Responses of Oaks to Mammal and Insect Herbivory A

Home , Datana ministra, Gryllus integer

RESPONSES OF OAKS TO MAMMAL AND INSECT HERBIVORY

A dissertation submitted

to Kent State University in partial fulfillment

of the requirements for the

degree of Doctor of Philosophy

Cynthia Lynn Perkovich

May 2021

Except for previously published materials

Dissertation written by

Cynthia Lynn Perkovich

B.S., Kent State University, 2013

M.S., University of Nebraska-Lincoln, 2016

Ph.D., Kent State University, 2021

Approved by

David Ward, Ph.D. , Chair, Doctoral Dissertation Committee

Oscar Rocha, Ph.D., Members, Doctoral Dissertation Committee

Matthew Lehnert, Ph.D.

Don Cipollini, Ph.D.

Edgar Kooijman, Ph.D.

Accepted by

Laura G. Leff, Ph.D. , Chair, Department of Biological Sciences

Mandy Munro-Stasiuk, Ph.D. , Interim Dean, College of Arts and Sciences

TABLE OF CONTENTS TABLE OF CONTENTS ...... iii LIST OF FIGURES ...... vi LIST OF TABLES ...... ix ACKNOWLEDGEMENTS ...... xi

I. INTRODUCTION...... 1

REFERENCES...... 14

II. HERBIVORE-INDUCED DEFENSES ARE NOT UNDER PHYLOGENETIC

CONSTRAINTS IN THE GENUS QUERCUS (OAK): PHYLOGENETIC

PATTERNS OF GROWTH, DEFENSE, AND STORAGE ...... 25

ABSTRACT ...... 25

INTRODUCTION ...... 27

METHODS ...... 31

RESULTS ...... 37

DISCUSSION ...... 51

CONCLUSIONS...... 58

REFERENCES ...... 60

III. ABOVEGROUND HERBIVORY CAUSES BELOWGROUND CHANGES IN

TWELVE OAK (QUERCUS) SPECIES: A PHYLOGENETIC ANALYSIS OF

ROOT BIOMASS AND NUTRIENT RE-ALLOCATION ...... 80

ABSTRACT ...... 80

INTRODUCTION ...... 82

iii

METHODS ...... 85

RESULTS ...... 92

DISCUSSION ...... 105

CONCLUSIONS...... 112

REFERENCES ...... 114

IV. DIFFERENTIATED DEFENSE SYNDROMES IN RESPONSE TO VARYING

HERBIVORE PRESSURES: OAK TREES INCREASE DEFENSES IN

RESPONSE TO INSECTS AND DECREASE NUTRITIVE QUALITY IN

RESPONSE TO DEER ...... 130

ABSTRACT ...... 130

INTRODUCTION ...... 132

METHODS ...... 135

RESULTS ...... 141

DISCUSSION ...... 151

CONCLUSIONS...... 155

REFERENCES ...... 157

V. PERIODICAL CICADAS INCREASE DEFENSES IN NORTH AMERICAN

FOREST TREES: BEFORE, DURING, AND AFTER A MASS OUTBREAK

...... 168

ABSTRACT ...... 168

INTRODUCTION ...... 170

METHODS ...... 172

RESULTS ...... 175

DISCUSSION ...... 186

CONCLUSIONS...... 190

REFERENCES ...... 192

VI. PROTEIN:CARBOHYDRATE RATIOS IN THE DIET OF GYPSY MOTH

LYMANTRIA DISPAR AFFECT ITS ABILITY TO TOLERATE TANNINS

...... 201

ABSTRACT ...... 201

INTRODUCTION ...... 203

METHODS ...... 205

RESULTS ...... 208

DISCUSSION ...... 214

CONCLUSIONS...... 219

REFERENCES ...... 221

VII. CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH

...... 229

OVERVIEW ...... 229

SUMMARY AND FUTURE DIRECTIONS ...... 230

REFERENCES ...... 239

LIST OF FIGURES

Figure 1. The phylogenetic relationships of the 12 Quercus species used for analysis of growth, defense, and nutrient reallocation in response to simulated herbivory ...... 32

Figure 2. Diagram of the treatments applied to the Quercus saplings with varying location and intensity of simulated herbivory ...... 33

Figure 3. Trade-offs between Quercus constitutive traits and phylomorphospace projections of the Quercus phylogeny ...... 42

Figure 4. Individual Quercus species apical shoot relative growth rates plotted by location and intensity of simulated herbivory...... 45

Figure 5. The mean condensed tannin concentrations of individual Quercus species in response to location of simulated herbivory (apical vs auxiliary) in each treatment...... 47

Figure 6. The average leaf shape factor for all simulated herbivory treatments, plotted against the

Quercus phylogenetic tree to show phylogenetic relationships...... 49

Figure 7. The phylogenetic relationships of the 12 Quercus species used for analysis of belowground responses to simulated herbivory...... 86

Figure 8. Diagram of the treatments applied to the Quercus saplings with varying location and intensity of simulated herbivory...... 88

Figure 9. Pattern of increased investment by Quercus species in belowground biomass relative to aboveground regrowth in response to varying location and intensity of simulated herbivory. .... 95

Figure 10. Patterns of investment by Quercus species in belowground biomass in response to differing locations and intensities of simulated herbivory...... 96

Figure 11. Investment of oak saplings in coarse and fine root biomass in response to differing locations of simulated herbivory...... 97

Figure 12. Investment in non-structural carbohydrate re-allocation to roots by oaks in response to differing locations and intensities of simulated herbivory...... 99

Figure 13. Ratio of the investment by Quercus species in non-structural carbohydrate re- allocation to roots and total belowground biomass in response to varying locations and intensities of simulated herbivory...... 100

Figure 14. Phylogenetic principal component analysis plots of Quercus responses to varying locations and intensities of simulated herbivory...... 103

Figure 15. Phylogenetic relationships and relative significance of response traits for Quercus species derived from phylogenetic principal components weightings of individual variables... 104

Figure 16. Plot of deer exclosure experimental design ...... 136

Figure 17. Scar characteristics and definitions of the insect functional group ...... 139

Figure 18. Patterns of changes in foliar nitrogen concentrations of Quercus bicolor in response to differentiated herbivore type ...... 144

Figure 19. Changes in foliar polyphenol and total tannin concentrations of Quercus bicolor in response to differentiated herbivore type...... 146

vii

Figure 20. Changes in the ratio of nitrogen to antinutritive traits of Quercus bicolor in response to differentiated herbivore type...... 148

Figure 21. Proportion of insect functional group on foliage of Quercus bicolor relative to the browseline ...... 150

Figure 22. Mean number of insect functional groups per leaf relative to leaf placement above or below the browseline of Quercus bicolor located inside and outside of deer exclosures...... 151

Figure 23. Changes in foliar chemistry of white oaks before, during, and after periodical cicada outbreak...... 177

Figure 24. Changes in root chemistry of white oaks before, during, and after periodical cicada outbreak...... 181

Figure 25. Differences in the foliar chemistry between damaged and undamaged branches of white oaks during and after periodical cicada emergence...... 185

Figure 26. Gypsy moth growth and development on diets with varying macronutrient ratios and tannin concentrations...... 210

Figure 27. The mean number of days for gypsy moth pupation from the 4th to the 5th stadium on diets with varying macronutrient ratios and tannin concentrations...... 211

Figure 28. Gypsy moth consumption, conversion efficiency, and food digestibility on diets with varying macronutrient ratios and tannin concentrations ...... 212

Figure 29. Gypsy moth frass excretion amount and tannin concentrations on varying diets of macronutrients and tannin...... 214

viii

LIST OF TABLES

Table 1. Predictions and assumptions of prevalent plant defense hypotheses...... 7

Table 2. Results of phylogenetic least squares regression of phylogenetic signal (Blomberg’s K with significance (P)) for Quercus responses to varying locations and intensities of simulated herbivory...... 39

Table 3. MANOVA results for the effects of location and intensity of simulated herbivory on oak growth, defense, and nutrient allocation ...... 44

Table 4. ANOVA results of the effects of location and intensity of simulated herbivory on oak growth, defense, and nutrient allocation ...... 50

Table 5. ANOVA results of the effects of location and intensity of simulated herbivory on oak belowground traits ...... 93

Table 6. Mean and median trait values of Quercus belowground traits to varying locations and intensities of simulated herbivory...... 94

Table 7. Eigenvalues and % variance explained by phylogenetic principal components analyses of Quercus responses to varying locations and intensities of simulated herbivory ...... 102

Table 8. Pearson’s correlations (and significance) between aboveground biomass regrowth and root starch concentrations in oak responses to varying locations and intensities of simulated herbivory...... 105

Table 9. Multivariate analysis of variance (MANOVA) results of growth and phytochemical responses by Quercus bicolor to differentiated herbivore types...... 142

Table 10. Univariate ANOVA results of macronutrient ratio and tannin on growth and nutritional indices of fourth-instar gypsy moths...... 209

ACKNOWLEDGMENTS

I dedicate this dissertation to my family who has always put my success ahead of their own. A dissertation is a monumental achievement which cannot be accomplished without a truly supportive environment. Through the dissertation process, I have learned that many things in life are far more important than research and degrees behind your name. A doctoral degree must truly be a passion for one to achieve it. Thank you to my advisor, Dr. David Ward, for his support and patience throughout this process. David always pushed me to my full potential by giving me additional tasks and objectives outside of my comfort zone. Thank you to my entire committee, including Dr. Oscar Rocha, Dr. Matthew Lehnert, and Dr. Don Cipollini, and to the department – especially Susan Kieklak, Robin Wise, and Jennifer Kipp for their endless support.

Thank you to Dr. Megan Griffiths-Ward and Dr. Tiffany Pillay for their additional advice and support with writing my dissertation. Also, to the Ward lab manager, Christian Combs, for his help ordering supplies, reviewing manuscripts, as well as setting up and maintaining project experiments.

I would also like to acknowledge the Art and Margaret Herrick Foundation and Kent

State’s Graduate Student research awards for financial support. These funds provided the opportunity for this research and facilitated countless hours of mentorship for undergraduate students. I would like to thank several specific undergraduate research assistants for their help in completion of this work, including John Christakis, Meggie Moore, Jalin Gillespie, Daytona

Johnson, and Harry Price. I hope that the research experience has helped you to further your passions for scientific investigation. Also, thanks to the many graduate students that helped critique and review my research, including Bethany Schmidt and Samia Hamati.

Finally, thank you to my supportive family. Thank you to my aunts, Kathy West and

Lonna Thomas, and my grandmother, Eleanore Thomas, for their support and never letting me give up. Thank you to my husband, Mike, for his support, encouragement, and understanding through the highs and lows and so much time spent in the field and lab. He did his best to critique my writing and keep our house maintained.

xii

CHAPTER I

INTRODUCTION

Herbivory is a major driver of plant community dynamics and evolution. Herbivore pressures have driven the evolution of a number of plant traits (Farmer 2016; Karban and Baldwin 1997;

Schuman and Baldwin 2016), including plant defenses that are used to prevent or minimize herbivore attacks (Mithöfer and Boland 2012; War et al. 2012; Zaynab et al. 2018). Defenses can be mechanical, such as thorns and spines (Ford et al. 2014; Rohner and Ward 1997; Zinn et al.

2007), or chemical, such as polyphenols and alkaloids (Cipollini et al. 2005; Erb and

Kliebenstein 2020; Feeny 1976; Ward and Young 2002). By definition, plant defenses should function to increase a plant’s reproductive success, which in evolutionary terms will increase a plant’s genetic contribution to the next generation (Erb 2018). However, the direct effects of herbivory on fitness are often difficult to measure and are diverse depending on environmental factors (Karban 2020; Maron and Crone 2006). Due to the complex nature and expression of plant defenses, it is important to investigate ecological and evolutionary effects of herbivory on plant defenses in key ecological species.

Herbivore diversity presents additional challenges for plants and exacerbates the need for plants to maintain a diverse arsenal of defenses (Moreira et al. 2014; Poelman and Kessler 2016).

Physical constraints on certain herbivores, such as large mammals, allow them to only feed on herbaceous plants and low hanging foliage (Hoffman and Stewart 1972; Nopp-Mayr et al. 2020). Contrastingly, insect herbivores comprise a large diversity of feeding guilds, with strategies that may include feeding on roots, burrowing into tree trunks to feed on inner cambium, or flying to reach foliage at greater heights (Kristensen et al. 2020; Peeters 2002).

Furthermore, dietary requirements may also dictate herbivore feeding behavior (Jeude and

Fordyce 2014; Marsh et al. 2018; Perkovich and Ward 2020). For example, Marsh et al. (2014) found that the dietary preference (e.g., the ratio of protein to chemical defense concentration) of a mammalian herbivore caused the herbivores to favor leaves of a specific age. Because herbivore feeding behavior is variable among herbivores and can vary interspecifically, a plant must be able to adapt to—and defend against—a multitude of herbivore assaults on various tissues.

Types of defenses Defenses can be classified as qualitative or quantitative (Feeny 1976;

Wiggins et al. 2016). According to Feeny (1976), qualitative defenses are plant defenses that are toxic in low concentrations, whereas quantitative defenses are dose dependent. The plant apparency hypothesis (Feeny 1976) predicts that specific plant defenses have different effects on specific types of herbivores (Müller-Schärer et al. 2004). For example, qualitative defenses, such as alkaloids, are effective toxins against generalist herbivores but some specialist herbivores have evolved mechanisms to detoxify or minimize the negative effects of these compounds

(Alba et al. 2011; McArthur et al. 1991; Perkovich and Ward 2020). Contrastingly, quantitative defenses, such as polyphenols and tannins (a subset of polyphenols), are effective against generalist and specialist herbivores at high concentrations (Chen et al. 2010; Feeny 1976;

Younginger et al. 2017). For example, mammalian herbivores often produce dietary enzymes in their saliva that reduce the effects of polyphenols but are still deterred by the presence of phenolic compounds (Bordage et al. 2017; Ward et al. 2020).

In addition to plant defenses being qualitative or quantitative, defenses may be constitutive or induced (Karban and Baldwin 1997). Constitutive defenses are defensive traits that are always expressed (Poelman et al. 2008; Wittstock and Gershenzon 2002). Examples of constitutive defenses include bark, waxy epidermal cuticles, and baseline concentrations of some chemical compounds. Plants also maintain an arsenal of inducible defenses that are produced in direct response to stimuli (Karban and Baldwin 1997). Inducible defenses are a form of phenotypic plasticity that allow plants to adapt to unpredictable and heterogeneous selective pressures from a diverse herbivore community (Adler and Karban 1994; Agrawal and Hastings

2019). Inducible defenses are often costly to produce, and are therefore only produced when the benefits of production outweigh the energetic costs (Cipollini et al. 2003; Neilson et al. 2013).

History of Plant Defense Theory The theory of plant defenses is a body of knowledge that has been derived from decades of research, dedicated to explaining and predicting phenotypic, genetic, and geographical patterns of plant defenses (Cipollini et al. 2014; Feeny 1976; Rhoades and Cates 1976; Stamp 2003). The earliest record of the term “secondary metabolites,” which describes compounds that are not used in primary metabolic function and are used as defense compounds, began in the 1950’s (Dethier 1954; Fraenkel 1959; Stamp 2003). A coevolutionary model to explain the diversification of plants and herbivores further developed plant defense theory by suggesting that macroevolutionary patterns in plant defense compounds could explain plant–insect affiliations (Ehrlich and Raven 1964). The notion of coevolution is that in the case

3 of plant–herbivore interactions, a plant species develops a new defense that reduces efficiency of the herbivores. As time progresses, the plant can proliferate into new niches. The Red Queen

Hypothesis predicts that, eventually, herbivores will coevolve and develop countermeasures to the new defense, ultimately exploiting the diverged plant species (Decaestecker and King 2018; van Valen 1977). With each new coevolved trait, parallel cladogenesis is not necessarily produced by successive adaptive radiations (Thompson 1999).

Of the many plant defense theories, none have been formally rejected (Berenbaum 1995;

Burkepile and Parker 2017; Stamp 2003) and several have provided frameworks for understanding patterns and making predictions. Here I briefly discuss each hypothesis and the assumptions associated with that particular framework to provide a clearer understanding of the rationale and philosophy behind this dissertation. Table 1 provides the predictions and assumptions of several prevalent hypotheses; however, it should be noted that this is by no means an exhaustive list and for further details and additional hypotheses, see Stamp (2003).

Optimal Defense Hypothesis (ODH; Feeny 1975, 1976; Rhoades 1979; Rhoades and

Cates 1976). ODH states that defenses are energetically costly and decrease fitness by diverting resources away from growth and reproductive processes (Rhoades 1979; Stamp 2003). The ODH framework suggests that: a) the risk from herbivores influences the proportion of defenses produced, which is inversely proportional to the energetic expense of defense production (Feeny

1976); b) the value of plant tissue proportionally affects the amount of defense investment, which is inversely proportional to the energetic expense of production (Zangerl and Bazzaz,

1992); c) allocation of constitutive and inducible defense strategies changes depending on the immediate threat (Adler and Karban 1994; Scogings 2018); and d) there is a trade-off between primary metabolic functions (such as growth and reproduction) and defense (Guo et al. 2018;

Rhoades 1979; Züst and Agrawal 2017). Unfortunately, ODH is difficult to directly test because there are no formal predictions (Fagerstrom et al. 1987; Stamp 2003). The hypothesis is deemed unfalsifiable because it predicts that if adaptive, every outcome is possible (Rhoades 1979).

Resource Availability Hypothesis (RAH; Coley 1987a, b; Coley et al. 1985) predicts that under conditions with optimal resources, constitutive defenses (either chemical or mechanical) should increase as growth rate decreases. In practice, this hypothesis can be reduced to a more basic concept that in low nutrient environments, plants are more likely to increase defenses because resources are not readily available for regrowth (Coley et al. 1985). Slow-growing plant species are predicted to have a fixed investment in constitutive defenses, which are energetically

“cheap” defenses such as lignins, so they are constantly defended from herbivores (Coley et al.

1985). Likewise, plants in high nutrient environments are more likely to invest in regrowth and decrease defenses (Coley 1987a). Fast-growing plant species are predicted to have a greater investment in inducible defenses that are produced in quick succession of stimuli (Coley 1987a).

Growth–Differentiation Balance Hypothesis (GDBH) predicts a trade-off between growth

(processes with significant cell division or elongation) and differentiation (enhancement of pre- existing structures and functions) (Ballaré and Austin 2019; Guo et al. 2018; Loomis 1932,

1953). Herms and Mattson (1992) elaborated on the terms “growth” and “differentiation,” depicting “growth” as primary metabolic functions (growth and reproduction) and

“differentiation” as secondary metabolic functions (e.g., defenses). GDBH makes predictions about plant resource allocation (i.e., to growth or differentiation) in limited, intermediate, and high resource conditions. Plants with limited resources should prioritize growth over differentiation (Stamp 2004; Waring and Pitman 1985) and therefore have a relatively low biomass and secondary metabolite concentrations. At intermediate resource levels,

5 photosynthesis is not as restricted as it is under low resource levels (Chapin 1980; Körner 1991), allowing secondary metabolites to be produced relatively cheaply (Stamp 2004). Plants with high resource availability are not limited by growth or photosynthesis and should therefore allocate a greater proportion of resources to growth than to differentiation (Herms and Mattson 1992). The

GDBH provides a valuable framework that integrates energetic costs of defense production with a resource gradient (Stamp 2004).

Table 1 Predictions and assumptions of prevalent plant defense hypotheses.

Plant Defense Hypothesis Predictions Assumption Relevant literature Optimal defense hypothesis • No formal predictions • Defenses are genetically McKey 1974, 1979; Rhoades 1979 • “any defensive pattern is variable possible if it is adaptive”— • Defenses are produced Stamp (2003) primarily against herbivores • Herbivory is reduced by defense production Plant apparency hypothesis • Plants that are easily • Plants are either “apparent” Feeny 1975, 1976; Rhoades and discovered by herbivores or “unapparent,” with no Cates 1976; Smilanich et al. 2016 are “apparent” and therefore intermediate apparency to invest in “quantitative” herbivores defenses that are dosage • Specialist herbivores are not dependent adapted to host-specific • Plants with stochastic compounds herbivore threats are “unapparent” and therefore invest in “qualitative” defenses that are toxic in low doses Carbon–nutrient balance hypothesis • The amount of carbon and • Mineral nutrients dictate Bryant et al. 1983; Tuomi et al. 1988, nitrogen available to a plant carbon gain and growth 1991 predicts the concentration of • When mineral nutrients are defense chemicals in plant adequately supplied, carbon tissues is allocated to growth • Any mineral nutrients supplied beyond what is needed for growth are allocated to defense production • Nutrient limitation inhibits growth more than photosynthesis • Defenses are primarily selected for by herbivore pressures

Resource availability hypothesis • Growth and constitutive • Growth rate is determined Coley 1987a, b; Coley et al. 1985; defense rates are inversely by nutrient availability Endara and Coley, 2011 related • Defenses are primarily • Species in nutrient-rich selected for by herbivore environments have faster pressures growth rates, and therefore lower constitutive defense concentrations than species in nutrient-poor environments Growth–differentiation balance • Low levels of resources • Growth, more than Guo et al. 2018; Herms and Mattson hypothesis should be preferentially photosynthesis, is limited by 1992; Loomis 1932, 1953 invested in growth resource availability processes • Growth is limited by • Plants with intermediate resource limitation and levels of resources should therefore maximum growth have high defense is determined by resource investment and intermediate availability growth • There is a trade-off between • High levels of resources growth and differentiation should be allocated to (i.e., growth and defense) photosynthates and • Herbivore damage is therefore have greater reduced by defenses investment in growth • Defenses divert resources • With increased light, away from growth and defenses will increase therefore are costly proportionally to growth

Importance of phylogeny and evolution in plant defense research Many current studies focus on understanding plant defense hypotheses in a phylogenetic and evolutionary context (e.g.,

Agrawal and Hastings 2019; Moreira et al. 2020). Comparative ecological studies among species should also consider phylogenetic relatedness of those species to accurately meet statistical assumptions of non-independence (Ackerly and Donoghue 1995; Mundry 2014; Pennell and

Harmon 2013). Closely related species may express shared characteristics that do not represent independent evolutionary events (exaptations sensu Gould and Vrba 1982). In plant defense studies, more closely related species may have similar defensive strategies due to shared evolutionary relationships (Ehrlich and Raven 1964; Pearse and Hipp 2009).

Felsenstein (1985) argued that different species are manifestly non-independent because they come from branching phylogenies where some shared traits are inherited from common ancestors. He proposed a method using independent contrasts for analyses using statistical tests that assume independence of individual data points. Independent contrasts use phylogenetic tree topography and branch length to incorporate phylogenetic relatedness into the analysis. Instead of using raw trait data, the independent contrasts are used in the analysis to meet statistical assumptions of independence. The use of independent contrasts has been further developed so that comparative studies can now quantify phylogenetic constraints by testing for phylogenetic signal and strength (Blomberg 2003; Münkemüller et al. 2012; Revell et al. 2008). The use of various phylogenetic comparative methods is further discussed in Chapters II and III.

Oaks (Fagaceae: Quercus) as a model system I chose the oak (Quercus) genus for my model system for several reasons. First, from an ecological standpoint, oaks are the most abundant and diverse woody plant taxon in North America (Nixon 1997; Rodríguez-Correa et al. 2015). Data

9 from forest inventories show that oaks have the highest biomass and species diversity of all woody plant genera in the United States and Mexico (Cavender-Bares 2016), making members of the oak genus integral species in many temperate ecosystems. Second, from an evolutionary standpoint, oaks have been used as model systems for studies on adaptive radiation (Hipp et al.

2014), hybridization and introgression (Eaton et al. 2015), and genome evolution (Petit et al.

2013), to name a few. These evolutionary studies have laid the foundation for further research on the evolution of plant–herbivore interactions (e.g., Pearse and Hipp 2009) and have provided a well-resolved molecular phylogeny (Hipp et al. 2014; 2020). Oaks are therefore an excellent system in which to conduct studies on plant–herbivore interactions using phylogenetic comparative methods (Pearse and Hipp 2009; Moreira et al. 2018).

Aims and objectives of this study Understanding how plants differentiate defense strategies in response to various herbivore pressures is an ongoing goal of plant science. This dissertation sought to better understand how oaks respond to mammalian and insect herbivory in an ecological and phylogenetic context.

The specific objectives of this study were:

1. Use phylogenetic comparative methods to understand oak patterns of growth,

defense, and nutrient allocation in response to various locations and intensities of

tissue damage by herbivores.

2. Determine whether aboveground herbivore damage has belowground consequences in

oaks.

3. Determine if oaks deploy differentiating defense strategies dependent on the

herbivore present (i.e., mammalian vs. insect).

4. Understand oak defense strategies in response to extreme herbivore events such as

large insect outbreaks.

5. Understand the effects of common oak defense strategies (i.e., tannin production and

variation in nutrient quality of foliage) on a generalist herbivore’s growth and

development.

Thesis structure Several hypotheses are tested in this dissertation. I give a brief introduction to the key theories of plant defenses and how more recent studies have focused on specific aspects of the ecology and evolution of plant defenses in Chapter I. The following chapters each give a more thorough explanation of the plant defense hypotheses that are being addressed within that chapter.

In Chapter II (Herbivore-induced defenses are not under phylogenetic constraints in the genus Quercus (oak): phylogenetic patterns of growth, defense, and storage), I present the results of a greenhouse experiment in which I simulated varying herbivory locations (apical vs. lateral) and intensities (25% vs. 75%) of plant tissue removal on 12 species of oak. I used a phylogenetic least squares regression to measure phylogenetic signal of responses among the 12 species. This study showed that there are limited phylogenetic constraints on induced defenses, and these induced defenses are more likely to result from local adaptations. Furthermore, it showed that the trade-off predicted by the growth–differentiation balance hypothesis only holds true for constitutive defenses and not induced traits. This chapter has been published in Ecology and

Evolution.

Chapter III (Aboveground herbivory causes belowground changes in twelve oak

(Quercus) species: a phylogenetic analysis of root biomass and nutrient re-allocation) was created from the same experimental design as the previous chapter but analyzed belowground responses of the 12 oak species in a phylogenetic context. For this experiment, I first showed that total root biomass increased in response to aboveground herbivory, and that root types (coarse vs. fine) are differentially affected by the location and intensity of aboveground herbivory. I then used a phylogenetic principal component analysis to show that phylogenetic constraints on root phenotypes exist when trees are severely damaged at the apical meristem (i.e., 75% removal).

This chapter is in review with Oikos.

After showing phylogenetic patterns and constraints on oak responses to simulated damage, I tested for differentiated responses of a single oak species to varying herbivore pressures in Chapter IV (Differentiated defense syndromes in response to varying herbivore pressures: oak trees increase defenses in response to insects and decrease nutritive quality in response to deer). I used deer exclosures to analyze chemical and morphological traits of swamp white oak (Q. bicolor) in herbivore-free environments, insect-only environments, deer-only environments, and environments with both deer and insects present. Behavioral differences of deer and insect herbivores allowed me to test for differentiated responses of this oak species. I found that swamp white oak responding to deer by lowing nutrient quality of foliage but did not increase chemical production. Contrastingly, swamp white oaks increased chemical production but did not change leaf nutrient quality in response to insect herbivores. This chapter has been submitted to Oecologia.

After assuming continuous feeding by insect herbivores in the previous chapter,

Chapter V (Periodical cicadas increase defenses in North American forest trees: before, during,

12 and after a mass outbreak) analyzes how trees respond to extreme fluctuations in insect feeding densities. I measured oak tree chemistry before, during, and after the emergence of 17-year periodical cicadas to analyze how oaks respond to large insect outbreaks. There are clear responses above- and belowground to the emergence. Aboveground defense production increased during the emergence and returned to pre-emergence concentrations the year after an emergence. This study shows that oaks balance costs of defense production by fluctuating between constitutive and induced response strategies. This chapter has been submitted to

Science.

Chapters I–V focus on oak responses to herbivory under varying conditions. For Chapter

VI (Protein:carbohydrate ratios in the diet of gypsy moth Lymantria dispar affect its ability to tolerate tannins), I analyzed how the differentiating defense mechanisms employed by oaks effects the growth and development of a generalist herbivore, the gypsy moth (Lymantria dispar). Gypsy moth larvae were fed diets varying in nutrient ratios and concentrations of tannins (a defense chemical commonly deployed by oaks). I found that generalist herbivores are able to tolerate small amounts of tannin concentrations, given the adequate protein:carbohydrate ratio. This chapter is published in the Journal of Chemical Ecology.

Finally, Chapter VII presents the general conclusions of the entire body of experimental results presented in this dissertation. I also propose new avenues for future research of plant defenses. Because all chapters (excluding Chapters I and VII) are submitted/ in review/ published in different journals, there is some unavoidable repetition and there are inconsistencies in formatting.

REFERENCES Ackerly DD, Donoghue MJ (1995) Phylogeny and ecology reconsidered. J Ecol 83:730–733

Adler FR, Karban R (1994) Defended fortresses or moving targets? Another model of inducible

defenses inspired by military metaphors. Am Nat 144:813–832

Agrawal AA, Hastings AP (2019) Trade-offs constrain the evolution of an inducible defense

within but not between plant species. Ecology 100:e02857

Alba JM, Glas JJ, Schimmel BC, Kant MR (2011) Avoidance and suppression of plant defenses

by herbivores and pathogens. J Plant Interact 6:221–227

Ballaré CL, Austin AT (2019) Recalculating growth and defense strategies under competition:

keys roles of photoreceptors and jasmonates. J Exp Bot 70:3425–3436

Berenbaum MR (1995) The chemistry of defense: theory and practice. Proc Nat Acad Sci 92:2–8

Blomberg SP, Garland Jr T, Ives AR (2003) Testing for phylogenetic signal in comparative data:

behavioral traits are more labile. Evolution 57:717–745

Bordage S, Pham TN, Zedet A, Gugglielmetti AS, Nappey M, Demougeot C, Girard-Thernier C

(2017) Investigation of mammal arginase inhibitory properties of natural ubiquitous

polyphenols by using an optimized colorimetric microplate assay. Planta Med 83:647–

653

Bryant JP, Chapin FS III (1983) Carbon/ nutrient balance of boreal plants in relation to

vertebrate herbivory. Oikos 40:357–368

Burkepile DE, Parker JD (2017) Recent advances in plant–herbivore interactions. F1000 Res

6:e119

Cavender-Bares J (2016) Diversity, distribution and ecosystem services of the North American

oaks. Int Oaks 27:37–48

Chapin FS III (1980) The mineral nutrition of wild plants. Annu Rev Ecol Syst 11:233–260

Chen F, Wang M, Zheng Y, Luo J, Yang X, Wang X (2010) Quantitative changes of plant

defenses and phytohormone I biocontrol of cucumber Fusarium wilt by Bacillus subtilis.

World J Microbiol Biotechnol 26:675–684

Cipollini D, Mbagwu J, Barto K, Hillstrom C, Enright S (2005) Expression of constitutive and

inducible chemical defenses in native and invasive populations of Alliaria petiolata. J

Chem Ecol 31:1255–1267

Cipollini D, Purrington CB, Bergelson J (2003) Costs of induced responses in plants. Basic Appl

Ecol 4:79–89

Cipollini D, Walters D, Voeckel C (2014) Costs of resistance in plants: from theory to evidence.

Annu Plant Rev 47:263–307

Coley PD (1987a) Interspecific variation in plant anti-herbivore properties: the role of habitat

quality and rate of disturbance. New Phytol 106:251–263

Coley PD (1987b) Effects of plant growth rate and leaf lifetime on the amount and type of anti-

herbivore defense. Oecologia 74:531–536

Coley PD, Bryant JP, Chapin FS III (1985) Resource availability and plant antiherbivore

defense. Science 230:895–899

Decaestecker E, King K (2018) Red queen dynamics. In Fath BD (ed) Encyclopedia of ecology

vol. 3 (2nd ed). Elsevier, Amsterdam, pp 185–192

Diether VG (1954) Evolution of feeding preferences in phytophagous insects. Evolution 8:33–54

Eaton DA, Gonzalez-Rodríguez A, Hipp AL, Cavender-Bares J (2015) Historical introgression

among American live oaks and the comparative nature of tests for introgression.

Evolution 69:2587–2601

Ehrlich PR, Raven PH (1964) Butterflies and plants: a study in coevolution. Evolution 18:586–

608

Endara MJ, Coley PD (2011) The resource availability hypothesis revisited: a meta-analysis.

Funct Ecol 25:389–398

Erb M (2018) Plant defenses against herbivory: closing the fitness gap. Trends Plant Sci 23:187–

194

Erb M, Kliebenstein DJ (2020) Plant secondary metabolites as defenses, regulators, and primary

metabolites: the blurred functional trichotomy. Plant Physiol 184:39–52

Fagerstrom T, Larsson S, Tenow O (1987) On optimal defence in plants. Funct Ecol 1:73–81

Farmer EE (2016) Leaf defence. Oxford University Press, Oxford

Feeny P (1975) Biochemical coevolution between plants and their insect herbivores. In Gilbert

LE, Raven PH (eds) Coevolution of animals and plants. University of Texas Press,

Austin, TX, pp 3–19

Feeny P (1976) Plant apparency and chemical defense. In Wallace JW, Mansell RL (eds)

Biochemical interaction between plants and insects. Springer, Boston, MA, pp 1–40

Felsenstein J (1985) Phylogenies and the comparative method. Am Nat 125:1–15

Ford AT, Goheen JR, Otieno TO, Bidner L, Isbell LA, Palmer TM, Ward D, Woodroffe R,

Pringle M (2014) Large carnivores make savanna tree communities less thorny. Science

346:346–349

Fraenkel GS (1959) The raison d’étre of secondary plant substances. Science 129:1466–1470

Gould SJ, Vrba ES (1982) Exaptation—a missing term in the science of form. Paleobiol 8:4–15

Guo Q, Major IT, Howe GA (2018) Resolution of growth–defense conflict: mechanistic insights

from jasmonates signaling. Curr Opin Plant Biol 44:72–81

Herms DA, Mattson WJ (1992) The dilemma of plants: to grow or defend. Q Rev Biol 67:283–

335

Hipp AL, Eaton DA, Cavender-Bares J, Fitzek E, Nipper R, Manos PS (2014) A framework

phylogeny of the American oak clade based on sequenced RAD data. PloS One 9:e93975

Hipp AL, Manos PS, Hahn M, Avishai M, Bodénès C, Cavender-Bares J, Crowl AA, Deng M,

Denk T, Fitz-Gibbon S, Gailing O (2020) Genomic landscape of the global oak

phylogeny. New Phytol 226:1198–1212

Hoffman RR, Stewart DRM (1972) Grazer or browser: a classification based on the stomach

structure and feeding habits of East African ruminants. Mammalia 36:226–240

Jeude SE, Fordyce JA (2014) The effects of qualitative and quantitative variation of aristolochic

acids on the performance and preference of a generalist herbivore. Entomol Exp Appl

150:232–239

Karban R (2020) The ecology and evolution of induced responses to herbivory and how plants

perceive risk. Ecol Entomol 45:1–9

Karban R, Baldwin IT (1997) Induced Responses to Herbivory. University of Chicago Press,

Chicago

Körner CH (1991) Some often overlooked plant characteristics as determinants of plant growth:

a reconsideration. Funct Ecol 5:162–173

Kristensen JÅ, Rouosk J, Metcalfe DB (2020) Below-ground responses to insect herbivory in

ecosystems with woody plant canopies: a meta-analysis. J Ecol 108:917–930

Loomis WE (1932) Growth–differentiation balance vs carbohydrate–nitrogen ratio. Proc Am Soc

Hortic Sci 29:240–245

Loomis WE (1953) Growth and differentiation—an introduction and summary. In Loomis WE

(ed) Growth and differentiation in plants. Iowa State College Press, Ames, IA, pp 1–40

Maron JL, Crone E (2006) Herbivory: effects on plant abundance, distribution and population

growth. Proc R Soc B Biol Sci 273:2575–2584

Marsh KJ, Ward J, Wallis IR, Foley WJ (2018) Intraspecific variation in nutritional composition

affects the leaf age preferences of a mammalian herbivore. J Chem Ecol 44:62–71

McArthur C, Hagerman AE, Robbins CT (1991) Physiological strategies of mammalian

herbivores against plant defenses. In Palo RT, Robbins CT (eds) Plant defenses against

mammalian herbivory. CRC Press, Boca Raton, FL, pp 103–114

Mithöfer A, Boland W (2012) Plant defense against herbivores: chemical aspects. Annu Rev

Plant Biol 63:431–450

Moreira X, Abdala-Roberts L, Galmán A, Bartlow AW, Berny-Mier y Teran JC, Carrari et al

(2020) Ontogenetic consistency in oak defense syndromes. J Ecol 108:1822–1834

Moreira X, Abdala-Roberts L, Parra-Tabla V, Mooney KA (2014) Positive effects of plant

genotypic and species diversity on anti-herbivore defenses in a tropical tree species. PloS

One 9:e105438

Moreira X, Glauser G, Abdala-Roberts L (2018) Assessing the influence of biogeographical

region and phylogenetic history on chemical defences and herbivory in Quercus species.

Phytochemistry 153:64–73

Müller-Schärer H, Schaffner I, Steinger T (2004) Evolution in invasive plants: implications for

biological control. Trends Ecol Evol 9:417–422

Mundry R (2014) Statistical issues and assumptions of phylogenetic generalized least squares. In

Garamszegi LZ (ed) Modern phylogenetic comparative methods and their application in

evolutionary biology. Springer, Berlin, pp 131–153

Münkemüller T, Lavergne S, Bzeznik B, Dray S, Jomart T, Schiffers K, Thuiller W (2012) How

to measure and test phylogenetic signal. Methods Ecol Evol 3:743–756

Neilson EH, Goodger JQ, Woodrow IE, Møller BL (2013) Plant chemical defenses: at what

cost? Trends Plant Sci 18:250–258

Nitao JK, Zangerl AR, Berenbaum MR, Hamilton JG, DeLucia EH (2002) CNB: Requiescat in

pace? Oikos 98:540–546

Nixon KC (1997) Quercus Linnaeus. In Flora of North America north of Mexico. Oxford

University Press, New York, pp 431–506

Nopp-Mayr U, Reimoser S, Reimoser F, Sachser F, Obermair L, Gratzer G (2020) Analyzing

long-term impacts of ungulate herbivory on forest–recruitment dynamics at community

and species level contrasting tree densities versus maximum heights. Sci Rep 10:e20274

Pearse IS, Hipp AL (2009) Phylogenetic and trait similarity to a native species predict herbivory

on non-native oaks. Proc Nat Acad Sci 106:18097–18102

Peeters PJ (2002) Correlations between leaf structural traits and the densities of herbivorous

insect guilds. Biol J Linn Soc 77:43–65

Pennell MW, Harmon LJ (2013) An integrative view of phylogenetic comparative methods:

connections to population genetics, community ecology, and paleobiology. Ann New

York Acad Sci 1289:90–105

Perkovich C, Ward D (2020) Protein:carbohydrate ratios in the diet of gypsy moth Lymantria

dispar affect its ability to tolerate tannins. J Chem Ecol 46:299–307

Perkovich C, Ward D (2021) Herbivore-induced defenses are not under phylogenetic constraints

in the genus Quercus (oak): phylogenetic patterns of growth, defense, and storage. Ecol

Evol in press

Petit RJ, Carlson J, Curtu AL, Loustau M-L, Plomion C, Gonzalez-Rodríguez A, Sork V,

Ducousso A (2013) Fagaceae trees as models to integrate ecology, evolution and

genomics: IUFRO Conference, Genetics of Fagaceae and Nothofagaceae, in Bordeaux,

France, October 2012. New Phytol 197:369–371

Poelman EH, Kessler A (2016) Keystone herbivores and the evolution of plant defenses. Trends

Plant Sci 21:477–485

Poelman EH, van Loon JJ, Dicke M (2008) Consequences of variation in plant defense for

biodiversity at higher trophic levels. Trends Plant Sci 13 535–541

Revell LJ, Harmon LJ, Collar DC (2008) Phylogenetic signal, evolutionary process, and rate.

Syst Biol 57:591–601

Rhoades DF (1979) Evolution of plant chemical defense against herbivores. In Rosenthal GA,

Janzen DH (eds) Herbivores: their interaction with secondary plant metabolites.

Academic Press, New York, pp 1–55

Rhoades DF, Cates RG (1976) Toward a general theory of plant antiherbivore chemistry. In

Wallace JW, Mansell RL (eds) Recent advances in phytochemistry. Plenum Press, New

York, pp 168–213

Rodríguez-Correa H, Oyama K, MacGregor-Fors I, González- Rodríguez A (2015) How are

oaks distributed in the neotropics? A perspective from species turnover, areas of

endemism, and climatic niches. Int J Plant Sci 176:222–231

Rohner C, Ward D (1997) Chemical and mechanical defense against herbivory in two sympatric

species of desert Acacia. J Veg Sci 8:717–726

Schuman MC, Baldwin IT (2016) The layers of plant responses to insect herbivores. Annu Rev

Entomol 61:373–394

Scogings PF (2018) Foliar flavonol concentration in Sclerocarya birrea saplings responds to

nutrient fertilization according to growth–differentiation balance hypothesis. Phytochem

Lett 23:180–184

Smilanich AM, Fincher RM, Dyer LA (2016) Does plant apparency matter? Thirty years of data

provide limited support but reveal clear patterns of the effects of plant chemistry on

herbivores. New Phytol 210:1044–1057

Stamp N (2003) Out of the quagmire of plant defense hypotheses. Q Rev Biol 78:23–55

Stamp N (2004) Can the growth–differentiation balance hypothesis be tested rigorously? Oikos

107:439–448

Thompson JN (1999) What we know and do not know about coevolution: insect herbivores and

plants as a test case. In Olff H, Brown VK, Drent RH (eds) Herbivores: between plants

and predators. Blackwell Science, London, pp 7–30

Tuomi J, Fagerstrom T, Niemelä P (1991) Carbon allocation, phenotypic plasticity, and induced

defenses. In Tallamy DW, Raupp MJ (eds) Phytochemical induction by herbivores.

Wiley, New York, pp 85–104

Tuomi J, Niemelä P, Chapin FS III (1988) Defensive responses of trees in relation to their

carbon, nutrient balance. In Mattson WJ, Levieux J, Bernard-Dagan C (eds) Mechanisms

of woody plant defenses against insects: search for pattern. Springer, New York, pp 57–

72 van Valen L (1977) The Red Queen. Am Nat 111:809–810

War AR, Paulraj MG, Ahmad T, Buhroo AA, Hussain B, Ignacimuthu S, Sharma HC (2012)

Mechanisms of plant defense against insect herbivores. Plant Signal Behav 7:1306–1320

Ward D, Schmitt MH, Shrader AM (2020) Are there phylogenetic differences in salivary tannin-

binding proteins between browsers and grazers, and ruminants and hind-gut fermenters?

Ecol Evol 10:10426–10439

Ward D, Young TP (2002) Effects of large mammalian herbivores and ant symbionts on

condensed tannins of Acacia drepanolobium in Kenya. J Chem Ecol 28:913–929

Waring RH, Pitman GB (1985) Modifying lodgepole pine stands to change susceptibility to

mountain pine beetle attack. Ecology 66:889–898

Wiggins NL, Forrister DL, Endara MJ, Coley PD, Kursar TA (2016) Quantitative and qualitative

shifts in defensive metabolites define chemical defense investment during leaf

development in Inga, a genus of tropical trees. Ecol Evol 6:478–492

Wittstock U, Gershenzon J (2002) Constitutive plant toxins and their role in defense against

herbivores and pathogens. Curr Opin Plant Biol 5:300–307

Younginger BS, Sirová D, Cruzan MB, Ballhorn DJ (2017) Is biomass a reliable estimate of

plant fitness? Appl Plant Sci 5:e1600094

Zangerl AR, Bazzaz FA (1992) Defense allocation. In Fritz RS, Simms EL (eds) Plant resistance

to herbivores and pathogens: ecology, evolution, and genetics. University of Chicago

Press, Chicago, pp 363–391

Zaynab M, Fatima M, Abbas S, Sharif Y, Umair M, Zafar MH, Bahadar K (2018) Role of

secondary plant metabolites in plant defense against pathogens. Microb Pathog 124:198–

202

Zinn AD, Ward D, Kirkman K (2007) Inducible defences in Acacia sieberiana in response to

giraffe browsing. Afr J Range For Sci 24:123–129

Züst T, Agrawal AA (2017) Trade-offs between plant growth and defense against insect

herbivory: an emerging mechanistic synthesis. Annu Rev Plant Biol 68:513–534

CHAPTER II

HERBIVORE-INDUCED DEFENSES ARE NOT UNDER PHYLOGENETIC CONSTRAINTS

IN THE GENUS QUERCUS (OAK): PHYLOGENETIC PATTERNS OF GROWTH,

DEFENSE, AND STORAGE

This work is published in

Perkovich C, Ward D (2021) Herbivore-induced defenses are not under phylogenetic constraints

in the genus Quercus (oak): phylogenetic patterns of growth, defense, and storage. Ecol Evol

doi.org/10.1002/ece3.7409

ABSTRACT

The evolution of plant defenses is often constrained by phylogeny. Many of the differences between competing plant-defense theories hinge upon the differences in the location of meristem damage (apical vs. auxiliary) and the amount of tissue removed. We analyzed the growth and defense responses of 12 Quercus (oak) species from a well-resolved molecular phylogeny using phylogenetically independent contrasts. Access to light is paramount for forest-dwelling tree species, such as many members of the genus Quercus. We therefore predicted a greater investment in defense when apical meristem tissue was removed. We also predicted a greater investment in defense when large amounts of tissue were removed and a greater investment in growth when less tissues were removed. We conducted five simulated-herbivory treatments

25 including a control with no damage and alterations of the location of meristem damage (apical vs. auxiliary shoots) and intensity (25% vs. 75% tissue removal). We measured growth, defense, and nutrient re-allocation traits in response to simulated herbivory. Phylomorphospace models were used to demonstrate the phylogenetic nature of trade-offs between characteristics of growth, chemical defenses, and nutrient re-allocation. We found that growth–defense trade-offs in control treatments were under phylogenetic constraints, but phylogenetic constraints and growth–defense trade-offs were not common in the simulated-herbivory treatments. Growth– defense constraints exist within the Quercus genus, although there are adaptations to herbivory that vary among species.

INTRODUCTION Herbivory is a key ecological process that acts as a selective pressure and regulates the evolutionary trajectories of plant defenses (Agrawal and Fishbein 2008; Poelman and Kessler

2016; Züst et al. 2012). Depending on herbivore pressures, plant defenses may be either constitutive (i.e., fixed) or induced (i.e., activated in response to a stimulus), where these strategies can trade-off to maximize fitness in a given ecological context (Moreira et al. 2014;

Rasmann et al. 2011). Induced plant defenses are thought to be more costly because energy and resources are diverted away from primary functions to produce defenses, thus creating a growth– defense trade-off (Campos et al. 2014; Cippolini et al. 2014; Herms and Mattson 1992; Huang et al. 2019; Züst and Agrawal 2017). Growth–defense trade-offs are also found between growth and constitutive defenses (e.g., Züst and Agrawal 2017). More recent studies have given us a better understanding of the physiological mechanisms that produce a growth–defense trade-off in plants (Ballaré and Austin 2019; Campos et al. 2014; Guo et al. 2018; Havko et al. 2016; Züst and Agrawal 2017). While we are starting to better understand physiological mechanisms and costs that sculpt growth–defense trade-offs (Ballaré and Austin 2019; Guo et al. 2018), our understanding of the ecological and evolutionary role of herbivory behind these trade-offs is less clear (Metcalf 2016).

Evolutionary theories of plant investment in defenses Resources are often limited so that plants are unable to attain sufficient nutrients to maximize both growth and secondary physiological processes, such as defense production (Coley et al. 1985; Lorio 1986; Scogings

2018). The resource availability hypothesis (RAH) (Coley et al. 1985) and the growth- differentiation balance hypothesis (GDBH) (Herms and Mattson 1992) are two plant defense

27 theories that can be used together to best predict levels of defenses within an ecological context.

The RAH predicts that a plant’s ability to access nutrients restricts allocation of those resources so that plants from high-stress environments will have slower growth rates and will have a greater investment in defenses to minimize herbivory than plants in low-stress environments

(Coley et al. 1985; Grime 2006; Karban and Baldwin 1997). Consequently, plants in high-stress environments are more likely to have evolved higher levels of constitutive chemical defenses than plants in low-stress environments (Coley 1988; Grime 2006). The GDBH hypothesizes that investments in growth (cell division and elongation) and differentiation (all other metabolic processes, including defense production) are mutually exclusive (Loomis 1932; 1953). GDBH predicts that plants in low-stress environments will have a greater investment in growth than defense, whereas plants in high-stress environments will invest less in growth, and more in differentiation (i.e., defense production) (Herms and Mattson 1992). RAH and GDBH make contradictory predictions about when a plant can provide a maximum defense. RAH predicts that maximum defense will occur when nutrient availability is low (Coley et al. 1985; Grime 2006).

GDBH predicts that a plant’s maximum defense will occur at intermediate levels of nutrient availability. That is, when nutrient availability is sufficiently high to synthesize the chemical defenses (Herms and Mattson 1992), but not high enough that replacement of lost tissues is less costly (Endara and Coley 2011; Glynn et al. 2007; Hattas et al. 2017; Scogings 2018).

Due to trade-offs between growth and defense, plants may differentiate between regrowth and defense strategies to maximize fitness. For example, location of meristem damage may cause differential allocation of resources to growth and defense (Bonser and Aarssen 1996; Ward

2010). Plants that express apical dominance (the main central shoot of a plant grows more quickly than auxiliary shoots) may lead to compensatory regrowth of the apical shoot when

28 damaged (Aarssen 1995; Ballaré and Austin 2019; Ward 2010). Contrastingly, auxiliary shoots may be produced when the apical shoot is damaged, leading to an increase in auxiliary shoot growth (Gadd et al. 2001; Ward 2010). In the genus Quercus, which are predominantly forest- dwelling species, defending the apical meristem is more beneficial than defending the auxiliary meristems to ensure access to light (sensu Banta et al. 2010).

Phylogenetic constraints on plant defenses It is important in comparative ecological studies among species to also consider phylogenetic information to address the statistical non- independence between species (Ackerly and Donoghue 1995; Mundry 2014; Pennell and

Harmon 2013). Species are descended from one another in a hierarchical fashion that violates assumptions of independence of data points (Blomberg et al. 2003; Felsenstein 1985). More closely related species will have similar defensive chemistry because of shared evolutionary relationships (Craft et al. 2013; Ehrlich and Raven 1964; Pearse and Hipp 2009). Previous research has investigated the influence of phylogeny on constitutive (i.e., fixed) and induced defenses (i.e., activated by an herbivore), but it is unknown how phylogeny influences growth– defense trade-offs of these two modes of plant defense. However, recent studies have suggested that there are few phylogenetic constraints on plant responses to herbivory (Endara et al. 2017;

Moreira et al. 2018; Rasmann and Agrawal 2011) which questions the importance of phylogenetic constraints on patterns of plant defenses. Moreover, none of these previous studies have manipulated the locations and intensities of herbivory (e.g., Moreira et al. 2018; Pearse and

Hipp 2012).

In this study, we assessed the response traits in Quercus (oaks). Quercus deploy a wide range of potential antiherbivore chemical defenses (e.g., Cavender-Bares et al. 2004; Feeny

1976; Hattori et al. 2004; Moctezuma et al. 2014; Pearse and Hipp 2012). Several studies have also suggested that Quercus species alter nitrogen investment and the distribution of non- structural carbohydrates (NSC) in foliage to deter herbivores (e.g., Forkner and Hunter 2000;

Peschiutta et al. 2018; Rieske and Dillaway 2008). Nitrogen concentrations have been shown to decrease in leaves when injured (Boo and Pettit 1975; Frost and Hunter 2008). Quercus species prioritize the storage of NSC relative to growth and reproduction when defoliated (Wiley et al.

2017). This prioritization is due to the essential role of NSC in regrowth and the production of structures such as new leaves and branches (Fornara and Du Toit 2008). Quercus traits often show phylogenetic patterns due to the evolutionary convergence of Quercus phenotypic traits

(Cavender-Bares et al. 2004; 2015). We were interested in determining whether growth–defense trade-offs exist in the Quercus genus and how phylogeny influences strategies among species.

We used control treatments to simulate constitutive modes of defense and manipulated location and intensity of damage to evaluate induced modes of defenses in 12 Quercus species (Felton

2008; Giordanengo et al. 2010). We sought to: (i) assess phylogenetic constraints on constitutive and induced modes of defense, (ii) assess growth–defense trade-offs under various degrees of herbivory, and (iii) evaluate patterns of responses without phylogenetic considerations.

Moreover, we predicted that due to the energetic costs involved in defense production, investment in defense should increase as severity of damage to tissues increases (Kessler 2015;

Neilson et al. 2013). This will lead to the production of an inducible defense rather than a constitutive (fixed) defense. As predicted by GDBH, we expect to find a trade-off between growth and defense so that as a plant’s defense production increases, the plant’s growth will decrease. Additionally, increased damage should increase allocation of NSC to belowground storage (Wiley et al. 2017), decreasing leaf NSC concentrations. Finally, we hypothesized that

30 more closely related species will demonstrate similar patterns of growth, defense, and nutrient allocation strategies in response to varying location and intensity of simulated herbivory.

METHODS

Quercus taxa, phylogeny, and herbivory treatments Using a well-resolved phylogeny of the

American oak clade (Hipp et al. 2018), we chose 12 species (pruned tree shown in Fig. 1) that spanned the phylogeny to get a representation of the biogeographical and environmental diversity of the genus Quercus. Due to the lack of availability of saplings of certain taxa, we sampled from three of the five major groups in the American oak clade (as defined by Manos et al. (1999) and Hipp et al. (2018)). We sampled Q. coccinea, Q. laurifolia, Q. nigra, Q. palustris, and Q. rubra from Quercus section Lobatae; Q. virginiana from Quercus section Quercus series

Virentes; Q. alba, Q. macrocarpa, Q. michauxii, and Q. muehlenbergii from Quercus section

Quercus; and Q. sinuata and Q. stellata from Quercus section Quercus subsection

Texas/northern Mexico (see Figure 2 Hipp et al. 2018). We followed the nomenclature described by the Oaks Names Database (Trehane 2007). Interspecific hybridization is common within certain species’ combinations in the genus Quercus (Petit et al. 2004; Rushton 1993), so we avoided species that are known to result from hybridization (e.g., Q. schuettei was avoided because it is a species that is known to be a hybrid of Q. macrocarpa and Q. bicolor (Bray

1960)). These 12 species of Quercus represent a broad spectrum of environmental and ecological diversity within the genus (Hipp et al. 2018).

Lobatae Sect.

Series Series Virentes

Quercus Quercus

Sect.

Subsection Subsection Mexico Texas/N.

Figure 1. The phylogenetic relationships of the 12 Quercus species used for analysis of growth,

defense, and nutrient reallocation in response to simulated herbivory. Phylogenetic information

pruned from complete Quercus phylogeny by Hipp et al. (2018).

Quercus saplings were purchased from Mossy Oak Nativ Nursery in West Point, MS,

United States. We used saplings that were the same age to avoid adaptive responses to damage caused by ontogenetic differences (Gruntman and Novoplansky 2011). We applied five treatments to mimic variations in location and intensity of simulated herbivory. Each species received all five treatments, which were replicated five times for a total of 25 individuals per species. The five treatments were as follows:

1. Control: No removal of tissues (Fig. 2a).

2. 25% apical removal: Removal of the dominant apical meristem and 25% apical shoot

(Fig. 2b).

3. 75% apical removal: Removal of the dominant apical meristem and 75% apical shoot

(Fig. 2c).

4. 25% auxiliary removal: Removal of all apical meristems (except for dominant meristem)

and 25 % of auxiliary shoots (Fig. 2d).

5. 75% auxiliary removal: Removal of all apical meristems (except for dominant meristem)

and 75 % of auxiliary shoots (Fig. 2e).

Figure 2. Diagram of the treatments applied to the Quercus saplings with varying location and intensity of simulated herbivory. The treatments include: (a) control, (b) 25% apical removal, (c) 75% apical removal,

(d) 25% auxiliary removal, and (e) 75% auxiliary.

Measurements of Quercus defensive traits Trees were harvested one year after treatment application. For chemical defense traits, leaf and root samples were dried in an oven at 65 ℃ for

48 h until plant tissues were completely dry. To assess possible differential investments in

33 different types of tannins, we measured total polyphenols and two types of tannins (tannins constitute a type of polyphenol). Polyphenols and tannins were extracted from the oven-dried plant tissues (Hagerman 1988) using a 70% acetone solvent (Graca and Barlocher 2005;

Hagerman 2011). Once extracted, total polyphenol concentrations in the Quercus tissue were analyzed using the Prussian Blue Assay (Price and Butler 1977) with modifications for use on a microplate reader (Hagerman 2011). We used gallic acid as a standard (gallic acid equivalents

“G.A.E.”). Total tannin concentrations in the Quercus tissue were analyzed using the Radial

Diffusion Assay and standardized against tannic acid (tannic acid equivalents “T.A.E.”)

(Hagerman 1987). Condensed tannin concentrations were analyzed using the Acid Butanol

Assay for proanthocyanidins (Gessner and Steiner 2005; Hagerman 2011) and standardized against quebracho tannin (quebracho equivalents “Q.E.”). Note that there are no unique concentrations for polyphenols or tannins, so they are expressed as equivalents of a specific polyphenol or tannin (Hagerman 2011).

To calculate trichome density, a hole punch was used to punch discs of 7 mm diameter from each leaf. The discs were placed on a microscope, and trichome density was calculated as the number of trichomes/dry mass (g) of the 7 mm disc. The average number of trichomes/dry mass (g) of the three discs from each sapling was recorded as the trichome density.

Quercus growth and leaf morphology After treatments were applied, individual Quercus tree growth was measured. We measured the height of the apical shoot (height), and the lengths of all auxiliary shoots (auxiliary growth). The Quercus saplings were kept in a greenhouse under optimal conditions for one year. Growth measurements for each individual tree were measured

34 biweekly for analysis of relative growth rates. Relative growth rates were calculated for each growth variable (height and auxiliary growth) defined as RGR in the equation:

ln⁡(푊 ) − ln⁡(푊 ) 푅퐺푅 = 2⁡ 1 푡2 − 푡1

where W1 and W2 are a measurement of the plant’s height or auxiliary growth at times t1 and t2.

RGR calculations minimize bias caused by variance in initial measurements of plant size

(Hoffmann and Poorter 2002; Rees et al. 2010). All growth measurements were taken biweekly throughout the year following treatment application. The final growth measurements were taken once trees were harvested, one year after treatments were applied.

Leaf morphological samples were taken during harvesting, one year after treatment application. Leaves were scanned on a CI-202 leaf area meter from CID BioScience. After scanning, leaves were dried and weighed. We measured specific leaf area (leaf area divided by the leaf’s dry weight), leaf aspect ratio (maximum leaf breadth/ maximum leaf length), and leaf shape factor (leaf area/ perimeter) by removing three leaves from each sapling and following leaf measurement protocols, as described by Lu et al. (2012).

Quercus nutrient allocation responses The samples were tested for the concentration of total non-structural carbohydrates using the method by Fournier (2001) that uses a phenol–sulfuric acid solvent for a colorimetric reaction of sugars and starches extracted from leaf tissues (see

Tomlinson et al. 2013). Non-structural carbohydrate analyses were done in a single lab to avoid differences from varying labs and techniques (Landhäusser et al. 2018). Nitrogen was analyzed using a rapid N exceed® nitrogen analyzer.

Statistical analysis: Measuring phylogenetic signal and phylogenetic correlations

Phylogenetic comparative methods (PCM) are statistical tools that are commonly used to help address the issue of non-independence among data points (Ackerly 2009; Felsenstein 1985;

Forthman and Weirauch 2018; Pennell et al. 2016). Access to phylogenetic information is a major advance in developing PCM that places an emphasis on detecting phylogenetic signal

(Mounce et al. 2018; Pennell et al. 2016). Phylogenetic signal ascertains whether there is an effect of molecular phylogeny on any particular trait using phylogenetic distances (Blomberg et al. 2003; Revell et al. 2008).

We calculated phylogenetic signal using a phylogenetic generalized least-squares (PGLS) regression (Cornwell and Nakawagaw 2017; Garland 1989). We accounted for within-species variation in the PGLS regressions by using the pgls.Ives (Ives et al. 2007) function in the phytools package in R (Revell 2012). Following the recommendations of Münkemüller et al.

(2012), we report Blomberg’s K because of its suitability for use with relatively few species

(Blomberg et al. 2003). A value of K < 1 indicates that species are less similar than expected by phylogenetic relationships and do not follow the Brownian model of evolution; a value K > 1 indicates a greater similarity between species than predicted by the Brownian model. It is important to stress that even a non-significant value does not necessarily mean that there is no phylogenetic signal, especially in relatively small data sets (Münkemüller et al. 2012).

Phylogenetic correlations between response variables were performed within each treatment using independent contrasts (Garland et al. 1999; Pagel 1999). To account for the possibility of spurious correlations, we performed a Bonferroni correction to adjust the α (Conneely and

Boehnke 2007). This method divides the α by the number of correlations (n = 5) to counteract the problem of multiple comparisons. We created phylomorphospace plots to project the

36 phylogeny onto the correlation of the two variables being analyzed (Sidlauskas 2008) to visualize how the data points are phylogenetically related and to visualize how trade-offs were influenced by phylogeny. We used the phytools package (Revell 2012) in R version 3.6.0 (R

Development Core Team 2019) for these phylomorphospace plots.

Statistical analysis: Ignoring phylogeny For those variables showing no phylogenetic signal, we first evaluated trait responses across the 12 species using a multivariate analysis of variance

(MANOVA) for multiple dependent variables (i.e., height, auxiliary growth, leaf total polyphenol, total tannins, condensed tannin concentrations, and root and leaf non-structural carbohydrate concentrations) to minimize type I statistical error. Thereafter, we used univariate

ANOVA (and Scheffé post hoc tests) for each significant response variable. The model included herbivory treatment as a fixed factor and species as a random factor. Both MANOVA and

ANOVA tests were run using IBM SPSS version 26 software (IBM 2019). To better understand responses across species, we used a non-parametric sign test (Siegel and Castellan 1981) to analyze overall trends. Trait variances often display trends that give insights into ecological and evolutionary processes that are not always visible when analyzing mean effects alone (Sánchez-

Tόjar et al. 2020).

RESULTS

Phylogenetic constraints on constitutive and induced modes of defense We found a significant phylogenetic signal for constitutive concentrations of total tannin (i.e., in control treatments; K = 1.091, P < 0.01; Table 2). However, there was no evidence of a significant

37 phylogenetic signal for inducible concentrations of total tannin (i.e., simulated herbivory treatments; Table 2). Trichome production was the only inducible morphological defense that showed a significant phylogenetic signal (Table 2). We found that there was a significant phylogenetic signal for apical relative growth rates (aRGR) in control treatments, but not in herbivory treatments (Table 2). There was also a significant phylogenetic signal for constitutive and induced leaf aspect ratios (Table 2). Similarly, induced specific leaf area and leaf shape showed a significant phylogenetic signal (Table 2).

Control 25% apical 75% apical 25% auxiliary 75% auxiliary removal Removal removal removal

K P K P K P K P K P Growth Responses RGR height 0.836 0.043* 0.639 0.323 0.611 0.387 0.545 0.653 0.538 0.632 RGR auxiliary 0.328 0.729 0.843 0.639 0.241 0.272 0.555 0.332 0.537 0.053 growth Defense Responses Polyphenols 0.658 0.295 0.751 0.152 0.743 0.214 0.633 0.366 0.528 0.782 Total tannins 1.091 0.013* 0.531 0.686 0.395 0.987 0.686 0.239 0.606 0.412 Condensed 0.455 0.897 0.650 0.305 0.671 0.258 0.685 0.214 0.878 0.038* tannins Trichome 0.287 0.316 0.878 0.615 0.456 0.688 0.312 0.999 1.089 0.982 density Morphological Traits Specific leaf area 0.715 0.817 0.841 0.049* 1.067 0.013* 1.003 0.028* 0.886 < 0.001* Leaf aspect ratio 0.963 < 0.001* 0.786 0.001* 0.876 < 0.001* 1.016 0.039* 0.977 < 0.001* Leaf shape factor 0.313 0.514 0.814 0.018* 1.001 < 0.001* 0.971 0.004* 0.999 < 0.001*

Nutrient Allocation Foliar NSC 0.416 0.288 0.770 0.144 0.743 0.187 0.610 0.379 0.502 0.347 Root NSC 0.753 0.165 0.643 0.400 0.520 0.540 0.562 0.085 0.537 0.689 Foliar nitrogen 0.540 0.132 0.643 0.068 0.510 0.740 0.641 0.361 0.646 0.323

Growth–defense correlations Using independent phylogenetic contrasts, we found a significant trade-off (strong negative correlation) between growth and defense (r = -0.71, P = 0.01; Fig. 3a, b), growth and investment in leaf morphology (r = -0.73, P = 0.007; Fig. 3c, d), and between growth and nutrient allocation (r = -0.49, P = 0.019; Fig. 3e, f). However, these trade-offs were not observed in responses induced by simulated herbivory. We also found a significant positive correlation between trichome production and growth (r = 0.513, P = 0.033). We did not find any other positive correlations between growth and defense responses (r range -0.113–0.396, P range

0.039 to > 0.05).

r = -0.71, P = 0.011

r = -0.73, P = 0.007

r = -0.49, P = 0.019

Figure 3. Trade-offs between Quercus constitutive traits and phylomorphospace projections of the

Quercus phylogeny. Each data point represents an individual species’ growth and constitutive trait plotted in morphospace (for more details, see Methods). a) The phylogenetic trade-off between growth (i.e.,

42 apical shoot relative growth rate) and constitutive chemical defenses (tannin concentration). b) A phylomorphospace plot of (a). c) The trade-off between growth and leaf morphology (specific leaf area). d) A phylomorphospace plot of (c). e) The trade-off between growth and constitutive nutrient allocation

(root non-structural carbohydrate storage. f) A phylomorphospace plot of (e). Particular colors represent the same section of the phylogeny. Circles in red = Lobatae, blue = Quercus, green = Quercus series

Virentes, and yellow = Quercus subsection Texas/N. Mexico. “aRGR” = apical shoot relative growth rate,

“T.A.E.” = tannic acid equivalents, “G.E.” = glucose equivalents.

Quercus responses ignoring phylogeny We assessed Quercus responses, ignoring phylogeny, using a MANOVA. After one year of regrowth, Quercus species showed significant responses to location of simulated herbivory, intensity of simulated herbivory, and the interaction of location and intensity of simulated herbivory (Table 3). The patterns of responses to location and intensity of simulated herbivory differed significantly among species (Table 3). We used post hoc univariate ANOVA to further analyze significant results (discussed below).

Table 3. MANOVA results for the effects of location and intensity of simulated herbivory on oak growth, defense, and nutrient allocation. Reported F values are equivalent F values based on

Wilks’ λ. Significant values indicated in bold with “*”.

Treatment Wilks’ λ F P Location of simulated herbivory (apical vs. auxiliary) 0.825 4.661 < 0.001* Intensity of simulated herbivory (25% vs. 75%) 0.861 3.566 < 0.001* Location X intensity of simulated herbivory 0.844 4.072 < 0.001* Species (random effect) 0.004 13.942 0.019*

Quercus defense responses There was no significant change in induced concentrations of total polyphenols or total tannins (Table 4). In general, albeit not statistically significant, members of

Quercus section Lobatae (Q. coccinea, Q. laurifolia, Q. nigra, Q. palustris, and Q. rubra) decreased condensed tannin production when damaged, and members of Quercus section

Quercus (Q. alba, Q. macrocarpa, Q. michauxii, and Q. muehlenbergii) increased or did not change investments in condensed tannins (Table 4, Fig. 4).

alba michauxii sinuata

control

25% removal

75% removal

macrocarpa muehlenbergii stellata

95% (mm/day) CI

palustris nigra virginiana

laurifolia coccinea rubra

+/ growth rate shoot Apical relative

Control Apical Auxiliary Control Apical Auxiliary Control Apical Auxiliary

Figure 4. Individual Quercus species apical shoot relative growth rates plotted by location and intensity of simulated herbivory. Scheffé post hoc tests result in p values < 0.05 for all species, except Q. michauxii (P = 0.08), Q. sinuata (P = 0.16), Q. palustris (P = 0.38), and Q. rubra (P = 0.47). 95% C.I. = 95% confidence interval, circle = control, triangle = 25% removal, square = 75% removal.

Quercus growth and leaf morphology Regardless of location of simulated herbivory, Quercus saplings (except for Q. alba) with 25% removal of tissue did not increase apical shoot relative growth rates (aRGR) in response to simulated herbivory (Table 4, Fig. 5). A non-parametric sign test (Siegel and Castellan 1981) showed a trend of decreased aRGR when 75% of tissue was removed (sign test: P = 0.02; Fig. 5) compared to control and 25% removal treatments (Fig. 5).

Figure 5. The mean condensed tannin concentrations of individual Quercus species in response to location of simulated herbivory (apical vs auxiliary) in each treatment. Taxa inside the black dotted line belong to Quercus section Quercus (various series) of the Quercus phylogeny and those outside the dashed lines belong to the Lobatae section. “+” = a positive trend from the control, “-” = a negative trend from the control. For two species (Q. macrocarpa and Q. muehlenbergii), there was no significant effect and consequently neither a “+” nor “-” is indicated. Q.E. = quebracho equivalents (see methods); 95%

C.I. = 95% confidence interval.

Quercus specific leaf area did not change in response to simulated herbivory (Table 4).

Leaf-aspect ratio increased (leaves became more elongated) with 75% removal of tissue, regardless of damage location, in five species (Q. coccinea, Q. laurifolia, Q. nigra, Q. stellata and Q. virginiana) (Table 4). Leaf-shape factor decreased (i.e., leaves became smaller) in seven

Quercus species (Q. macrocarpa, Q. michauxii, Q. muehlenbergii, Q. nigra, Q. sinuata, Q. stellata, Q. virginiana) when damaged at the apical shoot regardless of the amount of tissue removed (Table 4, Fig. 6).

p < 0.01 f f b b b a a e e c e d

Figure 6. The average leaf shape factor for all simulated herbivory treatments, plotted against the Quercus phylogenetic tree to show phylogenetic relationships. The average leaf shape factor was significantly affected by phylogeny for all simulated herbivory treatments. The phylogenetic tree shows that the relationship between species and more closely related species frequently have similar specific leaf areas

(mm2 g-1).

Quercus nutrient allocation responses Quercus species had similar constitutive concentrations of NSC in root storage (intraspecific variation ranging from 0.1–0.5 mg mL-1), with the exception of Q. stellata (0.9 ± 0.01 mg mL-1), which differed significantly from the remaining eleven species (P < 0.01). All Quercus species, except for Q. stellata, increased NSC concentrations in root storage when damaged, regardless of the intensity of the simulated

49 herbivory (Table 4). We found no changes to foliar nitrogen concentrations (all P values > 0.05;

Table 4).

Table 4. ANOVA results of the effects of location and intensity of simulated herbivory on oak growth, defense, and nutrient allocation. Significant values indicated in bold with “*”. Growth responses were measured as relative growth rates (RGR), and nutrients include total non- structural carbohydrates (NSC).

Location of Simulated Intensity of Location X Intensity of Herbivory (apical vs Simulated Simulated Herbivory auxiliary) Herbivory (25% vs 75%) Growth Responses F P F P F P RGR height 19.792 < 0.001* 21.411 < 0.001* 23.909 < 0.001* RGR auxiliary growth 0.001 1.000 0.042 0.838 0.854 0.356 Defense Responses Polyphenols 2.054 0.153 0.811 0.369 0.885 0.348 Tannins 0.208 0.649 4.170 0.051 0.058 0.810 Condensed tannins 0.933 0.335 6.149 0.014* < 0.001 0.995 Trichome density 0.481 0.489 0.960 0.328 1.398 0.597 Morphological Traits Specific leaf area 0.406 0.524 0.573 0.450 1.928 0.166

Leaf aspect ratio 0.280 0.607 2.695 0.029* 3.026 0.108 Leaf shape factor 1.105 0.005* 0.407 0.536 0.016 0.900 Nutrient Allocation

Foliar NSC 2.054 0.153 0.811 0.369 0.885 0.348 Root NSC 2.368 < 0.001* 2.870 0.092 3.156 0.077

Foliar nitrogen 0.280 0.597 0.115 0.735 1.166 0.281

DISCUSSION

Quercus defenses are driven by evolutionary selective pressures (such as herbivory) and the environment (Ackerly 2002; Firmat et al. 2017; Mitter et al. 1991; Pearse and Hipp 2012).

Having a broad geographic distribution, the genus Quercus contends with many environments containing diverse herbivore pressures (Cavender-Bares et al. 2004; 2016). We found that most of the effects of simulated herbivory were not explained by phylogeny. However, variations in constitutive relative growth rate and total tannin concentrations are explained by phylogenetic relationships. With certain locations and intensities of herbivory, more closely related species shared similar investments in growth and induced leaf-morphological traits, but phylogeny did not explain patterns of induced concentrations of total tannins. The results of this study, and those of previous studies (Hipp and Pearse 2012; Moreira et al. 2018), suggest that constitutive chemical defenses and inducible morphological traits are under significant phylogenetic constraints, but inducible chemical defenses appear to be species-specific.

Quercus chemical defenses, such as tannins (Feeny 1970; Rossiter et al. 1988; Visakorpi et al. 2019), may often be upregulated as an inducible defense against herbivores (Moctezuma et al. 2014; Rohner and Ward 1997; Ward 2006). Furthermore, Quercus may alter the types of tannins present, such as condensed and hydrolysable tannins, as chemical defenses against specialist herbivores (Clausen et al. 1992). We found that that some Quercus species such as Q. alba and Q. michauxii increased condensed tannin production relative to the amount of tissue removed (Fig. 5). We note that many studies have found defenses are induced by specific salivary enzymes and proteins (Berman 2002; Rooke 2003). However, herbivore saliva does not always induce these defenses (Keefover-Ring et al. 2015), and defenses are often induced without such catalysts (Huang et al. 2019).

How much variance in phenotypic traits does phylogeny explain? In this study, we asked whether phylogenetic constraints could explain patterns of growth–defense trade-offs between and within Quercus (oak) species. Phylogeny and adaptation define two ends of a continuum of biological explanations (Agrawal 2020; Cavender-Bares et al. 2016; Leimar et al. 2019; Stearns

1992). We note that it is crucial to keep the idea of a continuum in mind when interpreting phylogenetic analyses of genera, especially Quercus, that have broad, overlapping geographic distributions (McVay et al. 2017; Moreira et al. 2018). Furthermore, a phylogenetic pattern of phenotypic traits does not necessarily indicate that the trait is not adaptive (Agrawal, 2020;

Heslop-Harrison 2017; Stearns 1992). Ackerly (2002) explained how ecological sorting processes and selection can lead to adaptive evolution. He further gives a framework for how species’ distributions can lead to patterns of phylogenetic niche conservatism. In this study, we found correlations between phylogenetic relatedness and similarity of life-history traits in the genus Quercus (demonstrated by phylogenetic patterns in leaf morphological traits), similar to studies such as Cavender-Bares et al. (2004).

Physiology and genetics are two well-studied sources of constraints on adaptations of plant defenses (e.g., Ballaré and Austin 2019; Endara et al. 2017; Keith 2017; Ochoa-Lopez et al.

2018). Studies focusing on genetic constraints of adaptation often fail to consider limitation and assimilation capacity of resources (Ballaré and Austin 2019; Mole 1994), just as studies of physiological constraints often fail to evaluate heritability of traits (Ehrlich et al. 2020; Ward et al. 2012). Both types of constraints further fail to explain differentiation of traits expressed across levels of biological organization due to selective pressures (Barthelemy and Caraglio

2007; Hahn and Moran 2016; Züst and Agrawal 2017). For example, certain plant defenses have been shown to trade-off with plant growth or reproduction within individual species, but general

52 patterns of plant-defense trade-offs are less frequently recorded across related species (Agrawal and Fishbein 2008; Peiman and Robinson 2017; Züst and Agrawal 2017; Züst et al. 2015). More recent advances in phylogenetics have created a better understanding of patterns of plant-defense trade-offs across biological scales and sparked an interest in phylogenetic constraints of plant- defense adaptations (Hinman et al. 2019; Moreira et al. 2018; Pausas and Verdu 2010).

Phylogenetic constraints may not always be present but analyzing ecological variation in a phylogenetic context provides important information, even if phylogenetic signal is not detected

(Garland et al. 2005; Losos 2008).

Plant phylogeny often explains much of the variance in key defensive traits (Pearse and

Hipp 2009). Many Quercus species undergo leaf morphological changes that may act as defenses against herbivores (Dawra et al. 1988; Moctezuma et al. 2014) but it is often difficult to directly link leaf morphology to defense (Moctezuma et al. 2014). For example, Q. virginiana has a thick, waxy cuticle that acts as a defense (Eigenbrode and Espelie 1995), perhaps because insects with smaller mandibles find it difficult to try to cut through the tough cuticle (Raupp 1985). We found several changes to leaf morphology that were induced by simulated herbivory treatments, making it possible for us to conclude that these traits are related to defense. Furthermore, there is considerable evidence that similar leaf morphologies are a result of phylogenetic relatedness

(Hickey and Wolfe 1975; Kadereit et al. 2006; Oyston et al. 2016). If similar herbivore pressures affect certain lineages more consistently than others, we would expect lineage-specific adaptations that will reflect a phylogenetic pattern (Donoghue 1989; Lauder 1981; Walden et al.

2019). However, several studies (e.g., Moreira et al. 2018; Pearse and Hipp 2009; 2012), including ours, suggest that the tendencies of species to retain ancestral traits cannot entirely account for variations in inducible chemical traits. Even so, we did find examples of inducible

53 defenses that demonstrate phylogenetic effects. One example is the similarity in condensed tannin production of Q. virginiana, a species from Quercus series Virentes, and the closely related Quercus section Quercus (Q. alba and Q. michauxii) (Fig. 6). We also found that phylogeny explains some variation of inducible leaf-morphological traits. Nonetheless, we also found that condensed tannin production in this genus is also differentiated based on the simulated herbivore pressures. For example, except for Q. rubra, members of Quercus section Lobatae (Q. coccinea, Q. laurifolia, Q. palustris, and Q. nigra) decreased condensed tannin concentrations when 75% of tissues were removed.

What do positive and negative (trade-off) correlations tell us? Plant scientists have long considered a cost-benefit paradigm when trying to better understand plant defenses (Cippolini et al. 2014; Huang et al. 2019; Steppuhn and Baldwin 2008). In this regard, we would expect trade- offs to be common. The growth-differentiation balance hypothesis (GDBH), as well as the

Resource Availability hypothesis (Coley et al. 1985), predicts that slow-growing plants will have more resources available for investment in defenses because they need to limit loss (Hattas et al.

2017; Herms and Mattson 1992; Scogings 2018). This may result in species-specific trade-offs and are not generally extrapolable at the generic level (Agrawal 2020; Futuyma and Moreno

1988). Species-specific trade-offs may explain some of the patterns we observed in this study.

For example, the trade-off we found between apical shoot relative growth rate (aRGR) and total tannin concentration indicates that even though plants received the same resources, species with slower growth rates invested more in defenses than those with higher growth rates (see Results).

Individual species in control treatments in Quercus section Lobatae tend to invest more in total tannin production and less in aRGR relative to species in Quercus section Quercus. However, we

54 did not find the expected trade-off between aRGR and induced tannin concentrations in herbivory treatments. The absence of this trade-off may be due to specific genotype by environment interactions (i.e., adaptive phenotypic plasticity sensu van Kleunen and Fischer

2004; Via et al. 1995; Ward et al. 2012) within certain species of Quercus. Other studies have shown substantial evidence of local adaptation as well as adaptive differentiation of Quercus species that are closely related (e.g., Cavender-Bares and Ramirez-Valiente 2017; Gonzalez-

Rodriguez and Oyama 2005; Valladares et al. 2002). Furthermore, foliar NSC concentrations did not significantly change in any of the Quercus species, yet several species (Q. alba, Q. macrocarpa, Q. rubra) increased aRGR. We postulate that increasing aRGR increases photosynthetic capacity by increasing height and access to sunlight, regardless of the increased re-allocation to belowground (root) storage in the herbivory treatments. For example, Q. rubra has been shown to increase photosynthetic rates by up to 22% (Woolery and Jacobs 2011) and increase NSC concentrations in foliar tissues (Frost and Hunter 2008) following simulated herbivory. Wiley et al. (2017) also showed that Quercus species prioritized NSC root re- allocation relative to growth in Q. rubra. Rieske and Dillaway (2008) found that defoliation of

Q. velutina had no effect on relative height or non-structural carbohydrate reserves, but Q. alba decreased investments in both relative height and NSC in root reserves.

The role of nutrient acquisition in hiding trade-offs Trade-offs may result from genetic associations between growth/reproduction and defense (antagonistic pleiotropy—reviewed in

Hedrick 1999; Johnson et al. 2015; Keith and Mitchell-Olds 2019; Rose 1982; Wright 1968) or from optimization strategies regarding nutrient acquisition and allocation (Metcalf 2016; van

Noordwijk and de Jong 1986). Although antagonistic pleiotropy may be a plausible explanation

55 for trade-offs in Quercus species, an equally plausible hypothesis pertains to differences in nutrient acquisition and allocations to growth and defense (Bochdanovits and de Jong 2004; van

Noordwijk and de Jong 1986; Ward and Young 2002). Following the latter hypothesis, a trade- off will occur if there is a relatively small difference in nutrient acquisition between individual plants and a relatively large difference in the allocation of those nutrients (to growth or defense) between individuals (van Noordwijk and de Jong 1986). However, if there is a relatively large difference in nutrient acquisition between individual plants and a relatively small difference of nutrient allocation (to growth or defense) between individual plants, a trade-off will not occur

(van Noordwijk and de Jong 1986). In our study, all individuals received the same nutrients and water, although we removed different amounts of photosynthetic material, resulting in reduced acquisition with greater simulated herbivory. The absence of some trade-offs may result from the plasticity of traits in species that differ in their acquisition and allocation of resources (Metcalf

2016; van Noordwijk and de Jong 1986). Indeed, Armbruster et al. (2004) suggested that intraspecific correlations (positive and negative) between growth and defense traits are indicative of adaptations independent of phylogenetic constraints.

Under the GDBH, we would not expect to find positive correlations between growth and defense traits (Ward and Young 2002). However, resource acquisition may allow for some individuals to allocate more resources to multiple functions (growth and defense) (van

Noordwijk and de Jong 1986; Ward and Young 2002; Zera and Harshman 2001). We found a positive correlation between growth rate and trichome density in herbivory treatments, after we controlled for phylogeny. Trichomes have been linked to plant defenses and are energetically expensive to produce (Holeski et al. 2010; Levin 1973; Tian et al. 2012). Given access to the same resources (water and sunlight), we would expect to see a trade-off between aRGR and

56 trichome density because of the high cost of producing trichomes (Hare et al. 2003; Levin 1973;

Züst et al. 2011). We speculate that the positive correlation between growth rate and trichome density may be a result of a cascade of responses to increased size, resulting in a positive correlation. Furthermore, increases in trichome density can increase water moisture retention, thus increasing photosynthetic capacity (Brewer and Smith 1994) and resources available to increase growth rate. More research is needed to understand the nature of the genetic correlations

(MacTavish and Anderson 2020) of Quercus traits to determine if positive correlations could infer adaptation of defensive traits, or whether they are simply the consequence of allometric scaling (Falster et al. 2015).

Are leaf traits indicative of induced resistance to herbivory? Energetic costs associated with the production of constitutive and inducible defenses may be offset by the optimization of multiple metabolic pathways resulting in a trade-off between types of defenses (Gershenzon

1994; Neilson et al. 2013). In a meta-analysis of trade-offs between various plant defenses,

Koricheva et al. (2004) suggested that ecological costs of defense production may cause a differential investment between constitutive and inducible plant defenses. Differential investment in constitutive and induced chemical defenses makes it essential to consider the two types of defenses independently (Martinez-Swatson et al. 2020). Furthermore, phylogeny often constrains phenotypic expression of constitutive defenses (Moreira et al. 2018; Ralph et al. 2007). Inducible defenses may be under greater species-specific selective pressures and are more likely to be adaptive (Baldwin 1999; Koricheva et al. 2004; Moreira et al. 2018). Understanding the costs of defense production in both constitutive and inducible defenses is essential to understanding the evolution of plant defenses (Galman et al. 2019a, b; Martinez-Swatson et al. 2020). We found

57 that herbivore damage induced a greater aRGR (apical relative growth rate), greater production of condensed tannins, alterations in leaf aspect and leaf ratio, as well as induced non-structural carbohydrate re-allocation to root storage. Contrastingly, in our study, we found that polyphenol concentration, total tannin concentration, and trichome density remained constant regardless of damage (i.e., constitutive). Leaf traits that evolved for primary functions, such as specific leaf area (Knight et al. 2006), probably contribute to defense against herbivores as well (Agrawal

2004). We simulated herbivory allowing us to make assumptions about which morphological traits may be directly related to herbivore defense. Our study suggests that certain induced-leaf morphological traits (e.g., specific leaf area, leaf aspect ratio, leaf shape factor) may also be under phylogenetic constraints (see also Pearse and Hipp 2012). For example, we found that leaf- shape factor decreased depending on the location of damage, with closely related species behaving more similarly (Table 4).

CONCLUSIONS

Our study suggests that a combination of RAH and GDBH predictions construct a better representation of how ecological selective pressures, such as herbivory, affect a plant’s investment in growth and defense production (Endara and Coley 2011; Glynn et al. 2007; Hattas et al. 2017; Scogings 2018; Martinez-Swatson et al. 2020). At low levels of resources, plants may be able to do little other than grow, while at intermediate levels of resources, there are sufficient nutrients to grow rapidly and produce chemical defenses (GDBH only) as evidenced by the lack of growth-defense trade-offs in herbivory treatments. At higher levels of resources, plants may focus on growth-based life strategies and regrow any lost material (Coley et al. 1985;

Maron et al. 2014; Pearson et al. 2017). Resource allocation, in addition to herbivore pressures,

58 are likely to be factors that drive adaptations of chemical defense in Quercus. Overall, our results show uniquely that there are phylogenetic constraints on growth, constitutive tannin concentrations, and the trade-off between these two variables. Herbivore-induced condensed tannin concentrations and leaf morphological traits are also under significant phylogenetic constraints (K > 1). However, inducible chemical traits (except for condensed tannin concentrations) are influenced by adaptive selection pressures (K < 1). Considering the contrasting findings of previous studies about Quercus storage of non-structural carbohydrates

(e.g., Rieske and Dillaway 2008; Wiley et al. 2017), our study shows that NSC re-allocation strategies within the genus Quercus were related to location of meristem damage and not the intensity. We predicted greater responses to apical shoot damage but found that auxiliary shoot damage consistently caused a greater re-allocation of NSC storage in the roots. However, the overall relationships between growth and NSC re-allocation appear to represent species-specific adaptations to selective pressures imposed by herbivory.

REFERENCES

Aarssen LW (1995) Hypotheses for the evolution of apical dominance in plants: implications for

the interpretation of overcompensation. Oikos 74:149–156

Ackerly DD (2002) Community assembly, niche conservatism, and adaptive evolution in

changing environments. Int J Plant Sci 164:e165–e184

Ackerly DD (2009) Phylogenetic methods in ecology. In Encyclopedia of life sciences. Wiley,

Chichester, UK, doi: 10.1002/9780470015902.a0021223

Ackerly DD, Donoghue MJ (1995) Phylogeny and ecology reconsidered. J Ecol 83:730–733

Agrawal AA (2004) Plant defense and density dependence in the population growth of

herbivores. Am Nat 164:113–120

Agrawal AA (2020) A scale-dependent framework for trade-offs, syndromes, and specialization

in organismal biology. Ecology 101:e02924

Agrawal AA, Fishbein M (2006) Plant defense syndromes. Ecology 87:132–149

Agrawal AA, Fishbein M, Halitschke R, Hastings AP, Rabosky DL, Rasmann S (2009) Evidence

for adaptive radiation from a phylogenetic study of plant defenses. Proc Nat Acad Sci

USA 106:18067–18072

Armbruster SW, Pelabon C, Hansen TF, Mulder CPH (2004) Floral integration, modularity, and

accuracy. In Pigliucci M, Preston K (eds) Phenotypic integration. Oxford University

Press, Oxford, pp e23–e49

Baldwin IT (1999) Inducible nicotine production in native Nicotiana as an example of adaptive

phenotypic plasticity. J Chem Ecol 25:3–30

Ballaré CL, Austin AT (2019) Recalculating growth and defense strategies under competition:

keys roles of photoreceptors and jasmonates. J Exp Bot 70:3425–3436

Banta JA, Stevens MH, Pigliucci M (2010) A comprehensive test of the ‘limiting resources’

framework applied to plant tolerance to apical meristem damage. Oikos 119:359–369

Barthelemy D, Caraglio Y (2007) Plant architecture: a dynamic, multilevel and comprehensive

approach to plant form, structure and ontogeny. Ann Bot 99:375–407

Bergman M (2002) Can saliva from moose, Alces alces, affect growth responses in the sallow,

Salix caprea? Oikos 96:164–168

Blomberg SP, Garland Jr T, Ives AR (2003) Testing for phylogenetic signal in comparative data:

behavioral traits are more labile. Evolution 57:717–745

Bochdanovits Z, de Jong G (2004) Antagonistic pleiotropy for life-history traits at the gene

expression level. Proc R Soc Lond B 271:e75–e78

Bonser SP, Aarssen LW (1996) Meristem allocation: a new classification theory for adaptive

strategies in herbaceous plants. Oikos 77:347–352

Boo RM, Pettit R (1975) Carbohydrate reserves in roots of sand shin oak in west Texas. J Range

Manag 28:469–472

Bray JR (1960) A note on hybridization between Quercus macrocarpa Michx. and Quercus

bicolor Willd. in Wisconsin. Can J Bot 38:701–704

Brewer CA, Smith WK (1994) Influence of simulated dewfall on photosynthesis and yield in

soybean isolines (Glycine max [L.] merr. CV Williams) with different trichome densities.

Int J Plant Sci 155:460–466

Campos ML, King JH, Howe GA (2014) Jasmonate-triggered plant immunity. J Chem Ecol

40:657–675

Cavender-Bares J, Ackerly DD, Baum DA, Bazzaz FA (2004) Phylogenetic overdispersion in

Floridian oak communities. Am Nat 163:823–843

Cavender-Bares J, Ackerly DD, Hobbie SF, Townsend PA (2016) Evolutionary legacy effects on

ecosystems: biogeographic origins, plant traits, and implications for management in the

era of global change. Annu Rev Ecol Evol Syst 47:433–462

Cavender-Bares J, Gonzalez-Rodriquez A, Eaton DA, Hipp AL, Beulke A, Manos PS (2015)

Phylogeny and biogeography of the American live oaks (Quercus subsection Virentes): a

genomic and population genetics approach. Mol Ecol 24:3668–3687

Cavender-Bares J, Ramirez-Valiente JA (2017) Physiological evidence from common garden

experiments for local adaptation and adaptive plasticity to climate in American live oaks

(Quercus section Virentes): implications for conservation under global change. In Gil-

Pelegrin E, Peguero-Pina JJ, Sancho-Knapik D (eds) Oaks physiological ecology.

Exploring the functional diversity of genus Quercus L. Springer, New York, pp 107–105

Cippolini D, Walters D, Voelckel C (2014) Costs of resistance in plants: from theory to

evidence. In Voelckel C, Jander G (eds) Insect–plant interactions. Wiley, Chichester, UK,

pp 263–307

Clausen TP, Reichardt PB, Bryant JP, Provenza F (1992) Condensed tannin in plant defense: a

perspective on classical theories. In Hemingway RW, Laks PE (eds) Plant polyphenols.

Springer, Boston, MA, pp 639–651

Coley, PD (1988) Effects of plant growth rate and leaf lifetime on the amount and type of anti-

herbivore defense. Science 230:895–899

Coley PD, Bryant JP, Chapin FS III (1985) Resource availability and plant antiherbivore

defense. Science 230:895–899

Conneely KN, Boehnke M (2007) So many correlated tests, so little time! Rapid adjustment of P

values for multiple correlated tests. Am J Hum Genet 81:1158–1168

Cornwell WK, Nakagawa S (2017) Phylogenetic comparative methods. Curr Biol 27:e327–e338

Craft JD, Paul VJ, Sotka EE (2013) Biogeographic and phylogenetic effects on feeding

resistance of generalist herbivores toward plant chemical defenses. Ecology 94:18–24

Dawra RK, Makkar HPS, Singh B (1988) Total phenolics, condensed tannins, and protein-

precipitable phenolics in young and mature leaves of oak species. J Agric Food Chem

36:951–953

Donoghue MJ (1989) Phylogenies and the analysis of evolutionary sequences, with examples

from seed plants. Evolution 43:1137–1156

Ehrlich PR, Raven PH (1964) Butterflies and plants: a study in plant coevolution. Evolution

18:586–608

Ehrlich E, Kath NJ, Gaedke U (2020) The shape of a defense–growth trade-off governs seasonal

trait dynamics in natural phytoplankton. ISME J 14:1451–1462

Eigenbrode SD, Espelie KE (1995) Effects of plant epicuticular lipids on insect herbivores. Annu

Rev Entomol 40:171–194

Endara M-J, Coley PD (2011) The resource availability hypothesis revisited: a meta-analysis.

Funct Ecol 25:389–398

Endara M-J, Coley PD, Ghabash G, Nicholls JA, Dexter KG, Donoso DA, Stone GN,

Pennington RT, Kusar TA (2017) Coevolutionary arms race versus host defense chase in

tropical herbivore–plant system. Proc Nat Acad Sci USA 114:e7499–e7505

Falster DS, Duursma RA, Ishihara MI, Barneche DR, Fitzjohn RG, Vårhammar A, Aiba M,

Ando M, Anten N, Aspinwall MJ, et al (2015) BAAD: A Biomass and Allometry

Database for woody plants. Ecology 96:e1445

Feeny P (1970) Seasonal changes in oak leaf tannins and nutrients as a cause of spring feeding

by winter moth caterpillars. Ecology 51:565–581

Feeny P (1975) Biochemical evolution between plants and their insect herbivores. In Gilbert LE,

Raven PH (eds) Coevolution of animals and plants. University of Texas Press, Austin,

TX, pp 3–19

Feeny P (1976) Plant apparency and chemical defenses. Recent Adv Phytochem 10:1–42

Felsenstein J (1985) Phylogenies and the comparative method. Am Nat 125:1–15

Felton GW (2008) Caterpillar secretions and induced plant responses. In Schaller A (ed) Induced

plant resistance to herbivory. Springer, Dordrecht, The Netherlands, pp 369–387

Firmat C, Delzon S, Louvet JM, Parmentier J, Kremer A (2017) Evolutionary dynamics of the

leaf phenological cycle in an oak metapopulation along an elevation gradient. J Evol Biol

30:2116–2131

Forkner RE, Hunter MD (2000) What goes up must come down? Nutrient addition and predation

pressure on oak herbivores. Ecology 81:1588–1600

Fornara D, Du Toit JT (2008) Responses of woody saplings exposed to chronic mammalian

herbivory in an African savanna. Ecoscience 15:129–135

Forthman M, Weirauch C (2018) Phylogenetic comparative analysis supports aposematic

colouration–body size association in millipede assassins (Hemiptera: Reduviidae:

Ectrichodiinae). J Evol Biol 31:1071–1078

Fournier E (2001) Colorimetric quantification of carbohydrates. Curr Prot Food Anal Chem 1:1–

Frost CJ, Hunter MD (2008) Herbivore-induced shifts in carbon and nitrogen allocation in red

oak seedlings. New Phytol 178:835–845

Futuyma DJ, Moreno G (1988) The evolution of ecological speciation. Annu Rev Ecol Syst

19:207–234

Gadd ME, Young TP, Palmer TM (2001) Effects of simulated shoot and leaf herbivory on

vegetative growth and plant defense in Acacia drepanolobium. Oikos 92:515–521

Galman A, Abdala-Roberts L, Covelo F, Rasmann S, Moreira X (2019a) Parallel increases in

insect herbivory and defenses with increasing elevation for both saplings and adult trees

of oak (Quercus) species. Am J Bot 106:1558–1565

Galman A, Petry WK, Abdala-Roberts L, Butron A, de la Fuente M, Francisco M, Kergunteuil

A, Rasmann S, Moreira X (2019b) Inducibility of chemical defences in young trees is

stronger in species with high elevational ranges. Tree Physiol 39:606–614

Garland Jr T, Bennett AF, Rezende EL (2005) Phylogenetic approaches in comparative

physiology. J Exp Biol 208:3015–3035

Garland Jr T, Midford PE, Ives AR (1999) An introduction to phylogenetically based statistical

methods, with a new model for confidence intervals and ancestral values. Am Zool

39:374–388

Gershenzon J (1994) The cost of plant chemical defense against herbivory: a biochemical

perspective. In Bernays EA (ed) Insect–plant interactions. CRC Press, Boca-Raton, FL,

pp 105–176

Gessner MO, Steiner D (2005) Acid butanol assay for proanthocyanidins (condensed tannins). In

Graca M (ed) Methods to study litter decomposition. Springer, Amsterdam, The

Netherlands, pp 107–114

Giordanengo P, Brunissen L, Risterucci C, Vincent C, van Bel A, Dinant S, Girousse C, Faucher

M, Bonnemain JL (2010) Compatible plant–aphid interactions: how aphids manipulate

plant responses. C R Biol 333:516–523

Glynn C, Herms DA, Orians CM, Larsson S (2007) Testing the growth–differentiation

hypothesis: dynamic responses of willows to nutrient availability. New Phytol 176:623–

634

Gonzalez-Rodriguez A, Omaya K (2005) Leaf morphometric variation in Quercus affina and Q.

laurina (Fagaceae), two hybridizing Mexican red oaks. Bot J Linn Soc 147:427–435

Graca M, Barlocher F (2005) Radial diffusion assay for tannins. In Graca M (ed) Methods to

study litter decomposition. Springer, Amsterdam, The Netherlands, pp 101–105

Grafen A (1989) The phylogenetic regression. Philos Trans R Soc Lond B 326:119–157

Grime JP (2006) Plant strategies, vegetation processes, and ecosystem properties. Wiley,

Chichester, UK

Gruntman M, Novoplansky A (2011) Ontogenetic contingency of tolerance mechanisms in

response to apical damage. Ann Bot 108:965–973

Guo Q, Major IT, Howe GA (2018) Resolution of growth-defense conflict: mechanistic insights

from jasmonate signaling. Curr Opin Plant Biol 44:72–81

Hagerman A (1987) Radial diffusion method for determining tannin in plant extracts. J Chem

Ecol 14:453–461

Hagerman A (1988) Extraction of tannin from fresh and preserved leaves. J Chem Ecol 13:437–

449

Hagerman A (2011) Tannin handbook. Miami University, Oxford, OH.

https://www.users.miamioh.edu/hagermae/. Accessed 22 February 2019

Hahn PG, Maron JL (2016) A framework for predicting intraspecific variation in plant defense.

Trends Ecol Evol 31:646–656

Hare JD, Elle E, van Dam NM (2003) Costs of glandular trichomes in Datura wrightii: a three-

year study. Evolution 128:1021–1055

Hattas D, Scogings PF, Julkunen-Tiitto R (2017) Does the growth–differentiation balance

hypothesis explain allocation to secondary metabolites in Combretum apiculatum, an

African savanna woody species? J Chem Ecol 43:153–163

Hattori K, Ishida TA, Kaori MI, Suzuki M, Kimura MT (2004) Differences in response to

simulated herbivory between Quercus crispula and Quercus dentata. Ecol Res 19:323–

329

Havko NE, Major IT, Jewell JB, Attaran E, Howe GA (2016) Control of carbon assimilation and

partitioning by jasmonate: an accounting of growth-defense tradeoffs. Plants 5:e7

Hedrick PW (1999) Antagonistic pleiotropy and genetic polymorphism: a perspective. Heredity

82:126–133

Herms DA, Mattson WJ (1992) The dilemma of plants: to grow or defend. Q Rev Biol 67:283–

335

Heslop-Harrison JP (2017) Morphology, phylogeny, and adaptation. Ann Bot 120:621–624

Hickey LJ, Wolfe JA (1975) The bases of angiosperm phylogeny: vegetative morphology. Ann

Missouri Bot Gard 62:538–589

Hinman ED, Fridley JD, Parry D (2019) Plant defense against generalist herbivore in the forest

understory: a phylogenetic comparison of native and invasive species. Biol Invasions.

21:1269–1281

Hipp AL, Manos PS, Gonzalez-Rodriguez A, Hahn M, Kaproth M, McVay JD, Avalos SV,

Cavender-Bares J (2018) Sympatric parallel diversification of major oak clades in the

Americas and the origins of Mexican species diversity. New Phytol 217:439–452

Hoffmann WA, Poorter H (2002) Avoiding bias in calculations of Relative Growth Rate. Ann

Bot 80:37–42

Holeski LM, Chase-Alone R, Kelly JK (2010) The genetics of phenotypic plasticity in plant

defense: trichome production in Mimulus guttatus. Am Nat 175:391–400

Huang J, Hammerbacher A, Weinhold A, Riechelt M, Gleixner G, Behrendt T, van Dam NM,

Sala A, Gershenzon J, Trumbore S (2019) Eyes on the future – evidence for trade-offs

between growth, storage, and defense in Norway spruce. New Phytol 222:144–158

IBM Corp. (2016) IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY: IBM Corp

Ives AR, Midford PE, Garland Jr T (2007) Within-species variation and measurement error in

phylogenetic comparative methods. Syst Biol 56:252–270

Johnson MT, Campbell SA, Barrett SC (2015) Evolutionary interactions between plant

reproduction and defense against herbivores. Annu Rev Ecol Evol Syst 46:191–213

Kadereit G, Mucina L, Freitag H (2006) Phylogeny of Salicornioidaea (Chenopodiaceae):

diversification, biogeography, and evolutionary trends in leaf and flower morphology.

Taxon 55:617–642

Karban R, Baldwin IT (1997) Induced responses to herbivory. University of Chicago Press,

Chicago

Keefover-Ring K, Rubert-Nason KF, Bennett AE, Lindroth RL (2015) Growth and chemical

responses of trembling aspen to simulated browsing and ungulate saliva. J Plant Ecol

9:474–484

Keith RS (2017) Genetic constraints and the evolution of chemical defenses in multiple tissues of

Boechera stricta. PhD thesis, Duke University, Durham, NC

Keith RS, Mitchell-Olds T (2019) Antagonistic selection and pleiotropy constrain the evolution

of plant chemical defenses. Evolution 73:947–960

Kessler A (2015) The information landscape of plant constitutive and induced secondary

metabolite production. Curr Opin Insect Sci 8:47–53

Knight CA, Vogel H, Kroymann J, Shumate A, Witsenboer H, Mitchell-Olds T (2006)

Expression profiling and local adaptation of Boechera holboelli populations for water use

efficiency across a naturally occurring water stress gradient. Mol Ecol 15:1229–1237

Koricheva J, Nykanen H, Gianoli E (2004) Meta-analysis of trade-offs among plant anti-

herbivore defenses: are plants jacks-of-all-trades, masters of all? Am Nat 163:e64–e75

Lauder GV (1981) Form and function: structural analysis in evolutionary morphology. Paleobot

7:430–442

Landhäusser SM, Chow PS, Dickman LT, Furze ME, Kuhlman I, Schmid S, Wiesenbauer J,

Wild B, Gleixner G, Hartmann H, Hoch G, McDowell NG, Richardson AD, Richter A,

Adams HD (2018) Standardized protocols and procedures can precisely and accurately

quantify non-structural carbohydrates. Tree Physiol 38:1764–1778

Leimar O, Dall SRX, McNamara JM, Kuijper B, Hammerstein P (2019) Ecological genetic

conflict: genetic architecture can shift the balance between local adaptation and plasticity.

Am Nat 193:70–80

Levin DA (1973) The role of trichomes in plant defense. Q Rev Biol 48:3–15

Loomis WE (1932) Growth–differentiation balance vs carbohydrate–nitrogen ratio. Proc Am Soc

Hortic Sci 29:240–245

Loomis WE (1958) Growth and differentiation – an introduction and summary. In Loomis WE

(ed) Growth and differentiation in plants. Iowa State College Press, Ames, IA, pp 1–17

Lorio PL (1986) Growth–differentiation balance: a basis for understanding southern pine beetle–

tree interactions. For Ecol Manag 14:259–273

Losos JB (2008) Phylogenetic niche conservatism, phylogenetic signal and the relationship

between phylogenetic relatedness and ecological similarity among species. Ecol Lett

11:995–1003

Lu H, Jiang W, Ghiassi M, Lee S, Nitin M (2012) Classification of Camellia (Theaceae) species

using leaf architecture variations and pattern recognition techniques. PloS One 7:e29704

MacTavish R, Anderson JT (2020) Resource availability fitness trade-offs: implications for

evolution of stressful environments. Am J Bot 107:308–318

Manos PS, Doyle JJ, Nixon KC (199). Phylogeny, biogeography, and processes of molecular

differentiation in Quercus subgenus Quercus (Fagaceae). Mol Phylogenet Evol 12:333–

349

Maron JL, Baer KC, Angert AL (2014) Disentangling the drivers of context-dependent plant–

animal interactions. J Ecol 102:1485–1496

Martinez-Swatson K, Kjoller R, Cozzi F, Simonsen HT, Ronsted N, Barnes C (2020) Exploring

evolutionary theories of plant defence investment using field populations of the deadly

carrot. Ann Bot 125:737–750

McVay JD, Hipp AL, Manos PS (2017) A genetic legacy of introgression confounds phylogeny

and biogeography in oaks. Proc R Soc Lond B 284:e20170300

Metcalf C (2016) Invisible trade-offs: van Noordwijk and de Jong and life-history evolution. Am

Nat 187:3–5

Mitter C, Farrell B, Futuyma DJ (1991) Phylogenetic studies of insect–plant interactions:

insights into the genesis of diversity. Trends Ecol Evol 6:290–293

Moctezuma C, Hammerbacher A, Heil M, Gershenzon J, Mendez-Alonzo R, Oyama K (2014)

Specific polyphenols and tannins are associated with defense against insect herbivores in

the tropical oak Quercus oleiodes. J Chem Ecol 40:458–467

Mole S (1994) Trade-offs and constraints in plant–herbivore defense theory: a life-history

perspective. Oikos 71:3–12

Moreira X, Abdala-Roberts L, Galman A, Francisco M, de la Fuente M, Butron A, Rasmann S

(2018) Assessing the influence of biogeographical region and phylogenetic history on

chemical defences and herbivory in Quercus species. Phytochemistry 153:64–73

Moreira X, Mooney KA, Rasmann S, Petry WK, Carrillo-Galvan A, Zas R, Sampedro L (2014)

Trade-offs between constitutive and induced defences drive geographical and climatic

clines in pine chemical defences. Ecol Lett 17:537–546

Mounce R, Rivers M, Sharrock S, Smith P, Brockington S (2018) Comparing and contrasting

threat assessments of plant species at the global and sub-global level. Biodivers 27:907–

930

Mundry R (2014) Statistical issues and assumptions of phylogenetic generalized least squares. In

Garamszegi LZ (ed) Modern phylogenetic comparative methods and their application in

evolutionary biology. Springer, Berlin, pp 131–153

Münkemüller T, Lavergne S, Bzeznik B, Dray S, Jombart T, Schiffers K, Thuiller W (2012)

How to measure phylogenetic signal. Methods Ecol Evol 3:743–756

Neilson EH, Goodjer JQ, Woodrow IE, Moller BL (2013) Plant chemical defense: at what cost?

Trends Plant Sci 18:250–258

Ocha-Lopez S, Rebollo R, Barton KE, Fornoni J, Boege K (2018) Risk of herbivore attack and

heritability of ontogenetic trajectories in plant defense. Oecologia 187:413–426

Oyston JW, Hughes M, Gerber S, Wills MA (2016) Why should we investigate the

morphological disparity of plant clades? Ann Bot 117:859–879

Pagel M (1999) Inferring the historical patterns of biological evolution. Nature 401:877–884

Pausas JG, Verdu M (2010) The jungle of methods for evaluating phenotypic and phylogenetic

structure of communities. BioSci 60:614–625

Pearse IT, Hipp AL (2009) Phylogenetic and trait similarity to a native species predict herbivory

on non-native oaks. Proc Nat Acad Sci USA 106:18097–18102

Pearse IT, Hipp AL (2012) Global patterns of defenses in oak species. Evolution 66:2272–2286

Pearson DE, Ortega YK, Maron MK (2017) The tortoise and the hare: Reducing resource

availability shifts competitive balance between plant species. J Ecol 105:999–1009

Peiman KS, Robinson BW (2017) Comparative analyses of phenotypic trait covariation within

and among populations. Am Nat 190:451–468

Pennell MW, FitzJohn RG, Cornwell WK (2016) A simple approach for maximizing the overlap

of phylogenetic and comparative data. Methods Ecol Evol 7:751–758

Pennell MW, Harmon LJ (2013) An integrative view of phylogenetic comparative methods:

connections to population genetics, community ecology, and paleobiology. Ann New

York Acad Sci 1289:90–105

Peschiutta ML, Scholz FG, Goldstein G, Bicci SJ (2018) Herbivory alters plant carbon

assimilation, patterns of biomass allocation and nitrogen use efficiency. Acta Oecol 86:9–

Petit RL, Bodenes C, Ducousso A, Russel G, Kremer A (2004) Hybridization as a mechanism of

invasion in oaks. New Phytol 161:151–164

Poelman EH, Kessler A (2016) Keystone herbivores and the evolution of plant defenses. Trends

Plant Sci 21:477–485

Price M, Butler L (1977) Rapid visual estimation and spectrophotometric determination of tannin

content in sorghum grain. J Agric Food Chem 25:1269–1273

R Development Core Team. (2019) R: a language and environment for statistical computing. R

foundation for statistical computing, Vienna, Austria. URL https:///www.R.project.org/

Ralph SG, Jancsik J, Bohlmann J (2007) Dirigent proteins in conifer defense II: extended gene

discovery, phylogeny, and constitutive and stress-induced gene expression in spruce

(Picea spp.). Phytochemistry 68:1975–1991

Rasmann S, Agrawal AA (2011) Latitudinal patterns in plant defense: evolution of cardenolides,

their toxicity, and induction following herbivory. Ecol Lett 14:476–483

Raupp MJ (1985) Effects of leaf toughness on mandibular wear of the beetle Plagiodera

versicolora. Ecol Entomol 10:73–79

Rees M, Osborne CP, Woodward FI, Hulme SP, Turnbull LA, Taylor SH (2010) Partitioning the

components of relative growth rate: How important is plant size variation? Am Nat

176:e152–e161

Revell LJ (2012) Phytools: An R package for phylogenetic comparative biology (and other

things). Methods Ecol Evol 3:217–223

Revell LJ, Harmon LJ, Collar DC (2008) Phylogenetic signal, evolutionary process, and rate.

Syst Biol 57:591–601

Rieske LK, Dillaway DN (2008) Response of two oak species to extensive defoliation: Tree

growth and vigor, phytochemistry, and herbivore sustainability. For Ecol Manag

256:121–128

Rohner C, Ward D (1997) Chemical and mechanical defense against herbivory in two sympatric

species of desert Acacia. J Veg Sci 8:717–726

Rooke T (2003) Growth responses of woody species to clipping and goat saliva. Afr J Ecol

41:324–328

Rose MR (1982) Antagonistic pleiotropy, dominance, and genetic variation. Heredity 8:63–78

Rossiter M, Schultz JC, Baldwin IT (1988) Relationships among defoliation, red oak phenolics,

and gypsy moth growth and reproduction. Ecology 69:267–277

Rushton BS (1993) Natural hybridization within the genus Quercus L. Ann Sci For 50:73–90

Sánchez-Tόjar A, Moran NP, O’Dea R, Reinhold K, Nakagawa S (2020) Illustrating the

importance of meta-analyzing variances alongside means in ecology and evolution. J

Evol Biol 33:1216–1223

Scogings PF (2018) Foliar flavonol concentration in Sclerocarya birrea saplings responds to

nutrient fertilisation according to growth–differentiation balance hypothesis. Phytochem

Lett 23:180–184

Sidlauskas B (2008) Continuous and arrested morphological diversification in sister clades of

characiform fishes: a phylomorphospace approach. Evolution 62:3135–3156

Siegel S, Castellan NJ (1981) Nonparametric statistics for the behavioral sciences. McGraw Hill,

New York

Stearns SC (1992) The evolution of life histories. Oxford University Press, New York

Steppuhn A, Baldwin IT (2008) Induced defenses and the cost–benefit paradigm. In Schaller A

(ed) Induced plant resistance to herbivory. Springer, Berlin, pp 61–83

Tian D, Tooker J, Peiffer M, Chung SH, Felton GW (2012) Role of trichomes in defense against

herbivores: comparison of herbivore response to woolly and hairless trichome mutants in

tomato (Solanum lycopersicum). Planta 236:1053–1066

Tomlinson KW, van Langevelde F, Ward D, Bongers F, Da Silva DA, Prins HHT, De Bie S,

Sterck FJ (2013) Deciduous and evergreen trees differ in juvenile biomass allometries

because of differences in allocation to root storage. Ann Bot 112:575–587

Trehane P (2007) The Oak Names Checklist. URL http://wwww.oaknames.org. Accessed 13

October 2016.

Valladares F, Balaguer L, Martinez-Ferri E, Perez-Corona E, Manrique E (2002) Plasticity,

instability, and canalization: is the phenotypic variation in seedlings of sclerophyll oaks

consistent with the environmental unpredictability of Mediterranean ecosystems? New

Phytol 156:457–467 van Kleunen M, Fischer M (2004) Constraints on the evolution of adaptive phenotypic plasticity

in plants. New Phytol 166:49–60 van Noordwijk, AJ, de Jong G (1986) Acquisition and allocation of resources: their influence on

variation in life history tactics. Am Nat 128:137–142

Via S, Gomulkiewicz R, de Jong G, Scheiner SM, Schlichting CD, van Tienderen PH (1995)

Adaptive phenotypic plasticity – consensus and controversy. Trends Ecol Evol 10:212–

217

Visakorpi K, Riutta T, Mainez-Bauer AE, Salminen JP, Gripenberg S (2019) Insect community

structure covaries with host plant chemistry but is not affected by prior herbivory.

Ecology 100:e02739

Walden N, Lucek K, Willi Y (2019) Lineage-specific adaptation to climate involves flowering

time in North American Arabidopsis lyrata. Mol Ecol 29:1436–1451

Ward D (2006) Long-term effects of herbivory on plant diversity and functional types in arid

ecosystems. In Danell K, Bergström R, Duncan P, Pastor J (eds) Large herbivore

ecology, ecosystem dynamics and conservation. Cambridge University Press, Cambridge,

pp 142–169

Ward D (2010) The effects of apical meristem damage on growth and defences of two Acacia

species in the Negev Desert. Evol Ecol Res 12:589–602

Ward D, Shrestha MK, Golan-Goldhirsh A (2012) Evolution and ecology meet molecular

genetics: Adaptive phenotypic plasticity in two isolated Negev desert populations of

Acacia raddiana at either end of a rainfall gradient. Ann Bot 109:247–255

Ward D, Young TP (2002) Effects of large mammalian herbivores and ant symbionts on

condensed tannins of Acacia drepanolobium in Kenya. J Chem Ecol 28:921–937

Wiley E, Casper BB, Helliker BR (2017) Recovery following defoliation involves shifts in

allocation that favour storage and reproduction over radial growth in black oak. J Ecol

105:412–424

Woolery PO, Jacobs DF (2011) Photosynthetic assimilation and carbohydrate allocation of

Quercus rubra seedlings in response to simulated herbivory. Ann For Sci 68:617–624

Wright S (1968) Evolution and the genetics of populations. Volume 1: Genetic and biometric

foundations. University of Chicago Press, Chicago

Zera AJ, Harshman LG (2001) The physiology of life history trade-offs in animals. Annu Rev

Ecol Syst 32:95–126

Züst T, Agrawal AA (2017) Trade-offs between plant growth and defense against insect

herbivory: an emerging mechanistic synthesis. Annu Rev Plant Biol 68:513–534

Züst T, Joseph B, Shimizu KK, Kliebenstein DJ, Turnbull LA (2011) Using knockout mutants to

reveal the growth costs of defensive traits. Proc R Soc Lond B 278:2598–2603

Züst T, Rasmann S, Agrawal AA (2015) Growth–defense trade-offs for two major anti-herbivore

traits of the common milkweed Asclepias syriaca. Oikos 124:1404–1415

Züst T, Heichinger C, Grossniklaus U, Harrington R, Kliebenstein, DJ, Turnball LA (2012)

Natural enemies drive geographic variation in plant defenses. Science 338:116–119

CHAPTER III

ABOVEGROUND HERBIVORY CAUSES BELOWGROUND CHANGES IN TWELVE

OAK (QUERCUS) SPECIES: A PHYLOGENETIC ANALYSIS OF ROOT BIOMASS AND

NUTRIENT RE-ALLOCATION

ABSTRACT

Plant ecosystem structure is understood to be a result of complex multitrophic interactions. Most multitrophic studies focus on plant aboveground adaptations to aboveground herbivore pressures, neglecting belowground adaptations in response to aboveground damage. Differential investment in root structures may allow plants to compensate for tissue loss or damage due to herbivores.

Furthermore, phylogeny may constrain a plant’s ability to adapt belowground. We examined the belowground responses of 12 species of oak (Quercus) to varying locations (apical vs lateral meristem) and intensities (25% vs 75% tissue removal) of simulated herbivore damage. We first established that oak belowground traits responded to aboveground herbivory by measuring patterns of investment in coarse vs fine root structures and re-allocation of non-structural carbohydrates (NSC) to root storage. We then tested whether phylogeny could explain variations in investment patterns using phylogenetic independent contrasts. Plant adaptations to aboveground herbivory included allocating biomass and carbon reserves to root structures, depending on the location and intensity of herbivore damage. NSC re-allocation to root storage was observed when oak species experienced any type of damage, but damage to lateral tissues

80 caused a greater re-allocation than apical damage or control treatments. We found that most belowground responses to aboveground herbivory are species-specific and may be adapted for environmental conditions or type of herbivory. Some responses to herbivore damage, such as changes in fine-root mass and root sugar concentrations, were phylogenetically constrained.

Phylogenetic constraints generally occur when there is severe damage at the apical meristem.

Plants may adapt to aboveground tissue loss due to varying herbivore pressures (i.e., varying location and intensity of damage) by differentially investing in root types and NSC re-allocation to root storage. Understanding linkages between and phylogenetic constraints of plant belowground responses to aboveground herbivory will improve our understanding of the ecological processes involved in multitrophic interactions.

INTRODUCTION

Many studies on herbivory focus on aboveground plant responses (e.g., Schuman et al. 2018;

War et al. 2018; Züst and Agrawal 2017). These responses include fitness costs (Cipollini et al.

2014; Rusman et al. 2020) growth and compensatory regrowth (McNaughton 1983; Ward et al.

1997; Zhang et al. 2020), plant secondary metabolite (PSM) production (Couture et al. 2016;

Karban and Baldwin 2007; Kessler 2015), changes in nutrient quality (Tomlinson et al. 2016;

Tuomi et al. 1984), and tri-trophic interactions (Agrawal 2000; Shikano et al. 2017; van der

Putten et al. 2001). Over the last few decades, there has been an increased interest in linking above- and belowground interactions (Bargett and van der Putten 2014; Papadopoulou and van

Dam 2017). Several studies have analyzed feedbacks between above- and belowground herbivory (e.g., Bezemer et al. 2003; Masters and Brown 1993; van Dam and Heil 2011). An understudied aspect is the effect on morphology and chemistry of plants belowground when affected by changes aboveground (Bardgett et al. 2014).

Although roots are generally used for water and nutrient uptake (Hodge 2010; Jackson et al. 1990) some are also used for anchoring ability (Knorr et al. 2005; Tobin et al. 2007). Fine roots have a greater capacity for nutrient absorption than coarse roots (Johnson et al. 2008). We hypothesize that fine roots may play an important role in allowing plants to adapt to herbivore pressures. Fine roots are defined as having diameters < 2 mm, whereas coarse roots are defined as having diameters ≥ 2 mm (McCormack et al. 2015; Pregitzer et al. 2002). The diameters are often associated with the functional roles of roots (Cusack et al. 2009; Silver and Miya 2001).

Coarse roots are generally associated with root network structural support and delivering nutrients from fine roots to the rest of the plant (Knorr et al. 2005; Tobin et al. 2007).

Differences in functionality of fine and coarse roots may cause a differentiation in re-allocation

82 of carbon to the various root types as an adaptive response to herbivory. Herbivory often decreases photosynthetic capacity (Holland et al. 2017; Nabity et al. 2009), consequently decreasing nutrient uptake (Leon-Sanchez et al. 2020) so that investment in fine roots may be hypothesized as an adaptive response to compensate for the loss of nutrients.

Herbivory is an ecological process that regulates and selects for specific plant traits (Aide

1988; Brown and Lawton 1991; Maron et al. 2019). One of the most important aspects of herbivory is due to variation in location and intensity of herbivore damage (Ballaré and Austin

2019; Ward 2010). Many plants express apical dominance where there is preferential growth of the apex shoot, or apical meristem over lateral tissues (Aarssen 1995; Cline 1991; Kebrom

2017). Several studies have shown that plants overcompensate in other tissues due to removal or destruction of the apical meristem (e.g., Aarssen and Turkington 1987; Ward 2010). For example, damage to the apical meristem may cause an increase in lateral tissue growth (Gadd et al. 2001; Perkovich and Ward 2021; Ward 2010). Root-trait responses may be related to adaptation, phylogeny or a combination of adaptation and phylogeny (Valverde-Barrantes et al.

2015; 2017). We assessed how aboveground herbivory affected belowground traits of 12 related species in the genus Quercus (oaks; Fagaceae). Oaks are abundant in many temperate systems, have a wide geographical range (found on five continents), and demonstrate tens of million years of speciation, providing a model system for ecological and evolutionary studies (Cavender-Bares

2016; Kissing and Powers 2010; Manos and Stanford 2001; Pearse and Hipp 2012; Petit et al.

2013). Much research has been done on developing a highly resolved molecular phylogeny of

American oak clades (Hipp et al. 2014, 2018; McVay et al. 2017). Having a well-resolved phylogeny is crucial for comparative studies because of the non-independence between species, caused by hierarchical relationships (Ackerly and Donoghue 1995; Huey et al. 2019). Felsenstein

(1985) suggested a method for weighting character values of closely related species when performing comparative analyses. Sister species may have similar trait values because of common descent and are therefore nested in a phylogenetic hierarchy. These relationships violate the assumption of independence of data points. Felsenstein’s (1985) method uses independent contrasts (calculated as the absolute difference (“contrasts”) between the actual values (“tips”) among individual species, using the square root of the variance for standardization) (also see

Garland et al. 1992; Ward et al. 2020). Independent contrasts create statistical independence of data points, determining if traits may be similar because of phylogeny and not representations of individual adaptations.

The aim of this study was to provide a better understanding of evolutionary patterns that may explain and/or predict belowground trait responses to aboveground herbivory. We formulated these three hypotheses:

(1) There should be an increase in nutrient re-allocation belowground (Perkovich and

Ward 2021; Wiley et al. 2017), generating increased investment in root biomass when damaged aboveground. To compensate for nutrients lost to herbivory, there should be a greater investment in the production of fine roots than in coarse roots.

(2) Phylogeny should explain variations in oak belowground responses, with more closely related species responding in similar ways (Phillips et al. 2018; Xie et al. 2016).

(3) Because oak species often prioritize NSC storage over regrowth (Wiley et al. 2017), we predict a trade-off (strong negative correlation) between aboveground biomass and NSC in root storage. Furthermore, NSC re-allocation should influence root biomass (Kobe et al. 2010).

We therefore predict a positive correlation between root biomass and the re-allocation of NSC to root storage.

METHODS

Oak phylogeny and experimental design We used a well-resolved molecular phylogeny containing 146 species in the American oak clade (Hipp et al. 2018). Twelve species were chosen across the phylogeny to give an adequate representation of biogeographical and environmental diversity within the oak genus. We followed oak nomenclature described in the

Oaks Names Database (Trehane 2007). We excluded species where interspecific hybridization and introgression may occur, such as among the white oaks (Q. alba complex) and red oaks (Q. rubra complex) (Hipp and Weber 2008; Lexer et al. 2006; Moran et al. 2012; Whittemore and

Schaal 1991). We sampled the following 12 species: Q. alba, Q. coccinea, Q. laurifolia, Q. macrocarpa, Q. michauxii, Q. muehlenbergii, Q. nigra, Q. palustris, Q. rubra, Q. sinuata, Q. stellata and Q. virginiana (Fig. 7).

Figure 7. The phylogenetic relationships of the 12 Quercus species used for analysis of belowground responses to simulated herbivory. Species tree was pruned from the Hipp et al. (2018) phylogeny of

Quercus species in North America. (Modified from Figure 1 in Perkovich and Ward 2021).

Simulated herbivory treatments Oak saplings from each of the 12 species were purchased from Mossy Oak Nativ Nursery in West Point, MS, United States. To avoid differences in responses due to ontogeny, all saplings were the same age (approximately 3–4 years old)

(Gruntman and Novoplansky 2011). Saplings were planted in May 2017, in the same type of soil

(all-purpose Pro-Mix potting soil) to ensure that there were no nutritional or microbial effects from different soils (Pangesti et al. 2014; van der Putten et al. 2001). Throughout the experiment, pots containing the saplings were weeded weekly. The saplings were kept in optimal conditions in a greenhouse with a permanent schedule of 12 h of light and 12 h of dark at a constant 22 °C.

Daily watering was administered from an irrigation system to ensure each sapling received the same amount of water. We applied five treatments to simulate variations in meristem location

(apical vs. lateral) and intensity (25% vs 75% tissue removal) of herbivory. The initial height of each sapling was recorded before clipping treatments were applied to account for bias related to initial size. Each treatment was replicated five times for each of the twelve species for a total of

25 individuals per species, with each plant developing in a single pot. The treatments were as follows (see also Perkovich and Ward 2021):

1. Control: No vegetation removed (5 individuals/ oak species) (Fig. 8a).

2. 25% apical removal: Removal of the dominant apical meristem and 25% apical shoot (5

individuals/species) (Fig. 8b).

3. 75% apical removal: Removal of the dominant apical meristem and 75% apical shoot (5

individuals/species) (Fig. 8c).

4. 25% lateral removal: Removal of all apical meristems (except for dominant meristem)

and 25 % of lateral shoots (5 individuals/species) (Fig. 8d).

5. 75% lateral removal: Removal of all apical meristems (except for dominant meristem)

and 75 % of lateral shoots (5 individuals/species) (Fig. 8e).

Figure 8. Diagram of the treatments applied to the Quercus saplings with varying location and intensity of simulated herbivory. The treatments include (a) control, (b) 25% apical removal, (c) 75% apical removal,

(d) 25% lateral removal, and (e) 75% lateral removal. (Figure 2 from Perkovich and Ward 2021).

Aboveground regrowth, root biomass and total non-structural carbohydrate concentrations Saplings were harvested one year after planting and treatment application (May

2018). Aboveground tissues were separated from belowground tissues by cutting the trees at soil level. Aboveground tissues were dried at 65 ℃ for 48 h and weighed to calculate the total aboveground biomass. We estimated aboveground biomass regrowth to determine investment in aboveground regrowth relative to root biomass. Measuring aboveground biomass regrowth is especially difficult in plants as we cannot weigh the plants initially without uprooting and damaging root structure. As root structure was instrumental for this study, we therefore estimated aboveground biomass regrowth as the residuals from a regression of final height and biomass

(see Peig and Green 2009; Schulte-Hostedde et al. 2005) for each treatment to account for differences in growth responses between treatments. Oak roots were harvested one year after planting (May 2018), and treatment application. Dead root material was excluded from all analyses. Root samples were dried at 65℃ for 48 h. Root samples from individual trees were classified as either tap-root, coarse root, or fine root. For this study, we considered the taproot to

88 be the largest, central root from which subsidiary root branches developed. Coarse roots were classified as roots with a diameter > 2.0 mm, excluding the central tap-root. Fine roots were roots that were < 2.0 mm in diameter (McCormack et al. 2015; Pregitzer et al. 2002). The dry mass of each root classification was measured for each individual sapling. Total root biomass was calculated as the sum of all three root classes from a single individual.

After roots were dried and weighed, we took samples from coarse roots of each individual sapling and milled the dried material using a using a 2 mm mesh in a Wiley mill for analysis of non-structural carbohydrate (NSC) concentrations. NSC concentrations were measured using coarse root material because NSC are commonly stored in fourth-order roots

(McCormack et al. 2015). We used the NSC extraction protocol of Fournier (2001). In this protocol, sugars are first extracted using an ethanol wash. After several washes to remove sugars, the tissues are placed in 1% hydrochloric acid and set in a hot water bath at 100℃ for 1 h. After sugars and starches were extracted from the tissues, a phenol–sulfuric acid solvent was used to create a colorimetric reaction and measured using a microplate reader (Tomlinson et al. 2013).

All analyses were done in our laboratory to ensure the same conditions (Landhäusser et al. 2018;

Quentin et al. 2015).

Testing for root morphological responses to aboveground tissue damage (Hypothesis 1) We used a mixed model statistical analysis. To minimize Type I statistical error with multiple dependent variables, we tested for significant responses to aboveground damage using a multivariate analysis of covariance (MANCOVA) with two treatment factors; each treatment had three levels (treatment location with levels “control”, “apical”, and “lateral”, and intensity with levels “control”, “25%”, and “75%”) and initial height if sapling as the covariate. Because the

89 covariate did not have a significant effect, we proceeded with univariate ANOVA (and Scheffé post hoc tests) for significant response variables using IBM SPSS version 26 software (IBM

2019). Our model included treatment variables (location and intensity) as fixed factors. Species was designated as a random factor to estimate the variability that is attributable to this factor

(Gelman 2005; Hector 2015; Whitlock and Schluter 2014). For both MANCOVA and ANOVAs, data was transformed accordingly, to meet assumptions of these tests (i.e., Shapiro-Wilks’ test for normality and Levene’s test for homogeneity of variance).

Testing for phylogenetic patterns of root traits in response to aboveground damage

(Hypothesis 2) We tested for phylogenetic signal (sensu Münkemüller et al. 2012) of response variables (i.e., root structure measurements and NSC concentration) with Hipp et al.’s (2018) molecular phylogeny using R version 3.6.0 (R Development Core Team 2019). We performed a phylogenetic generalized least-squares regression (PGLS) (Grafen 1989; Symonds and Blomberg

2014). Our experimental design included replication within treatments. Consequently, we used the pgls.Ives (Ives et al. 2007) function in the phytools package to account for intraspecific variation (Revell 2012). There are several indices for calculating phylogenetic signal that have been reviewed for their strengths and weaknesses (Münkemüller et al. 2012). For the purposes of this study, we used Blomberg’s K (Blomberg et al. 2003) because of our relatively small sample sizes (reviewed by Münkemüller et al. 2012). Blomberg’s K uses a scale from 0–1, where 0 indicates no phylogenetic signal, and 1 indicates a very strong signal. However, because of the computations used in this index, it is possible to obtain a phylogenetic signal > 1 (Münkemüller et al. 2012).

We also tested for differences in root characteristics among species in response to each treatment in a multivariate approach. To do so, we created matrices of the average of individual species’ responses for each treatment ((n = 60 (i.e., 5 individuals per species * 5 treatments * 12 species)). We then performed a phylogenetic principal component analysis (pPCA) in the phytools package (Revell 2009) in R version 3.6.0 (R Development Core Team, 2019). Finally, we tested for differences in species’ responses at the multivariate level by performing a redundancy analysis (RDA; Oksanen et al. 2019; ter Braak 1986) with treatments as explanatory variables. The RDA was run using independent contrasts to account for phylogeny (Felsenstein

1985). RDA is a canonical form of principal component analysis (PCA) (Legendre et al. 2011;

Oksanen et al. 2019). We tested the significance of the first canonical axis (Legendre et al.

2011). The RDA was performed in the vegan package (Oksanen et al. 2019) in R version 3.6.0

(R Development Core Team 2019).

Correlations between biomass and root NSC concentrations (Hypothesis 3) We tested for a relationship between root morphological traits and NSC re-allocation of each treatment in response to aboveground damage using Pearson’s correlations, adjusted for phylogeny. The phylogenetic adjustments were calculated using phylogenetically independent contrasts to account for the non-independence of data points due to relatedness (Felsenstein 1985).

Computations were run using the phytools package (Revell 2009) in R version 3.6.0 (R

Development Core Team 2019).

RESULTS

Testing for root morphological responses to aboveground tissue damage (Hypothesis 1) The

MANCOVA results showed a significant response in root architecture (i.e., fine vs. coarse root) and NSC concentrations to the interaction of damage location and intensity (Wilks’ λ = 0.860,

F(2, 208) = 93.822, P < 0.001), as well as location of damage (Wilks’ λ = 0.0466, F(2, 208) = 28.755,

P < 0.001) and intensity of damage (Wilks’ λ = 0.789, F(2, 208) = 5.934, P < 0.001). Initial sapling size did not have a significant effect (Wilks’ λ = 0.825, F(2, 208) = 27.663, P = 0.391).

Consequently, we did not consider this factor in subsequent analyses. Individual species had different root-trait responses (species as random factor: Wilks’ λ = 0.016, F(11, 416) = 129.42, P <

0.001).

Differential investment in root classes and biomass We found that there was a significant interaction between the effects of location and intensity on the ratio of belowground biomass: aboveground regrowth (Table 5). There was a large increase in this ratio in the apical- and lateral meristem-damaged treatments compared to the control (Scheffé post hoc tests: P < 0.05, Fig. 9; also see Table 6).

Table 5. ANOVA results of the effects of location and intensity of simulated herbivory on oak belowground traits. Significant values indicated with “*”. Other root = remaining roots except the taproot. Initial plant size was non-significant in MANOVA and is therefore not shown in ANOVA table.

Species (random Location Intensity Location * intensity effect) F(df = 2) P F(df = 2) P F(df = 2) P F(df = 11) P Belowground 5.97 0.015* 0.08 0.783 32.46 < 0.001* 0.19 0.664 biomass Taproot biomass 0.78 0.377 1.98 0.16 32.36 < 0.001* 1.01 0.316 Coarse root biomass 6.37 0.012* 0.16 0.687 7.06 < 0.001* 1.25 0.266 Fine root biomass 5.74 0.017* 0.59 0.442 10.50 < 0.001* 0.51 0.476 Fine: coarse root 3.00 0.085 0.06 0.804 1.76 0.063 4.23 0.041* biomass ratio Tap: other root 0.76 0.386 0.62 0.433 0.51 0.894 0.74 0.391 biomass ratio Below: aboveground 20.28 < 0.001* 16.73 < 0.001* 11.50 0.001* 22.07 < 0.001* biomass regrowth ratio Aboveground 150.22 < 0.001* 582.2 < 0.001* 618.13 < 0.001* 184.03 < 0.001* biomass Root sugar conc. 144.66 < 0.001* 15.42 < 0.001* 90.21 < 0.001* 6.36 0.012* Root starch conc. 78.21 < 0.001* 21.65 < 0.001* 79.07 < 0.001* 10.36 0.001* NSC: belowground 49.03 < 0.001* 3.03 0.084 26.9 < 0.001* 0.21 0.645 biomass ratio

Table 6. Mean and median trait values of Quercus belowground traits to varying locations and intensities of simulated herbivory.*Other root = remaining roots except the taproot.

25% apical 75% apical 25% lateral 75% lateral Control removal removal removal removal

X̄ median X̄ median X̄ median X̄ median X̄ median Belowground 15.01 10.24 14.77 8.77 15.68 10.23 12.22 9.17 12.02 10.04 biomass Taproot 10.10 9.41 7.97 7.77 8.13 7.65 7.27 6.53 8.18 7.96 biomass Coarse root 0.61 0.61 0.66 0.48 0.73 0.70 1.05 0.97 0.88 0.97 biomass Fine root 1.75 1.49 1.75 1.72 1.77 1.48 1.37 1.25 1.55 1.44 biomass Fine: coarse root biomass 2.68 1.68 4.40 3.08 3.13 2.78 1.61 1.30 3.38 1.94 ratio Tap: other root biomass 6.11 4.25 4.06 3.71 4.36 4.50 10.45 3.55 4.39 4.22 ratio Above: belowground 0.23 0.17 0.70 0.68 0.35 0.50 0.31 0.24 0.34 0.30 biomass ratio Aboveground 62.58 60.63 47.11 44.25 26.89 19.44 46.41 42.86 40.74 44.71 biomass Root sugar 0.12 0.11 0.58 0.55 0.68 0.64 0.79 0.99 0.81 0.94 conc. Root starch 0.26 0.16 1.06 1.11 1.19 1.25 1.27 1.38 1.29 1.40 conc. NSC: root 0.05 0.03 0.17 0.18 0.19 0.18 0.22 0.24 0.23 0.23 biomass ratio

F = 11.50, P < 0.001 No tissue removal

25% tissue removal

x 75% tissue removal

Figure 9. Pattern of increased investment by Quercus species in belowground biomass relative to aboveground regrowth in response to varying location and intensity of simulated herbivory. Aboveground biomass regrowth was estimated using residuals from a final height to final biomass regression (see

Methods for details).

The total belowground biomass significantly increased depending on the location and intensity of damage (Fig. 10, Table 5). Total belowground biomass was significantly lower for the lateral-meristem treatments than the 75% apical-meristem treatments (Scheffé post hoc test:

P = 0.013; Fig. 10). When damaged at the lateral meristem, oak saplings significantly increased investment in coarse-root production (Fig. 11a, Table 5) while significantly decreasing investment in fine-root production (Fig. 11b, Table 5).

F = 32.46, P < 0.001

No tissue removal

25% tissue removal

x 75% tissue removal

Figure 10. Patterns of investment by Quercus species in belowground biomass in response to differing locations and intensities of simulated herbivory.

F = 6.37, P = 0.012

F = 5.74, P = 0.017

Figure 11. Investment of oak saplings in coarse and fine root biomass in response to differing locations of simulated herbivory. Oak investment in (a) coarse root biomass and

(b) investment in fine root biomass.

Re-allocation of non-structural carbohydrates to root storage There was a significant increase in root sugar- and starch concentrations in response to the interaction of location and intensity of aboveground damage (Fig. 12a, b, Table 5). Intensity of tissue removal caused significantly greater sugar concentrations in root storage in treatments with 75% apical removal than 25% apical removal (Scheffé post hoc test: P = 0.039, Fig. 12a) but had no significant effect on starch concentrations in root storage within apical or lateral removal treatments (Scheffé post hoc test: P > 0.05, Fig. 12b). Location of damage to the lateral meristem caused a greater re- allocation of sugars and of starch to root storage than damage to the apical meristem (Fig. 12a, b,

Table 5).

F = 90.21, P < 0.001

No tissue removal

25% tissue removal

x 75% tissue removal

F = 79.07, P < 0.001

No tissue removal

25% tissue removal

x 75% tissue removal

Figure 12. Investment in non-structural carbohydrate re-allocation to roots by oaks in response to differing locations and intensities of simulated herbivory. (a) Root sugar and (b) root starch concentrations significantly increased in response to simulated herbivory. “G.E.” = glucose equivalents.

Ratio of non-structural carbohydrates (NSC): total belowground biomass There was a significant interaction effect of location and intensity of damage on the ratio of NSC: total belowground biomass (Fig. 13, Table 5). Lateral damage caused a significantly greater increase in the ratio of NSC: total belowground biomass than apical damage (Scheffé post hoc test: P <

0.001). The intensity of damage (25% vs 75% tissue removal) did not have a significant effect within a specific location of damage (i.e., there were no significant differences between saplings with 25% apical damage compared to 75% apical damage, Scheffé post hoc test: P > 0.05).

F = 26.9, P < 0.001

No tissue removal

25% tissue removal

x 75% tissue removal

Figure 13. Ratio of the investment by Quercus species in non-structural carbohydrate re-allocation to roots and total belowground biomass in response to varying locations and intensities of simulated herbivory. “G.E.” = glucose equivalents.

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Testing for phylogenetic patterns of root traits to aboveground damage (Hypothesis 2)

There was no significant effect of phylogeny on differential investment in root classes, except when 75% of apical tissue was removed. When 75% of apical meristem tissue was removed, we detected phylogenetic signal for fine-root biomass (Blomberg’s K = 0.949, P = 0.026), and the ratio of taproot biomass: remaining root biomasses (i.e., coarse and fine) combined (Blomberg’s

K = 0.894, P = 0.048). We found a significant phylogenetic signal for root-sugar concentration when saplings were damaged at the apical meristem, regardless of the amount of tissue removed

(25% tissue removal: Blomberg’s K = 1.067, P = 0.019; 75% tissue removal: Blomberg’s K =

0.8592, P = 0.042).

A phylogenetic principal components analysis showed that the control and treatments with apical tissue removal, aboveground biomass regrowth and root biomass were the main response variables explaining variation in responses (Table 7, Fig. 14a–c). Trait responses varied within treatments, dependent on the oak species (canonical traits tested by RDA among species;

P < 0.001, adjusted R2 = 0.11). We also found that Q. michauxii and Q. muehlenbergii were at opposite ends of pPCA2, when apical meristems were removed (Fig. 14b, c). There were inconsistent patterns when lateral meristems were removed (Fig. 14d, e). There were also differences in the weightings of individual response variables for each species within a given treatment (Fig. 15).

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Table 7. Eigenvalues and % variance explained by phylogenetic principal components analyses of Quercus responses to varying locations and intensities of simulated herbivory. Each row represents a different treatment. (pPCA plots shown in Figure 15).

Treatment pPCA1 pPCA2

Eigenvalue % variance Eigenvalue % variance Control 7.54 76.4 2.11 19.9

25% apical 7.32 78.2 1.99 20.3 removal 75% apical 6.08 80.8 1.17 17.5 removal 25% lateral 6.51 64.0 2.80 29.2 removal 75% lateral 8.26 83.8 1.16 11.6 removal

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a b c

d e Figure 14 variable legend: A- Aboveground biomass B- Belowground biomass C- Taproot biomass D- Ratio of tap root mass: other root biomass E- Fine root mass: coarse root biomass

Figure 14. Phylogenetic principal component analysis plots of Quercus responses to varying locations and intensities of simulated herbivory. a) control; b) removal of 25% of apical tissues and meristem; c) removal of 75% of apical tissues and meristem; d) removal of 25% of lateral tissues and meristem; and e) removal of 75% of lateral tissues and meristem. For all plots, axis values represent the first two pPCA axes (% explained variation and eigenvalues are recorded in Table 7). Response variables that varied from the centroid value are labeled (A, B, C, D,

E); all other response variables (not shown) clustered around the centroid origin. Individual species are shown as acronyms on each plot: “al”

= Q. alba, “co” = Q. coccinea, “ma” = Q. macrocarpa, “mi” = Q. michauxii, “mu” = Q. muehlenbergii, “ni” = Q. nigra, “pa”= Q. palustris,

“ru” = Q. rubra, “si” = Q. sinuata, “st” = Q. stellata, “vi” = Q. virginiana.

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root root mass

rootmass

NSC: root NSC: mass

Root starch Root conc.

Root sugar Root conc.

Fine

Belowground mass Coarse

% contribution to pPCA1 for controls

Figure 15. Phylogenetic relationships and relative significance of response traits for Quercus species derivedF from phylogenetic principal components weightings of individual variables (only treatment

values are shown). Symbol size indicates variance explained by phylogenetic principal component analysis (see % contribution scale for details). “NSC” = non-structural carbohydrates.

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Correlations between biomass and root NSC concentrations (Hypothesis 3) Using independent contrasts, we did not find the hypothesized correlations between aboveground or root biomass traits and NSC concentrations (r values between -0.31 and 0.29 for all correlations;

P > 0.05). When phylogeny was ignored, we found a significant trade-off between aboveground biomass regrowth and root-starch concentrations in most of the damaged treatments (Table 8).

Treatment

r P 25% apical -0.57 < 0.001* removal 75% apical -0.71 < 0.001* removal 25% lateral -0.28 < 0.001* removal 75% lateral -0.61 < 0.001* removal

DISCUSSION

Testing for root responses to aboveground tissue damage (Hypothesis 1) Our results support

Hypothesis 1 and show that belowground traits are affected by aboveground herbivore pressures.

Many studies have shown that plants respond to aboveground herbivory using compensatory regrowth strategies (McNaughton 1983; McNickle and Evans 2018). As we hypothesized, damage increased investment in total root biomass. In contrast to previous studies showing a

105 decrease in root biomass allocation (e.g., Eyles et al. 2009; Geiger and Thomas 2002; Pinkard and Beadle 1998), we suggest that belowground biomass also increases in response to aboveground herbivore pressures, with a higher investment than aboveground biomass. Former studies have often analyzed root biomass allocation in response to damage under varying nutrient supplies or synchronously with other stressors (e.g., Eyles et al. 2009; Geiger and Thomas 2002).

For example, Eyles et al. (2009) found that Eucalyptus globulus decreased root biomass allocation in response to defoliation. This study was performed under varying water and nutrient availability. Under varying resource availabilities, plants may alter resource acquisition strategies as well as allocation strategies (Metcalf 2016; van Noordwijk and de Jong 1986). Studies manipulating nutrient availability do not allow us to determine differences between acquisition and allocation strategies. In our design, plants received the same resources and therefore we can determine that our results are directly related to allocation strategies.

Investment in fine roots versus coarse roots Aboveground herbivory reduces photosynthetic capacity (Holland et al. 2017; Mooney and Gulmon 1982). Some plants may compensate for lower photosynthetic capacities by consequently increasing or decreasing their foliar nutrient concentrations (Millard et al. 2001). Assuming there are no significant changes to foliar nutrient concentrations due to herbivory (see Perkovich and Ward 2021), there should be greater investment in fine-root production to increase nutrient uptake (Johnson et al. 2008; McCormack et al. 2015). We expected a greater investment in fine-root biomass than coarse-root biomass because fine roots have a greater nutrient absorption capacity than coarse roots (Johnson et al.

2008, McCormack et al. 2017 ; Wells and Eissenstat 2003). Our hypothesis was not supported: saplings displayed an increase in coarse-root investment and a decrease in fine-root investment

106 when damage occurred at the lateral meristem. Aboveground herbivory often causes increases in nutrient storage in roots (Dong et al. 2018), therefore we expected an increase in fine-root biomass to help increase with nutrient uptake. Other studies have found that aboveground herbivory reduces fine-root biomass (e.g., Luostarinen and Kauppi 2005; Saikkonen et al. 1999).

For example, herbivory by moose on deciduous tree species resulted in a decrease in fine-root biomass accompanied by a decrease in the longevity of fine roots (Kielland et al. 1997; Ruess et al. 1998). Thus, a possible explanation for our results is that the overall fine-root biomass production per unit time did not necessarily decrease, but rather there was an increase in turnover rate for fine roots as a result of herbivory. Unfortunately, we did not measure root longevity over time. Another possible explanation for decreased fine-root production may be that the effects of herbivore damage depend on nutrient availability (Burton et al. 2000; McCormack and Guo

2014) and the composition of rhizobacteria species in the soil (Pangesti et al. 2014; van der

Putten et al. 2001). However, our experimental design controlled for differences in soils because all plants were planted in the same potting soil so that explanation is unlikely.

Re-allocation of non-structural carbohydrates belowground Masters and Brown (1997) posited that aboveground herbivores should negatively affect root herbivores by reducing carbohydrate availability belowground (e.g., Ramirez et al. 2018; van der Putten et al. 2001; Wei et al. 2016). Studies on the re-allocation of non-structural carbohydrates (NSC) to root storage differ, depending on the species in question (e.g., Frost and Hunter 2008; Palacio et al. 2020;

Wiley et al. 2017). For example, some species of oak have been shown to reduce belowground

NSC when damaged by herbivores (Frost and Hunter 2008; Palacio et al. 2020), but others have been shown to prioritize NSC storage over growth (Wiley et al. 2017). We found that

107 significantly greater concentrations of NSC (both sugars and starch) were re-allocated to root storage when plants were damaged aboveground. Contra Masters and Brown’s (1997) model, it is common for species of deciduous trees to move carbon to belowground reserves, making

(albeit non-strategically) NSC readily available for belowground herbivores (Bezemer and van

Dam 2005; Wiley et al. 2017).

Carbon re-allocation to root storage versus root biomass Canham et al. (1999) and Zavala and Ravetta (2001) have pointed out that it is important to differentiate between carbon allocation to carbohydrate storage and carbon allocation to root biomass. However, there is a great deal of uncertainty when interpreting the importance of carbon in root storage and root biomass (Adame et al. 2017). Kobe (1997) suggested that increases in carbohydrate reserves contribute to the maintenance of metabolic functions when plants are stressed (e.g., due to herbivore damage). Contrastingly, root structural biomass is thought to play a significant role during droughts and thermal stress (Gotschalk 1985, Nippert et al. 2012; Sipe and Bazzaz 1994).

We would expect that because we are simulating herbivore damage under optimal greenhouse conditions, NSCs should be prioritized to root storage and biomass is therefore likely to increase.

NSCs were re-allocated to belowground storage. However, we found that despite an increase in

NSC re-allocation to belowground storage, biomass decreased in lateral removal treatments and remained unchanged in apical treatments.

Our findings suggest that plants may also differentiate carbon re-allocation responses dependent on the location of herbivore damage. Lateral damage caused a greater re-allocation of sugars and starch to root storage than apical damage. Re-allocation of sugars and starch to root storage is thought to be an adaptation to promote post-defoliation survival (Canham et al. 1999;

108

Orians et al. 2011). However, species with higher storage rates often have a trade-off resulting in slower growth rates (Myers and Kitajima 2007; Paula and Pausas 2011). Oaks are primarily forest-dwelling species which means they compete with other forest-dwelling trees to reach sunlight (Eloy et al. 2017; Grime and Jeffrey 1965). If increases in storage concentrations are inversely related to growth rate, when apical tissues are removed, it may be an advantage to regrow damaged tissue rather to slow growth. Otherwise slowed growth could lead to mortality if the individual is unable to adequately compete with nearby trees (Gause 1932; Magal and

Zhang 2017).

Testing for phylogenetic patterns of root traits to aboveground damage (Hypothesis 2) We pause here to note two important statistical limitations that should be considered when interpreting the lack of a phylogenetic signal. First, due to the practicality of experimental manipulations on several species with replication, we only included 12 species. In phylogenetic comparative studies with relatively small numbers of species, phylogenetic signal may sometimes be undetectable (Münkemüller et al. 2012). Seeing as phylogenetic signal was detected for some traits, too few species is not likely to have caused a lack of phylogenetic signal. Second, detection of phylogenetic signal and phylogenetic patterns is often dependent on the scale of the analysis (Agrawal 2020). Expression of traits is often associated with specific processes that generate those patterns. These processes are often scale-dependent (Agrawal 2020;

Züst and Agrawal 2017). For example, the trade-off between flower size and the number of flowers is a life-history trait that is observed within and between species but is not always present at the population level because of resource availability and local adaptations (Sargent et al. 2007).

109

While phylogenetic structuring has been shown to explain functional traits of fine roots

(Iversen et al. 2017; Valverde-Barrantes et al. 2015; 2017), we did not find phylogenetic effects on allocation patterns to coarse or fine roots. There are two possible reasons for phylogenetic similarities or differences: phylogenetic niche conservatism (Losos 2008) and phylogenetic overdispersion (Cavender-Bares et al. 2004). Losos (2008) discussed the possibility that closely related species may be more ecologically similar than expected by chance. This might occur where closely related species have similar ecological roles but occur allopatrically.

Contrastingly, Cavender-Bares et al. (2004) found a pattern of phylogenetic overdispersion (i.e., closely related species are more ecologically different and show less niche overlap than expected) of traits for multiple co-occurring species in Florida that are important for habitat differentiation within the genus Quercus. In our study, under stressed conditions (such as 75% apical meristem removal) the closely related species responded similarly, suggesting that there may be some form of phylogenetic niche conservatism when stressed (closely related species are more ecologically similar than expected by chance) (Losos 2008). A lack of phylogenetic signal does not dictate that phylogenetic comparative methods are unnecessary (Losos 2008). Instead, analyzing ecological variation in a phylogenetic context should be viewed as necessary to determine the role phylogeny has, even if phylogenetic constraints are minimal (Garland et al.

2005; Losos 1999). We found that responses to location and intensity of herbivore damage is likely to be adaptive due to the lack of phylogenetic signal.

Phylogeny may play an important role in explaining functional traits, but it is also possible that allocation patterns to root biomass are subject to local adaptation (Manschadi et al.

2006). The Quercus species may have similar responses to severe damage that are not under phylogenetic constraints, but instead are convergent adaptive responses to similar selective

110 pressures (Cavender-Bares et al. 2018). Adaptive phenotypic plasticity (sensu Murren et al.

2015) within the genus may allow for similar responses to a given selective pressure, even though the initial responses (seen in the controls, Fig. 15a) are much more diverse. For example,

Q. muehlenbergii and Q. michauxii, which are closely related (Fig. 7), can further explain this point: In the control treatment (Fig 15a), the variation in responses of Q. michauxii is mostly explained by aboveground biomass, whereas the variation in responses of Q. muehlenbergii is mostly explained by belowground biomass. When these species incur 75% removal of the lateral meristem, we see that both species have a similar response (Fig. 15e). We also found a clustering of the closely related Q. laurifolia, Q. palustris, and Q. rubra yet did not find a phylogenetic signal for any responses in the treatments with 75% of lateral tissues removed. Despite phylogenetic constraints on functional traits recorded by other authors in the Quercus genus

(Cavender-Bares et al. 2004, 2018; Hipp et al. 2018), we were unable to find possible phylogenetic patterns, with the exception of possible phylogenetic trait conservatism (sensu

Cavender-Bares et al. 2006; Losos 2008).

Correlations between biomass and root NSC concentrations (Hypothesis 3) We found that there was no phylogenetic trade-off between aboveground biomass regrowth and NSC re- allocation belowground. Hence, we considered effects without accounting for variability due to phylogenetic relatedness. Recent studies have found that herbivore-induced storage of NSC in roots may decrease NSC available for growth aboveground (Dong et al. 2017; 2018). We note that these studies were performed on aquatic herbaceous species so there are limits on the relatability to oaks. However, there is limited knowledge of how NSC storage aboveground is impacted by the re-allocation of NSC to belowground storage. Perkovich and Ward (2021) found

111 that despite increases in NSC concentrations belowground, NSC concentrations aboveground did not decrease. This suggests that oaks are perhaps capable of compensating for NSC re-allocation belowground by increasing photosynthetic capacity (Frost and Hunter 2008; Woolery and Jacobs

2011). Woolery and Jacobs (2011) also found that Quercus rubra (red oak) increased photosynthetic capacity by up to 22% when damaged. Furthermore, we did not find a positive correlation between NSCs re-allocated to root storage and root biomass. Interestingly, when phylogeny was ignored, there was a trade-off (negative correlation) between aboveground biomass regrowth and root-starch concentrations in all damage treatments (r range = -0.57 to

-0.78, all P values < 0.001). This suggests that starch concentrations are increasing belowground with a fitness cost to aboveground regrowth. The re-allocation of NSC above- and belowground is a common occurrence across many plant genera (Smith et al. 2018). Many studies suggest that concentrations of stored NSC in roots fluctuate to keep balance between supply via photosynthesis and demand for primary metabolic functions such as growth and respiration

(Tixier et al. 2020). Starch storage is thought to act as a reservoir for future use during periods of slow growth due to environmental stress (Martínez-Vilalta et al. 2016). This storage is also common at the end of the growing season when carbon assimilation becomes more limited due to decreased photosynthetic capacity (Tixier et al. 2020). Removing plant tissues greatly reduces photosynthetic capacity and may cause prioritization of NSC over growth (Wiley et al. 2017).

CONCLUSIONS

A growing body of evidence suggests that aboveground and belowground processes are intrinsically linked (e.g., Bardgett et al. 2014; van der Putten et al. 2009). Conventional feedback models such as the Masters and Brown model (1997) suggest that there are negative feedbacks

112 between above- and belowground herbivores. However, biomass allocation patterns vary depending on the plant functional type of woody species (Puglielli et al. 2021). In the case with oak species, aboveground herbivory increases NSC re-allocation to root storage and may therefore provide a positive feedback for belowground herbivores. Broad phylogenetic differences exist in carbon re-allocation strategies, but these patterns may become less distinct at lower levels of taxonomic classes. Phylogenetic constraints are less prevalent at the species level

(Agrawal 2020; Agrawal and Hastings 2019). More manipulative experiments are needed at varying levels of organization (i.e., population, genus, species) to obtain a broader understanding of how phylogenetic constraints influence trait responses to herbivore pressures (Agrawal 2020;

Tiffin 2000). Thereafter, we can initiate analyses of the relative importance of phylogeny and adaption on community structure and processes.

113

REFERENCES

Aarssen LW (1995) Hypotheses for the evolution of apical dominance in plants: implications for

the interpretation of overcompensation. Oikos 74:149–156

Aarssen LW, Turkington R (1987) Responses to defoliation in Holcus lanatus, Lolium perenne,

and Trifolium repens from three different aged pastures. Can J Bot 65:1364–1370

Ackerly DD, Donoghue MJ (1995) Phylogeny and ecology reconsidered. J Ecol 83:730–733

Adame MF, Cherian S, Reef R, Stewart-Koster B (2017) Mangrove root biomass and the

uncertainty of belowground carbon estimations. For Eco Manag 403:52–60

Agrawal AA (2000) Mechanisms, ecological consequences and agricultural implications of tri-

trophic interactions. Curr Opin Plant Biol 3:329–335

Agrawal AA (2020) A scale-dependent framework for trade-offs, syndromes, and specialization

in organismal biology. Ecology 101:e02924

Agrawal AA, Hastings AP (2019) Trade-offs constrain the evolution of an inducible defense

within but not between species. Ecology 100:e02857

Aide T (1988) Herbivory as a selective agent on the timing of leaf production in a tropical

understory community. Nature 336:574–575

Ballaré CL, Austin AT (2019) Recalculating growth and defense strategies under competition:

key roles of photoreceptors and jasmonates. J Exp Bot 70:3425–3436

Bardgett RD, Mommer L, De Vries FT (2014) Going underground: root traits as drivers of

ecosystem processes. Trends Ecol Evol 29:692–699

Bardgett RD, van der Putten WH (2014) Belowground biodiversity ad ecosystem functioning.

Nature 515:505–511

114

Bezemer TM, Wagenaar R, van Dam NM, Wäckers FL (2003) Interactions between above- and

belowground insect herbivores as mediated by the plant defense system. Oikos 101: 555–

562

Bezemer TM, van Dam NM (2005) Linking aboveground and belowground interactions via

induced plant defenses. Trends Ecol Evol 20:617–624

Blomberg SP, Garland Jr T, Ives AR (2003) Testing for phylogenetic signal in comparative data:

behavioral traits are more labile. Evolution 57:717–745

Brown VK, Lawton JH (1991) Herbivory and the evolution of leaf size and shape. Philos Trans

R Soc Lond B Biol Sci 333:265–272

Burton AJ, Pregitzer KS, Hendrick RL (2000) Relationships between fine root dynamics and

nitrogen availability in Michigan northern hardwood forests. Oecologia 125:389–399

Canham CD, Kobe RK, Latty EF, Chazdon RL (1999) Interspecific and intraspecific variation in

tree seedling survival: effects of allocation to roots versus carbohydrate reserves.

Oecologia 121:1–11

Cavender-Bares J (2016) Diversity, distribution and ecosystem services of the North American

oaks. Inter Oaks 27:37–48

Cavender-Bares J, Ackerly DD, Baum DA, Bazzaz FA (2004) Phylogenetic overdispersion in

Floridian oak communities. Am Nat 163:823–843

Cavender-Bares J, Keen A, Miles B (2006) Phylogenetic structure of Floridian plant

communities depends on taxonomic and spatial scale. Ecology 87:e109–e122

Cavender-Bares J, Ackerly DD, Hobbie SE, Townsend PA (2016) Evolutionary legacy effects on

ecosystems: biogeographic origins, plant traits, and implications for management in the

era of global change. Annu Rev Ecol Evol Syst 47:433–462

115

Cavender-Bares J, Kothari S, Meirles JE, Kaproth MA, Manos PS, Hipp AL (2018) The role of

diversification in community assembly of the oaks (Quercus L.) across the continental

U.S. Am J Bot 105:565–586

Cipollini D (2004) Stretching the limits of plasticity: can a plant defend against both competitors

and herbivores? Ecology 85:28–37

Cippolini D, Walter D, Voelckel C (2014) Costs of resistance in plants: from theory to evidence.

In Voelckel C, Jander G (eds) Insect–plant interactions. Wiley, New York, pp 263–307

Cline MG (1991) Apical dominance. Bot Rev 57:318–358

Couture JJ, Mason CJ, Habeck CW, Lindroth RL (2016) Behavioral and morphological

responses of an insect herbivore to low nutrient quality are inhibited by plant chemical

defenses. Arthropod Plant Interact 10:341–349

Cusack DF, Chou WW, Yang WH, Harmon ME, Silver WL, Team L (2009) Controls on long-

term root and leaf litter decomposition in neotropical forests. Global Chang Biol

15:1339–1355

Dong BC, Fu T, Luo FL, Yu FH (2017) Herbivory-induced maternal effects on growth and

defense traits in the clonal species Alternanthera philoxeroides. Sci Total Environ

605:114–123

Dong BC, Wang MZ, Liu RH, Luo FL, Li HL, Yu FH (2018) Direct and legacy effects of

herbivory on growth and physiology of a clonal plant. Biol Invasions 20:3631–3645

Eloy C, Fournier M, Lacointe A, Moulia B (2017) Wind loads and competition for light sculpt

trees into self-similar structures. Nat Commun 8:1–12

116

Eyles A, Pinkard EA, Mohammed C (2009) Shifts in biomass and resource allocation following

defoliation in Eucalyptus globulus growing with varying water and nutrient supplies.

Tree Physiol 29:753–764

Felsenstein J (1985) Phylogenies and the comparative method. Am Nat 125:1–15

Fournier E (2001) Colorimetric quantification of carbohydrates. Curr Protoc Food Anal Chem

1:1–8

Frost CJ, Hunter MD (2008) Herbivore-induced shifts in carbon and nitrogen allocation in red

oak seedlings. New Phytol 178:835–845

Gadd ME, Young TP, Palmer TM (2001) Effects of simulated shoot and leaf herbivory on

vegetative growth and plant defense in Acacia drepanolobium. Oikos 92:515–521

Garland Jr T, Harvey PH, Ives AR (1992) Procedures for the analysis of comparative data using

phylogenetically independent contrasts. Syst Biol 41:18–32

Garland Jr T, Bennett AF, Rezende EL (2005) Phylogenetic approaches in comparative

physiology. J Exp Biol 208:3015–3035

Gause GF (1932) Experimental studies on the struggle for existence: l. Mixed population of two

species of yeast. J Exp Biol 9:389–402

Gelman A (2005) Analysis of variance—why it is more important than ever. Ann Stat 33:1–53

Gieger T, Thomas FM (2002) Effects of defoliation and drought stress on biomass partitioning

and water relations of Quercus robur and Quercus petraea. Basic Appl Ecol 3:171–181

Gottschalk KW (1985) Effects of shading and development of northern red oak, black oak,

black cherry, and red maple seedlings. In Dawson JO, Majerus KA (eds) Proceedings of

the Fifth Hardwood Forest Conference. Society of American Foresters. University of

Illinois, Champaign-Urbana, IL, pp 189–195

117

Grafen A (1989) The phylogenetic regression. Philos Trans R Soc Lond B Biol Sci 326:119–157

Grime JP, Jeffrey DW (1965) Seedling establishment in vertical gradients of sunlight. J Ecol

53:621–642

Gruntman M, Novoplansky A (2011) Ontogenetic contingency of tolerance mechanisms in

response to apical damage. Ann Bot 108:965–973

Hector A (2015) The new statistics with R: an introduction for biologists. Oxford University

Press, Oxford

Hipp AL, Eaton DA, Cavender-Bares J, Fitzek E, Nipper R, Manos PS (2014) A framework

phylogeny of the American oak clade based on sequenced RAD data. PloS One 9:e93975

Hipp AL, Manos PS, González-Rodríguez A, Hahn M, Kaproth M, McVay JD, Avalos SV,

Cavender-Bares J (2018) Sympatric parallel diversification of major oak clades in the

Americas and the origins of Mexican species diversity. New Phytol 217:439–452

Hipp AL, Weber JA (2008) Taxonomy of Hill’s Oak (Quercus ellipsoidalis; Fagaceae): evidence

from AFLP data. Syst Biol 33:148–158

Hodge A (2010) Roots: the acquisition of water and nutrients from the heterogeneous soil

environment. Prog Bot 71:307–337

Holland EP, Mugford J, Binny RN, James A (2017) How herbivore browsing strategy affects

whole-plant photosynthetic capacity. Bull Math Biol 79:772–787

Huey RB, Garland Jr T, Turelli M (2019) Revisiting a key innovation in evolutionary biology:

Felsenstein’s “phylogenies and the comparative method”. Am Nat 193:755–772

IBM Corp. (2019) IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp

Iversen CM, McCormack ML, Powell AS, Blackwood CB, Freschet GT, Kattge J, Roumet C,

Stover DB, Soudzilovskaia NA, Valverde-Barrantes OJ, van Bodegam PM, Violle C

118

(2017) A global fine-root ecology database to address below-ground challenges in plant

ecology. New Phytol 215:15–26

Ives AR, Midford PE, Garland Jr T (2007) Within-species variation and measurement error in

phylogenetic comparative methods. Syst Biol 56:252–270

Jackson RB, Manwaring JH, Caldwell MM (1990) Rapid physiological adjustment of roots to

localized soil enrichment. Nature 344:58–60

Johnson NC, Rowland DL, Corkidi L, Allen EB (2008) Plant winners and losers during

grassland N-eutrophication differ in biomass allocation and mycorrhizas. Ecology

89:2868–2878

Karban R, Baldwin IT (2007) Induced responses to herbivory. University of Chicago Press,

Chicago

Kebrom TH (2017) A growing stem inhibits bud outgrowth – the overlooked theory of apical

dominance. Front Plant Sci 8:e1874

Kessler A (2015) The information landscape of plant constitutive and induced secondary

metabolite production. Curr Opin Insect Sci 8:47–53

Kielland K, Bryant JP, Ruess RW (1997) Moose herbivory and carbon turnover of early

successional stands in interior Alaska. Oikos 80:25–30

Kissing L, Powers J (2010) Coarse woody debris stocks as a function of forest type and stand age

in Costa Rican tropical dry forest: long-lasting legacies of previous land use. J Trop Ecol

26:467–471

Kobe RK (1997) Carbohydrate allocation to storage as a basis of interspecific variation in

sapling survivorship and growth. Oikos 80:226–223

119

Kobe RK, Iyer M, Walters MB (2010) Optimal partitioning theory revisited: nonstructural

carbohydrates dominate root mass responses to nitrogen. Ecology 91:166–179

Landhäusser SM, Chow PS, Dickman LT, Furze ME, Kuhlman I, Schmid S, Wiesenbauer J,

Wild B, Gleixner G, Hartmann H, Hoch G, McDowell NG, Richardson AD, Richter A,

Adams HD (2018) Standardized protocols and procedures can precisely and accurately

quantify non-structural carbohydrates. Tree Physiol 38:1764–1778

Legendre P, Oksanen J, ter Braak CJ (2011) Testing the significance of canonical axes in

redundancy analysis. Methods Ecol Evol 2:269–277

Leόn-Sánchez L, Nicolás E, Prieto I, Nortes P, Maestro FT, Querejeta JI (2020) Altered leaf

elemental composition with climate change is linked to reductions in photosynthesis,

growth and survival in a semi-arid shrubland. J Ecol 108:47–60

Lexer C, Kremer A, Petit RJ (2006) Shared alleles in sympatric oaks: recurrent gene flow is a

more parsimonious explanation than ancestral polymorphism. Mol Ecol 15:2007–2012

Losos JB (1999) Uncertainty in the reconstruction of ancestral character states and limitations on

the use of phylogenetic comparative methods. Anim Behav 58:1319–1324

Losos JB (2008) Phylogenetic niche conservatism, phylogenetic signal and the relationship

between phylogenetic relatedness and ecological similarity among species. Ecol Lett

11:995–1003

Luostarinen K, Kauppi A (2005) Effects of coppicing on the root and stump carbohydrate

dynamics of birch. New For 29:289–303

Magal P, Zhang Z (2017) Competition for light in forest population dynamics: from computer

simulator to mathematical model. J Theor Biol 419:290–304

120

Manschadi AM, Christopher J, deVoil P, Hammer G (2006) The role of root architectural traits

in adaptation of wheat to water-limited environments. Funct Plant Biol 33:823–837

Manos PS, Stanford AM (2001) The historical biogeography of Fagaceae: Tracking the tertiary

history of temperate and subtropical forests in the Northern Hemisphere. Int J Plant Sci

162:e77–e93

Maron JL, Agrawal AA, Schemske DW (2019) Plant–herbivore coevolution and plant

speciation. Ecology 100:e02704

Martínez-Vilalta J, Sala A, Asensio D, Galiano L, Hoch G, Palacio S, Piper FI, Loret FL (2016)

Dynamics of non-structural carbohydrates in terrestrial plants: a global synthesis. Ecol

Monogr 86:495–516

Masters CJ, Brown VK (1997) Host-plant mediated interactions between spatially separated

herbivores: effects of community structure. In Gange AC, Brown VK (eds) Multitrophic

interactions in terrestrial systems – 36th symposium of the British Ecological Society.

Blackwell, Oxford, pp 217–237

McCormack ML, Dickie IA, Eissenstat DM, Fahey TJ, Fernandez CW, Guo D, Helmisaari HS,

Zadworny M (2015) Redefining fine roots improves understanding of below-ground

contributions to terrestrial biosphere processes. New Phytol 207:505–518

McCormack ML, Guo D, Iversen CM, Chen W, Eissenstat DM, Fernandez CW, Li L, Ma C, Ma

Z, Poorter H, Reich PB, Zadworny M, Zanne A (2017) Building a better foundation:

improving root-trait measurements to understand and model plant and ecosystem

processes. New Phytol 215:27–37

McCormack ML, Guo D (2014) Impacts of environmental factors on fine root lifespan. Front

Plant Sci 5:e205

121

McNaughton SJ (1983) Compensatory plant growth as a response to herbivory. Oikos 40:329–

336

McNickle GG, Evans WD (2018) Toleration games: compensatory growth by plants in response

to enemy attack is an evolutionarily stable strategy. Ann Bot Plants 10:e035

McVay JD, Hipp AL, Manos PS (2017) A genetic legacy of introgression confounds phylogeny

and biogeography in oaks. Proc R Soc Lond B 284:e20170300

Metcalf C (2016) Invisible trade-offs: van Noordwijk and de Jong and life-history evolution. Am

Nat 187:3–5

Millard P, Hester A, Wendler R, Baillie G (2001) Interspecific defoliation responses of trees

depend on the sites of winter nitrogen storage. Funct Ecol 15:535–543

Mooney HA, Gulmon, SL (1983) Constraints on leaf structure and function in reference to

herbivory. BioScience 32:198–206

Moran EV, Willis J, Clark JS (2012) Genetic evidence for hybridization in red oaks (Quercus

sect. Lobatae, Fagaceae). Am J Bot 99:92–100

Münkemüller T, Lavergne S, Bzeznik B, Dray S, Jombart T, Schiffers K, Thuiller W (2012)

How to measure phylogenetic signal. Methods Ecol Evol 3:743–756

Murren CJ, Auld JR, Callahan H, Ghalambor CK, Handelsman CA, Heskel MA, Kingslover JG,

Schlichting CD (2015) Constraints on the evolution of phenotypic plasticity: limits and

costs of phenotype and plasticity. Heredity 115:293–301

Myers JA, Kitjima K (2007) Carbohydrate storage enhances seedling shade and stress tolerance

in neotropical forest. J Ecol 95:383–395

Nabity PD, Zavala JA, DeLucia EH (2009) Indirect suppression of photosynthesis on individual

leaves by arthropod herbivory. Ann Bot 103:655–663

122

Nippert JB, Wieme RA, Ocheltree TW, Craine JM (2012) Root characteristics of C4 grasses limit

reliance on deep soil water in tallgrass prairie. Plant Soil 355:385–394

Oksanen J, Blanchet F, Kindt R, Legendre P, Minchin PR, O’hara RB, Simpson P, Solymos MH,

Stevens H, Wagner H (2019) Vegan: Community Ecology Package. R package version

2.5–6. https://CRAN.R-project.org/package=vegan

Orians CM, Thorn A, Gόmez S (2011) Herbivore-induced resource sequestration in plants: why

bother? Oecologia 167:1–9

Palacio S, Paterson E, Hester A, Nogués S, Lino G, Anadon-Rosell A, Maestro M, Millard P

(2020) No preferential carbon-allocation to storage over growth in clipped birch and oak

saplings. Tree Physiol 40:621–636

Pangesti N, Pineda A, Dicke M, van Loon JJ (2014) Variation in plant-mediated interactions

between rhizobacteria and caterpillars: potential role of soil composition. Plant Biol

17:474–481

Papadopoulou GV, van Dam NM (2017) Mechanisms and ecological implications of plant-

mediated interactions between belowground and aboveground insect herbivores. Ecol Res

32:13–26

Paula S, Pausas JG (2011) Root traits explain different foraging strategies between resprouting

life histories. Oecologia 165:321–331

Pearse IS, Hipp AL (2012) Global patterns of leaf defenses in oak species. Evolution 66:2272–

2286

Peig J, Green AJ (2009) New perspectives for estimating body condition from mass/length data:

the scaled mass index as an alternative method. Oikos 118:1883–1891

123

Perkovich C, Ward D (2021) Herbivore-induced defenses are not under phylogenetic constraints

in the genus Quercus (oak): phylogenetic patterns of growth, defense, and storage. Ecol

Evol in press

Petit RJ, Carlson J, Curtu AL, Loustau ML, Plomion C, González-Rodríguez A, Sork V,

Ducousso A (2013) Fagaceae trees as models to integrate ecology, evolution and

genomics. New Phytol 197:369–371

Phillips AG, Töpfer T, Rahbek C, Böhning-Gaese K, Fritz SA (2018) Effects of phylogeny and

geography on ecomorphological traits in passerine bird clades. J Biogeogr 45:2337–2347

Pinkard EA, Beadle CL (1998) Regulation of photosynthesis in Eucalyptus nitens (Deane and

Maiden) Maiden following green pruning. Trees Struct Funct 12:366–376

Pregitzer KS, DeForest JL, Burton AJ, Allen MF, Ruess RW, Hendrick RL (2002) Fine root

architecture of nine North American trees. Ecol Monogr 72:293–309

Puglielli G, Laanisto L, Poorter H, Niinemets Ü (2021) Global patterns of biomass allocation in

woody species with different tolerance of shade and drought. New Phytol 229:308–322

Quentin AG, Pinkard EA, Ryan MG, Tissue DT, Baggett LS, Adams HD, Maillard P, Woodruff

DR (2015) Non-structural carbohydrates in woody plants compared among laboratories.

Tree Physiol 11:1146–1165

R Development Core Team. (2019) R: a language and environment for statistical computing. R

foundation for statistical computing, Vienna, Austria. URL https:///www.R.project.org/

Ramirez KS, Geisen S, Morriën E, Snoek BL, van der Putten WH (2018) Network analyses can

advance above–belowground ecology. Trends Plant Sci 23:759–768

Revell LJ (2009) Size-correction and principal components for comparative studies. Evolution

63:6258–3268

124

Revell LJ (2012) Phytools: An R package for phylogenetic comparative biology (and other

things). Methods Ecol Evol 3:217–223

Ruess RW, Hendrick RL, Bryant JP (1998) Regulation of fine root dynamics by mammalian

browsers in early successional Alaskan taiga forests. Ecology 79:2706–2720

Rusman Q, Lucas-Barbosa D, Hassan K, Poelman EH (2020) Plant ontogeny determines strength

and associated plant fitness consequences of plant mediated interactions between

herbivores and flower visitors. J Ecol 108:1046–1060

Saikkonen K, Ahonen-Jonnarth U, Markkola AM, Helander M, Tuomi J, Riotto M, Ranta H

(1999) Defoliation and mycorrhizal symbiosis: a functional balance between carbon

sources and below-ground sinks. Ecol Lett 2:19–26

Sargent RD, Goodwillie C, Kalisz S, Ree RH (2007) Phylogenetic evidence for a flower size and

number trade-off. Am J Bot 94:2059–2062

Schulte-Hostedde AI, Zinner B, Millar JS, Hickling GJ (2005) Restitution of mass-size residuals:

validating body condition indices. Ecology 86:155–163

Schuman MC, Meldau S, Gaquerel E, Diezel C, McGale E, Greenfield S, Baldwin IT (2018) The

active jasmonates JA-lle regulates a specific subset of plant jasmonates-mediated

resistance to herbivores in nature. Front Plant Sci 9:e787

Shikano I, Rosa C, Tan CW, Felton GW (2017) Tritrophic interactions: microbe-mediated plant

effects on insect herbivores. Annu Rev Phytopathol 55:313–331

Silver WL, Miya RK (2001) Global patterns in root decomposition, comparisons of climate and

litter quality effects. Oecologia 129:407–419

Sipe TW, Bazzaz FA (1994) Gap partitioning among maples (Acer) in central New England:

shoot architecture and photosynthesis. Ecology 75:2318–2332

125

Smith MG, Miller RE, Arndt SK, Kasel S, Bennett LT (2018) Whole-tree distribution and

temporal variation of non-structural carbohydrates in broadleaf evergreen trees. Tree

Physiol 38:570–581

Symonds MR, Blomberg SP (2014) A primer on phylogenetic generalized least squares. In

Garamszegi L (ed) Modern phylogenetic comparative methods and their application in

evolutionary biology. Springer, Berlin, pp 105–130 ter Braak CJF (1986) Canonical correspondence analysis: a new eigenvalue technique for

multivariate direct gradient analysis. Ecology 67:1167–1179

Tiffin P (2000) Mechanisms of tolerance to herbivore damage: what do we know? Evol Ecol 14:

523–536

Tixier A, Guzmán-Delgado P, Sperling O, Roxas AA, Laca E, Zwieniecki MA (2020)

Comparison of phenological traits, growth patterns, and seasonal dynamics of non-

structural carbohydrate in Mediterranean tree crop species. Sci Rep 10:1–11

Tomlinson KW, van Langevelde F, Ward D, Bongers F, Da Silva DA, Prins HHT, De Bie S,

Sterck FJ (2013) Deciduous and evergreen trees differ in juvenile biomass allometries

because of differences in allocation to root storage. Ann Bot 112:575–587

Tomlinson KW, van Langevelde F, Ward D, Prins HH, de Bie S, Vosman B, Sampaio EV,

Sterck FJ (2016) Defence against vertebrate herbivores trades off into architectural and

low nutrient strategies amongst savanna Fabaceae species. Oikos 125:126–136

Trehane P (2007) The Oak Names Checklist. URL http://wwww.oaknames.org. Accessed 13

October 2016.

Tuomi J, Niemelä P, Haukioja E, Sirén S, Neuvonen S (1984) Nutrient stress: an explanation for

plant anti-herbivore responses to defoliation. Oecologia 61:208–210

126

Valverde-Barrantes OJ, Smemo KA, Feinstein LM, Kershner MW, Blackwood CB (2013) The

distribution of below-ground traits is explained by intrinsic species differences and

intraspecific plasticity in response to root neighbors. J Ecol 101:933–942

Valverde-Barrantes OJ, Smemo KA, Blackwood CB (2015) Fine root morphology is

phylogenetically structured, but nitrogen is related to the plant economic spectrum in

temperate trees. Funct Ecol 29:796–807

Valverde-Barrantes OJ, Freschet GT, Roumet C, Blackwood CB (2017) A worldview of root

traits: the influence of ancestry, growth form, climate and mycorrhizal association on the

functional trait variation of fine-root tissues in seed plants. New Phytol 215:1562–1573 van Dam NM, Heil M (2011) Multitrophic interactions below and above ground: en route to the

next level. J Ecol 99:77–88 van der Putten WH, Vet LE, Harvey JA, Wäckers FL (2001) Linking above- and belowground

multitrophic interactions of plants, herbivores, pathogens, and their antagonists. Trends

Ecol Evol 16:547–554 van Noordwijk AJ, de Jong G 1986 Acquisition and allocation of resources: their influence on

variation in life history tactics. Am Nat 128:137–142

War AR, Taggar GK, Taggar MS, Nair RM, Sharma HC (2018) Plant defence against herbivory

and insect adaptations. Ann Bot 10:e037

Ward D (2010) The effects of apical meristem damage on growth and defences of two Acacia

species in the Negev Desert. Evol Ecol Res 12:589–602

Ward D, Spiegel M, Saltz D (1997) Gazelle herbivory and interpopulation differences in calcium

oxalate content of leaves of a desert lily. J Chem Ecol 23:333–346

127

Ward D, Schmitt MH, Shrader AM (2020) Are there phylogenetic differences in salivary tannin‐

binding proteins between browsers and grazers, and ruminants and hindgut fermenters?

Ecol Evol 10:10426–10439

Wei H, He M, Lu X, Ding J (2016) Differences in interactions aboveground and belowground

herbivores on the invasive plant Alternanthera philoxeroides and native host A. sessilis.

Biol Invasions 18:3437–3447

Wells CE, Eissenstat DM (2003) Beyond the roots of young seedlings: the influence of age and

order on fine root physiology. J Plant Growth Regul 21:324–334

Whitlock MC, Schluter D (2009) The analysis of biological data. Roberts and Company

Publishers, Greenwood Village, CO

Whittemore AT, Schaal BA (1991) Interspecific gene flow in sympatric oaks. Proc Nat Acad Sci

USA 88:2540–2544

Wiley E, Casper BB, Helliker BR (2017) Recovery following defoliation involves shifts in

allocation that favour storage and reproduction over radial growth in black oak. J Ecol

105:412–424

Woolery PO, Jacobs DF (2011) Photosynthetic assimilation and carbohydrate allocation of

Quercus rubra seedlings in response to herbivory. Ann For Sci 68:617–624

Wu X, Ding C, Baerson SR, Lian F, Lin X, Zhang L, Wu C, Hwang SY, Zeng R, Song Y (2018)

The roles of jasmonate signalling in nitrogen uptake and allocation in rice (Oryza sativa

L.). Plant Cell Environ 42:659–672

Xie J, Shi H, Du Z, Wang T, Liu X, Chen S (2016) Comparative genomic and functional analysis

reveal conservation of plant growth promoting traits in Paenibacillus polymyxa and its

closely related species. Sci Rep 6:1–12

128

Zavala JA, Ravetta DA (2001) Allocation of photoassimilates to biomass, resin and

carbohydrates in Grindelia chileonsis as affected by light intensity. Field Crops Res

69:143–149

Zhang Z, Gong J, Wang B, Li X, Ding Y, Yang B, Zhu C, Liu M, Zhang W (2020) Regrowth

strategies of Leymus chinensis in response to different grazing intensities. Ecol Appl

30:e02113

Züst T, Agrawal AA (2017) Trade-offs between plant growth and defense against insect

herbivory: an emerging mechanistic synthesis. Annu Rev Plant Biol 68:513–534

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CHAPTER IV

DIFFERENTIATED DEFENSE SYNDROMES IN RESPONSE TO VARYING HERBIVORE

PRESSURES: OAK TREES INCREASE DEFENSES IN RESPONSE TO INSECTS AND

DECREASE NUTRITIVE QUALITY IN RESPONSE TO DEER

ABSTRACT

Plant defensive syndromes against herbivory include nutritional and antinutritional qualities.

Plants may alter defense expression dependent on the specific herbivore present. Many studies focus on the optimization of these defenses against a single herbivore but in an ecological context, there may be a myriad of different herbivores at any given time. We designed a study to examine defenses employed against insects and large mammals by swamp white oak (Quercus bicolor). We established six deer exclosures with trees located inside and outside of each exclosure. Outside the exclosures, physical constraints only allow a deer to feed up to a browseline on trees, whereas insects can access all portions of the tree. Trees inside exclosures were only accessible to insect herbivores. Furthermore, insecticides were applied to half of the exclosures to create insect-free (i.e., insecticide applied), deer-free environments (i.e., inside exclosures), deer-only (i.e., outside exclosures with insecticide) and herbivore-free environments

(i.e., inside exclosures with insecticide). This design allowed us to analyze simultaneous defensive responses of individual trees to mammal and insect herbivores. Plant defense strategies varied spatially on individual trees. When only insects were present (i.e., above the browseline),

130 nutritional constituents did not change but antinutritional constituents increased. Contrastingly, when deer were present, both nutritional and antinutritional constituents decreased. Our findings suggest that trees may partition defense syndromes depending on the most prominent herbivore pressure. Understanding plant defense syndromes under multiple herbivore pressures provides a more accurate representation of optimization and evolutionary drivers of plant defense syndromes.

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INTRODUCTION

Trees maintain an arsenal of defenses against herbivores (Agrawal 2020; Erb 2018; Maron et al.

2019). This arsenal includes, but is not limited to, chemical defense production (Mithöfer and

Boland 2012; Sánchez-Sánchez and Morquecho-Contreras 2017), changing the nutritive quality of foliage (Castagneyrol et al. 2018; Haukioja et al. 1991; Lundberg and Åström 1989) and re- allocation of nutrients (Pérez-de-Lis et al. 2016; Wiley et al. 2017). Defense production is often energetically costly (Coley 1987a, b; Coley et al. 1985; Endara and Coley 2011); therefore, trees may employ a combination of defenses to optimize fitness and balance metabolic costs dependent on specific herbivore pressures (Agrawal and Fishbein 2006; Benevenuto et al. 2020;

Fornoni et al. 2004). Although it is more convenient to only analyze a single trait, plants often utilize many defensive traits in unison in what has been termed a “plant defense syndrome”

(Duffey and Stout 1996).

There may be differentiation of defense syndromes used by trees that is dependent on the individual herbivore (i.e., mammalian vs. insect herbivores). Large mammalian herbivores, such as white-tailed deer (Odocoileus virginianus), typically consume vegetation on the lower parts of a tree up to a maximum height known as the “browseline” (typically about 1.5 – 2 m; Hoffman and Stewart 1972; Nopp-Mayr et al. 2020). Deer represent a random herbivore threat that, due to their size, may cause a relatively large reduction of photosynthetic tissues. When a deer forages, trees may employ induced defenses, which are upregulated quickly, to deter immediate herbivore threats (Baldwin 1998; Baldwin and Karban 1997; Benevenuto et al. 2018). Investment in induced-defense production is considered expensive and must therefore be used in direct response to an herbivore threat (Kessler and Chautá 2020; Orrock et al. 2018). Contrastingly, insects can potentially exploit every plant tissue and, due to their diversity and abundance, are

132 most always present. Trees may invest in constitutive chemical defenses (defenses that are always present) due to the risk of damage by insect herbivores to any given tissue at any given time (Lill and Marquis 2003). Such constitutive defenses are not considered expensive to produce (Erb 2018; Stamp 2003). An optimal defense strategy predicts that trees may allocate resources to both constitutive and induced chemical production in a way that optimizes fitness

(Adler and Karban 1994; Agrawal and Hastings 2019; Agrawal and Karban 1999). Supporting evidence for an optimal defense strategy focuses on plant defenses against a single type of herbivore (e.g., Agrawal and Fishbein 2006; da Silva and Batalha 2011). However, trees are susceptible to simultaneous attacks from multiple herbivores at any given time (Iwao and

Rausher 1997; Kim 2017) and may differentiate defense syndromes accordingly.

Chemical defense production is only one example of defensive strategies. Another possible defense syndrome that may minimize herbivory is a reduction in the nutritive quality of foliar tissues (Castagneyrol et al. 2018; Haukioja et al. 1991;Lundberg and Åström 1989). The notion of reducing nutritive quality as a defense against mammalian herbivores is well supported

(Lundberg and Åström 1989; Wigley et al. 2018). We note that reducing the nutrients lost per unit of biomass consumed does not necessarily reduce the amount of biomass lost. For example, reducing nutrient quality may be an effective defense against mammalian herbivores that are limited by gut fill (Demment and van Soest 1985; Müller et al. 2013) but insects may increase consumption rates to compensate for nutritional deficiencies (Perkovich and Ward 2020).

In some plant species, herbivory induces re-allocation of NSC to storage in roots (Orians et al. 2011; Perkovich and Ward 2021; Wiley et al. 2017). Re-allocating non-structural carbohydrates to storage is thought to be an adaptive trait that acts as a buffer to fluctuating environmental factors such as herbivory (Kobe et al. 2010; Paula and Pausas 2011).

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Competition between herbivores Several studies have shown the critical damage of foraging deer to trees (e.g., Begley-Miller and Cady 2015; Suzuki et al. 2012). Deer foraging often simplifies or entirely removes vegetative structures so that insects that feed on foliar tissues may be negatively affected by deer herbivory (Rooney and Waller 2003; Stewart 2001). Furthermore, competition among herbivores may result in resource partitioning among them (Davis et al.

2018; Xi et al. 2017), further increasing the need for trees to differentiate between defensive syndromes above- and below the browseline (Rooney and Waller 2003). Nonetheless, there is little evidence that we are aware of that demonstrates alterations in defense syndromes within an individual tree under herbivore pressures by different types of organisms (but see Dicke et al.

2009; Ward and Young 2002).

We designed an experiment to examine the effects of simultaneous insect and mammal herbivory on an individual plant. We used swamp white oak (Quercus bicolor) as our model plant because oak defense syndromes by insects and deer are well understood (Karban and

Myers 1989; Moreira et al. 2020; Pearse and Hipp 2012). We set up exclosures to exclude mammalian herbivores. We grew trees on both the inside and the outside of these exclosures.

Insecticide was applied to half the trees to exclude insect herbivores. We hypothesized that:

1. There should be greater defenses and reduced nutritional quality below the browseline.

2. Inside the exclosures, biomass of trees should be larger than outside the exclosures and

there should be a reduction in defenses below the browseline inside the exclosures.

3. Root non-structural carbohydrate concentrations (NSC) should be greater in trees located

outside the deer exclosure where insects and deer have access than in trees located inside

the exclosures.

4. Insecticide-treated trees should have a reduction in defenses.

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METHODS

Field site We built six deer exclosures in May 2017 at Liberty Park in Summit County, Ohio,

United States. Each exclosure is 5 m x 5 m in area. We used a deer-exclusion fence

(ALLFENZ®) with a 2 cm mesh. The mesh style is sufficient to keep large herbivores out, while not too dense to cause shading. The fences’ perimeters were checked monthly to ensure no damage occurred. The height of the fences is 2 m, which is high enough to keep out deer and other common large herbivores in the area (Palmer et al. 1985; VerCauteren et al. 2006). Each exclosure had four swamp white oak (Quercus bicolor) saplings (oaks were approximately 2 m tall) planted in each of the four corners of the exclosure and four on the outside of each exclosure, one along each side of the fence (see Fig. 16 for details). Insecticides were applied to three of the six exclosures, on trees both inside and outside of the exclosure. We used two types of insecticide with different modes of action: one provided a systemic form of control

(Spectracide® Triazide®) and the other controls pests on contact (Sevin®). Both insecticides were applied according to their manufacturer’s recommendations at appropriate time intervals

(Spectracide® Triazide® bi-monthly; Sevin® every three months).

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Figure 16. Plot of deer exclosure experimental design. The deer exclosures measured 5 m x 5 m and were

2 m tall. Each exclosure had eight swamp white oaks (Quercus bicolor); four located outside the exclosure with one along each side of the fence and four located inside the exclosure with one in each corner. These exclosures were set up in Liberty Park, Summit County Metroparks, OH, USA.

Insect damage and phytochemical analyses Plant tissue samples were collected in September of each year, starting in 2017. From each tree, we sampled five leaves above the browseline, five leaves below the browseline, and a portion of root material (samples were about 10 cm in length and of coarse-root material that was 2.5–3 mm in diameter for standardization). From the leaves sampled, we first measured the amount of insect damage on each leaf. We used a CI-202 portable laser leaf area meter (CID BioScience) to scan the area of the leaf. This leaf area meter measured leaf area that excluded the holes and punctures removed by insect feeding. We then placed tape over the damaged areas and re-scanned the leaf to calculate the total area of the leaf as if no tissue had been removed. We calculated % damage as the damaged leaf area subtracted

136 from the total leaf area multiplied by 100. We then dried the leaves for 48 h at 65 °C. The dried leaf material was ground using a 2 mm mesh in a Wiley mill.

We measured non-structural carbohydrates with a modified method that uses an acid to dissolve the sugars and starches (Fournier 2001; Tomlinson et al. 2013). To ensure consistency of NSC measurements, these analyses were all done in a single laboratory (Landhäusser et al.

2018). Leaf nitrogen content was measured on a Rapid N® Exceed nitrogen analyzer. Polyphenol and tannin analyses were performed according to standard protocols by Hagerman (2011). We extracted polyphenols and tannins with a 70% acetone solvent (Graca and Barlocher 2005;

Hagerman 2011). Total polyphenol concentrations were analyzed using a modification to the

Prussian Blue assay (Hagerman 2011; Price and Butler 1977), using gallic acid as a standard.

Total tannin concentrations were analyzed using a Radial Diffusion Assay, standardized against tannic acid (Hagerman 1987; 2011). Finally, condensed tannin concentrations were analyzed using the Acid Butanol Assay for proanthocyanidins, standardized against quebracho tannin

(Gessner and Steiner 2005; Hagerman 2011). Note that there is no standard unit for measuring polyphenols and tannins, so equivalents are used (Hagerman 2011). Root samples were dried and also run through the analyses described above.

Biomass and relative growth rate measurements At the end of the experiment in September

2020 (i.e., after four years), tree sapling height was measured, and each sapling was harvested in its entirety. We calculated sapling (non-reproductive) relative growth rate (RGR) using the formula below:

ln⁡(푊 ) − ln⁡(푊 ) 푅퐺푅 = 2⁡ 1 푡2 − 푡1

137 where W1 and W2 are a measurement of the plant’s height or auxiliary growth at times t1and t2.

RGR calculations minimize bias caused by variance in initial measurements of plant size

(Hoffmann and Poorter 2002; Rees et al. 2010).

We separated below-ground biomass (i.e., roots) from above-ground biomass. We further separated above-ground biomass into two categories: 1) biomass below the browseline (i.e., biomass below 2 m) from 2) biomass above the browseline (i.e., biomass above 2 m). The constituents of the three biomass categories (below-ground, below browseline, and above browseline) were dried and weighed.

Diversity of insect herbivores (foliar and below-ground) For foliar insect-herbivores, which may have substantial mobility, we determined the dominant functional group on each tree, both above and below the browseline immediately before harvesting. We did not identify the individual insect, but instead classified functional group dominance based on leaf scars left behind. For example, if a leaf had heavy tissue damage from chewing insects and slight tissue damage from sucking insects, we classified the dominant functional group as chewing.

Functional group assignments often vary among researchers (Hawkins and MacMahon 1989;

Novotny et al. 2010). For foliar insect-herbivores, we categorized the functional group based on behavioral traits that affect the plant directly (see Kristensen et al. 2020; Peeters 2002) using the following classifications:

1. Chewing – feeding occurred with no avoidance of leaf veins (a, Fig. 17).

2. Gall-making – insect causes a gall to form on leaf (b, Fig. 17).

3. Mining – feeding occurs within the upper and lower cuticle of a leaf (c, Fig. 17).

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4. Netting – feeding avoided primary and secondary leaf veins (d, Fig. 17).

5. Skeletonizing – feeding occurs around main veins of leaf (e, Fig. 17).

6. Sucking – insect pierces leaf cuticle with stylet to suck up liquid (f, Fig. 17).

7. None – no feeding occurred on the leaf (not illustrated).

d c e

b f

Figure 17. Scar characteristics and definitions of the insect functional group the scars represent. a)

Chewing – feeding occurred with no avoidance of leaf veins; b) Gall-making – insect causes a gall to form on leaf; c) Mining – feeding occurs within the upper and lower cuticle of a leaf; d) Netting – feeding avoided primary and secondary leaf veins; e) Skeletonizing – feeding occurs around main veins of leaf; f)

Sucking – insect pierces leaf cuticle with stylet to suck up liquid. Insect functional group classifications are similar to classifications from Kristensen et al. (2020).

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For root insect-herbivores, we removed all soil from a 1 m diameter around the roots for each tree at the conclusion of the experiment. The soil was brought back to the lab and sifted using a sieve with 2 mm mesh. Insects were collected and identified to genus.

Statistical analyses Our independent variables were position relative to browseline (foliage above or below), tree position (located inside or outside of the deer exclosure), insecticide

(applied or not applied (control)), and year. We simultaneously analyzed multiple dependent variables (i.e., foliar phytochemistry, root phytochemistry, the ratios of the beneficial constituents (nitrogen): anti-nutritional components (polyphenol, tannin, condensed tannin), % leaf damage by insects, biomass, and relative growth rates) using a multivariate analysis of variance (MANOVA) to minimize Type I statistical error. We controlled for variation by treating exclosures as separate blocks (i.e., we treated plots as a random factor) to assure that the plot that the tree was in did not play a role in the outcome. For variables that were statistically significant, we then ran a univariate analysis of variance (ANOVA) followed by Scheffé post hoc tests in the above-mentioned MANOVA. Statistical analyses were run in SPSS version 26 (IBM 2019).

We analyzed variation in the diversity of insect functional group on foliar tissues in terms of the following parameters: position relative to browseline (above vs. below), tree position

(inside vs. outside the exclosure), and insecticide (applied vs. not applied (control)). β diversity is generally used for finding differences in the diversity of species across gradients (Legendre

2019; Legendre and De Carceres 2013; Legendre and De Condit 2019). Here we used

Whittaker’s β diversity to find differences in functional groups between treatments and treatment levels. We also used Whittaker’s β diversity to test for differences in root herbivores between treatments and treatment levels. For below-ground diversity, we used an analysis with the same

140 parameters, excluding position relative to browseline as this parameter is not applicable to below-ground herbivores. For both foliar and below-ground diversity analyses, the data were normalized using the decostand function in the vegan package (Oksanen et al. 2020) in R statistical software (R Development Core Team 2019). We used grouping variables (listed above) with replicated data to compare diversity indices using bootstrap resampling. To examine the effects of foliar herbivory, we selected a dominant functional group based on its frequency on each tree for those above the browseline and those below the browseline. We selected five leaves above and five leaves below the browseline from each of the 48 trees to determine functional abundance. To examine the effects of belowground herbivory, we identified insects present on each root ball from trees inside and outside the exclosure (n = 48 trees). We used 999 iterations for both analyses. We used the simboot (Scherer and Pallmann 2017) and vegan (Oksanen et al.

2020) packages in R. For parameters with significant differences between groups, we used the vegan package in R (Oksanen et al. 2020) to run a Multi-Response Permutation Procedure

(MRPP). This test is similar to analysis of variance because it compares dissimilarity between and within groups (Gardener 2014) but uses a resampling technique (default = 999 iterations) to calculate p values. For the diversity analyses of both foliar and below-ground insect-herbivores, we used a Euclidean metric to create the dissimilarity matrix and performed the MRPP using

Whittaker’s β diversity (Oksanen et al. 2020).

RESULTS

Significant multivariate effects were found for the majority of independent variables and their interactions (Table 9). Subsequently, we used univariate ANOVA to analyze the significance of the significant dependent variables.

141

Table 9. Multivariate analysis of variance (MANOVA) results of growth and phytochemical responses by Quercus bicolor to differentiated herbivore types. * Significant values are shown in bold. Variables include browseline (position of foliage above or below the browseline), position

(tree position inside vs. outside the exclosure), insecticide (applied vs. not applied), and year.

Treatments Wilk’s λ F P

Browseline 0.490 39.50 < 0.001

Position 0.440 48.39 < 0.001

Insecticide 0.577 27.85 < 0.001

Year 0.655 5.78 < 0.001

Browseline X Position 0.535 33.06 < 0.001

Browseline X Insecticide 0.977 0.89 0.519

Browseline X Year 0.819 2.63 < 0.001

Position X Insecticide 0.558 30.15 < 0.001

Position X Year 0.726 4.28 < 0.001

Insecticide X Year 0.743 3.97 < 0.001

Browseline X Position X Insecticide 1.000 282.00 0.516

Browseline X Position X Year 1.000 773.36 < 0.001

Browseline X Insecticide X Year 1.000 282.00 0.076

Position X Insecticide X Year 1.000 282.00 0.322

Browseline X Position X Insecticide X Year 1.000 282.00 0.487

Block effect 0.215 1.84 0.011

Tree biomass and growth Below-ground biomass was significantly lower in trees located outside of the deer exclosure (F = 6.98, P = 0.012). Tree relative growth rate (RGR) was not

142 affected by placement of the trees inside or outside of the exclosures (F = 0.80, P = 0.377).

Insecticide application did not significantly affect tree biomass or growth (F = 2.43, P = 0.05).

Location of the deer exclosure (block effect) had a significant effect on above-ground and belowground biomass (F = 5.05, P = 0.001, and F = 6.23, P < 0.001, respectively).

Nutritional quality of foliage: nitrogen and non-structural carbohydrates At the start of the experiment, there was no significant difference in nitrogen concentration above or below the browseline (Fig. 18a). One year later, foliar nitrogen was significantly lower in foliage below the browseline (F = 106.14, P < 0.001, Fig. 18a). In trees located outside the exclosure where deer could access them, nitrogen was lower compared to trees inside the exclosure (F = 9.35, P <

0.001, Fig. 18b). Insecticide application did not cause a significant change in nitrogen content of trees (F = 0.46, P = 0.498). Non-structural carbohydrates were not significantly different across treatments (all values P > 0.05).

143

a a a a a a

b b b

Above browseline

Below browseline F = 9.35, P < 0.001

b a a a a a

b b b

Outside exclosure

Inside exclosure

F = 17.09, P < 0.001

Figure 18. Patterns of changes in foliar nitrogen concentrations of Quercus bicolor in response to differentiated herbivore type. Foliar nitrogen concentrations were compared in a) foliar tissues above and

144 below the browseline and b) tissues from trees inside vs. outside of the deer exclosures starting in 2017 when the exclosures were set up in Liberty Park, Summit County Metroparks, OH, USA. Significant differences are indicated by differences in the letters above the error bars on the graphs (Scheffé post hoc tests).

Nutritional quality of foliage: anti-nutritional constituents At the start of the experiment, anti-nutritional constituents showed no differentiation (P value range = 0.19–0.387). However, anti-nutritional constituents decreased in foliage below the browseline compared to above the browseline in subsequent years (polyphenol concentration: F = 18.45, P < 0.001, Fig. 19a; total tannin concentration (radial diffusion assay): F = 2.69, P = 0.046, Fig. 19b). Overall, condensed tannin concentrations (acid butanol assay) were significantly lower in foliage below the browseline (F = 179.97, P < 0.001), and outside the exclosures (F = 288.46, P < 0.001). Trees without insecticide applications had significantly higher concentrations of polyphenols (F =

10.69, P = 0.001) and total tannins (F = 31.94, P < 0.001) than trees with insecticide applications.

145

a Above browseline Below browseline a

a a a a

b b

F = 2.69, P = 0.046

b a a a a a

b b

b Above browseline

Below browseline

F = 18.45, P < 0.001

Figure 19. Changes in foliar polyphenol and total tannin concentrations of Quercus bicolor in response to differentiated herbivore type. a) Polyphenol concentrations (G.A.E. = gallic acid equivalents) and b) total

146 tannin concentrations (T.A.E. = tannic acid equivalents) were collected above and below the browseline from foliar tissues inside and outside of deer exclosures set up in Liberty Park, Summit County

Metroparks, OH, USA. Significant differences are indicated by differences in the letters above the error bars on the graphs (Scheffé post hoc tests).

Nutritional quality of foliage: ratio of nitrogen to anti-nutritional constituents The ratio of nitrogen: total tannin concentration was greater in trees outside the exclosure with insecticide applied (i.e., no herbivory, F = 21.12, P < 0.001, Fig. 20a). The ratio of nitrogen: condensed tannin was greater in foliage below the browseline of trees outside the exclosure (F = 50.96, P <

0.001, Fig. 20b). Trees with insecticide applications had a significantly greater nitrogen: total tannin concentration (F = 189.94, P < 0.001) than trees without insecticide applications.

147

a b No insecticide

Insecticide

F = 21.12, P < 0.001

a a a

Outside exclosure b Inside exclosure

F = 50.96, P < 0.001

a a a

Figure 20. Changes in the ratio of nitrogen to antinutritive traits of Quercus bicolor in response to differentiated herbivore type. a) The interactive effects of tree placement (inside vs. outside the deer exclosures) and insecticide application on the ratio of nitrogen: total tannin concentration. b) The interactive effects of foliage location (relative to the browseline), and tree placement (inside vs. outside the deer exclosure) on the ratio of nitrogen: condensed tannin concentrations (Q.E. = quebracho equivalents). Deer exclosures were set up in Liberty Park, Summit County Metroparks, OH, USA.

148

Significant differences are indicated by differences in the letters above the error bars on the graphs

(Scheffé post hoc tests).

Intensity of insect damage and diversity of insect herbivores (foliar and below-ground)

There were no significant differences in intensity of insect damage among treatments (MRPP: P

> 0.05), except for greater leaf damage due to insects in treatments without insecticide applications (MRPP: P < 0.01). β diversity of insect folivores was not significantly different above or below the browseline (P = 0.408) regardless of whether the tree was inside or outside the exclosure. However, trees located outside of the exclosure had a lower diversity of functional groups than trees placed inside the exclosure (MRPP: P < 0.001, Fig. 21a, b) and trees with insecticide applications showed a lower diversity of guilds than trees without insecticide (MRPP:

P < 0.001). When we analyzed β diversity of exclosures without insecticide applications, we found that trees located outside the exclosure had a significantly lower diversity of insect functional groups on foliage above the browseline than below the browseline (MRPP: P = 0.021,

Fig. 21b; 22). β diversity of below-ground insect-herbivores was not affected by the presence of deer (trees inside vs. outside of exclosures; P = 0.747) but was significantly reduced on trees that received insecticide applications (MRPP: P = 0.001).

149

a b

Leaf placement relative to browseline

Figure 21. Proportion of insect functional group on foliage of Quercus bicolor relative to the browseline. We compared insect feeding guild diversity of trees without insecticide applications, located a) inside and b) outside of deer exclosures. Deer exclosures were set up in Liberty Park,

Summit County Metroparks, OH, USA. (See Fig. 17 for feeding-guild categories).

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a a Above browseline

Below browseline

F = 27.17, P < 0.001

Figure 22. Mean number of insect functional groups per leaf relative to leaf placement above or below the browseline of Quercus bicolor located inside and outside of deer exclosures. Deer exclosures were set up in Liberty Park, Summit County Metroparks, OH, USA. Significant differences are indicated by differences in the letters above the error bars on the graphs (Scheffé post hoc tests).

DISCUSSION

Plants use various defense syndromes to defend against a myriad of herbivores including, but not limited to, decreasing nutritional quality of tissues (Hoffland et al. 2000; Quintero and Bowers

2018) and increasing anti-nutritional compounds such as tannins (Mithöfer and Boland 2012;

Sánchez-Sánchez and Morquecho-Contreras 2017). Utilization of these syndromes in response to various herbivores has mainly been investigated under single herbivore pressures (i.e., insect or mammal; Agrawal and Fishbein 2006; da Silva and Batalha 2011). Our experimental design

151 allowed us to investigate differences in responses to insect and mammal herbivory on the same plant. We found that, at the individual plant level, plants may execute multiple syndromes simultaneously that are dependent on the specific herbivore involved.

We found a reduction in nutritional quality (i.e., lower nitrogen content) of foliage that deer can access (i.e., below the browseline and in trees outside the exclosures), but not in foliage that only insects can feed on (i.e., above the browseline and inside the exclosures). Moran and

Hamilton (1980) defined three assumptions for low nutritive quality to act as a defense against herbivores: (1) herbivores must be able to detect the differences in nutritive quality, (2) herbivores must be able to move to a different host plant, or (3) reducing nutritive quality directly causes mortality or makes herbivores more susceptible to enemies (e.g., predators and parasitoids). Mammal and insect herbivores meet these three assumptions. However, decreased nutritional quality has been more widely accepted as a defense against mammal herbivores

(Lundberg and Åström 1989; Schmitt et al. 2016; Shrader et al. 2012) than against insect herbivores (Couture et al. 2016; Moran and Hamilton 1980). Reducing nutritive quality as a defense against insect herbivores has received a great deal of criticism (Augner 1995; Hervé and

Erb 2019; Moran and Hamilton 1980). Decreasing nutritional quality may cause an exponential increase (i.e., not a decrease) in feeding by insects (Perkovich and Ward 2020; Waldbauer 1968).

For example, Perkovich and Ward (2020) found that gypsy moth larvae (Lymantria dispar) that were fed diets with low protein compensated for the reduction in nutrients by increasing consumption rates. As a consequence, individual plants may suffer a greater loss of biomass from herbivory due to insects than deer (Ramirez 2016), so that decreasing foliar-nutritive value across the entire tree may not be an advantageous defense against insect herbivores.

152

Mammal and insect herbivores have vastly different physiologies and likely have different dietary requirements and tolerances. An example of a major physiological difference is that many mammal herbivores produce tannin-binding proteins that enable them to tolerate diets with higher tannin concentrations (McArthur et al. 1991; Schmitt et al. 2016; Ward et al. 2020).

These tannin-binding proteins often make tannin production an ineffective defense against mammal herbivores (McArthur et al. 1995; Schmitt et al. 2016; Shimada 2006). Instead, the ratio between nutritional and antinutritional properties may play a larger role in herbivore preference

(Perkovich and Ward 2020; Shrader et al. 2012). Therefore, successful defense syndromes to select herbivores may include a multitude of trait adaptations.

Identifying and understanding adaptive traits is challenging, especially for plant-defense syndromes that most commonly consist of a suite of traits (Agrawal and Fishbein 2006; Schluter

2000). Adaptations to specific herbivores (or selective contexts) may encompass multiple covarying traits (Agrawal and Fishbein 2006; da Silva and Batalha 2011; Okamura et al. 2016).

The plant apparency hypothesis argues that apparent plants, such as oak trees, must deploy plant defense syndromes consisting of multiple covarying traits, including decreased nutritional value, and increased chemical defenses as our results also suggest (Feeny 1976; Smilanich et al. 2016).

We consider the concept of an optimal defense syndrome (Adler and Karban 1994;

Agrawal and Hastings 2019) by suggesting that these traits may be partitioned within an individual tree to maximize defense based on the selective pressure (or in our case, type of herbivore present). We found that reductions in nutritional quality were more prominent in trees and foliage where deer had access to them (i.e., outside the exclosure and below the browseline).

We also found that tannin concentrations were increased in tree foliage where deer were not

153 present, and only insects had access to them (i.e., above the browseline and inside the exclosures).

We would expect that trees located inside the exclosure and insecticide applied would have the highest ratio of nitrogen: tannin concentration. However, we found that trees outside the exclosure with insecticide applied had a higher ratio of nitrogen: tannin concentration than those inside the exclosure or without insecticide. This perhaps gives insight into which herbivore group is providing a greater selective pressure for a specific defensive trait or defensive syndrome. We show that when insects are present, tannin concentrations are increased. For the ratio of nitrogen: tannin concentration to be high over the entire plant, when insects are present (resulting in higher tannin concentrations), nitrogen concentrations must consequentially be considerably higher. It may be that large mammal herbivores, such as deer, act as selective pressures for defense syndromes that result in decreased nutritional value, while insect herbivores are drivers for selection of defense syndromes that use chemical defenses.

Diversity of insect herbivores Unsurprisingly, the main source of variation in insect diversity

(above- and below-ground) was due to insecticide application. However, when comparing leaf for leaf, insect functional groups were significantly less diverse in trees located outside of the exclosures (Fig. 22). We acknowledge that there may be multiple reasons for this occurrence.

For example, deer exclosures could keep insectivorous birds from entering as easily to feed on insects inside the exclosure. There may also be competition between deer and insects that forces insects to forage on foliage where deer do not have access. Patterns of selection on plant defenses, and the responses to this selection, depend on the herbivore community composition

(Stinchcombe and Rausher 2002). Previous herbivores could affect plant defense phenotypes and

154 cause a historical contingency (also known as priority effects), therefore influencing future insect-herbivore communities (Fukami 2015; Rasmann et al. 2012). However, we would anticipate that induced-chemical defenses would deter current, not historical, herbivory

(Bakhtiari et al. 2018; Rasmann et al. 2012) because induced defenses are advantageous against immediate herbivores only.

As a consequence of these induced defenses, plants should have lower insect functional group diversity. Contrastingly, we found that trees located outside of the exclosure had lower condensed tannin concentrations than those inside the exclosure, but insect functional groups were more diverse on trees inside exclosures (where deer were absent). Furthermore, we expected to find a significantly greater diversity of insect functional groups above the browseline than below the browseline if deer were altering the chemical composition of the trees and thus changing insect functional group composition. Instead, trees located outside the exclosure where deer and insects were present, had a greater diversity of insect functional groups on foliage below the browseline than above (Fig. 22). Our diversity findings are an anomaly. Our only explanation is that there may have been some protection from wind inside the exclosures that promoted foraging by insects from a greater number of functional groups.

CONCLUSIONS

Ecological and evolutionary mechanisms select for defensive phenotypes that are advantageous in a specific environment (Agrawal 2007; da Silva and Batalha 2011). Plant defense theories have sought to better understand phylogenetic and geographical patterns of plant-defense traits

(Stamp 2003). Plants in a given environment often express similar traits because they are under

155 similar selective pressures, so that plant defense syndromes are often easily recognized (Agrawal and Fishbein 2006; Kursar and Coley 2003). Understanding how plants differentiate defenses to variable selective pressures may help provide a more accurate framework for understanding the mechanisms behind phylogenetic and environmental patterns of plant defense syndromes.

156

REFERENCES

Adler FR, Karban R (1995) Defended fortress or moving target? Another model of inducible

defenses inspired by military metaphors. Am Nat 144:813–832

Agrawal AA (2007) Macroevolution of plant defense strategies. Trends Ecol Evol 22:103–109

Agrawal AA (2020) A scale-dependent framework for trade-offs, syndromes, and specialization

in organismal biology. Ecology 101:e02924

Agrawal AA, Fishbein M (2006) Plant defense syndromes. Ecology 87:e132–e149

Agrawal AA, Hastings AP (2019) Trade-offs constrain the evolution of an inducible defense

within but not between plant species. Ecology 100:e02857

Agrawal AA, Karban R (1999) Why induced defenses may be favored over constitutive

strategies in plants. In Tollrain R, Harvell CD (eds) The ecology and evolution of

inducible defenses. Princeton University Press, Princeton, pp 45–61

Augner M (1995) Low nutritive quality as a plant defence: effects of herbivore-mediated

interactions. Evol Ecol 9:605–616

Bakhtiari M, Glauser G, Rasmann S (2018) Root JA induction modifies glucosinolate profiles ad

increases subsequent aboveground resistance to herbivore attack in Cardamine hirsute.

Front Plant Sci 9:e1230

Baldwin I (1998) Jasmonate-induced responses are costly but benefit plants under attack in

native populations. Proc Nat Acad Sci USA 95:8113–8118

Begley-Miller D, Cady A (2015) White-tailed deer browsing of soybeans significantly changes

plant morphology and reduces yield, contributing to large financial losses. Ohio J Sci

115:56–61

157

Benevenuto RF, Hegland SJ, Töpper JP, Rydgren K, Moe SR, Rodriguez-Saona C, Seldal T

(2018) Multiannual effects of induced plant defenses: are defended plants good or bad

neighbors? Ecol Evol 8:8940–8950

Benevenuto RF, Seldal T, Polashock J, Moe SR, Rodriguez-Saona C, Gillespie MA, Hegland SJ

(2020) Molecular and ecological plant defense responses along an elevational gradient in

a boreal system. Ecol Evol 10:2478–2491

Coley PD (1987a) Interspecific variation in plant anti-herbivore properties: the role of habitat

quality and rate of disturbance. New Phytol 106:251–263

Coley PD (1987b) Effects of plant growth rate and leaf lifetime on the amount and type of anti-

herbivore defense. Oecologia 74:551–536

Coley PD, Bryant JP, Chapin FS III (1985) Resource availability and plant antiherbivore

defense. Science 230:895–899

Couture JJ, Mason CJ, Habeck CW, Lindroth RL (2016) Behavioral and morphological

responses of an insect herbivore to low nutrient quality are inhibited by plant chemical

defenses. Arthropod Plant Interact 10:341–349

Castagneyrol B, Jactel H, Moreira X (2018) Anti-herbivore defences and insect herbivory:

interactive effects of drought and tree neighbours. J Ecol 106:2043–2057 da Silva DM, Batalha MA (2011) Defense syndromes against herbivory in a cerrado plant

community. Plant Ecol 212:181–193

Davis NE, Gordon IR, Coulson G (2018) The influence of evolutionary history and body size on

partitioning of habitat resources by mammalian herbivores in south-eastern Australia.

Aust J Zool 65:226–239

158

Demment MW, van Soest PJ (1985) A nutritional explanation for size patterns of ruminant and

non-ruminant herbivores. Am Nat 125:641–672

Dicke M, van Loon JJ, Soler R (2009) Chemical complexity of volatiles from plants induced by

multiple attack. Nat Chem Biol 5:317–324

Duffey SS, Stout MJ (1996) Antinutritive and toxic components of plant defense against insects.

Arch Insect Biochem Physiol 32:e3–e7

Endara MJ, Coley PD (2011) The resource availability hypothesis revisited: a meta-analysis.

Funct Ecol 25:389–398

Erb M (2018) Plant defenses against herbivory: closing the fitness gap. Trends Plant Sci 23:187–

194

Feeny PP (1976) Plant apparency and chemical defense. In Wallace JW, Mansell RL (eds)

Biochemical interaction between plants and insects. Plenum, New York, pp 1–40

Fornoni J, Nunez-Farfán J, Valverde PL, Rausher MD (2004) Evolution of mixed strategies of

plant defense allocation against natural enemies. Evolution 58:1685–1695

Fournier E (2001) Colorimetric quantification of carbohydrates. Curr Prot Food Anal Chem 1:1–

Fukami T (2015) Historical contingency in community assembly: integrating niches, species

pools, and priority effects. Annu Rev Ecol Evol Syst 46:1–23

Gardner M (2014) Community ecology: analytical methods using R and Excel. Pelagic

Publishing, Exeter, UK

Gessner MO, Steiner D (2005) Acid butanol assay for proanthocyanidins (condensed tannins). In

Graca M (ed) Methods to study litter decomposition. Springer, Amsterdam, The

Netherlands, pp 107–114

159

Graca M, Barlocher F (2005) Radial diffusion assay for tannins. In Graca M (ed) Methods to

study litter decomposition. Springer, Amsterdam, The Netherlands, pp 101–105

Hoffland E, Dicke M, van Tintelen W, Dijkman H, Beusichem ML (2000) Nitrogen availability

and defense of tomato against two-spotted spider mite. J Chem Ecol 26:2697–2711

Hagerman A (1987) Radial diffusion method for determining tannin in plant extracts. J Chem

Ecol 14:453–461

Hagerman A (1988) Extraction of tannin from fresh and preserved leaves. J Chem Ecol 13:437–

449

Hagerman A (2011) Tannin handbook. Miami University, Oxford, OH.

https://www.users.miamioh.edu/hagermae/. Accessed 7 July 2020

Haukioja E, Ruohomäki K, Suomela J, Vuorisalo T (1991) Nutritional quality as a defense

against herbivores. For Ecol Manag 39:237–245

Hawkins CP, MacMahon JA (1989) Guilds: the multiple meanings of the concept. Annu Rev

Entomol 34:423–451

Hervé MR, Erb M (2019) Distinct defense strategies allow different grassland species to cope

with root herbivore attack. Oecologia 191:127–139

Hoffman R, Stewart D (1972) Grazer or browser: a classification based on the stomach-structure

and feeding habits of East African ruminants. Mammalia 36:226–240

Hoffmann WA, Poorter H (2002) Avoiding bias in calculations of Relative Growth Rate. Ann

Bot 80:37–42

IBM Corp. (2019) IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp

Iwao K, Rausher MD (1997) Evolution of plant resistance to multiple herbivores: quantifying

diffuse coevolution. Am Nat 149:316–335

160

Karban R, Baldwin IT (1997) Induced responses to herbivory. University Press, Chicago

Karban R, Myers JH (1989) Induced plant responses to herbivory. Annu Rev Ecol Syst 20:331–

348

Kessler A, Chautá A (2020) The ecological consequences of herbivore-induced plant responses

on plant-pollinator interactions. Emerging Top Life Sci 1:33–43

Kim TN (2017) How plant neighborhood composition influences herbivory: testing four

mechanisms of associational resistance and susceptibility. PloS one 12:e0176499

Kobe RK, Iyer M, Walters MB (2010) Optimal partitioning theory revisited: nonstructural

carbohydrates dominate root mass responses to nitrogen. Ecology 91:166–179

Kristensen JÅ, Rouosk J, Metcalfe DB (2020) Below-ground responses to insect herbivory in

ecosystems with woody plant canopies: a meta-analysis. J Ecol 108:917–930

Kursar TA, Coley PD (2003) Convergence in defense syndromes of young leaves in tropical

rainforests. Biochem Syst Ecol 31:929–949

Landhäusser SM, Chow PS, Dickman LT, Furze ME, Kuhlman I, Schmid S, Wiesenbauer J,

Wild B, Gleixner G, Hartmann H, Hoch G, McDowell NG, Richardson AD, Richter A,

Adams HD (2018) Standardized protocols and procedures can precisely and accurately

quantify non-structural carbohydrates. Tree Physiol 38:1764–1778

Legendre P (2019) A temporal beta-diversity index to identify sites that have changed in

exceptional ways in space–time surveys. Ecol Evol 9:3500–3514

Legendre P, Condit R (2019) Spatial and temporal analysis of beta diversity in the Barro

Colorado Island forest dynamics plot, Panama. For Ecosyst 6:e7

Legendre P, De Caceres M (2013) Beta diversity as the variance of community data:

dissimilarity coefficients and partitioning. Ecol Lett 16:951–963

161

Lill J, Marquis R (2003) Ecosystem engineering by caterpillars increases insect herbivore

diversity on white oak. Ecology 84:682–690

Lundberg P, Åström M (1989) Low nutritive quality as a defense against optimally foraging

herbivores. Am Nat 135:547–562

Maron JL, Agrawal AA, Schemske DW (2019) Plant–herbivore coevolution and plant

speciation. Ecology 7:e02704

McArthur C, Hagerman AE, Robbins CT (1991) Physiological strategies of mammalian

herbivores against plant defenses. In Palo RT, Robbins CT (eds) Plant defenses against

mammalian herbivory. CRC Press, Boca Raton, FL, pp 103–114

McArthur C, Sanson GD, Beal AM (1995) Salivary proline-rich proteins in mammals: roles in

oral homeostasis and counteracting dietary tannin. J Chem Ecol 21:663–691

Mithöfer A, Boland W (2012) Plant defenses against herbivores: chemical aspects. Annu Rev

Plant Biol 63:431–450

Moran N, Hamilton WD (1980) Low nutritive quality as a defense against herbivores. J Theor

Biol 86:247–254

Moreira X, Abdala-Roberts L, Bruun HH, Covelo F, De Frenne P, Galmán A, Gatán Á, Raimo J,

Pulkkinen P, Ten Hoopen JPG, Timmermans BGH, Tack AJ, Castagneroyl B (2020)

Latitudinal variation in seed predation correlates with latitudinal variation in seed

defensive and nutritional traits in a widespread oak species. Ann Bot 125:881–890

Müller DWH, Codron D, Meloro C, Munn A, Schwarm A, Hummel J, Clauss M (2013)

Assessing the Jarman-Bell Principle: scaling of intake, digestibility, retention time and

gut fill with body mass in mammalian herbivores. Comp Biochem Physiol A 164:129–

140

162

Nopp-Mayr U, Reimoser S, Reimoser F, Sachser F, Obermair L, Gratzer G (2020) Analyzing

long-term impacts of ungulate herbivory on forest-recruitment dynamics at community

and species level contrasting tree densities versus maximum heights. Sci Rep 10:e20274

Novotny V, Miller SE, Baje L, Balagawi S, Basset Y, Cizek, L, Craft KJ, Dem F, Drew RAI,

Hulcr J, Leps J, Lewis OT, Pokon R, Stewart AJ, Samuelson GA, Weiblen GD (2010)

Guild-specific patterns of species richness and host specialization in plant-herbivore food

webs from a tropical forest. J Anim Ecol 79:1193–1203

Okamura Y, Sawada Y, Hirai MY, Murakami M (2016) Effects of different secondary

metabolite profiles in plant defense syndromes on specialist and generalist herbivores.

Entomol Sci 19:97–103

Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara

RB, Simpson GL, Solymos P, Stevens HH, Szoecs E, Wagner H (2020) vegan:

Community Ecology Package. R package version 2.5–7. https://CRAN.R-

project.org/package=vegan

Orians CM, Thorn A, Gόmez S (2011) Herbivore-induced resource sequestration in plants: why

bother? Oecologia 167:1–9

Orrock JL, Connolly BM, Choi WG, Guiden PW, Swanson SJ, Gilroy S (2018) Plants eavesdrop

on cues produced by snails and induce costly defenses that affect insect herbivores.

Oecologia 186:703–710

Palmer WL, Payne JM, Wingard RG, George JL (1985) A practical fence to reduce deer damage.

Wildl Soc Bull 13:240–245

Paula S, Pausas JG (2011) Root traits explain different foraging strategies between resprouting

life histories. Oecologia 165:321–331

163

Pearse IS, Hipp AL (2009) Phylogenetic and trait similarity to a native species predict herbivory

on non-native oaks. Proc Nat Acad Sci USA 106:18097–18102

Peeters PJ (2002) Correlations between leaf structural traits and the densities of herbivorous

insect guilds. Biol J Linn Soc 77:43–65

Pérez-de-Lis G, García-González I, Rozas V, Olano JM (2017) Feedbacks between earlywood

anatomy and non-structural carbohydrates affect spring phenology and wood production

in ring-porous oaks. Biogeosci 13:5499–5510

Perkovich C, Ward D (2020) Protein:carbohydrate ratios in the diet of gypsy moth Lymantria

dispar affect its ability to tolerate tannins. J Chem Ecol 46:299–307

Perkovich C, Ward D (2021) Herbivore-induced defenses are not under phylogenetic constraints

in the genus Quercus (oak): phylogenetic patterns of growth, defense, and storage. Ecol

Evol in press

Price ML, Butler LG (1977) Prussian blue assay for total phenols. J Agric Food Chem 25:1268–

1273

Quintero C, Bowers MD (2018) Plant and herbivore ontogeny interact to shape the preference,

performance and chemical defense of a specialist herbivore. Oecologia 187:401–412

R Development Core Team. (2019) R: a language and environment for statistical computing. R

foundation for statistical computing, Vienna, Austria. URL https:///www.R.project.org/

Ramirez S (2016) Deer management strategies. URL

http://smartgrowth.umd.edu/assets/plsc.480.9_deer_management_strategies.pdf.

[accessed 23 November 2020]

164

Rasmann S, De Vos M, Casteel CL, Tian D, Halitschke R, Sun JY, Agrawal AA, Felton GW,

Jander G (2012) Herbivory in the previous generation primes plants for enhanced insect

resistance. Plant Physiol 158:854–863

Rees M, Osborne CP, Woodward FI, Hulme SP, Turnbull LA, Taylor SH (2010) Partitioning the

components of relative growth rate: how important is plant size variation? Am Nat

176:e152–e161

Rooney TP, Waller DM (2003) Direct and indirect effects of white-tailed deer in forest

ecosystems. For Ecol Manag 181:165–176

Sánchez-Sánchez H, Morquecho-Contreras A (2017) Chemical plant defense against herbivores.

In: Shields, V.D.C. (ed) Herbivores. IntechOpen doi: 10.5772/67346

Scherer R, Pallmann P (2017) simboot: Simultaneous Inference for Diversity Indices. R package

version 0.2–6. https://CRAN.R-project.org/package=simboot

Schluter D (2000) The ecology of adaptive radiation. Oxford University Press, Oxford

Schmitt MH, Ward D, Shrader AM (2016) Incorporating secondary metabolites, tannin-binding

proteins, and diet breadth into carrying-capacity models of African elephants. Ecol

Modell 332:8–18

Shimada T (2006) Salivary proteins as a defense against dietary tannins. J Chem Ecol 32:1149–

1163

Shrader AM, Bell C, Bertolli L, Ward D (2012) Forest of the trees: at what scale do elephants

make foraging decisions? Acta Oecol 42:3–10

Smilanich AM, Fincher RM, Dyer LA (2016) Does plant apparency matter? Thirty years of data

provide limited support but reveal clear patterns of the effects of plant chemistry on

herbivores. New Phytol 210:1044–1057

165

Stamp N (2003) Out of the quagmire of plant defense hypotheses. Q Rev Biol 78:23–55

Stewart AJA (2001) The impact of deer on lowland woodland invertebrates: a review of the

evidence and priorities for future research. Int J For Res 74:259–270

Stinchcombe JR, Rausher MD (2002) The evolution of tolerance to deer herbivory:

modifications caused by the abundance of insect herbivores. Proc R Soc Lond B

269:1241–1246

Suzuki M, Miyashita T, Kabaya H, Ochiai K, Asada M, Kikvidze Z (2012) Deer herbivory as an

important driver of divergence of ground vegetation in temperate forests. Oikos 122:104–

110

Tomlinson KW, van Langevelde F, Ward D, Bongers F, Da Silva DA, Prins HHT, De Bie S,

Sterck FJ (2013) Deciduous and evergreen trees differ in juvenile biomass allometries

because of differences in allocation to root storage. Ann Bot 112:575–587

VerCauteren KC, Lavelle MJ, Hygnstrom S (2006) Fences and deer-damage management: a

review of designs and efficacy. Wildl Soc Bull 34:191–200

Waldbauer GP (1968) The consumption and utilization of food by insects. Adv Insect Physiol

5:229–288

Ward D, Schmitt MH, Shrader AM (2020) Are there phylogenetic differences in salivary tannin-

binding proteins between browsers and grazers, and ruminants and hindgut fermenters?

Ecol Evol 10:10426–10439

Ward D, Young T (2002) Effects of large mammalian herbivores and ant symbionts on

condensed tannins in Acacia drepanolobium in Kenya. J. Chem. Ecol. 28: 921–937.

Wigley BJ, Fritz H, Coetsee C (2018) Defence strategies in African savanna trees. Oecologia

187:797–809

166

Wiley E, Casper BB, Helliker BR (2017) Recovery following defoliation involves shifts in

allocation that favour storage and reproduction over radial growth in black oak. J Ecol

105:412–424

Xi X, Yang Y, Yang Y, Segoli M, Sun S (2017) Plant-mediated resource partitioning by

coexisting parasitoids. Ecology 98:1660–1670

167

CHAPTER V

PERIODICAL CICADAS INCREASE DEFENSES IN NORTH AMERICAN FOREST

TREES: BEFORE, DURING, AND AFTER A MASS OUTBREAK

ABSTRACT

Periodical cicadas have mass outbreaks once every 13 or 17 years. Such an outbreak causes a massive change in the defenses that trees need to withstand, later dropping back to ambient levels for many years. We examined the defensive responses of the common dominant white oaks (Quercus alba) strategies prior to, during, and subsequent to a 17-year periodical-cicada

(Magicicada spp.) emergence in western Pennsylvania, U.S.A. During the emergence, total tannin and condensed tannin concentrations increased in foliar tissue, while simultaneously decreasing in root tissue. Non-structural carbohydrates were low prior to the mass outbreak but were re-allocated to belowground storage during the emergence year and dropped thereafter. In the year after the emergence, there was a relaxation of foliar defenses and root defenses returned to pre-emergence concentrations. We also tested for local effects in damaged and undamaged branches during (2019) and the year after the emergence (2020). Both damaged and undamaged branches had significantly higher concentrations of chemical defenses (polyphenols, total tannins, and condensed tannins) during the emergence than in the following year when there was no outbreak. We propose that re-allocation of resources may help maximize oak tree fitness by moving resources away from areas that are not in immediate threat to areas that are under

168 immediate threat. Changes in aboveground and belowground phytochemistry in response to mass outbreaks of periodical cicadas may help us better understand which resource re-allocation strategies are used by plants to minimize the effects of insect emergences and outbreaks.

169

INTRODUCTION

Periodical cicadas (Magicicada spp.) emerge in mass outbreaks every 13- or 17-years (Karban

2014). These outbreaks occur in “broods” scattered across the northeastern United States (Dybas and Davis 1962; Marshall and Cooley 2000; Williams and Simon 1995). During emergence years, densities of periodical cicadas can reach nearly 400 cicadas m-2, making their emergences the largest recorded of any insect (Dybas and Davis 1962; Karban 2014).

During the developmental years (13 or 17 years), periodical cicada nymphs feed on roots of deciduous trees (Dybas and Lloyd 1974; Karban 2014; White 1980; Williams and Simon

1995). At the beginning of the emergence, adult cicadas emerge from belowground. They form large chorusing centers for mating. Thereafter, female cicadas leave the chorusing center to search for a branch to lay their eggs. Stored proteins and lipids from feeding during the nymphal stage are used during the adult stage. Adults may sometimes feed on xylem fluids from stems and foliar tissues to replenish reserves, although there is debate as to whether they feed as adults

(Brown and Chippendale 1973; Williams and Simon 1995).

The damaging effects of cicada oviposition on woody tree growth are well understood, often resulting in the subsequent loss of the distal part of the branch (e.g., Clay et al. 2009; Cook and Holt 2002; Flory and Mattingly 2008; Miller and Crowley 1998). However, these studies have focused on growth and woody accumulation (e.g., Karban 1980; Koenig and Liebhold

2003; Yang and Karban 2019). We have limited knowledge of the physiological and biochemical responses of trees to periodical-cicada emergences (Boyce et al. 2019; Cook and Holt 2002;

Nguyen et al. 2020). White (1981) showed that resin secretion and callus formation were induced responses of host trees to cicada oviposition. Similarly, Karban (1983) reported that cherry trees

(Prunus serotina) form gum deposits around the egg nests. Researchers have largely overlooked

170 plant defense responses to periodical cicadas because there is limited evidence of feeding as adults; consequently, these insects are not likely to be affected by chemical defense production

(Christensen and Fogel 2011). However, even though plant secondary metabolite (PSM) production may not influence the behaviors of adult periodical cicadas, PSM production may draw energy and resources away from other tree functions (Douma et al. 2017; Zangerl et al.

1997). According to all prominent plant-defense hypotheses (Fiorucci 2020; Herms and Mattson

1992), physiological costs of defense production limit metabolic processes so that when defenses are produced, resources are allocated away from other processes. The divergence of resources to defense production, along with physical injury from cicada “flagging” (females insert their ovipositors, creating a series of injuries on the host tree (Kritsky 2004; White 1981; Williams and Simon 1995)), may leave trees more susceptible to diseases as well as reducing growth and plant fitness (Clay et al. 2009a; Ostry and Anderson 1983).

Oak (Quercus) species are preferred host trees for periodical cicadas (Clay et al. 2009b,

Perkovich and Ward, unpublished data). In response to herbivory, oak species have several strategies to prevent further tissue removal. One strategy is to increase defensive chemical production (e.g., polyphenols and tannins) (Pearse and Hipp 2012; Wold and Marquis 1997).

Tannins are a class of organic compounds that precipitate proteins, making the proteins unusable to herbivores (Bernays et al. 1989; Tayal et al. 2020). A second strategy used by oaks, not mutually exclusive from the defense hypothesis, is to re-allocate nutrients. For example, oaks have been shown to decrease nitrogen content in the leaves (Frost and Hunter 2008; Wold and

Marquis 1997) and to re-allocate non-structural carbohydrates to belowground storage

(Perkovich and Ward 2021a; Wiley et al. 2017) in response to tissue removal.

171

We predict that, during mass outbreak years, oak trees will increase foliar polyphenol and tannin concentrations in response to damage from adult periodical cicadas. The damage should also cause a decrease in foliar nitrogen along with a decrease in foliar non-structural carbohydrate concentrations (Frost and Hunter 2008; Wold and Marquis 1997) because of trade- offs in the production of defenses and tree quality (as measured by nitrogen and non-structural carbohydrates). Branches that are damaged should have a greater concentration of defenses than undamaged branches on the same tree (Tuomi et al. 1988). Concomitantly, non-structural carbohydrate concentrations should be re-allocated to the roots because of aboveground damage by adult periodical cicadas (Perkovich and Ward 2021a; Wiley et al. 2017). As a result of nymphs ceasing to feed on roots and the emergence of adults, defensive compound concentrations in the roots should decrease. We predict that the defensive compound and nutrient concentrations of individual trees should return to pre-emergence concentrations in the year following the periodical-cicada emergence because of diminished resource allocation and high cost of production of defenses (Huntzinger et al. 2004; Young and Okello 1998;). More specifically, a damaged branch will have an increased concentration of defense compounds compared with undamaged branches, and that same damaged branch will relax (decline) these defense concentrations the following year.

METHODS

Study Site and Cicada Brood VIII We sampled white oak (Quercus alba) trees in Keystone

State Park, Westmoreland County, Pennsylvania, in the United States. Keystone State Park is part of the mixed-mesophytic Appalachian Forest with robust oak stands (McCarthy et al. 2001).

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Brood VIII of the periodical cicada (Magicicada spp.) emerges in this forest every 17 years and emerged in late May/early June of 2019 (Cooley et al. 2009; Simon 1988).

Sampling for foliar changes before, during, and after a cicada emergence We sampled in early June 2018 (i.e., one year before the emergence), early June 2019 (i.e., during the emergence), and early June 2020 (i.e., one year after the emergence). During sampling each year, we collected samples of leaf tissues by removing 5 leaves from each tree (n = 50). Trees were marked in 2018, and sequential years’ samplings were collected from the same tree.

Sampling for root chemical changes before, during, and after a cicada emergence Root samples were collected from the same trees used in the foliar sampling (see above). We collected root samples that were between 2.5 and 3.5 mm in diameter to standardize samples between multiple trees. In general, as the nymphs grow larger, they feed on larger roots (R. Karban, pers. comm.).

Sampling for changes between damaged vs. undamaged branches within individual trees

We sampled branches (n = 100 from 50 different trees) that were damaged during the emergence

(i.e., flagging was present) and compared them to branches that were undamaged on the same tree. Foliar tissue from branches that had clear flagging scars, but whose foliage had not senesced, was used for the sampling of damaged branches. We measured branch diameter to standardize the branches we sampled. The sampled branches were marked in 2019 (i.e., during the emergence) and the same branches were re-sampled in 2020.

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Chemical analyses After collection, foliar and root samples were dried at 60 °C for 48 h and then ground using a Wiley mill (mesh size = 2 mm). We extracted polyphenols and tannins using a 70% acetone solution (Graca and Barlocher 2005; Hagerman 2011). Total polyphenol concentrations were analyzed using the Prussian Blue Assay (Price and Butler 1977), modified for use on a microplate reader (Hagerman 2011) and standardized using gallic acid. Total tannin concentrations were measured using the Radial Diffusion Assay and standardized against tannic acid (Hagerman 1987; 2011). We also measured condensed tannin concentrations (which are a type of tannin) using the Acid Butanol Assay for proanthocyanidins (Gessner and Steiner 2005;

Hagerman 2011) standardized against quebracho tannin. There are no unique concentrations for polyphenols or tannins; they are therefore expressed as equivalents of the standard used

(Hagerman 2011).

Non-structural carbohydrates were measured using Fournier’s (2001) method, using phenol–sulfuric acid as a solvent to dissolve sugars and starches (see Tomlinson et al. 2013). A rapid N exceed® analyzer by Elementar was used to measure percent nitrogen.

Statistical Analyses For whole tree responses, we used a multivariate analysis of variance

(MANOVA) to minimize Type I statistical error from analyzing multiple dependent variables

(e.g., polyphenol, tannin, and condensed tannin concentrations) simultaneously, with diameter at breast height (DBH) used as a covariate to standardize for variation in tree sizes. Significant dependent variables were then run through a univariate analysis of variance (ANOVA) followed by Scheffé post hoc tests. We used the same approach for statistically analyzing differentiation in responses between damaged and undamaged branches within an individual tree. We used branch

174 diameter in the middle of flagging scars as a covariate to standardize for variation in branch sizes. All statistical analyses were run in SPSS version 26 (IBM 2019).

RESULTS

Oak trees had significant phytochemical changes to periodical-cicada emergences (Wilk’s λ =

0.008, F = 93.18, P < 0.001). However, the size of the tree did not have a significant effect

(Wilk’s λ = 0.892, F = 1.127, P = 0.34).

Foliar changes before, during, and after a cicada emergence The average foliar tannin and condensed tannin concentrations increased nearly threefold during the emergence year (2019) (F

= 156.73, P < 0.001 and F = 217.98, P < 0.001, respectively, Fig. 1A and B). Total tannin and condensed-tannin concentrations in the year after the emergence (2020) returned to concentrations that were not statistically different from the year preceding the emergence (2018)

(P = 0.37 and P = 0.09, respectively, Fig. 23A and B). Foliar nitrogen exhibited a similar significant trend (F = 16.83, P < 0.001, Fig. 23C), but the year after the emergence (2020) did not return to the concentrations of the year preceding the emergence (2018). Foliar non-structural carbohydrate concentrations decreased during the emergence (2019) and further decreased the following year (2020) (F = 36.90, P > 0.001, Fig. 23D).

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B A F = 156.73, P < 0.001

B B F = 217.98, P < 0.001

176

C B F = 17.46, P < 0.001

D A F = 57.18, P < 0.001

Figure 23. Changes in foliar chemistry of white oaks before, during, and after periodical cicada outbreak.

Changes in foliar chemistry are shown as changes in A) total tannin concentrations (measured in tannic acid equivalents (T.A.E) using the Radial Diffusion Assay); B) condensed tannin concentrations

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(measured in quebracho equivalents (Q.E.) using the Acid Butanol Assay); C) foliar nitrogen; and D) foliar non-structural carbohydrate concentrations (measured in glucose equivalents (G.E.)). Significant differences are indicated by differences in the letters above the error bars on the graphs (Scheffé post hoc tests).

Root chemical changes before, during, and after a cicada emergence Root polyphenol concentrations significantly increased during the emergence (2019) and significantly decreased post-emergence (2020) (F = 63.80, P < 0.001, Fig. 24A). Total tannin and condensed-tannin concentrations followed the same trend, but in the opposite direction of foliar tannin and condensed tannin concentrations. These concentrations significantly decreased during the emergence (2019), and significantly increased post-emergence (2020) (F = 14.84, P < 0.001 and

F = 14.11, P < 0.001, respectively, Fig. 24B and C). Root nitrogen significantly decreased during the emergence (2019) and significantly decreased post-emergence (2020) (F = 30.86, P < 0.001,

Fig. 24D).

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A B F = 62.41, P < 0.001

B F = 15.13, P < 0.001 A

C B

179

A A

F = 14.32, P < 0.001

F = 31.53, P < 0.001 A

180

E B F = 16.89, P = 0.019

Figure 24. Changes in root chemistry of white oaks before, during, and after periodical cicada outbreak.

Changes in root chemistry are shown as changes in A) total polyphenol concentrations (measured in gallic acid equivalents (G.A.E.) using the Prussian blue assay; B) total tannin concentrations (measured in tannic acid equivalents (T.A.E.) using the radial diffusion assay; C) condensed tannin concentrations

(measured in quebracho equivalents (Q.E.) using the acid butanol assay); D) nitrogen; and E) non- structural carbohydrate concentrations (measured in glucose equivalents (GE)). Significant differences are indicated by differences in the letters above the error bars on the graphs (Scheffé post hoc tests).

Changes of damaged vs. undamaged branches within individual trees There was a significantly higher concentration of defensive compounds (polyphenols, tannins, and condensed tannins) during the emergence (2019) than the year following the emergence (2020) (Wilk’s λ =

0.292, F = 190.00, P < 0.001, Fig. 25A–C). Post-emergence (2020), defensive compounds were

181 significantly higher in branches that had been damaged during the emergence (Wilk’s λ = 0.365,

F = 190.00, P < 0.001, Fig. 25A–C).

Damaged branches had significantly lower nitrogen content post-emergence (2020) than during the emergence (2019) (P < 0.001, Fig. 25D). Likewise, non-structural carbohydrate concentrations in damaged branches were significantly lower post-emergence (P = 0.03, Fig.

25E).

182

A A F = 48.8, P < 0.001 A

C Damaged Undamaged

A B A A

F = 292.68, P < 0.001

Damaged Undamaged B

183

C F = 17.22, P < 0.001 A

Damaged C Undamaged D

D A F = 6.35, P < 0.001

AB B

Damaged Undamaged

184

E A F = 5.08, P < 0.03

Damaged A Undamaged

Figure 25. Differences in the foliar chemistry between damaged and undamaged branches of white oaks during and after periodical cicada emergence. Changes shown are changes in A) polyphenol concentrations (measured in gallic acid equivalents (G.A.E.) using the Prussian blue assay); B) condensed tannin concentrations (measured in quebracho equivalents (Q.E.); C) total tannin concentrations

(measured in tannic acid equivalents (T.A.E.) using the radial diffusion assay); D) nitrogen; and E) non- structural carbohydrate concentrations (measured in glucose equivalents (G.E.)). Significant differences are indicated by differences in the letters above the error bars on the graphs (Scheffé post hoc tests).

185

DISCUSSION

During the periodical-cicada emergence, oak trees increase chemical defenses and significantly alter nutrient allocation strategies. Optimal defense theory predicts that plants increase defenses to minimize immediate herbivore threats (Karban 2020; Zangerl and Rutledge 1996). However, these inducible defenses are energetically costly, which is why they are not consistently produced (i.e., constitutive) (Rhoades 1979; Stamp 2003). Costs of defense production may take the form of allocation costs that divert resources away from plant growth and reproduction

(Fiorucci 2020; Herms and Mattson 1992; Karban and Baldwin 1997). Induction of defenses is less effective and less cost-efficient if the plant is unable relax (i.e., reduce) expensive defenses when there is no immediate herbivory (Huntzinger et al. 2004; Levins 1968; Young and Okello

1998). We found that in most cases, the foliar increases in defenses and changes in nutrient re- allocation patterns relax the following year. Contrastingly, the root chemical defenses decreased during the emergence (2019) and returned to the higher, pre-emergence concentrations the year after the emergence (2020). We believe that the relaxation of root polyphenol and condensed tannin concentrations may be a response to the nymphs discontinuing belowground feeding when they emerge as adults. This relaxation of root defenses may provide necessary allocation of resources to produce foliar defenses as the adult periodical cicadas begin mating. This presupposes that it is the emergence of periodical cicadas that imposes much of the induction of plant defenses (Cook and Holt 2002; Karban 1983) and not inherent levels of herbivory by other insects that may occupy these trees.

Assuming there is no other significant form of herbivory on these trees, the relaxation of defense chemicals in the roots during the emergence suggests that oak trees are re-allocating resources to optimize their fitness. Pre-emergence, most damage occurs belowground when the

186 nymphs are feeding. However, during the emergence, damage occurs aboveground. In response to changes to location of herbivore attack, plants may alter their defensive phenotype to better protect themselves from the immediate threat and minimize energetic costs (Adler and Karban

1994). According to Adler and Karban’s (1994) model, variation in herbivore threat favors an optimal inducible defense strategy. The optimal inducible-defense strategy states that when herbivores are absent, plants will be less defended, investing in increased growth. In the presence of a single herbivore population, plants will switch to a more defended state at the cost of reduced growth rate (Adler and Karban 1994). These predictions are consistent with our data.

Periodical cicadas are a single population that feed belowground for 17 years. Concordant with predictions from Adler and Karban’s (1994) model, the root tannin and condensed tannin concentrations decrease when the nymphs emerge. Aboveground, the adults stimulate an inducible response, with concentrations of tannin and condensed tannins increasing. Oak trees may be altering resource allocation strategies to optimize defenses (Adler and Karban 1994;

Perkovich and Ward 2021b, in review).

Acquisition and allocation of resources are plastic traits within a population (Metcalf

2016; Noordwijk and de Jong 1986). Individuals with access to fewer resources may use alternative allocation strategies to maximize defense (Metcalf 2016; Orians and Ward 2010; van

Noordwijk and de Jong 1986; Ward and Young 2002). Similarly, individuals experiencing higher levels of stress may also alter allocation strategies to maximize defense (Adler and Karban 1994;

McCormick et al. 2019). Again, assuming that belowground herbivory from non-periodical cicadas is low during a periodical-cicada emergence, individual trees that experience higher levels of damage aboveground may re-allocate resources away from belowground defense production. The re-allocated resources may provide additional support to minimize the

187 aboveground threat. Additionally, a study by Cook and Holt (2002) found that oviposition damage was ineffective, or trees were able to sufficiently compensate. We propose that the changes in defense strategy and nutrient re-allocation may be an evolutionary mechanism allowing for physiological compensation, assuming ceteris paribus that the periodical cicadas are causing the maximal negative effect relative to other sources of herbivory or damage.

Foliar responses to periodical-cicada emergences We found total tannin production to be the most common induced response as damaged branches had significantly higher concentrations of total tannins, but not polyphenols or condensed tannins during the emergence. Tannins are abundant plant-defense compounds, particularly in oaks (Barbehenn and Constabel 2011;

Bernays et al. 1989; Clausen et al. 1992). However, insects, unlike vertebrates, suffer few antinutritional effects of tannins and mainly suffer from toxic effects (Barbehenn and Constabel

2011; Farahat et al. 2018; Hafeez et al. 2019). We cannot confirm that white oaks were increasing tannin production in response to general injury, or if the increased tannin production was in direct response to an insect herbivore. Certain biosynthetic pathways (such as jasmonic acid and ethylene pathways) are often activated in response to plant-defense elicitors present in insect saliva, frass, or oviposition fluids (Acedevo et al. 2019; Hogenhout and Bos 2011; Musser et al. 2005). A repertoire of defenses may be synthesized in a specific manner, dependent on the activation cue (Erb et al. 2012). For example, the saliva of the fall armyworm (Spodoptera frugiperda) contains phytohormones that elicit a variety of responses in different plant species

(Acevedo et al. 2019). This repertoire of defenses is also consistent with Adler and Karban’s

(1994) moving targets model where investments in defense strategies may change to maximize plant fitness.

188

In the case of many insect herbivores, larvae feed on the same tree where they hatched.

The preference–performance hypothesis (a.k.a. the ‘mother-knows-best hypothesis’ or the ‘naïve adaptationist hypothesis’) predicts that insect herbivores should not lay eggs on plants that are heavily fed upon (Courtney and Kibota 1990; Jaenike 1978; Valladares and Lawton 1991). An adaptive strategy is for phytophagous insects to lay eggs on plants that are not heavily consumed

(Clark et al. 2011; Lambert et al. 2018; Mayhew 1997). In the case of nonfeeding adults, we would predict that the induction of defenses would have a negative effect on the root-feeding nymphs. Although we focused on the effects of the female flagging behavior, it is also possible that they are selecting the trees based on the inclusive fitness to their offspring (Birch 2017;

Hamilton 1964). That is, by choosing these particular host trees, they are maximizing fitness for the next generation (and thereby improving their own fitness). This is not mutually exclusive from the results we have discussed above.

During the emergence, oak trees displayed a systemic response with similar concentrations of polyphenols and condensed tannins in damaged and undamaged branches.

There was a differentiation of defensive compounds between damaged and undamaged branches the following year (2020). During the year post-emergence (2020), polyphenol, total tannin, and condensed tannin concentrations were higher in branches that received flagging damage during the emergence, than in undamaged branches.

Non-structural carbohydrate re-allocation As we hypothesized, there was an increase in root non-structural carbohydrate concentrations during the emergence year. Oaks have been shown to increase root storage of non-structural carbohydrates in response to foliar damage (Perkovich and

Ward 2021a; Wiley et al. 2016). If non-structural carbohydrates are being shuttled to root storage

189 in response to foliar damage, periodical cicada nymphs may take advantage of this. Nutritional differences in the xylem influence nymphal growth (White and Lloyd 1985) and excess nutrient availability may stimulate neonatal growth as shown in other insect species (e.g., Cohen 2015;

Woods et al. 2019). Faster growth provides a competitive advantage for the nymphs that generally compete for resources such as space and nutrition (Karban 1981; Lloyd and Dybas

1966; White and Lloyd 1975).

Female cicadas may select oviposition locations based on whether a plant has low defenses or higher non-structural carbohydrate concentrations to increase inclusive fitness

(Hamilton 1964; Birch 2017). There may be selection for periodical-cicada nymphs that fall belowground to feed on a tree that has a higher concentration of non-structural carbohydrates in root storage. Insects are shown to develop faster with increased carbohydrate consumption (Kılcı and Altun 2020; Shen et al. 2017; Silverman 1995).

CONCUSIONS

Oak tree phytochemistry significantly changes in response to periodical-cicada emergences.

Changes in production of chemical defense and nutrient re-allocations are dependent on the plant tissue (i.e., foliar vs. root). In many cases, these changes relax, or return to pre-emergence concentrations the following year. The changes followed by relaxation further support the notion that the trees are directly responding to the periodical-cicada emergence and not to long-term effects of herbivory by other organisms. This is likely an effect of the outbreak phenomenon in periodical cicadas. Changes in chemical defense and nutrient concentrations may be an evolutionary mechanism that allow oaks to maximize their fitness during a period of high stress.

Re-allocating resources to better defend tissue that face immediate threats, as predicted by

190 optimal defense theories (Barto and Cipollini 2005; Rhoades 1979;), is clearly shown by the changes in tannin concentration during the year of an emergence. Periodical-cicada outbreaks not only induce changes in the concentration of plant defenses, but also stimulate a cascade of other phytochemical responses, including the induction of non-structural carbohydrate storage in the roots.

191

REFERENCES

Acevedo FE, Smith P, Peiffer M, Helms A, Tooker J, Felton GW (2019) Phytohormones in fall

armyworm saliva modulate defense response in plants. J Chem Ecol 45:598–609

Adler FR, Karban R (1994) Defended fortresses or moving targets? Another model of inducible

defenses inspired by military metaphors. Am Nat 144:813–832

Barbehenn RV, Constabel CP (2011) Tannins in plant–herbivore interactions. Phytochemistry

72:1551–1565

Barto EK, Cipollini D (2005) Testing the optimal defense theory and the growth–differentiation

balance hypothesis in Arabidopsis thaliana. Oecologia 146:169–178

Bernays EA, Driver GC, Bilgener M (1989) Herbivores and plant tannins. Adv Ecol Res 19:263–

302

Birch J (2017) The inclusive fitness controversy: finding a way forward. R Soc Open Sci

4:e170335

Boyce GR, Gluck-Thaler E, Slot JC, Stajich JE, Davis WJ, James TY, Cooley JR, Panaccione

DG, Eilenberg J, et al (2019) Psychoactive plant- and mushroom- associated alkaloids

from two behavior modifying cicada pathogens. Fungal Ecol 41:147–164

Brown JJ, Chippendale GM (1973) Nature and fate of the nutrient reserves of the periodical (17

year) cicada. J Insect Physiol 19:607–614

Christensen H, Fogel ML (2011) Feeding ecology and evidence for amino acid synthesis in

periodical cicada (Magicicada). J Insect Physiol 57:211–219

Clark KE, Hartley SE, Johnson SN (2011) Does mother know best? The preference–performance

hypothesis and parent–offspring conflict in aboveground–belowground life cycles. Ecol

Entomol 36:117–124

192

Clausen TP, Reichardt PB, Bryant JP, Provenza F (1992) Condensed tannins in plant defense: a

perspective on classical theories. In Hemingway RW, Laks PE (eds) Plant polyphenols:

synthesis, properties, significance. Plenum Press, New York, pp 639–651

Clay K, Shelton AL, Winkle C (2009a) Effects of oviposition by periodical cicadas on tree

growth. Can J For Res 39:1688–1697

Clay K, Shelton AL, Winkle C (2009b) Differential susceptibility of tree species to oviposition

by periodical cicadas. Ecol Entomol 34:277–286

Cohen AC (2015) Insect diets: science and technology. CRC Press, Boca Raton, FL

Cook WM, Holt RD (2002) Periodical cicada (Magicicada cassini) oviposition damage: visually

impressive yet dynamically irrelevant. Am Midl Nat 147:214–224

Cooley J, Kritsky G, Edwards M, Zyla J, Marshall D, Hill K, Simon C (2009) The distribution of

periodical cicada. Am Entomol 55:106–112

Courtney SP, Kibota TT (1990) Mother doesn’t know best: selection of hosts by ovipositing

insects. In Bernays EA (ed) Insect–plant interactions. CRC Press, Boca Raton, FL, pp

161–188

Douma JC, Vermeulen PJ, Poelman EH, Dicke M, Anten NP (2017) When does it pay off to

prime for defense? A modeling analysis. New Phytol 216:782–797

Dybas HS, Davis DD (1962) A population census of seventeen-year periodical cicadas

(Homoptera: Cicadidae: Magicicada). Ecology 43:432–444

Dybas HS, Lloyd M (1974) The habitats of 17-year periodical cicadas (Homoptera: Cicadidae:

Magicicada spp.). Ecol Monog 44:279–324

Erb M, Medlau S, Howe GA (2012) Role of phytohormones in insect-specific plant reactions.

Trends Plant Sci 17:250–251

193

Farahat NM, Zyaan OH, Khaled AS, Hussein M (2018) Toxic and biochemical effects of

imidacloprid and tannic acid on the Culex pipiens larvae (Diptera: Culicidae). Int J Mosq

Res 5:111–115

Flory SL, Mattingly WB (2008) Response of host plants to periodical cicada oviposition damage.

Oecologia 156:649–656

Fiorucci AS (2020) To grow or defend? More on the plant Cornelian dilemma. Plant Physiol

183:437–438

Fournier E (2001) Colorimetric quantification of carbohydrates. Curr Prot Food Anal Chem 1:1–

Frost CJ, Hunter MD (2008) Herbivore-induced shifts in carbon and nitrogen allocation in red

oak seedlings. New Phytol 178:835–845

Gessner MO, Steiner D (2005) Acid butanol assay for proanthocyanidins (condensed tannins). In

Graca M (ed) Methods to study litter decomposition. Springer, Amsterdam, pp 107–114

Graca M, Barlocher F (2005) Radial diffusion assay for tannins. In Graca M (ed) Methods to

study litter decomposition. Springer, Amsterdam, pp 101–105

Hafeez M, Liu S, Jan S, Gulzar A, Fernández-Grandon GM, Qasim M, Khan KA, Ali B, Kedir

SJ, Fahad M, Wang M (2019) Enhanced effects of dietary tannic acid with

chlorantraniliprole on life table parameters and nutritional physiology of Spodoptera

exigua (Hübner). Pestic Biochem Physiol 155:108–118

Hagerman A (1987) Radial diffusion method for determining tannin in plant extracts. J Chem

Ecol 14:453–461

Hagerman A (2011) Tannin Handbook. Miami University, Oxford, OH.

https://www.users.miamioh.edu/hagermae/. Accessed 2 February 2020

194

Hamilton WD (1964) The genetical evolution of social behaviour. I and II. J Theor Biol 7:1–52

Herms DA, Mattson WJ (1992) The dilemma of plants: to grow or defend. Q Rev Biol 67:283–

335

Hogenhout SA, Bos JIB (2011) Effector proteins that modulate plant–insect interactions. Curr

Opin Plant Biol 14:422–428

Huntzinger M, Karban R, Young TP, Palmer TM (2004) Relaxation of induced indirect defenses

of acacias following exclusion of mammalian herbivores. Ecology 85:609–614

IBM Corp. (2019) IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp

Jaenike J (1978) On optimal oviposition behaviour in phytophagous insects. Theor Popul Biol

14:350–356

Karban R (1980) Periodical cicada nymphs impose periodical oak tree wood accumulation.

Nature 287:326–327

Karban R (1981) Effects of local density on fecundity and mating speed for periodical cicadas.

Oecologia 51:260–264

Karban R (1983) Induced responses of cherry trees to periodical cicada oviposition. Oecologia

59:226–231

Karban R (2014) Transient habitats limit developmental time for periodical cicadas. Ecology

95:3–8

Karban R (2020) The ecology and evolution of induced responses to herbivory and how plants

perceive risk. Ecol Entomol 45:1–9

Karban R, Baldwin IT (1997) Induced responses to herbivory. University of Chicago Press,

Chicago

195

Kılcı L, Altun N (2020) The effect of carbohydrates on nutritional preference and development

of Mediterranean flour moth, Ephestia kuehniella Zeller (Lepidoptera: Pyralidae). J

Stored Prod Res 87:e101620

Koenig WD, Liebhold AM (2003) Regional impacts of periodical cicadas on oak radial

increment. Can J For Res 33:1084–1089

Kritsky G (2004) Periodical cicadas: the plague and the puzzle. Indiana Academy of Science,

Indianapolis, IN

Lambert P, Rutter I, Grillas P, Stoks R (2018) Oviposition plant choice maximizes offspring

fitness in an aquatic predatory insect. Hydrobiol 1:1–12

Levins R (1968) Evolution in changing environments. Princeton University Press, Princeton

Lloyd M, Dybas HS (1966) The periodical cicada problem. II. Evolution. Evolution 20:466–505

Marshall DC, Cooley JR (2000) Reproductive character displacement and speciation in

periodical cicadas, with description of a new species, 13-year Magicicada neotredecim.

Evolution 54:1313–1325

Mayhew PJ (1997) Adaptive patterns of plant–host selection by phytophagous insects. Oikos

79:417–428

McCarthy BC, Small CJ, Rubino DI (2001) Composition, structure, and dynamics of Dysart

Woods, an old-growth mixed mesophytic forest of southeastern Ohio. For Ecol Manag

140:193–213

McCormick AC, Irmisch S, Boeckler GA, Gershenzon J, Köllner TG, Unsicker SB (2019)

Herbivore-induced volatile emission from old-growth black poplar trees under field

conditions. Sci Rep 9:1–10

196

Metcalf CJE (2016) Invisible trade-offs: van Noordwijk and de Jong and life-history evolution.

Am Nat 187:iii–v

Miller F, Crowley W (1998) Effects of periodical cicada ovipositional injury on woody plants. J

Arboric 24:248–253

Musser RO, Cipollini DF, Hum-Musser SM, Williams SA, Brown JK, Felton GW (2005)

Evidence that the caterpillar salivary enzyme glucose oxidase provides herbivore offence

in solanaceous plants. Arch Insect Biochem Physiol 58:128–137

Nguyen HQ, Serret H, Bae Y, Ji S, Chae S, Kim YI, Ha J, Jang Y (2020) Not all cicadas increase

thermal tolerance in response to a temperature gradient in metropolitan Seoul. Sci Rep

10:1–12

Orians CM, Ward D (2010) Evolution of plant defenses in nonindigenous environments. Annu

Rev Entomol 55:439–459

Ostry ME, Anderson NA (1983) Infection of trembling aspen by Hypoxylon mammatum through

cicada oviposition wounds. Phytopathol 73:1092–1096

Pearse IS, Hipp AL (2012) Global patterns of leaf defenses in oak species. Evolution 66:2272–

2286

Perkovich C, Ward D (2021a) Herbivore-induced defenses are not under phylogenetic

constraints in the genus Quercus (oak): phylogenetic patterns of growth, defense, and

storage. Evol Ecol in press

Perkovich C, Ward D (2021b) Differentiated defense syndromes in response to varying

herbivore pressures: oak trees increase defenses in response to insects and decrease

nutritive quality in response to deer. Ecological Applications in review

197

Price M, Butler L (1977) Rapid visual estimation and spectrophotometric determination of tannin

content in sorghum grain. J Agri Food Chem 25:1269–1273

Rhoades DF (1979) Evolution of plant chemical defense against herbivores. In Rosenthal GA,

Janzen DH (eds) Herbivores: their interaction with secondary metabolites. Academic

Press, New York, pp 4–53

Shen QD, Yang MM, Xie GQ, Wang HJ, Zhang L, Qui LY, Wang SG, Tang B (2017) Excess

trehalose and glucose affects chitin metabolism in brown planthopper (Nilaparvata

lugens). J Asia Pac Entomol 20:449–455

Silverman J (1995) Effects of glucose-supplemented diets on food intake, nymphal development,

and fecundity of glucose-averse, non-glucose-averse, and heterozygous strains of the

German cockroach, Blattella germanica. Entomol Exp Appl 76:7–14

Simon C (1988) Evolution of 13- and 17-year periodical cicadas (Homoptera: Cicadidae:

Magicicada). Bull Entomol Soc Am 34:163–176

Stamp N (2003) Out of the quagmire of plant defense hypotheses. Q Rev Biol 78:23–55

Tayal M, Somavat P, Rodriguez I, Thomas T, Christoffersen B, Kariyat R (2020) Polyphenol-

rich purple corn pericarp extract adversely impacts herbivore growth and development.

Insect 11:e98

Tomlinson KW, van Langevelde F, Ward D, Bongers F, Da Silva DA, Prins HHT, De Bie S,

Sterck FJ (2013) Deciduous and evergreen trees differ in juvenile biomass allometries

because of differences in allocation to root storage. Ann Bot 112:575–587

Tuomi J, Niemelä P, Rousi M, Siren S, Vuorisalo T (1988) Induced accumulation of foliage

phenols in mountain birch: branch response defoliation? Am Nat 132:602–608

198

Valladares G, Lawton JH (1991) Host-plant selection in the holly leaf-miner: does mother know

best? J Anim Ecol 60:227–240 van Noordwijk AJ, de Jong G (1986) Acquisition and allocation of resources: their influence on

variation in life history traits. Am Nat 128:137–142

Ward D, Young TP (2002) Effects of large mammalian herbivores and ant symbionts on

condensed tannins of Acacia drepanolobium in Kenya. J Chem Ecol 28:921–937

White J (1980) Resource partitioning by ovipositing cicadas. Am Nat 115:1–28

White J (1981) Flagging: hosts defences versus oviposition strategy in periodical cicadas

(Magicicada spp., Cicadidae, Homoptera). Can Entomol 113: 727–738

White J, Lloyd M (1975) Growth rates of 17- and 13-year periodical cicadas. Am Midl Nat

94:127–143

White J, Lloyd M (1985) Effect of habitat size on nymphs in periodical cicadas (Homoptera:

Cicadidae: Magicicada spp.). J Kans Entomol Soc 58:605–610

Wiley E, Casper BB, Helliker BR (2017) Recovery following defoliation involves shifts in

allocation that favour storage and reproduction over radial growth in black oak. J Ecol

105:412–424

Williams KS, Simon C (1995) The ecology, behavior, and evolution of periodical cicadas. Annu

Rev Entomol 40:269–295

Wold EN, Marquis RJ (1997) Induced defense in white oaks: effects on herbivores and

consequences for the plant. Ecology 78:1356–1369

Woods MJ, Hoffman LC, Pieterse E (2019) Artificial diets for neonatal black soldier fly

(Hermetia illucens) larvae. J Insects Food Feed 5:99–105

199

Yang LH, Karban R (2019) The effects of pulsed fertilization and chronic herbivory by

periodical cicadas on tree growth. Ecology 100:e02705

Young TP, Okello BD (1998) Relaxation of an induced defence after exclusion of herbivores:

spines on Acacia drepanolobium. Oecologia 111:508–513

Zangerl AR, Arntz AM, Berenbaum MR (1997) Physiological price of an induced chemical

defense: photosynthesis, respiration, biosynthesis, and growth. Oecologia 109:433–441

Zangerl AR, Rutledge CE (1996) The probability of attack and patterns of constitutive and

induced defense: a test of optimal defense theory. Am Nat 147:599–608

200

CHAPTER VI

PROTEIN:CARBOHYDRATE RATIOS IN THE DIET OF GYPSY MOTH LYMANTRIA

DISPAR AFFECT ITS ABILITY TO TOLERATE TANNINS

This work is published in

Perkovich C, Ward D (2020) Protein:carbohydrate ratios in the diet of gypsy moth Lymantria

dispar affect its ability to tolerate tannins. J Chem Ecol 46:299–307

ABSTRACT

Generalist insect herbivores may regulate nutrient balance in their diets, including the incorporation of carbohydrates as well as proteins. However, secondary metabolites, including tannins, are likely to interact with protein:carbohydrate ratios in insect herbivores. We investigated the effects of protein:carbohydrate ratios, tannin, and the interaction between macronutrient ratios and tannin on the performance of gypsy moth, Lymantria dispar. We designed a 6 x 3 factorial experiment, with six protein:carbohydrate ratios and three tannin concentrations. We monitored the development time and size of gypsy moths on the different diets. We conducted 4th stadium feeding trials to measure consumption, digestibility, and overall efficiency of ingestion/ digestion. Gypsy moths fed a 1:1 protein:carbohydrate ratio without tannin, grew larger and developed faster than those fed a 1:2 protein:carbohydrate ratio.

Increasing protein in the diet above the 1:1 protein:carbohydrate ratio (i.e., 2:1 or 7:1) did not

201 have a significant effect on gypsy moth growth or development. Approximate digestibility was greatest in treatments with a low protein:carbohydrate ratio (1:2). Gypsy moths grew faster and larger on no-tannin diets than those with tannin in the diet. However, the specific concentration of tannin did not affect growth. The results of the interaction between protein:carbohydrate ratio and tannin showed that there may be a trade-off between development time and efficiency of food assimilation. We also found that feeding gypsy moths an optimal protein:carbohydrate ratios may be more important for tolerating tannin than the amount of protein ingested alone.

202 INTRODUCTION

Nutrients, allelochemicals, and physical structures affect an insect’s ability to consume plants.

Optimal defense theory suggests that plants will defend themselves using physiological and chemical changes whenever necessary (Feeny 1976; Rhoades and Cates 1976). Diversification of plant defense strategies has generated several hypotheses for determining which strategy is evolutionarily advantageous under specific conditions and herbivore pressures (Agrawal 2007;

Coley 1983; van der Meijden et al. 1988). The complexity of biochemical changes such as nutrient availability and production of secondary compounds has become an increasingly important topic of plant-defense research (e.g., Gonçalves et al. 2016; Züst and Agrawal 2017).

Plant secondary metabolites produced in response to herbivory may reduce insect feeding by decreasing nutrient quality through production of entomotoxic proteins (Franco et al. 2002;

Negreiros et al. 1991) that disrupt physiological processes such as digestion (Wetzel et al. 2016).

Many studies conducted on the effects of toxins and nutrients in the diets of insect herbivores focus on the amount of protein in the diet. Insects have been found to increase consumption and utilization efficiencies of poor-quality foods to counter protein limitations in their diets (Felton 1996; Slansky and Wheeler 1989). However, protein may not be the only important factor. A study by Behmer (2009) suggested that generalist insect herbivores may also regulate nutrient balance in their diets when possible, including carbohydrates as well as proteins. To do this, insects employ pre- and post-ingestive regulatory methods to maintain a nutritional balance of protein and carbohydrates. However, to our knowledge, no study appears to have addressed the relationships between protein:carbohydrate balance and plant secondary metabolites.

203 Several theoretical models have been proposed to depict nutritional interactions between the ecological environment (biotic and abiotic) and organisms (considered as the organism’s function, mechanism, development, and history as described by Tinbergen (1963)). Proposed models include Optimal Foraging Theory (MacArthur and Pianka 1966; Pyke et al. 1977),

Geometric Framework (Raubenheimer and Simpson 1993, 1994, 1997; Simpson and

Raubenheimer 1993; 1995; 1999), and Ecological Stoichiometry (Elser 2006). In this study, we manipulated macronutrient ratios (protein:carbohydrate) to use the Geometric Framework. The

Geometric Framework is a nutritionally explicit model that uses key variables from other frameworks to disambiguate effects of individuals and interactions of insects and nutritional components.

Plant defense compounds such as secondary metabolites, like tannins, may reduce growth of insect herbivores and decrease plant tissue digestibility. (Feeny 1968; Schultz 1989). Tannins are a subclass of polyphenols that have been shown to decrease protein digestion in insect herbivores by forming tannin–protein precipitates with dietary proteins (Barbehenn and

Constabel 2011; Bernays et al. 1989). Access to protein in insect diets is considered to help detoxify plant defenses that decrease host-plant nutrient quality (Lemoine and Shantz 2016;

Moise et al. 2019).

Fluctuations in plant chemistry and physiology may affect the population dynamics of many herbivorous insects (Altieri et al. 1984; Elkinton and Liebhold 1990; Muller and Orians

2018). For example, Liebhold et al. (2000) found that inter-annual fluctuations in gypsy moth

(Lymantria dispar) populations were significantly influenced by phytochemical changes in host foliage quality. The gypsy moth is a well-known example of a generalist insect herbivore that feeds on over 500 different species of plants, favoring species of Quercus, Populus, and Larix

204

(Davidson et al. 1999; Elkinton and Liebhold 1990; Sharov et al. 2002). There is a negative correlation between gypsy moth performance and concentrations of phenolics (including tannins) and protein in the diet (Lindroth and Bloomer 1991). Previous studies suggest that generalist insect herbivores may perform well on host plants containing toxic compounds if the nutritional quality of the host plant is optimal (e.g., Boeckler et al. 2014; Couture 2016).

In this study, we focused on the importance of macronutrient ratios

(protein:carbohydrate) in insect diets. We wanted to assess the interactive effects of nutrient ratios and allelochemicals (tannin) on gypsy moth performance. Dietary proteins and macronutrient ratios are known to affect gypsy moth performance (Rossiter 1987; Stockhoff

1993a). Furthermore, tannins are a common constituent of gypsy moth diets and may reduce larval performance (Lazarevic et al. 2017; Rossiter et al. 1988). Due to the protein-binding potential of tannins, we hypothesized that larvae fed diets with high protein:carbohydrate ratios and low tannin concentrations would (1) grow larger; (2) develop faster; and (3) have better performance in terms of nutritional indices than larvae fed diets with low protein:carbohydrate ratios and high tannin concentrations. We also hypothesized that larvae fed diets with high protein:carbohydrate ratios and high tannin concentrations would be able to eliminate tannin more readily than larvae fed diets with low protein:carbohydrate ratios and high tannin concentrations (Lazarevic et al. 2017; Rossiter et al. 1988).

METHODS

Insect Rearing Lymantria dispar eggs were provided by the USDA-APHIS, Otis Air National

Guard Base, Massachusetts. Larvae were reared in an environmentally controlled chamber

205

(25°C, 60% R.H., 14:10 h L:D period). Individual caterpillars were placed in clear plastic 2 oz. cups. Each larva received a weighed amount of a specific treatment diet.

Gypsy Moth Diets The artificial diet from Frontier Scientific (#F9630B, USDA Hamden

Formula) was used as a baseline for dietary needs, vitamins, and antibiotics. We used a 6 x 3 factorial experimental design with 6 levels of macronutrient ratios (protein:carbohydrate – 1:7,

1:5, 1:2, 1:1, 2:1, and 7:1) and three levels of % dry weight hydrolysable tannin (no tannin, 2.5%, and 5%). The base diet was the Frontier Scientific gypsy moth diet with known protein:carbohydrate ratios. We made additions of dextrose and casein to adjust the macronutrient (protein:carbohydrate) ratios. We added cellulose, a non-nutritional compound, to standardize the amount of water in the diets so that moisture content would not be a covariate between treatments. Tannic acid (purchased from Sigma-Aldrich Co., St. Louis, Missouri) was used to represent tannins in the diet because it is a type of hydrolysable tannin that is commonly found in many plants (Dawra et al. 1988; Feeny 1970). Gypsy moth larva can survive on diets containing high percentages of tannins (e.g., Schultz and Baldwin 1982). We were interested in establishing how tannin concentrations interact with macronutrient ratios and chose hydrolysable-tannin concentrations that would ensure survival (Feeny and Bostock 1968). We added tannic acid so that tannin was equal to 2.5% and 5% of the dry weight in the low-tannin and high-tannin treatments, respectively.

Effects of Diet on Gypsy Moth Performance Food was weighed and equally dispensed for each specific treatment. Cups were cleaned and replenished daily. Remaining food and frass

206 were collected, dried, and weighed. Remaining food was subtracted from the original amount given for each individual larva to calculate the amount eaten. For standardization of initial mass of food given and mass of food uneaten, we used a wet-to-dry mass regression. At the end of each stadia, dry masses of food and frass were used to calculate nutritional indices. Nutritional indices included approximate digestibility (AD), efficiency of conversion of ingested food (ECI), efficiency of conversion of digested food (ECD), relative growth rate (RGR) and relative consumption rate (RCR) from standard formulas (Scriber 1977; Waldbauer 1968). Once larvae molted into the 5th stadium, they were frozen, dried, and weighed. There is generally a difference in size between newly molted male and female 5th stadium larvae, but we did not find any significant differences in larval weight among the sexes and therefore did not separate them. The amount of tannin excreted in the frass was measured using a radial diffusion assay standardized with tannic acid, as described by Hagerman (2011).

Statistical Analyses Larval performance data were analyzed by multivariate analysis of variance

(MANOVA) because we had multiple dependent variables and wanted to minimize Type I statistical error. For those variables that were significant in the MANOVA, we used univariate

(ANOVA) tests followed by a Scheffé post hoc test of the main effects. We used IBM SPSS version 24 (IBM 2016).

207

RESULTS

To work within the parameters of the Geometric Framework, we used six macronutrient ratios

(see methods). However, due to high mortality and logistical reasons, the two lowest protein:carbohydrate ratios (1:7 and 1:5 macronutrient ratios) were not used in the analysis.

There were no statistical differences in larval development or nutritional indices until the

4th stadium (P range = 0.198–0.878). Because of this, only the 4th stadium data are discussed. A

MANOVA showed a statistically significant difference in gypsy moth performance for protein:carbohydrate ratio (Wilks’ λ = 0.561, P < 0.001), tannin concentration (Wilks’ λ = 0.002,

147 P < 0.001), and the interaction between protein:carbohydrate ratio and tannin concentration

(Wilks’ λ < 0.001, P < 0.001). Consequently, we used univariate ANOVA to test for significance of the dependent variables (Table 10; see below).

208

Table 10. Univariate ANOVA results of macronutrient ratio and tannin on growth and nutritional indices of fourth-instar gypsy moths. RGR = relative growth rate, RCR = relative consumption rate, AD = approximate digestibility, ECI = efficiency of conversion of ingested food, ECD = efficiency of conversion of digested food, TAE = tannic acid equivalents.

Protein:carbohydrate Protein:carbohydrate Tannin concentration ratio X tannin ratio concentration F P F P F P Fresh mass (g) 20.53 <0.001 2.91 0.058 6.57 <0.001 Dry mass (g) 3.71 0.007 0.962 0.385 2.07 0.044 Stadium duration (days) 60.94 <0.001 15.99 <0.001 11.12 <0.001 RGR (g/g/day) 27.58 <0.001 2.54 0.084 18.54 <0.001 RCR (g/g/day) 539.23 <0.001 799.65 <0.001 475.01 <0.001 AD (%) 7.91 <0.001 17.9 <0.001 3.58 0.001 ECI (%) 4.65 0.002 2.01 0.138 3.24 0.002 ECD (%) 3.83 0.006 2.43 0.093 2.38 0.008 Frass mass (g) 48.76 <0.001 120.35 <0.001 21.06 <0.001 Tannin concentration in frass (TAE) 253.93 <0.001 1587.23 <0.001 118.81 <0.001

Gypsy Moth Growth and Development Larval dry mass was significantly higher for larvae fed diets with high protein:carbohydrate ratios and high tannin content (Fig. 26A; F = 2.071, P =

0.04). Larval fresh mass increased as protein:carbohydrate ratios and tannin concentrations increased (Fig. 26B; F = 6.57, P < 0.001). With a minimum protein:carbohydrate ratio of 1:1, increasing tannin in the diet did not significantly increase development rates. At the lowest macronutrient ratio (1:2 protein:carbohydrate ratios), larvae fed diets with a high tannin concentration took longer to mature into the next stadium than larvae with low or no tannin (Fig.

27; P < 0.001, and P < 0.001, respectively). In contrast, larvae fed diets with macronutrient ratios at or above the 1:1 protein:carbohydrate ratio did not show significant differences in growth rate

209 with varying tannin concentrations (P range of 0.143–0.936). Larvae fed diets with the lowest protein:carbohydrate ratio had significantly longer stadia than any other macronutrient ratio (P <

0.001 for all treatment ratios).

AA B

Figure 26. Gypsy moth growth and development on diets with varying macronutrient ratios and tannin concentrations. A) The mean dry mass of larvae after molting into the 5th stadium and B) the mean fresh mass of larvae after molting into 5th stadium. Open diamond = No-tannin diet, open circle = low-tannin diets, and solid diamond = high-tannin diets.

210

Figure 27. The mean number of days for gypsy moth pupation from the 4th to the 5th stadium on diets with varying macronutrient ratios and tannin concentrations. Open diamond = No-tannin diet, open circle

= low-tannin diets, and solid diamond = high-tannin diets.

Gypsy Moth Consumption, Digestibility, and Conversion Efficiency High tannin concentrations significantly decreased efficiency of conversion of ingested food (ECI) when protein:carbohydrate ratios were greater than 1:1 (Fig. 28A; F = 3.24, P = 0.002). Tannin alone did not influence gypsy moth ECI (F = 2.01, P = 0.138). ECI was highest for diets containing a

1:1 protein:carbohydrate ratio (P = 0.023). Except for the low protein:carbohydrate ratio (1:2), there is a general trend that high-tannin diets have a higher approximate digestibility (AD) (Fig.

28B; F = 7.91, P = 0.001). Diets with the lowest macronutrient ratio (1:2) had an opposite trend, where greater tannin concentrations had a significantly lower AD (P = 0.022).

Larvae fed the lowest protein:carbohydrate ratio (1:2) had a significantly lower RCR than other protein:carbohydrate ratios (Fig. 28C; P < 0.001 for all treatments). For the 1:2 protein:carbohydrate ratio, larvae with no tannin diets consumed significantly more than larvae fed the high tannin treatments (P < 0.001).

211

A B

Figure 28. Gypsy moth consumption, conversion efficiency, and food digestibility on diets with varying

macronutrient ratios and tannin concentrations. A) The mean efficiency of conversion of ingested food

(ECI) for 4th stadia larvae, B) the mean approximate digestibility (AD) of food by larvae in the 4th stadium, and C) the mean relative consumption rate (RCR) of larvae during the 4th stadium. Open diamond = No-tannin diet, open circle = low-tannin diets, and solid diamond = high-tannin diets.

212

Gypsy Moth Excretion of Frass and Tannin As protein:carbohydrate ratios increased and tannin concentrations decreased in the diet, larvae excreted a greater amount of frass (Fig. 29A;

F = 21.06, P < 0.001). The exception to this pattern were larvae fed diets with a 1:2 protein:carbohydrate ratio. Larvae on this diet had significantly greater amounts of frass excreted with high tannin concentrations in the diet (P < 0.05 for all tannin treatments).

There were significantly higher concentrations of tannin excreted in the frass of larvae fed a 2:1 protein:carbohydrate and high tannin diet (Fig. 29B; F = 118.81, P < 0.001) than larvae on any other diet. There was a trend of increasing tannin concentration in the frass as the protein:carbohydrate ratio increased, peaking at the 2:1 protein:carbohydrate ratio. Larvae fed diets with a 7:1 protein:carbohydrate ratio exhibited a significant decrease in tannin concentration in the frass. As tannin concentration increases in the diet, the concentration of tannin excreted in the frass increased proportionately across each protein:carbohydrate ratios

(ANCOVA with protein:carbohydrate ratio as the covariate, F = 121.96, P < 0.001). In general, the tannin concentration in the frass increased as protein:carbohydrate ratios increased, peaking at the 2:1 protein:carbohydrate ratio. The 7:1 protein:carbohydrate ratio had the lowest tannin concentration in the frass, regardless of the tannin concentration.

213

A B

Figure 29. Gypsy moth frass excretion amount and tannin concentrations on varying diets of macronutrients and tannin. A) the mean frass excreted from individual larvae during the 4th stadium (g) and B) the mean concentration of tannin in tannin acid equivalents (TAE) of larvae during the 4th stadium. Open diamond = No-tannin diet, open circle = low-tannin diets, and solid diamond = high-tannin diets.

DISCUSSION

Early insect nutritional ecologists focused on the importance of protein in the insect’s diet (e.g.,

Crawley 1983; White 1993). In the past few decades, insect nutritional ecologists have developed new frameworks, such as the Geometric Framework, to capture the dynamic variables involved in optimal nutrient acquisition (Raubenheimer and Simpson 1994, 1997; Simpson and

Raubenheimer 2001). Few studies have examined the relationship between macronutrient ratios and other nutritional elements such as tannins (Simpson and Raubenheimer 2001; Trier and

Mattson 2003). Many studies have suggested that tannins decrease available dietary proteins

(e.g., Arnold and Schultz 2002; Peters and Constabel 2002; War et al. 2012), begging the question of how nutrient ratios effect herbivore performance when tannins are present.

Vegetative leaf quality may vary due to phenological, ontogenetic, and physiological changes

214 within a plant (Baldwin et al. 1987; Baldwin and Schultz 1983; Muiruri et al. 2019). Gypsy moths are generalist herbivores who must deal with continually changing plant phytochemistry.

This study produced several anomalies that contradicted our original predictions. Due to high mortality rates with relatively low protein:carbohydrate ratio diets (1:7 and 1:5) and logistical reasons, we were unable to fully compare the impact of low protein:carbohydrate ratio diets on gypsy moth larval growth and nutritional indices. We were still able to ascertain valuable information about how macronutrient ratios effect gypsy moth larvae’s ability to perform on tannins.

As we predicted, larvae fed diets with higher protein:carbohydrate ratio weighed more than those with lower protein:carbohydrate ratio diets. Surprisingly, tannin increased larval weight as well. In this last-mentioned regard, Hemming and Lindroth (1995) also found a positive correlation between tannin concentrations and pupal mass, but did not explicitly address this issue. One possible reason may be that lipophorins, (a class of lipoproteins that are formed in the insect hemolymph to transport lipids for absorption and storage (Chino et al. 1981) and believed to bind to tannins (Martin and Martin 1984; Tebib et al. 1994)), are unable to be transported once bound in a tannin–protein precipitate and therefore accumulate. Further research is needed to understand the relationship between increased body mass and tannin.

While teasing apart multivariate interactions is a daunting task, there is one key pattern that stands out in our data. Tannin has a reverse effect when protein:carbohydrate ratios fall below a 1:1 ratio on many of the response variables measured (i.e., stadium duration, approximate digestibility, relative consumption rate, and frass dry mass). For example, we expected that larvae with tannin in their diet should have a reduction in approximate digestibility

(AD) because tannins may inhibit protein digestion in some insects (Feeny 1970; Hafeez et al.

215

2019; Klocke and Chan 1982). At a 1:2 protein:carbohydrate ratio, tannin did decrease AD of the food, but at protein:carbohydrate ratios of 1:1 or greater, tannin increased the AD of the food.

Locusts fed on grasses with high protein:carbohydrate ratios are unable to assimilate as much protein as those fed on grasses with lower protein:carbohydrate ratios (Clissold et al.

2006). Clissold et al. (2006) concluded that excess protein in the diet impeded adequate assimilation of protein into biomass. This could explain why gypsy moths more readily digest high protein diets when tannin concentrations are higher. Diets with excess protein and inadequate carbohydrate availability may not be optimal for gypsy moths to properly use the protein. Adding tannin, a protein-binding molecule, may bind to the excess protein, enabling gypsy moth larvae to better digest food with higher protein:carbohydrate ratios.

Despite a higher approximate digestibility of food, the development time was much longer for the larvae fed diets with a low protein:carbohydrate ratio than those with a high protein:carbohydrate ratio. There appears to be a tradeoff between stadium duration and AD. It may be that nutrients are more efficiently used by gypsy moth larvae on low protein:carbohydrate ratio diets at the cost of development time. For example, Trakimas et al.

(2019) found a trade-off between nutrient assimilation and development speed in western stutter- trilling crickets (Gryllus integer). Crickets that had significantly higher body carbon developed more slowly than those that had significantly higher protein concentrations in their body

(Trakimas et al. 2019). We found that at low protein:carbohydrate ratios, gypsy moths consumed food at a slower rate, and consequently grew more slowly. However, the larvae from low protein:carbohydrate ratio foods had a much higher AD. These larvae consumed less food but incorporated more of it into growth and metabolic functions at the cost of increased stadium duration. This may have to do with the gypsy moths’ ecology. Gypsy moths are known to slow

216 growth and consumption during the 4th and 5th stadium. Our data is from the 4th stadium, where the larvae may be going through physiological changes, and are more likely to allocate nutrients to physiological aspects of development. There may be a tradeoff for the gypsy moth larvae on the low protein:carbohydrate ratio diets to allocate nutrients to physiological changes at the cost of overall development rate (i.e., it may be more important to take a longer time and properly mature than to quickly develop and not be properly matured). Tannin in the diet further increased stadium duration (Fig. 28). Our findings are consistent with other studies that found that phenolics in the diet of gypsy moths increased the time needed to develop from the 4th instar into the 5th instar (Hemming and Lindroth 1995; Roth et al. 1994).

For high tannin diets, the efficiency of conversion of ingested food (ECI) decreased with increasing protein:carbohydrate ratios, for all diets except the 1:2 protein:carbohydrate ratio diet.

This means that increasing the protein in the diet did not aid gypsy moths’ conversion of food ingested as we predicted it would.

Again, there is a reversed effect of tannin on the amount of frass produced. Tannin increased the amount of frass produced by gypsy moth larvae for low protein:carbohydrate ratios

(1:2) but decreased frass produced for all other macronutrient ratios (Fig. 29A). Decreased frass production with increased protein:carbohydrate ratios again support the previous idea that tannin–protein precipitates are accumulating in the gypsy moths’ system.

Regarding tannin found in the frass, our findings support the idea that generalist herbivores, such as gypsy moths, have found a way to tolerate tannins in the diet. Mechanisms for tolerating tannins have been found in several other generalist insect herbivore species (e.g.,

Barbehenn and Martin 1992; Berenbaum 1983; Martin et al. 1987). Herbivores may use lysophospholipids to bind to proteins (De Veau and Schultz 1992) or alter gut pH to be more

217 basic (Appel and Maines 1995; Martin and Martin 1984) to minimize negative effects of tannins in their diets. It is possible that physiological processes for eliminating the effects of tannins might have altered tannin structure so that it did not register in our radial diffusion assay.

Is there an optimal protein:carbohydrate ratio? Insects are not always limited by protein in their diets. For example, Schroeder (1986) studied yellow-necked caterpillars, Datana ministra, that were fed American basswood/linden, Tilia americana (Malvaceae) with and without added protein. When receiving high-protein diets, Schroeder found that these caterpillars developed faster and did not metabolize the protein compared to those caterpillars receiving no protein supplement. Schroeder (1986) concluded that additional protein in the diet may impose an osmotic imbalance, leading to improper assimilation of excess proteins. Optimal macronutrient ratios may also reflect ontogenetic changes as gypsy moths mature (Stockhoff 1993a). Stockhoff

(1993b) further found that mean protein in the gypsy moth diet cannot be used as a predictor of growth and that macronutrient ratio interactions in the diet may play a more important role in protein utilization and assimilation than the amount of protein alone. Furthermore, a study by

Trier and Mattson (2003) found that as carbohydrate increased in the diet of budworm

(Choristoneura fumiferana), the larvae were able to assimilate more protein into growth. Diets that contained high protein, but low carbohydrates, had a lower assimilation of protein into growth and allocated more protein into metabolic processes (diet-induced thermogenesis) (Trier and Mattson 2003).

To our knowledge, there have been no studies that define the optimal diet for gypsy moth larvae (Lymantria dispar). Numerous studies have repeatedly found that gypsy moth larvae perform better on high protein diets (e.g., Jankovic-Tomanic and Lazarevic 2012; Lindroth et al.

218

2008; Stockhoff 1993b). However, no studies have claimed an optimal protein:carbohydrate ratio. The artificial diet that we used is a standard diet produced by the USDA and commonly used in laboratory experiments. Grayson et al. (2015) showed that gypsy moths perform better on natural hosts than on artificial diets. The reason for this has yet to be explained. The USDA artificial diet consists of a protein:carbohydrate ratio of 7:1. We assumed at the start of the experiment that the optimal diet was high protein, (in line with Optimal Foraging Theory assumptions) and that the artificial diet used, mimicked the gypsy moth’s optimal diet. Our study suggests that when tannin is not present, larvae fed a diet with 7:1 protein:carbohydrate ratio grew larger than lower protein:carbohydrate diets, but do not develop faster when they are fed a

1:1 protein:carbohydrate ratio or greater. Furthermore, we also found in this study that larvae fed a diet with a 7:1 protein:carbohydrate ratio without tannin also have a higher relative consumption rate which may account for the larger size. When tannin is present in the diet, we found that there are trade-offs between larger size and amount of time needed to develop.

Specifically, larvae with high tannin concentrations tend to be larger, but also develop more slowly.

CONCLUSIONS

Our study suggests that generalist herbivores, such as the gypsy moth, may require optimal protein:carbohydrate ratios rather than just high amounts of protein depending on the presence of tannin in the diet. Many studies have shown the positive effects of increased protein alone (Lee et al. 2010; Stockhoff 1993a) and negative effects of tannin (Feeny 1968; Schultz 1989). We need to further analyze how generalist herbivores may use fitness tradeoffs or alter diets to achieve optimal macronutrient ratios in response to plant secondary metabolites. The need to

219 analyze macronutrient ratios rather than individual macronutrient amounts supports the

Geometric Framework over the classical Optimal Foraging Theory approach.

220

REFERENCES

Agrawal A (2007) Macroevolution of plant defense strategies. Trends Ecol Evol 22:103–109

Altieri M, Letourneau D, Risch S (1984) Vegetation diversity and insect pest outbreaks. Crit Rev

Plant Sci 2:131–169

Appel H, Maines L (1995) The influence of host plant on gut conditions of gypsy moth

(Lymantria dispar) caterpillars. J Insect Physiol 41:241–246

Arnold TM, Schultz JC (2002) Induced sink strength as a prerequisite for induced tannin

synthesis in developing leaves of Populus. Oecologia 130:585–593

Baldwin IT, Schultz JC (1983) Rapid changes in tree leaf chemistry induced by damage:

evidence for communication between plants. Science. 221:277–279

Baldwin IT, Schultz JC, Ward D (1987) Patterns and sources of leaf tannin variation in yellow

birch (Betula alleghenisnsis) and sugar maple (Acer saccharum). J Chem Ecol 13:1069–

1078

Barbehenn R, Constabel CP (2011) Tannins in plant–herbivore interactions. Phytochemistry

72:1551–1565

Barbehenn R, Martin M 1(992) The protective role of the peritrophic membrane in tannin

tolerant larvae of Orgyia leucostigma (Lepidoptera). J Insect Physiol 38:973–980

Behmer S (2009) Insect herbivore nutrient regulation. Annu Rev Entomol 54:165–187

Berenbaum M (1983) Effects of tannins on growth and digestion in two species of papilinoids.

Entomol Exp Appl 34:245–250

Bernays EA, Driver GC, Bilgener M (1989) Herbivores and plant tannins. In Begon M, Fitter A,

Ford E, Macfayden A (eds). Advances in ecological research vol 19. Elsevier, London,

pp 263–302

221

Boeckler GA, Towns M, Unsicker, SB, Mellway RD, Yip L, Hilke I, Gershenzon J, Constabel

CP (2014) Transgenic upregulation of the condensed tannin pathway in poplar leads to a

dramatic shift in leaf palatability for two tree-feeding Lepidoptera. J Chem Ecol 40:150–

158

Chino H, Downer RH, Wyatt GR, Gilbert LI (1981) Lipophorins, a major class of lipoproteins of

insect haemolymph. Insect Biochem 11:e491

Clissold F, Sanson G, Read J (2006) The paradoxical effects of nutrient ratios and supply rates

on an outbreaking insect herbivore, the Australian plague locust. J Anim Ecol 75:1000–

1013

Coley PD (1983) Herbivory and defensive characteristics of tree species in a lowland tropical

forest. Ecol Monogr 53:209–234

Couture J, Mason C, Habeck C, Lindroth R (2016) Behavioral and morphological responses of

an insect herbivore to low nutrient quality are inhibited by plant chemical defenses.

Arthropod Plant Interact 10:341–349

Crawley MJ (1983) Herbivory: The dynamics of animal–plant interactions. Blackwell Scientific

Publications, Oxford, UK

Davidson CB, Gottschalk KW, Johnson JE (1999) Tree mortality following defoliation by the

European gypsy moth (Lymantria dispar L.) in the United States: a review. For Sci

45:74–84

Dawra RK, Makkar HPS, Singh B (1988) Total phenolics, condensed tannins, and protein-

precipitable phenolics in young and mature leaves of oak species. J Agric Food Chem

36:951–953

222

De Veau EI, Schultz JC (1992) Reassessment of interaction between gut detergents and tannins

in Lepidoptera and significance for gypsy moth larvae. J Chem Ecol 18:1437– 1453

Elkinton J, Liebhold A (1990) Population dynamics of gypsy moth in North America. Annu Rev

Entomol 35:571–596

Elser J (2006) Biological stoichiometry: A chemical bridge between ecosystem ecology and

evolutionary biology. Am Nat 168:25–35

Feeny P (1968) Effect of oak leaf tannins on larval growth of the winter moth Operophtera

brumata. Insect Physiol 14:805–817

Feeny P (1970) Seasonal changes in oak leaf tannins and nutrients as a cause of spring feeding

by winter moth caterpillars. Ecology 51:565–581

Feeny P (1976) Plant apparency and chemical defense. In Wallace JW, Mansell RL (eds)

Biochemical interaction between plants and insects. Springer, Boston, MA, pp 1–40

Feeny P, Bostock H (1968) Seasonal changes in the tannin content of oak leaves.

Phytochemistry 7:871–880

Felton GW (1996) Nutritive quality of plant protein: sources of variation and insect herbivore

responses. Arch Insect Biochem Physiol 32:107–130

Goncalves AZ, Mercier H, Oliveira RS, Romero GQ (2016) Trade-off between soluble protein

production and nutritional storage in Bromeliaceae. Ann Bot 118:1199–1208

Grayson KL, Parry D, Faske TM, Hamilton A, Tobin PC, Agosta SI, Johnson DM (2015)

Performance of wild and laboratory-reared gypsy moth (Lepidoptera: Erebidae): A

comparison between foliage and artificial diet. Environ Entomol 44:864–873

223

Hafeez M, Liu S, Jan S, Gulzar A, Fernandez-Grandon GM, Qasim M et al (2019) Enhanced

effects of dietary tannic acid with chlorantraniliprole on life table parameters and

nutritional physiology of Spodoptera exigua. Pestic Biochem Physiol 155:108–118

Hagerman A (2011) Tannin handbook. Miami University, Oxford, OH.

https://www.users.miamioh.edu/hagermae/. Accessed 5 May 2019

Hemming JD, Lindroth RL (1995) Intraspecific variation in aspen phytochemistry: effects on

performance of gypsy moths and forest tent caterpillars. Oecologia 103:79–88

IBM Corp. (2016) IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY: IBM Corp

Jankovic-Tomanic M, Lazarevic J (2012) Effects of temperature and dietary nitrogen on genetic

variation and covariation in gypsy moth larval performance traits. Arch Biol Sci

64:1109–1116

Klocke J, Chan B (1982) Effects of cotton condensed tannin on feeding and digestion in the

cotton pest, Heliothis zea. J Insect Physiol 28:911–915

Lazarevic J, Jankovic-Tomanic M, Savkovic U, Dordevic M, Milanovic S, Stojkovic B (2017)

Host-associated divergence in the activity of digestive enzymes in two populations of the

gypsy moth Lymantria dispar (Lepidoptera: Erebidae). Entomol Sci 20:189–194

Lee KP, Simpson SJ, Wilson K (2008) Dietary protein-quality influences melanization and

immune function in an insect. Funct Ecol 22:1052–1061

Lemoine NP, Shantz AA (2016) Increased temperature causes protein limitation by reducing the

efficiency of nitrogen digestion in the ectothermic herbivore Spodoptera exigua. Physiol

Entomol 41:143–151

Liebhold A, Elkinton J, Williams D, Muzika RM (2000) What causes outbreaks of gypsy moth

in North America? Popul Ecol 42:257–266

224

Lindroth RL, Bloomer MM (1991) Biochemical ecology of the forest tent caterpillar: responses

to dietary protein and phenolic glycosides. Oecologia 86:408–413

Lindroth RL, Klein KA, Hemming JDC, Feucker AM (2008) Variation in temperature and

dietary nitrogen affect performance of the gypsy moth (Lymantria dispar L.). Physiol

Entomol 22:55–64

MacArthur RH, Pianka ER (1966) On optimal use of a patchy environment. Am Nat 100:603–

609

Martin J, Martin M, Bernays E (1987) Failure of tannic acid to inhibit digestion or reduce

digestibility of plant protein in gut fluids of insect herbivores. J Chem Ecol 13:605–621

Martin M, Martin J (1984) Surfactants: their role in preventing the precipitation of proteins by

tannins in insect guts. Oecologia 61:342–345

Moise ER, McNeil JN, Hartley SE, Henry HA (2019) Plant silicon effects on insect feeding

dynamics are influenced by plant nitrogen availability. Entomol Exp Appl 167:91–97

Muiruri EW, Barantal S, Iason G, Salminen JP, Perez-Fernandez E, Koricheva J (2019) Forest

diversity effects on insect herbivores: do leaf traits matter? New Phytol 221:2250–2260

Muller C, Orians CM (2018) From plants to herbivores: novel insights into the ecological and

evolutionary consequences of plant variation. Oecologia 187:357–360

Negreiros ANM, Carvalho MM, Xavier-Filho J, Blanco-Labra A, Shewry PR, Richardson M

(1991) The complete amino acid sequence of the major Kunitz trypsin inhibitor from the

seeds of Prosopsis juliflora. Phytochemistry 30:2829–2833

Peters DJ, Constabel CP (2002) Molecular analysis of herbivore-induced plant volatilesin maize.

Planta 234:207–215

225

Pyke GH, Pulliam HR, Charnov DL (1977) Optimal foraging: a selective review of theory and

tests. Q Rev Biol 52:137–154

Raubenheimer D, Simpson SJ (1993) The geometry of compensatory feeding in the locust. Anim

Behav 45:953–964

Raubenheimer D, Simpson SJ (1994) The analysis of nutrient budgets. Funct Ecol 8: 783–791

Raubenheimer D, Simpson SJ (1997) Integrative models of nutrient balancing: application to

insects and vertebrates. Nutr Res 10:151–179

Rhoades DF, Cates RG (1976) Toward a general theory of plant antiherbivore chemistry. In

Wallace J, Mansell R (eds) Biochemical interaction between plants and insects. Springer,

Boston, MA, pp 168–213

Rossiter M (1987) Genetic and phenotypic variation in diet breadth in a generalist herbivore.

Evol Ecol 1:272–282

Rossiter M, Schultz JC, Baldwin IT (1988) Relationships among defoliation, red oak phenolics,

and gypsy moth growth and reproduction. Ecology 69:267–277

Roth SK, Lindroth RL, Montgomery ME (1994) Effects of foliar phenolics and ascorbic acid on

performance of the gypsy moth (Lymantria dispar). Biochem Syst Ecol 22:341–351

Schroeder LA (1986) Protein limitation of a tree leaf feeding lepidopteran. Entomol Exp Appl

41:115–120

Schultz JC (1989) Tannin–insect interactions. In Hemingway J, Karchesy J, Branham S (eds)

Chemistry and significance of condensed tannins. Springer, Boston, MA, pp 417–433

Schultz JC, Baldwin IT (1982) Oak leaf quality declines in response to defoliation by gypsy

moth larvae. Science 217:149–151

226

Sharov AA, Leonard D, Liebhold AM, Roberts EA, Dickerson W (2002) "Slow the

spread": a national program to contain the gypsy moth. J For 100:30–36

Simpson SJ, Raubenheimer D (1993) Multi-level analysis of feeding behavior: geometry of

nutritional decisions. Phil Trans R Soc Lond B 342:381–402

Simpson SJ, Raubenheimer D (1995) The geometric analysis of feeding and nutrition: a user's

guide. J Insect Physiol 7:545–553

Simpson SJ, Raubenheimer D (1999) Assuaging nutritional complexity: a geometric approach.

Proc Nutri Soc 58:779–789

Simpson SJ, Raubenheimer D (2001) The geometric analysis of nutrient–allelochemical

interactions: a case study using locusts. Ecology 82:422–439

Slansky F, Wheeler GS (1989) Compensatory increases in food consumption and utilization

efficiencies by velvet bean caterpillars mitigate impact of diluted diets on growth.

Entomol Exp Appl 51:175–187

Stockhoff BA (1993a) Ontogenetic change in dietary selection for protein and lipid by gypsy

moth larvae. J Insect Physiol 39:677–686

Stockhoff BA (1993b) Diet heterogeneity: implications for growth of a generalist herbivore, the

gypsy moth. Ecology 74:1939–1949

Tebib K, Besancon P, Rouanet JM (1994) Dietary grape seed tannins affect lipoproteins,

lipoprotein lipases, and lipids in rats fed hypercholesterolemic diet. Nutr J 12:2451–2457

Trakimas G, Krams R, Krama T, Kortet R, Haque S, Luoto S et al (2019) A link between

developmental speed and physiological stress in an omnivorous insect. Front Behav

Neurosci 13:e42

227

Trier TM, Mattson WJ (2003) Diet-induced thermogenesis in insects: a developing concept in

nutritional ecology. Environ Entomol 32:1–8

van der Meijden E, Wijn M, Verkaar HJ (1988) Defence and regrowth, alternative plant

strategies in the struggle against herbivores. Oikos 51:355–363

Waldbauer GP (1968) The consumption and utilization of food by insects. In Advances in insect

physiology. Academic Press, Cambridge, pp 229–288

War AR, Paulraj MG, Ahmad T, Buhroo AA, Hussain B, Ignacimuthu S, Sharma HC (2012)

Mechanisms of plant defense against insect herbivores. Plant Signal Behav 7:1306–1320

Wetzel WC, Kharouba HM, Robinson M, Holyoak M, Karbon R (2016) Variability in plant

nutrients reduces insect herbivore performance. Nature 539:425–427

White TCR (2012) The inadequate environment: nitrogen and the abundance of animals.

Springer, Berlin

Züst T, Agrawal AA (2017) Trade-offs between plant growth and defense against insect

herbivory: an emerging mechanistic synthesis. Annu Rev Plant Biol 68:513–534

228

CHAPTER VII

CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH

OVERVIEW

The effects of herbivory on plant defenses are difficult to measure, primarily due to the complexity of plant-defense expression and variability of herbivore pressures in a given environment (Erb 2018; Poleman and Kessler 2016). Current plant-defense studies focus on understanding how evolutionary and ecological pressures drive the expression of plant-defense traits (Agrawal and Hastings 2019; Ballaré and Austin 2019). Scientists have formulated numerous plant-defense hypotheses (see Chapter I), but we have yet to fully understand and make accurate predictions of how these evolutionary and ecological selective pressures interact to shape plant-defense expression (Agrawal 2020; Erb 2018). For example, the growth- differentiation balance hypothesis (Herms and Mattson 1992; Loomis 1932) has been supported by numerous studies (e.g., Guo et al. 2018; Hattas et al. 2017; Perkovich and Ward 2021a), but we have yet to understand why this pattern is scale-dependent (i.e., only observed at higher taxonomic levels and is not observed within species (Agrawal 2020; Barto and Cipollini 2005)).

This dissertation aimed to investigate the effects of herbivory on key Quercus species to develop a further understanding of phylogenetic constraints and herbivore-community dynamics on the expression of plant-defense traits.

229 SUMMARY AND FUTURE DIRECTIONS

Phylogenetic constraints on plant defenses - Chapter II (Herbivore-induced defenses are not under phylogenetic constraints in the genus Quercus (oak): Phylogenetic patterns of growth, defense, and storage) and Chapter III (Aboveground herbivory causes belowground changes in twelve oak (Quercus) species: A phylogenetic analysis of root biomass and nutrient re- allocation) of this dissertation analyzed the effects of location (apical vs. lateral tissue damage) and intensity (25% vs. 75% tissue removal) on oak responses to herbivory. We found that there were limited phylogenetic constraints on induced responses, both above- (Chapter II) and belowground (Chapter III). This could suggest that phenotypic plasticity (i.e., variations in genotypes to produce multiple phenotypes) may explain the patterns of simulated herbivory.

However, adaptive phenotypic plasticity (caused by selective pressures unique to that organism’s environmental surroundings sensu Bayonee Mboumba and Ward 2008; Conover and Schultz

1995; Ghalambor et al. 2007) could also be an important process involved in plant-defense expression in the genus Quercus (Ackerly 2002; Cavender-Bares et al. 2004). Different types of phenotypic plasticity can uniquely contribute to adaptive phenotypic plasticity when populations are faced with new or altered environments (Ghalambor et al. 2007). Adaptive phenotypic plasticity should promote establishment and persistence in a new environment, as long as there is selection that will cause adaptive divergence between populations (Ward et al. 2012).

Comparative methods such as the ones performed in Chapters II and III are unable to differentiate between confounding mechanisms unless genetic variation and covariation is directly measured (de Villemereuil et al. 2018; Leroi et al. 1994). However, a quantitative genetic analysis of expressed traits was outside of this dissertation’s goals (e.g., Anderson et al.

2013; Anderson and Gezon 2015). Furthermore, interactions between genotype and environment

230 are some of the most influential phenomena that create the ecological environments we observe

(Pigliucci 2005). Commonly, these studies are performed on short-lived organisms (e.g., Gao et al. 2018; Volis 2009). However, long-lived organisms, such as oaks, offer opportunities to study evolutionary processes on a spatiotemporal scale (Du et al. 2020; Long et al. 2017). Future studies may explore quantitative-genetic patterns and genotype-by-environment interactions on long-lived organisms to create a better understanding of the evolutionary drivers that determine induced-trait expression.

Previous studies that have examined phylogenetic constraints on oak defenses have come to different conclusions (Moreira et al. 2018; Pearse and Hipp 2012). When testing plant-defense hypotheses, it is important to understand the limitations within studies and to acknowledge how the differing limitations may affect the conclusions. For example, Pearse and Hipp (2012) found that variations in oak-defense traits could be partly explained by phylogeny, although these authors also showed that there was a pattern of association that was consistent with a seasonality gradient and was consistent with the resource availability hypothesis. However, Moreira et al.

(2018) concluded that there was no phylogenetic signal for most oak-defense traits but there were significant differences between Palearctic and Nearctic Quercus species (i.e., at a higher taxonomic level). It is difficult to compare the results from Pearse and Hipp (2012) and Moreira et al. (2018) because of the differences in experimental design. For example, Pearse and Hipp

(2012) failed to consider differences between constitutive and inducible leaf defenses.

Contrastingly, Moreira et al. (2018, 2020) considered differences in the types of defenses; however, their design ignored differences in locations and intensities of herbivore feeding.

There are additional challenges to interpreting results of phylogenetic comparative analyses (Cavender-Bares et al. 2009; Freckleton 2009; Cornwell and Nakagawa 2017). To

231 properly analyze and make conclusions from phylogenetic comparative studies, one must understand the limitations of the statistical approach used (Folk et al. 2018). The first limitation to consider is sample size. Phylogenetic comparative methods struggle to detect phylogenetic signal with a limited number of species (Münkemüller et al. 2012). There are over 600 extant species of oaks (Nixon 1997; Quercus 2017); sampling from every oak species is clearly impractical, especially because replication is fundamental (Freckleton 2009; Weber and Agrawal

2012). Instead of focusing on phylogenetic signal and strength as done in phylogenetic least squares regressions (PGLS; Chapter II), plant-defense studies could utilize phylogenetic principal components analyses (pPCA; Chapter III) or similar methods to generate overall patterns. For example, in Chapter II, response patterns were not easily detected when analyzing individual species’ responses due to within-species variation (see Fig. 4, 5). However, we found patterns in sections of the oak phylogeny where, for example, Quercus section Lobatae differed from Quercus section Quercus by having a greater investment in defenses and less in growth

(i.e., higher taxonomic level, shown in sign-test analysis in Chapter II). Furthermore, the usefulness of pPCA originates from how ecologists analyze trait associations. Ecosystems are inherently multi-dimensional. Because of this multi-dimensionality, ecologists frequently focus on the many covarying traits using multivariate analyses (Hervé et al. 2018; James and

McCulloch 1990; Nye 2011). Multivariate analyses allow for the analysis of relationships between multiple dependent variables (Grimm and Yarnold 1995; Wang et al. 2018). However, some multivariate analyses, such as linear regressions, assume the absence of multicollinearity among dependent variables (Dennis 2018; Grimm and Yarnold 1995). The different methods of statistical analysis prompt differential interpretations and discussions of evolutionary mechanisms that may be driving the expressed phenotypes in Chapters II and III. While each

232 chapter contained different datasets, the difference in statistical approaches generated fundamentally distinct methods of data interpretation. When using PGLS in Chapter II, we struggled to find overall patterns, especially at the within-species level (Fig. 4 and 5). We ascertained that response traits were often species-specific. Subsequently, when we used a pPCA in Chapter III, we also found that trait expression was species-specific, but we were able to make further inferences on which traits were possibly adaptive for specific treatments (i.e., location and intensity of herbivory). I do not mean to suggest that one method is better than another (PGLS vs. pPCA), but I wish to highlight that both methods have limitations that should be recognized. For example, using a pPCA (Chapter III), we were unable to analyze bivariate traits and therefore were unable to determine evolutionary trade-offs as we did with PGLS

(Chapter II). As with all statistical methods, the ecological question drives method selection

(Stephens et al. 2007; Zuur et al. 2007; Cadotte et al. 2017). My effort for discussing differences in interpretations of statistical methods is to highlight that studies should also consider the ecological correlations that are possible in their experimental design. For studies with many variables, some of which may be collinear, pPCA methods may be the better approach to reduce multi-dimensionality (Nye 2011).

Leroi et al. (1994) suggested that lower taxonomic levels may experience greater phylogenetic effects on trait expression. More recently, plant defense studies have shifted to focus on correlations among defensive phenotypes and less so on the expression of a single trait

(Agrawal and Hastings 2019; Erb 2018; Züst and Agrawal 2017). Interestingly, when we analyze trait correlations and not the expression of individual traits, phylogenetic patterns are often lost when analyzed at the within-species level (Agrawal 2020). So why do individual traits display phylogenetic patterns, but correlations of these traits do not? This may largely be due to trade-

233 offs within species being constrained physiologically (Agrawal 2020; Coley and Kursor 1996;

Herms and Mattson 1992) or genetically (Agrawal and Hastings 2019; Tiffin and Rausher 1996).

Competitive ecological demands are experienced over time and space, so we do not necessarily expect a trade-off to exist within species (Agrawal 2020; Sartori et al. 2019; Wang et al. 2019).

We should still ask questions and make predictions about species-level trait correlations but interpret our results with caution. We can confidently draw conclusions about trait evolution at the species level if we find significant trait relationships after correcting for phylogeny.

However, ascribing specific evolutionary drivers is difficult. Albeit a rigorous and monumental task, testing hypothesized drivers through experimental manipulation may help to differentiate among a plethora of evolutionary mechanisms (Perkovich and Ward 2021a; Weber and Agrawal

2012).

Differentiation of defense syndromes dependent on herbivore type Expressing differentiated defense syndromes may be an effective strategy to maintain suboptimal nutritional quality due to differences in herbivore feeding behavior and physiology (Okamura et al. 2016; Perkovich and

Ward 2021b, submitted). We found that differentiation of defense syndromes may be adaptive, depending on the type of herbivore present (Chapter IV - Differentiated defense syndromes in response to varying herbivore pressures: oak trees increase defenses in response to insects and decrease nutritive quality in response to deer). In response to mammalian herbivory, trees invested more heavily in growth and reduced nutrient quality of foliar tissues. Contrastingly, trees increased total tannin production in response to insect herbivores. Mammal and insect herbivores have vastly different physiologies, which include, but are not limited to, differences in nutritional requirements and antinutritional tolerances (Rosenthal and Berenbaum 2012). For

234 example, tannins are often not as effective of a defense against large mammalian herbivores because they possess tannin-binding proteins in their saliva (Schmitt et al. 2016; Ward et al.

2020). Insects lack tannin-binding salivary enzymes, so that tannins may be a successful defense against insect herbivores (Barbehenn and Constabel 2011). Our findings suggest that individual plants may differentiate against herbivore types and adjust their defensive responses accordingly.

This opens an interesting avenue for future research to explore the simultaneous expression of multiple plant-defense syndromes.

Some simultaneously expressed traits could correlate with volatile organic compounds

(VOCs; Schmitt et al 2020). VOCs are plant defense compounds that are released as gases. The expression of VOCs may act as attractants (Kalske et al. 2019; Ranger et al. 2010) or deterrents to herbivores (Scokzek et al. 2017; Schmitt et al. 2020). Olfactory cues such as plant odors have largely been overlooked (Bedoya-Pérez et al. 2014; Schmitt et al. 2018). However, the use of plant VOC’s could give insight to selective pressures on both plant defenses and herbivore foraging decisions. That is, plants may use VOCs to prevent damage before herbivory occurs

(Bedoya-Pérez et al. 2014). Likewise, herbivores may use VOCs as pre-ingestive cues or as cues to make long-range foraging decisions (Villalba et al. 2015; Schmitt et al. 2018). Future observations of volatile organic compounds (VOCs) should prove to be powerful tools to understand pre-ingestive cues (Schmitt et al 2020).

By conducting a before, during, and after experiment with insect outbreaks, we found further differentiation of defense strategies on a temporal scale (Chapter V - Periodical cicadas increase defenses in North American forest trees: before, during, and after a mass outbreak). We suggest that ecologists should take advantage of large insect outbreaks to explore patterns of plant defenses in a community, particularly because of the high phenotypic plasticity of plant-

235 defense traits. Statistical analyses of plant-defense concentrations are often inconclusive due to within-species variation (Dodd et al. 1999; Ives et al. 2007). For example, we were unable to determine differences among simulated-herbivory treatments in the greenhouse for many individual oak species, including the white oaks (Q. alba) that are consumed by periodical cicadas (Chapter II). However, when we analyzed defense-trait expression of white oaks during the periodical cicada outbreak, we were able to see clear patterns despite the within-species variation (Chapter V). This may occur because, during insect outbreaks, these herbivores act as a greater selective pressure for particular defense traits than non-outbreaking insect herbivores.

Extreme outbreak events, such as the periodical cicada, offer an opportunity to make powerful observations of plant-defense expression.

How effective are oak defense syndromes against herbivores? There are several frameworks for nutritional ecology that predict optimum nutritional requirements for herbivores (i.e., optimal foraging theory (Pyke 1984; Owen-Smith et al. 2010; Ruedenauer et al. 2016), the geometric framework for nutrition (Simpson and Raubenheimer 1995; Simpson and Raubenheimer 2012), and ecological stoichiometry (Sterner and Elser 2002)). Nutritional ecologists have not identified a single framework to satisfy the criterion for ecological applications (Raubenheimer et al.

2009). However, recent studies have largely supported the geometric framework (e.g., Helm et al. 2017; Morimoto and Lihoreau 2020; Perkovich and Ward 2020) which predicts that optimal macronutrient ratios are more important than overall protein and/or energy consumption (as predicted by optimal foraging theory sensu Pyke 1984). Most recently, Raubenheimer and

Simpson (2018) have further suggested that the geometric framework provides a more accurate prediction of nutritional requirements because it integrates the other proposed theories. Protein is

236 indeed an important dietary component (as promoted by optimal foraging theory), but optimal macronutrient ratios may play a more vital role than protein consumption alone (Raubenheimer and Simpson 2018; Perkovich and Ward 2020). Generalist herbivores may be able to tolerate acceptable concentrations of antinutritional compounds in their diet, given an optimal ratio of macronutrients (Chapter VI - Protein: carbohydrate ratios in the diet of gypsy moth Lymantria dispar affect its ability to tolerate tannins). Further research in both nutritional ecology and chemical ecology should begin to incorporate findings from each other’s fields so that nutritional ecologists incorporate antinutritional dietary components as well as macronutrient ratios within optimal foraging strategies and chemical ecologists begin to incorporate varying macronutrient ratios as well as plant defense concentrations into their studies (Shrader et al. 2012).

Where do we go from here? It is clear that several fields of study have a consistent overlap in their research questions and approaches. These fields should begin to concentrate on creating a better dialogue (i.e., ecologists and evolutionary biologists, nutritional ecologists and chemical ecologists). I want to focus on what the future of chemical ecology could look like. The findings of this dissertation have made it quite clear that plants invest in multiple defense syndromes dependent on herbivore pressures. Chemical ecologists need to focus on plant secondary metabolite production as well as variations in nutritional quality to fully understand how herbivores are making foraging decisions, that ultimately influence the evolution of plant defenses. A better understanding of the interaction of nutritional and antinutritional factors should provide an improved framework to make predictions about herbivore foraging decisions as well as the trajectory of the evolution of plant defenses. Furthermore, chemical ecologists should start exploring the various classes of plant secondary metabolites to understand plant

237 defense on a multi-dimensional level. That is, we largely understand how plant defenses work as post-ingestive cues, but some compounds may play a larger role in foraging decisions before ingestion occurs. Future research should focus on identifying pre-ingestive cues in Quercus that may affect herbivore foraging decisions.

238

REFERENCES

Ackerly DD (2002) Community assembly, niche conservatism, and adaptive evolution in

changing environments. Int J Plant Sci 164:e165-e184

Agrawal AA (2020) A scale-dependent framework for trade-offs, syndromes, and specialization

in organismal biology. Ecology 2:e02924

Agrawal AA, Fishbein M, Halitschke R, Hastings AP, Rabosky DL, Rasmann S (2009) Evidence

for adaptive radiation from a phylogenetic study of plant defenses. Proc Nat Acad Sci

USA 106:18067–18072

Agrawal AA, Hastings AP (2019) Trade-offs constrain the evolution of an inducible defense

within but not between plant species. Ecology 100:e02857

Anderson JT, Gezon ZJ (2015) Plasticity in functional traits in the context of climate change: A

case study of the subalpine forb Boechera stricta (Brassicaceae). Glob Chang Biol

21:1689–1703

Anderson JT, Lee C-R, Rushworth CA, Colautti RI, Mitchell-Olds T (2013) Genetic trade-offs

and conditional neutrality contribute to local adaptation. Mol Ecol 22:699–708

Ballaré CL, Austin AT (2019) Recalculating growth and defense strategies under competition:

keys roles of photoreceptors and jasmonates. J Exp Bot 70:3425–3436

Barbehenn RV, Constabel CP (2011) Tannins in plant-herbivore interactions. Phytochemistry

72:1551–1565

Barto EK, Cipollini D (2005) Testing the optimal defense theory and the growth-differentiation

balance hypothesis in Arabidopsis thaliana. Oecologia 146:169–178

239

Bayonne Mboumba G, Ward D (2008) Phenotypic plasticity and local adaptation in two extreme

populations of Acacia karroo. Afr J Range For Sci 25:121–130

Bedoya-Pérez MA, Isler I, Banks PB, McArthur C (2014) Roles of volatile terpene, 1,8-cineole,

in plant-herbivore interactions: a foraging odor cue as well as a toxin? Oecologia

174:827–837

Cadotte MW, Davies TJ, Peres‐Neto PR (2017) Why phylogenies do not always predict

ecological differences. Ecol Monogr 87:535–551

Cavender-Bares J, Ackerly DD, Baum DA, Bazzaz FA (2004) Phylogenetic overdispersion in

Floridian oak communities. Am Nat 163:823–843

Cavender-Bares J, Kozak KH, Fine PV, Kenbel SW (2009) The merging of community ecology

and phylogenetic biology. Ecol Lett 12:693–715

Coley PD, Kursor TA (1996) Anti-herbivore defenses of young tropical leaves: physiological

constraints and ecological trade-offs. In Mulkey SS, Chazdon RL, Smith AP (eds)

Tropical forest plant ecophysiology. Springer, Boston, MA, pp 305–336

Conover DO, Schultz ET (1995) Phenotypic similarity and the evolutionary significance of

countergradient variation. Trends Ecol Evol 10:248–252

Cornwell W, Nakagawa S (2017) Phylogenetic comparative methods. Curr Biol 27:e333–e336 de Villemereuil P, Mouterde M, Gaggiotti OE, Till-Bottraud I (2018) Patterns of phenotypic

plasticity and local adaptation in the wide elevation range of the alpine plant Arabis

alpine. J Ecol 106:1952–1971

240

Denis DJ (2018) SPSS data analysis for univariate, bivariate, and multivariate statistics. Wiley,

New York

Dodd ME, Silvertown J, Chase MW (1999) Phylogenetic analyses of trait evolution and species

diversity variation among angiosperm families. Evolution 53:732–744

Du FK, Wang T, Wang Y, Ueno S, de Lafontaine G (2020) Contrasted patterns of local

adaptation to climate change across the range of an evergreen oak, Quercus aquifolioides.

Evol Appl 13:2377–2391

Erb, M. 2018. Plant defenses against herbivory: closing the fitness gap. Trends Plant Sci 23:187–

194.

Folk RA, Soltis PS, Soltis DE, Guralnick R (2018) New prospects in the detection and

comparative analysis of hybridization in the tree of life. Am J Bot 105:364–375

Freckleton RP (2009) The seven deadly sins of comparative analysis. J Evol Biol 22:1367–1375

Gao S, Mo L, Zhang L, Zhang J, Wu J, Wang J, Zhao N, Gao Y (2018) Phenotypic plasticity vs.

local adaptation in quantitative traits differences of Stipa grandis in semi-arid steppe,

China. Sci Rep 8:1–8

Ghalambor CK, McKay JK, Reznik DN (2007) Adaptive versus non-adaptive phenotypic

plasticity and the potential for contemporary adaptation in new environments. Funct Ecol

21:394–407

Grimm LG, Yarnold PR (1995) Reading and understanding multivariate statistics. American

Psychological Association, Washington, DC

241

Guo Q, Major IT, Howe, GA (2018) Resolution of growth–defense conflict: mechanistic insights

from jasmonates signaling. Curr Opin Plant Biol 44:72–81

Hattas D, Scogings PF, Julkunen-Tiitto R (2017) Does the growth differentiation balance

hypothesis explain allocation to secondary metabolites in Combretum apiculatum, an

African savanna woody species? J Chem Ecol 43:153–163

Helm BR, Slater GP, Rajamohan A, Yocum GD, Greenlee KJ, Bowsher JH (2017) The

geometric framework for nutrition reveals interactions between protein and carbohydrate

during larval growth in honey bees. Biol Open 6:872–880

Herms DA, Mattson WJ (1992) The dilemma of plants: to grow or defend. Q Rev Biol 67:283–

335

Hervé MR, Nicolè F, Lê Cao KA (2018) Multivariate analysis of multiple datasets: a practical

guide for chemical ecology. J Chem Ecol 44:215–234

Ives AR, Midford PE, Garland Jr T (2007) Within-species variation and measurement error in

phylogenetic comparative methods. Syst Biol 56:252–270

James FC, McCulloch CE (1990) Multivariate analysis in ecology and systematics: panacea or

Pandora’s box? Annu Rev Evol Syst 21:129–166

Kalske A, Shiojiri K, Uesugi A, Sakata Y, Morrell K, Kessler A (2019) Insect herbivory selects

for volatile-mediated plant-plant communication. Curr Biol 29:3128–3133

Leroi AM, Rose MR, Lauder GV (1994) What does the comparative method reveal about

adaptation? Am Nat 143:381–402

242

Moreira X, Abdala-Roberts L, Galmán A, Bartlow AW, Berny-Mier y Teran JC, Carrari E,

Covelo F, de la Fuente M, Ferrenberg S et al (2020) Ontogenetic consistency in oak

defense syndromes, J Ecol 108:1822–1834

Moreira X, Glauser G, Abdala-Roberts L (2018) Assessing the influence of biogeographical

region and phylogenetic history on chemical defences and herbivory in Quercus species.

Phytochemistry 153:64–73

Morimoto J, Lihoreau M (2020) Open data for open questions in comparative nutrition. Insects

11:e236

Münkemüller T, Lavergne S, Bzeznik B, Dray S, Jombart T, Schiffers K, Thuiller W (2012)

How to measure and test phylogenetic signal. Methods Ecol Evol 3:743–756

Nixon KC (1997) Quercus Linnaeus. In Flora of North America north of Mexico. Oxford

University Press, New York, pp 431–506

Nye TMW (2011) Principal components analysis in the space of phylogenetic trees. Ann Stat

39:2716–2739

Okamura Y, Sawada Y, Hirai MY, Murakami M (2016) Effects of different secondary

metabolite profiles in plant defense syndromes on specialist and generalist herbivores.

Entomol Sci 2:97–103

Owen-Smith N, Fryxell JM, Merrill EM (2010) Foraging theory upscaled: the behavioural

ecology of herbivore movement. Phil Trans R Soc Lond B 365:2267–2287

Pearse IS, Hipp AL (2012) Global patterns of leaf defenses in oak species. Evolution 66:2272–

2286

243

Perkovich C, Ward D (2020) Protein: carbohydrate ratios in the diet of gypsy moth Lymantria

dispar affect its ability to tolerate tannins. J Chem Ecol 49:299-307

Perkovich C, Ward D (2021a) Herbivore-induced defenses are not under phylogenetic

constraints in the genus Quercus (oak): phylogenetic patterns of growth, defense, and

storage. Ecol Evol in press

Perkovich C, Ward D (2021b) Differentiated defense syndromes in response to varying

herbivore pressures: oak trees increase defenses in response to insects and decrease

nutritive quality in response to deer. Oikos submitted

Pigliucci M (2005) Evolution of phenotypic plasticity: where are we going now? Trends Ecol

Evol 20:481–486

Poelman EH, Kessler A (2016) Keystone herbivores and the evolution of plant defenses. Trends

Plant Sci 21:477–485

Pyke GH (1984) Optimal foraging theory: a critical review. Annu Rev Ecol Syst 15:523–575

Quercus L (2017) Plants of the world online. Royal Botanic Gardens, Kew. [retrieved 30

September 2020]

Ranger CM, Reding ME, Persad AB, Herms DA (2010) Ability of stress-related volatiles to

attract and induce attacks by Xylosandrus germanus and other ambrosia beetles. Agri For

Entomol 12:177–185

Raubenheimer D, Simpson SJ (2018) Nutritional ecology and foraging theory. Curr Opin Insect

Sci 27:38–45

244

Raubenheimer D, Simpson SJ, Mayntz D (2009) Nutrition, ecology and nutritional ecology:

toward an integrated framework. Funct Ecol 23:4–16

Rosenthal GA, Berenbaum MR (2012) Herbivores: their interactions with secondary plant

metabolies: ecological and evolutionary processes (Vol. 2). Academic Press, Cambridge,

Ruedenauer FA, Spaethe J, Leonhardt SA (2016) Hungry for quality—individual bumblebees

forage flexibility to collect high-quality pollen. Behav Ecol Sociobiol 70:1209–1217

Sartori K, Vasseur F, Violle C, Baron E, Gerard M, Rowe N, Ayala-Garay O, Christophe A, De

Jalon LG, Masclef D, Harscouet E (2019) Leaf economics and slow-fast adaptation

across geographic range of Arabidopsis thaliana. Sci Rep 9:1-12

Schmitt MH, Shuttleworth A, Shrader AM, Ward D (2020) The role of volatile plant secondary

metabolites as pre-ingestive cues and potential toxins dictating diet selection by African

elephants. Oikos 129:24-34

Schmitt MH, Shuttleworth A, Ward D, Shrader A (2018) African elephants use plant odours to

make foraging decisions across multiple spatial scales. Anim Behav 141:17–27

Schmitt MH, Ward D, Shrader AM (2016) Incorporating secondary metabolites, tannin-binding

proteins, and diet breadth into carrying-capacity models of African elephants. Ecol

Modell 332:8–18

Shrader AM, Bell C, Bertolli L, Ward D (2012) Forest or the trees: at what scale do elephants

make foraging decisions? Acta Oecol 3–10

245

Simpson SJ, Raubenheimer D (1995) The geometric analysis of feeding and nutrition: a user’s

guide. J Insect Physiol 41:545–553

Simpson SJ, Raubenheimer D (2012) The nature of nutrition: a unifying framework. Aust J Zool

59:350–368

Skoczek A, Piesik D, Wenda-Piesik A, Buszewski B, Bocianowski J, Wawrzyniak M (2017)

Volatile organic compounds released by maize following herbivory or insect extract

application and communication between plants. J Appl Entomol 141:630–643

Stephens PA, Burskirk SW, del Rio CM (2007) Inference in ecology and evolution. Trends Ecol

Evol 22:192–197

Sterner RW, Elser JJ (2002) Ecological stoichiometry: the biology of elements from molecules to

the biosphere. Princeton University Press, Princeton, NJ

Tiffin P, Rausher MD (1999) Genetic constraints and selection acting on tolerance to herbivory

in the common morning glory Ipomoea purpurea. Am Nat 154:700–716

Villalba J, Provenza F, Catanese F, Distel R (2015) Understanding and manipulating diet choice

in grazing animals. Anim Prod Sci 55:261–271

Wang Y, Si Y, Huang B, Lou Z (2018) Survey of the theoretical research and engineering

applications of multivariate statistics process monitoring algorithms: 2008–2017. Can J

Chem Eng 96:2073–2085

Wang Y, Donovan LA, Temme AA (2019) Allometric scaling impacts plasticity in biomass

allocation, morphology and anatomical traits due to above and belowground resource

limitation in cultivated sunflower (Helianthus annuus L.). bioRxiv doi: 10.1101/504316

246

Ward D, Schmitt MH, Shrader AM (2020) Are there phylogenetic differences in salivary tannin-

binding proteins between browsers and grazers, and ruminants and hindgut fermenters?

Ecol Evol 10:10426–10439

Ward D, Shrestha MK, Golan-Goldhirsh A (2012) Evplution and ecology meet molecular

genetics: adaptive phenotypic plasticity in two isolated Negev desert populations of

Acacia raddiana at either end of a rainfall gradient. Ann Bot 209:247–255

Weber MG, Agrawal AA (2012) Phylogeny, ecology, and the coupling of comparative and

experimental approaches. 27:394–403

Züst T, Agrawal AA (2017) Trade-offs between plant growth and defense against insect

herbivory: an emerging mechanistic synthesis. Ann Rev Plant Biol 68:513–534

Zuur A, Ieno EN, Smith GM (2007) Analyzing ecological data. Springer, Darmstadt

247