i 
 
BIOCHEMICAL, PHYSIOLOGICAL, AND GENETIC INVESTIGATIONS OF MULTIPLE 
HERBICIDE RESISTANT AVENA FATUA L. 
by 
 
Lucas Arlin Wright 
 
 
 
 
 
 
A thesis submitted in partial fulfillment 
of the requirements for the degree 
 
of 
 
Master of Science 
 
in 
 
Plant Sciences 
 
 
 
MONTANA STATE UNIVERSITY 
Bozeman, Montana 
 
 
 
December 2023
 
 
 
 
 
 
 
©COPYRIGHT 
 
by 
Lucas Arlin Wright 
2023 
All Rights Reserved
ii 
 
ACKNOWLEDGEMENTS 
I would like to express my deepest gratitude to my advisor, Dr. William Dyer, whose 
invaluable guidance and support made it possible for me to pursue this path. A special thank you 
to Dr. Barbara Keith, whose assistance was crucial throughout this journey. My utmost 
appreciation goes to my committee members, Dr. Jennifer Lachowiec and Dr. Jason Cook, for 
their instrumental role in my academic and professional growth. I am also grateful to the faculty 
who provided the knowledge and resources necessary for achieving this milestone. A big thank 
you to the many student workers whose help has been crucial in my projects. To my fellow 
students, thank you for the friendships and camaraderie we have shared. My family deserves my 
deepest thanks for their support and encouragement over the years. Lastly, a very special 
acknowledgment to Sally Johnson, whose unwavering support and ability to keep me grounded 
throughout this journey. 
Finally, thank you to the many organizations that funded this research, the USDA-NIFA-
AFRI, the US EPA Strategic Agricultural Initiative, the Montana Wheat and Barley Committee, 
and the Montana Agricultural Experiment Station. 
  
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TABLE OF CONTENTS 
INTRODUCTION .................................................................................................................... 1 
Multiple Herbicide Resistant Avena fatua .......................................................................... 1 
Volatile Organic Compounds .................................................................................. 5 
Plant Pigments and Non-Photochemical Quenching .............................................. 8 
Quantitative Trait Loci Analysis of MHR Avena fatua ......................................... 13 
Research Objectives .............................................................................................. 15 
Project Significance .............................................................................................. 16 
INSECT GENERALIST PREFERENCE TO MHR AVENA FATUA ..................................... 17 
Summary ........................................................................................................................... 17 
Introduction ....................................................................................................................... 18 
Methods............................................................................................................................. 20 
Plant Material ........................................................................................................ 20 
Spodoptera exigua Choice and No-choice Herbivory Experiments ..................... 20 
Greenhouse Experiments .......................................................................... 20 
Field Experiments ..................................................................................... 21 
Measurements ........................................................................................... 22 
Spodoptera exigua Olfaction Choice Assay ......................................................... 22 
Statistics ................................................................................................................ 23 
Results ............................................................................................................................... 24 
Greenhouse Choice Experiments .......................................................................... 24 
Greenhouse No-choice Experiments ..................................................................... 26 
Field Choice and No-choice Experiments ............................................................ 26 
Olfactory Preferences of S. exigua........................................................................ 28 
Discussion ......................................................................................................................... 31 
Conclusion ........................................................................................................................ 35 
EXOGENOUS VOLATILE ORGANIC COMPOUNDS EFFECTS ON 
HERBICIDE RESISTANCE IN TWO AVENA FATUA LINES ............................................. 36 
Summary ........................................................................................................................... 36 
Introduction ....................................................................................................................... 37 
Methods............................................................................................................................. 39 
Plant Material ........................................................................................................ 39 
Exogenous VOC Treatments ................................................................................. 40 
Herbicide Treatments ............................................................................................ 41 
Statistics ................................................................................................................ 41 
Results ............................................................................................................................... 42 
Open Bag Experiments ......................................................................................... 42 
CO2 Concentrations .............................................................................................. 44 
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TABLE OF CONTENTS CONTINUED 
 
Discussion ......................................................................................................................... 51 
Conclusion ........................................................................................................................ 57 
PLANT PIGMENTS DETECTION, PHOTOSYNTHETIC AND ENERGY 
MANAGEMENT PARAMETERS OF MULTIPLE HERBICIDE 
RESISTANT AVENA FATUA ................................................................................................. 58 
Summary ........................................................................................................................... 58 
Introduction ....................................................................................................................... 59 
Methods............................................................................................................................. 61 
Plant Material ........................................................................................................ 61 
Thin Layer Chromatography................................................................................. 61 
Spectrophotometer Assay...................................................................................... 62 
Pulse Amplitude Modulated Fluorometry ............................................................. 62 
Statistics ................................................................................................................ 63 
Results ............................................................................................................................... 64 
Thin Layer Chromatography................................................................................. 64 
Spectrophotometer Assay...................................................................................... 65 
Pulse Amplitude Modulated Fluorometry Assay .................................................. 68 
Discussion ......................................................................................................................... 73 
Conclusion ........................................................................................................................ 77 
QUANTITATIVE TRAIT LOCI ANALYSIS OF MULTIPLE HERBICIDE 
RESISTANT AVENA FATUA ................................................................................................. 79 
Summary ........................................................................................................................... 79 
Introduction ....................................................................................................................... 80 
Methods............................................................................................................................. 82 
Plant Material and Experimental Design .............................................................. 82 
Phenotypic Data .................................................................................................... 82 
Genotyping and Linkage Map Construction ......................................................... 83 
QTL Analysis ........................................................................................................ 84 
RNA-seq Analysis ................................................................................................. 84 
Results ............................................................................................................................... 85 
Genotyping and Linkage Map Construction ......................................................... 85 
QTL Analysis ........................................................................................................ 86 
RNA-Seq Analysis ................................................................................................ 90 
Discussion ......................................................................................................................... 92 
Conclusion ........................................................................................................................ 96 
REFERENCES CITED ........................................................................................................... 97 
  
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LIST OF TABLES 
Table                Page  
Table 1.1: Mean A. fatua leaf areas consumed (in mm²) in greenhouse S. 
exigua choice experiments. Two-way comparisons were between line S2 and 
one of six MHR lines within the same pot. ............................................................................. 25 
Table 1.2: Mean A. fatua leaf areas consumed (in mm²) in field S. exigua 
choice experiments. Two-way comparisons were between line S1 and one of 
four MHR lines in the same pot. . ........................................................................................... 27 
Table 1.3: Mean A. fatua leaf areas consumed (in mm²) in field S. exigua 
choice experiments. Two-way comparisons were between line S2 and one of 
four MHR lines in the same pot.  ............................................................................................ 27 
Table 1.4: Mean A. fatua leaf areas consumed (in mm²) in field S. exigua no-
choice experiments. Multi-way comparisons were between six A. fatua lines. ..................... 27 
Table 1.5: Mean A. fatua leaf areas consumed (in mm²) in field grasshopper 
choice experiments. Two-way comparisons were between line S1 by 20R1 and 
20R2, and S2 by 20R1 and 20R2 in the same pot. ................................................................. 28 
Table 1.6: Mean A. fatua leaf areas consumed (in mm²) in field grasshopper 
no-choice experiments. Multi-way comparisons were between four A. fatua 
lines.. ....................................................................................................................................... 28 
Table 2.1: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for S2 
Lines with Exogenous Linalool Across Herbicide Treatments in Open Bags 
with P-Values .......................................................................................................................... 43 
Table 2.2: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for S2 
Lines with Exogenous Limonene Across Herbicide Treatments in Open Bags 
with P-Values .......................................................................................................................... 44 
Table 2.3: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for S2 
Lines with Exogenous Linalool Across Herbicide Treatments in Closed Bags 
with P-Values .......................................................................................................................... 46 
Table 2.4: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for S2 
Lines with Exogenous Limonene Across Herbicide Treatments in Closed Bags 
with P-Values .......................................................................................................................... 47 
 
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LIST OF TABLES CONTINUED 
Table 2.5: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for R4 
Lines with Exogenous Linalool Across Herbicide Treatments in Closed Bags 
with P-Values .......................................................................................................................... 48 
Table 2.6: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for R4 
Lines with Exogenous Limonene Across Herbicide Treatments in Closed Bags 
with P-Values .......................................................................................................................... 49 
Table 2.7: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for S2 
Lines with Exogenous Combined VOCs Across Herbicide Treatments in 
Closed Bags with P-Values ..................................................................................................... 50 
Table 2.8: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for R4 
Lines with Exogenous Combined VOCs Across Herbicide Treatments in 
Closed Bags with P-Values ..................................................................................................... 51 
Table 3.1: Fluorescence Measurements Used for Photosynthetic and Energy 
Management Parameters of All A. fatua Lines Tested.  .........................................................  70 
Table 4.1. Complete list of QTLs found in the 93 RIL population using MIM. ..................... 87 
Table 4.2. Complete list of QTLs found in the 06 RIL population using MIM. ..................... 89 
Table 4.2 Continued ................................................................................................................ 90 
Table 4.3. DEGs underlying QTL peaks in R4 lines............................................................... 91 
 
 
 
 
 
 
 
 
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LIST OF FIGURES 
Figure                Page  
Figure 1.2: Bar graph comparing the mean area consumed by S. exigua (in 
mm2) between A. fatua HS line S1 and MHR lines within the same pot. a) S1 
v 18R, b) S1 v 20R1, c) S1 v 20R2, d) S1 v 93R, e) S1 v R3, f) S1 v R4 .............................. 25 
Figure 1.3: Bar graph comparing the mean area consumed by S. exigua (in 
mm2) between A. fatua lines.  ................................................................................................ 26 
Figure 1.4 - Bar graph illustrates the olfactory choices of S. exigua regarding 
A. fatua lines R4 and S1. Each bar represents the count of olfactory choices 
made by S. exigua. .................................................................................................................. 29 
Figure 1.5 – Bar graph illustrates the olfactory choices of S. exigua regarding 
Lactuca sativa L. and filtered airflow. Each bar represents the count of 
olfactory choices made by S. exigua. ...................................................................................... 30 
Figure 1.6 – Bar graph illustrates the olfactory choices of S. exigua regarding 
filtered airflow and filtered airflow. Each bar represents the count of olfactory 
choices made by S. exigua. ..................................................................................................... 31 
Figure 3.1 – Thin layer chromatography of acetone extracts of S2 (left panel) 
and R4 (right panel) plants. ..................................................................................................... 65 
Figure 3.2 - Thin layer chromatography of acetone extracts of two R4 plants 
grown in different conditions.. ................................................................................................ 65 
Figure 3.3 – Spectrophotometer Full scan of MHR and HS A. fatua lines 
ranging from 400- 750 nm. ..................................................................................................... 66 
Figure 3.4 – Spectrophotometer, Acetone soluble pigment concentrations of A. 
fatua lines detected at 480 nm. ............................................................................................... 67 
Figure 3.5 – Spectrophotometer, Acetone soluble pigment concentrations of A. 
fatua lines detected at 652 nm. ............................................................................................... 68 
Figure 3.6 – Field derived lines measuring a. Y(NO) – Non-regulated energy 
dissipation, b. Y(NPQ) - Regulated energy dissipation, c. Y(II) – Effective 
Photochemical yield, and d. ETR - Electron transfer rate. ..................................................... 71 
 
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LIST OF FIGURES CONTINUED 
Figure 3.7 – 93 RIL groups and progenitor lines measuring a. Y(NO) – Non-
regulated energy dissipation, b. Y(NPQ) - Regulated energy dissipation, c. 
Y(II) – Effective Photochemical yield, and d. ETR - Electron transfer rate........................... 72 
 Figure 3.7 – 06 RIL groups and progenitor lines measuring a. Y(NO) – Non-
regulated energy dissipation, b. Y(NPQ) - Regulated energy dissipation, c. 
Y(II) – Effective Photochemical yield, and d. ETR - Electron transfer rate........................... 73 
Figure 4.1 – Linkage Groups for the 06 RIL Population. Black Bands 
Represent a SNP. ..................................................................................................................... 86 
  
ix 
 
ABSTRACT 
Intense herbicide usage has led to the evolution of herbicide resistant weeds, which 
threaten food production and security. The multiple herbicide resistant (MHR) Avena fatua (wild 
oat) lines investigated here are resistant to all members of selective herbicide families available 
for A. fatua control. The research in this thesis is designed to help understand some of the 
ecological, biochemical, and genetic aspects of MHR. First, MHR lines with elevated volatile 
organic compound (VOC) levels and herbicide susceptible lines were used to compare the 
feeding behavior of Spodoptera exigua (beet armyworm), and potential role of VOCs to mitigate 
herbicide injury. Results for feeding behavior were mixed, possibly being influenced by 
environmental and genetic changes more than VOCs. Exposing VOCs to A. fatua lines found 
that linalool reduced flucarbazone injury of HS plants, while a combined VOC treatment 
generally increased herbicide injury. MHR responded differently than HS plants to some 
treatments, suggesting that MHR has fundamental VOC perception alterations. Other studies 
compared plant pigments and energy management capabilities and showed that MHR lines had 
higher beta-carotene and chlorophyll b concentrations, as well as enhanced photosynthetic and 
excess energy management capabilities in MHR lines. Finally, two populations of recombinant 
inbred lines (RILs) were phenotyped for herbicide resistance and used to discover several 
quantitative trait loci (QTL) associated with resistance. Overall, this work contributes to our 
understanding of MHR and will lay the groundwork for future studies.  
 
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CHAPTER ONE 
INTRODUCTION 
Multiple Herbicide Resistant Avena fatua 
Weeds are the primary biotic cause of crop yield loss, reducing yield by an annual 
worldwide average of 28% (Vila et al., 2021). Before the creation of synthetic herbicides, 
enormous efforts were required to control weed populations by manual weeding or tillage. The 
adoption of herbicides has since revolutionized weed control by substantially reducing costs and 
manual labor, as well as increasing the overall efficacy of weed control (Heap 2014). The 
economic cost of weed management in the United States is estimated to be $33 billion annually 
(Chauhan 2020). In most modern crop production systems, producers use selective herbicides, 
which do not injure crops while killing weeds. However, the very properties that facilitated their 
continuous use have also led to overreliance and overuse, imposing intense selection pressure on 
target weed species. As a result, herbicide-resistant populations of many weeds are now 
widespread across worldwide cropping systems. The first recorded observation of herbicide 
resistance was to 2,4-dichorophenoxyacetic (2,4-D), an auxin mimic herbicide that was 
discovered in 1950 (Reddy and Nandula 2012). Since 1950, approximately 521 unique cases of 
herbicide-resistant weeds (species x site of action) have been discovered, or a rate of 16 new 
cases reported annually (Heap 2014). Large amounts of herbicides are used each year. For 
reference, glyphosate, one of the most widely used herbicides, is applied at an annual rate of 
804,000 tons (Rivas-Garcia et al., 2022). 
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Herbicide resistant weed populations can be resistant to one herbicide (HR) or multiple 
herbicides (MHR). Two types of biological mechanisms confer herbicide resistance: target site 
resistance (TSR) and non-target site resistance (NTSR). TSR, the most common mechanism, 
confers resistance due to a mutation in the gene encoding the herbicide target enzyme. This 
change inhibits the herbicide from binding to the target site or leads to overexpression of the 
target enzyme, allowing the enzyme to still function in the presence of herbicide (Powles and Yu 
2010; Fang et al., 2019). NSTR includes other mechanisms that confer herbicide resistance 
outside of the target enzyme, such as a reduction in herbicide uptake or translocation, enhanced 
herbicide metabolism, decreased rate of herbicide activation and/or sequestration, or a point 
mutation that confers herbicide resistance (Délye et al., 2013, Dyer 2018, Jugulam and Shyam 
2019). 
The focus of this literature review is Avena fatua L. (wild oat). As an annual, 
allohexaploid, and predominantly self-pollinating monocotyledonous weed, A. fatua thrives 
across temperate agricultural regions globally (Dahiya et al., 2019). Its adaptability extends to 
cereal systems, root crops, legumes, vegetables, and even ornamental plants, demonstrating its 
competitive nature (Beckie et al., 2012). Ranked as the second worst grass weed in the world 
(Beckie et al., 2012), A. fatua presents severe challenges for agricultural management. It exhibits 
various physiological traits that enhance its competitive advantage, including extended seed 
dormancy and early seed shattering and dispersal before harvest (Mahajan and Chauhan 2021). 
According to estimates, A. fatua has infested over 11 million hectares of agricultural land in the 
U.S. Great Plains and the Pacific Northwest and have caused an annual crop loss valued at 
approximately $1 billion (Beckie et al., 2012). 
3 
 
Avena fatua’s persistence surpasses borders, affecting cereal crops in Europe, North 
America, and Australia (Wrzesińska et al., 2016; Jäck et al., 2017; Dyer 2018; Mahajan and 
Chauhan 2021; Ņečajeva et al., 2021). Herbicide resistance biotypes of A. fatua is present among 
all these locations. For example, random survey in the Western Australian grain belt looked at 
677 cropping fields and found 107 A. fatua populations that had range of resistant capabilities 
(Owen et al., 2009). In North America, and several European countries, A. fatua populations are 
being discovered to have multiple herbicide resistant capabilities (Travlos et al., 2011; Beckie et 
al., 2012; Adamczewski et al., 2019). Indicating that herbicide resistant A. fatua is becoming a 
greater threat to food production, and food security globally.  
Overall, research on Avena fatua resistant biotypes is divided into multiple sections; 
1.finding the mechanisms conferring herbicide resistance like enhanced metabolism or target site 
mutations (Ahmad-Hamdani et al., 2013; Yu et al., 2013), 2. managing these populations to avoid 
more herbicide resistance emergence (Nietschke et al., 1996; Cavan et al., 2001), and 3. 
exploring the differences in resistant of A. fatua as compared to other resistant weeds, or a 
sensitive biotype (De Prado and Franco, 2004; Alizade et al., 2020). This thesis is investigating 
the later and the final chapter relates to finding mechanism conferring herbicide resistance. 
In Teton County, Montana, USA, producers controlled A. fatua populations exclusively 
with the preemergence herbicide triallate (HRAC Group 15) for over three decades after its 
commercialization in 1962. Herbicide Resistance Action Committee (HRAC) grouping is a 
classification system used to categorize herbicides based on their mode of action (Beffa et al., 
2019). Producer complaints about triallate lack of control of A. fatua in 1993 led the Dyer lab to 
collect seeds and test them for resistance in the controlled condition of a greenhouse. These 
4 
 
populations were confirmed to be triallate resistant and were subjected to recurrent selection with 
triallate for five generations to create the inbred line 93R (Kern et al., 2002). This line was also 
found to be cross-resistant to the unrelated herbicide, difenzoquat (HRAC Group Ø), which has 
an unknown mode of action. An additional sensitive line HS2 was derived from a nondormant 
biotype used for seed dormancy research (Naylor and Jana 1976, Johnson et al., 1995). In 2006, 
after a complaint from producers that pinoxaden (HRAC Group 1) failed to control A. fatua 
during its first year of commercial use, seeds collected from fields near the 93R source were 
subjected to recurrent selection using pinoxaden for two generations to create the R3 and R4 
inbred lines (Keith et al., 2015). They were confirmed to be resistant to members of the acetyl-
CoA carboxylase (HRAC Group 1), acetolactate synthase (HRAC Group 2), and photosystem I 
(HRAC Group 22) inhibitors, as well as triallate and difenzoquat (Lehnhoff et al., 2013). An 
herbicide susceptible A. fatua line (HS1) was created from seeds collected nearby and was later 
confirmed to be 100% HS (Lehnhoff et al., 2013). Other resistant lines 18R, 20R2, and 20R1, 
were derived from seeds collected in 2018 (18R) and 2020 (20R1 and 20R2) from nearby fields 
and were suspected to be resistant to glyphosate as well as all R3 and R4 herbicides (Keith et al 
in preparation). 
In 2002, the Dyer lab made reciprocal crosses between 93R and HS1 to determine the 
genetic basis for triallate resistance. Triallate resistance segregated in a 15:1 HS to HR ratio in 
the F2 generation, indicating that two recessive genes controlled the resistance phenotype (Kern 
et al., 2002). No target site mutations known to confer resistance to Group 1 or Group 2 
inhibitors were found in R3 and R4 A. fatua lines, indicating that NTSR mechanisms were 
responsible for the MHR phenotype (Keith et al., 2015). Additionally, RNA - Seq analysis 
5 
 
showed that R3 and R4 lines have constitutively elevated gene transcript levels relative to HS1, 
including genes with functions in xenobiotic catabolism, stress response, redox maintenance, and 
transcriptional regulation (Keith et al., 2017). R3 and R4 resistance to flucarbazone, 
imazmethabenz, and pinoxaden is controlled by three closely linked nuclear genes (Burns et al., 
2018). Finally, the R3 and R4 lines' response to herbicide stress is similar to other abiotic 
stressors, as determined by proteomic biochemical and immunological assays (Burns et al., 
2018). The findings from these studies reveal that the resistant lines possess NTSR mechanisms 
to multiple herbicides, making them a distinctive model for research. 
Volatile Organic Compounds 
Plants are stationary organisms: they have evolved numerous defense mechanisms to 
combat abiotic and biotic stresses. One response to stress is the release of biogenic volatile 
organic compounds (VOCs) that not only function to attract pollinators and in plant–plant 
communication, but also induce plant defense mechanisms against insects and pathogens (Ueda 
et al., 2012; Kalske et al., 2019). When a plant perceives a stress, it emits a unique VOC profile 
which can contain a mix of hundreds of VOCs, and these profiles differ among plant species 
(Landi et al., 2020; Tiwari et al., 2020; Fitzky et al., 2021). Plant VOCs include members of the 
very large terpene family, including hemiterpenes (C5), monoterpenes (C10), sesquiterpenes 
(C15), homoterpenes (C11 and C16), diterpenes (C20), and triterpenes (C30) (Vivaldo et al., 
2017, Zhou and Jander 2021). Mechanically injured plants release a mix of green leaf volatiles 
(GLVs) consisting of aldehydes, esters, and alcohols of six-carbon compounds (Ameye et al., 
2018). 
6 
 
 Notably, all plants constitutively emit VOCs; however, when they encounter stress, they 
release different VOC profiles dependent on response. Emitted VOCs from a stressed plant can 
be detected by surrounding receiver plants alerting them to prepare for incipient stresses by 
priming defensive mechanisms, including enhanced production of defensive chemicals, 
induction of defensive proteins and enzymes, and activation or reinforcement of physical barriers 
(Llusia et al., 2005; Brilli et al., 2009; Yuan et al., 2009; Kalske et al., 2019; Sako et al., 2020; 
Zhou and Jander 2021). For example, drought stress causes a plant to warn other plants around it 
and induce a defensive mode to counteract the stress, like closing the stomata to reserve water 
(Copolovici et al., 2014). In contrast, plants experiencing a strong temperature change emit a 
different profile of VOCs that elicit different physiological responses (Holopainen and 
Gershenzon 2010).  
The previously described Dyer lab MHR A. fatua line (R4) was found to constitutively 
emit a different VOC profile than the HS1 line (Keith et al., in preparation). The MHR and HS 
lines were placed individually in glass collection chambers, and VOCs were collected on a super-
Q trap (Alltech Associates Inc, Deerfield, IL) for 6-hour periods, eluted, and quantified using gas 
chromatography/mass spectrometry. Of more than 75 VOCs detected, nine were emitted at 
significantly different levels between untreated HS and MHR plants. Of these nine, five were 
identified as monoterpenoids, three as GLVs, and one as methyl salicylate. The A. fatua 
monoterpenoids emitted at significantly higher levels in MHR plants were β-myrcene, limonene, 
linalool, α-terpinene, and α-pinene. GLVs identified as decanal was also emitted at higher levels 
in MHR lines.   
7 
 
Of the nine VOCs listed above, linalool, limonene, and methyl salicylate were chosen for 
further study. In addition, methyl jasmonate was included because of its well-documented role in 
defense signaling (Tamogami et al., 2008). The jasmonate signaling pathway regulates the plant 
response to abiotic stressors like drought, salt, and cold (Wang et al., 2021). This pathway also 
regulates responses to biotic stress imposed by insect herbivores, pathogens, or other fungal 
diseases. (Turner et al., 2002). Transcriptome studies show that jasmonate signaling is also 
involved in changes in defense gene expression in response to mechanical and herbivory plant 
wounding, host plant resistance to phloem-feeding insects, enhanced chemical and VOC based 
defenses, priming direct and indirect defenses, pathogen resistance and systemic transmission of 
defense signaling (Howe and Jander 2008).  
The monoterpenoid linalool enhances direct and indirect defenses mostly against biotic 
stressors like insects, pathogens, and fungal infections (Zhang et al., 2023). Exposure to linalool 
can also induce similar responses. For example, exposure to exogenous linalool has been shown 
to activate oxidative bursts and inhibit infection by Colletotichum acutatum, a pathogen which 
causes citrus post-bloom fruit drop in Citrus × aurantium (Sweet Orange) (Sun et al., 2014). 
Similarly, exogenous exposure to limonene has been shown to activate disease resistances to 
multiple organisms and confer limited thermotolerance in Quercus ilex (Holm Oak) (Llusia et al., 
2005; Fujioka et al., 2015). Therefore, the inherent properties of linalool and limonene protect 
plants from biotic stressors and likely have broader regulatory roles in plant stress response. 
Methyl salicylate, another methyl ester compound, has major signaling roles in systemic 
acquired resistance (SAR), an induced defense response that confers long-lasting protection 
against a broad spectrum of microorganisms (Park et al., 2007). Methyl salicylate is primarily 
8 
 
associated with biotic stressors and is released by plants undergoing stress such as pathogen 
attack. (Chen et al., 2019). In addition, methyl salicylate itself has direct antimicrobial properties 
and can act as a local defense compound within the emitting plant as Aphidoidea (aphid) and 
other herbivores have been documented to be deterred by methyl salicylate (Verma et al., 2023). 
In receiver plants, methyl salicylate can be demethylated to salicylic acid, which further 
amplifies the defense signaling network by upregulating the expression of pathogenesis-related 
genes and priming the plant's defenses (Chen et al., 2019). 
Changes in VOC emission profiles have been investigated after herbicide treatment of 
susceptible plants and in transgenic herbicide-tolerant plants. Exposure to sublethal glyphosate 
doses altered VOC/GLV emission patterns but did not alter parasitoid behavior on herbicide 
susceptible Zea mays (maize) plants (D’Alessandro et al., 2006). Transgenic glyphosate-resistant 
Glycine max (soybean) plants with an insensitive 5-enolpyruvoylshikimate-3-phosphate synthase 
did not show constitutive VOC changes. However, emissions of several monoterpenoids were 
preferentially elevated in response to herbivory by Chrysodeixis includens (soybean looper) in 
the resistant line, as compared to their glyphosate sensitive counterparts (Strapasson et al., 2016). 
In Oryza sativa (rice), no VOC differences were detected between transgenic glufosinate-
resistant and non-transgenic cultivars (Choi and Kim 2013). Besides these studies on transgenic 
plants, I am not aware of any VOC analyses of field-selected, herbicide resistant populations of 
any plant species. 
Plant Pigments and Non-Photochemical Quenching 
Chapter Four of this thesis examines plant pigment concentrations and non-
photochemical quenching (NPQ) capacity of MHR and HS A. fatua lines. Quantifying plant 
9 
 
pigments and NPQ can give insight into linkages with abiotic stress defense mechanisms and 
possibly with NTSR mechanisms. As mentioned above, NTSR mechanisms are mechanisms that 
confer herbicide resistance by reducing herbicide uptake or translocation, enhancing rates of 
herbicide metabolism, decreasing rates of herbicide activation and/or sequestration, possibly by a 
point mutation in a gene other than one encoding the herbicide’s target enzyme, or other 
unidentified mechanisms (Délye et al., 2013; Dyer 2018; Jugulam and Shyam 2019). Since many 
herbicides cause increased reactive oxygen species (ROS) content (Traxler et al., 2023), an 
enhanced ability to quench ROS could also confer NTSR. Herbicides function by specifically 
binding to target enzymes that disrupt essential metabolic pathways within plants. This 
interaction leads to a cascade of events that ultimately leads to the plant’s mortality. Some 
herbicides like HRAC groups 5, 6, 10, 12, 13, 14, 22, 23, 32, 33, 34, and others bind enzymes in 
plant photosystems, (Beffa et al., 2019) and generally, shut down electron transport in the 
photosystem. As a result, ROS generation can increase rapidly and dramatically (Matzrafi et al., 
2017; Caverzan et al., 2019; Traxler et al., 2023). 
ROS molecules are integral in plant signaling, growth, development, and response to 
biotic and abiotic factors (Tripathy and Oelmüller 2012, Das and Roychoudhury 2014). There are 
four primary types of ROS: free radicals O2•− (superoxide radical), •OH (hydroxyl radical), non-
radicals H2O2 (hydrogen peroxide), and 1O2 (singlet oxygen). While these ROS molecules are 
beneficial in small amounts, large amounts can damage DNA, lipids, and proteins and ultimately 
cause apoptosis (Das and Roychoudhury 2014). Plants ameliorate excessive ROS levels using 
enzymatic and non-enzymatic mechanisms, including plant pigments, which play a significant 
role in ROS homeostasis (Choudhary et al., 2017). 
10 
 
Plants contain molecules called biochromes, or more commonly, pigments, that absorb or 
reflect light. Many biological processes depend on visible light, using wavelengths ranging from 
ultraviolet light (<380 nm) to far-red light (>700 nm). Plant pigments absorb energy specific 
wavelengths to power and influence biological processes (Carvalho et al., 2011), like 
photosynthesis, germination, fruiting, growth direction, and pigment formation. Light quality, 
quantity, duration, direction, and intensity are all involved in photomorphogenesis, the growth 
and development of plants in response to light (Arsovski et al., 2012). Photomorphogenesis is 
influenced by protein photoreceptors that interpret incoming light and activate processes to 
regulate signaling pathways (Paik and Huq 2019). A critical part of photomorphogenesis is 
regulating plant pigment levels in response to environmental variables. For example, abiotic 
stress can cause an accumulation of carotenoids and anthocyanins as part of an overall defense 
response (Carvalho et al., 2011; Demmig-Adams et al., 2020; Stewart et al., 2021).  
Plant pigments are classified into four categories: flavonoids, carotenoids, chlorophyll, 
and betalains, all of which have roles in photosynthesis, light harvesting, and photoprotection 
(Chen 2015). Since betalains are only found in the order Caryophyllales, my research focused on 
the other three main plant pigment classes. 
Flavonoids are essential secondary metabolites that play critical roles in plant 
development and defense. They are classified into 12 subclasses based on the oxidation degree of 
their central heterocycle (Shen et al., 2022). These pigments reflect wavelengths that confer 
purple, blue, and red pigmentation in fruits, vegetables, and flowers (Carvalho et al., 2011). 
Anthocyanidins, an unstable subclass of flavonoids, generally exist in their glycosylated form as 
anthocyanins. This large subclass contains over 650 compounds which act as free radical 
11 
 
acceptors that can quench and neutralize ROS before they cause damage to plant cells (Han et 
al., 2012; Shen et al., 2022).  
The second class of plant pigments consisting of carotenoids, lipophilic secondary 
metabolites crucial for photoprotection and light harvesting, are grouped into two sub-classes: 
carotenes and xanthophylls. Carotenes are non-oxygenated molecules, while xanthophylls 
contain oxygen (Amorim-Carrilho et al., 2014), and both are responsible for the orange, yellow, 
and red colors in fruits, vegetables, and flowers (Carvalho et al., 2011). Carotenes and 
xanthophyll both play photoprotective roles by dissipating photosynthetic excitation energy as 
heat scavenges ROS and suppresses lipid peroxidation (Tiwari et al., 2017). Additionally, 
carotenes and xanthophylls are antenna molecules capable of harvesting light and transmitting 
photon energy to the photosystems. Importantly, they significantly detoxify ROS (Han et al., 
2012; Maoka 2020). 
Carotenes exist in either a ground state or in one of two excited states after photon 
absorption. In its lower excited state, transferred energy is used in photosynthesis, while the 
higher energy state (triplet–triplet) releases transferred energy as heat through polyene vibration. 
However, the triplet–triplet state can also potentially create ROS, particularly singlet oxygen. 
Excess ROS is primarily quenched by antioxidants, but more stressful conditions can cause 
cellular damage, such as lipid peroxidation and protein oxidation, ultimately leading to DNA 
damage (Choudhary et al., 2017; Hashimoto et al., 2018; Maoka 2020).  
Xanthophylls are also involved in light harvesting and photoprotection. They dissipate 
excess energy by a cyclic enzymatic process that removes epoxy groups, forming de-epoxidized 
xanthophylls. These de-epoxidized xanthophylls, particularly zeaxanthin, play a vital role in 
12 
 
enhancing the structural integrity of chloroplast membranes, enhancing their resistance to 
oxidative damage by ROS. This cycle promotes the dissipation of excess energy, effectively 
preventing it from overexciting the photosynthetic reaction centers. During periods of intense 
light exposure, violaxanthin, which contains epoxy groups, is enzymatically converted into 
zeaxanthin, the de-epoxidized form, passing through the intermediary antheraxanthin. This safely 
releases energy before interacting with oxygen molecules (Maoka 2020). 
Genetic and transgenic efforts to alter xanthophyll biosynthesis and overexpress beta-
carotene hydroxylase genes have improved the abiotic stress tolerance of Arabidopsis thaliana, 
Nicotiana tabacum, and Oryza sativa. (Davison et al., 2002; Götz et al., 2002; Römer and Fraser 
2005; Du et al., 2010). Beta-carotene hydroxylase genes are responsible for the biosynthesis of 
zeaxanthin and carotenoid precursors, which can confer drought and oxidative stress resistance 
(Du et al., 2010). To my knowledge, enhanced herbicide tolerance based on elevated non-
enzymatic antioxidants has not been documented. However, the detection of plant pigment 
concentrations in MHR and HS Avena fatua lines warrants further analysis since it is a potential 
NTSR mechanism. 
The third plant pigment class consists of chlorophylls, chlorophyll a and b, which are 
located in chloroplast light-harvesting complexes and capture solar energy used to drive 
photosynthesis and other biological processes (Murchie and Lawson 2013). If light energy is not 
used for photochemistry, then it is emitted as heat or light that could cause ROS generation. 
These outcomes do not exist in isolation but rather in competition, and are dependent upon light 
intensity, light quality, and genetics (Genesio et al., 2021). If light intensity exceeds the 
photosynthetic maximum, and if the generated excess energy is not quenching by the plant, it 
13 
 
could cause ROS generation and induces photooxidative damage (Roach and Krieger 2014). 
Antioxidant mechanisms can be activated to dissipate energy from excess light; however, 
photoreceptors such as far-red, red, blue, and UV are primarily utilized first. As far-red 
photoreceptors, indicated by the name are influenced by far-red light (Batschauer 1998). Plant 
photoreceptors can initiate light avoidance by causing chloroplasts to relocate inside plant cell 
(Carvalho et al., 2011; Tripathy and Oelmüller 2012). My research focused on photochemical 
yield and excess light quenching through regulated and non-regulated mechanisms, factors that 
are linked to abiotic stress tolerance (Chaerle et al., 2007; Hussain et al., 2023), and perhaps to 
NTSR to herbicides. Since ROS generation commonly occurs with herbicide injury, any 
mechanism that has an enhanced role to dissipate, quench ROS, would likely enhance NTSR 
herbicide resistance.  
Quantitative Trait Loci Analysis of MHR Avena fatua 
The issue of identifying herbicide resistant mechanisms is a complex and troublesome 
problem worldwide. As the global reliance on herbicides continues, so does the selection 
pressure, leading to a rapid increase in cases of herbicide resistance. Improved genetic analysis 
of NTSR mechanisms in A. fatua became more accessible with the availability of a reference 
genome for the closely related species Avena sativa (common oat) in 2021 (Yao et al., 2022). 
Avena sativa and Avena fatua are in the same genus; both are hexaploid (42 chromosomes), can 
be crossed without barriers, and a high degree of synteny is expected (Koroluk et al., 2022). 
Since previous studies in the Dyer lab have shown that several genes are potentially responsible 
for the MHR phenotype (Burns et al., 2018; Keith et al., 2017; Keith et al., 2015), genomic 
14 
 
approaches using quantitative statistical methodology is warranted to search for casual genes 
(Leon et al., 2021). 
QTL analysis is a statistical method that links phenotypic and genotypic data to specific 
regions of chromosomes. This tool attempts to explain the genetic basis of variation in complex 
traits (Farquhar, 1998). High quality phenotypic data results in higher predictive power to detect 
potential QTL markers (Würschum, 2012). Genotypic data requirements are stringent and often 
necessitate the use of recombinant inbred lines (RILs) for accurate analysis. This involves a cross 
between two parental lines, typically one possessing the trait of interest and the other lacking it. 
The progeny from this initial cross is then subjected to sibling mating or selfing for multiple 
generations to achieve isogeneity. The resulting RIL thus allows precise genetic mapping at 
specific loci (Broman, 2005). The Dyer lab has three RIL populations that were utilized for this 
research. The 93R inbred line was crossed with the HS S2 line to create a population of 124 RILs 
(Kern, Myers et al. 2002) and the inbred MHR line R4, which were also crossed with S2 to 
create the 06 (n = 120) RIL populations, respectively (Burns, Keith et al. 2018). 
To determine genomic differences between resistant and sensitive A. fatua lines that may 
contribute to herbicide resistance, genotyping-by-sequencing (GBS) was performed on all 
parental and RILs populations. GBS fragments the genome into small sequences and is aligned 
to a reference genome to identify single nucleotide polymorphisms (SNPs) (Deschamps et al., 
2012). SNPs represent parental variations at individual nucleotides in the DNA sequence. To 
create linkage maps, SNPs from the genomic data are placed on a map according to their position 
on the chromosome and how often they are inherited together. The distance between adjacent 
SNPs on the map is measured in centimorgans (cM), units representing the distance between 
15 
 
genetic markers on a chromosome (Oliver et al., 2001). One cM represents a 1% chance that a 
marker at one genetic locus will be separated from a marker at a second locus due to 
recombination during meiosis. The closer the SNPs are to each other on a chromosome, the more 
likely they will be passed on together to the next generation (Ganal et al., 2009). SNPs are 
valuable for this purpose due to their abundance throughout the genome and the relative ease 
with which their frequency and location can be determined in the mapped individuals (Sudan, 
Sharma et al. 2023). While some SNPs can be directly associated with phenotypic differences, 
their primary utility in this context is to serve as markers for genetic mapping (Myles & Wayne, 
2008). 
QTL analyses use the linkage map created from the SNPs and phenotypic data to 
calculate QTL positions using a statistical estimate known as the logarithm of the odds ratio 
(LOD). LOD scores thus estimate the relative strength of evidence for the presence of a QTL at a 
particular location (Van Ooijen, 1999). QTL analysis measures intervals between adjacent pairs 
of markers along each chromosome and gives a corresponding LOD score. Using these methods, 
one can find accurate and reliable QTL markers associated with potential candidate genes that 
confer NTSR mechanisms in A. fauta. 
Research Objectives 
1) Evaluate the olfactory and herbivory preferences of a generalist insect species between MHR 
and HS lines of A. fatua under choice and no-choice conditions. 
2) Examine how exposure to exogenous VOC applications affects the physiological responses of 
HS and MHR A. fatua lines to subsequent herbicide treatments. 
16 
 
3) Characterize and compare plant pigment concentrations and non-photochemical quenching 
capacities of MHR and HS A. fatua lines to determine if they are associated with herbicide 
resistance phenotypes. 
4) Perform quantitative trait loci analysis using recombinant inbred lines of three A. fatua 
populations to identify genomic regions potentially associated with MHR phenotypes. 
Project Significance 
This project aims to understand the mechanisms of MHR in Avena fatua using 
physiological, biochemical, and molecular genetic tools. Insect herbivory studies will help to 
understand the associations between elevated VOC emission profiles and insect orientation and 
feeding choices and may have ecological implications for MHR plants in agroecosystems. 
Examining the effects of VOC exposure on subsequent herbicide efficacy will add to our 
understanding of the maintenance and evolution of MHR as affected by biochemical plant 
signaling. Comparative analysis of plant pigments and non-photochemical quenching capacities 
will add to our knowledge of previously undescribed NTSR mechanisms related to 
photooxidative stress tolerance. Discovery and analysis of quantitative trait loci for MHR will 
reveal specific genetic regions associated with this trait and represents a novel molecular genetic 
approach in weedy species to unravel the underlying mechanisms that confer resistance, 
potentially aiding the development of more effective, targeted control strategies. 
 
 
 
 
17 
 
CHAPTER TWO 
 
INSECT GENERALIST PREFERENCE TO MHR AVENA FATUA 
Summary 
This study aimed to explore the feeding preferences of the generalist herbivore, 
Spodoptera exigua (beet armyworm), between multiple herbicide resistant (MHR) and herbicide 
sensitive (HS) lines of Avena fatua (Wild oat) in both greenhouse and field settings. Beet 
armyworm Spodoptera exigua preferred the R4 (MHR) line over the S1 (HS) line and preferred 
S1 over 93R (MHR) in a controlled greenhouse environment. However, no consistent 
preferences were observed in other choice and no-choice trials or in field experiments. Olfactory 
tests produced inconclusive results between the R4 and S1 lines. This study suggests that 
environmental factors and genetic variation influence herbivory more than the released VOCs for 
the Avena fatua lines used in this study. The inconsistent findings across experiments call for 
further investigation into the intricate factors affecting herbivore preferences, including but not 
limited to VOC profiles and genetic variations in A. fatua lines. This study provides a foundation 
for understanding the potential ecological implications of herbicide resistance on herbivore 
behavior, which may have broader impacts on both weed and crop management strategies. 
 
 
 
 
 
18 
 
Introduction 
Plant volatile organic compounds (VOCs) and green leaf volatiles (GLVs) are primary 
determinants of insect attraction, orientation, and feeding preferences (Ameye et al., 2018). Most 
published research on attracting insects to plants focuses on indirect defenses utilizing VOCs to 
attract beneficial insects. This attraction of predatory/parasitoid species that colonize the insect 
herbivores is a form of biological control (Baldwin et al., 2002; Kessler & Baldwin, 2001; 
Rodriguez-Saona et al., 2011). However, for weedy or undesirable plants, VOCs could also play 
a role in attracting herbivores to aid in pest management. 
VOC sensing is detected by an organism’s capacity of olfaction, the sense of smell, to 
detect and extract information from said VOCs. What sets insects apart is their heavy reliance on 
olfaction to ensure survival and reproduction. For insects, this primarily involves sensing food, 
hosts, mates, and prey and performing group communication for aggregation and avoidance 
(Ache & Young, 2005; Fan et al., 2011)  
Without damage, plants constantly emit VOCs that influence their ecology. VOC 
differences between the MHR and HS A. fatua lines may influence the feeding preferences of an 
insect generalist herbivore. MHR and HS A. fatua line’s (VOC) emissions differ. Of the 75 
unique VOCs detected from the A. fatua lines, nine were produced significantly more in the 
MHR R4 line. When the HS S2 plant was mechanically wounded, its emission profile was 
similar to the untreated MHR R4 line. This, along with several other biochemical, genetic, and 
proteomic changes, indicates that the MHR A. fatua lines being examined are constitutively 
primed to resist stressors (Burns et al., 2018; Dyer, 2018; Keith et al., 2017). It begs the question 
19 
 
of whether these VOC profiles also act as a subtle deterrent or attractant, possibly influencing the 
feeding preferences of an insect generalist herbivore. 
VOC attraction of insect herbivores has been studied for many plant and insect species 
(Anderson & Alborn, 1999; Rojas, 1999; Sun et al., 2014). For specific VOCs, limonene, and 
methyl salicylate have been shown to attract Helicoverpa armigera (cotton bollworm) larvae in 
petri dish and two-choice olfactometer assays (Di et al., 2017; Gregg et al., 2010). The 
constitutively elevated emissions of limonene and methyl salicylate in MHR R4 A. fatua led me 
to hypothesize that they might similarly attract S. exigua and consume more of the MHR lines.  
Research in many species has illuminated the molecular details of plant deterrence of 
insect pests, including the defensive mechanisms that the plant incorporates when being fed 
upon. Several defenses are typically induced following an herbivore attack. Herbivory damage 
increases cytosolic calcium levels, increases protein phosphorylation after activation of mitogen-
activated protein kinases (MAPK), NADPH oxidase is activated, and the generation of reactive 
oxygen species (Mostafa et al., 2022). Additionally, the plant releases VOCs to attract beneficial 
insects or to repel the insects that are consuming it (Howe & Jander, 2008).   
Avena fatua is fed upon by many insects including Spodoptera exigua (beet armyworm) 
and Acridomorpha (grasshopper species). Spodoptera exigua (beet armyworm), a polyphagous 
insect is a significant threat to agriculture and one of the most destructive insects in the 
Spodoptera genus (Hafeez et al., 2021). Beet armyworm larvae feed on economically important 
crops like cotton, vegetables, and flowers (Hafeez et al., 2021; Moulton et al., 2000). Since this 
species is not native to Montana, I also tested the Montana-endemic Acridomorpha (grasshopper 
species) for a relevant comparison using a natural herbivore in field conditions. Grasshoppers are 
20 
 
monophagous to grass species but can be polyphagous depending on the scarcity of sustenance, 
making wild oat an applicable experimental plant species (Franzke et al., 2010; Joern, 1979). 
Methods 
Plant Material  
The Avena fatua MHR lines utilized in these experiments consisted of 93R, R4, R3, 18R, 
20R2, 20R1, and the S1 and S2 sensitive lines. These plants were cultivated under controlled 
conditions in a greenhouse, following a 16-hour photoperiod, which included natural sunlight 
supplemented with mercury vapor lamps delivering an intensity of 165 μmol m−2 sec−1. The 
temperature was maintained at 25 ± 4°C. They were planted in a standard greenhouse soil mix 
comprising Bozeman silt, loam, and Sunshine mix #12 (Sun Gro Horticulture, Inc., Bellvue, 
WA) in a 1:1:1 ratio enriched with perlite. Weekly fertilization was carried out using Jack’s 
water-soluble 20 N–20 P–20 K fertilizer (JR Peters Inc., Allentown, PA). All plants for each 
experiment were uniformly located on the same greenhouse bench.  
Spodoptera exigua Choice and No-choice Herbivory 
Experiments 
Greenhouse Experiments Fourth instar larvae of Spodoptera exigua (beet armyworm) 
were purchased from Benzon Research Inc. (Carlisle, PA). On arrival, the larvae were 
maintained at 4°C and provided with an artificial diet supplied by Benzon Research Inc. Before 
the start of each experiment, the larvae were subjected to a 24-hour starvation period at room 
temperature.  
Whole plant choice and no-choice herbivory experiments were conducted using A. fatua 
plants at Zadok's stage 13 under the greenhouse conditions described above. For choice 
21 
 
experiments two plants were transplanted into a 20.32 cm diameter pot and for no choice one 
plant was transplanted into a 20.32 cm diameter pot 7 days before experiment. Each trial 
involved placing a single S. exigua larva on the soil surface, positioned 4 cm from the base of 
either a single MHR or HS plant (no-choice) or equidistantly between MHR and HS plants 
growing together in the same pot (choice), in 20.32 cm diameter pots. 
To prevent larvae escape, pots were covered with 70 cm x 30 cm wide, 300 µ mesh nylon 
insect rearing sleeves (MegaView Science Co., Ltd. Taiwan). The larvae were allowed to feed for 
6 hours, after which they were removed, and all plant shoots were harvested at the soil surface. 
Individual leaves were separated, taped to graph paper, labeled, and photographed for 
quantification of herbivory. The extent of leaf area consumed was determined using the open-
source Java image processing and analysis program ImageJ (Schneider et al., 2012).   
Field Experiments  Whole plant choice and no-choice herbivory experiments were also 
conducted under field conditions using A. fatua plants and S. exigua. Plants were grown at the 
Arthur H. Post Research Farm near Bozeman, MT, located at latitude 35.67 N, longitude 111.15 
W, and an elevation of 1,455 m. The soil type at this location is an Amsterdam silt loam. Each 
plant was grown in a buried 20.32 cm diameter pot with 3.81 cm extending above the soil 
surface to allow insect-rearing sleeves to be fastened around the pot base to prevent insect 
escape. Spodoptera exigua larvae used in field trials were stored, starved, and allowed to feed as 
described above, and leaf areas consumed were recorded as above. Acridomorpha (grasshopper 
species) were gathered directly at the Arthur H. Post Farm site. Grasshopper collection involved 
using a bug net to sweep native grasses in the vicinity of field plots. The captured grasshoppers 
were subsequently placed in individual 50 mL tubes, each fitted with an airhole on the cap. This 
22 
 
collection took place one day prior to the experiment, ensuring a 24-hour period of starvation 
similar to what S. exigua experienced. All experiments were initiated at dawn or early morning. 
This early morning timing of experiment initiated was determined upon environmental factors, 
mainly temperature, as S. exigua was observed to seek heat avoidance with high temperatures.  
Measurements In the analysis of herbivory results, two distinct metrics were employed: mean 
leaf area consumed by S. exigua larvae, expressed in square millimeters (mm²), and mean 
average leaf consumption, expressed as a percentage of the total plant leaf area. Data presented 
will employ the leaf area consumed in (mm2). S1 or S2 leaf consumption was compared with 
each MHR line in pairwise comparison. 
Spodoptera exigua Olfaction Choice Assay 
Olfaction preference experiments were performed by monitoring S. exigua orientation to 
individual air flows from two different, intact plants growing in soil (Fig. 1.1). Avena fatua lines 
R4 and S1 and Lactuca sativa L. (as an unrelated, broadleaf control species) were grown 
separately in 10.16 cm diameter pots enclosed in scentless, clear, closed polyethylene bags 
(12.70 x 10.16 x 60.96 cm). Air flow was controlled at 6 liters min-1 from a commercial grade air 
pump (EcoPlus ECOair 1) attached to 3/16” clear vinyl tubes (Eastrans Inc. Bayboro, NC). Air 
streams were filtered through C290 activated charcoal filters (Etsy, Fort Jones, CA), and then 
passed through LZQ-7 gas flow meters (Hilitand, China), and into the polyethylene bags via slits 
at the bottom. Exhaust tubes from plant bags were secured into slits at the top of each bag, 
leading to a cardboard platform with 2.54 cm diameter holes spaced 15.24 cm apart and covered 
with stainless steel mesh screen (1 mm), so that air from two bagged plants was passing through 
the two holes.  At the initiation of each experiment, one S. exigua larva was placed on the 
23 
 
platform between the two mesh holes, and the number of orientations towards one or the other 
air hole, or no response, was recorded during a one-minute trial. Direct comparisons were made 
between the R4 and S1 lines, filtered airflow and L. sativa, and a control study where no plants 
were assessed. 
 
Figure 1.1. Experimental set up for S. exigua olfaction choice assay. The “x” in the middle of the 
mesh screen represents where the S. exigua was placed for each repetition. 
Statistics 
Herbivory choice and no-choice data from the greenhouse were analyzed using linear 
mixed-effects models in R Studio (RStudio TEAM, 2023). Differences in total leaf area 
consumed between different lines were assessed. Differences in average leaf area consumed were 
also recorded, but the patterns were the same as total leaf area consumed so those results are not 
presented. Choice data was subset to only compare changes in leaf area consumed between 
directly compared lines. An interaction term between target and competing lines was included to 
make this assessment. The same analyses were performed on data collected from the field but an 
additional factor representing insect (S. exigua and Acridomorpha) was included. For the 
olfactory experiments we used linear mixed-effects models to assess the choices made by S. 
24 
 
exigua (choice A, choice B, or none). Variation within and between trials was accounted for with 
random intercept adjustments for all models. Analysis of variance tests (ANOVA) were 
performed to assess treatment group differences. Estimated marginal means (EMMs) with Tukey 
adjustments were used for post-hoc comparisons between treatment groups (Searle et al., 1980). 
All treatments were pseudo replicated 2 to 20 times within an experiment, and experiments were 
run 1 to 7 times. Data from multiple trials were combined after confirming homoscedasticity 
using diagnostic plots. If only one experiment was performed, a linear model was used with no 
mixed effects. 
Results 
Greenhouse Choice Experiments 
In the greenhouse S. exigua preference varied across genotypes. Choice experiments 
showed that R4 leaves were consumed more than S1, as expressed in areas consumed and 
average areas consumed (P ≤ 0.001 and P ≤ 0.05, respectively). Conversely, S1 was consumed 
more than 93R in mean area and mean average consumed (both with significance levels of P ≤ 
0.05) (Figure 1.2). No significant differences were detected for other combinations of S1 and 
other MHR lines or in the S2 choice trials (Table 1.1). There were also no significant differences 
found between S2 and S1 choice trails (data not shown). 
25 
 
Figure 1.2: Mean leaf area consumed in mm2 by S. exigua between A. fatua HS line S1 and 
MHR lines within the choice experiments. No * indicates no significant difference. * Indicates P 
≤ 0.05. *** indicates P ≤ 0.001. a) 18R v S1, b) 20R1 v S1, c) 20R2 v S1, d) 93R v S1, e) R3 v 
S1, f) R4 v S1 
Table 1.1: Mean A. fatua leaf areas consumed (in mm²) in greenhouse S. exigua choice 
experiments. Two-way comparisons were between line S2 and one of six MHR lines within the 
same pot. ‘n’ replicates; p > 0.05 indicates no significant difference between treatments. 
Leaf Area Consumed (mm2) S2 vs MHR lines Greenhouse, Choice 
HS HS Area Consumed MHR MHR Area Consumed 
2 2 n P-Value 
Line (mm ) Line (mm ) 
S2 19.95 (± 8.17) 18R 45.12 (± 13.68) 11 P > 0.05 
S2 25.33 (± 8.04) 20R1 46.99 (± 9.88) 16 P > 0.05 
S2 39.92 (± 15.94) 20R2 66.55 (± 23.81) 17 P > 0.05 
S2 40.16 (± 12.51) 93R 15.71 (± 6.78) 16 P > 0.05 
S2 43.67 (± 10.24) R3 52.7 (± 10.66) 13 P > 0.05 
S2 61.36 (± 16.23) R4 66.61 (± 10.90) 16 P > 0.05 
 
 
26 
 
Greenhouse No-choice Experiments 
In no-choice greenhouse experiments, S. exigua demonstrated significantly higher 
consumption of R4 and S2 compared to 20R2 in terms of mean leaf areas consumed (mm2) 
(Figure 1.3). 
 
Figure 1.3: Mean leaf area consumed in mm2 comparing all A. fatua lines in the greenhouse in 
the no choice experiments. Compact letter display is employed to illustrate significant 
differences. 
Field Choice and No-choice Experiments 
Under field conditions, there were no significant differences in leaf area consumption 
among any plant line pairwise comparisons, for both S. exigua and grasshopper insect herbivores 
(Tables 1.2, 1.3, 1.4, 1.5, 1.6) 
 
 
 
27 
 
Table 1.2: Mean A. fatua leaf areas consumed (in mm²) in field S. exigua choice experiments. 
Two-way comparisons were between line S1 and one of four MHR lines in the same pot. ‘n’ 
replicates; p > 0.05 indicates no significant difference between treatments. 
Leaf Area Consumed (mm2) S1 vs MHR Lines Field, Choice 
HS Area Consumed MHR Area Consumed 
Line (mm2) Line (mm2) n P-Value 
S1 20.11 (± 7.11) 20R1 28.90 (± 8.21) 8 P > 0.05 
S1 28.43 (± 7.78) 93R 10.28 (± 3.31) 6 P > 0.05 
S1 11.74 (± 7.49) R3 36.23 (± 13.70) 6 P > 0.05 
S1 14.38 (± 4.43) R4 25.43 (± 6.83) 10 P > 0.05 
Table 1.3: Mean A. fatua leaf areas consumed (in mm²) in field S. exigua choice experiments. 
Two-way comparisons were between line S2 and one of four MHR lines in the same pot. ‘n’ 
replicates; p > 0.05 indicates no significant difference between treatments. 
Leaf Area Consumed (mm2) S2 vs MHR Lines Field, Choice 
HS Area Consumed MHR Area Consumed 
Line (mm2 n  P-Value 
) Line (mm2) 
S2 14.53 (± 6.41) 20R1 22.46 (± 5.70) 7 P > 0.05 
S2 32.65 (± 11.90) 20R2 34.92 (± 12.79) 4 P > 0.05 
S2 26.70 (± 9.31) 93R 30.84 (± 13.44) 8 P > 0.05 
S2 30.07 (± 8.19) R4 49.08 (± 7.78) 9 P > 0.05 
Table 1.4: Mean A. fatua leaf areas consumed (in mm²) in field S. exigua no-choice experiments. 
Multi-way comparisons were between six A. fatua lines. ‘n’ replicates; compact letter design 
employed to see statistical differences. 
Leaf Area Consumed (mm2) All Lines, Field, No-Choice 
A. fatua Line Area Consumed (mm2) n Compact Letter Design 
S2 41.42 (± 10.27) 12 a 
S1 42.11 (± 7.43) 14 a 
93R 31.00 (± 7.87) 9 a 
R4 66.02 (± 16.53) 12 a 
20R1 26.35 (± 6.06) 11 a 
20R2 29.57 (± 11.18) 7 a 
 
 
 
28 
 
Table 1.5: Mean A. fatua leaf areas consumed (in mm²) in field grasshopper choice experiments. 
Two-way comparisons were between line S1 by 20R1 and 20R2, and S2 by 20R1 and 20R2 in 
the same pot. ‘n’ replicates; p > 0.05 indicates no significant difference between treatments. 
Leaf Area Consumed (mm2) HS vs MHR Lines, Field, Choice, Grasshopper 
HS Area Consumed MHR Area Consumed 
2 2 n P-Value 
Line (mm ) Line (mm ) 
S1 51.27 (± 10.36) 20R1 18.39 (± 7.65) 2 P > 0.05 
S1 92.69 (± 46.06) 20R2 55.63 (± 39.01) 3 P > 0.05 
S2 100.56 (± 59.14) 20R1 37.29 (± 23.52) 4 P > 0.05 
S2 22.62 (± 22.62) 20R2 37.29 (± 28.17) 2 P > 0.05 
Table 1.6: Mean A. fatua leaf areas consumed (in mm²) in field grasshopper no-choice 
experiments. Multi-way comparisons were between four A. fatua lines. ‘n’ replicates; compact 
letter design employed to see statistical differences. 
Leaf Area Consumed (mm2) Four Lines, Field, No-Choice, Grasshopper 
A. fatua Line Area Consumed (mm2) n Compact Letter Design 
S2 72.16 ± (26.5) 7 a 
S1 102.09 ± (37.5) 9 a 
93R 77.61 ± (54.3) 4 a 
18R 31.99 ± (9.3) 7 a 
 
Olfactory Preferences of S. exigua 
 Greenhouse S. exigua olfactory preference experiments did not detect differences in 
orientation between MHR and HS A. fatua plants (Figure 1.4). However, orientations towards 
Lactuca plants occurred significantly more often than to filtered air alone (Figure 1.5). This can 
be seen as the number of times a S. exigua motioned to a hole emitting headspace VOCs. Control 
experiments comparing two streams of filtered air showed a no significant preference to filtered 
air flow (Figure 1.6). This indicates that the headspace VOCs being pushed through the tubes are 
being sensed, and S. exigua is indicating towards a VOC profile. 
29 
 
Figure 1.4 - Olfactory choices of S. exigua regarding A. fatua lines R4 and S1. Each bar 
represents the count of olfactory choices made by S. exigua. Compact letter design is employed 
to indicate significant differences among the groups. 
 
 
30 
 
Figure 1.5 –Olfactory choices of S. exigua regarding Lactuca sativa L. and filtered airflow. Each 
bar represents the count of olfactory choices made by S. exigua. Compact letter design is 
employed to indicate significant differences among the groups. 
 
 
31 
 
Figure 1.6 – Bar graph illustrates the olfactory choices of S. exigua regarding filtered airflow and 
filtered airflow. Each bar represents the count of olfactory choices made by S. exigua. Compact 
letter design is employed to indicate significant differences among the groups. 
Discussion 
The main objective of this study was to investigate the attraction and feeding choices of 
the insect generalist herbivore S. exigua between MHR and HS A. fatua seedling plants in 
greenhouse and field settings. Field experiments were replicated using native grasshoppers.  
In greenhouse choice trials, larvae of the generalist herbivore S. exigua consumed R4 
significantly more than S1. However, S2 choice trials did not yield statistically significant 
results. This behavior aligns with S. exigua's documented polyphagous nature, indicating its 
willingness to consume plant species it prefers (Hafeez et al., 2021; Moulton et al., 2000; 
Vickerman & Trumble, 1999). However, when S2 and R4 were used in a choice experiment, 
their consumption was relatively equal. This suggests that S2 is preferable over S1, but only in 
the presence of R4, indicating that damage by an herbivore does not consistently differ among 
32 
 
resistance and sensitive lines. When S2 and S1 were present in a choice experiment there was no 
significant difference between them. However, a reversal occurred in the presence of 93R, where 
S1 was consumed significantly more. Indicating that S. exigua does prefer S1 over some other 
lines, showing that they are making a conscious choice towards any line. Also, S2 and 93R did 
not show any significant difference. So, these results may show that R4 is heavily preferred over 
S1 but not compared to other lines. This trend was consistent across both metrics of 
measurement: area consumed (mm2) and the average consumed area by to the total shoot area.  
In no-choice experiments, S2 and R4 were found to be the most consumed lines, but data 
was only significantly larger than 20R2 in total area consumed. This data highlights the 
preference for R4 and S2 and indicates that S. exigua does not consume one more than the other 
during choice experiments. Notably, the comparison between S2 and 20R2 yields a noteworthy 
contrast, with the pattern changing when a competing choice was offered. While these lines 
exhibited a significant difference in the no-choice experiment, the presence of both in a choice 
experiment resulted in a consumption pattern where 20R2 was consumed more, albeit the 
differences were not significant. Observations in the field did not reveal any significant 
differences in total leaf area consumed in all lines, and insect generalist. This implies that 
environmental factors can have a mediating effect on herbivore choice and total consumption. 
For example, wind, sun, temperature can all influence the feeding behavior as well as VOC 
production from plants (Tauber et al., 1976; Holopainen and Gershenzon, 2010). 
The olfactory preference experiments yielded contradictory results compared to the 
choice experiments between R4 and S1. In the choice experiments, no significant differences 
were observed between R4 and S1. These differences suggest that S. exigua may exhibit a 
33 
 
preference for VOCs from R4 and S1, although consumption was greater for R4 plants, 
indicating that while VOCs may guide initial orientation or attraction, other factors such as 
nutritional value, physical plant characteristics, or secondary metabolites could be influencing 
the ultimate feeding choice of an insect herbivore (Moreira et al., 2016). A lack of consistency in 
olfactory data led me to conduct additional olfactory preference experiments to troubleshoot the 
experimental design for potential flaws. 
These experiments were done to extend and complement S. exigua studies previously 
performed using only S1 and R4 A. fatua lines (Keith et al, in preparation). The pilot study that 
was conducted found that S. exigua consumed MHR R4 and R3 lines 2.2 to 3.0-fold more than 
HS S1 lines. It is important to note that the data collection methods and experimental design 
were nearly identical to these experiments documented here. The R4 and S1 comparison holds 
true for this experiment; however, the findings for R3 and S1 were not. 
Considering the relatively low number of significant findings across all experiments, 
perhaps the previous studies our experiments were motivated by was not representative of the 
true relationship between plant stress and insect generalist. Research in this area remains 
relatively scarce, and the comparison between herbicide resistance and herbivore behavior can be 
convoluted. However, some previous findings offer valuable insights, particularly in the context 
of R4 and S1 preferences discussed earlier. Two studies by the same research team found that 
herbicide resistant Amaranthus hybridus were more susceptible to damage from herbivores 
(Gassmann & Futuyma, 2005; Gassmann, 2005). These papers support the findings for the R4 
and S1 choice experiments but the 93R and S1 choice experiments present a contrast. 
34 
 
As the majority of these herbivory experiments have yielded insignificant results, and 
several of these experiments, particularly those conducted in controlled greenhouse 
environments, have been thoroughly tested. Therefore, alternative factors must be explored that 
can influence the preference of an insect generalist. One key factor that could potentially 
contribute to these preferences is genetic differences among these A. fatua lines, which may 
control mechanisms that render one line more preferable to another. This could be changing the 
expression of a gene that unintentionally makes a plant more attractive to an herbivore. For 
example, transgenerational effects and genome polyploidy could modify the generation of VOCs’ 
profiles of offspring (Picazo-Aragonés et al., 2020). VOCs have also been documented to play a 
significant role in herbivory defenses (Brilli et al., 2019; Zhou & Jander, 2021). As R4 and S1 
headspace VOC profiles have been collected and analyzed, the rest of the lines have not been. R4 
was consumed significantly more than S1 in choice trials possibly indicating that the 9 elevated 
VOCs from R4 could be attracting S. exigua. However, the VOC emissions from the other lines 
are unknown, and would require further investigation.  
It should be noted that several factors could have influenced the outcomes of this study. 
These may include unidentified variables altering plant attractiveness, genetic variations among 
A. fatua lines, or even the possibility of human errors during data collection. Consequently, 
further research is required to completely explore and understand these factors. The potential 
application of this research, which investigates the use of an insect generalist species to control 
invasive weeds, presents certain challenges. Notably, our experiments lack a comparison with 
crop species. It would be interesting to compare different A. fatua lines with various crop species 
and assess their attractiveness to insect herbivores. Further, conducting an in-depth analysis of all 
35 
 
A. fatua lines VOC profiles would be extremely valuable. Such an analysis could carry 
significant ecological implications if herbicide-resistant weeds are indeed becoming more 
attractive to insect generalists. This scenario might lead to an increase in insect damage among 
both crop and weed species, potentially posing a greater challenge for producers than the innate 
issues of an herbicide-resistant weed. 
Conclusion 
Avena fatua R4 lines were significantly preferred over S1 lines and S1 was preferred 
compared to 93R for choice experiments in a controlled greenhouse setting. Unfortunately, the 
remaining experiments, whether in choice or no-choice conditions, did not provide clear 
evidence of a preference by the insect generalist. Our field experiments also yielded inconclusive 
results, and in the greenhouse, only two choice experiments produced significant findings. One 
of these indicated that an MHR line was not preferred. This means that there are other 
differences being observed other than herbicide resistant capabilities between these sensitive and 
multiple resistant A. fatua lines. However, this research has highlighted certain aspects that could 
be responsible for this outcome. For example, both environmental changes and genetic variations 
are suspected of strong influence on the behavior of herbivorous insects. This observation, drawn 
from the outcomes of both greenhouse and field experiments, contributes to the ongoing 
discussion about the complexities of herbicide resistance and ecological implications. 
 
 
 
 
36 
 
CHAPTER THREE 
 
EXOGENOUS VOLATILE ORGANIC COMPOUNDS EFFECTS ON HERBICIDE 
RESISTANCE IN TWO AVENA FATUA LINES 
Summary 
This research is aimed to determine the effects of VOCs on the efficacy of herbicides in 
multiple herbicide resistant (MHR) R4 and herbicide sensitive (HS) S2 Avena fatua lines. Prior 
evidence has shown that MHR A. fatua lines are constitutively primed to resist stress, and their 
elevated VOC emissions are maybe a part of that defense. This study explored the potential of 
VOCs in plant-plant communication and their influence on herbicide resistance. R4 and S2 
plants were exposed to VOCs, including linalool or limonene, or a combination VOC treatment 
including linalool, limonene, methyl salicylate, and methyl jasmonate, followed by a herbicide 
treatment of flucarbazone, imazamethabenz, or pinoxaden at sublethal doses. Linalool 
demonstrated a protective effect against flucarbazone in S2 lines, but this was not observed with 
other VOCs or herbicides. The combined VOC treatment enhanced herbicide injury in both S2 
and R4 lines, particularly against imazamethabenz. This study contributes to understanding 
VOCs’ priming roles in herbicide injury responses. Future research should focus on isolating 
VOC effects in controlled environments and exploring the mechanisms underlying VOC-
mediated stress tolerance, thereby enhancing our understanding of VOC roles in plant defenses. 
 
 
 
37 
 
Introduction 
Plants exhibit many complex defense mechanisms, including the ability to emit volatile 
organic compounds (VOCs) in response to stress. In addition to inducing internal defense 
responses, these compounds serve as chemical messengers, alerting neighboring plants to 
upcoming environmental stress and reinforcing defensive mechanisms (Brilli et al., 2019). These 
defenses are critical in response to environmental stressors like temperature extremes and 
drought (Bezerra et al., 2021; Bolsoni et al., 2018). The influence of VOCs on plant resilience to 
abiotic and biotic stressors has been well-documented in the literature (Brilli et al., 2019; 
Dudareva et al., 2013).  
Methyl jasmonate and methyl salicylate are methyl ester VOCs involved in several 
regulatory roles in stress response pathways. Methyl salicylate is critical for activating defense 
responses mainly associated with biotic stressors like pathogen infections (Li et al., 2019). 
Methyl jasmonate has known roles in priming plant defense mechanisms (Santino et al., 2013) 
and is mainly associated with defense responses to abiotic stressors like drought, soil salinity, 
and temperature change (Wang et al., 2021). The exogenous application of VOCs such as methyl 
salicylate and methyl jasmonate has been documented to alter the plant’s herbicide response. 
Herbicides can generate reactive oxygen species (ROS). Methyl salicylate pre-treatment 
generally attenuated ROS content and increased ROS-scavenging enzyme activity after 
applications of several herbicides such as halosulfuron-methyl and mesosulfuron-methyl 
(Khatami et al., 2022; Li et al., 2020). In contrast, methyl salicylate pre-treatment exacerbated 
atrazine injury in Nicotiana tabacum (Tobacco) and Arabidopsis thaliana (Thale cress) 
(Silverman et al., 2005). Pre-treatment with methyl jasmonate has shown a reduction in the 
38 
 
efficacy of paraquat (PSI inhibitor, Group 22) in Hordeum vulgare (barley) and Nicotiana 
tabacum (Tobacco), lessening the herbicide’s impact on photosynthetic efficiency (Hristova & 
Popova, 2002; Silverman et al., 2005). One example of an exogenous VOC effect on herbicide 
resistant plants, methyl jasmonate was found to decrease the efficacy of the growth-inhibitory 
herbicide quinclorac in resistant biotypes of Echinochloa crus-galli (barnyardgrass), while it did 
not affect herbicide response in quinclorac-sensitive strains (Jingjing, 2020).  
The VOC profile emitted can vary between herbicide resistant and sensitive biotypes and 
may contribute to differential herbicide responses. For example, in Avena fatua, monoterpenoids 
such as linalool and limonene, which were found to be released in higher quantities from R4 
lines (Keith et al in preparation). Linalool, when applied in high concentrations, has been 
demonstrated to negatively affect growth parameters in Lolium rigidum (rigid ryegrass), 
suggesting that it may prime plants to be more susceptible to herbicide treatments (Vasilakoglou 
et al., 2013). Contrastingly, limonene did not exhibit a similar inhibitory impact in this study. 
However, limonene has been recognized for its role in conferring resistance against abiotic 
stresses, and linalool is primarily linked to the defense against biotic stresses (Fujioka et al., 
2015; Llusia et al., 2005; Sun et al., 2014; Zhang et al., 2023). These findings illustrate the 
intricate roles that such compounds play in plant physiology and stress response mechanisms. 
MHR A. fatua plants constitutively emit elevated levels of six monoterpenoid, decanal, 
and methyl salicylate VOCs, and preferentially induce their emission in response to insect 
herbivory and mechanical wounding (Keith et al in preparation). These results raise the 
interesting possibility that VOCs are directly involved in MHR maintenance and evolution. 
Specifically, elevated VOCs from MHR plants could influence neighboring HS A. fatua plants in 
39 
 
their tolerance to an herbicide. A plant’s use of VOCs for stress communication is a well-
established defense strategy, suggesting that MHR A. fatua may be “warning” neighboring plants 
against stressors, specifically herbicide stress. My goal was to determine if exogenous VOCs 
change HS or MHR plant responses to subsequent herbicide treatments.  
This work is based on preliminary studies that demonstrated that limonene and linalool 
improve resistance in S2 plants to the herbicides flucarbazone and pinoxaden, respectively. The 
preliminary findings contradict existing studies which indicate that exogenous linalool and 
limonene exacerbate the effects of herbicides, suggesting instead that the VOCs may confer 
protective benefits (Vasilakoglou et al., 2013). The goal is to investigate the influence of these 
VOCs individually and combined effect with all four on the efficacy of herbicides in MHR R4 
and HS S2 A. fatua lines. It is well-established that VOCs can induce an enhanced stress 
response in plants facing abiotic challenges (Holopainen & Gershenzon, 2010). Including 
reactions to high temperatures, drought, and salinity. These responses underscore the complex 
role of VOCs in enhancing plant resistance (Possell & Loreto, 2013). 
Methods 
Plant Material 
 Avena fatua lines S2 and R4 were grown in individual 10.16 cm2 pots to Zadok’s stage 13 
size. All experiments were initiated mid-morning to minimize any potential circadian effects. 
Each individual experiment was run with A. fatua lines, S2 or R4. Growing conditions as 
described in Chapter 2: Insect Generalist Preference to MHR Avena fatua  
 
40 
 
Exogenous VOC Treatments 
Plants were placed in clear 61 cm x 61 cm (38L) polyethylene bags held upright with 
wire tomato cages, and the top of the bag was left open, for open bag systems. Closed bag was 
held up with a bamboo stake without wire tomato cages. Linalool (Sigma Aldrich, L2602) and 
limonene (Sigma Aldrich, 62118) were diluted in 100% ethanol to final concentrations of 0.56 M 
and 0.62 M, respectively. Control treatments received 100% ethanol only. 10 µL of each solution 
was dispensed onto 7.1 cm2 of 3mm filter paper (Whatman, United Kingdom), which was taped 
to the top of a 46 cm tall bamboo stake and secured upright in the center of plant pots. CO2 
concentrations in closed and open bags were monitored (SCD30 CO2 Module, Sensirion, 
Switzerland). In both open and closed systems, plants were exposed to VOC treatments or 
ethanol for 6 hours under ambient greenhouse conditions. 
VOC mixtures were used to assess a synergistic treatment comprising linalool, limonene, 
methyl jasmonate (Sigma Aldrich, 392707), and methyl salicylate (Sigma Aldrich, 76631). 
Methyl jasmonate and methyl salicylate were gradually diluted in glass-distilled water to achieve 
a final concentration of 3 mM for each compound. To this solution, we added 0.125% (v/v) of a 
nonionic surfactant (X-77, Loveland Products Inc., Loveland, CO). Plants were relocated to a 
darkened room where they were uniformly misted with either the 3 mM methyl jasmonate and 
methyl salicylate solution or a control solution (consisting solely of 0.125% surfactant in water) 
until the point of runoff. After the application, the plants remained in the dark for two hours to 
avoid photodegradation of methyl salicylate and methyl jasmonate. Following this, the plants 
were transferred to the greenhouse where 0.56 M linalool and 0.62 M limonene was applied as 
described above. Linalool and limonene were administered onto the same piece of filter paper, 
41 
 
which was then removed after a 6-hour exposure, culminating in an 8-hour period for the 
combined VOC treatment. 
Herbicide Treatments 
After VOC treatment, plants were treated with a low concentration of herbicide. S2 plants 
were treated with flucarbazone (EverestTM), imazamethabenz (ASSERTTM), and pinoxaden 
(AXIALTM). Plants were treated with either 73.9, 83.7, or 98.8 g a.i. ha-1 (R4 plants), or 3, 4.9, 
6.9, or 9.9 g a.i. ha-1 (S2 plants) flucarbazone, or 896.9, 1,029.4, or 1,175.2 g a.i. ha-1 (R4 plants) 
or 66.3, 110.5, 154.6, or 221 g a.i. ha-1 (S2 plants) imazamethabenz, or 13.1, 17.5, or 21.9 g a.i. 
ha-1 (R4 plants), or 2.6, 4.4, 6.1, or 8.8 g a.i. ha-1 (S2 plants) pinoxaden. Herbicides were applied 
using a moving nozzle sprayer in a total volume of 94L ha-1 as described (Keith et al., 2015). 
After herbicide sprays had dried, plants were returned to the greenhouse for 21 days, after which 
shoots were harvested, dried at 40°C for one week, and weighed.  
Statistics 
Mean dry biomass data from exogenous VOC and supplemental herbicide treatment were 
analyzed using linear mixed-effects models in R Studio (RStudio TEAM, 2023). To determine 
weight differences among treatments. Mean dry biomass data was subset by VOC and herbicide 
treatment to compare changes in the same herbicide, A. fatua line, and VOC treatments. 
Variation within and between trials was determined using random intercept adjustments for all 
models. Analysis of variance (ANOVA) was performed to assess treatment group differences. 
Estimated marginal means (EMMs) with Tukey adjustments were used for post-hoc comparisons 
among treatment groups (Searle et al., 1980). The S2 open bag exogenous linalool treatments 
included 16-31 technical replicates, which each experiment repeated 3-7 times. The rest of the 
42 
 
treatment combinations in open and closed bags were included 5-33 technical replicates with 1-3 
experimental repeats. Data from multiple trials using the same VOC and herbicide, and all 
control treatments, were combined after confirming homoscedasticity using diagnostic plots. The 
R4 closed bag testing exogenous limonene with flucarbazone and pinoxaden, and exogenous 
linalool with all three herbicide treatments were performed only once, and so a linear model was 
used with no mixed effects for data analysis. 
Results 
Open Bag Experiments 
 Two control treatments were used for open bag experiments: a 'no VOC' treatment and an 
'ethanol' treatment. Ethanol, while not classified as a VOC, was utilized as a solvent in treatments 
involving linalool and limonene. Comparative analysis revealed no significant difference in the 
outcomes between the 'no VOC' and 'ethanol' treatments. Given this finding and ethanol's role in 
other treatments, we have designated ethanol as a standard control for all experimental 
comparisons and will be documented as “None”. This approach enables a consistent standard 
across treatments, focusing on VOC effects. 
Table 2.1 shows the mean dry biomass of S2 lines with exogenous linalool effects in open 
bag experiments. Linalool significantly reduced herbicide injury of plants treated with 3 g a.i.  
ha-1 flucarbazone, and increased herbicide injury with 4.4 g a.i. ha-1 pinoxaden. 
 
 
 
43 
 
Table 2.1: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for S2 Lines with 
Exogenous Linalool Across Herbicide Treatments in Open Bags with P-Values 
Mean Dry 
Herbicide VOC Biomass             n P 
Herbicide Dose (g ± SE) 
None 1.55 (± 0.04)  63 
None None > 0.05 
Linalool 1.53 (± 0.04)  67 
            
None 0.39 (± 0.03)  31 
flucarbazone 4.9 g a.i. ha-1 ≤ 0.05 
Linalool 0.57 (± 0.08)  27 
            
None 0.33 (± 0.02)  31 
flucarbazone 9.9 g a.i. ha-1 > 0.05 
Linalool 0.30 (± 0.01)  27 
            
None 0.58 (± 0.06)  20 
imazamethabenz 110.5 g a.i. ha-1 > 0.05 
Linalool 0.70 (± 0.06)  20 
            
None 0.45 (± 0.04)  21 
imazamethabenz 221 g a.i. ha-1 > 0.05 
Linalool 0.45 (± 0.03)  20 
            
None 1.48 (± 0.08)  16 
pinoxaden 4.4 g a.i. ha-1 ≤ 0.001 
Linalool 0.64 (± 0.10)  16 
            
None 0.34 (± 0.02)  16 
pinoxaden 8.8 g a.i. ha-1 ≤ 0.01 
Linalool 0.64 (± 0.10)  16 
 
Table 2.2 shows the mean dry biomass of S2 lines with exogenous limonene effects in 
open bag experiments. There was no significance detected among any of the treatments. 
 
 
 
 
 
 
44 
 
Table 2.2: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for S2 Lines with 
Exogenous Limonene Across Herbicide Treatments in Open Bags with P-Values 
Mean Dry 
Herbicide Herbicide Dose VOC Biomass           n p 
(g ± SE) 
None 1.72 (± 0.06) 8 
None None > 0.05 
Limonene 1.64 (± 0.06) 8 
            
None 0.48 (± 0.08) 8 
flucarbazone 4.9 g a.i. ha-1 > 0.05 
Limonene 0.59 (± 0.13) 8 
            
None 0.41 (± 0.05) 8 
flucarbazone 9.9 g a.i. ha-1 > 0.05 
Limonene 0.34 (± 0.04) 8 
            
None 0.62 (± 0.09)  8 
imazamethabenz 110.5 g a.i. ha-1 > 0.05 
Limonene 0.59 (± 0.05)  8 
            
None 0.40 (± 0.05)  8 
imazamethabenz 221 g a.i. ha-1 > 0.05 
Limonene 0.39 (± 0.04)  8 
            
None 1.01 (± 0.12)  12 
pinoxaden 4.4 g a.i. ha-1 > 0.05 
Limonene 1.12 (± 0.12)  12 
            
None 0.43 (± 0.05)  12 
pinoxaden 8.8 g a.i. ha-1 > 0.05 
Limonene 0.34 (± 0.05)  12 
 
CO2 Concentrations 
The rest of the experiments were performed using a closed bag system, as cross-
contamination or evaporation could be occurring in open bag experiments. CO2 concentrations 
were monitored in a closed system and found that the CO2 levels did decrease to 150 ppm and 
stayed steady at 150 ppm for the remainder of the experiment, indicating the plants were 
experiencing stress. In contrast, the open bag systems had CO2 levels above 350 ppm. CO2 
above 350 ppm is considered non-stress (Dippery et al., 1995; Gerhart & Ward, 2010). 
 
45 
 
The control treatments used for closed bag experiments were “no VOC in closed bag”, 
and “ethanol in a closed bag”. Table 2.3 shows the mean dry biomass of S2 lines with exogenous 
linalool effects in a closed bag experiment. For flucarbazone treatments at 3 and 6.9 g a.i. ha-1, 
the linalool treated plants had significantly higher biomass compared to the control. The 
imazamethabenz treatments yielded no significant differences. For the pinoxaden-treated group, 
a significant difference was recorded at the 2.6 g a.i. ha-1 dose as the linalool treatment was 
significantly lower than the control. There were no other significant differences among the other 
pinoxaden treatments. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
46 
 
Table 2.3: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for S2 Lines with 
Exogenous Linalool Across Herbicide Treatments in Closed Bags with P-Values 
Mean Dry 
Herbicide Herbicide Dose VOC n p 
Biomass (g ± SE) 
None 1.48 (± 0.04) 29 
None None > 0.05 
Linalool 1.32 (± 0.07) 33 
            
None 0.51 (± 0.04) 18 
flucarbazone 3 g a.i. ha-1 ≤ 0.01 
Linalool 0.63 (± 0.05) 18 
            
None 0.52 (± 0.03) 18 
flucarbazone 4.9 g a.i. ha-1 > 0.05 
Linalool 0.55 (± 0.05) 18 
            
None 0.39 (± 0.02)  18 
flucarbazone 6.9 g a.i. ha-1 ≤ 0.01 
Linalool 0.50 (± 0.03)  18 
            
None 0.47 (± 0.07)  5 
imazamethabenz 66.3 g a.i. ha-1 > 0.05 
Linalool 0.45 (± 0.04)  5 
            
None 0.37 (± 0.04)  11 
imazamethabenz 110.5 g a.i. ha-1 > 0.05 
Linalool 0.30 (± 0.02)  11 
            
None 0.37 (± 0.04)  5 
imazamethabenz 154.6 g a.i. ha-1 > 0.05 
Linalool 0.43 (± 0.02)  5 
            
None 1.19 (± 0.05)  15 
pinoxaden 2.6 g a.i. ha-1 ≤ 0.01 
Linalool 1.00 (± 0.08)  15 
            
None 0.45 (± 0.06)  15 
pinoxaden 4.4 g a.i. ha-1 > 0.05 
Linalool 0.47 (± 0.06)  15 
            
None 0.34 (± 0.04) 15 
pinoxaden 6.1 g a.i. ha-1 > 0.05 
Linalool 0.32 (± 0.03) 15 
 
Table 2.4 shows the mean dry biomass of S2 lines with exogenous limonene effects in a 
closed bag experiment. Limonene reduced the herbicide injury significantly for the 4.9 g a.i. ha-1 
dose of flucarbazone. No other significant differences were found. 
47 
 
Table 2.4: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for S2 Lines with 
Exogenous Limonene Across Herbicide Treatments in Closed Bags with P-Values 
Mean Dry 
Herbicide Herbicide Dose VOC n P 
Biomass (g ± SE) 
None 1.28 (± 0.05)  27 > 
None None 
Limonene 1.14 (± 0.06)  31 0.05 
            
None 0.24 (± 0.02) 8 
flucarbazone 4.9 g a.i. ha-1 ≤ 
 
Limonene 0.30 (± 0.02) 8 0.001 
            
None 0.74 (± 0.10)  10 > 
imazamethabenz 66.3 g a.i. ha-1 
Limonene 0.56 (± 0.04)  10 0.05 
            
None 0.41 (± 0.04)  10 > 
imazamethabenz 110.5 g a.i. ha-1 
Limonene 0.39 (± 0.01)  10 0.05 
            
None 0.36 (± 0.02)  10 > 
imazamethabenz 154.6 g a.i. ha-1 
Limonene 0.35 (± 0.02)  10 0.05 
            
None 0.92 (± 0.14)  9 > 
pinoxaden 2.6 g a.i. ha-1 
Limonene 0.90 (± 0.09)  9 0.05 
            
None 0.83 (± 0.07)  9 > 
pinoxaden 4.4 g a.i. ha-1 
Limonene 0.80 (± 0.07)  9 0.05 
            
None 0.35 (± 0.04)  9 > 
pinoxaden 6.1 g a.i. ha-1 
Limonene 0.29 (± 0.02)  9 0.05 
 
Table 2.5 shows the mean dry biomass of R4 lines with exogenous linalool effects in a 
closed bag experiment. No significant differences were observed. 
 
 
 
 
48 
 
Table 2.5: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for R4 Lines with 
Exogenous Linalool Across Herbicide Treatments in Closed Bags with P-Values 
A. Mean Dry 
fatua Herbicide Herbicide Dose VOC Biomass             n P 
Line (g ± SE) 
None 1.36 (± 0.07) 26 
R4 None None > 0.05 
Linalool 1.36 (± 0.07) 26 
              
None 0.73 (± 0.05) 6 
R4 flucarbazone 73.9 g a.i. ha-1 > 0.05 
Linalool 0.66 (± 0.05) 6 
              
None 0.66 (± 0.05)  6 
R4 flucarbazone 78.8 g a.i. ha-1 > 0.05 
Linalool 0.71 (± 0.06)  6 
              
None 0.69 (± 0.05)  6 
R4 flucarbazone 83.7 g a.i. ha-1 > 0.05 
Linalool 0.82 (± 0.08)  6 
              
None 1.03 (± 0.13)  11 
R4 imazamethabenz 896.9 g a.i. ha-1 > 0.05 
Linalool 0.97 (± 0.09)  11 
              
None 0.82 (± 0.06)  11 
R4 imazamethabenz 1,029.4 g a.i. ha-1 > 0.05 
Linalool 0.96 (± 0.09)  11 
              
None 0.79 (± 0.08)  11 
R4 imazamethabenz 1,175.2 g a.i. ha-1 > 0.05 
Linalool 0.80 (± 0.04)  11 
              
None 0.74 (± 0.06)  9 
R4 pinoxaden 17.5 g a.i. ha-1 > 0.05 
Linalool 0.69 (± 0.05)  9 
 
Table 2.6 shows the mean dry biomass of R4 lines with exogenous limonene effects in a 
closed bag experiment. No significant differences were observed. 
 
 
49 
 
Table 2.6: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for R4 Lines with 
Exogenous Limonene Across Herbicide Treatments in Closed Bags with P-Values 
A. Mean Dry 
fatua Herbicide Herbicide Dose VOC Biomass         n P 
Line (g ± SE) 
None 0.85 (± 0.03)  26 
R4 None None > 0.05 
Limonene 0.85 (± 0.03)  26 
              
None 0.28 (± 0.04)  8 
R4 flucarbazone 78.8 g a.i. ha-1 > 0.05 
Limonene 0.39 (± 0.01)  8 
              
None 0.39 (± 0.05)  12 
R4 imazamethabenz 1,029.4 g a.i. ha-1 > 0.05 
Limonene 0.40 (± 0.07)  12 
              
None 0.74 (± 0.06)  8 
R4 pinoxaden 17.5 g a.i. ha-1 > 0.05 
Limonene 0.77 (± 0.08)  8 
 
Table 2.7 shows the mean dry biomass of S2 lines with the combined exogenous VOCs 
and its effects on herbicide potency. The combined VOCs had a significantly negative effect for 
the imazamethabenz and pinoxaden treatment.  
 
 
 
 
 
 
 
 
 
50 
 
Table 2.7: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for S2 Lines with 
Exogenous Combined VOCs Across Herbicide Treatments in Closed Bags with P-Values 
Mean Dry Biomass 
Herbicide Herbicide Dose VOC n P 
(g ± SE) 
None 1.17 (± 0.06) 42 
> 
None None Combined 
1.16 (± 0.06) 42 0.05 
VOCs 
            
None 0.33 (± 0.02) 18 
-1 > 
flucarbazone 4.9 g a.i. ha  Combined 
0.33 (± 0.02) 18 0.05 
VOCs 
            
None 0.43 (± 0.04) 33 
-1 ≤ 
imazamethabenz 110.45 g a.i. ha  Combined 
0.27 (± 0.02) 32 0.001 
VOCs 
            
None 0.36 (± 0.04) 16 
≤ 
pinoxaden 4.4 g a.i. ha-1 Combined 
0.26 (± 0.02) 16 0.05 
VOCs 
 
Table 2.8 shows the mean dry biomass of R4 lines with the combined exogenous VOCs 
and its effects on herbicide potency. Combined VOCs had a significantly negative effect on dry 
biomass for imazamethabenz treatment. No significant differences were found for the other 
treatments. 
 
 
 
 
 
 
 
 
51 
 
Table 2.8: Pairwise Comparisons of Mean Dry Biomass (Grams ± SE) for R4 Lines with 
Exogenous Combined VOCs Across Herbicide Treatments in Closed Bags with P-Values 
Mean Dry 
A. fatua 
Herbicide Herbicide Dose VOC Biomass      n P 
Line 
(g ± SE) 
None 1.18 (± 0.10) 26 
R4 None None Combined > 0.05 
1.14 (± 0.11) 28 
VOCs 
              
None 0.81 (± 0.08) 18 
R4 flucarbazone 78.8 g a.i. ha-1 Combined > 0.05 
0.69 (± 0.06) 18 
VOCs 
              
None 1.06 (± 0.11) 18 
R4 imazamethabenz 1,029.4 g a.i. ha-1 Combined ≤ 0.05 
0.77 (± 0.10) 18 
VOCs 
              
None 0.39 (± 0.01) 25 
R4 pinoxaden 17.5 g a.i. ha-1 Combined > 0.05 
0.35 (± 0.04) 25 
VOCs 
 
Discussion 
 The objective of these studies was to investigate whether VOCs influence the efficacy of 
herbicides in MHR R4 and HS S2 A. fatua lines. Prior research has demonstrated that MHR A. 
fatua lines have an inherent readiness to withstand various stressors (Burns et al., 2018; Dyer, 
2018; Keith et al., 2017), and that they exhibit higher VOC emissions than S2 counterparts, 
indicating a possible role in plant-plant communication. To test these ideas, MHR and HS plants 
were exposed to exogenous VOCs, followed by treatments with flucarbazone, imazamethabenz, 
and pinoxaden herbicides at several sublethal doses. 
Preliminary experiments, in a closed bag system, demonstrated that limonene and linalool 
exhibited a protective effect against flucarbazone and pinoxaden applications, respectively. It 
was later suggested by a colleague that these enclosed environments might impose CO2 
52 
 
limitations on the treated plants. Therefore, subsequent experiments I conducted were in an open 
bag setup to mitigate CO2 constraints. Despite the open-bag approach, concerns regarding 
potential cross-contamination and evaporation led to a reversion to the closed-bag method in 
later experiments. CO2 levels within these closed environments were monitored, revealing a 
decline in concentration after the first hour, eventually stabilizing at 150 ppm. At 150 ppm a 
plant is considered CO2 stressed (Dippery et al., 1995; Gerhart & Ward, 2010). Consequently, all 
experiments conducted in closed bags were classified as experiencing CO2-stressed conditions. 
Notably, control experiments within these CO2-stressed environments displayed no significant 
differences in plant growth compared to those conducted without bag confinement. This outcome 
suggests that the CO2 limitation inherent to the closed-bag setup did not notably impact plant 
growth under the conditions of our study. 
Linalool had a protective effect in closed and open bags to flucarbazone, however it 
increased injury to pinoxaden. Pretreatment of S2 plants with linalool reduced injury from 4.9 g 
a.i. ha-1 flucarbazone in the open bag system (Table 2.1). In the closed bag system, 3, and 6.9 g 
a.i. ha-1 flucarbazone treatments found that linalool treated plants had higher dry biomass weight 
(Table 2.3). Indicating that linalool pre-treatments have a protective role to sublethal 
flucarbazone doses. However, the pinoxaden dose of 4.4 g. a.i. ha-1 shows a potentiating effect of 
pinoxaden, as it was significantly lower weight than the control in the open bag systems. For the 
closed bag systems, linalool had the same negative effect for 2.6 g a.i. ha-1 dose of pinoxaden. 
This indicates that linalool has a protective role against flucarbazone, but a non-protective role 
for pinoxaden. There were no significant differences found for any of the imazamethabenz 
53 
 
sublethal herbicide doses. R4 plant exposed to linalool in the closed system did not show any 
significance either (Table 2.5). 
Limonene had a slight protective effect against flucarbazone but did not show any 
protective roles for the other herbicides used. The pretreatment of exogenous limonene did not 
confer protection to S2 plants against any herbicide treatments when conducted in an open bag 
system (Table 2.2). However, in a closed system environment, limonene pretreatment was 
observed to mitigate the damage caused by a 4.9 g a.i. ha-1 dose of flucarbazone, suggesting a 
specific protective effect of limonene against this herbicide dose (Table 2.4). R4 plants treated 
with limonene did not exhibit any significant response to various herbicide treatments (Table 
2.6). The absence of a significant protective effect in several cases implies that limonene may not 
provide resistance to herbicide induced stress. These findings imply that the role of limonene 
might be more aligned with conferring resistance to different abiotic stresses rather than 
mitigating the effects of herbicide induced abiotic stress (Fujioka et al., 2015; Llusia et al., 
2005).  
The combined effects of the four VOCs generally increased the injury to all the 
herbicides showing a lack of protection across all experiments. The application of linalool, 
limonene, methyl salicylate, and methyl jasmonate in a combined treatment did not grant any 
enhanced protection against imazamethabenz or pinoxaden with S2 plants and instead enhanced 
the effects of those herbicides significantly (Table 2.7). Priming those plants to be more prone to 
herbicide injury. R4 plants exposed to the combined VOC treatment had a similar effect for the 
imazamethabenz treatment (Table 2.8). However, there was no effect with flucarbazone and 
pinoxaden for the R4 plants.  
54 
 
The application of linalool to S2 plants demonstrated an attenuation of the effects of 
flucarbazone across various sublethal doses, suggesting a protective role of linalool against this 
specific herbicide. However, this protective effect was not observed when linalool was combined 
with other VOCs such as limonene, methyl salicylate, and methyl jasmonate. The combined 
treatment of these VOCs with linalool appeared to exacerbate herbicide induced injury, 
indicating a potential synergistic effect that intensifies herbicide injury. Intriguingly, although 
imazamethabenz shares the same target enzyme (acetolactate synthase) as flucarbazone, they 
belong to different chemical classes: imazamethabenz to the imidazolinones and flucarbazone to 
the sulfonylureas. This difference could imply that linalool's protective role may be more specific 
to the sulfonylurea class of herbicides, as opposed to the imidazolinones.  
The protective role of limonene in our study presents an area of uncertainty. Notably, 
limonene was observed to confer protection to S2 plant lines against a specific dose of 
flucarbazone 4.9 g a.i. ha-1. This finding was from experiments conducted within a closed bag 
system. This isolated instance might suggest a potential protective role of limonene. However, 
such protective effects were not found in the open bag system. Furthermore, similar experiments 
involving R4 plants also failed to demonstrate any protective effects of limonene against the 
tested herbicides. These contrasting results imply that limonene's protective role is either non-
existent or highly conditional, possibly influenced by the environmental setup of the experiment. 
The absence of a consistent protective effect across different experimental conditions and plant 
lines suggests that limonene may not play a significant role in herbicide resistance for the 
herbicides tested. 
55 
 
In the combined VOC treatments experiments, the combined application of linalool, 
limonene, methyl salicylate, and methyl jasmonate demonstrated an exacerbation of herbicide 
induced injury in both S2 and R4 plant lines. For the S2 lines, this combined VOC treatment 
intensified the effects of imazamethabenz and pinoxaden. Similarly, in R4 lines, the combined 
VOC application was observed to potentiate the herbicide injury of imazamethabenz. These 
findings suggest that the simultaneous presence of these specific VOCs does not confer a 
protective effect against the herbicides tested. Instead, it appears to enhance the herbicidal injury. 
This outcome indicates that the protective roles, if any, of individual VOCs like linalool or 
limonene do not translate into a combined treatment context.  
This research, while yielding significant results, should be viewed in the context of 
certain methodological issues. One notable limitation was the small-time frame of the combined 
VOC treatments, which restricted the experiments to a single herbicide dose. I also encountered 
variability in plant responses, particularly in terms of dry biomass weight following herbicide 
application. This was most evident in the S2 lines, where even low herbicide concentrations led 
to high herbicide injury, indicating a higher sensitivity than anticipated. Additionally, changes in 
dry weight responses within the same herbicide and VOC treatment groups could be credited to 
variabilities in experimental application and environmental conditions, such as differential light 
exposure (cloudy or sunny days) and watering regimes. Despite these challenges, the validity of 
the results is supported by the control treatments within each experiment. These controls were 
subjected to the same conditions as the treated plants, thereby providing a reliable baseline for 
comparison.  
56 
 
These results add to the discussion of VOCs and priming stress response mechanisms. It 
has been documented numerous times that VOCs through plant-plant communication can warn 
other plants of impending stress events, and receiver plants effectively mitigating or lessening 
the impact of the stressor (Brilli et al., 2019; Ueda et al., 2012). These indirect defenses can alter 
the way plants handle abiotic and biotic effects. As there are some significant differences among 
the S2 lines response to herbicide after being exposed to a VOC, but there is not a consistent 
outcome with specific herbicides and VOCs. Additionally, some results contrast certain herbicide 
treatments, therefore adding to the complexity of VOC priming defenses for herbicide 
applications.  
Future studies should research precise mechanisms of volatile organic compounds that 
are known to enhance stress tolerance such as methyl jasmonate and methyl salicylate. It would 
also be beneficial to employ a more precise experimental setup, such as the use of a sealed 
growth chamber. This would allow VOC treatments to be primarily focused without the 
confounding factors of cross-contamination or evaporation, while also facilitating the critical 
regulation of CO2 levels. In addition to a controlled environment, a detailed methodical approach 
would incorporate testing ranges of VOCs concentrations, and incorporating other VOCs that are 
emitted from the MHR R4 lines, thereby fulling understanding the complex and nuanced 
mechanisms that go into the VOC mediated herbicide resistance for A. fatua lines.  
 
 
 
 
57 
 
Conclusion 
In conclusion, this study has provided valuable insights into the interactions between 
VOCs and herbicides in MHR R4 and HS S2 A. fatua lines. The research revealed responses of 
these lines to various combinations of VOCs and herbicides, to understand the complexity of 
plant stress responses. While linalool demonstrated a protective effect against flucarbazone in S2 
plants under certain conditions, this effect was not seen in R4 plants. Limonene did not have a 
strong effect to prime plants against herbicide stress. The combined application of multiple 
VOCs tended to exacerbate herbicide induced injury, suggesting that these compounds may 
interact synergistically to strengthen herbicide injury rather than lessen them. More research is 
required to see if methyl jasmonate and methyl salicylate have significant effects towards 
herbicide application. These findings contribute to the understanding of plant-plant 
communication and VOCs' role in priming stress response mechanisms, particularly in the 
context of herbicide resistance. 
 
 
 
 
 
 
 
 
 
58 
 
CHAPTER FOUR 
 
PLANT PIGMENTS DETECTION, PHOTOSYNTHETIC AND ENERGY MANAGEMENT 
PARAMETERS OF MULTIPLE HERBICIDE RESISTANT AVENA FATUA 
Summary 
Due to the observations of color differences of R4 and S2 A. fatua lines grown in a 
growth chamber, plant pigment and photosynthetic fluorometry of multiple herbicide sensitive 
and resistant Avena fatua lines, and two derived recombinant inbred lines (RILs) populations 
were examined. Thin layer chromatography (TLC) to physically separate and observe pigments, 
revealed some pigment differences among lines, warranting further investigation. 
Spectrophotometric analyses were then used to compare pigment absorbance values of acetone 
extracts, revealing significantly higher levels of beta-carotene in and chlorophyll b in MHR 
plants. Through pulse amplitude modulated (PAM) fluorometry, changes in distinct light energy 
management capabilities, such as lower non-regulated energy quenching and higher effective 
photochemical yield, were identified in R4 and R3 plants. This indicates that these lines are 
better equipped to utilize light energy and quench excess energy through non-destructive 
mechanisms. This research provides foundational insights into pigment levels, photosynthetic 
behavior, and light-energy management of HS and MHR A. fatua lines. Future work will be 
required to determine if there is a direct connection to non-target site resistance (NTSR) 
mechanisms. 
 
 
59 
 
Introduction 
The multiple herbicide resistance capabilities conferred to Avena fatua lines arise through 
NTSR mechanisms (Burns et al., 2018; Dyer, 2018; Keith et al., 2017). NTSR mechanisms 
include a reduction in herbicide uptake or translocation, enhanced rates of herbicide metabolism, 
a decreased rate of herbicide activation and sequestration, a mutation in a gene other than one 
encoding the herbicide’s target enzyme, or other unidentified mechanisms (Délye et al., 2013; 
Dyer, 2018; Jugulam & Shyam, 2019). Since many herbicides cause increased reactive oxygen 
species (ROS) content (Traxler et al., 2023), an enhanced ability to quench ROS could also 
confer NTSR capabilities. Plants have innate abilities to quench ROS through antioxidants, 
specifically enzymatic and non-enzymatic antioxidants (Ahmad et al., 2010; Sharma et al., 
2022). Non-enzymatic antioxidants such as carotenoids and flavonoids play crucial roles in 
quenching ROS. The upregulation of carotenoids and flavonoids has been documented to 
alleviate abiotic stress in crops (Davison et al., 2002; Du et al., 2010; Götz et al., 2002; Römer & 
Fraser, 2005). If these protective molecules are found to be in higher concentration in MHR lines 
than HS, this might be an accomplice or causal to the NTSR mechanism. 
A plants’ non-photochemical quenching (NPQ) capacity is also linked to abiotic stress 
tolerance (Müller et al., 2001). Chlorophyll fluorometry, a non-invasive technique for assessing 
PSII activity, offers a tool to measure and compare photosynthetic mechanisms and physiological 
characteristics across plants. This method provides detailed insights into the efficiency and health 
of the photosynthetic process, allowing for a deeper understanding of plant responses under 
various environmental conditions (Murchie & Lawson, 2013). These assays show several 
fluorescence parameters that can show maximum/minimum chlorophyll fluorescence, PSII 
60 
 
operating efficiency, and quantum yield of photochemistry. These parameters commonly quantify 
photosynthetic performance, such as electron transfer rate and total photochemical yield of 
photons (Murchie & Lawson, 2013). In addition, chlorophyll fluorometry can also measure non-
photochemical quenching (NPQ) mechanisms, which are photoprotective processes that remove 
excess energy through excited states. Non-stressed plants rely on these processes to allow the 
quenching of excitation energy (Ruban et al., 2012). Simplified, chlorophyll fluorometry can 
quantify the amount of light being quenched and the amount being used in photochemistry 
(Baker, 2008).   
The approach pulse amplitude fluorometry (PAM) estimates the number of photons being 
used in photochemistry versus NPQ. For this procedure, a plant is exposed to an intense light 
beam and the number of photons emitted is determined. PAM can also calculate the electron 
transport rate (ETR) of electrons passing through PSII, giving a reliable estimate of the plant's 
photosynthetic capacity (Raymond & Bunthawin, 2010). As such, ETR can be used to estimate 
the plant's overall photosynthetic electron transport efficiency. This efficiency is a crucial 
determinant of how effectively the plant converts light energy into chemical energy during 
photosynthesis, directly influencing its growth and productivity (Murchie & Lawson, 2013). 
Thus, plant pigment detection/quantification and fluorometry can give a reliable idea of how 
plants utilize light energy for photochemistry, manage excess energy and ROS. Several 
parameters were measured to identify these metrics and their potential role in NTSR mechanisms 
in MHR Avena fatua lines.  
 
 
61 
 
Methods 
Plant Material 
 All Avena fatua lines were grown to Zadok’s stage 13 under the same greenhouse 
conditions described in Chapter 2: Insect Generalist Preference to MHR Avena fatua. RILs were 
used for pulse amplitude modulated fluorometry readings. These included the 93R inbred line 
that was crossed with the S2 HS line to create 124 RILs (Kern et al., 1996). Similarly, seed from 
the population found in 2006 were used to create the inbred MHR lines R4, which were crossed 
with S2 to create the 06 RIL (n = 120) population. Progeny from both crosses was advanced 
using single seed descent (Jinks & Pooni, 1976) for six generations to achieve a homozygosity 
rate of 98.44 %. Out of the 120 06 RILs, 35 lines were used, that all had a range of herbicide 
resistance capabilities. Out of the 124 93 RILs, 23 lines were used, that all had a range of 
herbicide resistant capabilities as well.  
Thin Layer Chromatography 
For thin layer chromatography, plants were grown in either controlled environment 
chambers (500 µmol m-2 sec1 PAR; high light) or under the greenhouse conditions (~300 µmol 
m-2 sec1 PAR; low light). Shoots from HS2 and MHR plants were harvested at the soil surface, 
and 0.6 g (FW) were immediately frozen in liquid nitrogen and stored at -80 °C. All subsequent 
steps were conducted under very low light conditions (~50 µmol m-2 sec1) to protect light-
sensitive pigments. Shoot material was ground three times in a liquid nitrogen pre-cooled pestle 
and mortar, transferred into 2 mL Eppendorf tubes, acetone (400 µL) was added and vortexed for 
2 minutes. Tubes were centrifuged 14,500 RPM at 2 °C for 5 minutes, and 150 µL of the 
supernatant was removed and spotted onto silica gel plates (20 x 20 cm TLC plates, Sigma 
62 
 
Aldrich, Z122785, St. Louis, MO) at 2 cm above the bottom edge with 20 µL microcapillary 
pipettes (Drummond Microcaps, 1-000-9000, Broomall, PA). TLC plates were placed into a 20 
cm rectangular glass chromatography tank (Biostep, BS120.173A, Sun Valley, CA) containing 
40:60 petroleum ether: acetone (by vol.) to a depth of 1.5 cm. After 20 minutes, plates were 
removed, dried, and photographed under room light and UV light. 
Spectrophotometer Assay 
Shoot material was harvested from HS and MHR plants was immediately frozen in liquid 
nitrogen and stored at -80 °C. Shoot material (0.3 g FW) was placed in pre-chilled 2 mL 
Eppendorf tubes containing three 2 mm glass beads. The frozen tubes were placed in a bead 
beater (Mini-Beadbeater-96 Biospec, Bartlesville, OK) and ran for one minute. Acetone (200 µL) 
was added to each tube, followed by vortexing (2 minutes) and centrifugation at 14,500 RPM at 
2 °C for 5 minutes. Supernatant (100 µL) was removed to a new tube and kept on ice. A 
subsample (15 µL) of supernatant was mixed into 985 µL acetone in a 2 mL quartz cuvette 
(Sigma Aldrich, St. Louis, MO), and samples were loaded into a microplate reader (SpectraMax 
M2, Molecular Devices, San Jose, CA) and scanned using two modes. Mode 1 was a full scan of 
300 nm – 750 nm at 2 nm intervals, and Mode 2 was a kinetic scan at 451, 480, 530, 538, 652, 
and 665 nm, which are absorbance maxima for Beta-carotene, betaxanthin, anthocyanin, 
betacyanin, chlorophyll b, and chlorophyll a, respectively (Peters & Noble, 2014).  
Pulse Amplitude Modulated Fluorometry 
Avena fatua field derived lines and 06R and 93R RIL populations were grown as 
described above. Individual RILs were grouped according to their resistance phenotypes. 06 
RILs were grouped according to their resistance towards flucarbazone and triallate. 93 RILS 
63 
 
were grouped based on their resistance levels to triallate. These resistance groups are defined as 
Low (< 24% of resistant parent’s effective dose), Medium (25% to 75% of resistant parent’s 
effective dose), and High (>76% of resistant parent’s effective dose). Within the 06R RIL 
population, there were 14 lines classified as 'Low' resistance, 8 as 'Medium', and 13 as 'High'. For 
the 93R RILs, there were 7 lines in the 'Low' resistance group, 9 in the 'Medium', and 7 in the 
'High' resistance category. Plants were transferred to a darkened room (~10 µmol m2 sec) and 
kept for 30 minutes before the experiment continued. These low light conditions were 
maintained throughout experiments. A two-saturation pulse analysis was performed using a PAM 
MINI II instrument (Heinz WALZ, Effeltrich, Germany). Intact plants were first exposed to a one 
second initial actinic light saturation pulse, followed by a secondary saturation pulse of one 
second after 30 seconds. Actinic light from a quasi-constant light source in the instrument was 
utilized to stimulate photosynthesis, while saturation pulses were used to saturate primary 
photosynthetic molecules. The parameters measured were the fluorescence minimum, maximum, 
effective photochemical yield, regulated and non-regulated quenching, and electron transport 
rate. 
Statistics 
Plant pigment and PAM data were analyzed using linear mixed-effects models in R 
Studio (RStudio TEAM, 2023). Variation within and between trials was assessed with random 
intercept adjustments for all models. Analysis of variance tests (ANOVA) were performed to 
assess treatment group differences. Estimated marginal means (EMMs) with Tukey adjustments 
were used for post-hoc comparisons among treatment groups (Searle et al., 1980). The Plant 
pigment detection using spectrophotometry experiments were replicated 4 times, and treatments 
64 
 
were technically replicated 7-10 times. Plant fluorimetry experiments were replicated 3 times 
and treatments were technically replicated 3-12 times. Field derived A. fatua lines were utilized 
more for both and are responsible for the high number of replicates. Data from experimental 
repeats were combined after confirming homoscedasticity using diagnostic plots. 
Results 
Thin Layer Chromatography 
TLC assays were used to semi-quantitatively detect pigment levels in S2 and R4 plants 
grown under growth chamber (high light) and greenhouse (low light) conditions. Pigments were 
tentatively identified based on Rf values from similar published assays (Quach et al., 2004). An 
observation was made about the diminished presence of the pheophytin band in R4 plants grown 
in the growth chamber, compared to their greenhouse counterparts (Figure 3.1). However, the 
opposite was observed in the R4 plants when the experiment was repeated; the pheophytin band 
was much stronger in R4 plants grown in a growth chamber compared to greenhouse conditions 
(Figure 3.2). Pheophytin is a chlorophyll derivative that functions as primary electron acceptor 
and has partial roles in preventing photodamage under high light conditions (Hou, 2014). No 
consistent differences were observed across plant samples for the other plant pigments. However 
due to the inherit limitations of TLC a more robust approach was required. These observations 
highlighted potential differences between the plant lines. Motivating the quantification of plant 
pigment concentrations through spectrophotometric analysis of plants grown under consistent 
light conditions. 
65 
 
Figure 3.1 – Thin layer chromatography of acetone extracts of S2 (left panel) and R4 (right 
panel) plants. The left and right hand extracts in both panels are from plants grown in the 
greenhouse and growth chamber, respectively. 
           
Figure 3.2 - Thin layer chromatography of acetone extracts of two R4 plants grown in different 
conditions. The left extract is the R4 A. fatua line grown in the greenhouse, and the right extract 
is the R4 line grown in the growth chamber.  
Spectrophotometer Assay 
 Spectrophotometer assays showed absorbance values of acetone extracts from MHR and 
HS plants in 400 to 750 nm scans and at specific wavelengths. Direct quantification of pigment 
concentrations from absorbance values is possible but relies on pure samples without interfering 
substances (Lichtenthaler, 1987; Wellburn, 1994). Therefore, I reported only absorbance data 
66 
 
from my pigment mixed samples here, and then used these data to determine estimated pigment 
concentrations subject to some interpretation.  
 A full scan of A. fatua field derived lines (Table 3.3) reveals a notable distinction in the 
absorption concentration of acetone soluble pigments. Specifically, the lines R3, R4, and 20R2 
exhibit higher absorption levels and possess a greater concentration of acetone soluble pigments 
when compared to the S1, S2, and 93R lines.  
 
Figure 3.3 – Spectrophotometer Full scan of MHR and HS A. fatua lines ranging from 400- 750 
nm. 
Pigment absorbance values from two HS and four MHR A. fatua lines showed no 
significant differences at 451 nm, 530 nm, 538 nm, or 665 nm. The concentration of acetone 
soluble pigments detected at 480 nm showed that 20R2’s concentration was significantly higher 
than the concentration of 93R (Figure 3.4). The other field derived lines were not significantly 
67 
 
different from one another. Extraction of shoot material in the polar solvent acetone favored 
solvation of chlorophyll and carotenoids, while flavonoids like anthocyanins would be present in 
low abundance (Silva et al., 2017). The most common plant pigment that has an absorption 
maximum at 480 is beta-carotene.  
 
Figure 3.4 – Spectrophotometer, Acetone soluble pigment concentrations of A. fatua lines 
detected at 480 nm. Compact letter design is employed to illustrate significant differences. The 
line center in box is median, the box represents the interquartile range of data observed. 
Figure 3.5 displays the acetone soluble pigment concentrations detected at 652 nm. R4 
was significantly higher in concentration compared to 93R. The plant pigment commonly found 
here with acetone extractions is chlorophyll b. 
68 
 
Figure 3.5 – Spectrophotometer, Acetone soluble pigment concentrations of A. fatua lines 
detected at 652 nm. Compact letter design is employed to illustrate significant differences. The 
line center in the box is the median, the box represents the interquartile range of data observed. 
Pulse Amplitude Modulated Fluorometry Assay 
 PAM assays revealed several informative photosynthetic and energy management 
parameters from HS and MHR plants. These parameters include: 1) Y(NPQ), representing 
effective non-photochemical energy dissipation. This shows a change in regulated quenching 
capacity, where these plants are releasing excess energy as heat, that prevents damage to the 
photosynthetic mechanism. This utilizes the xanthophyll cycle to dissipate excess energy; 2) 
Y(NO) represents quantum yield of non-regulated energy dissipation, or the fraction of absorbed 
light that dissipated as heat through non-regulated processes with the triplet-triplet energy 
release; 3) Y(II) which represents quantum yield of PSII photochemistry, or the fraction of 
absorbed light that is used in the photochemical process, and 4) (ETR) represents electron 
69 
 
transport rate and is an estimate of the rate at which electrons are moving through the 
photosynthetic electron transport chain (Hashimoto et al., 2018; Raymond & Bunthawin, 2010). 
The sum of the first three measurements is equal to 1 for a given absorbed photon flux: 
Y(II)+Y(NPQ)+Y(NO) = 1. Note that mean values can create a total that deviates slightly from 
1.0.  
 Equations for these metrics are as follows: Y(II) = Fm′−F / Fm′; Y(NO) = F / Fm’; 
Y(NPQ) = F/Fm’ – F/Fm. Where Fm′ is the fluorescence level at the saturation pulse, F is the 
fluorescence level before the saturation pulse and Fm is the maximum fluorescence level. Table 
3.1 shows the values of Fm’, F, and Fm for all the lines used for the PAM assays. Chlorophyll 
fluorescence measurements represent the intensity of fluorescence and are typically 
dimensionless and presented in arbitrary units (Baker, 2008; Murchie & Lawson, 2013; 
Raymond & Bunthawin, 2010).  
 
 
 
 
 
 
 
 
 
 
70 
 
Table 3.1: Fluorescence Measurements Used for Photosynthetic and Energy Management 
Parameters of All A. fatua Lines Tested (measurements are arbitrary units) 
A. fatua Lines and 
RIL resistant groups Fm' F Fm 
S2 2034.9 1637.1 2890.8 
S1 2019.6 1578.1 2918.4 
R3 1901.5 1424.7 3284.6 
R4 1771.1 1279.6 3143.8 
20R2 2344.4 2012.9 3198.1 
93R 2101.6 1674.7 3014.6 
        
Low resistant group 
for 06 RILs  2001.2 1497.8 3085.1 
Medium resistant 
group for 06 RILS 2005.5 1512.1 3112.9 
High resistant group 
for 06 RILs 1961.5 1501.1 3055.6 
     
Low resistant group 
for 93 RILs 2446.2 2033.0 3339.4 
Medium resistant 
group for 93 RILs 2217.6 1827.4 3312.7 
High resistant group 
for 93 RILs 2271.5 1900.1 3245.0 
 
Figure 3.6 shows Y(NPQ) regulated quenching capacity, Y(NO) non-regulated quenching 
capacity, Y(II) effective photochemical yield values, and the electron transfer rate for HS and 
MHR plants. Y(NPQ) values were not significantly different among A. fatua lines, while Y(NO) 
values were lower in both R4 and R3 plants compared to the other lines. Y(II) values were 
significantly higher in R3 and R4 plants than in other lines. Y(II) values in 20R2 were lower than 
in S1, R3, and R4 plants. ETR values of R3 and R4 were significantly higher than the other lines 
and that 20R2 ETR value is significantly less than S1, R3, and R4. In total, these PAM values 
indicate that R3 and R4 utilize light energy more for photochemical reactions, and release excess 
energy through non-regulated ways significantly less.   
71 
 
Figure 3.6 – Field derived lines measuring a. Y(NO) – Non-regulated energy dissipation, b. 
Y(NPQ) - Regulated energy dissipation, c. Y(II) – Effective Photochemical yield, and d. ETR - 
Electron transfer rate. Compact letter design is employed to display significant differences.   
A single significant difference existed between the 93 RILs and their progenitor lines 
(Figure 3.7). The medium resistant group (25 - 75%) has a higher Y(NPQ) value compared to S2 
and the low (<24%) resistant group. This indicates that the medium resistant group has an ability 
to quench excess energy through regulated mechanisms. 
72 
 
Figure 3.7 – 93 RIL groups and progenitor lines measuring a. Y(NO) – Non-regulated energy 
dissipation, b. Y(NPQ) - Regulated energy dissipation, c. Y(II) – Effective photochemical yield, 
and d. ETR - Electron transfer rate. Compact letter design is employed to display significant 
differences.   
Several significant differences were found for the 06 RIL groups and their progenitor 
lines (Figure 3.8). Y(NO) values showed that S2 was significantly higher than R4 and the RIL 
groups. S2 was also found to be significantly lower for Y(II) and ETR R4 and RIL groups. 
Y(NPQ) values showed that R4 was significantly higher than S2 and RIL groups. S2 was 
significantly lower than R4 and the high RIL resistant group for Y(NPQ). This indicates that R4 
and the high RIL resistant group utilize more energy for photochemical processes and releases 
energy through regulated methods. This also shows that S2 consistently utilizes energy at a lower 
rate and releases energy through non-regulated mechanisms.  
73 
 
 
Figure 3.7 – 06 RIL groups and progenitor lines measuring a. Y(NO) – Non-regulated energy 
dissipation, b. Y(NPQ) - Regulated energy dissipation, c. Y(II) – Effective Photochemical yield, 
and d. ETR - Electron transfer rate. Compact letter design is employed to display significant 
differences.   
Discussion 
 This study aimed to explore plant pigment and fluorometry differences between HS and 
MHR, A. fatua lines. Thin layer chromatography and spectrophotometer assays was used to 
compare pigment levels. Avena fatua MHR and HS field derived lines were used along with RIL 
groups based on their resistant phenotype and were measurements to compare photosynthetic 
parameters.  
Thin layer chromatography showed preliminary evidence that pheophytin, a chlorophyll 
pigment that functions as a primary electron acceptor, was present in the two S2 plants grown 
under high and low light (Hou, 2014). However, the pheophytin band was not as pronounced in 
the R4 plant grown under high light. This experiment was replicated and found the opposite, 
74 
 
showing that R4 grown in greenhouse conditions had a less pronounced pheophytin band. While 
this difference is subtle, it may indicate that slight environmental conditions may influence the 
pheophytin band. Cloudy days with less light energy being absorbed by the plant could lead to a 
reduction of this pigment, for plants grown in the greenhouse. Or it could be minor energy 
fluctuations in the growth chamber that altered the pigments bands. It has been documented that 
plants can control the synthase of plant pigments based on the light conditions they receive 
(Carvalho et al., 2011; Stewart et al., 2021). For example, Lemna gibba (duckweed) showed 
rapid changes in chlorophyll after exposure to either high or low light. However, because of 
these initial findings, further investigation was conducted on the detection of plant pigments 
using spectrophotometry. 
 Spectrophotometry assays showed significant differences for acetone soluble pigments at 
480 and 652 nm. 20R2 had a significant increase in concentration from 93R. Beta-carotene has 
an absorption maximum near 480 nm, indicating a higher concentration of beta-carotene in 
20R2. Increase in beta-carotene can lead to enhanced photoprotection, enhanced photosynthetic 
activity, and higher stress tolerance to abiotic factors (Chen, 2015; Maoka, 2020). Interestingly, 
this pigment is not elevated in 93R plants, possibly indicating a lower abiotic stress tolerance. 
The 93R line is resistant to two herbicides, and 20R2 is resistant to nine herbicides. This possibly 
indicates that 20R2 could have evolved with high synthesis capabilities of beta-carotene to 
mitigate herbicide stress. Researchers have found that by overexpressing beta-carotene 
hydroxylase in Arabidopsis led to enhanced abiotic stress tolerance (Davison et al., 2002). 
Hydroxylase is the gene responsible for beta-carotene production. However, more research must 
be conducted to see if this gene is overexpressed in 20R2 lines.  
75 
 
Additionally, R4 was in greater concentration at 652 nm than 93R. Chlorophyll b has an 
absorption peak at this wavelength (Lichtenthaler, 1987; Wellburn, 1994). This indicates that R4 
lines have higher chlorophyll b concentrations than 93R or HS lines. This difference could result 
in better light harvesting and more effective energy transfer under suboptimal conditions (Chen, 
2015; Voitsekhovskaja & Tyutereva, 2015). Additionally, the PAM assays revealed that 93R 
underperformed for effective photochemical yield (YII) and electron transport rate and R4 
performed higher in the same metrics. These results suggest that 93R has less chlorophyll b and 
is underutilizing light energy for photochemical processes, and that R4 is utilizing more light 
energy to drive photochemical processes. 
PAM fluorometry revealed 3 significant differences between MHR and HS lines in 
Y(NPQ), Y(NO), Y(II), and ETR parameters. The Y(NO) measurement was significantly lower 
in the R4 and R3 lines, showing that these two lines don’t utilize non-regulated quenching 
mechanism as much as the other lines. Y(II) and ETR measurements showed that R4 and R3 
were significantly higher than the other field derived lines. This indicates that R4 and R3 also 
have a greater extent of utilizing captured energy and that R4 and R3 are better suited to 
capitalize on absorbed light and quenching excess energy through a regulated process. The other 
MHR lines 93R and 20R2 performed equivalently to the HS A. fatua lines. These non-regulated 
quenching mechanisms refer to the triplet-triplet energy release. As mentioned above, this energy 
release is usually non-destructive and is controlled by antioxidants present in cells. However, if 
the light stress becomes too high, or additional stresses arise, excess ROS molecules can be 
generated and cause damage to the cells (Das & Roychoudhury, 2014; Hashimoto et al., 2018). 
This shows that 93R, 20R2, S1, and S2 rely more on non-regulated quenching mechanisms and 
76 
 
could experience higher photooxidative stress in strong light conditions. This could also mean 
that these lines rely more heavily on other antioxidants to suppress ROS generation during high 
stress situations.  
The 93 RIL population and its progenitor lines showed minimal significant differences. 
The regulated quenching proportion, Y(NPQ), for S2 and the low resistance group was 
significantly lower than the medium resistance group. However, no significant differences were 
observed in non-regulated quenching, effective photochemical yield proportions, or electron 
transport rate. Given that the parental lines 93R and S2 did not differ significantly in any of these 
measures, the consistency observed among the 93 RIL and its progenitor lines was expected. 
These results indicate that S2 and the medium resistant group don’t utilize or do not have enough 
xanthophyll molecules to mitigate this energy release, instead relying more heavily on triplet-
triplet energy transfer. 
The 06 RIL population had significant differences in that all showed S2 being different 
than the R4 and RIL resistant groups, which was observed from the PAM results from the field 
derived lines. The S2 line showed a notably higher non-regulated quenching proportion, Y(NO), 
than the other lines and was significantly less for Y(NPQ), YII, and ETR. For regulated 
quenching proportion Y(NPQ), R4 was significantly increased than the other progenitor line and 
resistance groups. The high resistance group was significantly different from S2, and R4 was an 
intermediate between the two lines, which means that R4 consistently has the highest Y(NPQ) 
value compared to the other RIL groups and S2. However, it is important to note that this 
fluorometry data is not co-segregating with resistance. If it was, the data would have shown the 
77 
 
low resistant groups and the high resistant groups to have similar data spreads to its parental 
lines. The results indicate that these resistant groups are an intermediary from both parental lines.  
Using the 06 and 93 RIL populations, the goal was to see if these traits co-segregated 
with herbicide resistance. (Broman, 2005). However, since most RIL resistance group 
measurements were insignificant and most RIL resistance groups did not differ amongst 
themselves, this outcome is unlikely. Future work should incorporate two procedures: plant 
pigment detection using high-performance liquid chromatography coupled with absorbance 
detection and mass spectrometry to identify pigments definitively (Naharwal et al., 2023). And 
another PAM assay utilizing the photosynthesis inhibitor paraquat to see if MHR lines have an 
enhanced capability to quench excess energy through regulated or non-regulated mechanism, as 
well as examining if the paraquat stress significantly impacts their photochemical yield and 
electron transport rate. 
Conclusion 
This study involved the examination of plant pigment and fluorometry measurements 
among different A. fatua lines under controlled conditions. Initial observations from growth 
chamber experiments led to a deeper investigation using thin layer chromatography, revealing a 
pheophytin band difference between the lines. This initiated spectrophotometric assays to 
identify these differences, finding differences for 93R, 20R2 in beta-carotene concentrations and 
differences between 93R and R4 chlorophyll b concentrations. Through pulse amplitude 
modulated fluorometry, R3 and R4 lines showed increased capabilities in managing absorbed 
light energy, potentially hinting at their enhanced resistance against herbicide stress. The 06 RIL 
population demonstrated an intermediator of the progenitor lines, while the 93 RIL population 
78 
 
mirrored its progenitor lines. Although this study provides valuable insights into the 
photosynthetic and light-management capabilities of A. fatua lines, the direct linkage between 
these traits and non-target site resistance mechanisms remains to be definitively established. 
Future research, potentially leveraging advanced techniques like high-performance liquid 
chromatography, and fluorometry measurements with photosystem inhibitors could offer clearer 
insights for potential NTSR mechanism.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
79 
 
CHAPTER FIVE 
 
QUANTITATIVE TRAIT LOCI ANALYSIS OF MULTIPLE HERBICIDE RESISTANT 
AVENA FATUA 
Summary 
Quantitative trait Loci (QTL) discovery and mapping were employed to delve into the 
genetic basis of multiple herbicide resistance in Avena fatua. Two recombinant inbred line (RIL) 
populations, 93 RIL(93R x S2), and 06 RIL (R4 x S2) were used developed for this study. Three 
QTLs were associated with triallate resistance in the 93 RIL population, and five QTLs were 
found to associate with flucarbazone and triallate in the 06 RIL population. Present in the 06 RIL 
population, a major QTL 3D_427541473 (LOD of 41.21) was identified as associated with 
triallate resistance, while QTL 4A_358747067 (LOD of 4.79) emerged as a significant candidate 
for flucarbazone resistance. Significant QTLs were not identified for difenzoquat resistance in 
either population. Identifying QTLs associated with triallate and flucarbazone resistance 
provides valuable preliminary genetic markers for future fine mapping and functional validation 
studies. In related research, differentially expressed genes (DEGs) from previous transcriptome 
assays were mapped with the QTLs reported here, providing potential candidate genes for MHR. 
This research advances the genetic characterization of A. fatua MHR lines, with the long-term 
goal of identifying specific herbicide resistance mechanisms. 
 
 
 
80 
 
Introduction 
 The issue of identifying herbicide resistant mechanisms is a complex and troublesome 
problem worldwide. As the global reliance on herbicides continues, so does the selection 
pressure, leading to a rapid increase in cases of herbicide resistance. Since 1950, 521 unique 
cases of herbicide-resistant weeds (species x site of action) have been discovered, or a rate of 16 
unique cases reported annually (Heap, 2014). Given this alarming rate and the widespread 
implications of MHR for global agricultural production and food security, understanding the 
genetic mechanisms and causal genes that confer MHR is of utmost importance. 
Our species of study, Avena fatua (wild oat), has developed resistance to multiple 
herbicides through nontarget site resistance (NTSR) mechanisms, leading to numerous studies to 
identify specific mechanisms responsible for the MHR phenotype. In 2002, inheritance studies of 
reciprocal crosses of A. fatua lines 93R with S2 found that triallate resistance segregated in a 
15:1 S:R ratio, indicating that two recessive genes confer triallate resistance (Kern et al., 2002). 
Additionally, 93R’s resistance to triallate is conferred by reduced rates of sulfoxidase activity, a 
pre-herbicide activation step required for toxicity (Kern et al., 1996). In 2015, DNA sequencing 
of 06R line R4 found no known target site mutations that confer resistance to acetolactate 
synthase (ALS; HRAC Group 2) or acetyl coA carboxylase (ACCase; Group 1) inhibitors, 
confirming the involvement of NTSR mechanisms (Keith et al., 2015). In 2017, R4 was shown 
to exhibit constitutively elevated transcript levels of differentially expressed genes that had roles 
in xenobiotic catabolism, stress response, redox maintenance, and transcriptional regulators, as 
compared to S1 levels (Keith et al., 2017). In 2018, additional inheritance studies showed that 
81 
 
three closely linked nuclear genes control the resistance of R3 and R4 lines to flucarbazone, 
imazmethabenz, and pinoxaden (Burns et al., 2018).  
QTL analysis is a statistical method that links phenotypic and genotypic data to specific 
regions of chromosomes. This tool attempts to explain the genetic basis of variation in complex 
traits (Farquhar, 1998). QTL positions can be found using a linkage map of the RIL populations 
and phenotypic data for each line in those populations. Accompanying this position is the 
verification estimate known as the logarithm of the odds ratio (LOD). LOD scores estimate the 
relative strength of evidence for the presence of a QTL at a particular location (Van Ooijen, 
1999). QTL analysis measures intervals between adjacent pairs of markers along each 
chromosome and gives a corresponding LOD score. Using these methods, accurate and reliable 
QTL markers associated with potential candidate genes that confer NTSR mechanisms in A. 
fauta can be found. 
QTL analyses have helped researchers investigate NTSR mechanisms for single 
herbicides very recently in Eleucine indica (goosegrass), Sorghum bicolor (grain sorghum), 
Alopercurus myosuroides (blackgrass) and Phaseolus vulgaris (snap bean) (Cai et al., 2023; Cha 
et al., 2014; Dakota et al., 2022; Pandian et al., 2022; Pandian et al., 2020). In this work, two RIL 
populations segregating for MHR were genotyped and used along with associated phenotypic 
data to find genetic markers by a QTL analysis. With the end goal of discovering the causal 
genes for the herbicide resistance traits in A. fatua lines. 
 
 
 
82 
 
Methods 
Plant Material and Experimental Design 
RILs were used described in Chapter 4: Plant Pigment Detection, Photosynthetic and 
Energy Management Parameters of Multiple Herbicide Resistant Avena fatua. All plants were 
grown in the greenhouse conditions described in Chapter 2: Insect Generalist Preference to MHR 
Avena fatua. 
Phenotypic Data 
Seeds of the 93R and S2 parental lines and derived 93 RILs were treated with pre-
emergence applications of 513 g a.i. ha-1, 1,026 g a.i. ha-1 or 2,052 g a.i. ha-1 doses of triallate. 
Triallate was applied to 1,33.5 cm3 of moistened soil with a moving nozzle sprayer as described 
in Chapter Three: Exogenous Volatile Organic Compounds Effects on Herbicide Resistance in 
Two Avena fatua Lines, mixed thoroughly, and treated soil spread evenly over seeds placed on 
top of 5 cm of untreated greenhouse soil in 15.24 cm pots. Pots were lightly watered and 
fertilized as above, and after 21 days, percent germination (of 8 seeds planted), shoot height, and 
a visual herbicide injury rating (0 = no injury to 5 = complete death) were recorded. These three 
parameters were combined by averaging the scores to create an overall resistance score, which 
was then used to rank RILs. This resulted in 12 individual phenotypic measurements to use for 
QTL mapping.  
06R and S2 parental and derived 06 RIL lines were grown in the greenhouse conditions 
described above and treated with individual herbicides when they reached Zadok’s stage 13. The 
06R lines were treated with flucarbazone (Everest™) at 19.7 g a.i. ha-1 or 39.4 g a.i. ha-1 doses or 
triallate (FAR-GO™) at 513 g a.i. ha-1, 1,026 g a.i. ha-1 or 2,052 g a.i. ha-1 doses, difenzoquat 
83 
 
(Avenge™) at 712.05 g a.i. ha-1, or pinoxaden (Axial™) at 17.5 g a.i. ha-1. Three weeks after 
treatment, visual injury scores were recorded, plant shoots were harvested at the soil surface, 
tissues were dried at 40°C for two weeks, and dry weights (DW) were recorded. Phenotypic data 
from the 06 lines were expressed as the percent of either an untreated progenitor line or untreated 
RIL control grown simultaneously with the treated RIL line. All treatments were applied to 3 or 4 
biological replicates. For 06 lines, a basic injury rating system ranging from 1 to 5 was employed 
(0 = no injury to 5 = complete death). This resulted in 17 phenotypic measurements to use for 
QTL mapping. Additionally, 06R and S2 parental and derived 06 RIL lines were phenotyped for 
triallate as described above. 
Genotyping and Linkage Map Construction 
 Reference genome alignment and linkage map construction were completed following the 
procedures described in (Fiedler et al., 2015). Briefly, genomic DNA was extracted from 
lyophilized young leaves of parental and RIL population plants by Novogene Corp. Inc. 
(Sacramento, CA). Genotyping was conducted through Genotype-by-Sequencing (GBS) 
techniques (Deschamps et al., 2012) on restriction enzyme fragmented DNA, and resulting 
sequences were aligned to the Avena sativa reference genome OT3098v2 (Peng et al., 2022). 
This process allows single nucleotide polymorphisms to be identified, which are single 
nucleotide changes at a specific position in the genome. SNP identification and calling used the 
criteria of 1) a minimum of four reads for each SNP call, 2) SNPs must be present in 50% of the 
relevant RIL population, and 3) have a minor allele frequency of at least 0.01. The filtered SNPs 
were then used for linkage mapping using Joinmap (Ooijen, 2018). Due to the limitations of 
84 
 
GBS techniques and the aggressive filtering of non-ideal and redundant SNPs, a relatively small 
number of SNPs were mapped for each population. 
QTL Analysis 
Standard and multiple interval mapping (SIM and MIM, respectively) were conducted 
using the maximum likelihood method as described by (Broman, 2005), using the R-studio 
packages qtl and ASMap (Taylor & Butler, 2017). Standard interval mapping was first used to 
focus on a single QTL at each locus and calculate LOD scores for each marker location. 
Permutation tests for QTL significance were simultaneously conducted with 1,000 replications, 
providing a statistical foundation for LOD score to determine QTL significance (Doerge & 
Rebaï, 1996; Kao et al., 1999). For this phase, Haley-Knott (HK) regression (Haley & Knott, 
1992) was specifically applied as the regression-based method for QTL mapping. Briefly, HK 
linearly regresses phenotypic values of individuals against marker probabilities, which are 
calculated based on known marker genotypes and their respective recombination fractions 
(Haley & Knott, 1992). After completion of standard interval mapping, multiple interval 
mapping (Kao et al., 1999) was used to fit multiple QTLs into a model simultaneously. Then a 
stepwise QTL function (Kao et al., 1999) was employed, allowing for the addition and removal 
of QTLs based on their statistical significance. For 06 RILs, a LOD score > 3.0 and a 
significance level set at P < 0.05 were utilized for QTL detection. For 93 RILs, weaker 
phenotypic data required that LOD scores > 2.5 were considered significant.  
RNA-seq Analysis 
 In this study, an RNA-seq analysis on the Avena fatua transcriptome (Keith et al., 2017) 
was reanalyzed utilizing the Avena sativa reference genome OT3098v2 (Peng et al., 2022)to 
85 
 
align sequence reads. Quality control of the sequencing reads was conducted using BBmap 
version 38.98 (Bushnell, 2014). Reads were aligned using Spliced Transcripts Alignment to a 
Reference (STAR) software version 2.7.10a (Dobin et al., 2013). This approach yielded an 
average mapping rate of approximately 75%, a significant improvement over the original 30% 
mapping rate achieved with the Oryza sativa reference genome. Post-alignment filtering using 
FeatureCounts ver. 2.0.3 (Liao et al., 2014) further refined the dataset, resulting in approximately 
60% of the reads remaining aligned. Differential gene expression analysis was carried out using 
DESeq2 version 1.36.0 (Love et al., 2014), and differentially expressed genes (DEGs) with a log 
2-fold change > 0.85 were mapped to identified QTLs. Gene annotations were based on E-values 
≤ 10−4 
Results 
Genotyping and Linkage Map Construction 
 Genotyping resulted in 1,504 mapped SNPs in 24 linkage groups in the 93 RIL population 
and the 06 RILs had 1,834 mapped SNPs in 30 linkage groups. Linkage groups for the 06 RIL 
population are shown in Figure 4.1. Once all SNP markers were mapped, the allele frequency 
within each RIL population was assessed. Given that these RIL populations originated from a 
single cross between two parental lines and were advanced to the sixth generation through single 
seed descent, it would be expected that the proportion of each parental allele would approximate 
50% (Hill et al., 2008). However, the frequency at individual loci could deviate from this 
expectation due to factors such as random segregation and recombination. Consistent with this, 
the 06 and 93 RILs displayed an allele frequency close to 50% for each parent. 
 
86 
 
Figure 4.1 – Linkage groups for the 06 RIL population. Black bands represent a SNP. 
 
QTL Analysis 
 Three QTLs for triallate resistance were identified in the 93 RIL population (Table 4.1).  
First, 3D_427541473 was detected on chromosome 3D at 19.6 cM and was detected for seven 
different phenotypic measurements with LOD scores ranging from 3.27 to 5.56. Second, QTL 
3D_347553285 was detected on chromosome 3D at 13.4 cM for one phenotypic measurement 
with a LOD score of 8.01. Third, QTL 1A_9291409 was detected at chromosome 1A at 1.2 cM 
for one phenotypic measurement with a LOD score of 2.66. 
 
 
 
87 
 
Table 4.1. Complete list of QTLs found in the 93 RIL population using MIM. Tri suffix 
represents Triallate (FAR-GO). All QTLs were detected using an alpha of 0.05, and LOD score 
above 2.5 for significant threshold. 
Position 
QTL ID Trait Chromosome LOD Flanking Markers 
(cM) 
Visual rating 513 g 
4.71 
a.i ha-1 Tri 
Size rating 1,026 g 
-1 3.27 
a.i ha Tri 
Size scale 2,052 g a.i 
-1 4.50 
ha Tri 
Score 2,052 g a.i ha-1 
5.56 3D_427541473 - 
3D_427541473 Tri 3D 18.6 
3D_433601457 
Total Score 2,052 g 
-1 3.54 
a.i ha Tri 
Rank Overall  
5.38 
Phenotype of visual 
rating 1,026 g a.i ha-1 5.14 
Tri 
Germination scale 3D_347553285 - 
3D_347553285 3D 13.4 8.01 
1,026 g a.i ha-1 Tri 3D_346231075 
Ranking based on 
1A_9291409 - 
1A_9291409 injury 1,026 g a.i ha- 1A 1.2 2.66 
1A_251522473 
1 Tri 
 
Five distinct QTLs associated with resistance to triallate or flucarbazone were identified 
in the 06 RIL population (Table 4.2). These QTLs were localized to five different chromosomes: 
4A, 1D, 2D2, 3D2, and 7A. The most prominent QTL for flucarbazone resistance, designated as 
4A_358747067, was located on chromosome 4A at 15.8 cM. This QTL was identified across 
seven flucarbazone phenotype measures, displaying LOD scores that ranged from 3.15 to 4.76. 
Two additional QTLs implicated in flucarbazone resistance were identified as 1D_16113776, 
located on chromosome 1D at 0 cM, was found in three different phenotypic measures with LOD 
88 
 
scores between 3.20 and 3.3; and 2D_410716933, located on chromosome 2D2 at 4.2 cM, was 
found in four phenotypic measurements with LOD scores ranging from 3.51 to 3.85. 
For triallate resistance, a major QTL labeled 3D_1907891 was located on chromosome 
3D at 22.0 cM. It was identified in four phenotypic assays for triallate resistance, exhibiting LOD 
scores that ranged from 26.67 to 41.21, the highest among all identified QTLs. Another QTL, 
7A_66986043, was found on chromosome 7A at 0 cM and was identified in five triallate 
phenotypic assays with LOD scores ranging from 3.40 to 4.91. Notably, no QTLs were identified 
for resistance to either pinoxaden or difenzoquat. Moreover, no shared QTLs were found 
between flucarbazone and triallate resistance measures. 
 
 
 
 
 
 
 
 
 
 
 
 
 
89 
 
Table 4.2. Complete list of QTLs found in the 06 RIL population using MIM. The Flu suffix 
represents flucarbazone (Everest) Tri suffix represents Triallate (FAR-GO). All QTLs are above 
the alpha = 0.05, and LOD score above 3 for significant threshold. 
Peak 
QTL Flanking 
Trait Chromosome Position LOD 
Identification Markers 
(cM) 
Percent of Control 19.7 
3.95 
g a.i ha-1 Flu 
Percent of R4 19.7 g 
-1 4.49 
a.i ha  Flu 
Percent of S2 19.7 g a.i 
4.76 
ha-1 Flu 
4A_358747067 
Visual Score 19.7 g a.i 
4A_358747067 -1 4A 15.8 4.38 - 
ha  Flu 
4A_358747067 
Percent of R4 39.4 g 
-1 3.41 
a.i. ha  Flu 
Percent of S2 39.4 g 
a.i. ha-1 3.15 
 Flu 
Visual Score 39.4 g a.i. 
-1 4.52 
ha  Flu 
Percent of R4 19.7 g 
3.20 
a.i. ha-1 Flu 
Percent of R4 39.4 g 1D_16113776 - 
1D_16113776 1D 0 3.28 
a.i. ha-1 Flu 1D_16113776 
Visual score 39.4 g a.i. 
ha-1 3.3 
 Flu 
Percent of S2 19.7 g 
3.85 
a.i. ha-1 Flu 
Percent of R4 39.4 g 
3.51 
a.i. ha-1 Flu 2D_234378723 
2D_410716933 2D2 4.2 - 
Percent of S2 39.4 g 2D_410716933 
3.66 
a.i. ha-1 Flu 
Visual Score of 39.4 g 
3.66 
a.i. ha-1 Flu 
 
90 
 
Table 4.2 Continued. 
Peak 
QTL Flanking 
Trait Chromosome Position LOD 
Identification markers 
(cM) 
Percent of Control 
41.21 
1,026 g a.i ha-1 Tri 
Percent of R4 1,026 g 
41.21 
a.i ha-1 Tri 3D_1907899 - 
3D_1907891 3D2 22.0 
Percent of Control 3D_6229596 
26.67 
2,052 g a.i ha-1 Tri 
Percent of R4 2,052 g 
26.67 
a.i. ha-1 Tri 
Percent of Control 
3.40 
1,026 g a.i. ha-1 Tri 
Percent of R4 1,026 g 
3.40 
a.i. ha-1 Tri 
7A_6698604 Percent of Control 7A_66986043 - 
7A 0 4.91 
3 2,052 g a.i. ha-1 Tri 7A_66986043 
Percent of R4 2,052 g 
4.91 
a.i. ha-1 Tri 
Visual Score of 2,052 g 
4.47 
a.i. ha-1 Tri 
 
RNA-Seq Analysis 
 RNA-seq analysis identified 13 DEGs located within QTL peaks that met predefined 
selection criteria (Figure 4.3). Four of these were significantly overexpressed in the R4 lines, 
while the remaining nine were attenuated in R4 compared to S2. Notably, all four genes showing 
elevated expression in R4 were localized to the 3D_1907891 major QTL for triallate resistance. 
Specifically, the genes overexpressed in R4 include 73C10 and U73C6, both of which are 
annotated as UDP-glycosyl-transferases, and LEUNIG, a transcriptional co-repressor, and NPL4, 
characterized as an NPL4-like protein. In contrast, within the same major-effect QTL 
(3D_1907891), three other genes—BOR4, FB345, and TBL1—were attenuated in R4. BOR4 is 
91 
 
identified as a boron transporter, FB345 as an F-box protein, and TBL1 as a trichome 
birefringence-like 1 protein. 
Under the 2D_410716933QTL for flucarbazone resistance, six genes displayed lower 
expression levels in R4 than in S2 lines. These genes were annotated as U71K2, a UDP-
glycosyl-transferase; CAR8, possessing a protein C2 domain; RA51D, a DNA repair protein; 
FBT8, a likely folate-biopterin transporter; PIP21, an aquaporin protein; and HFA2B, a heat 
stress transcription factor. 
Table 4.3. DEGs underlying QTL peaks in R4 lines. Alias is the identifier for genes. Log 2-fold 
change values > 0.8 and P < 0.5 represent the magnitude and direction of gene expression, where 
positive and negative numbers represent under- and over-expression in R4 compared to S2. 
Log 2-
QTL DEG fold 
Identification Alias change P-Value Annotation 
73C10 -1.6974 0.0001456 UDP-glycosyl-transferase 
U73C6 -1.7595 0.000000024 UDP-glycosyl-transferase 
LEUNIG -2.0964 0.00000025 Transcriptional co-repressor 
3D_1907891 NPL4 -1.0317 0.0000042 NPL4-Like protein 
BOR4 3.1637 0.00000000000019 Boron transporter 4 
FB345 5.6962 0.00000000000023 F-box protein 
Protein trichome 
TBL1 8.4688 0.000000000004 birefringence 
U71K2 0.9975 0.00044746 UDP-glycosyl-transferase 
Protein C2-Domain ABA-
CAR8 0.8970 0.00000123 
related 
2D_41071693 RA51D 1.8205 0.0000000001 DNA repair Protein 
3 FBT8 0.8333 0.00050535 Folate-biopterin transporter 
PIP21 2.0713 0.0000127 Aquaporin protein 
Heat stress transcription 
HFA2B 0.8537 0.00021586 
factor 
 
 
 
92 
 
Discussion 
 The primary aim of this study was to employ QTL mapping techniques to identify 
genomic regions associated with herbicide resistance across three RIL populations. QTL 
mapping is a widely recognized approach for dissecting complex traits and pinpointing genomic 
regions that influence quantitative attributes in plant species (Farquhar, 1998; Myles & Wayne, 
2008; Würschum, 2012). In the 93 RIL population, three QTLs were identified. 3D_427541473, 
with LOD scores ranging from 3.27 to 5.56. 3D_347553285 and 1A_9291409 with LOD scores 
of 8.01 and 2.66 respectively. 3D_427541473 was detected multiple times using different 
phenotypic parameters in the QTL analysis, strongly indicating that this region houses a gene 
conferring triallate resistance. The other two QTLs were only detected once, using the scoring 
system phenotypic parameter which suggests that these may not be strong genomic markers for 
herbicide resistance. Crucially, the resistant trait for triallate in 93R segregates to a 15:1 
(sensitive to resistant) ratio. This lead the RIL population to have 7 out of 124 fully resistant 
lines, with phenotypic values being concentrated at low end of each phenotypic metric. Without a 
range of phenotypic values, QTL analysis becomes extremely difficult (Consortium, 2003). 
For the 06 RIL population, QTL analysis identified three regions associated with 
flucarbazone resistance: 4A_358747067, 1D_16113776, and 2D_410716933. Among these, 
4A_358747067 was the most recurrent, appearing in seven different phenotypic measurements 
and displaying a higher LOD score than the other flucarbazone-associated QTLs. For the other 
flucarbazone resistance trait in the 06 RIL population, two additional QTLs, 1D_16113776 and 
2D_410716933, exhibited lower LOD scores compared to 4A_358747067. Specifically, 
1D_16113776 had LOD scores ranging from 3.20 to 3.30, while 2D_410716933 registered 
93 
 
scores between 3.51 and 3.85. Intriguingly, the latter QTL had flanking markers within which 
several DEGs were identified. As two recessive genes control triallate resistance in 93R (Kern et 
al., 2002), and the mechanism responsible for triallate resistant in R4 normally segregates. This 
means that although QTL are being mapped onto 3D chromosome, they are not indicating the 
same mechanism are responsible for this phenotype. The lack of strong LOD scores could be 
caused by the two recessive genes required for triallate resistance. All the DEGs meeting our 
criteria were downregulated in the R4 population and included U71K2, a UDP-glucosyl-
transferase implicated in xenobiotic resistance, and CAR8, a C2-Domain Protein involved in 
ABA receptor recruitment. C2-domain proteins have significant roles in signal transduction and 
membrane transport (Edel & Kudla, 2016). RA51D is a DNA repair protein, and PIP21, which 
was identified as an aquaporin, both being under expressed would could have a role for herbicide 
resistance. FBT8 is a folate-biopterin transporter that could grant some herbicide resistant 
capabilities, but only to asulam (DHPS inhibitor, Group 8), an herbicide that targets folate 
biosynthesis, which cannot control monocots. Notably, HFA2B, a heat stress transcription factor, 
was detected to be under-expressed in R4, which is similar to the findings presented in (Keith et 
al., 2017). 
 In relation to triallate resistance in the 06 RIL population, two QTLs were detected: 
3D_1907891 and 7A_66986043. The latter appeared five times across various measurements 
with LOD scores between 3.40 and 4.91, while 3D_1907891 was found to have a major effect 
QTL, evident by its high LOD scores ranging from 26.67 to 41.21. Subsequent DEG analysis 
under this major QTL peak revealed that four genes, 73C10, U73C6, LEUNIG, and NPL4, were 
94 
 
expressed at higher levels in R4 than in S2 lines. Conversely, three genes, namely BOR4, FB245, 
and TBL1, were upregulated in S2 lines. 
Of the DEGs overexpressed in R4, 73C10 and U73C6 were categorized as UDP-
glycosyl-transferases, enzymes previously noted for their role in xenobiotic resistance (Keith et 
al., 2017). These enzymes are part of a broader superfamily, the uridine diphosphate sugar-
utilizing glycosyl-transferases, involved in various metabolic pathways, including xenobiotic 
resistance (Dimunová et al., 2022; Meech et al., 2012). Their presence under a major-effect QTL 
highlights their possible role in herbicide resistance mechanisms. LEUNIG, a transcriptional co-
repressor, and its homologs have been reported to regulate both abiotic and biotic stress 
responses in plants (Gonzalez et al., 2007; Sitaraman et al., 2008). Nuclear Protein Localization 
4 (NPL4) operates as a heterodimeric cofactor within the ubiquitin-proteasome pathway, a 
critical catabolic mechanism that targets unwanted or damaged proteins for degradation (Bays & 
Hampton, 2002). This pathway has been implicated in herbicide treatment responses; for 
instance, glyphosate treated Pisum sativa (Common pea) exhibited elevated levels of 
ubiquitinated proteins alongside heightened proteasome activity (Zulet et al., 2013). The 
ubiquitin-proteasome system has also modulated the effects of various abiotic stresses on plant 
growth and development (Mackinnon & Stone, 2022). 
 In the R4 lines, DEGs with lower expression included BOR4, a Boron transporter; 
FB345, an F-box protein; and TBL1, a protein involved in trichome birefringence. FB345 is part 
of the SCF (Skip1, Cullin, F-box) complex, which facilitates the ubiquitination of target proteins, 
marking them for degradation by the 26S proteasome (Jackson & Eldridge, 2002). TBL1 is a 
95 
 
binding protein that interacts with pectin and other cell wall polysaccharides, potentially 
affecting defense mechanisms (Bischoff et al., 2010). 
The attenuated expression of FB345 in R4 is intriguing, especially when considering its 
functional similarities with NPL4, a gene expressed at higher levels in R4 lines. Both are 
involved in proteasome-mediated degradation; however, the SCF complex and the ubiquitin-
proteasome pathway in which NPL4 is complex, housing many different genes (Roos‐Mattjus & 
Sistonen, 2004). The upregulation of genes involved in this pathway but found in different lines 
suggests that proteasome activity may be modulated context-dependent. It is also plausible that 
these pathways intersect or are involved in different parts of herbicide resistance or stress 
response. 
While this raises questions about the specific roles of these proteins and their interaction 
in herbicide resistance, it also opens avenues for future research. For instance, a thorough 
functional validation could find whether the down regulation of FB345 in R4 lines indeed affects 
proteasome activity in a manner like or different from NPL4. The genes that have been found in 
previous work for A. fatua should also be examined as their occurrence, highlighted by this QTL 
analysis, further suggests a possible role in herbicide resistance (Keith et al., 2017).  
Given that the GBS data for this study was aligned to the Avena sativa reference genome, 
it would be warranted to re-align the GBS data once the Avena fatua genome becomes available. 
Subsequent QTL analyses could then be conducted to compare QTL regions of interest. 
Additionally, advanced methodologies such as fine mapping could be employed to refine our 
understanding of the identified regions. Fine mapping is a targeted genetic technique designed to 
narrow down the precise location of a SNP as a marker associated with a specific trait. It is 
96 
 
especially useful when studying heterozygous individuals who possess different alleles at the 
locus of interest (Noroozi & Sattari, 2015). The fine mapping approach can pinpoint the gene or 
QTL more accurately than broader mapping methods. 
Conclusion 
In conclusion, this study employed QTL mapping to investigate the genomic regions 
associated with herbicide resistance across different RIL populations. Highlighted outcomes 
were the identification of specific QTLs, such as 3D_427541473 for triallate resistance and 
4A_358747067 for flucarbazone resistance for 06 RILs. These QTLs stand as genomic regions 
likely to harbor genes conferring herbicide resistance, thereby serving as significant markers for 
future genetic studies. Intriguingly, the DEG analysis within these QTL regions provided insights 
into the complex biochemical pathways potentially involved in herbicide resistance. For 
instance, UDP-glycosyl-transferases were found to be overexpressed in R4 lines and were 
situated under a major-effect QTL, highlighting their likely importance in herbicide resistance 
mechanisms. The study also noted upregulation of genes related to proteasome-mediated 
degradation pathways in both R4 and S2 lines. The following steps should involve fine mapping 
techniques to narrow these identified QTL regions. This would be a logical extension to provide 
a more accurate pinpointing of the genes responsible for herbicide resistance. Cross-referencing 
these findings with the soon-to-be-released Avena fatua genome could yield even more precise 
results. 
 
 
 
97 
 
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