, in addition to three Montana 47 cultivars, and Persia-20 (AUS5202; CI 11283), an Iranian landrace used as the resistant parent in the initial breeding of our breeding project due to previously reports of its resistance to both P. neglectus and P. thornei (Smiley et al., 2014b; Sheedy et al., 2008a; Sheedy et al., 2007; Sheedy et al., 2008b; Vanstone et al., 2008). The different lines showed variable results in nematode density per root biomass, as well as reproduction factor. The line ‘54DH31’, previously selected due to its resistance to P. neglectus under greenhouse conditions (Consoli et al., 2021) proved to be an excellent host to P. thornei. Similarly, one of the parental lines (RLN145) also showed to be an excellent host to P. thornei while being classified as a poor host to P. neglectus. While it is well established that resistance in plants to migratory endoparasitic nematodes is not governed by a single gene (Peng & Moens, 2003), the mechanisms underlying resistance to root lesion nematodes remain poorly understood. However, it is known that plants resistant to Pratylenchus neglectus are not necessarily resistant to Pratylenchus thornei. Nevertheless, wheat lines resistant to both nematode species have been identified in Australia (Sheedy et al., 2022). Two of the lines tested in this research, (i.e., 54DH60 and 34DH41), have demonstrated poor host performance for both P. neglectus and P. thornei, indicating their potential for managing these nematodes, although their genotype remains unknown. To date, wheat genotyping for known P. neglectus resistant loci was conducted on the initial phase of this winter wheat breeding project with variable success (May et al., 2015), while phenotyping has only been performed for the double haploid lines. Genotyping these lines would provide a higher confidence level for the resistant status of these lines. To date, potential candidate genes linked to P. neglectus resistance is the Rlnn1, which can be detected using Kompetitive Allele Specific PCR (KASP) markers uat128 and uat129, and the QTL QRlnn.lrc- 48 2B, located in chromosome 2B near the P. thornei -resistant QTL, QRlnt.lrc-2B (Zwart et al., 2010). Conclusion Plant resistance in combination with rotational crops is one of the best management strategies for reducing plant-parasitic nematode damage and preserving yields. Cover crops can also be useful for managing these nematodes, provided they are poor or non-hosts for the target nematode species. In the present study, two of the tested winter wheat lines, ‘54DH60’ and ‘34DH41’, were found to be poor hosts for both P. neglectus and P. thornei. This suggests they have the potential for managing these two Pratylenchus species and should be tested under field conditions. In contrast, the winter wheat line ‘54DH31’ was a poor host for P. neglectus, but an excellent host for P. thornei. Similarly, pea was a poor host for P. neglectus while serving as an excellent host for P. thornei. These findings highlight the potential of these breeding lines and rotational crops for an integrated management of P. neglectus and P. thornei, but also demonstrate the importance of accurate diagnostics for the Pratylenchus species present in the field, as this is essential for making management recommendations, particularly when both species co-occur in the same field. References Alcaniz, E., Pinochet, J., & Fernandez A. (1996) Evaluation of rootstocks for root-lesion nematode resistance. HortScience 31:1013-1016 Bhuiyan, S. A., & Garlick, K. (2021) Evaluation of root-lesion nematode (Pratylenchus zeae) resistance assays for sugarcane accession lines. Journal of Nematology, 53: e-2021-67 49 Brown, R. H. (1987) Control strategies in low value crops. In R. H. Brown and B. 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A.(2005a) Suppression of wheat growth and yield by Pratylenchus neglectus in the Pacific Northwest. Plant Disease. 89: 958-968 Smiley, R. W., Whittaker, R. G., Gourlie, J. A., & Easley, S. A. (2005b) Pratylenchus thornei associated with reduced wheat yield in Oregon. Journal of Nematology, 37: 45-54 Smiley, R. W., Yan, G. P., & Gourlie, J. A. (2014a) Selected Pacific Northwest crops as hosts of Pratylenchus neglectus and P. thornei. Plant Disease. 98:1341-1348 Smiley, R. W., Gourlie, J. A., Yan, G., and Rhinhart, K. E. L. (2014b) Resistance and tolerance of landrace wheat in fields infested with Pratylenchus neglectus and P. thornei. Plant Disease. 98:797-805. Stirling G., Blair, B., Wildon, E., & Stirling, M. (2002) Crop rotation for managing nematode pests and improving soil health in sugarcane cropping systems. Proceedings of the Conference of the Australian Society of Sugar Cane Technologists. Cairns, Queensland, Australia, 129-134 Taylor, S. P., Hollaway, G. J., & Collen, H. H. (2000) Effect of field crops on population densities of Pratylenchus neglectus and P. thornei in Southeastern Australia; Part 1: P. neglectus. Journal of Nematology 32(4S):591-599 Vanstone, V. A., Hollaway, G. J., & Stirling, G. R. (2008) Managing nematode pests in the southern and western regions of the Australian cereal industry: continuing progress in a challenging environment. Australasian Plant Pathology, 37:220-234. Viaene, N., Hallmann, J., & Leendert, P. G. M. (2020). Methods for nematode extraction. In Techniques for Work with Plant and Soil Nematodes, Perry, R. N., Hunt, D. J., & Subbotin, S.A. CAB International, Wallingford Oxfordshire, UK, 35-77 Wesemael, W. (2020) Screening plants for resistance/ susceptibility to plant-parasitic nematodes. In Techniques for Work with Plant and Soil Nematodes, Perry, R. N., Hunt, D. J., & Subbotin, S.A. CAB International, Wallingford Oxfordshire, UK, 103-117 Wickham, H. (2016) ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org https://ggplot2.tidyverse.org/ 52 Zwart, R. S., Thompson, J. P., Milgate, A. W., Bansal, U. K., Williamson, P. M., Raman, H., & Bariana, H. S. (2010) QTL mapping of multiple foliar disease and root-lesion nematode resistances in wheat. Molecular Breeding, 26:107-124 Zuck, P. C. (2010) Evaluation of alternative crops for management of Pratylenchus neglectus in Montana winter wheat production. Master Thesis. Montana State University, College of Agriculture, Bozeman MT. 80p. Available at https://scholarworks.montana.edu/handle/1/2612 https://scholarworks.montana.edu/handle/1/2612 53 CHAPTER THREE FIELD ASSESSMENT OF WINTER WHEAT LINES FOR RESISTANCE TO PRATYLENCHUS NEGLECTUS Contribution of Authors and Co-Authors Manuscript in Chapter 3 Author: Erika Consoli Contributions: conceptualization, methodology, validation, formal analysis, investigation, data curation, writing – original draft, visualization. Co-Author: Alan T. Dyer Contributions: conceptualization, validation, resources, writing – review and editing, project administration, funding acquisition. 54 Manuscript Information Erika Consoli, & Alan T. Dyer (2024) Field assessment of winter wheat lines for resistance to Pratylenchus neglectus. Status of Manuscript: ☒ Prepared for submission to a peer-reviewed journal ☐ Officially submitted to a peer-reviewed journal ☐ Accepted by a peer-reviewed journal ☐ Published in a peer-reviewed journal 55 Summary 1. Pratylenchus neglectus distribution was highly variable within a field. 2. A low pre-plant density of Pratylenchus neglectus resulted in a higher rate of nematode multiplication compared to higher initial densities. 3. Yield and grain quality were significantly reduced in susceptible phenotypes under higher pre-plant P. neglectus densities, while resistant phenotypes maintained both yield and grain quality, regardless of initial nematode density. Abstract The root lesion nematode (RLN), Pratylenchus neglectus, is an important plant-parasitic nematode that causes yield loss in wheat cropping systems worldwide. In Montana, one of the largest wheat producing states in the United States, P. neglectus is widely distributed. In 2016, winter wheat yield losses due to P. neglectus infestations in the state were estimated at 15%, equivalent to US$84 Million. Due to its polyphagous nature, the use of resistant and/or tolerant wheat cultivars is the most effective strategy to control this nematode. However, there are no commercial wheat cultivars identified as resistant to Pratylenchus spp. in the United States. This research aimed to evaluate the potential value of pre-screened winter wheat double haploid lines at reducing P. neglectus densities under field conditions and compare the agronomics of resistant entries with those of Montana wheat cultivars under varying nematode initial densities. Pre-plant P. neglectus densities varied significantly across the trial and within plots. The nNematode multiplication rate was inversely correlated with e initial nematode density. Results also identified a negative correlation between pre-plant nematode densities and yield for the 56 susceptible lines, while the inverse was observed for resistant lines. Increases of P. neglectus densities resulted in a decline of 1,000-kernel and test weights for the susceptible lines, while resistant lines were unaffected. These results indicated that lines identified as resistant in greenhouse screenings are tolerant to nematode attack under field conditions, and these entries would provide value in terms of improved yield and grain quality in fields where P. neglectus pressure is high. Introduction The root lesion nematode, Pratylenchus neglectus Rensch, 1924 (Tylenchida: Pratylenchidae) is a polyphagous, migratory endoparasite nematode affecting various crops and pasture species across temperate regions (Williams, 2002), including Montana (Johnson, 2007). This nematode has been linked to significant yield losses in key crops such as wheat and barley, which are staple crops in Montana’s agricultural landscape. Among these, wheat stands out as a major host for P. neglectus, particularly in the state’s expansive dryland farming areas. The presence of P. neglectus can lead to yield reductions of approximately 20% in wheat, underscoring the importance of effective management strategies (Taylor et al., 1998; Vanstone et al., 1998). Montana is a leading United States producer of wheat, barley, sugar beets, and pulse crops serving as a major driver of Montana’s economy. In 2023, farmers planted 2.2 million hectares of wheat, which includes 750,000 hectares of winter wheat, 1.1 million hectares of spring wheat, and 285,000 hectares of durum wheat. This positions Montana as the third largest wheat producer in the nation (NASS, 2024). Winter wheat is a key cash crop for Montana 57 growers and typically offers higher yield potential than spring wheat, particularly when moisture level is adequate (McVay et al., 2010). For the past five years (2019-2024) that spring wheat produced an average of 30.4 bushels per acre, while winter wheat yielded an average of 44.9 bushels per acre statewide. A statewide survey conducted between 2006-2007 P. neglectus as widespread across Montana (Johnson, 2007). The survey revealed greater yield loss in crops where winter wheat was grown in the previous season, suggesting that this nematode multiplies more extensively in winter wheat compared to other crops. This is partially due to the longer growing season of winter wheat (approximately nine months) compared to the shorter three-month growth season of spring wheat. Additionally, winter wheat provides both food and shelter for the nematode during the colder winter months, further contributing to population buildup. Reducing initial nematode densities is crucial for minimizing the damage caused by P. neglectus. In Montana's low-input cropping systems, one effective approach is the cultivation of resistant wheat varieties. However, to date, no wheat cultivars resistant to P. neglectus are available. Resistance is defined as the plant's ability to limit nematode multiplication, thereby protecting yields (Mokrini et al., 2019; Trudgill, 1991). Developing P. neglectus resistant cultivars with comparable agronomic performance to that of current winter wheat varieties would be an important step toward sustainable nematode management in the state’s low-input agricultural systems. This project aimed to evaluate whether selected winter wheat double haploid lines, which were resistance to P. neglectus under greenhouse conditions, were also effective in reducing P. 58 neglectus densities in the field. Additionally, agronomic performance was compared to that of popular Montana winter wheat varieties when grown under low nematode densities. By understanding the interactions between P. neglectus and winter wheat lines, Montana can make informed decisions about the introduction of nematode resistant lines into Montana’s production systems. Materials and Methods Plant Material Sixteen winter wheat entries were selected for evaluation under field conditions. These include six resistant double haploid lines, five susceptible double haploid lines, the two resistant parental lines, and three Montana varieties (i.e., ‘Warhorse’, ‘Yellowstone’, and ‘Judee’) (Table 3.1). 59 Table 3.1. Plant material used in the field trials. a Entries number 1 to 6 corresponds to the P. neglectus resistant (R) double haploid lines, entries 7 to 11 corresponds to the susceptible (S) double haploid lines, entries 12 and 13 are the parental (P) lines, and entries 14 to 16 correspond to the susceptible (S) Montana varieties. Entry number a Line Phenotype 1 34DH32 R 2 34DH41 R 3 54DH31 R 4 54DH41 R 5 54DH55 R 6 54DH60 R 7 54DH52 S 8 54DH37 S 9 34DH28 S 10 34DH35 S 11 34DH39 S 12 RLN145-1 P 13 RLN84-3 P 14 Yellowstone Variety, S 15 Judee Variety, S 16 Warhorse Variety, S Experimental Sites The trials were initially conducted at three locations during the 2022-2023 growing season and repeated in growing season 2023-2024 (Figure 3.1). These locations were chosen because they represented distinct wheat-growing environments in Montana: dryland with high rainfall (Bozeman location, Gallatin County), dryland irrigated (Chester location, Liberty County), and dryland low rainfall (Havre location, Hill County). These sites were naturally infested with varying densities of P. neglectus, as determined by a field survey conducted in the fall of 2021. At that time, P. neglectus densities at the Chester site ranged from 4,906 to 13,063 P. neglectus/ kg soil, while at the Havre site, densities ranged from 3,219 to 4,406 P. neglectus/ kg 60 soil. For comparisons, 2,000 P. neglectus /kg of soil were previously associated with a 7% reduction in yield (Johnson 2007). Pratylenchus neglectus densities at the Bozeman site were not assessed at that time due to the pending field allocation, but the site was known for having natural infestations of P. neglectus (Dyer, personal communication). The Bozeman field site was in field section P7 at Montana State University Arthur Post Farm Research Station (1,455 meters of elevation, 45°40’47” N 111°08’54” W). The soil at this site is characterized as silty loam, and the research plot was fallowed the previous year. Overall, the site receives an average annual rainfall of 412 mm, with most of precipitation occurring in late spring to early summer (May/June), and an average snowfall of 1,270 mm. Chester field site was in a grower’s field southeast of Chester (885 meters of elevation, 48°17’03” N 110°46’28” W). The soil at this farm is characterized as clay soil, and the research plot was cropped with winter wheat the previous cropping season. The site receives an average annual rainfall of 280 mm, with most of the precipitation in early summer (June), and snowfall from October to May, ranging from 51 mm to 177 mm. Field location Bozeman Chester Havre Figure 3.1. Field locations in Montana selected for the study. 61 Havre field site was in a grower’s field located southeast of Havre (826 meters of elevation, 48°31’32” N 109°36’20” W). The soil in this site is characterized as a clay-loamy soil, and the field was cropped with winter wheat the previous cropping season. The site receives an average annual rainfall of 284 mm, with most of precipitation occurring in late spring (May), and average snowfall of 965 mm. The mean monthly temperatures from 2022 to 2024 for the field locations is displayed in Table A3.1 (Appendix A). Experimental Design At each location, the experimental design consisted of two factors: winter wheat lines (16 lines), with or without seed treatment. Treatments were applied to the same plots across two growing seasons (2023 and 2024). For treatments that received seed treatment (T), the seeds were treated to protect against fungal soil-borne pathogens. Seed treatments consisted of a combination of Syngenta’s Cruiser Max Vibrance (active ingredients: Thiamethoxam 20.8%, Mefenoxam 3.13%, Fludioxonil 1.04%, Sedaxane 1.04%; application rate: 2.95 ml per pound of seed) and MetaStart ST (Chemtura Corp., active ingredient: Metalaxyl 29.99%; application rate: 0.12 ml per pound of seed). MetaStart is a systemic fungicide and was used to prevent Phytium and downy mildew damage, while Cruiser Max was used to prevent damage caused by Fusarium crown and root rot, while also offering some levels of protection against bunts and smuts. The experiment was conducted using a randomized complete block design. Each treatment was replicated six times. Randomization of the experiment was performed using the R packaged ‘Agricolae’ version 1.3.7 (de Mendiburu, 2023). Plots dimensions were 3m in length and 4m in width, with 91 cm buffer rows between blocks. This design was consistent across all field locations, except Bozeman, where only 62 untreated seed treatments were applied due to the smaller field size. The fields in Havre and Chester measured one acre each, whereas in Bozeman the field was half an acre. Block 12 was not planted at the Chester site due to the lack of visibility. In total, the study included 11 blocks of 16 plots in Chester, 12 blocks of 16 plots in Havre, and 6 blocks of 16 plots in Bozeman, resulting in 464 plots across the three field sites. In both years, wheat was planted during the second week of October and harvested in the second week of August the following year. Seeds were sown at a density of 244 seeds/ square meter. The soil was maintained under no-till conditions. Weeds were controlled with a non- selective herbicide before planting (Roundup® at 0.8 lb/acre a.i.), and selective herbicide post wheat emergence (Huskie® Herbicide at 11 oz/acre with non-ionic surfactant at 0.25% v: v). Both were applied with a sprayer. The remaining volunteer wheat and weeds were removed manually. Grain was harvested after drying to approximately 10% moisture content in the field. All field operations, from planting to harvest, were conducted by the authors, except for fertilization in Chester, which was carried out by the grower. Plants were harvested from the 4 row plots, and plot length was measured after harvest to obtain the exact area harvested. A detailed list of planting, sampling, and harvesting dates is provided in Table A3.2. Soil Sampling, Extraction, and Nematode Quantification Nematode population densities were assessed at pre-planting (initial nematode density; Pi) and post-harvest (final nematode density; Pf). Soil samples were collected two weeks before planting and after wheat harvest for each growing season. Due to the soil hardness, a shovel was used to break the soil, and soil samples were collected from a depth of 0-15 cm using a metal trowel. Each sample was a composite of five soil cores collected in a zigzag pattern, bulked in a 63 Ziplock bag, and stored in a cooler for transportation. Nematodes were extracted from 400 ml of soil using the modified Baermann tray method (Viaene et al., 2020; Figure A3.1) for 48 hours, as previous research found no significant differences between the modified Barmann tray and centrifugal floatation techniques on nematode densities (Al-Khafaji, 2018). The water suspension containing the extracted nematodes was collected and concentrated using a 20 µm sieve and transferred to a 50 ml Falcon tube for quantification. After nematode extraction, the tubes were stored in the refrigerator at 7°C and nematodes were counted immediately. If immediate nematode quantification was not possible, the nematodes were fixed using TAF fixative (EPPO, 2021) and quantified later. Nematodes counts were performed from three 1 ml aliquots using a stereomicroscope at 40X magnification, using an opaque nematode counting slide (Clalex, Utah, USA). The nematode quantification focused on the root-lesion nematode, P. neglectus. Additionally, all nematodes present in the samples were classified according to their trophic groups (i.e., bacterial feeders, fungal feeders, predators, omnivores, and plant-parasitic). Reproduction Factor (Rf) was calculated by dividing the final nematode density (Pf) by the initial nematode density (Pi), as follows: Reproduction factor (R𝑓) = Final Nematode Density (P𝑓) Initial Nematode Density (P𝑖) Agronomics Agronomics measurements included grain yield (bushel per acre), protein content (%), 1000-kernel weight (grams), and test weight (pounds per bushel). Test weight, which estimates the plumpness of wheat kernels, serves as an overall indicator of grain quality by reflecting the potential for efficient milling, while 1,000-kernel weight reflects the size and weight of 64 individual kernels, and are important for determining yield, milling, baking, and planting rates (Mallory et al., 2012). The yield was calculated by measuring the grain weight from the harvested area and extrapolating the results to bushels per acre. Plant height was measured from the soil surface to the top of the wheat head, excluding the awns (Tracy et al., 2022). Grain protein, moisture, and test weight were analysed at the Montana State University Cereal Quality Laboratory using an Infratec 1241 (FossAnalytics, Denmark). The 1,000-kernel weight was measured by automatically counting 1,000 kernels using a CGOLDENWALL automatic seed counter and weighting the kernels in a precision scale. Statistical Analysis Statistical analyses were performed in R-Studio version 3.2 (R core team, 2024). The effect of initial nematode density on plant agronomics was determined by a linear model, according to the experimental design.To meet model assumptions, nematode counts were log-e transformed prior to analysis, and later they were back-transformed to the original scale to be presented in the tables and graphs. When the main effect was significant (P < 0.05), means were separated using Post-hoc Tukey’s High Significance Difference (HSD) test at α = 0.05. Results Nematode Quantification Nematodes were quantified for each plot at pre-planting and post-harvest in each growth season. Overall, Bozeman field site had the highest percentage of plant-parasitic nematodes at pre-planting and post-harvest during the whole extend of the field trial. Figure 3.2 and 3.3 shows 65 the percentage of nematode trophic groups per field site at the beginning and end of the first year of the field trials for the three field locations, while Figure 3.4 shows the percentage of trophic groups at the end of the trials in 2024 for Bozeman and Chester. At pre-planting in 2022, 56% of nematodes at the Bozeman location were plant-parasitic nematodes (PPN), increasing to 67% in 2023, and to 82% at the end of the trial in 2024. Mean PPN genera found at this location were Pratylenchus (P. neglectus and P. thornei), Paratylenchus, Merlinius, Tylenchorynchus, and Hoplolaimus. From 2022 to 2024, percentages of bacterial feeder nematodes went from 20% to 11%, and from 11% to 8% of fungal feeders, and omnivores went from 13% to 2%. At the Chester location, from 2022 to 2024, the percentages of PPN went from 41% (2022) to 28% (2023) to 46% (2024), while at Havre location, the PPN percentage varied from 27% (2022) to 29% (2024). The main PPN genera found at Chester and Havre locations were Pratylenchus (P. neglectus), Merlinius, and Tylenchorynchus. Nematode densities in Havre were not accessed at the end of the field trial because a hailstorm affected wheat yield, and grains started to emerge before the soil samples were collected, affecting the nematode counts. 66 Figure 3.3. Percentage of nematode tropic groups per 400 cc of soil at each field site at post- harvest. 67 28 29 15 47 42 16 12 18 2 13 11 0 20 40 60 80 100 Bozeman Chester Havre Plant-Parasitic Bacterivores Fungivores Omnivores 56 41 27 20 34 41 11 7 6 13 18 26 0 20 40 60 80 100 Bozeman Chester Havre Plant-Parasitic Bacterivores Fungivores Omnivores Figure 3.2. Percentage of nematode tropic groups per 400 cc of soil at each field site at pre- planting at the beginning of the trials (2022) Figure 3.4. Percentage of nematode tropic groups per 400 cc of soil at Bozeman and Chester sites at post-harvest in 2024. 46 82 31 11 8 5 14 2 0 20 40 60 80 100 Chester Bozeman Plant Parasitic Bacterivors Fungivores Omnivores 67 Pratylenchus neglectus densities were quantified at pre-planting and post-harvest to assess the effect of wheat phenotypes on the nematode reproduction factor and plant agronomics. Pratylenchus neglectus density was highly heterogeneous both across locations (ANOVA, p < 0.001) and between plots within a field (ANOVA, p < 0.001). Figure 3.5 illustrates the P. neglectus distribution within the Chester field in the first year of the trial. A summary of initial P. neglectus densities for each of the location-year is shown in Table A3.3. Overall, the Bozeman location, which was fallowed the year before the trial started, had the lowest pre-planting Pratylenchus density among the three field sites (ANOVA, p < 0.001) in the first year of the trial, while Chester location had the highest pre-planting P. neglectus density when the trial started. P. neglectus Kg soil- Figure 3.5. Distribution of Pratylenchus neglectus densities at pre-planting in Chester field during the first year of the field trials. 68 The nematode reproduction factor was calculated by dividing the nematode density at harvest (Pf) by the nematode density at pre-planting (Pi). Table 3.2 summarizes the reproduction factor (Rf) of P. neglectus across locations for the 2023 and 2024 cropping season. The reproduction factor in Havre was measured only in 2023, as a hailstorm affected yields and seeds started emerging before soil samples could be collected, which in turn affected the post-harvest nematode counts. There was a significant difference in the nematode reproduction factor (Rf) among locations (ANOVA, p < 0.001), year (ANOVA, p = 0.001), and there was an interaction between location and year (ANOVA, p < 0.001). The greatest Rf was observed for the first year (Rf 2023) of field trials at Bozeman location, where reproduction factors ranged from 1.4±0.9 to 18.8±2.6. In the second year (Rf 2024) at Bozeman location, the reproduction factor ranged from 0.9±0.2 to 5.8±3.1. At the Chester location for the first year (Rf 2023), reproduction factor ranged from 0.3±0.1 to 3.4±2.5, and from 0.9±0.3 to 4.4±2.4 in the second year (Rf 2024). At Havre location, reproduction factor ranged from 0.6±0.3 to 8.1±4.2 (Rf 2023). Overall, the reproduction factors obtained in the field were not always consistent with the greenhouse phenotyping which considers the plant to be resistant if Rf < 1.0. Pratylenchus neglectus reproduction factor for the lines ‘54DH4’ and ‘54DH60’ was 1.4 ± 0.9 and 2.2 ± 0.3, respectively, and P. neglectus the susceptible line ‘54DH37’ had a P. neglectus Rf of 10.4 ± 3.3 but no statistical differences were detected among lines (ANOVA, p = 0.39). The same pattern of no significant differences among lines or resistance phenotype was found for Chester and Havre. There was no correlation between the reproductive factors obtained in the greenhouse and in the field for each location- year (Table 3.2). 6 9 Table 3.2. Pratylenchus neglectus reproduction factor (Rf) across field locations and years. Values are mean ±standard error. aReproduction factor (Rf ± SE). bMean across 2023-2024 field seasons. cCombined mean cross field location and years. Means followed by the same letter are not statistically different at P < 0.05 according to ANOVA using Least Significant Differences to separate means. Location Bozeman Chester Havre Line Rf a2023 Rf 2024 Meanb Rf Rf 2023 Rf 2024 Mean Rf Rf 2023 Overall Meanc Rf 34DH41 6.1 ± 2.6 1.6 ± 0.4 3.4 ± 1.2 0.9 ± 0.2 1.9 ± 0.6 1.1 ± 0.3 0.5 ± 0.2 4.9 ± 1.6 a 54DH37 10.4 ± 3.3 2.9 ± 0.9 6.2 ± 1.9 0.6 ± 0.1 1.7 ± 0.4 1.1 ± 0.2 1.6 ± 1.4 2.4 ± 0.6 a 54DH41 2.2 ± 0.3 2.2 ± 1.3 2.2 ± 0.8 0.4 ± 0.1 1.4 ± 0.3 0.8 ± 0.2 1.7 ± 1.2 1.4 ± 0.4 a 34DH28 3.5 ± 0.1 5.8 ± 3.1 4.1 ± 2.3 0.4 ± 0.1 2.8 ± 0.6 1.6 ± 0.4 1.1 ± 0.4 2.9 ± 1.1 a 54DH31 7.6 ± 4.5 1.4 ± 0.6 4.1 ± 2.1 0.2 ± 0.1 4.4 ± 2.4 2.2 ± 1.2 1.6 ± 0.6 2.5 ± 0.8 a 34DH32 9.8 ± 2.3 3.4 ± 1.6 5.6 ± 1.6 0.4 ± 0.1 2.5 ± 0.7 1.4 ± 0.4 1.7 ± 0.7 3.3 ± 0.9 a 34DH35 3.5 ± 2.1 0.9 ± 0.2 1.5 ± 0.5 0.4 ± 0.1 3.1 ± 0.7 1.5 ± 0.4 2.2 ± 1.0 1.7 ± 0.3 a 34DH39 4.1 ± 2.8 2.0 ± 0.6 2.9 ± 1.2 0.3 ± 0.1 2.3 ± 0.3 1.2 ± 0.3 1.4 ± 0.9 2.4 ± 0.9 a 54DH52 7.6 ± 2.6 3.1 ± 1.0 4.8 ± 1.4 0.6 ± 0.2 1.7 ± 0.4 1.1 ± 0.2 0.7 ± 0.2 4.1 ± 1.3 a 54DH55 3.4 ± 0.2 1.4 ± 0.3 2.2 ± 0.2 0.4 ± 0.1 2.4 ± 0.5 1.4 ± 0.3 2.1 ± 1.1 2.8 ± 1.1 a 54DH60 1.4 ± 0.9 1.1 ± 0.3 1.2 ± 0.4 0.5 ± 0.2 2.2 ± 0.7 1.3 ± 0.4 3.5 ± 2.2 1.9 ± 0.6 a RLN145 9.1 ± 5.6 1.6 ± 0.3 4.3 ± 1.6 3.4 ± 2.5 0.9 ± 0.3 1.7 ± 0.2 8.1 ± 4.2 5.2 ± 1.6 a RLN84 18.8 ± 2.6 1.4 ± 0.3 4.1 ± 2.1 0.4 ± 0.1 1.5 ± 0.6 0.9 ± 0.3 0.6 ± 0.4 1.4 ± 0.5 a Judee 8.2 ± 5.3 2.8 ± 0.7 3.9 ± 2.5 1.0 ± 0.3 3.4 ± 1.3 1.9 ± 0.6 2.1 ± 0.7 2.4 ± 0.5 a Warhorse 3.7 ± 1.4 1.5 ± 0.4 2.3 ± 0.6 0.3 ± 0.1 1.8 ± 0.4 1.0 ± 0.2 2.7 ± 1.3 1.7 ± 0.4 a Yellowstone 7.6 ± 2.5 0.8 ± 0.3 3.5 ± 1.4 0.7 ± 0.3 2.1 ± 0.6 1.3 ± 0.3 4.9 ± 2.4 2.8 ± 0.8 a Greenhouse phenotyping R2 = 0.04 R2 = 0.006 - R2 = 0.02 R2 = 0.04 - R2 = 0.04 - p = 0.86 p = 0.74 - p = 0.64 p = 0.46 - p = 0.50 - 70 Agronomics Overall, grain yield was influenced by field location (ANOVA, p < 0.001), reflecting the environment factors present in Montana. A significant interaction between location and year was observed for both grain yield and seed quality (ANOVA, p < 0.001 and p < 0.001, respectively; Table A3.4). As a result, the analysis was conducted separately for each year-location. In the second year of the field trials, Bozeman and Havre sites experienced extensive damage from a hailstorm, resulting in significantly lower yields. Consequently, yields were only quantified for Chester and Bozeman location in the second year. Agronomic data for each line, location and year are presented in Tables 3.3 to 3.6. At the Bozeman location, a statistically significant difference in yield was not observed for 2023, but there was a significant difference in yield for year 2024 (ANOVA, p = 0.82 and p = 0.01, respectively). Yield at Bozeman location varied from 72.8 ± 8.2 to 116 ± 10.4 bushels per acre, with the susceptible line ‘54DH35’ having the relatively lower yields, and the resistant line 54DH41 yielding the relatively higher yields. In 2023, when the Bozeman location was hit by a hailstorm, yield ranged from 52.6 ± 3.0 to 80.6 ± 2.9 bushels per acre, with Yellowstone and ‘34DH39’ having the lowest yields, and ‘54DH60’ and ‘RLN145’ having the highest yields. At the Chester location, no significant differences in yield were observed (ANOVA, p = 0.12 and p = 0.52 for 2023 and 2024, respectively). Yield ranged from 47.3 ± 7.1 to 69.1 ± 5.9 bushels per acre and 53.8 ± 5.5 to 67.1 ± 6.4 bushels per acre in 2023 and 2024, respectively. Similarly for the Havre location, no significant differences in yield were observed (ANOVA, p = 0.6). Yield in Havre ranged from 28.4 ± 3.9 to 38.3 ± 3.0. Across years and locations, the resistant line ‘54DH41’ yielded 67.1 bushels per acre and the susceptible line ‘34DH35’ yielded 52.6 bushels 71 per acre, whereas Montana’s widest grown cultivar, ‘Warhorse’, yielded a mean of 59.5 bushels/acre across years and locations. Considering the winter wheat price of US$6.4/bushel (NASS, 2024), this difference of 14.5 bushels/ acre and 7.5 bushels/acre between the highest and lowest yielding lines and Warhorse, represents and additional US$92.8/ acre and US$48/ acre for growers. 7 2 Table 3.3. Yield of winter wheat lines at Bozeman, Chester and Havre locations for the 2023 and 2024 field seasons. Values represent mean ± standard error. In the same column, similar letters are not statistically different after ANOVA followed by Tukey HSD test (α = 0.05). Line Yield (bushels per acre) Overall yield Bozeman 2023 Bozeman 2024 Chester 2023 Chester 2024 Havre 2023 34DH41 108 ± 11.6 a 60.5 ± 3.8 ab 66.8 ± 7.6 a 62.2 ± 6.2 a 30.6 ± 3.1 a 63.1 ± 4.0 54DH37 110 ± 17.9 a 59.8 ± 4.0 ab 51.8 ± 5.9 a 60.9 ± 7.5 a 30.4 ± 3.2 a 61.0 ± 4.7 54DH41 116 ± 10.4 a 66.2 ± 3.6 ab 65.7 ± 57 a 67.1 ± 6.4 a 38.3 ± 3.0 a 67.1 ± 5.0 34DH28 113 ± 10.8 a 60.9 ± 3.5 ab 55.8 ± 7.6 a 59.5 ± 5.1 a 30.2 ± 1.8 a 57.5 ± 4.7 54DH31 110 ± 10.0 a 56.1 ± 2.3 ab 50.3 ± 4.2 a 57.9 ± 5.7 a 31.1 ± 3.9 a 59.1 ± 4.5 34DH32 112 ± 11.9 a 61.7 ± 2.2 ab 59.5 ± 5.3 a 62.0 ± 6.3 a 28.4 ± 3.9 a 63.9 ± 5.1 34DH35 72.8 ± 8.2 a 52.6 ± 3.0 ab 47.3 ± 7.1 a 53.8 ± 5.5 a 29.8 ± 2.5 a 52.6 ± 3.7 34DH39 121 ± 4.6 a 58.6 ± 4.8 b 61.5 ± 7.4 a 57.1 ± 6.9 a 34.1 ± 2. 5 a 57.9 ± 4.9 54DH52 109 ± 11.6 a 60.6 ± 3.6 ab 53.0 ± 7.2 a 57.6 ± 5.1 a 33.1 ± 4.2 a 59.3 ± 4.3 54DH55 116 ± 8.9 a 60.7 ± 2.8 ab 60.5 ± 8.4 a 60.5 ± 7.0 a 33.4 ± 2.6 a 58.5 ± 5.3 54DH60 100 ± 15.4 a 80.6 ± 2.9 a 68.6 ± 5.3 a 61.8 ± 6.1 a 35.4 ± 2.9 a 64.5 ± 4.3 Judee 115 ± 13.2 a 74.8 ± 2.4 ab 63.1 ± 2.2 a 62.7 ± 8.4 a 34.2 ± 2.6 a 56.5 ± 4.9 RLN145 112 ± 17.9 a 74.1 ± 2.1 ab 60.7 ± 5.1 a 60.5 ± 6.1 a 32.2 ± 3.2 a 63.4 ± 4.5 RLN84 96.7 ± 6.2 a 80.5 ± 1.9 a 62.7 ± 6.8 a 56.7 ± 5.8 a 35.1 ± 3.5 a 57.0 ± 3.9 Warhorse 114 ± 12.6 a 72.9 ± 3.2 ab 60.8 ± 4.7 a 61.3 ± 7.2 a 36.3 ± 2.9 a 59.5 ± 4.2 Yellowstone 98 ± 7.43 a 64.4 ± 3.4 b 69.1 ± 5.9 a 62.2 ± 7.7 a 35.2 ± 3.8 a 60.9 ± 3.9 Yield p = 0.82, F=0.62 p = 0.01, F=2.4 p = 0.12, F=1.5 p = 0.52, F=0.9 p = 0.6, F=0.8 7 3 Table 3.4. Protein content of winter wheat lines at Bozeman, Chester and Havre locations for the 2023 and 2024 field season. Values are mean ± standard error. In the same column, similar letters are not statistically different after ANOVA followed by Tukey HSD test (α = 0.05). Line Protein Content (%) Overall mean Bozeman 2023 Bozeman 2024 Chester 2023 Chester 2024 Havre 2023 34DH41 14.0 ± 0.2 a 14.6 ± 0.1 ab 14.4 ± 0.2 ab 13.7 ± 0.2 a 11.8 ± 0.3 a 13.8 ± 0.2 54DH37 13.2 ± 0.2 bc 14.1 ± 0.5 bc 14.6 ± 0.2 bc 13.8 ± 0.1 a 11.8 ± 0.5 a 13.7 ± 0.2 54DH41 12.5 ± 0.1 c 13.4 ± 0.1 c 14.3 ± 0.1 ab 13.4 ± 0.2 a 11.7 ± 0.3 a 13.5 ±0.2 34DH28 14.8 ± 0.1 d 15.4 ± 0.1 a 14.6 ± 0.2 bc 14.3 ± 0.1 a 12.5 ± 0.2 a 14.4 ± 0.2 54DH31 14.2 ± 0.1 ad 15.0 ± 0.2 ab 14.9 ± 0.2 a-c 14.2 ± 0.3 a 12.3 ± 0.5 a 14.2 ± 0.2 34DH32 13.9 ± 0.1 ab 14.7 ± 0.2 ab 14.5 ± 0.2 bc 14.0 ± 0.1 a 12.5 ± 0.4 a 14.0 ± 0.2 34DH35 13.9 ± 0.1 ade 14.6 ± 0.1 ac 13.8 ± 0.1 b-d 13.9 ± 0.1 a 12.5 ± 0.4 a 13.8 ± 0.2 34DH39 14.0 ± 0.1 ad 14.6 ± 0.1 ab 14.9 ± 0.2 a-c 14.0 ± 0.1 a 12.2 ± 0.3 a 13.9 ± 0.2 54DH52 13.1 ± 0.1 bc 14.2 ± 0.1 acc 14.4 ± 0.1 bc 13.8 ± 0.1 a 12.3 ± 0.4 a 13.8 ± 0.2 54DH55 12.9 ± 0.1 c - 13.9 ± 0.2 bde 13.5 ± 0.1 a 12.2 ± 0.4 a 13.5 ± 0.2 54DH60 12.7 ± 0.2 c 13.3 ± 0.2 c 13.3 ± 0.2 d 13.3 ± 0.2 a 12.4 ± 0.3 a 13.3 ± 0.2 Judee 13.6 ± 0.1 ac 14.6 ± 0.2 ab 15.3 ± 0.2 c 14.0 ± 0.2 a 12.1 ± 0.4 a 14.0 ± 0.3 RLN145 13.1 ± 0.1 bce 14.1 ± 0.2 bcd 13.8 ± 0.2 b-d 13.6 ± 0.2 a 12.4 ± 0.4 a 13.6 ± 0.2 RLN84 14.4 ± 0.2 abd 14.7 ± 0.1 ab 14.4 ± 0. 1 bc 13.8 ± 0.1 a 11.6 ± 0.3 a 13.7 ± 0.3 Warhorse 13.9 ± 0.1 ade 15.1 ± 0.2 ad 14.8 ± 0.2 ace 14.0 ± 0.2 a 11.6 ± 0.3 a 14.0 ± 0.3 Yellowstone 12.9 ± 0.1 bc 13.9 ± 0.4 bc 14.3 ± 0.1 ab 13.5 ± 0.2 a 11.8 ± 0.4 a 13.3 ± 0.2 Protein p < 0.001, F=13.1 p < 0.001, F=7.7 p < 0.001, F=7.2 p = 0.2, F=1.3 p = 0.5, F=0.9 7 4 Table 3.5. Test weight of winter wheat lines at Bozeman, Chester and Havre locations for the 2023 and 2024 field season. Values are mean ± standard error. In the same column, similar letters are not statistically different after ANOVA followed by Tukey HSD test (α = 0.05). Line Test Weight (pounds per bushel) Overall mean Bozeman 2023 Bozeman 2024 Chester 2023 Chester 2024 Havre 2023 34DH41 58.2 ± 0.2 a 59.6 ± 0.6 a 59.5 ± 0.4 ab 59.5 ± 0.4 a 59.8 ± 0.4 a 59.5 ± 0.2 54DH37 59.7 ± 0.2 ab 59.7 ± 0.5 a 58.1 ± 0.4 ac 59.6 ± 0.3 a 60.6 ± 0.2 a 59.6 ± 0.2 54DH41 58.1 ± 0.5 a 60.1 ± 0.6 a 58.9 ± 0.3 ab 59.7 ± 0.2 a 60.5 ± 0.3 a 59.7 ± 0.2 34DH28 58.6 ± 0.1 ab 59.2 ± 0.3 a 59.3 ± 0.5 ab 59.9 ± 0.3 a 61.1 ± 0.4 a 59.9 ± 0.2 54DH31 57.8 ± 0.3 a 58.5 ± 0.4 a 56.3 ± 0.9 c 58.8 ± 0.1 a 60.8 ± 0.3 a 58.8 ± 0.3 34DH32 59.4 ± 0.4 ab 60.1 ± 0.4 a 59.4 ± 0.4 ab 59.7 ± 0.3 a 59.6 ± 0.4 a 59.7 ± 0.2 34DH35 59.0 ± 0.2 ab 60.4 ± 0.2 a 60.3 ± 0.4 ab 60.1 ± 0.2 a 60.1 ± 0.4 a 60.2 ± 0.2 34DH39 59.1 ± 0.1 ab 59.3 ± 0.3 a 58.9 ± 0.4 ab 59.6 ± 0.2 a 60.5 ± 0.4 a 59.6 ± 0.2 54DH52 58.9 ± 0.3 ab 59.8 ± 0.4 a 59.6 ± 0.4 ab 59.9 ± 0.2 a 60.2 ± 0.3 a 59.9 ± 0.2 54DH55 60.2 ± 0.5 b - 58.7 ± 0.4 bc 59.6 ± 0.3 a 59.9 ± 0.4 a 59.5 ± 0.2 54DH60 58.5 ± 0.3 ab 58.5 ± 0.5 a 58.8 ± 0.6 bc 59.6 ± 0.2 a 60.7 ± 0.3 a 59.6 ± 0.3 Judee 60.7 ± 0.2 b 61.1 ± 0.5 a 58.7 ± 0.3 bc 59.7 ± 0.3 a 59.8 ± 0.5 a 59.6 ± 0.2 RLN145 59.6 ± 0.3 ab 59.8 ± 0.6 a 60.1 ± 0.5 ab 60.1 ± 0.4 a 60.7 ± 0.3 a 60.2 ± 0.2 RLN84 58.0 ± 0.3 ab 59.7 ± 0.2 a 60.8 ± 0.4 b 60.2 ± 0.1 a 60.3 ± 0.3 a 60.2 ± 0.2 Warhorse 58.5 ± 0.4 ab 59.7 ± 0.3 a 58.7 ± 0.5 bc 59.5 ± 0.2 a 60.2 ± 0.4 a 59.5 ± 0.2 Yellowstone 59.1 ± 0.4 ab 59.9 ± 0.4 a 58.6 ± 0.4 bc 59.6 ± 0.2 a 60.6 ± 0.3 a 59.6 ± 0.2 Test Weight p < 0.001, F=3.4 p = 0.2, F=1.3 p <0.001, F=4.3 p = 0.5, F=1.0 p = 0.3, F=1.2 7 5 Table 3.6. Thousand kernel weight of winter wheat lines at Bozeman, Chester and Havre locations for the 2023 and 2024 field season. Values mean ± standard error. In the same column, similar letters are not statistically different after ANOVA followed by Tukey HSD test (α = 0.05). Line 1,000-Kernel Weight (TKW; grams) Overall mean Bozeman 2023 Bozeman 2024 Chester 2023 Chester 2024 Havre 2023 34DH41 39.5 ± 0.5 a 32.6 ± 0.9 ab 32.8 ± 1.3 ac 35.2 ± 0.7 a 35.1 ± 1.2 a 35.2 ± 0.6 54DH37 33.4 ± 0.6 bc 28.1 ± 0.8 bc 27.6 ± 0.9 b 31.8 ± 0.6 a 34.9 ± 0.9 a 31.8 ± 0.6 54DH41 34.4 ± 0.8 bd 30.7 ± 1.2 ab 28.3 ± 0.7 bd 32.8 ± 1.1 a 34.9 ± 0.8 a 32.7 ± 0.6 34DH28 32.7 ± 0.3 b 27.7 ± 0.5 b 30.5 ± 1.5 bce 32.1 ± 0.9 a 33.7 ± 0.9 a 32.0 ± 0.6 54DH31 36.4 ± 0.7 bce 31.9 ± 1.0 ab 32.1 ± 1.3 ac 34.4 ± 1.1 a 34.8 ± 1.1 a 34.1 ± 0.5 34DH32 35.7 ± 0.6 abce 30.2 ± 0.6 ab 31.8 ± 1.2 ac 33.5 ± 0.7 a 34.7 ± 0.9 a 33.2 ± 0.5 34DH35 36.9 ± 1.1 acde 30.4 ± 0.3 ab 32.1 ± 1.7 ac 34.1 ± 0.4 a 35.4 ± 1.7 a 33.8 ± 0.7 34DH39 37.7 ± 0.7 ae 31.4 ± 0.6 ab 32.4 ± 1.2 ac 34.1 ± 0.9 a 34.7 ± 0.9 a 33.9 ± 0.5 54DH52 34.2 ± 0.4 bcf 29.5 ± 1.0 bc 29.4 ± 1.0 bce 33.0 ± 0.7 a 35.4 ± 1.2 a 32.6 ± 0.9 54DH55 37.9 ± 0.3 aef - 33.4 ± 1.7 ae 35.5 ± 0.6 a 35.3 ± 1.7 a 35.3 ± 0.6 54DH60 38.4 ± 0.6 ae 34.8 ± 1.4 ac 33.7 ± 1.6 ade 35.7 ± 0.7 a 35.8 ± 1.4 a 35.4 ± 0.6 Judee 37.9 ± 0.8 abce 31.7 ± 1.1 ab 29.4 ± 1.2 bce 33.4 ± 1.4 a 35.2 ± 1.1 a 32.9 ± 0.6 RLN145 36.6 ± 0.6 ac 31.4 ± 1.1 ab 31.9 ± 1.1 ac 34.0 ± 0.8 a 34.9 ± 0.9 a 33.8 ± 0.5 RLN84 41.7 ± 0.5 ae 33.9 ± 0.6 a 34.3 ± 0.9 a 35.7 ± 0.9 a 34.6 ± 1.3 a 34.9 ± 0.4 Warhorse 32.8 ± 0.7 bd 28.8 ± 0.6 bc 27.8 ± 1.0 bc 32.5 ± 1.1 a 35.7 ± 1.2 a 32.4 ± 0.7 Yellowstone 37.2 ± 0.7 ac 31.9 ± 1.1 ab 31.5 ± 1.1 ac 34.1 ± 1.7 a 36.1 ± 1.1 a 34.2 ± 0.6 TKW p < 0.001, F=8.3 p < 0.001, F=3.5 p < 0.001, F=4.9 p = 0.05, F=1.7 p = 0.9, F=0.5 76 Effect of Pratylenchus neglectus Densities on Agronomics To investigate the effect of nematode density on agronomics, the initial nematode density (Pi) per plot was used. All locations combined, there was a statistically significant effect of initial nematode density (Pi) on yield (ANOVA, p < 0.001), 1,000 kernel weight (p < 0.001), test weight (ANOVA, p < 0.001), and protein (p = 0.05). A closer look by location and plant phenotype based on greenhouse phenotyping (resistant and susceptible) showed a clear distinction between resistant and susceptible phenotypes according to the initial nematode density. Resistant phenotypes maintained 1,000 kernel weight and test weight independently of the initial nematode density, while for the susceptible phenotypes, initial nematode density negatively affected 1,000 kernel weight (Figure 3.6B), and a similar trend was found for test weight (Figure 3.6C). Yield was reduced at higher initial nematode densities for the susceptible phenotypes, while for the resistant phenotypes, there was a significant yield increase at higher nematode densities (Figure 3.6A). For the protein content, no interaction between initial nematode density and phenotype was detected (ANOVA, p = 0.42), however, there was an effect of initial nematode density (ANOVA, p < 0.02) and phenotype (ANOVA, p < 0.004) on protein content across all locations and years. In general, there was a slight decline in protein content (Figure 3.6D) with the increase of nematode density, and overall, susceptible phenotypes had higher protein contents than the resistant phenotypes (ANOVA, p < 0.01). A summary of effects accounting for grain quality is displayed in Table A3.3. 77 T es t W ei g h t (P o u n d s p er B u sh el ) Figure 3.6. Effect of initial nematode densities on agronomics between resistant and susceptible wheat phenotypes. A: Yield (bushels/acre). B: Thousand kernel weight (grams). C: Test weight (pounds/bushel). D: Protein content (%). BA C Resistant Susceptible Resistant Susceptible P ro te in c o n te n t (% ) D 78 Discussion Field trials were conducted using no-till production practices to assess the impact of winter wheat lines, exhibiting greenhouse resistance to the root lesion nematode Pratylenchus neglectus, on nematode suppression and agronomic performance, in comparison with nematode- susceptible lines and Montana cultivars. The study took place at three field locations, each representing a typical wheat growing environment in Montana, including two growers’ fields known for experiencing high nematode pressures in their farms. In this study, root-lesion nematode density exhibited a significant horizontal variation both across and within field locations, with hotspots of higher nematode densities observed within the fields, as shown in Figure 3.5. This variability in nematode density has been well- documented in the literature. Research indicates that Pratylenchus populations are typically aggregated in fields, with spatial dependence ranging from 110 to 147 meters (Gorny et al., 2020). Nematode populations often exhibit seasonal fluctuations, with density peaks observed during the summer and autumn months (Ciancio et al., 1995). Moreover, the level of aggregation tends to decrease as mean population density increases (Pennacchio et al., 1985). This aggregation distribution can be influenced by soil texture or other environmental factors (Avendano et al., 2004; Bell & Watson, 2001), and the spatial nematode distribution of nematodes may persist over time, allowing for potential site-specific management strategies. However, this variation in nematode densities must be considered when evaluating plant resistance under real field scenarios, as the pre-planting nematode pressure can lead to different nematode reproduction rates (Phillips et al., 1984). 79 In this study, the Bozeman location, which was fallowed in the season before the start of the trials, had the lowest pre-planting P. neglectus densities, with a mean of 618 P. neglectus/kg soil. In comparison, the Chester site started the trials with a mean of 3,833 P. neglectus/ kg soil. Consistent evidence shows that initial nematode population densities significantly impact crop yields. Studies across various crops, including tobacco, chili peppers, tomatoes, wheat, and barley, demonstrate yield reductions that correlate with higher pre-planting nematode densities (Fanning et al, 2018; Namouchi-Kachouri, 2008; Nicol et al., 1999; LaMondia, 1995). Considering these, spatial and temporal dynamics are essential for developing effective sampling protocols and site-specific management strategies (Gorny et al., 2020; Morgan et al., 2002). By accounting for initial nematode densities in each plot, we aimed to more accurately assess the impact of nematodes on the agronomics of different wheat lines and phenotypes. Our results showed that lower initial nematode densities were associated with a higher reproduction factor during the current growth season, as observed at the Bozeman location for the first year of the field trials. This may be due to a range of ecological and biological factors, such as reduced competition for resources, which can facilitate greater nematode reproduction (Fanning et al., 2019). The literature shows an inverse relationship between initial nematode density and reproduction factor in both susceptible and resistant plant varieties, though resistant varieties typically maintain lower reproduction factors across all density levels (Fourie et al., 2010; Phillips, 1984). Indeed, our study showed that at lower nematode densities, the resistant lines ‘54DH60’ and ‘54DH41’ displayed the lowest reproduction factors. Conversely, other studies have shown that as initial nematode densities increase, nematode multiplication rates tend to decrease (Fanning et al., 2019; Ortiz-Monasterio & Nicol, 2004; Ferris, 1985), which was also 80 observed in our study. This suggests that management strategies must account for initial nematode densities, as nematodes can quickly build up when planting susceptible lines under such conditions. Our results revealed distinct differences in plant response by comparing the agronomic performance of resistant and susceptible wheat phenotypes exposed to varying nematode densities. At higher nematode densities, susceptible phenotypes experience a decline in yield, while resistant phenotypes show an increase in grain yield. A similar pattern has been observed in previous studies evaluating plant resistance under different nematode pressures, where resistant phenotypes had a yield increase of up to 30% (Corbett et al., 2011), highlighting the fitness advantage of resistant plants under such conditions. This study did not find significant differences in yield between resistant and susceptible lines at most location-years. For example, at the Bozeman location in 2023, although yields ranged from 72 to 116 bushels per acre, high variability within replicates led to the conclusion that there were no significant yield differences. This lack of differences may also be attributed to the low nematode pressure at this location, as Bozeman had the lowest initial nematode density among the tested sites. In the second year of trials, a hailstorm caused over 60% yield loss at both the Bozeman and Havre locations, preventing meaningful correlations between yields and initial nematode densities. However, at the Bozeman site, significant yield differences were observed among the tested lines, with the resistant line ‘54DH60’ and the parental line ‘RLN84’ showing relatively higher yields. This suggests that the P. neglectus line ‘54DH60’ may be suitable for maintaining productivity even under adverse weather conditions. 81 The resistant phenotypes maintained stable test weight and 1,000 kernel weights even under higher nematode pressure. This stability helps explain the yield loss observed in susceptible lines, as grain volume and density are key factors influencing overall yield. Furthermore, the impact of nematodes on grain quality has significant implications for growers and industry. Lower kernel weights can affect seeding rates, grain storage requirements, seed vigor, seedling growth, plant performance, and milling yield (Deivasigamani & Swaminathan, 2018), ultimately influencing the farmer’s profitability. On the other hand, both resistant and susceptible lines experienced a decline in protein content under higher nematode pressure, with resistant lines consistently showing a 0.5% lower protein content compared to susceptible lines, regardless of the nematode density. Farmers are paid premium prices for higher grain protein, but the amount varies yearly (Lamb et al., 2015). We assessed the nematode reproduction factor under field conditions by calculating the ratio of the final nematode population (Pf) to the initial population (Pi). The reproduction factor exhibited considerable variability depending on the year and field location, with higher reproduction factors typically observed when initial nematode densities were lower, as discussed earlier. Our study did not find a positive correlation between greenhouse and field reproduction factors. Notably, lines that were categorized as resistant in greenhouse trials had a reproduction factor greater than one (Rf > 1.0) under field conditions. This discrepancy can be attributed to the fact that the reproduction factor, when used solely, may not be the most reliable indicator for phenotyping, especially under field conditions, as several biotic and biotic factors can influence nematode reproduction in the field. Additionally, the high variability in nematode densities 82 within field plots may have prevented accurate detection of population changes, especially if the number of soil cores sampled was insufficient. Variations in initial nematode densities also affected the reproduction factor significantly, as observed for the Bozeman site. This can be explained by the need of using multiple assessment rates such as nematode per root biomass in combination with reproduction factor and host suitability as it gives an indication of the effect of nematodes on plant growth reduction (Bhuiyan & Garlick, 2021). Similarly, comparing the root biomass of infected and non-infected plants could also give a better estimation of plant growth reduction due to nematode infection. Root biomass was not obtained from the field trials as sampling was performed before planting when no roots are present, and after harvest when the root is no longer functional. Quantifying nematode per root weight is also challenging under field conditions due to the root depth and root structure of monocots, and the nematode dispersal over the plant growth season. The sampling depth (0-15cm) from which nematodes were sampled may also have impacted the accuracy of nematode density estimates. Literature suggests that both P. neglectus and P. thornei often migrate to deeper soil layers (Smiley et al., 2008; Pudasani et al., 2006; Taylor & Evans, 1998), and this could have resulted in an underestimation of their population density if they were present in greater depths. Given the compactness of soils in the experimental sites due to harsh soil textures, the use of an automated soil sampler would be ideal for reaching greater depths and enhancing the precision of nematode density measurements. Additionally, understanding the distribution patterns of root-lesion nematodes across the various Montana soil types could help inform more effective nematode sampling strategies. 83 Conclusion This study evaluated the efficacy of winter wheat phenotypes displaying greenhouse resistance to the root-lesion nematode, Pratylenchus neglectus, with the aim of assessing the potential of these lines at reducing nematode densities and enhancing yield under field conditions of high nematode pressure. Although nematode suppression varied across years and field locations, resistant phenotypes were able to better cope with nematode pressure, displaying higher yield, and better grain quality than the susceptible lines under higher P. neglectus pressure. These results suggest that these lines are likely tolerant or less sensitive to nematode attack under field conditions, and they have the potential to improve both grain yield and quality for Montana growers dealing with high P. neglectus pressures in their fields. Acknowledgements We thank Montana Wheat and Barley Committee for the financial support, Jeff Johnston, Hannah Johnson, Jack Svijka, Erika Emerson & Rowyn Morehouse for their support with planting and harvesting the field trials, and Lorenzo Bonomi and Dipiza Oli for their support with soil sampling. We also thank Deanna Nash and Jeanne Gripentrog from the MSU Cereal Quality Laboratory for instructions with the grain quality analysis. Description of Appendix Material Appendix for Chapter 3 consist of a table showing the mean monthly temperature from 2022 to 2024 (Table A3.1), a summary of dates of specific field activities (Table A3.2), A summary of the initial nematode densities for each location-year (Table A3.3), and the statistics 84 for each of the parameters used in statistical models (Table A3.4). Figure A3.1 is a scheme of the modified Baermann tray used for nematode extraction. References Al-Khafaji, R. T. (2018) An assessment of nematodes affecting wheat in Montana. PhD Dissertation. Montana State University College of Agriculture. Bozeman, MT. 80p Avendano F., Pierce, F. J., & Melakeberhan, H. (2004) The relationship between soybean cyst nematode seasonal population dynamics and soil texture. Nematology, 6:511-525 Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) Fitting linear mixed-effects models Usinglme4. Journal of Statistical Software, 67(1). https://doi.org/10.18637/jss.v067.i01 Bell, N. 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(1984) The effect of initial population density on the reproduction of Globodera pallida on partially resistant potato clones derived from Solanum vernei. Nematropica 30(1):57-65 https://quickstats.nass.usda.gov/ https://www.weather.gov/wrh/climate 87 Pudasaini, M. P., Schomaker, C. H., Been, T. H. & Moens, M. (2006) Vertical distribution of the plant-parasitic nematode, Pratylencus penetrans, under four crop systems. Phytopathology, 96(3): 226-233 Smiley, R. W., Sheedy, J. G., & Easley, S. A. (2008) Vertical distribution of Pratylenchus spp. in silt loam and Pacific Northwest dryland crops. Plant Disease, 9(12): 1662-1668. https://doi.org/10.1094/PDIS-92-12-1662 Tracy, J. D., Mondal, S., Berg, J. E., Bruckner, P. L., Ramsfield, R., Holen, D., Nash, D., Eberly, J., ..., & Schafer, T. (2022) Montana winter wheat variety performance summary. Montana State University Agricultural Experiment Station, Montana State University, Bozeman, MT. 23p R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: https://www.R-project.org Taylor, S. P., & Evans, M. L. (1998) Vertical and horizontal distribution of and soil sampling for root lesion nematodes (Pratylenchus neglectus and P. thornei) in South Australia. Australasian Plant Pathology 27:90-96. https://doi.org/10.1071/AP98011 Trudgill, D. (1991) Resistance to and tolerance of plant parasitic nematodes in plants. Annual Review of Phytopathology, 29:167 Williams, K. J., Taylor, S. P., Bogacki, P., Pallotta, M., Bariana, H. S., & Wallwork, H. (2002) Mapping of the root lesion nematode (Pratylenchus neglectus) resistance gene Rlnn1 in wheat. Theor. Appl. Genet. 104, 874-879 Vanstone, V. A., Rathjen, A. J., Ware, A. H., & Wheeler, R. D. (1998) Relationship between root lesion nematodes (Pratylenchus neglectus and P. thornei) and performance of wheat varieties. Aust. J. Exp. Agric. 38: 181-188 Viaene, N., Hallmann, J., & Molendijk, L. P. G. (2020) Methods for nematode extraction. In: Perry, R. N., Hunt, D. J., & Subbotin, S. A. (eds). Techniques for Work with Plant and Soil Nematodes. CAB International, Oxfordshire, UK. 12-41 https://doi.org/10.1094/PDIS-92-12-1662 https://www.r-project.org/ https://doi.org/10.1071/AP98011 88 CHAPTER FOUR SOIL MICROBIOME DRIVEN BY ROOT LESION NEMATODE DENSITY IN WHEAT FIELDS Contribution of Authors and Co-Authors Manuscript in Chapter 4 Author: Erika Consoli Contributions: conceptualization, methodology, validation, formal analysis, investigation, data curation, writing – original draft and review and editing, visualization Co-Author: Alan T. Dyer Contributions: conceptualization, methodology, validation, funding acquisition Co-Author: Jed O. Eberly Contributions: conceptualization, methodology, formal analysis, investigation, data curation, funding acquisition. 89 Summary 1. The geographic location and presence of plants are the main driver of microbiome composition 2. No statistical differences between plant phenotypes on bacterial and fungal microbiome were detected 3. Lower P. neglectus densities at Bozeman location were correlated with a diverse bacterial community than higher P. neglectus densities. Abstract Recent research suggests the plant microbiome plays a crucial role in plant health under various environmental stressors, including soil-borne pathogens. Different studies have highlighted the significant influence of the plant host on plant-microbiome assembly, with plant genes involved in development, immunity, nutrient uptake, and root exudation regulating the structure of the rhizosphere community. On the other hand, soilborne pests such as root lesion nematodes affect plant health by directly feeding and facilitating infections by other soilborne pathogens. This study aimed to characterize the rhizosphere microbiome of root-lesion nematode-resistant and susceptible winter wheat phenotypes under three different field conditions. While nematodes can penetrate the roots of both resistant and susceptible phenotypes, we hypothesize that resistant phenotypes, upon nematode recognition, are able to manipulate their microbial communities to mitigate nematode-induced damage. By sequencing bacterial 16S DNA and fungal ITS DNA from the soil prior to planting and from the rhizosphere of resistant and susceptible wheat lines, our results revealed that microbial composition is shaped 90 by environmental factors, such as soil characteristics and climate. Significant differences in microbial composition were also obtained according to the year samples were collected (p < 0.001). We found that bacterial alpha diversity differed significantly between soils sampled in the absence versus presence of plants while no such change was observed for fungal communities. For both bacterial and fungal communities, location emerged as the primary driver of microbial composition, with significant shifts in bacterial composition even within plots within a location. This study did not identify significant differences in beta diversity between plant genotypes, and plant phenotype was not a good explanatory variable for microbial composition. However, in the first year of the field trials, low soil densities of the root-lesion nematode Pratylenchus neglectus were associated with distinct bacterial compositions. By the second year, as P. neglectus densities increased, only minor changes in microbial composition were observed. This suggests that the nematodes themselves may be influencing and shaping the microbial communities over time, and as resistant phenotypes gradually decrease the nematode multiplication, more changes in microbial community composition are expected to be observed. Introduction Plant-microbial interactions play a crucial role in plant defense and stress resilience. Disease-suppressive soils serve as a prime example of microbe-mediated plant defense, where beneficial soil microbes help protect plants from pathogen infections (Topalovic et al., 2020). Disease suppressive soils, which are characterized by the inability of soil-borne pathogens to establish or persist, or, to reduce the pathogens’ ability to cause substantial disease, provide effective protection against soil-borne pathogens through the complex interactions of rhizosphere 91 microorganisms (Chiaramonte et al., 2021; Gomez Exposito et al., 2017, Chapelle et al., 2016; Baker & Cook, 1974). The rhizosphere microbiome, composed of diverse microbial communities, plays a crucial role in plant health and soil functioning. Extensive research has focused on understanding its influence on plant growth and health (Mendes et al. 2013, Mendes et al. 2018, Carrión et al. 2019, Liu et al. 2020, Yu et al. 2021, Faist et al. 2021), particularly given that numerous processes within this microbiome are influenced by external factors (Amoo et al., 2023). Plants actively shape their rhizosphere microbiome through root exudates and immune responses, selectively recruiting beneficial microorganisms that promote growth and suppress pathogens (Spooren et al., 2024; Berg & Smalla, 2012). The rhizosphere also provides a critical environment where soil-borne pathogens establish parasitic relationships with plants. To infect root tissue, these pathogens must compete with microbiome members for nutrients and microsites. In this way, the rhizosphere microbiome may represent the plants’ first line of defense against soil-borne pathogens (Mendes et al. 2018, Chiaramonte et al. 2021, Yin et al. 2021). Understanding the mechanisms that govern plant-microbe interactions holds the potential to enhance agricultural practices and boost crop productivity (Song et al., 2020). Recent studies suggest that rhizosphere microbial communities are responsive to plant genetic traits, which may actively influence microbial composition and function to enhance the suppression of root pathogens (Yao & Wu, 2010; Sun et al. 2013, Mendes et al. 2018, Faist et al. 2021, Lazcano et al. 2021). Plant genetic traits not only shape root structure (Comas et al., 2013) but also determine the profile of organic compounds released into the soil, which in turn affect microbial interactions (Bakker et al., 2012). Given the beneficial functions of the rhizosphere 92 microbiome and potential for manipulating it through plant genetics, understanding these interactions is of great agronomic interest. This knowledge could be a valuable tool for enhancing breeding programs, offering a complementary approach to improving plant health, and sustainable crop production. While beneficial microbial communities play key roles in plant defense, plant pathogenic microorganisms strive to break this protective microbial shield and evade the plant’s immune system to feed and reproduce, ultimately leading to disease development (Mendes et al., 2013). Pathogens and pests, which include pathogenic fungi, oomycetes, bacteria, and nematodes, are a costly burden to agricultural production globally, causing an estimated 30% of yield losses worldwide (Sikora et al., 2023). Among them, plant-parasitic nematodes pose a significant threat to crop production systems globally, substantially impacting food security. Worldwide, the economic damage caused by plant-parasitic nematodes is estimated at over US$100 billion, with approximately US$10 billion of those loss occurring in the in the United States alone (Kantor et at., 2022; Bernard et al. 2017; Elling, 2013). These are likely underestimates as growers and agriculturalists often attribute nematode damage to nutrient deficiencies or infections caused by secondary plant pathogens infections. In addition to direct crop losses resulting from nematode damage, their invasion of root tissues creates favorable conditions for subsequent infections by other pathogens. While this synergistic function is well documented (Back et al., 2002; Smiley et al., 2005; Khan, 2013; Al-Hazmi & Al-Nadary, 2015; Aghale et al., 2017), the full extent of the community dynamics underlying these interactions have received only limited exploration. Among plant-parasitic nematodes, the genus Pratylenchus are of worldwide economic importance due to their extensive host range and the synergies they create with other root 93 pathogens (Alake & Nasamu, 2024; Smiley 2021, Back et al. 2002, Bhatt & Vadhera 1997). In Montana, the root lesion nematode (RLN), Pratylenchus neglectus, represents a major pest of winter wheat, with estimated yield losses between 12 to 15% (Johnson 2007; May, 2015). The initial strategy to control this pest relied on the use of rotations to resistant crops: pea, lentil, and barley (May et al., 2016). However, the emergence of barley and lentil P. neglectus pathotypes (Al-Khafaji et al., 2019) has increased the need for more durable and sustainable alternatives for RLN management. In that study, different pathotypes were described as P. neglectus populations expressing differences in host specificity. As an alternative to resistant crop rotations, winter wheat crosses were made in 2008 to incorporated RLN resistance into Montana’s adapted winter wheat cultivars. Initially, backcross lines were produced between Persia-20, a land race with reported resistance to P. neglectus (Smiley et al., 2014), and Yellowstone, a popular Montana winter wheat cultivar. In greenhouse screenings, 200 backcross lines were tested for resistance to P. neglectus, resulting in the identification of six lines with significant RLN resistance. Genetic analyses of these lines using single nucleotide polymorphisms confirmed previous reports of resistance Quantitative Trait Loci (QTLs) on chromosomes 5BL, 7AL, and 7BL (Williams et al., 2002; Mulki et al., 2013; Thompson, 2013). While outperforming Yellowstone on yield, the resistant lines were too tall for commercial production and therefore two of the resistant lines (RLN84 and RLN145) were crossed with Warhorse, a shorter, solid-stemmed winter wheat cultivar. The resulting F1 generation was then used to produce 120 double haploid lines (May, 2015) that were subsequently screened in the greenhouse for P. neglectus resistance and in the field for agronomic performance. Machete was used as the susceptible control. Figure 4.1 shows the 94 range of nematode reproduction factors among double haploid lines and control varieties. Reproduction factor is the ratio between final and initial nematode densities. A plant is considered resistant when the reproduction factor is below 1, which indicates lack of reproduction. Materials and Methods Plant Material Sixteen winter wheat entries were selected for evaluation under field conditions. These include six resistant double haploid lines, five susceptible double haploid lines, the two resistant parental lines, and three Montana varieties (i.e., Warhorse, Yellowstone, and Judee). Table 4.1 provides a summary of the entries and their correspondent phenotypes. Figure. 4.1. Distribution of reproductive factors (Rf) of double haploid lines displaying significant variation in nematode resistance. The distribution includes 49 double haploid lines and Persia-20, ‘Warhorse’, ‘Yellowstone’ and ‘Machete’ (controls). Average root lesion nematode reproductive factor (Rf = final nematode density (Pf)/ initial nematode density (Pi)) of 1.0 indicates that nematode population at the end of the experiment numbered the same as those at the beginning. Error bars indicate standard error among five replicates. 95 Table 4.1. Plant material used in the field trials. a Entries number 1 to 6 corresponds to the P. neglectus resistant (R) double haploid lines, entries 7 to 11 corresponds to the susceptible (S) double haploid lines, entries 12 and 13 are the parental (P) lines, and entries 14 to 16 correspond to the susceptible (S) Montana varieties. Entry number a Line Phenotype 1 34DH32 R 2 34DH41 R 3 54DH31 R 4 54DH41 R 5 54DH55 R 6 54DH60 R 7 54DH52 S 8 54DH37 S 9 34DH28 S 10 34DH35 S 11 34DH39 S 12 RLN145-1 P 13 RLN84-3 P 14 Yellowstone Variety, S 15 Judee Variety, S 16 Warhorse Variety, S Field Experiments Site Descriptions The study was conducted during cropping seasons 2022-2023 and 2023-2024 at three field locations (Figure 4.2). Each of these locations were selected because they represent a distinct wheat-growing environment found in Montana wheat production: dryland with high rainfall (Bozeman location, Gallatin County), dryland irrigated (Chester location, Liberty County), and dryland low rainfall (Havre location, Hill County). These specific fields were selected based on natural infestation levels of P. neglectus, as determined by a field survey conducted in the fall of 2021 (data not shown). Previous studies reported different P. neglectus pathotypes are also present between these locations (Al-Khafaji et al., 2019). In that 96 study, different pathotypes were described as P. neglectus populations expressing differences in host specificity. Bozeman field site was in field section P7 at Montana State University Arthur Post Farm Research Station (1,455 meters of elevation, 45°40’47” N 111°08’54” W). The soil at this site is characterized as silty loam, and the research plot was fallowed the previous year. Overall, the site receives an average annual rainfall of 412 mm, with most of precipitation occurring in late spring to early summer (May/June), and an average snowfall of 1,270 mm. Chester field site is in a grower’s field southeast of Chester (885 meters of elevation, 48°17’03” N 110°46’28” W). The soil at this farm is characterized as a clay soil, and the research plot was cropped with winter wheat the previous cropping season. The site receives an average annual rainfall of 280 mm, with most of the precipitation in early summer (June), and snowfall from October to May, ranging from 51 mm to 177 mm. Figure 4.2. Field locations in Montana selected for the study. Field location Bozeman Chester Havre 97 Havre field site is in a grower’s field located southeast of Havre (826 meters of elevation, 48°31’32” N 109°36’20” W). The soil in this site is characterized as a clay-loamy soil, and the field was cropped with winter wheat the previous cropping season. The site receives an average annual rainfall of 284 mm, with most of precipitation occurring in late spring (May), and average snowfall of 965 mm. Experimental Design At each location, treatments consisted of sixteen winter wheat lines classified in two phenotypes (i.e., resistant or susceptible to P. neglectus) arranged in a complete randomized block design. Randomization of the experiment was performed using the R packaged ‘Agricolae’ version 1.3.7 (de Mendiburu, 2023). Each treatment had five replications. Plots dimensions were 3m in length and 4m in width, with 91cm buffer rows between blocks. This design was consistent across all field locations. In total, this study had 16 plots replicated five times, resulting in 240 plots across all field sites. Planting took place in the second week of October for all locations and years. Seeds were sown at a density of 244 seeds/ m2. Fields were not tilled, and seeds were not treated because this could affect the microbiome composition of fungal communities. Weeds were controlled by spraying non-selective herbicide before planting (Roundup® at 0.8 lb/acre a.i.), and selective herbicide post wheat emergence (Huskie® Herbicide at 11 oz/acre with non-ionic surfactant at 0.25% v: v). The remaining volunteer wheat and weeds were removed manually. Grain was harvested from 4 rows after drying to approximately 10% moisture content in the field with a combine. Plot lengths were measured after harvest to account for the harvested area. 98 Data Collection Nematodes Densities Nematode population densities were assessed at pre-planting (initial nematode density; Pi) and post-harvest (final nematode density; Pf). Soil samples were collected two weeks before planting and after the wheat harvest. Due to the soil hardness, a shovel was used to break the soil, and soil samples were collected from a depth of 0-15 cm using a metal trowel. Each sample was a composite of five soil cores collected in a zigzag pattern, bulked in a Ziplock bag, and stored in a cooler for transportation. Nematodes were extracted from 400 ml of soil using the modified Baermann tray method (Viaene et al., 2020) for 48 hours, as previous research found no significant differences between the modified Barmann tray and centrifugal floatation techniques on nematode densities (Al-Khafagi, 2018). The water suspension containing the extracted nematodes was collected and concentrated using a 20 µm sieve and transferred to a 50 ml Falcon tube for quantification. After nematode extraction, the tubes were stored in the refrigerator at 7°C and nematodes were counted immediately. If immediate nematode quantification was not possible, the nematodes were fixed using TAF fixative (EPPO, 2021) and quantified later. Nematodes counts were performed from three 1 ml aliquots using a stereomicroscope at 40X magnification, using an opaque nematode counting slide (Clalex, Utah, USA). The nematode quantification focused on the root-lesion nematode, P. neglectus. Additionally, all nematodes present in the samples were classified according to their trophic groups (i.e., bacterial feeders, fungal feeders, predators, omnivores, and plant-parasitic). Nematode Reproduction Factor (Rf) was calculated by dividing the final nematode density (Pf) by the initial nematode density (Pi), as follows: 99 Reproduction factor (R𝑓) = Final Nematode Density (P𝑓) Initial Nematode Density (P𝑖) Nematode trophic groups were used to assess the abundance of each feeding group over the seasons (Castillo et al., 2017), and to elucidate whether their densities have an influence on microbial abundance. Agronomic Data Agronomics measurements included grain yield (bushel per acre), protein content (%), 1000-kernel weight (grams), and test weight (pounds per bushel). The yield was calculated by measuring the grain weight from the harvested area and extrapolating the results to bushels per acre. Grain protein, moisture, and test weight were analysed at the Montana State University Cereal Quality Laboratory using an Infratec 1241 (FossAnalytics, Denmark). The 1000-kernel weight was measured by automatically counting 1,000 kernels using a CGOLDENWALL automatic seed counter and weighting the kernels in a precision scale. Microbiome The microbiome profile was assessed in 2022 at pre-planting (bare/ bulk soil) to assess the baseline microbiome, and at stem elongation (rhizosphere microbiome) in 2023 and 2024. For the pre-planting samples, soil samples were collected at the same time as the pre-planting nematode samples but kept in a separated Ziplock bag. Each sample was a composite of five soil cores collected in a zigzag pattern within the plot, bulked in a Ziplock bag, and stored in a cooler with ice for subsequent DNA extractions. For the rhizosphere microbiome, plant roots were sampled at stem elongation. The top six inches of the wheat root system were carefully excavated using a trowel, with loose soil shaken off. Plant leaves were attached together, and the entire plant was placed in a Ziplock bag for further DNA extraction of rhizosphere soil. Five plants were collected for each plot, with all roots from a single plot placed 100 into the same Ziplock bag. Samples were stored in a cooler with ice and DNA was extracted within two days. Total DNA extraction from pre-planting soil samples were extracted from 300 mg of soil following the DNeasy Power Soil Pro Kit (QIAGEN, Germantown, MD, USA) following the manufacturer’s instructions. For the rhizosphere samples, roots from five plants collected in each plot were free of lose soil, and roots with rhizosphere soil were placed in 50 mL sterile Falcon® conical tubes with 30 mL of sterile 0.9% NaCl solution. Samples were sonicated in a sonicating water bath for one minute. The resulting slurry was transferred to a cup through a metal mesh, and back to the conical tube, and the process was repeated three times to remove a greater amount of rhizosphere soil from the root system. The sample volume was increased to 30 mL with the 0.9% NaCl solution for centrifugation. Rhizosphere soil in the 50 mL tube was then pelleted by centrifugation at 2,000 x g for 5 minutes. The supernatant was discarded, pellets were resuspended in 1 mL of sterile water, and 1 mL of the suspension was transferred to 1.5 mL microcentrifuge tubes. Microcentrifuge tubes were centrifuged for 1 min at 2,000 g for 1 minute and the supernatant was discarded. Pelleted rhizosphere samples were stored at -80°C for DNA extraction. Total genomic DNA was extracted using the DNeasy Power Soil Pro kit (Qiagen, Germantown, MD, USA) following the manufacturer’s instruction. DNA purity was assessed using spectrophotometry (NanoDrop model, ThermoFisher, Waltham, MA). DNA library preparation and sequencing were performed by Novogene Inc. (Sacramento, CA, USA). Amplicon sequencing of the bacterial 16S V4-V5 region and fungal ITS2 region were sequenced using the Illumina NovaSeq 600 platform (Illumina, San Diego, CA, USA). 101 Bioinformatics and Statistical Analysis The sequencing dataset was processed in R version 4.3.2 (R Core Team, 2024), employing the DADA2 pipeline version 1.16 (Callahan et al., 2016). Reads were trimmed and filtered to exclude low-quality sequences, followed by merging the forward and reverse reads with at least 12 bases overlap. Chimeras were deleted using the consensus approach. Bacterial and fungal taxonomic assignment was performed using the SILVA 138 ribosomal RNA (rRNA) database (Quast et al., 2013) and the UNITE ITS (v8.2) database (Abarenkov et al., 2024), respectively. Phyloseq objects, which combine the operational taxonomic unit (OTU), taxonomic classification, phylogenetic tree, and field data, were created for downstream analysis using the “Phyloseq” package (McMurdie & Holmes, 2013). Ordination plots were made using the “Microeco” (Liu et al., 2021) and “Microviz” (Barnett et al., 2021) packages. Statistical differences between treatments were computed using Multivariate Analysis of Variance (MANOVA). To assess the effect of nematode densities on microbiome composition, nematode counts were converted into categorical data, as it is required for ordination plots. The categorization involved classifying nematode densities as low, medium, or high based on the distribution of counts at each location. Low nematode density was defined as counts in the first quartile, medium density as counts between the second and third quartiles, and high density as counts in the third to fourth quartile. This classification was applied according to the abundance of nematodes at each location and time point (pre-planting or post-harvest) when samples were collected. 102 Results Nematode Quantification Nematodes were quantified for each plot at pre-planting and post-harvest in each growth season. Overall, Bozeman field site had the highest percentage of plant-parasitic nematodes at pre-planting and post-harvest (Figures 4.3, 4.4 and 4.5), and the lowest percentage of bacterial feeder and omnivore nematodes. At pre-plant in 2022, 56% of nematodes at the Bozeman location were plant-parasitic nematodes (PPN), increasing to 67% in 2023, and to 82% at the end of the trial in 2024. Mean PPN genera found at this location were Pratylenchus (P. neglectus and P. thornei), Paratylenchus, Merlinius, Tylenchorynchus, and Hoplolaimus. From 2022 to 2024, percentages of bacterial feeder nematodes went from 20% to 11%, and from 11% to 8% of fungal feeders, and omnivores went from 13% to 2%. At Chester location, from 2022 to 2024, the percentages of PPN went from 41% (2022) to 28% (2023) to 46% (2024), while at Havre location, the PPN percentage varied from 27% (2022) to 29% (2024). The main PPN genera found at Chester and Havre locations were Pratylenchus (P. neglectus), Merlinius, and Tylenchorynchus. Nematode densities in Havre were not accessed at the end of the field trial because a hailstorm affected wheat yield, and grains started to emerge before the soil samples were collected, affecting the ability to enumerate nematode densities. Pratylenchus neglectus density was highly heterogeneous both across locations (ANOVA, p < 0.001) and between plots within a field (ANOVA, p < 0.001). A summary of initial P. neglectus densities for each of the locations and year is shown in Table B4.1 (Appendix B). Overall, Bozeman site, had the lowest pre-planting Pratylenchus density among the three field 103 sites (ANOVA, p < 0.001) in the first year of the trial, while Chester location had the highest pre- planting P. neglectus density. 104 56 41 27 20 34 41 11 7 6 13 18 26 0 20 40 60 80 100 Bozeman Chester Havre Plant-Parasitic Bacterivores Fungivores Omnivores Figure 4.3. Percentage of nematode tropic groups per 400 cc of soil at each field site at pre- planting. Figure 4.4. Percentage of nematode tropic groups per 400 cc of soil at each field site at post- harvest in 2023. 67 28 29 15 47 42 16 12 18 2 13 11 0 20 40 60 80 100 Bozeman Chester Havre Plant-Parasitic Bacterivores Fungivores Omnivores Figure 4.5. Percentage of nematode tropic groups per 400 cc of soil at Bozeman and Chester sites post-harvest in 2024. 46 82 31 11 8 5 14 2 0 20 40 60 80 100 Chester Bozeman Plant Parasitic Bacterivors Fungivores Omnivores 105 Overall Composition of Bacterial and Fungal Communities An average of 48,146,699 reads were generated from the NovaSeq sequencing for each of the sampling times. After filtering and processing, a total of 31,945,188 reads were retained. The rarefaction curve for bacterial 16S exhibited a plateau in species richness after 50,000 reads (Figure B4.2), indicating the sequencing depth was sufficient to cover most bacterial taxa in the samples. In the first year of field trials, the most abundant rhizosphere bacterial family were Pseudomonaceae, Sphingobacteriaceae, Oxalobacteraceae, and Micrococcaceae, while for Chester had a higher abundance of Geodermatophilaceae, Micrococcaceae, Rubrobacteriaceae, and Beijerinckiaceae, and Havre had a higher abundance of Rubrobacteriaceae, Micrococcaceae, Beijerinckiaceae, and Oxalobacteriaceae. In the second year of field trials, Bozeman had a higher abundance of Nocardioidaceae, Geodermatophilaceae, Micrococcaceae, Pseudonocardiaceae and Sphingobacteriaceae. For Chester and Havre, the most abundant taxa were Geodermatophilaceae, Nocardioidaceae, Beijerinckiaceae, Pseudonocardiaceae and Gammatimonadaceae. Surprisingly, Pseudomonaceae, the most abundant bacterial family at Bozeman location in the first year of the field trials, was not among the 25 most abundant taxa in the second year of the field trials. Figure B4.3 shows the relative abundance of the 25 most abundant bacterial families across locations and years. For the ITS, an average of 42,595,246 reads were generated from the NovaSeq sequencing for each of the sampling time. After filtering and processing, a total of 31,509,145 reads were retained, with an average of 144,537 reads per sample. The rarefaction curve exhibited a plateau in species richness after 50,000 reads for most of the samples (Appendix B4.4), indicating the sequencing depth was sufficient to cover most fungal taxa in the samples. 106 In the first year of field trials, the higher relative abundant rhizosphere fungal family at the Bozeman location were Erysiphaceae, Mortierellaceae, Nectriaceae, Helodaceae, and Piskurozymaceae, while for Chester had a higher abundance of Nectriaceae, Pleosporaceae, Erysiphaceae, Cladosporiaceae, and Lasiophaeriaceae, and Havre had a higher relative abundance of Erysiphaceae, Nectriaceae, Aspergillaceae, Pleosporaceae, and Phaeosphaeriaceae. In the second year of field trials, Bozeman had a higher abundance of Nocardioidaceae, Nectriaceae, Cladosphoraceae, Phaeosphaeriaceae, and Pleosporaceae. For Chester, the Nectriaceae, Pseudeudeuroliaceae, and Pleosporaceae had a relative higher abundance, while for Havre, Erysiphaceae, Phaeosphaeriaceae, Nectriaceae, Cladosphoraceae, and Pleosporaceae had a relative higher abundance. Figure B4.5 shows the 25 most abundant fungal families across locations and years. There was a trend clustering indicating the significant effect of sampling year on the dataset (MANOVA, p = 0.001, F = 10.7 and p=0.001, F = 34.5 for 16S and ITS, respectively; Figure B4.6). Alpha and Beta Diversity Significant differences in bacterial alpha diversity were observed with respect to field location (ANOVA, p < 0.001) and sampling time (ANOVA, p < 0.001). The Chester and Havre fields exhibited similar alpha diversity (ANOVA, p = 0.52), while both sites showed significantly different alpha diversity compared to the Bozeman site (ANOVA, p < 0.0001 for both Chester and Havre). Figure 4.6 shows a combined Principal Coordinate Analysis (PCoA) for location (Bozeman, Chester and Havre), and sampling time (bare soil/ bulk vs rhizosphere) for the 2022- 2023 field trial. A similar pattern was observed with a Principal Component Analysis (PCA) of 107 rhizosphere samples between locations (Figure 4.7A and 4.7B), with location representing 47.1% and 63.2% of the variation for 2023 and 2024, respectively. No significant differences between alpha diversity for wheat phenotype was observed within locations. Figure B4.7 and Figure B4.8 show the bacterial alpha diversity across locations and between plant phenotype within locations, respectively. Figure 4.6. Principal coordinate analysis (PCoA) based on Bray-Curtis distances comparing the composition of bacteria communities across locations and soil (bulk vs rhizosphere) for the first year of the field trials (2022-2023). 108 In contrast with the bacterial alpha diversity (Figure B4.9), there was no difference in fungal abundance between bulk/bare soil and rhizosphere soil (Figure B4.10). Principal component analysis indicated that location represented 28.8% and 32% of the fungal variation (Figure 4.6C and Figure 4.6D). No significance in alpha diversity related to plant phenotypes was observed for the Bozeman site (ANOVA, p = 0.17), Chester (ANOVA, p = 0.58), and Havre (ANOVA, p = 0.85) (Figure B4.11). Figure 4.7. Principal component analysis (PCA) of microbial communities in the rhizosphere samples based on Bray-Curtis dissimilarity matrix. A and B: bacterial taxa in 2023 and 2024 sampling years. C and D: fungal taxa in 2023 and 2024. Circles within the PCA are 95% confidence ellipses. B D A C 109 Effect of Nematode Density on Bacterial Community While no clear effect of plant phenotype on microbial taxa was observed (Figure B4.12), the geographic location of samples within the same field was observed to play a role, as blocking effect represented 25.8% and 18.8% of the bacterial variance at Bozeman location for the first and second year of field trials, respectively (Figure 4.8). In addition to the blocking effect, the densities of the root-lesion nematode, P. neglectus represented 24.4% of the variance obtained for the first year of the field trials at Bozeman location (Figure 4.9). ALDEx2 Differential Abundance Analysis (Figure 4.10) revealed the bacterial genus that significantly different between low and moderate/high P. neglectus densities. Some bacterial genera, such as Amycolatopsis spp. and Kribbella spp., were significantly more abundant in soils with low densities of P. neglectus. These genera are reported to produce antibiotics (Kisil et al., 2021; Virues-Segovia et al., 2022), which may help explain the lower Figure 4.8. Principal Component Analysis (PCA) plot of bacterial taxa based on Bray- Curtis dissimilarity metric. Circles within the PCA plot indicates 95% confidence ellipses. PF1 to PF5: block 1 to block 5 at Bozeman location. A: 2023, B: 2024. B A 110 abundance of P. neglectus in these soils. However, no studies have explored the nematicidal effects of these genera. In contrast, Bacillus spp., which were more abundant in soils with higher P. neglectus populations, include species or strains reported to act as disease suppressors and plant growth promoters (Miljakovic et al., 2020). Figure 4.9. Non-Dimensional Scaling (MDS) plot of bacterial taxa based on Bray- Curtis dissimilarity metric. Circles within the MDS plot are 95% confidence ellipses. Root lesion nematode counts were transformed to categories. Low: nematode density in the first quartile; medium: counts between the second and third quartiles, high density as counts in the third to fourth quartile. 111 Discussion This study assessed changes in microbial communities associated with winter wheat phenotypes exhibited as greenhouse defined resistance or susceptibility to the root-lesion nematode Pratylenchus neglectus. Root-lesion nematodes are migratory endoparasites that require plant roots to feed and complete their life cycle. By feeding and migrating through plant cells, these nematodes cause stress to plants through a combination of direct damage to the root system, interference with nutrient and water uptake, introduction to secondary pathogens, and induce physiological disruptions (Jones et al., 2016; Atkinson et al., 2013). It is known that root lesion nematodes can penetrate the roots of both resistant and susceptible plants, but resistant plants exhibit mechanisms to limit nematode reproduction and damage (Vieira et al., 2019; Villain et al., 2004). We hypothesized that resistant plant phenotypes, under nematode attack, can Figure 4.10. ALDEx2 Differential abundance analysis of bacterial genera among different abundance levels of Pratylenchus neglectus. 112 influence the microbial community in their growth environment, and that this shift in microbial composition contributes to enhanced nematode suppression compared to susceptible lines. Our results showed that microbial communities are strongly influenced by geographic location, with distinct microbial compositions observed across different field sites. Additionally, our findings indicated a shift in microbial composition between bulk soil and rhizosphere soils. Earlier studies have reported shifts in microbial composition from bulk soil to rhizosphere in agricultural systems, initially driven by soil properties and later shaped by the rhizosphere effect, which is influenced by plant selection and management practices (Köbel et al., 2020; Schmidt et al., 2019; Hargreaves et al., 2015). While long-term agricultural practices may lead to a loss of microbial diversity, the rhizosphere maintains functional resilience through the selective recruitment of specific taxa, resulting in a subset of bulk soil communities that are adapted to plant-specific conditions (Gross-Souza et al., 2019; Mendes et al., 2014). However, our results did not show any significant differences between resistant and susceptible phenotypes. We did find distinct bacterial community differences associated with low and high P. neglectus densities in the first year of the field trials at Bozeman location, indicating that nematodes drive changes in rhizosphere microbial community composition. The same observation was not detected in the second year of the field trials, potentially because of the overall greater numbers of root-lesion nematodes in the second season. Recent studies have explored the complex interactions between plants, nematodes, and soil microbiomes, highlighting the variable effects of plant resistance on microbiome composition. For instance, Tran et al (2024) found that soybean genotypes resistant to the soybean cyst nematode exhibit a small but significant alteration in bacterial composition 113 compared to susceptible genotypes under controlled greenhouse conditions. However, field trials revealed no significant differences between soybean phenotypes in terms of microbiome composition. Under controlled conditions, Barros et al. (2022) reported that increased bacterial diversity reduced the population density of the root-knot nematode, Meloidogyne javanica. However, the same treatment maintained the density of the root-lesion nematode, P. brachyurus, without affecting plant performance. In another study, Wurst et al (2009) investigated whether soil microorganisms could mediate plant defense against plant-parasitic nematodes. They concluded that plant resistance to nematodes is not driven by changes in the soil microbial community. However, nematode infection was found to affect root exudation patterns and rhizosphere fungal communities, with species-specific changes that did not necessarily promote indirect control of nematodes by antagonistic microorganisms. Conversely, Elhady et al. (2018) demonstrated that the rhizosphere microbiome associated with different pre-crops can influence plant resistance to plant-parasitic nematodes. In a later study, Elhady et al. (2021) showed that specific plant species recruit specific microbes that attach to and antagonize P. neglectus, thereby affecting nematode fitness and potentially reducing their impact on plants. Although no differences in microbiome composition were detected between plant phenotypes at the same location, it is important to note that, even if nematodes were present in the plots, the presence of nematodes in the plants used for rhizosphere sampling was not investigated. Therefore, conclusions were drawn solely on microbial composition across phenotypes, without considering whether nematode pressure might have stressed the plants at the time of samplings. 114 While bacterial 16S and fungal ITS sequencing are powerful tools for investigating microbial communities across a wide range of samples and environmental conditions, it is important to acknowledge their limitations when using these research tools. Short-read amplicon sequencing has limitations in predicting functional potential due to taxonomic resolution constraints and intragenomic variability (Heidrich & Beule, 2022). It is also important to consider that different taxa may contain varying numbers of gene copies, and the use of primer pairs targeting different variable regions can lead to divergent results (Lozupone and Knight, 2008). This variability can complicate cross-study comparisons and hinder the ability to draw consistent conclusions. In addition, bacterial and fungal taxa with identical or highly similar 16S and ITS sequences can have distinct ecological functions (Jaspers & Overmann, 2004). In addition, fungal and bacterial genera from different families or phyla can share the same functions. Thus, functional groupings can be very different from taxonomic relationships. This presents a potential problem for basing microbial extended phenotypes solely on taxonomic classifications. Recent studies have highlighted the importance of using both taxonomic and functional approaches to understand rhizosphere microbial communities. Metagenomics and metatranscriptomics provide comprehensive insights into microbial interactions and chemical diversity in the rhizosphere (Boparai & Sharma, 2021). Research has shown that the rhizosphere community is a subset of the bulk soil community, with selection occurring at both taxonomic and functional levels (Mendes et al., 2014; Yan et al., 2016). Examining both taxonomic and functional core rhizobiomes can lead to a more comprehensive understanding of microbial 115 relationships and their potential impact on soil health and agricultural sustainability (Castellano‐ Hinojosa & Strauss, 2021). Conclusion The objective of this study was to compare the microbial communities between resistant and susceptible wheat phenotypes to better understand the interaction between root-lesion nematodes, wheat phenotypes, and the surrounding microbiome. Drawing on recent literature suggesting that resistant plants may influence the microbiome to better cope with biotic and abiotic stressors, we hypothesized that resistant wheat phenotypes would harbor a distinct microbial community in response to nematode attack. However, our results did not support this hypothesis, as plant phenotype was not a significant driver of microbial community composition. Instead, location and presence of a host plant were best predictors of microbiome composition. Microbiome composition varied even within a location. At Bozeman site, nematode density proved to be a better predictor of microbiome composition. Description of Appendix Material The appendix material includes figures and tables of nematode counts and output from microbiome analysis. Table B4.1 shows the pre-planting densities of P. neglectus for each of the trial year-location. Figures B4.1 and B4.2 show the saturation curve for 16S sequencing and the most abundant bacterial taxa across locations. Figure B 4.3 and B4.4 display the saturation curve for ITS sequencing and the most abundant fungal taxa across locations. Figure 4.5 is a Bray- Curtis analysis for sampling year. Figure B4.6 and B4.7 show the bacterial alpha diversity across 116 locations and wheat phenotypes, respectively. Figure B4.8 and B4.09 display the 16S and ITS alpha diversity between bulk and rhizosphere soil. Figure B4.10 and B4.11 show the fungal alpha diversity across locations and wheat phenotype. Figure B4.12 shows a principal component analysis of bacterial taxa according to plant phenotype. Acknowledgements We thank the United States Department of Agriculture, National Institute of Food and Agriculture (USDA-NIFA) for funding this research. Thanks go to Asa Hurd at Central Ag Research Center in Moccasin for the assistance with sample processing, and to Jeff Johnson, & Erika Emerson (2023) and Rowyn Morehouse & Cooper Lysek (2024) for assisting with part of the rhizosphere microbiome sampling and processing. Thanks to Lorenzo Bonomi for assisting with nematode and microbiome samplings during the whole extent of this project. We also thank Deanna Nash and Jeanne Gripentrog from the Cereal Quality Laboratory for their instruction with the grain quality analysis. References Abarenkov, K., Nilsson, R. H., Larsson, K. H., Taylor, A. F. S., May, T. W., ..., & Kõljalg, U. 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Microbiome, 9:86 https://doi.org/10.1186/s40168-020-00997-5 https://doi.org/10.1186/s40168-020-00997-5 124 CHAPTER FIVE CONCLUSION AND FUTURE DIRECTIONS Root-lesion nematodes, particularly Pratylenchus neglectus and P. thornei, are major pests that significantly affect wheat production globally. In Montana, previous field surveys have revealed that P. neglectus is widespread in wheat fields. At that time, the economic impact of P. neglectus was estimated to cause US$84 million in yield losses to winter wheat alone. To further complicate the management of root-lesion nematodes in Montana, P. thornei has recently been reported in a winter wheat field in the state. The non-specific symptoms associated with these plant-parasitic nematodes make it difficult to detect their presence, and consequently, to manage the pest. Additionally, their polyphagous nature intensifies control challenges. Consequently, developing plant resistance and tolerance is essential for effectively managing these nematodes, particularly in low-input cropping systems like wheat. This dissertation is the culmination of a breeding research effort that started back in 2012 and took several steps until the selection of advanced winter wheat lines evaluated in this dissertation. Furthermore, the detection of P. thornei in wheat fields in Montana underscores the need for further investigation into the potential threats this Pratylenchus species may pose to Montana’s crops. Therefore, the objectives of this study were to: (i) compare the multiplication rates of P. neglectus and P. thornei on Montana crops and winter wheat lines under controlled conditions, (ii) assess P. neglectus suppression and plant agronomic parameters of selected winter wheat lines under field conditions, and (iii) compare the bacterial and fungal communities associated with resistant and susceptible wheat lines under varying field conditions and nematode densities. 125 Two decades ago, the presence and widespread distribution of P. neglectus in Montana was first reported. Later, P. thornei was identified in corn and alfalfa fields in eastern Montana (Ozbayrak et al., 2019), and more recently, its presence was confirmed in a wheat field in Montana (Consoli & Dyer, 2024). Peas are one of the primary crops used by Montana growers to reduce P. neglectus populations. However, the results of this study demonstrate that peas are highly susceptible to P. thornei. Therefore, to mitigate root-lesion nematode damage to Montana crops, it is essential to invest in field surveys and accurate diagnostics in order to recommend the most effective management practices for growers dealing with one or both nematode species. This study also emphasizes the importance of using multiple indices in breeding programs when screening for nematode resistance. The reproduction factor and host suitability, two commonly used indices for evaluating nematode multiplication, yielded contrasting results when compared to nematode densities per root biomass. Field trials were established to assess the efficacy of greenhouse-selected winter wheat phenotypes for managing P. neglectus. Overall, the results from our field trials highlight the challenges of translating research conducted under controlled conditions to real-world field scenarios. In the field, P. neglectus densities exhibit significant variability both among locations and within a single field, whereas a constant nematode population was introduced in greenhouse screenings. Nematode multiplication is known to be influenced by initial nematode density, with higher multiplication observed when starting densities are lower, as competition for resources can affect multiplication rates. Additionally, various biotic and abiotic factors in field conditions can impact nematode fitness. The presence of other plant-parasitic nematode species and 126 pathogens, which are uncontrolled variables, may also increase plant susceptibility to P. neglectus. Although our results did not show that greenhouse-resistant phenotypes consistently suppressed P. neglectus populations across all fields and trial years, two of the resistant lines exhibited a relatively lower reproduction factor under low nematode pressure, when the multiplication rates were greater for most of the tested lines. This suggests the potential of these lines to control nematode populations in fields. The impact of pre-planting P. neglectus densities on plant agronomics and grain quality revealed that the resistant phenotypes are better able to withstand high nematode pressures compared to the susceptible ones. This suggests that these resistant lines are also tolerant to P. neglectus and could help mitigate yield and grain quality losses in fields with high nematode densities. However, this study did not evaluate the effects of individual wheat lines, as both resistant and susceptible phenotypes were used. Each phenotype consisted of eight wheat lines, and variations in resistance and susceptibility among the individual lines were not considered, requiring further investigation. In addition to assessing the ability of different wheat phenotypes to suppress P. neglectus populations and improve plant agronomics, this study also compared the microbiomes between wheat genotypes. The results confirmed a well-established concept in the literature: the environment plays a key role in shaping the composition of the microbiome. Variations in microbiome composition between years, field locations, and presence or absence of the host plant were identified. Similarly to nematode distribution, variations in microbiome composition were also identified within field locations. At Bozeman location, this variation was explained by 127 the variation in densities of P. neglectus. Plots with lower P. neglectus densities had a higher abundance of bacteria known for their antibiotics production. However, no clear evidence of phenotype manipulating the microbiome was detected. That could be partially explained by the fact that it is unknow if the plants used for rhizosphere extraction were actually infected by root lesion nematodes, which may or may not have triggered plant defense. Secondly, each phenotype consisted of eight different lines, and variation of resistance within the resistant and susceptible phenotypes were not considered. Genotyping these lines and using those genotypes as reference for microbiome data analysis could provide clearer evidence of plant resistance than phenotyping alone. Overall, this study showed the potential of resistant wheat phenotypes at sustaining yield and grain quality at higher P. neglectus densities, and further trials would provide more evidence of the P. neglectus suppression potential present in these lines. Root lesion nematodes and the underground environment are complex. 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Bozeman Month Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual 2022 23.7 24.8 37.3 37.2 48.4 59.7 69 71 61 47.9 20 18.7 43.2 2023 21.7 23.7 25.8 39.5 56 57.9 67.4 67 58.5 43.8 36.4 29.7 43.9 2024 19.6 28.8 35.1 45.4 49.1 61.2 68.7 65.9 61.7 52.5 N/A N/A 48.8 Mean 27.9 29.6 34.7 44.4 55.8 64.7 75.1 73.7 66.3 51.1 34.4 28.2 50 Chester Month Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual 2022 20.3 20.8 31.7 35.8 50.2 60.7 71 72.2 61.7 49.2 21.5 8 41.9 2023 20.5 22.5 19.4 44.1 59.8 64.3 73.8 69.6 60.6 42.7 35.1 31.9 45.4 2024 12.5 26.6 30.6 46.1 51.7 61.2 71.8 68.3 62.3 50.1 N/A N/A 48.1 Mean 17.8 23.3 27.2 42.0 53.9 62.1 72.2 70.0 61.5 47.3 28.3 19.9 45.1 Havre Month Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual 2022 22.6 23.6 34.9 38.1 53.5 62.3 72.3 73.9 63.5 49.6 21.9 6.4 43.6 2023 17.9 23.4 17.8 41.8 60.5 64.6 71.3 70.5 61.7 42.8 36.5 32.6 45.1 2024 12.2 24.3 29 46.2 52.8 60.8 73.1 70 63.5 49.2 N/A N/A 48.1 Mean 17.6 23.8 27.2 42 55.6 62.6 72.2 71.5 62.9 47.2 29.2 19.5 45.6 149 Table A3.2. Planting dates and nematode sampling for the 2022-2023 and 2023-2024 cropping season. Location Year Bozeman Chester Havre Planting 2022 October 21st October 15th October 16th 2023 October 12th October 14th October 15th Harvest 2022 August 15th August 7th August 6th 2023 August 15th August 10th August 11th Nematode Sampling 2022 pre-planting October 16th October 14th October 14th 2023 post-harvest/pre-planting October 19th October 14th October 15th 2024 post-harvest September 15th August 22nd - 1 5 0 Table A3.3. Pre-planting P. neglectus densities across sites and years. Similar letters in the Location Total row indicates no significant differences in pre-planting P. neglectus densities based on ANOVA followed by Tukey HSD test at P < 0.05. Location Bozeman Chester Havre Line Pi 2023 Pi 2024 Pi 2023 Pi 2024 Pi 2023 Pi 2024 34DH41 870 ± 580 4,153 ± 1,816 2,484 ± 556 1,681 ± 438 1,259 ± 812 1,043 ± 436 54DH37 315 ± 117 2,437 ± 486 3,823 ± 993 1,340 ± 182 2,009 ± 400 596 ± 170 54DH41 620 ± 203 3,444 ± 1,562 4,510 ± 886 1,458 ± 345 2,000 ± 410 654 ± 138 DH28 1,092 ± 689 1,520 ± 642 3,570 ± 585 1,223 ± 294 1,363 ± 257 662 ± 131 DH31 1,583 ± 624 4,424 ± 2,181 4,919 ± 724 761 ± 163 1,319 ± 311 819 ± 136 DH32 379 ± 132 5,711 ± 2,178 3,348 ± 773 1,223 ± 282 2,018 ± 460 854 ± 317 DH35 1,175 ± 440 3,097 ± 525 3,919 ± 836 1,132 ± 240 1,837 ± 622 868 ± 195 DH39 713 ± 439 3,159 ± 1,255 3,919 ± 565 886 ± 137 1,875 ± 430 725 ± 128 DH52 796 ± 259 3,840 ± 1,992 4,500 ± 862 1,181 ± 200 1,296 ± 314 724 ± 154 DH55 481 ± 178 2,035 ± 461 3,868 ± 820 1,037 ± 194 1,472 ± 325 649 ± 144 DH60 454 ± 164 951 ± 256 3,722 ± 843 1,064 ± 172 1,893 ± 434 701 ± 149 Judee 491 ± 154 2,653 ± 617 3,338 ± 715 1,803 ± 345 1,000 ± 168 1,068 ± 261 RLN145 704 ± 352 2,812 ± 640 3,555 ± 715 1,579 ± 385 1,476 ± 414 994 ± 225 RLN84 194 ±119 3,507 ± 1,811 5,000 ± 879 1,231 ± 264 1,430 ± 397 638 ± 179 Warhorse 694 ± 214 2,604 ± 737 4,914 ± 822 1,113 ± 123 1,247 ± 358 492 ± 118 Yellowstone 611 ± 200 4,486 ± 908 2,969 ± 965 1,272 ± 191 837 ± 249 793 ± 103 Location Total 618 ± 251 a 2,932 ± 1,129 bc 3,833 ± 770 c 1,221 ± 247 b 1,475 ± 362 b 752 ± 186 a 151 Table A3.4. ANOVA results indicating the effect of different variables used in linear models for comparing grain yield and grain quality. p > 0.15 ns, p < 0.15 . , p < 0.05 *, p < 0.01 **, p < 0.001 *** Effects Grain yield 1,000-kernel weight Protein Test weight Year *** *** *** *** Location *** *** *** *** Seed Treatment ns . ns ns Resistance (R/S) ns *** *** ns Initial Nematode density (Pi) *** *** * ns Initial Nematode Density (Pi) vs Year * ns ns ns Location vs Initial Nematode density *** * ** * Location vs Year *** *** *** * Location vs Resistance ns ns ** ns Resistance vs Year ns ns . ns Year vs Seed Treatment *** ** ns * 152 Figure A3.1. Modified Barmann Tray. Soil is placed in a gause supported by a metal wire. Living nematodes move from soil through the bottom of the tray. (Source: Viaene et al., 2020). 153 APPENDIX B ADDITIONAL TABLES FOR CHAPTER 4 154 Table B4.1. Pre-planting P. neglectus densities across sites and years. Similar letters in the Location Total row indicates no significant differences in pre-planting P. neglectus densities based on ANOVA followed by Tukey HSD test at P < 0.05. Location Bozeman Chester Havre Line Pi 2023 Pi 2024 Pi 2023 Pi 2024 Pi 2023 Pi 2024 34DH41 870 ± 580 4,153 ± 1,816 2,484 ± 556 1,681 ± 438 1,259 ± 812 1,043 ± 436 54DH37 315 ± 117 2,437 ± 486 3,823 ± 993 1,340 ± 182 2,009 ± 400 596 ± 170 54DH41 620 ± 203 3,444 ± 1,562 4,510 ± 886 1,458 ± 345 2,000 ± 410 654 ± 138 DH28 1,092 ± 689 1,520 ± 642 3,570 ± 585 1,223 ± 294 1,363 ± 257 662 ± 131 DH31 1,583 ± 624 4,424 ± 2,181 4,919 ± 724 761 ± 163 1,319 ± 311 819 ± 136 DH32 379 ± 132 5,711 ± 2,178 3,348 ± 773 1,223 ± 282 2,018 ± 460 854 ± 317 DH35 1,175 ± 440 3,097 ± 525 3,919 ± 836 1,132 ± 240 1,837 ± 622 868 ± 195 DH39 713 ± 439 3,159 ± 1,255 3,919 ± 565 886 ± 137 1,875 ± 430 725 ± 128 DH52 796 ± 259 3,840 ± 1,992 4,500 ± 862 1,181 ± 200 1,296 ± 314 724 ± 154 DH55 481 ± 178 2,035 ± 461 3,868 ± 820 1,037 ± 194 1,472 ± 325 649 ± 144 DH60 454 ± 164 951 ± 256 3,722 ± 843 1,064 ± 172 1,893 ± 434 701 ± 149 Judee 491 ± 154 2,653 ± 617 3,338 ± 715 1,803 ± 345 1,000 ± 168 1,068 ± 261 RLN145 704 ± 352 2,812 ± 640 3,555 ± 715 1,579 ± 385 1,476 ± 414 994 ± 225 RLN84 194 ±119 3,507 ± 1,811 5,000 ± 879 1,231 ± 264 1,430 ± 397 638 ± 179 Warhorse 694 ± 214 2,604 ± 737 4,914 ± 822 1,113 ± 123 1,247 ± 358 492 ± 118 Yellowstone 611 ± 200 4,486 ± 908 2,969 ± 965 1,272 ± 191 837 ± 249 793 ± 103 Location Total 618 ± 251 a 2,932 ± 1,129 bc 3,833 ± 770 c 1,221 ± 247 b 1,475 ± 362 b 752 ± 186 a 155 A B Figure B4.1: Saturation curve for 16S sequencing for 2023 (A) and 2024 (B). 156 A B Figure B4.2. Relative abundance of bacterial family across locations for rhizosphere samples in 2023 (A) and 2024 (B). 157 Figure B4.3: Saturation curve for ITS sequencing for 2023 (B) and 2024 (B). A B 158 Figure B4.4. Relative abundance of fungal family across locations for rhizosphere samples in 2023 (A) and 2024 (B). 159 Figure B4.5. Principal component analysis of bacterial and fungal taxa based on Bray-Curtis dissimilarity metric according to sampling year. Circles within the PCA plot are 95% confidence ellipses. A B 160 Figure B4.7. Composition of alpha diversity across wheat phenotypes (R: resistant; S: Susceptible). A: Bozeman; B: Chester; D: Havre. A B C Figure B4.6. Composition of bacterial alpha diversity across locations. A: Observed; B: Chao1; C: Shannon; D: Simpson. A B C D 161 Figure B4.8. Alpha diversity of bacterial 16S for all locations according to the sampling time (bare soil/ bulk vs rhizosphere). Figure B4.9. Fungal alpha diversity between bulk / bare soil and rhizosphere soil. 162 Figure B4.11. Composition of fungal alpha diversity across wheat phenotypes (R: resistant; S: Susceptible). A: Bozeman; B: Chester; C: Havre. A B C Figure B4.10. Composition of fungal alpha diversity across location. A: Observed; B: Chao1; C: Shannon; D: Simpson. A C B D 163 Figure B4.12. Principal Component Analysis of bacterial taxa based on Bray-Curtis dissimilarity metric and fungal according to plant phenotype. A: Bacterial taxa, B: Fungal Taxa. Circles within the PCA plot are 95% confidence ellipses. Resistance: R: resistant, S: Susceptible phenotype. A B ©COPYRIGHT DEDICATION ACKNOWLEDGEMENTS TABLE OF CONTENTS TABLE OF CONTENTS CONTINUED TABLE OF CONTENTS CONTINUED LIST OF TABLES LIST OF FIGURES LIST OF FIGURES CONTINUED LIST OF FIGURES CONTINUED ABSTRACT INTRODUCTION Wheat: Importance, Production and Yield Losses Caused by Diseases Biology of Root Lesion Nematodes Management of Root Lesion Nematodes Root Lesion Nematodes in Montana Soil Microbial Communities and Plant Health Goals of This Research Research Hypothesis and Objectives References HOST SUITABILITY OF WINTER WHEAT BREEDING LINES AND ROTATIONAL CROPS TO THE ROOT LESION NEMATODES PRATYLENCHUS NEGLECTUS AND PRATYLENCHUS THORNEI Contribution of Authors and Co-Authors Manuscript Information Summary Abstract Introduction Material and Methods Root Lesion Nematode Cultures and Inoculum Preparation Evaluation of Breeding Lines Evaluations of Rotational Crops Nematode Inoculation Experiment Evaluations Nematode Quantification and Reproductive Factor Statistical Analysis Results Multiplication Comparison of P. neglectus and P. thornei in Winter Wheat Lines Multiplication Comparison of Pratylenchu neglectus and Pratylenchus thornei on Rotational Crops Discussion Conclusion References FIELD ASSESSMENT OF WINTER WHEAT LINES FOR RESISTANCE TO PRATYLENCHUS NEGLECTUS Contribution of Authors and Co-Authors Manuscript Information Summary Abstract Introduction Materials and Methods Plant Material Experimental Sites Experimental Design Soil Sampling, Extraction, and Nematode Quantification Agronomics Statistical Analysis Results Nematode Quantification Agronomics Effect of Pratylenchus neglectus Densities on Agronomics Discussion Conclusion Acknowledgements Description of Appendix Material References SOIL MICROBIOME DRIVEN BY ROOT LESION NEMATODE DENSITY IN WHEAT FIELDS Contribution of Authors and Co-Authors Summary Abstract Introduction Materials and Methods Plant Material Field Experiments Data Collection Bioinformatics and Statistical Analysis Results Nematode Quantification Overall Composition of Bacterial and Fungal Communities Alpha and Beta Diversity Effect of Nematode Density on Bacterial Community Discussion Conclusion Description of Appendix Material Acknowledgements References CONCLUSION AND FUTURE DIRECTIONS CUMULATIVE REFERENCE CITED APPENDICES ADDITIONAL TABLES FOR CHAPTER 3 ADDITIONAL TABLES FOR CHAPTER 4