Spotted Gums and Hybrids: Impact of Pests and Diseases, Ontogeny and Climate on Tree Performance

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Studies of plant interactions with pests and pathogens in Corymbia species and hybrids

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Sarti Bonora, F. (2020). Studies of plant interactions with pests and pathogens in Corymbia species and hybrids [University of the Sunshine Coast, Queensland]. https://research.usc.edu.au/discovery/fulldisplay/alma99463808902621/61USC_INST:ResearchRepository Document Type: Thesis

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Studies of plant interactions with pests and

pathogens in Corymbia species and hybrids

Flávia Sarti Bonora

Master of Science

This thesis is submitted in fulfilment of the requirements for the completion of

the degree of Doctor of Philosophy

University of the Sunshine Coast - Forest Industries Research Centre

Maroochydore DC, Queensland 4558, Australia

June 2020

Declaration of Originality

This thesis is submitted to the University of the Sunshine Coast in fulfilment of the requirements fora Doctor of Philosophy. I, the undersigned, hereby declare that the work here is the result of my own investigations and that all references to ideas and work of other researchers have been specifically acknowledged in the text. I hereby certify that the work embodied in this thesis has not been previously submitted, either in whole or in part, for a degree at any tertiary educational institution. This research was conducted under the supervision of A/Professor David Lee, Doctor Helen Nahrung and Doctor Andrew Hayes

(University of the Sunshine Coast), and has been prepared as a thesis to conform to the guidelines provided by the University of the Sunshine Coast.

Flavia Sarti Bonora, Date PhD Candidate

University of the Sunshine Coast

11 Certification

This is to certify that the thesis entitled 'Studies of plant interactions with pests and pathogens in Corymbia species and hybrids', submitted by Miss Flavia Sarti Bonora in fulfilment of the requirements for the award of the degree of Doctor of Philosophy to the

University of the Sunshine Coast, is a record of the student's own work carried out by her under my supervision and guidance. The report has not been submitted for the award of any other degree or certificate in this or any other University or Institute .

. !.�./4-�/��2-o A/ProfessorDavid John Lee Date Principal Supervisor

University of the Sunshine Coast

111

Abstract

Pests and pathogens are a major concern in eucalypts in native forests and plantations, as they cause loss of biodiversity and productivity and reduce tree survival. Pest and pathogen management requires multiple strategies to minimise impacts and maximise forest health, involving silvicultural practices, use of pesticides, biocontrol and selection of resistant genotypes through breeding programs. Greater knowledge of host interactions with pests and pathogens and consequences for plant performance and physiology may contribute significantly to the development of pest and disease management strategies.

This thesis focuses on plant interactions with pests and pathogens and understanding plant physical and chemical parameters in response to damage. The research included controlled and field experiments with Corymbia spp. and hybrids, ecologically and economically important eucalypt taxa. The research investigated:

(1) Corymbia citriodora subsp. variegata chemical and physical response to insect damage;

(2) Differences in physical and chemical foliar parameters between C. citriodora subsp. variegata plants resistant and susceptible to Quambalaria pitereka and Austropuccinia psidii;

(3) Comparison between C. citriodora subsp. variegata response to Q. pitereka and A. psidii infection;

(4) The levels of susceptibility of Corymbia species and hybrids to pests and pathogens in the field and the consequences of damage on plant performance;

The three controlled experiments were conducted in a shadehouse to examine specific plant responses to pest and pathogen damage, involving analyses of plant growth rate, leaf anatomy and leaf chemistry. In the first experiment, seedlings of C. citriodora subsp. variegata were subjected to Paropsis atomaria larval feeding, mechanical wounding and no damage (control), to examine the effect of leaf tissue removal on the growth rate, leaf toughness, leaf trichome

density and leaf secondary metabolites. Plant response was analysed in damaged leaves (local

response), undamaged leaves in damaged plants (systemic response), and in new leaves

produced post-treatment (delayed response). Plants did not exhibit a local chemical response,

nor differences in growth rate or measured leaf physical parameters following damage. There

was, however, a systemic chemical response to P. atomaria larval feeding and mechanical

wounding, with plants subjected to larval feeding also exhibiting a delayed chemical response.

Six compound classes were identified, of these, long-chain hydrocarbons were lower in the treatments relative to undamaged plants, whereas the proportion of monounsaturated hydrocarbons and monoterpenes was higher. When analysed across compound classes, larval mortality was significantly correlated with monounsaturated hydrocarbons and long-chain hydrocarbons. The potential mechanisms underlying the observed responses are discussed.

The second shadehouse trial examined plant-pathogen interactions. Corymbia citriodora subsp. variegata seedlings from different provenances were inoculated with either Q. pitereka or A. psidii, and resistance classes for the two pathogens were determined. Differential plant responses between severely infected (susceptible) and resistant plants (low levels of damage) for each pathogen were quantified. Growth rate, leaf toughness and leaf secondary metabolites were compared between uninoculated (control), resistant and susceptible plants for both pathogens. Post-treatment leaf samples were taken when plants were free of disease, to determine if secondary metabolite profiles would assist in susceptibility prediction. Susceptible plants infected by Q. pitereka had greater leaf toughness while those infected by A. psidii had

reduced plant growth and changes in the expression of secondary metabolites in comparison to

uninoculated controls and resistant plants. The plants severely damaged by A. psidii exhibited a reduction in the proportion of monoterpenes and monounsaturated hydrocarbons and an increased proportion of long chain hydrocarbons. Post-treatment samples did not differ from each other or control plants, suggesting secondary metabolites are not a good predictor of

susceptibility to these pathogens in C. citriodora subsp. variegata. The potential mechanisms

underlying the observed responses are discussed and contrasted with similar studies in other

species.

The third shadehouse trial involved seedlings of C. citriodora subsp. variegata from Woondum

(26° 25' S, 152° 81' E), assome families from this provenance may display resistance to Q.

pitereka and susceptibility to A. psidii. . Quambalaria pitereka innoculated plants, A. psidii

inoculated plants and uninoculated control plants were assessed to compare phenotypical

responses including growth rate, leaf toughness, leaf thickness, leaf chemistry and leaf

histochemistry. Only A. psidii infection resulted in alteration of leaf toughness and leaf

chemistry in comparison to Q. pitereka infection and controls. The histochemical analyses

suggest that both pathogens alter the distribution of polyphenols and tannins in comparison to

control plants. Further discussion underlying the differences between infections by these

pathogens are explored.

The field experiment studied four pure taxa – C. citriodora subsp. citriodora, C. citriodora

subsp. variegata, C. henryi and C. torelliana – and three hybrids – C. torelliana × C. citriodora subsp. citriodora, C. torelliana × C. citriodora subsp. variegata and C. torelliana × C. henryi.

The aim of the field trial was to examine tree performance, leaf ontogeny and susceptibility to pests and pathogens over time, through a series of assessments conducted over 24 months.

Throughout the experiment, plants were naturally affected by several generalist insect defoliators, and Q. pitereka, an endemic pathogen of Corymbia spp. that causes necrosis, leaf distortion, and affects plant performance and survival. Results indicated significant variations between taxa and across time for all assessed parameters and significant taxon × time interactions. The characteristics of each taxon and the influence of environmental conditions underlying the studied parameters of tree performance and pest and disease susceptibility are discussed.

These results reveal the effects of damage on tree performance, the variable susceptibility to

pests and pathogens between Corymbia species and hybrids, and suggest mechanisms

underlying leaf physical and chemical responses observed in C. citriodora subsp. variegata under pest and pathogen damage. The knowledge generated in this thesis benefits the development of pest and disease management, contributes to strategies to select resistant genotypes and adds a relevant understanding on plant responses and interactions with pests and pathogens in natural and controlled conditions.

vii

Acknowledgements

I express sincere appreciation to my principal supervisor, David Lee, and to my co-supervisors Helen Nahrung and Andrew Hayes, Forest Industries Research Centre - University of the Sunshine Coast for their great supervision, guidance, patience and ongoing encouragement throughout this project. It was a pleasure to work with three dedicated and considerate supervisors.

I would like to express my gratitude to Geoff Pegg for provision of facilities and training on pathogen culture and inoculation and for his collaboration on the preparation of Chapter 3 and 4.

I am thankful to Tanya Scharaschkin for her valuable assistance with the histochemical analyses and data interpretation, and for her collaboration with Chapter 4 preparation.

I wish to thank Manon Griffiths and Ngoc Hoan Le for helping me building the insect cages and for the assistance in collecting and maintaining the Paropsis atomaria colony.

I am grateful to Tracey Menzies, Anthony Burridge and John Oostenbrink for supporting me with plant propagation and fieldwork, and for helping me to implement and maintain my experimental trial in Traveston.

I gratefully acknowledge the financial support provided by the University of the Sunshine Coast International Research Scholarship, without which this course of study could not have been undertaken. I also acknowledge the HDR Research Excellence Scheme for financing the histochemical analyses of this thesis.

I am especially thankful to Emily Lancaster for helping me with my field, laboratory work, for her friendship and for always encouraging me throughout my PhD.

Thank you to my friends Cristiana Giffin, Adriano Marinho and Allan Gomes for all the fun times of food, drink and conversation, and for helping me in the last months of my studies. Many thanks to my cousin Fernanda Sarti for the long distance support and friendship.

Finally, I am forever grateful to my parents Eliana Glauce Sarti Bonora and Robelio Bonora Junior, for their encouragement, support and love.

viii

List of Publications and presentations arising from this thesis

Bonora FS, Nahrung HF, Hayes RA, Pegg GS, Lee DJ (2020). Does disease severity impact on plant foliar chemical and physical responses to two Corymbia citriodora subsp. variegata pathogens? Industrial Crops and Products. 148:112288. doi:https://doi.org/10.1016/j.indcrop.2020.112288.

Bonora FS, Hayes RA, Nahrung HF and Lee DJ (in press). Spotted gums and hybrids: impact of pests and diseases, ontogeny and climate on tree performance. Forest Ecology and Management.

Submitted manuscripts

Bonora FS, Hayes RA, Nahrung HF, Lee DJ. Paropsis atomaria larval feeding induces a chemical but not a physical response in Corymbia citriodora subsp. variegata. Submitted: Trees (under review).

Bonora FS, Nahrung HF, Hayes RA, Sharaschkin T, Pegg G, Lee DJ. Changes in leaf chemistry and anatomy of Corymbia citriodora subsp. variegata (Myrtaceae) in response to native and exotic pathogens. Submitted: Australasian Plant Pathology (under review).

Conference presentations

Bonora FS, Hayes RA, Nahrung HF, Lee DJ. Does leaf beetle larval feeding induce responses in Corymbia citriodora subsp. variegata (CCV)? In: Australian Entomological Society 50th AGM and Scientific Conference, 2019.

Bonora FS, Nahrung HF, Hayes RA, Lee DJ. Chemical and physical foliar response of Corymbia citriodora subsp. variegata (CCV) inoculated with two pathogens. In: XXV IUFRO world congress, 2019. – Awarded best student presentation in the session: Vanguards for research of myrtle rust, Austropuccinia psidii.

Bonora FS, Hayes RA, Nahrung HF, Lee DJ. Does leaf beetle larval feeding induce responses in Corymbia citriodora subsp. variegata (CCV)? In: XXV IUFRO world congress, 2019

Posters

Bonora FS, Nahrung HF, Hayes RA, Lee DJ. Correlation between Gonipterus sp. herbivory and leaf oil profile in Corymbia species and hybrids. In: Forestry at USC: Research Showcase and Institute Launch, 2020 – Awarded second runner up best poster.

Bonora FS, Hayes RA, Nahrung HF, Lee DJ. Can induced defences enhance pest resistance in industrial plantations? In: 2nd Biennial State Conference - Doing Timber Business in Queensland: Room to Grow, 2018.

Bonora FS, Nahrung HF, Hayes RA, Lee DJ. Correlation between Gonipterus sp. herbivory and leaf oil profile in Corymbia species and hybrids. In: Science Protecting Plant Health conference, 2017.

Table of Contents

Declaration of Originality ...... ii Certification...... iii Abstract ...... iv Acknowledgements ...... viii List of Publications and presentations arising from this thesis ...... ix Table of Contents ...... xi List of Tables ...... xiv List of Figures...... xv Chapter 1 Literature review – Plant interaction with pests and pathogens: spotted gum (Corymbia spp.) as a case study ...... 1 Introduction ...... 1 Patterns of defence mechanisms ...... 2 Specific plant responses to pests and pathogens ...... 6 Plant secondary metabolites ...... 9 Spotted gums and hybrids ...... 11 Paropsis atomaria ...... 14 Quambalaria pitereka ...... 15 Austropuccinia psidii ...... 16 Thesis aims and outline...... 18 References ...... 19 Chapter 2 Paropsis atomaria larval feeding induces a chemical but not a physical response in Corymbia citriodora subsp. variegata ...... 32 Statement of Intellectual Contribution...... 33 Abstract ...... 34 Introduction ...... 35 Material and Methods ...... 39 Experiment ...... 39 Tree growth rate ...... 40 Physical response - Leaf toughness and trichomes density ...... 41 Chemical response - Plant secondary metabolites ...... 42 Results ...... 43 Tree growth rate ...... 43

Physical response - Leaf toughness and trichomes density ...... 44 Chemical responses - Plant secondary metabolites ...... 45 Discussion ...... 49 Conclusion ...... 53 Acknowledgements ...... 54 References ...... 54 Chapter 3 Does disease severity impact on chemical and physical foliar responses from two Corymbia citriodora subsp. variegata pathogens? ...... 60 Statement of Intellectual Contribution...... 61 Abstract ...... 62 Introduction ...... 64 Methodology ...... 67 Plant material ...... 67 Inoculation ...... 68 Disease assessment ...... 69 Growth rate ...... 70 Sample collection ...... 70 Leaf Toughness ...... 71 Secondary Metabolites Profile ...... 72 Results ...... 73 Growth rate and leaf toughness ...... 73 Secondary metabolites ...... 74 Discussion ...... 77 Conclusion ...... 83 Acknowledgements ...... 84 References ...... 84 Chapter 4 Changes in leaf chemistry and anatomy of Corymbia citriodora subsp. variegata (Myrtaceae) in response to native and exotic pathogens...... 90 Statement of Intellectual Contribution...... 91 Abstract ...... 92 Introduction ...... 93 Material and Methods ...... 95 Plant material ...... 95 Inoculation ...... 96 xii

Leaf histochemistry and anatomy ...... 97 Leaf toughness ...... 98 Secondary metabolites profile ...... 99 Results ...... 100 Leaf histochemistry ...... 100 Secondary metabolite profiles ...... 106 Leaf morphology ...... 108 Discussion ...... 109 Acknowledgements ...... 116 References ...... 116 Chapter 5 Spotted gums and hybrids: impact of pests and diseases, ontogeny and climate on tree performance ...... 122 Statement of Intellectual Contribution...... 123 Abstract ...... 124 Introduction ...... 126 Methods ...... 129 Plant material and study site ...... 129 Assessment ...... 132 Statistical analyses ...... 133 Results ...... 135 Performance ...... 135 Ontogeny ...... 138 Damage...... 139 Correlations ...... 142 Discussion ...... 144 Acknowledgements ...... 151 References ...... 152 Chapter 6 General discussion ...... 159 Future studies ...... 165 References ...... 166 Appendix A - Supplementary material for Chapter 4 ...... 172 Appendix B - Supplementary material for Chapter 5 ...... 174

xiii

List of Tables

Table 2.1 Back-transformed mean ± standard errors relative area under the peak for compound classes used to distinguish systemic response (damaged leaves) between C- undamaged

controls, L- larval feeding by Paropsis atomaria and M- mechanical wounding; and to distinguish delayed response (new leaves produced post-treatments) between C and L...... 47 Table 3.1 Provenance range of uninoculated controls and resistant and susceptible plants inoculated with Quambalaria pitereka and Austropuccinia psidii...... 70 Table 3.2 Back-transformed mean ± s.e. relative percentage area of compound class used to distinguish uninoculated controls from Austropuccinia psidii resistant and susceptible Corymbia citriodora subsp. variegata plants...... 76 Table 4.1 Presence (+) or absence (-) of secondary metabolite compounds in each sample of each treatment identified based on the staining reaction to ruthenium red and toluidine blue (Figure 4.1): uninoculated controls (a - d), samples inoculated with Quambalaria pitereka (e - h) and samples inoculated with Austropuccinia psidii (i - l). Blue/green = polyphenols, tannin, lignin, essential oils, lipids; pink = mucilage; purple = pectin...... 106 Table 4.2 Comparison of leaf secondary metabolites of Corymbia citriodora subsp. variegata between treatments: uninoculated controls, inoculated with Quambalaria pitereka and Austropuccinia psidii. For each compound the back-transformed mean ± s.e. relative area are shown within rows, with different lowercase letters designating significant differences between treatments, following Kruskal-Wallis test with pairwise comparisons...... 107 Table 4.3 Comparison of compounds (back-transformed mean ± s.e. relative area) used to differentiate Corymbia citriodora subsp. variegata inoculated with Austropuccinia psidii from controls and Quambalaria pitereka plants...... 108 Table 5.1 List of studied taxa and hybrids. Eighteen plants of each family were used in the field trial, giving a total of 36 – 108 trees per taxon...... 131

xiv

List of Figures

Figure 2.1 Example of undamaged/control, larval feeding and mechanical wounding Corymbia citriodora subsp. variegata leaves and positions where the one cm² squares were placed to count the leaf trichomes...... 42 Figure 2.2 Mean ± standard error growth rate (cm day⁻¹) of Corymbia citriodora subsp. variegata subjected to (black) undamaged controls, (grey) larval feeding by Paropsis atomaria and (white) mechanical wounding treatments during treatment, post-treatment, and overall. Results of ANOVA comparing between damage treatments are shown above for each period...... 44 Figure 2.3 Mean ± standard error of (a) leaf toughness (mg cm⁻²) and (b) leaf trichomes cm⁻² determined for Corymbia citriodora subsp. variegata local (damaged leaves), systemic (undamaged leaves on damaged plants) and delayed (new leaves produced post-treatments) responses subjected to (black) undamaged controls, (grey) larval feeding by Paropsis atomaria and (white) mechanical wounding treatments. Results of ANOVA comparing between damage treatments are shown above for each period...... 45 Figure 2.4 Two-dimensional nMDS ordination of hexane extracts of the Corymbia citriodora subsp. variegata subjected to (square) undamaged controls, (circle) larval feeding by Paropsis atomaria and (cross) mechanical wounding treatments to detect (a) local (damaged leaves), (b) systemic (undamaged leaves on damaged plants) and (c) delayed (new leaves produced post- treatments) responses. The plots are based on square root transformed abundances and a Bray- Curtis similarity matrix. Extracts from plants cluster separately. Ellipses are used for ease of interpretation only...... 47 Figure 2.5 Compound class and back-transformed means ± standard errors of relative area under the chromatogram of components detected in hexane extracts detected in Corymbia citriodora subsp. variegata as (a) local (damaged leaves), (b) systemic (undamaged leaves on damaged plants) and (c) delayed (new leaves produced post-treatments) responses on plants subjected to (black) undamaged controls, (grey) larval feeding by Paropsis atomaria and (white) mechanical wounding treatments. Different lowercase letters designate significant differences in the overall profile (Kruskal-Wallis, P < 0.05)...... 48 Figure 2.6 Correlations between relative areas of (a) monounsaturated hydrocarbons and (b) long chain hydrocarbons present in Corymbia citriodora subsp. variegata leaves and percentage of larval mortality...... 49

Figure 3.1 Uninoculated controls, resistant and susceptible leaves from plants 14 days after inoculation with Quambalaria pitereka (left) and Austropuccinia psidii (right)...... 71 Figure 3.2 Mean ± standard error (a) growth rate (cm day⁻¹) and (b) leaf toughness (mg cm⁻²) of controls (black), resistant (grey) and susceptible (white) plants of Corymbia citriodora subsp. variegata inoculated with Quambalaria pitereka and Austropuccinia psidii. Different lowercase letters within treatments designate significant differences in growth rate and leaf toughness...... 74 Figure 3.3 Two-dimensional nMDS ordination of extracts of Corymbia citriodora subsp. variegata leaves from plants inoculated with (a) Quambalaria pitereka and (b) Austropuccinia psidii. The plots are based on square-root transformed abundances and a Bray-Curtis similarity matrix. Extracts from A. psidii susceptible plants cluster separately. Symbols: control (circle) resistant (cross) susceptible (square). Ellipses are used for ease of interpretation only...... 76 Figure 3.4 Back-transformed mean ± s.e. relative area of compound classes of controls (black), resistant (grey) and susceptible (white) plants of Corymbia citriodora subsp. variegata inoculated with (a) Quambalaria pitereka and (b) Austropuccinia psidii at day 14. Results of Kruskal-Wallis tests for each compound class are shown, with different lowercase letters designating significant differences between treatments...... 77 Figure 4.1 Comparison of leaf anatomy and histochemistry of asymptomatic tissues of inoculated and uninoculated leaves of Corymbia citriodora subsp. variegata. Top row (a - d), uninoculated controls, middle row (e - h) inoculated with Quambalaria pitereka and lower row (i - l) inoculated with Austropuccinia psidii. Abbreviations used: UE = upper epidermis; ↓↑ = stomata; PP = palisade parenchyma; BS = bundle sheath; A = airspace; SP = spongy parenchyma; LE = lower epidermis; Scale bar = 100 μm. Each figure corresponds to one sample. Leaf material: 5 μm transverse sections double stained in ruthenium red and toluidine blue...... 103 Figure 4.2 Comparision of pectin and lignin accumulation in uninoculated controls and symptomatic tissues of inoculated leaves of Corymbia citriodora subsp. variegata. Top row (a - d), uninoculated controls, middle row (e - h) inoculated with Quambalaria pitereka and lower row (i - l) inoculated with Austropuccinia psidii. Inoculated samples exhibited tissues displaying minor (e, f, i, j) and necrotic symptoms (g, h, k, l). An abundance of pectic substances (purple) can be seen in symptomatic tissues of inoculated samples (e, f, i, j) showing intercellular growth of Q. pitereka hyphae (f, ↑) and intracellular growth of A. psidii (i, j, ↑). Both pectic substances and lignin (purple and dark blue) occurred in necrotic tissues (g, h, k, l), also showing Q. pitereka conidiophore and conidia (g, ↑) and A. psidii uredinia (k, ↑). Scale xvi

bar = 100 μm. Within rows, each figure represents different areas of the same sampled leaf. Leaf material: 5 μm transverse sections double stained in ruthenium red and toluidine blue...... 104 Figure 4.3 Leaf anatomy and histochemistry of Corymbia citriodora subsp. variegata showing emergent oil glands of uninoculated controls (a), symptomatic tissue of leaf inoculated with Quambalaria pitereka (adaxial b, abaxial c), and symptomatic tissue of leaf inoculated with Austropuccinia psidii (d). Emergent oil glands remained intact during severe infection (b - d). The interior of the oil cavity stained in green-blue indicating the presence of essential oils. Scale bar = 100 μm. Leaf material: 5 μm transverse sections double stained in ruthenium red and toluidine blue...... 105 Figure 4.4 Two-dimensional nMDS ordination of the Corymbia citriodora subsp. variegata extracts inoculated with Quambalaria pitereka and Austropuccinia psidii. The plots are based on square root transformed abundances and a Bray-Curtis similarity matrix. Extracts from A. psidii treatments tend to cluster separately. Symbols: (circle) control (cross) Q. pitereka (square) A. psidii. (ANOSIM, P < 0.05)...... 108 Figure 4.5 Comparison of leaf toughness (mg cm⁻²) mean ± standard errors of Corymbia citriodora subsp. variegata between treatments: uninoculated controls, inoculated with Quambalaria pitereka and inoculated with Austropuccinia psidii. Different lowercase letters designate significant differences in leaf toughness (Kruskal-Wallis, P < 0.05)...... 109 Figure 5.1 Climatic conditions at Traveston site during the experimental period: columns = mean ± s.e. daily rainfall (mm); line = mean ± s.e. maximum temperature (°C); dashed line = mean ± s.e. minimum temperature (°C). Data obtained from SILO database...... 132 Figure 5.2 Resutlts of performance assessments of each taxon. Comparison between taxon at each assessment interval of (a) growth rate and (b) back transformed flushing percentage. Comparison of (c) diameter at hight breast over bark (DBH) ± s.e.taken at the end of the experiment (age 2), and (d) Plots of Kaplan-Meier curves to estimate taxon survival through time. Symbols: (circle; CCC) Corymbia citriodora subsp. citriodora, (square, CCV) C. citriodora subsp. variegata, (triangle, CH) C. henryi, (×-cross, CT) C. torelliana, hybrids (+- cross, CT × CCC) C. torelliana × C. citriodora subsp. citriodora, (diamond, CT × CCV) C. torelliana × C. citriodora subsp. variegata, (dash, CT × CH) C. torelliana × C. henryi. For DBH and survival, taxa sharing the same letters are not significantly different at the final assessment...... 137 Figure 5.3 Comparison of taxon with predominantly juvenile foliage and non-juvenile foliage over time between taxa over time. Symbols: (circle) Corymbia citriodora subsp. citriodora, xvii

(square) C. citriodora subsp. variegata, (triangle) C. henryi, (×-cross) C. torelliana, hybrids (+-cross) C. torelliana × C. citriodora subsp. citriodora, (diamond) C. torelliana × C. citriodora subsp. variegata, (dash) C. torelliana × C. henryi. Taxa sharing the same letters had similar proportions of juvenile foliage at the final assessment...... 139 Figure 5.4 Comparison of crown damage index (CDI) between taxa over time. Symbols: (circle) Corymbia citriodora subsp. citriodora, (square) C. citriodora subsp. variegata, (triangle) C. henryi, (×-cross) C. torelliana, hybrids (+-cross) C. torelliana × C. citriodora subsp. citriodora, (diamond) C. torelliana × C. citriodora subsp. variegata, (dash) C. torelliana × C. henryi...... 140 Figure 5.5 Results of damage parameters of each taxon. Comparison between taxon at each assessment interval of (a) mean defoliation and (b) mean necrosis scores. Comparison of percentage of trees of each taxon with high (grey; scores 3-5) and low (black; scores 0-2) levels of (c) defoliation and (d) necrosis at assessment four (April 2018), when the highest levels of damage occurred. Symbols: (circle; CCC) Corymbia citriodora subsp. citriodora, (square, CCV) C. citriodora subsp. variegata, (triangle, CH) C. henryi, (×cross, CT) C. torelliana, hybrids (+cross, CT × CCC) C. torelliana × C. citriodora subsp. citriodora, (diamond, CT × CCV) C. torelliana × C. citriodora subsp. variegata, (dash, CT × CH) C. torelliana × C. henryi. For prevalence of defoliation and necrosis, taxa sharing the same letters are not significantly different...... 141 Figure 5.6 Result of Spearman rank correlations between performance and damage parameters for each taxon. Correlations: (black) significantly positive; (grey) significantly negative;

(white) not significant. Symbols: (MR) mean daily rainfall; (Tmax) maximum temperature;

(Tmin) minimum temperature; (G) growth rate; (F) flushing percentage; (D) defoliation; (N) necrosis; (CCC) Corymbia citriodora subsp. citriodora, (CCV) C. citriodora subsp. variegata, (CH) C. henryi, (CT) C. torelliana, hybrids (CT × CCC) C. torelliana × C. citriodora subsp. citriodora, (CT × CCV) C. torelliana × C. citriodora subsp. variegata, (CT × CH) C. torelliana × C. henryi...... 143 Figure 5.7 Result of Spearman rank correlations between performance and damage parameters for each taxon over time. Correlations: (black) significantly positive; (grey) significantly negative; (white) not significant. Symbols: (G) growth rate; (F) flushing percentage; (D) defoliation; (N) necrosis (CCC) Corymbia citriodora subsp. citriodora, (CCV) C. citriodora subsp. variegata, (CH) C. henryi, (CT) C. torelliana, hybrids (CT × CCC) C. torelliana × C. citriodora subsp. citriodora, (CT × CCV) C. torelliana × C. citriodora subsp. variegata, (CT × CH) C. torelliana × C. henryi...... 143 xviii

Figure 6.1Synthesis of the results across this study for Corymbia citriodora subsp. variegata. Symbols: (up arrow) increased, (down arrow) reduced, (circle) altered distribution within leaf tissue, (dash) no significant effect, (blank) not addressed in this study, (DBH) diameter at breast height over bark...... 159

xix

Chapter 1

1 Chapter 1 Literature review – Plant interaction with pests and

2 pathogens: spotted gum (Corymbia spp.) as a case study

3 Introduction

4 In forest systems, globalization, climate change and domestication of trees into monoculture

5 systems have been altering the dynamics of tree interaction with pests and pathogens, creating

6 a favourable environment for insect and disease outbreaks (Wingfield et al. 2008; Paine et al.

7 2011; Burgess and Wingfield 2017). Pest and pathogen outbreaks have huge environmental

8 and economic impacts in forest systems, compromising biodiversity and ecological processes,

9 and reducing forest productivity and viability (Paine et al. 2011; Gong and Zhang 2014;

10 Gonçalves et al. 2013; Stenlid and Oliva 2016).

11 Studies of plant interactions with pests and pathogens may provide a better understanding of

12 outbreak dynamics and possibly assist control, prevention and management of them in forest

13 systems, by improving plant selection for resistance and reducing the costs associated with

14 biological control, use of pesticides and silvicultural practices (Eyles et al. 2010; Paine et al.

15 2011; Gonçalves et al. 2013; Naidoo et al. 2014, 2019). Pest and pathogen pressure in forest

16 systems has shaped the selection of phenotypical traits associated with plant resistance,

17 tolerance and escape strategies (Gong and Zhang 2014; Stenlid and Oliva 2016), including

18 physical structures, chemical compounds and growth strategies to reduce, minimise,

19 compensate or avoid damage (Feeny 1976; McNaughton 1981; Batish et al. 2008; Barton

20 2016). This chapter reviews literature on plant, particularly eucalypt, interactions with pests

21 and pathogens, including induced responses, and introduces spotted gums and hybrids and

22 important pests and pathogens of these taxa in Australia.

Chapter 1 24 Patterns of defence mechanisms

25 Plants have evolved mechanisms to minimise pest and pathogen pressure, expressing various

26 constitutive and induced defences involving physical and chemical traits to enhance plant

27 fitness (War et al. 2012; Fürstenberg-Hägg et al. 2013; Mitchell et al. 2016; Boots and Best

28 2018). Constitutive structures are permanent and form the first line of plant defence, creating

29 physical and/or chemical barriers between plants and damage agents (Karban and Baldwin

30 1997; Arnason and Bernards 2010). When constitutive barriers are trespassed by biotic or

31 abiotic damage, some plants can activate induced responses, altering physical structures and/or

32 chemical compounds (Karban 1990; Karban and Baldwin 1997; Eyles et al. 2010). These traits

33 may act as direct defences, negatively affecting pest and pathogen preference and performance

34 (e.g. tough leaves, production of toxic substances), or as indirect defences, producing resources

35 (e.g. food and shelter) and chemical cues to attract natural enemies. The variability of plant

36 defences is strongly associated with biotic and abiotic pressure, leading to differences across

37 species, individuals and plant organs depending on the plant’s developmental stage

38 (Fürstenberg-Hägg et al. 2013; Gong and Zhang 2014; Barton and Boege 2017).

39 To cope with generalist pests and pathogens plants have evolved broad-spectrum defence

40 mechanisms, while specialist pests and pathogens may also select for specific structures, in an

41 ongoing coevolutionary process in which changes in plant defence mechanisms are followed

42 by evolutionary responses of pests and pathogens (Ali and Agrawal 2012; Caseys et al. 2018).

43 In addition, environmental conditions influence the type and abundance of defence

44 mechanisms expressed in plants (Coley et al. 1985; Howe and Schaller 2008; Endara and Coley

45 2011). Due to these complexities, theories and hypotheses explaining patterns of plant defence

46 mechanisms and inter- and intra-specific variability fluctuate widely across studies of different

47 plant species (Stamp 2003; War et al. 2012; Fürstenberg-Hägg et al. 2013; Mitchell et al. 2016).

Chapter 1 48 As there is a metabolic cost associated with the production of defence structures, especially in

49 the absence of pests and pathogens, optimal defence theory predicts that plants balance the

50 costs and benefits associated with investing in defence to maximise plant fitness (Rhoades

51 1979; Fagerstrom et al. 1987; McCall and Fordyce 2010; Godschalx et al. 2016). It assumes

52 that plants will allocate more resources to defence of plant organs with the highest relevance

53 to plant fitness (McCall and Fordyce 2010; Godschalx et al. 2016). In Eucalyptus cladocalyx,

54 for example, a higher concentration of cyanogenic glycosides, a herbvore deterret compound,

55 was found in young, vegetative and reproductive tissues (Gleadow and Woodrow 2000). A

56 similar pattern was observed in Phaseolus lunatus (lima beans), with decreasing concentrations

57 of cyanogenic glycosides observed as plant organs matured (flower buds, flowers, pods and

58 young leaves; Godschalx et al. 2016). The concentration of cyanogenic glycosides was lower

59 in reproductive organs than in leaves, which seems contradictive as reproductive parts directly

60 determine plant fitness. However, removing up to 75% of flowers did not significantly affect

61 pod number, total seed or total viable seed, while removing up to 66% of young leaves

62 significantly reduced the production of these structures. This corroborates optimal defence

63 theory, as removal of young leaves was more important to plant fitness, and therefore leaves

64 were better defended than flowers and other reproductive parts (Godschalx et al. 2016).

65 The expression of constitutive and induced traits varies between plant organs according to their

66 likelihood of being damaged (Zangerl and Rutledge 1996; Agrawal 1999). As constitutive

67 defences are permanent and always expressed, they may be costly to produce and maintain,

68 while induced defences are only produced when plants are damaged, which may minimise the

69 investments in defence until they are needed (Karban and Myers 1989; Karban and Baldwin

70 1997; Agrawal 1999; Stamp 2003; Gong and Zhang 2014). Additionally, induced defences

71 may allow plants to cope with a variety of pests and pathogens and may slow counter-

72 adaptation to plant defences (Agrawal 1999). In Pastinaca sativa (parsnip), fruits are more

Chapter 1 73 likely than roots to be attacked by herbivores, which may explain the allocation of constitutive

74 xanthotoxin in fruits, while only induced xanthotoxin is expressed in roots (Zangerl and

75 Rutledge 1996). Constitutive and induced defences may also vary during a plant’s development

76 stages.

77 Heteroblastic plants show a marked variation in leaf morphology and anatomy as they

78 transition through ontogenetic stages, including seedling to juvenile, intermediate and mature

79 stages (Boege and Maquis 2005; Zoztz et al. 2011). The ontogenetic shifts potentially alter the

80 type and abundance of organisms hosted by plants, which may be associated with the

81 expression of different defence mechanisms in each leaf stage (Hanley et al. 2007). Early stages

82 of plant development (seedling and juvenile leaves) possibly favour induced over constitutive

83 defences, as at these stages, plants are developing and are more likely to invest in growth rather

84 than in constitutive defences, that are permanent and costly to produce (see review by Barton

85 and Koricheva 2010). On the other hand, plants at mature stages (adult leaves) are likely to

86 have greater expression of defence traits, because they are developed enough to produce and

87 maintain defence mechanisms (Herms and Mattson 1992; Boege 2005a, b; Boege and Maquis

88 2005; Elger et al. 2009).

89 In general, juvenile foliage of eucalypts are more frequently and severely damaged by pests

90 and pathogens than adult foliage (Lawrence et al. 2003; Sánchez Márquez et al. 2011;

91 Gherlenda et al. 2016). However, there is no clear pattern with regards to the distribution of

92 defences through ontogenetic stages, and they are likely to be nonlinear (Boege and Maquis

93 2005; Goodger et al. 2007; Barton and Koricheva 2010; Ochoa-López et al. 2015). For

94 instance, despite the morphological differences between juvenile and adult leaves of E. nitens

95 and E. regnans, these leaf stages did not differ in puntative anti-herbivore defences, such as

96 nutritional levels (water, protein and carbohydrates) or total phenolics (Gras et al. 2005).

97 Eucalyptus froggattii seedling leaves, on the other hand, had higher levels of defence

Chapter 1 98 metabolites such as total phenolics and terpenoids in comparison to adult plants (Goodger et

99 al. 2013), and adult leaves of several eucalypt species had higher levels of cyanogenic

100 glycosides than juvenile leaves (Goodger et al. 2006).

101 The resource availability hypothesis proposes that the development of defence mechanisms is

102 related to plant species’ environment in terms of water, nutrients and light (Coley et al. 1985).

103 This hypothesis suggests that plants adapted to areas rich in resources are likely to invest more

104 in growth than defence, as plants may escape or tolerate pests and pathogens by compensating

105 damage loss with increased growth. In contrast, plants adapted to areas with poor resources

106 grow slowly and allocate more resources to defence mechanisms, reducing the levels of pest

107 and pathogen attack (Coley et al. 1985; Endara and Coley 2011). For instance, in resource-rich

108 areas Melaleuca quinquenervia had high levels of compensatory growth, maintaining constant

109 levels of foliar biomass even after four consecutive years of intense Oxyops vitiosa Pascoe

110 (Coleoptera: Curculionidae) herbivory (Pratt et al. 2005).

111 Plant hybridization also influences plant interactions with pests and pathogens, as hybrids may

112 inherit physical and chemical defence traits from the parental species, which may influence

113 plant susceptibility to pests and pathogens (Whitham et al. 1994; Fritz et al. 1999). The

114 hybridization hypothesis suggests that hybrids can be more susceptible, more resistant,

115 intermediate or have no difference in susceptibility to pests and pathogens in comparison to

116 their parents (Fritz et al. 1999). The expression of chemical traits associated with resistance to

117 the fungi Cylindrocladium quinqueseptatum was higher in Corymbia citriodora subsp.

118 citriodora × C. torelliana hybrids than in the parental species (Varshney et al. 2012). Hybrids

119 between C. torelliana and C. citriodora subsp. variegata presented intermediate levels of

120 Paropsis atomaria Olivier (Coleoptera: Chrysomelidae) herbivory and chemical characteristics

121 in comparison to parental species (Nahrung et al. 2009). In other studies, leaf chemistry of

122 spotted gum hybrids (C. torelliana × C. citriodora subsp. citriodora, C. citriodora subsp.

Chapter 1 123 variegata or C. henryi) in comparison to parental taxa were variable and arthropod

124 susceptibility was strongly dependent on the environmental conditions rather than taxa

125 (Nahrung et al. 2010, 2012; Hayes et al. 2013).

126 The dynamics of plant interactions with their biotic and abiotic environment is very complex,

127 and the patterns associated with the expression of defence mechanisms are not always

128 consistent. Studies on traits associated with plant defences may provide insights for better

129 understanding of plant interactions with pests and pathogens.

130

131 Specific plant responses to pests and pathogens

132 Plants may recognize pest and pathogen attack through their interaction with plant tissues, such

133 as insect oral secretion (e.g. saliva), feeding pattern (e.g. scalloping, mining, skeletonising)

134 (Waterman et al. 2019), or pathogens’ penetration/feeding structures (e.g. appressoria,

135 haustoria) and colonization mechanisms (e.g. through stomata, intra/intercellularly) (Jones and

136 Dangl 2006; Carr et al. 2019). Once plants detect damage, they activate metabolic responses

137 that may alter physical structures and/or chemical compounds, which in some cases can

138 enhance plant defence and fitness (Agrawall et al. 1998; León et al. 2001; Björkman et al. 2008;

139 Carr et al. 2019; Waterman et al. 2019).

140 Physical plant structures such as trichomes, leaf thickness and toughness may hinder pest and

141 pathogen development and access to plant nutrients (Woodman and Fernandes 1991;

142 Steinbauer 2001; Hanley et al. 2007; Björkman et al. 2008; Malishev and Sanson 2015; Barton

143 2016). For instance, leaf physical parameters, such as tougher leaves, a denser palisade layer

144 and reduced intercellular air space, negatively influenced development of Teratosphaeria sp.

145 (Capnodiales: Teratosphaeriaceae) fungal infection in E. globulus and E. nitens (Smith et al.

146 2006, 2007a, 2017). Feeding by Extatosoma tiaratum Macleay (Phasmatodea: Phasmatidae)

147 on leaves of E. viminalis and E. ovata (Malishev and Sanson 2015) and P. atomaria larval

Chapter 1 148 mortality in Corymbia citriodora subsp. variegata (Nahrung et al. 2009) was influenced by

149 physical traits such as leaf veins and leaf toughness.

150 Chemical compounds such as terpenes, waxes, phenolics and pectins are associated with

151 repellent, toxicological and behavioural effects against many groups of organisms (Batish et

152 al. 2008; Daayf et al. 2012; Pusztahelyi et al. 2015). These compounds are produced through

153 secondary metabolism, exuded by specific structures (e.g. glands and trichomes) or liberated

154 after the rupture of the plant cell wall (Levin 1973; Boland et al. 1991; Wink 2003; Hartmann

155 2007; Pichersky and Lewinsohn 2011; Gong and Zhang 2014). Plant physical and chemical

156 responses may be triggered when plants experience pest and pathogen damage or by elicitors

157 such as jasmonic and salicylic acid which are phytohormones that mediate many plant

158 functions, including the induction of physical and chemical responses (Turner et al. 2002;

159 Smith et al. 2009; Naidoo et al. 2014).

160 Most of the knowledge in plant-induced responses has focused on annual herbaceous and short-

161 lived perennials (see review by Eyles et al. 2010). In these systems, alterations of leaf physical

162 and/or chemical structures due to damage or use of elicitors negatively influenced pest and

163 pathogen development and survival, favouring plant fitness (War et al. 2018). Insect feeding

164 and application of methyl jasmonate induced physical responses in several crop plants,

165 increasing leaf trichome density (Agrawal 1999; Traw and Dawson 2002; Boughton et al.

166 2005). For example, Pieris rapae Linnaeus (Lepidoptera: Pieridae) larval feeding in Raphanus

167 raphanistrum and R. sativus (wild radish; Agrawal 1999), P. rapae, Trichoplusia ni Hubner

168 (Lepidoptera: Noctuidae), and Phyllotreta cruciferae Goeze (Coleoptera: Chrysomelidae)

169 feeding in Brassica nigra (black mustard; Traw and Dawson 2002), and the application of

170 methyl jasmonate in Lycopersicon esculentum (tomato; Boughton et al. 2005) induced physical

171 plant responses. Furthermore, induced responses by Niccotiana attenuata (tobacco) treated

172 with methyl jasmonate included an increase in nicotine levels, influencing Manduca sexta

Chapter 1 173 Linneaus (Lepidoptera: Sphingidae) feeding preference and development (van Dam et al.

174 2000). In Triticum turgidum subsp. durum Desfontaines (durum wheat) damage by cereal

175 aphids - Schizaphis graminum Rondani, Sitobion avenae Fabricius, and Rhopalosiphum padi

176 Linnaeus (Hemiptera: Aphididae) - increased the levels of benzoxazinoid compounds, known

177 for their protective and anti-feeding effects against insects (Shavit et al. 2018). Zea mays

178 Linnaeus (maize) attack by several pests and pathogens induces an array of terpene volatiles

179 with antimicrobial activity (see review by Block et al. 2019).

180 Induced responses in perennial trees are more complex, as long-lived organisms may

181 experience a greater variety of biotic and abiotic stress, favouring the expression of constitutive

182 defences over induced responses (Hammerschmidt 2006; Eyles et al. 2010). For instance, E.

183 globulus and E. grandis treated with methyl jasmonate or damaged by P. atomaria or

184 Mnesampela privata Guenèe (Lepidoptera: Geometridae) did not induce responses, suggesting

185 that constitutive traits were more critical in interactions between these trees and herbivorous

186 insects (Rapley et al. 2007; Henery et al. 2008).

187 Conversely, induced responses to pests and pathogens are described from some forest systems

188 including poplar, eucalypts and pine trees. For instance, levels of monoterpenes and

189 sesquiterpenes increased in Populus nigra challenged by caterpillars of Lymantria dispar

190 Linnaeus (Lepidoptera: Erebidae), Laothoe populi Linnaeus (Lepidoptera: Sphingidae) and

191 Amata mogadorensis Blachier (Lepidoptera: Erebidae; Fabisch et al. 2019). Clones of E.

192 grandis and hybrids of E. camaldulensis × E. grandis damaged by Leptocybe invasa Fisher &

193 LaSalle (Hymenoptera: Eulophidae) modified their foliar essential oil profiles (Oates et al.

194 2015) and E. camaldulensis under Glycaspis brimblecombei Moore (Hemiptera: Psyllidae)

195 feeding modified leaf polyphenol profiles (Patton et al. 2018). Additionally, pathogen infection

196 induced systemic responses in clones of E. grandis damaged by the fungus Chrysoporthe

197 austroafricana Gryzenh. & M.J.Wingf. (Diaporthales: Cryphonectriaceae) by altering

Chapter 1 198 terpenoid levels in the leaves (Visser et al. 2015). Eucalyptus globulus and E. nitens infected

199 by Teratosphaeria sp. altered internal leaf structures (Smith et al. 2006; 2007a; 2017), and

200 Picea abies (Norway spruce) infected by the rust pathogen Chrysomyxa rhododendri De Bary

201 (Pucciniales: Coleosporiaceae) increased its concentration of leaf polyphenols (Ganthaler et al.

202 2017).

203 Hence, there is a growing interest in induced responses in trees, as they may add important

204 knowledge on plant interactions with pests and pathogens (Eyles et al. 2010; Naidoo et al.

205 2014), which may in turn contribute to management strategies, such as inducing resistant

206 characteristics or selection of resistant plants (Naidoo et al. 2014, 2019).

207

208 Plant secondary metabolites

209 Plant metabolism produces a diversity of low molecular weight compounds, classified as

210 primary and secondary metabolites, depending on their role (Pichersky and Gang 2000).

211 Primary metabolism covers all essential processes involved in growth and development,

212 therefore its constituents - chlorophylls, common sugars, protein amino acids, purines and

213 pyrimidines of nucleic acids - are indispensable, uniform, conserved and universally present

214 (Walton and Brown 1999)

215 Until the end of 19th century, secondary metabolites had no recognized role in the maintenance

216 of vital processes in plants, and thus, were historically regarded as metabolic waste,

217 detoxification products and inert compounds (Walton and Brown 1999; Wink 2003; Hartmann

218 2007). In fact, secondary metabolites are essential for plant survival, comprising a vast array

219 of compounds, including phenolics, pectins, terpenoids and waxes, which have physiological

220 functions and regulate plant interactions with biotic and abiotic stressors (Mitchell-Olds et al.

221 1998; Wink 2003; Hartmann 2007; Pichersky and Lewinsohn 2011).

Chapter 1 222 Specifically, polyphenols and pectins are essential secondary metabolites in plant physiology,

223 thought to regulate plant interactions with pests and pathogens (Lattanzio et al. 2006; Witzell

224 and Martín 2008; Pogorelko et al. 2013). Plant polyphenols include several compound classes,

225 such as tannins and lignins, that moderate pollinator attraction and UV protection among other

226 roles (Lattanzio et al. 2006; Miedes et al. 2014). Pectins are polysaccharides with a structural

227 role in plant primary and secondary cell walls, influencing plant growth and development

228 (Lattanzio et al. 2006; Pogorelko et al. 2013). Under pest and pathogen attack, pectins and

229 polyphenols undergo modification, increasing/decreasing their levels or generating new

230 compounds that may signal the presence of cell wall damage (Mandal and Mitra 2007;

231 Pogorelko et al. 2013). These signal compounds elicit biological responses related to

232 constitutive and induced defences, such as accumulation and/or production of compounds that

233 inhibit pathogen enzymes, and activation of cell wall strengthening mechanisms through the

234 deposition of lignin and other compounds (Smith et al. 2006, 2007a; Lattanzio et al. 2006;

235 Pogorelko et al. 2013; Miedes et al. 2014; Pusztahelyi et al. 2015; Ganthaler et al. 2017).

236 Terpenoids - including monoterpenes, sesquiterpenes and associated compounds - are the

237 prominent secondary metabolites in eucalypts, involving a vast array of functional groups such

238 as hydrocarbons, alcohols, aldehydes, ketones, acids and esters (Boland et al. 1991).

239 Terpenoids are associated with cold resistance, prevention of water loss, UV protection

240 (Boland et al. 1991), allelopathic effects (Zhang et al. 2016; Aleixo et al. 2016), attraction of

241 pollinators and natural enemies (Badenes-Perez 2014), and plant defence (Naidoo et al. 2014).

242 Some terpenoids have toxic, repellent and antibiotic effects (Batish et al. 2008). The genes

243 responsible for terpenoid syntheses have been identified in E. grandis, E. globulus and C.

244 citriodora subsp. variegata (Külheim et al. 2015; Butler et al. 2018). These genes are numerous

245 and highly conserved between species, potentially associated with evolutionary adaptations to

246 biotic and abiotic conditions (Külheim et al. 2015; Butler et al. 2018). This may allow plants

Chapter 1 247 to quickly adapt to environmental stress, which can include inducing or priming physical and

248 chemical responses when under stress, such as pest and pathogen attack (Külheim et al. 2015;

249 Butler et al. 2018).

250 Leaf waxes are important secondary metabolic components of eucalypts, comprising physical

251 and chemical characteristics that are essential to plant physiological functions, such as sealing

252 the leaf surface, UV protection, water and gas regulation and protection from pests and

253 pathogens (Müller and Riederer 2005; Gosney et al. 2016, 2017; dos Santos et al. 2019). The

254 highly hydrophobic properties of waxes reduce leaf wettability, influencing pest and pathogen

255 adhesion and development on the leaf surface (Steinbauer et al. 2004, 2009; Müller and

256 Riederer 2005; Paine et al. 2011; Smith et al. 2018). In addition, the secondary metabolites that

257 compose leaf waxes may work as chemical signals for pest and pathogen attack, inducing plant

258 responses and/or defence mechanisms (Müller and Riederer 2005). Steroids form part of the

259 cuticular wax composition of some eucalypts (dos Santos et al. 2019), and some steroids may

260 contribute to general defence in plants, as in vitro tests demonstrated their toxic effects to

261 several fungal pathogens (Roddick 1987).

262

263 Spotted gums and hybrids

264 The genus Corymbia is endemic to Australia, comprising 113 species (Parra-O et al. 2010)

265 distributed mostly in northern Australia, with a few species occurring in southern parts of the

266 continent (Hill and Johnson 1995). Spotted gums (Corymbia citriodora subsp. citriodora

267 (Hook.) KD. Hill & L.A.S. Johnson (CCC), C. citriodora subsp. variegata (F. Muell.) A.R.

268 Bean & M.W. McDonald (CCV), C. henryi (S.T. Blake) K.D. Hill & L.A.S. Johnson (CH) and

269 C. maculata (Hook.) K.D. Hill & L.A.S. Johnson) are important commercial taxa of Corymbia,

270 with hardwood plantations being developed in Australia (Lee 2007; Lee et al. 2010), South

Chapter 1 271 Africa (Gardner et al. 2007), Brazil (Alfenas et al. 2016; Cunha et al. 2019) and Asia (Zhou

272 and Wingfield 2011; Varshney et al. 2012; Chen et al. 2017).

273 Spotted gums occur naturally from around latitude 16 °S in northern Queensland to latitude 37

274 °S in eastern Victoria in a replacement series down the east coast (Hill and Johnson 1995).

275 They suit a vast range of site conditions, including poor soils and dry environments, showing

276 desirable field performance, wood quality and adaptation to environments where other

277 commercial eucalypts grow poorly (Lee 2007; Lee et al. 2009, 2010).

278 The use of spotted gums in forest plantations is hindered by flowering asynchrony that reduces

279 seed production and by low rates of vegetative propagation (Lee 2007). The development of

280 hybrids between spotted gums and C. torelliana (F. Muell.) KD. Hill & L.A.S. Johnson (CT)

281 allowed vegetative propagation (Lee 2007; Lee et al. 2009). Unlike spotted gums, CT is

282 considered a weed in some parts of Australia (Smith et al. 2007b), with limited potential for

283 commercialisation due to its large taper, poor form and growth; it is, however, commonly used

284 in windbreaks and amenity plantings outside its natural range (Lee 2007). In these areas, CT

285 naturally hybridizes with spotted gums, and the offspring frequently present desirable growth

286 and form, leading to the development of a controlled breeding and testing program (Lee 2007).

287 Field trials involving these spotted gum hybrids have confirmed their potential for outstanding

288 performance, with some families displaying good height and diameter growth, frost tolerance,

289 straightness and seedling/coppice rootability similar or better than their parental species (Lee

290 2007; Lee et al. 2009). In general, hybrids are more susceptible to pests and pathogens than

291 pure species (Whitham et al. 1994; Dungey et al. 2000; Potts and Dungey 2004; Nahrung et al.

292 2011, 2014). This may occur because tree selection for rapid growth may reduce plant

293 defensive traits (Henery 2011), or indirectly select for susceptibility (Dungey et al. 1997).

294 However, some spotted gum hybrids appear to have lower or similar susceptibility to pests and

295 pathogens than their parental species, as found for eriophyid mite (Rhombacus sp.) (Lee 2007),

Chapter 1 296 P. atomaria (Nahrung et al. 2009), and Q. pitereka (Dickinson et al. 2004; Lee 2007; Lee et al.

297 2009).

298 Different levels of tolerance/resistance to various arthropods (e.g. Lee et al. 2010; Nahrung et

299 al. 2012), and to the pathogens Q. pitereka, Austropuccinia psidii (G. Winter) Beenken

300 (Pucciniales: Sphaerophragmiaceae) and Calonectria pteridis Crous, MJ Wingf. & Alfenas

301 (Hypocreales: Nectriaceae) (e.g. Dickinson et al. 2004; Pegg et al. 2011a, c; Pegg et al. 2014;

302 Alfenas et al. 2016) were found among spotted gums and their hybrids. The differences in the

303 expression of physical and chemical foliar traits between hybrids and pure spotted gums

304 (Nahrung et al. 2009; Hayes et al. 2013) was potentially one of the factors influencing the

305 variable resistance between these taxa to pests and pathogens observed in field trials (Nahrung

306 et al. 2012). Hence, it is important to understand spotted gum and hybrid susceptibility to pests

307 and pathogens under glasshouse and field conditions, as it may influence tree performance and

308 their potential suitability for plantations (Lee et al. 2010; Pegg et al. 2011a, c; Nahrung et al.

309 2012).

310 Studies of spotted gums have primarily focused on CCV, with its performance being described

311 in field and controlled environmental conditions (e.g. Lee 2007; Johnson et al. 2009; Lee et al.

312 2010; Lan et al. 2011), resistance/tolerance to pests and pathogens (e.g. Lawson and McDonald

313 2005; Johnson et al. 2009; Pegg et al. 2009a; Pegg et al. 2011a, b; Nahrung et al. 2010, 2014;

314 Lan et al. 2011; Pegg et al. 2014) and genetic parameters (e.g. Brawner et al. 2011, Butler et

315 al. 2016, 2019; Freeman et al. 2019). CCV is important to hardwood plantations in New South

316 Wales and Queensland due to its desirable performance parameters (Dickinson et al. 2004; Lee

317 2007; Lee et al. 2010), including resistance to borer attack (Lee et al. 2010) and to Q. pitereka

318 (Dickinson et al. 2004). Field trials planted with different taxa, including Corymbia and other

319 eucalypt species, provenances and hybrids, found that two CCV provenances were among the

320 top-performing taxa for volume growth and lower incidence of Phoracantha spp. (Coleoptera:

Chapter 1 321 Cerambycidae) and Endoxyla cinerea Tepper (Lepidoptera: Cossidae) borer attack (Lee et al.

322 2010). CCV has been intensively tested for Q. pitereka tolerance at the provenance, family and

323 individual levels, and as clones (Johnson et al. 2009; Brawner et al. 2011; Lan et al. 2011; Pegg

324 et al. 2011a, c), providing opportunities to improve disease resistance through breeding and

325 selection.

326

327 Paropsis atomaria

328 Paropsis atomaria Olivier (Coleoptera: Chrysomelidae) occurs along the east coast of

329 Australia from central Queensland to southern Victoria and west to South Australia (Schutze

330 2008). It is a defoliator in its adult and larval stages with a broad host range of eucalypt species,

331 including spotted gums (Schutze 2006; Nahrung 2006). All larval stages of P. atomaria feed

332 preferentially on newly expanding foliage, threatening young, rapidly growing eucalypt

333 plantations (Carne 1966; Nahrung 2006).

334 Under natural conditions, P. atomaria occurs in low numbers, however, the expansion of

335 hardwood plantations created a favourable environment for pest outbreaks (Nahrung 2006;

336 Schutze 2008). The insect became a pest in many eucalypt plantations in Australia (Nahrung

337 2006; Carnegie et al. 2008; Nahrung et al. 2010, 2011) and has recently been identified as an

338 invasive species in Taiwan (iNaturalist.org 2019; Nahrung et al. submitted; Chris Reid,

339 Australian Museum pers. comm.). Paropsis atomaria damage causes tree dieback,

340 compromising tree performance and productivity (Carne 1966).

341 Field trials involving spotted gums and their hybrids with CT have found that the intensity of

342 damage caused by P. atomaria was different between taxa, also varying according to site

343 location and abundance of insects (Nahrung et al. 2009, 2012). Leaf chemical and physical

344 characteristics were markedly different between these taxa, which may influence plant-insect

345 interactions (Nahrung et al. 2009; Hayes et al. 2013). For instance, leaf toughness, lamina

Chapter 1 346 thickness, leaf glabrousness and leaf chemistry affected P. atomaria feeding preference and

347 performance (Nahrung et al. 2009).

348

349 Quambalaria pitereka

350 Spotted gums are susceptible to Q. pitereka (J.Walker & Bertus) J.A.Simpson

351 (Microstromatales: Quambalariaceae), an endemic fungal pathogen that affects new leaves and

352 shoots causing foliage spots, leaf and shoot blight, stem death and loss of apical dominance,

353 thereby compromising tree form, productivity and survival (Pegg et al. 2009a, b; Lan et al.

354 2011). Trees affected by Q. pitereka may not recover, which reduces the effective stocking and

355 yield of the plantation (Lee 2007; Pegg et al. 2011a, c). It affects planted spotted gum forests

356 in Queensland and New South Wales, and host species outside the disease’s natural range, such

357 as C. maculata and C. calophylla plantations in Western Australia (Paap et al. 2008; Ahrens et

358 al. 2019) and CCC plantations in China (Zhou et al. 2007).

359 Quambalaria pitereka occurs naturally with numerous Corymbia species (Dickinson et al.

360 2004), providing the primary source of inoculum on the edge of spotted gum plantations (Pegg

361 et al. 2011b). The pathogen has the potential to produce multiple disease cycles within a short

362 period of time when environmental conditions are favourable, increasing the risk of disease

363 outbreaks within plantations (Pegg et al. 2011b). Within three years of commercial-scale

364 plantation establishment, Q. pitereka outbreaks became a problem in spotted gum plantations

365 in Queensland and New South Wales, with pathogen infestation occurring three to six months

366 after planting (Self et al. 2002; Carnegie et al. 2007; Johnson et al. 2009).

367 Even though most spotted gums and hybrids are somewhat susceptible to Q. pitereka

368 (Dickinson et al. 2004; Carnegie 2007; Lee 2007), there are different levels of

369 tolerance/resistance within and between these taxa (Lee et al. 2010; Pegg et al. 2011a, c). For

370 example, there was a strong positive relationship between tree growth parameters and Q.

Chapter 1 371 pitereka resistance and faster-growing trees were less infected or escaped infection (Brawner

372 et al. 2011; Lan et al. 2011; Pegg et al. 2011a).

373 The geographical distribution of CCV resistance to Q. pitereka is possibly associated with

374 home-site climate, such as temperature and rainfall, resulting from ongoing coevolutionary

375 processes between host and pathogen (Freeman et al. 2019). Coevolution has also shaped the

376 variable resistance observed between native pathogens and host interactions, underlying the

377 expression of resistance (R-) genes and phenotypic traits (Butler et al. 2019). In CCV, specific

378 R-genes and large-effect quantitative trait loci regulate resistance to Q. pitereka, moderating

379 direct pathogen detection and activation of defence mechanisms such as a hypersensitive

380 response (Butler et al. 2019). Additionally, factors such as leaf anatomy and chemical

381 composition influence pathogen development and may play an important role in plant

382 resistance (Butler et al. 2019).

383

384 Austropuccinia psidii

385 Austropuccinia psidii (G. Winter) Beenken (Pucciniales: Sphaerophragmiaceae) is an exotic,

386 highly invasive, fungal pathogen that was first detected in Australia in 2010 (Carnegie et al.

387 2010). This pathogen has a broad host range of over 480 myrtaceous species / subspecies from

388 73 genera (Soewarto et al. 2019) in natural areas of Australia and planted forests globally,

389 causing enormous economic and environmental losses (Carnegie 2015; Pegg et al. 2017; Pegg

390 et al. 2018; Winzer et al. 2018).

391 Although this pathogen has not caused significant damage in spotted gums and hybrids in the

392 field (Carnegie 2015; Lee, pers. obs.), glasshouse screening studies found that they are highly

393 susceptible to A. psidii (Pegg et al. 2014), with the disease representing a real threat to native

394 and planted forests of spotted gums and hybrids. The pathogen infects young shoots and stems,

395 flowers, fruits, and coppice, causing leaf spots, severe shoot and stem blight, necrosis and

Chapter 1 396 distortion. These symptoms can lead to tree dieback, with high levels of mortality in some

397 species (Glen et al. 2007; Carnegie et al. 2016; Pegg et al. 2017; Winzer et al. 2018).

398 Due to the potentially devastating consequences of A. psidii infection, selecting for resistance

399 is one of the main strategies to control this pathogen in eucalypts (Pegg et al. 2014; Naidoo et

400 al. 2019). Resistance to A. psidii is variable in eucalypts, including pure species, subspecies,

401 hybrids and clones (Silva et al. 2013; Pegg et al. 2014; Silva et al. 2017; dos Santos et al. 2019).

402 Resistant plants exhibit immunity or a hypersensitive response, restricting pathogen spread

403 through plant tissue (Xavier et al. 2001; Junghans et al. 2003; Pegg et al. 2014; dos Santos et

404 al. 2019). Molecular studies have found that multiple small-effect genes tend to regulate

405 eucalypt resistance to A. psidii (Alves et al. 2012; Thumma et al. 2013; Butler et al. 2016,

406 2019), resulting in nonspecific pathogen recognition that activates generic disease response

407 mechanisms (Butler et al. 2019).

408 Leaf anatomy and chemistry play an important role in eucalypt resistance to A. psidii (dos

409 Santos et al. 2019; Yong et al. 2019). For instance, the variation of terpene expression in

410 eucalypts may be associated with protection against A. psidii (Hantao et al. 2013; Potts et al.

411 2016; Yong et al. 2019), thought to work as species-specific biomarkers for resistance to this

412 pathogen (Yong et al. 2019). Leaf waxes also play a role in pathogen recognition, potentially

413 reducing or favouring pathogen infection (Müller and Riederer 2005; dos Santos et al. 2019).

414 In addition, A. psidii penetrates directly through leaf cuticle and epidermis (Hunt 1968; Pegg

415 et al. 2014), and plant anatomical characteristics such as leaf toughness, thickness and cell

416 density may be important to mediate the severity of infection (dos Santos et al. 2019; Freeman

417 et al. 2019; Butler et al. 2019).

418

Chapter 1 419 Thesis aims and outline

420 The objective of this thesis was to study plant phenotypical traits underlying spotted gums and

421 their hybrids’ interaction with pests and pathogens. Physical and chemical responses to the pest

422 Paropsis atomaria Olivier (Coleoptera: Chrysomelidae), and the pathogens Quambalaria

423 pitereka (Microstromatales: Quambalariaceae) and Austropuccinia psidii (Pucciniales:

424 Sphaerophragmiaceae) were analysed in Corymbia citriodora subsp. variegata, the most well-

425 studied and important taxon of spotted gums. The effect of pests and pathogens on plant

426 performance in the field and the influence of leaf ontogeny on damage were studied in the pure

427 taxa C. citriodora subsp. citriodora (CCC), C. citriodora subsp. variegata (CCV), C. henryi

428 (CH) and C. torelliana (CT), and their hybrids (CT × CCC, CT × CCV and CT × CH), as the

429 whole group have potential/are used as plantation species in different environments.

430 Due to the importance of CCV as a plantation hardwood, three controlled experiments in the

431 shadehouse focussed on this taxon, aiming to understand its chemical and anatomical responses

432 to pests and pathogens. These experiments determined local, systemic and delayed plant

433 responses to mechanical and P. atomaria damage (Chapter 2), the differences in plant response

434 between resistant and susceptible individuals to Q. pitereka and A. psidii infection (Chapter 3)

435 and compared different responses to these pathogens (Chapter 4). The final research

436 experiment was a field trial conducted over two years, involving three spotted gum taxa (CCC,

437 CCV and CH), CT and three hybrids (CT × CCC, CT × CCV and CT × CH). The aims of the

438 field experiment were to evaluate the effects of pest and pathogen pressure on tree performance

439 of a broad range of Corymbia taxa with potential for plantation development (Chapter 5).

440 The four experiemntal chapters have been submitted for publication and are presented as

441 independent units, with statements of contribution and publication status of each manuscript

442 included in the beginning of each chapter. Each of these experimental chapters follows the

443 journal manuscript style, but with figures and tables integrated into the text. Chapter 6

Chapter 1 444 comprises a general discussion integrating the key findings as a whole and including

445 recommendations and future research needs.

446

447 References

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Chapter 1 701 Karban R, Myers JH (1989). Induced plant-responses to herbivory. Annual Review of 702 Ecology and Systematics. 20:331-348. 703 doi:https://doi.org/10.1146/annurev.es.20.110189.001555. 704 Karban R (1990). Herbivore outbreaks on only young trees: testing hypotheses about aging 705 and induced resistance. Oikos. 59:27-32. doi:https://doi.org/10.2307/3545118. 706 Karban R, Baldwin IT (1997). Induced responses to herbivory. University of Chicago Press, 707 Chicago. doi:https://doi.org/10.7208/chicago/9780226424972.001.0001. 708 Külheim C, Padovan A, Hefer C, Krause ST, Köllner TG, Myburg AA, Degenhardt J, Foley 709 WJ (2015). The Eucalyptus terpene synthase gene family. BMC Genomics. 16:18. 710 doi:https://doi.org/10.1186/s12864-015-1598-x. 711 Lan J, Raymond CA, Smith HJ, Thomas DS, Henson M, Carnegie AJ, Nichols JD (2011). 712 Variation in growth and Quambalaria tolerance of clones of Corymbia citriodora 713 subsp. variegata planted on four contrasting sites in north-eastern NSW. Australian 714 Forestry. 74:205-217. 715 Lattanzio V, Lattanzio VM, Cardinali A (2006). Role of phenolics in the resistance 716 mechanisms of plants against fungal pathogens and insects. Phytochemistry: 717 Advances in research. 661:23-67. 718 Lawrence R, Potts BM, Whitham TG (2003). Relative Importance of Plant Ontogeny, Host 719 Genetic Variation, and Leaf Age for a Common Herbivore. Ecology. 84:1171-1178. 720 doi:https://doi.org/10.1890/0012-9658(2003)084[1171:RIOPOH]2.0.CO;2. 721 Lawson S, McDonald J (2005). Emerging insect pests of Corymbia. Corymbia Research.20. 722 Lee DJ (2007). Achievements in forest tree genetic improvement in Australia and New 723 Zealand 2: Development of Corymbia species and hybrids for plantations in eastern 724 Australia. Australian Forestry. 70:11-16. 725 doi:https://doi.org/10.1080/00049158.2007.10676256. 726 Lee DJ, Huth JR, Brawner JT, Dickinson GR (2009). Comparative performance of Corymbia 727 hybrids and parental species in subtropical queensland and implications for breeding 728 and deployment. Silvae Genetica. 58:205-212. doi:https://doi.org/10.1515/sg-2009- 729 0026. 730 Lee DJ, Huth JR, Osborne DO, Hogg BW (2010). Selecting hardwood taxa for wood and 731 fibre production in Queensland's subtropics. Australian Forestry. 73:106-114. 732 doi:https://doi.org/10.1080/00049158.2010.10676316. 733 León J, Rojo E, Sánchez-Serrano JJ (2001). Wound signalling in plants. Journal of 734 Experimental Botany. 52:1. doi:https://doi.org/10.1093/jexbot/52.354.1. 735 Levin DA (1973). The role of trichomes in plant defense. The quarterly review of biology. 736 48:3-15. 737 Malishev M, Sanson GD (2015). Leaf mechanics and herbivory defence: how tough tissue 738 along the leaf body deters growing insect herbivores. Austral Ecology. 40:300-308. 739 doi:https://doi.org/10.1111/aec.12214. 740 Mandal S, Mitra A (2007). Reinforcement of cell wall in roots of Lycopersicon esculentum 741 through induction of phenolic compounds and lignin by elicitors. Physiological and 742 Molecular Plant Pathology. 71:201-209. 743 doi:https://doi.org/10.1016/j.pmpp.2008.02.003.

Chapter 1 744 McCall AC, Fordyce JA (2010). Can optimal defence theory be used to predict the 745 distribution of plant chemical defences? Journal of Ecology. 98:985-992. 746 doi:https://doi.org/10.1111/j.1365-2745.2010.01693.x. 747 McNaughton S (1983). Compensatory plant growth as a response to herbivory. Oikos Vol 40, 748 no 3 1983. 40 doi:https://doi.org/10.2307/3544305. 749 Miedes E, Vanholme R, Boerjan W, Molina A (2014). The role of the secondary cell wall in 750 plant resistance to pathogens. Frontiers in Plant Science. 5:358. 751 doi:https://doi.org/10.3389/fpls.2014.00358. 752 Mitchell-Olds T, Gershenzon J, Baldwin I, Boland W (1998). Chemical ecology in the 753 molecular era. Trends in Plant Science. 3:362-365. doi:https://doi.org/10.1016/s1360- 754 1385(98)01296-5. 755 Mitchell C, Brennan RM, Graham J, Karley AJ (2016). Plant defense against herbivorous 756 pests: exploiting resistance and tolerance traits for sustainable crop protection. 757 Frontiers in Plant Science. 7:8. doi:https://doi.org/10.3389/fpls.2016.01132. 758 Müller C, Riederer M (2005). Plant surface properties in chemical ecology. Journal of 759 Chemical Ecology. 31:2621-2651. doi:https://doi.org/10.1007/s10886-005-7617-7. 760 Nahrung HF (2006). Paropsine beetles (Coleoptera: Chrysomelidae) in south-eastern 761 Queensland hardwood plantations: identifying potential pest species. Australian 762 Forestry. 69:270-274. doi:https://doi.org/10.1080/00049158.2006.10676247. 763 Nahrung HF, Waugh R, Hayes RA (2009). Corymbia species and hybrids: chemical and 764 physical foliar attributes and implications for herbivory. Journal of Chemical Ecology. 765 35:1043-1053. doi:https://doi.org/10.1007/s10886-009-9682-9. 766 Nahrung HF, Waugh R, Lee DJ, Lawson SA (2010). Susceptibility of Corymbia species and 767 hybrids to arthropod herbivory in Australian subtropical hardwood plantations. 768 Southern Forests. 72:147-152. doi:https://doi.org/10.2989/20702620.2010.547247. 769 Nahrung HF, Waugh R, Hayes RA, Lee DJ (2011). Influence of Corymbia hybridisation on 770 crown damage by three arthropod herbivores. Journal of Tropical Forest Science. 771 23:383-388. 772 Nahrung HF, Hayes RA, Waugh R, Lawson SA (2012). Corymbia leaf oils, latitude, hybrids 773 and herbivory: a test using common-garden field trials. Austral Ecology. 37:365-373. 774 doi:https://doi.org/10.1111/j.1442-9993.2011.02284.x. 775 Naidoo S, Külheim C, Zwart L, Mangwanda R, Oates CN, Visser EA, Wilken FE, Mamni 776 TB, Myburg AA (2014). Uncovering the defence responses of Eucalyptus to pests and 777 pathogens in the genomics age. Tree physiology. 34:931-943. 778 doi:https://doi.org/10.1093/treephys/tpu075. 779 Naidoo S, Slippers B, Plett JM, Coles D, Oates CN (2019). The Road to Resistance in Forest 780 Trees. Frontiers in Plant Science. 10 doi:https://doi.org/10.3389/fpls.2019.00273. 781 Oates CN, Külheim C, Myburg AA, Slippers B, Naidoo S (2015). The transcriptome and 782 terpene profile of Eucalyptus grandis reveals mechanisms of defense against the 783 insect pest, Leptocybe invasa. Plant and Cell Physiology. 56:1418-1428. 784 doi:https://doi.org/10.1093/pcp/pcv064. 785 Ochoa-López S, Villamil N, Zedillo-Avelleyra P, Boege K (2015). Plant defence as a 786 complex and changing phenotype throughout ontogeny. Annals of Botany. 116:797- 787 806. doi:https://doi.org/10.1093/aob/mcv113.

Chapter 1 788 Paap T, Burgess TI, McComb JA, Shearer BL, Hardy G (2008). Quambalaria species, 789 including Q. coyrecup sp nov., implicated in canker and shoot blight diseases causing 790 decline of Corymbia species in the southwest of Western Australia. Mycological 791 Research. 112:57-69. doi:https://doi.org/10.1016/j.mycres.2007.10.005. 792 Paine TD, Steinbauer MJ, Lawson SA (2011) Native and exotic pests of Eucalyptus: a 793 worldwide perspective. Annual Review of Entomology. 56:181-201. 794 doi:https://doi.org/10.1146/annurev-ento-120709-144817. 795 Park RF, Keane PJ, Wingfield MJ, Crous PW (2000) Fungal diseases of eucalypt foliage. 796 In:Keane PJ, Kile GA, Podger FD, Brown BN (eds) Diseases and pathogens of 797 eucalypts. CSIRO publishing, Australia pp 153-239. 798 Parra-O C, Bayly MJ, Drinnan A, Udovicic F, Ladiges P (2010). Phylogeny, major clades 799 and infrageneric classification of Corymbia (Myrtaceae), based on nuclear ribosomal 800 DNA and morphology (vol 22, pg 384, 2009). Australian systematic botany. 23:141- 801 +. doi:https://doi.org/10.1071/sb09028_co. 802 Patton MF, Arena GD, Salminen JP, Steinbauer MJ, Casteel CL (2018). Transcriptome and 803 defence response in Eucalyptus camaldulensis leaves to feeding by Glycaspis 804 brimblecombei Moore (Hemiptera: Aphalaridae): a stealthy psyllid does not go 805 unnoticed. Austral Entomology. 57:247-254. doi:https://doi.org/10.1111/aen.12319. 806 Pegg GS, Carnegie AJ, Wingfield MJ, Drenth A (2009a). Quambalaria species: increasing 807 threat to eucalypt plantations in Australia. Southern Forests. 71:111-114. 808 doi:https://doi.org/10.2989/sf.2009.71.2.4.819. 809 Pegg GS, Webb RI, Carnegie AJ, Wingfield MJ, Drenth A (2009b). Infection and disease 810 development of Quambalaria spp. on Corymbia and Eucalyptus species. Plant 811 Pathology. 58:642-654. doi:https://doi.org/10.1111/j.1365-3059.2009.02087.x. 812 Pegg GS, Carnegie AJ, Wingfield MJ, Drenth A (2011a). Variable resistance to Quambalaria 813 pitereka in spotted gum reveal opportunities for disease screening. Australasian Plant 814 Pathology. 40:76-86. doi:https://doi.org/10.1007/s13313-010-0016-8. 815 Pegg GS, Nahrung H, Carnegie AJ, Wingfield MJ, Drenth A (2011b). Spread and 816 development of Quambalaria shoot blight in spotted gum plantations. Plant 817 Pathology. 60:1096-1106. doi:https://doi.org/10.1111/j.1365-3059.2011.02468.x. 818 Pegg GS, Shuey LS, Carnegie AJ, Wingfield MJ, Drenth A (2011c). Potential gains through 819 selecting for resistance in spotted gum to Quambalaria pitereka. Australasian Plant 820 Pathology. 40:197-206. doi:https://doi.org/10.1007/s13313-011-0030-5. 821 Pegg GS, Brawner JT, Lee DJ (2014). Screening Corymbia populations for resistance to 822 Puccinia psidii. Plant Pathology. 63:425-436. doi:https://doi.org/10.1111/ppa.12097. 823 Pegg GS, Taylor T, Entwistle P, Guymer G, Giblin F, Carnegie AJ (2017). Impact of 824 Austropuccinia psidii (myrtle rust) on Myrtaceae-rich wet sclerophyll forests in south 825 east Queensland. Plos One. 12 doi:https://doi.org/10.1371/journal.pone.0188058. 826 Pegg GS, Lee DJ, Carnegie AJ (2018). Predicting impact of Austropuccinia psidii on 827 populations of broad leaved Melaleuca species in Australia. Australasian Plant 828 Pathology. 47:421-430. doi:https://doi.org/10.1007/s13313-018-0574-8. 829 Pichersky E, Gang DR (2000). Genetics and biochemistry of secondary metabolites in plants: 830 an evolutionary perspective. Trends in Plant Science. 5:439-445. 831 doi:https://doi.org/10.1016/s1360-1385(00)01741-6.

Chapter 1 832 Pichersky E, Lewinsohn E (2011) Convergent Evolution in Plant Specialized Metabolism. 833 In:Merchant SS, Briggs WR, Ort D (eds) Annual Review of Plant Biology, Vol 62, 834 vol 62. Annual Review of Plant Biology. pp 549-566. 835 doi:https://doi.org/10.1146/annurev-arplant-042110-103814. 836 Pogorelko G, Lionetti V, Bellincampi D, Zabotina O (2013). Cell wall integrity: targeted 837 post-synthetic modifications to reveal its role in plant growth and defense against 838 pathogens. Plant Signaling & Behavior. 8 doi:https://doi.org/10.4161/psb.25435. 839 Potts BM, Dungey HS (2004). Interspecific hybridization of Eucalyptus: key issues for 840 breeders and geneticists. New Forests. 27:115-138. 841 Potts BM, Sandhu KS, Wardlaw T, Freeman J, Li HF, Tilyard P, Park RF (2016). 842 Evolutionary history shapes the susceptibility of an island tree flora to an exotic 843 pathogen. Forest Ecology and Management. 368:183-193. 844 doi:https://doi.org/10.1016/j.foreco.2016.02.027. 845 Pratt PD, Rayamajhi MB, Van TK, Center TD, Tipping PW (2005). Herbivory alters resource 846 allocation and compensation in the invasive tree Melaleuca quinquenervia. Ecological 847 Entomology. 30:316-326. doi:https://doi.org/10.1111/j.0307-6946.2005.00691.x. 848 Pusztahelyi T, Holb I, Pocsi I (2015). Secondary metabolites in fungus-plant interactions. 849 Frontiers in Plant Science. 6 doi:https://doi.org/10.3389/fpls.2015.00573. 850 Rapley LP, Allen GR, Potts BM, Davies NW (2007). Constitutive or induced defences - how 851 does Eucalyptus globulus defend itself from larval feeding? Chemoecology. 17:235- 852 243. doi:https://doi.org/10.1007/s00049-007-0382-z. 853 Rhoades DF (1979). Evolution of plant chemical defense against herbivores. Herbivores: 854 their interaction with secondary plant metabolites.3-54. 855 Roddick JG (1986). Antifungal activity of plant steroids. ACS Symposium series - American 856 Chemical Society.286-303. 857 Sánchez Márquez S, Bills G, Zabalgogeazcoa I (2011). Fungal species diversity in juvenile 858 and adult leaves of Eucalyptus globulus from plantations affected by Mycosphaerella 859 leaf disease. Annals of Applied Biology. 158:177-187. 860 doi:https://doi.org/10.1111/j.1744-7348.2010.00449.x. 861 Schutze MK, Mather PB, Clarke AR (2006). Species status and population structure of the 862 Australian Eucalyptus pest Paropsis atomaria Olivier (Coleoptera : Chrysomelidae). 863 Agricultural and Forest Entomology. 8:323-332. doi:https://doi.org/10.1111/j.1461- 864 9563.2006.00316.x. 865 Schutze MK (2008) The significance of genetic and ecological diversity in a wide-ranging 866 insect pest, Paropsis atomaria Olivier (Coleoptera: Chrysomelidae). Queensland 867 University of Technology. 868 Self NM, Aitken EAB, Dale MD (2002). Susceptibility of provenances of spotted gums to 869 ramularia shoot blight. New Zealand Plant Protection. 55:68-72. 870 Shavit R, Batyrshina ZS, Dotan N, Tzin V (2018). Cereal aphids differently affect 871 benzoxazinoid levels in durum wheat. Plos One. 13:14. 872 doi:https://doi.org/10.1371/journal.pone.0208103. 873 Silva PHM, Miranda AC, Moraes MLT, Furtado EL, Stape JL, Alvares CA, Sentelhas PC, 874 Mori ES, Sebbenn AM (2013). Selecting for rust (Puccinia psidii) resistance in

Chapter 1 875 Eucalyptus grandis in Sao Paulo State, Brazil. Forest Ecology and Management. 876 303:91-97. doi:https://doi.org/10.1016/j.foreco.2013.04.002. 877 Silva RR, da Silva AC, Rodella RA, Serrao JE, Zanuncio JC, Furtado EL (2017). Pre- 878 infection stages of Austropuccinia psidii in the epidermis of Eucalyptus hybrid leaves 879 with different resistance levels. Forests. 8:12. doi:https://doi.org/10.3390/f8100362. 880 Smith AH, Pinkard EA, Hunter GC, Wingfield MJ, Mohammed CL (2006). Anatomical 881 variation and defence responses of juvenile Eucalyptus nitens leaves to 882 Mycosphaerella leaf disease. Australasian Plant Pathology. 35:725-731. 883 doi:https://doi.org/10.1071/ap06070. 884 Smith AH, Gill WM, Pinkard EA, Mohammed CL (2007a). Anatomical and histochemical 885 defence responses induced in juvenile leaves of Eucalyptus globulus and Eucalyptus 886 nitens by Mycosphaerella infection. Forest Pathology. 37:361-373. 887 doi:https://doi.org/10.1111/j.1439-0329.2007.00502.x. 888 Smith AH, Potts BM, Ratkowsky DA, Pinkard EA, Mohammed CL (2018). Association of 889 Eucalyptus globulus leaf anatomy with susceptibility to Teratosphaeria leaf disease. 890 Forest Pathology. 48:10. doi:https://doi.org/10.1111/efp.12395. 891 Smith HJ, Henson M, Boyton S Forests NSW’spotted gum (Corymbia spp.) tree 892 improvement and deployment strategy. In: Proceeding of the Inaugural Australasian 893 Forest Genetics Conference, Breeding for Wood Quality, 2007b. pp 10-14 894 Smith JL, De Moraes CM, Mescher MC (2009). Jasmonate- and salicylate-mediated plant 895 defense responses to insect herbivores, pathogens and parasitic plants. Pest 896 Management Science. 65:497-503. doi:https://doi.org/10.1002/ps.1714. 897 Soewarto J, Giblin F, Carnegie AJ (2019). Austropuccinia psidii (myrtle rust) global host list. 898 Version 4. Australian Network for Plant Conservation. 899 http://www.anpc.asn.au/myrtle-rust. 900 Stamp N (2003). Out of the quagmire of plant defense hypotheses. The Quarterly Review of 901 Biology. 78:23-55. 902 Steinbauer MJ (2001). Specific leaf weight as an indicator of juvenile leaf toughness in 903 Tasmanian bluegum (Eucalyptus globulus ssp. globulus): implications for insect 904 defoliation. Australian Forestry. 64:32-37. 905 doi:https://doi.org/10.1080/00049158.2001.10676158. 906 Steinbauer MJ, Schiestl FP, Davies NW (2004). Monoterpenes and epicuticular waxes help 907 female autumn gum moth differentiate between waxy and glossy Eucalyptus and 908 leaves of different ages. Journal of Chemical Ecology. 30:1117-1142. 909 doi:https://doi.org/10.1023/B:JOEC.0000030267.75347.c1. 910 Steinbauer MJ, Davies NW, Gaertner C, Derridj S (2009). Epicuticular waxes and plant 911 primary metabolites on the surfaces of juvenile Eucalyptus globulus and E-nitens 912 (Myrtaceae) leaves. Australian Journal of Botany. 57:474-485. 913 doi:https://doi.org/10.1071/bt09108. 914 Stenlid J, Oliva J (2016). Phenotypic interactions between tree hosts and invasive forest 915 pathogens in the light of globalization and climate change. Philosophical Transactions 916 of the Royal Society B-Biological Sciences. 371:10. 917 doi:https://doi.org/10.1098/rstb.2015.0455.

Chapter 1 918 Thumma B, Pegg GS, Warburton P, Brawner J, MacDonell P, Yang X, Southernton S (2013). 919 Molecular tagging of rust resistance genes in eucalypts. Plant Health Australia, 920 Canberra. 921 Traw B, Dawson T (2002). Differential induction of trichomes by three herbivores of black 922 mustard. Oecologia. 131:526-532. doi:https://doi.org/10.1007/s00442-002-0924-6. 923 Turner J, Ellis C, Devoto A (2002). The jasmonate signal pathway. Plant Cell. 14:S153-S164. 924 doi:https://doi.org/10.1105/tpc.000679. 925 van Dam NM, Hadwich K, Baldwin IT (2000). Induced responses in Nicotiana attenuata 926 affect behavior and growth of the specialist herbivore Manduca sexta. Oecologia. 927 122:371-379. doi:https://doi.org/10.1007/s004420050043. 928 Varshney VK, Pandey A, Thoss V, Kumar A, Ginwal HS (2012). Foliar chemical attributes 929 of the hybrid bred from Eucalyptus citriodora x E. torelliana and its parental taxa, 930 and implications for fungal resistance. Annals of Forest Research. 55:53-60. 931 Visser EA, Mangwanda R, Becker JVW, Kulheim C, Foley WJ, Myburg AA, Naidoo S 932 (2015). Foliar terpenoid levels and corresponding gene expression are systemically 933 and differentially induced in Eucalyptus grandis clonal genotypes in response to 934 Chrysoporthe austroafricana challenge. Plant Pathology. 64:1320-1325. 935 doi:https://doi.org/10.1111/ppa.12368. 936 Walton NJ, Brown DE (1999). Chemicals from plants: perspectives on plant secondary 937 products. 938 War AR, Paulraj MG, Ahmad T, Buhroo AA, Hussain B, Ignacimuthu S, Sharma HC (2012). 939 Mechanisms of plant defense against insect herbivores. Plant Signaling & Behavior. 940 7:1306-1320. 941 War AR, Taggar GK, Hussain B, Taggar MS, Nair RM, Sharma HC (2018). Plant defence 942 against herbivory and insect adaptations. AoB PLANTS. 10 943 doi:https://doi.org/10.1093/aobpla/ply037. 944 Waterman JM, Cazzonelli CI, Hartley SE, Johnson SN (2019). Simulated herbivory: the key 945 to disentangling plant defence responses. Trends in Ecology & Evolution. 946 doi:https://doi.org/10.1016/j.tree.2019.01.008. 947 Whitham TG, Morrow PA, Potts BM (1994). Plant hybrid zones as centers of biodiversity - 948 the herbivore community of 2 endemic Tasmanian eucalypts. Oecologia. 97:481-490. 949 doi:https://doi.org/10.1007/bf00325886. 950 Wingfield MJ, Slippers B, Hurley BP, Coutinho TA, Wingfield BD, Roux J (2008). Eucalypt 951 pests and diseases: growing threats to plantation productivity. Southern Forests. 952 70:139-144. doi:https://doi.org/10.2989/south.for.2008.70.2.9.537. 953 Wink M (2003). Evolution of secondary metabolites from an ecological and molecular 954 phylogenetic perspective. Phytochemistry. 64:3-19. doi:https://doi.org/10.1016/s0031- 955 9422(03)00300-5. 956 Winzer LF, Carnegie AJ, Pegg GS, Leishman MR (2018). Impacts of the invasive fungus 957 Austropuccinia psidii (myrtle rust) on three Australian Myrtaceae species of coastal 958 swamp woodland. Austral Ecology. 43:56-68. doi:https://doi.org/10.1111/aec.12534. 959 Witzell J, Martin J (2008). Phenolic metabolites in the resistance of northern forest trees to 960 pathogens - past experiences and future prospects. Canadian Journal Of Forest

Chapter 1 961 Research-Revue Canadienne De Recherche Forestier. 38:2711-2727. 962 doi:https://doi.org/10.1139/X08-112. 963 Woodman RL, Fernandes GW (1991). Differential mechanical defense - herbivory, 964 evapotranspiration, and leaf-hairs. Oikos. 60:11-19. 965 doi:https://doi.org/10.2307/3544986. 966 Xavier AA, Alfenas AC, Matsuoka K, Hodges CS (2001). Infection of resistant and 967 susceptible Eucalyptus grandis genotypes by urediniospores of Puccinia psidii. 968 Australasian Plant Pathology. 30:277-281. doi:https://doi.org/10.1071/ap01038. 969 Yong WTL, Ades PK, Goodger JQD, Bossinger G, Runa FA, Sandhu KS, Tibbits JFG 970 (2019). Using essential oil composition to discriminate between myrtle rust 971 phenotypes in Eucalyptus globulus and Eucalyptus obliqua. Industrial Crops and 972 Products. 140:111595. doi:https://doi.org/10.1016/j.indcrop.2019.111595. 973 Zangerl AR, Rutledge CE (1996). The probability of attack and patterns of constitutive and 974 induced defense: a test of optimal defense theory. The American Naturalist. 147:599- 975 608. doi:https://doi.org/10.1086/285868. 976 Zhang CL, Li XW, Chen YQ, Zhao J, Wan SZ, Lin YB, Fu SL (2016). Effects of Eucalyptus 977 litter and roots on the establishment of native tree species in Eucalyptus plantations in 978 South China. Forest Ecology and Management. 375:76-83. 979 doi:https://doi.org/10.1016/j.foreco.2016.05.013. 980 Zhou XD, de Beer ZW, Xie YJ, Pegg GS, Wingfield MJ (2007). DNA-based identification of 981 Quambalaria pitereka causing severe leaf blight of Corymbia citriodora in China. 982 Fungal Diversity. 25:245-254. 983 Zhou XD, Wingfield MJ (2011). Eucalypt diseases and their management in China. 984 Australasian Plant Pathology. 40:339-345. doi:https://doi.org/10.1007/s13313-011- 985 0053-y. 986 Zotz G, Wilhelm K, Becker A (2011). Heteroblasty—A Review. The Botanical Review. 987 77:109-151. doi:https://doi.org/10.1007/s12229-010-9062-8.

Chapter 2

1 Chapter 2 Paropsis atomaria larval feeding induces a chemical but

2 not a physical response in Corymbia citriodora subsp. variegata

4 Authors: Flávia Sarti Bonoraa, Richard Andrew Hayesa, Helen F Nahrunga, David John

5 Leea

6 Affiliations: aForest Industries Research Centre, University of the Sunshine Coast - 90 Sippy

7 Downs Dr, Sippy Downs QLD 4556, Australia

9 *Corresponding author: [email protected] +610426163647

10 Journal: Trees (Springer)

11 Impact factor: 1.799

Chapter 2 13 Statement of Intellectual Contribution

15 I, Flávia Sarti Bonora declare that I have made substantial intellectual contribution (70%) to

16 the original research article ‘Paropsis atomaria larval feeding induces a chemical but not a

17 physical response in Corymbia citriodora subsp. variegata’ by F.S. Bonora, R.A. Hayes, H.F.

18 Nahrung and D.J. Lee, submitted to the journal Trees (Springer).

Chapter 2 21 Abstract

22 Plants have evolved strategies against herbivore pressure, relying on constitutive or induced

23 traits that create physical and chemical barriers, which may influence herbivore performance.

24 We evaluated the physical and chemical responses of Corymbia citriodora subsp. variegata,

25 an important hardwood plantation taxon, to feeding by Paropsis atomaria larvae, a pest that

26 causes severe defoliation in young trees. This was undertaken to obtain insights into plant-

27 herbivore interactions, aiming to identify atributes that may improve plant fitness and/or

28 protection that may benefit pest management in forestry plantations. Four-months-old

29 seedlings of C. citriodora subsp. variegata were submitted to the following treatments: no

30 damage, mechanical wounding, and P. atomaria larval feeding damage. Foliar samples were

31 collected during and after treatment at day 15 and 22, respectively, from damaged and

32 undamaged leaves to detect local, systemic, or delayed induced responses. Leaf samples were

33 analysed to determine whether there were induced physical (leaf toughness, trichome density)

34 or chemical (secondary metabolite profiles) responses to damage. No physical response in any

35 of the treatments was evident in C. citriodora subsp. variegata foliage at day 15 or 22. Systemic

36 chemical responses were observed for the larval feeding and mechanical treatments, with a

37 delayed response evident in the larval feeding treatment only. The proportion of long chain

38 hydrocarbons were reduced in these treatments relative to the control, whereas the proportion

39 of monounsaturated hydrocarbons and monoterpenes increased. When analysed across

40 treatments, larval mortality was negatively correlated with long chain hydrocarbons and

41 positively correlated with monounsaturated hydrocarbons. These findings suggest that induced

42 response to larval feeding may be associated with plant defence strategies.

44 Keywords: herbivory; induced response; secondary metabolites; leaf wax; leaf toughness; leaf

45 trichomes.

Chapter 2 46 Introduction

47 Plants have evolved strategies against herbivore pressure, relying on constitutive or induced

48 traits that create physical and chemical barriers, such as tough cell walls, trichomes and

49 secondary metabolites which may influence herbivore performance (Fürstenberg-Hägg et al.

50 2013; Mitchell et al. 2016; War et al. 2018). Physical leaf traits may affect herbivore

51 performance by influencing the effort required to rupture leaves (Feeny 1976; Steinbauer 2001)

52 while chemical leaf traits may have toxicological and behavioural effects (Mithöfer and Boland

53 2012). The traits that are pre-formed and continuously expressed are known as constitutive,

54 operating as the first line of plant defence (Karban and Baldwin 1997; Freeman and Beattie

55 2008). Unlike constitutive defences, induced traits are only triggered in response to plant

56 damage, which may allow plants to allocate resources to other functions (e.g. growth), in the

57 absence of damage and possibly confer advantages in terms of plant defence or resistance to

58 herbivore damage (Karban et al. 1999).

59 Induced responses against herbivores occur among a wide array of plants and are well

60 described for annual crops (Karban and Baldwin 1997; Mitchell et al. 2016). For instance,

61 herbivory increased the leaf trichome density in Raphanus raphanistrum (wild radish) and

62 Brassica nigra (black mustard) and Lycopersicon esculentum (tomato) (Agrawal 1999; Traw

63 and Dawson 2002; Boughton et al. 2005) and altered the leaf chemistry in Nicotiana attenuata

64 (tobacco), Triticum turgidum subsp. durum (durum wheat) and Zea mays (maize) (Karban and

65 Baldwin 1997; Shavit et al. 2018; Block et al. 2019).

66 In perennial trees, induced response can be more complicated, as they are long-lived and

67 experience different levels of herbivore pressure over time (Eyles et al. 2010). Populus nigra

68 showed induced local (damaged leaves) and systemic (undamaged leaves) chemical responses

69 when challenged by Lymantria dispar, Laothoe populi and Amata mogadorensis, increasing

70 the levels of monoterpenes and sesquiterpenes; other compounds, such as aldehydes, esters,

Chapter 2 71 and alcohols, were only induced locally (Fabisch et al. 2019). Induced responses also occurred

72 in Pinus pinaster and P. radiata exposed to herbivory by the bark-feeder weevil Hylobius

73 abietis, increasing the concentrations of non-volatile resin, volatile monoterpenes and

74 polyphenolics in stem tissues, and increased the concentration of non-volatile resin and

75 decreased polyphenolics in the needle tissues (Moreira et al. 2013). When these trees were

76 exposed to the leaf eating caterpillar Thaumetopoea pityocampa the induced response was

77 observed only in the stem, where the concentration of polyphenolics increased (Moreira et al.

78 2013).

79 In eucalypt trees, induced responses against insect herbivores are relatively less studied and

80 have controversial findings in comparison to annual crops and other perennial trees (Eyles et

81 al. 2010). For instance, Rapley et al. (2007) found no change in Eucalyptus globulus essential

82 oils, polyphenolic groups or foliar wax compounds, and a non-significant elevation in tannin

83 levels, in response to Mnesampela privata defoliation. Henery et al. (2008) also reported the

84 absence of an induced chemical response in tannins, terpenes or formylated phloroglucinol

85 compounds in E. grandis treated with methyl jasmonate or damaged by Paropsis atomaria

86 Olivier (Coleoptera: Chrysomelidae). Both studies suggested that constitutive traits would be

87 more critical in interactions between trees and herbivorous insects.

88 In contrast, Oates et al. (2015) found that clones of E. grandis and hybrids of E. camaldulensis

89 × E. grandis had a chemical response to Leptocybe invasa oviposition and larvae, modifying

90 essential oil profiles. Additionally, Patton et al. (2018) detected induced chemical responses in

91 E. camaldulensis under Glycaspis brimblecombei feeding, modifying polyphenol profiles

92 among treatments.

93 Secondary metabolites are substances that can be induced in response to insect damage

94 (Hartmann 2007). They comprise compounds such as leaf waxes (Müller and Riederer 2005)

95 and terpenes (Boland et al. 1991). Secondary metabolites can be induced by phytohormones

Chapter 2 96 such as methyl salicylate and methyl jasmonate, signalling molecules that activate local and

97 systemic responses when plants are subjected to wounding damage, possibly associated with

98 plant defence mechanisms (Turner et al. 2002).

99 Leaf waxes, mainly found on the leaf cuticle, are responsible for the hydrophobic

100 characteristics of the leaf surface, determining leaf wettability, preventing plant desiccation,

101 and influencing insect adhesion to leaf surface (Neinhuis and Barthlott 1997; Müller and

102 Riederer 2005). Their chemical composition may also shape the interaction between plants and

103 insect herbivores, acting as attractant cues for insects; influencing insect adhesion to leaf

104 surface; and promoting or hindering insect feeding (Müller and Riederer 2005). For instance,

105 P. charybdis caused less damage on glaucous juvenile leaves of E. nitens and E. globulus than

106 glossy adult leaves (Edwards 1982). Additionally, leaf wax chemistry was found to influence

107 E. globulus resistance to M. privata, with benzyl esters and phenylethyl esters being found in

108 more resistant genotypes (Jones et al. 2002; Rapley et al. 2004).

109 Terpenes are volatile substances stored in specific globular glands throughout the leaf

110 parenchyma that are known to influence plant interaction with insect herbivore and plant

111 responses to stress (Boland et al. 1991; Külheim et al. 2015). Eucalypts are rich in terpenes,

112 and their composition is strongly associated with insecticidal and repellent effects (Batish et

113 al. 2008; Barbosa et al. 2016). For instance, defoliation by Anoplognathus spp. (Christmas

114 beetles) was associated with the composition of the leaf terpenoids in six different species of

115 Eucalyptus, with defoliation being less severe in trees with higher levels of 1, 8-cineole

116 (Edwards et al. 1993). In E. melliodora and E. sideroxylon plants with higher levels of

117 sideroxylonals and 1, 8-cineole were less damaged by Christmas beetles (Matsuki et al. 2011)

118 than those with lower concentrations.

119 Moreover, the genes responsible for terpene syntheses related to the secondary metabolism in

120 E. grandis, E. globulus and C. citriodora subsp. variegata are numerous and highly variable

Chapter 2 121 (Külheim et al. 2015; Butler et al. 2018). This is potentially associated with stress response

122 mechanisms, allowing plants to rapidly adapt to environmental change, which can include

123 inducing or priming chemical responses when plants are under stress, such as herbivory (Butler

124 et al. 2018).

125 Around the world, eucalypts, including the genera Eucalyptusand Corymbia (Parra-O et al.

126 2006), are among the most widely deployed hardwood trees (Wingfield et al. 2008). The

127 expansion of forestry plantations has, however, increased the likelihood of pest outbreaks

128 (Paine et al. 2011; Wingfield et al. 2013). Studies on potential induced responses against

129 damage in eucalypt trees may provide a better understanding of plant-herbivore interactions

130 and possibly assist pest prevention and management in forestry plantations of eucalypts by

131 improving plant selection for resistance, thereby reducing the costs associated with biological

132 controls, use of pesticides and silvicultural practices (Eyles et al. 2010).

133 This study evaluated the potential induced responses, both physical and chemical, of Corymbia

134 citriodora subsp. variegata to P. atomaria larval feeding and mechanical wounding. Corymbia

135 citriodora subsp. variegata (CCV) is an increasingly important taxon for hardwood

136 plantations. CCV is vulnerable to P. atomaria (Carnegie et al. 2008), which can cause severe

137 defoliation, especially in young plantations, as their preferred host plants are at immature leaf

138 stages (Carne 1966). Leaf toughness, trichome density and leaf secondary metabolites, such as

139 terpenes and waxes, were assessed at three response levels: local (damaged leaves), systemic

140 (undamaged leaves on damaged plants) and delayed (new leaves produced post-treatments).

141 We also correlated larval mortality with the measured atributes to determine the influence of

142 plant growth rate and induced responses.

143

Chapter 2 144 Material and Methods

145 Experiment

146 Seeds from an open pollinated CCV provenance at Woondum (26° 25' S, 152° 81' E, Altitude:

147 400 m a.s.l; mean rainfall: 1536 mm year⁻¹) were sown in a glasshouse at the Queensland

148 Department of Agriculture and Fisheries’ facility at Gympie, Queensland in 8 × 5-celled QNT

149 trays (200 cc). Potting medium consisting of 50% pine bark fines (0–10 mm), 25% pine bark

150 peat, 25% coarse perlite, a mix of 8–9 month Osmocote Pro Low P (4 kg m⁻³), agricultural lime

151 (4 kg m⁻³); gypsum (1 kg m⁻³), Micromax (1 kg m⁻³) and a granular wetting agent, Hydroflow

152 (1 kg m⁻³). The seeds germinated after two weeks and were transferred to 50% shade cloth

153 under mist until the roots were well established. Plants were re-potted at two-months-old into

154 6 × 3-celled trays (600 cc) using the same potting medium, maintained under 50% shadecloth

155 and irrigated six times a day (3 × 10 min and 3 × 5 min) using overhead sprinklers.

156 Plants were transferred to a shadehouse at three months old at the Ecosciences Precinct

157 Brisbane, under 10% shadecloth and manually irrigated once a day. The experiment started

158 when the plants were approximately four months old, allowing them to acclimate to shadehouse

159 conditions. Seventy-two plants were randomly allocated to one of three treatments: undamaged

160 controls, larval feeding and mechanical wounding treatments. Each treatment had 24 replicate

161 plants. All plants were individually caged in frames (15 × 15 × 55 cm) constructed using wire,

162 with a cloth mesh bag cover sealed with a rubber band.

163 A P. atomaria colony was maintained to supply larvae for this experiment. Field-collected egg

164 batches were held in cages in the shadehouse, and hatching larvae were fed with fresh leaves

165 of E. camaldulensis (a non-test host) to avoid potential larval feeding bias before the

166 experiment started. Paropsis atomaria larvae are gregarious, with groups comprising

167 individuals of different instars to increase feeding establishment (Schutze 2008). Therefore, to

168 replicate larval colonization in the field, five mixed instar larvae were transferred to the first

Chapter 2 169 fully expanded leaves of experimental plants using a soft paintbrush. Plants were checked every

170 2-3 days, and any dead larvae and larvae that pupated were replaced to maintain constant larval

171 pressure (five larvae per plant) for the duration of the feeding period (15 days). Larval mortality

172 (total dead larvae divided by total larvae placed on each plant) was determined on each plant

173 under larval treatment at day 15.

174 The first fully expanded leaves of plants in the mechanical treatment were damaged using a

175 hole punch to remove part of the leaf lamina and simulate the loss of leaf tissue caused by

176 larvae (Baldwin 1990). Plants were punched three times during the experiment - 10 holes on

177 day one, 20 holes on day five and 40 holes on day twelve - totalling 70 holes, resulting in 15

178 cm2 of leaf area removed from each plant by the end of the experiment, approximating the

179 amount of leaf tissue consumed in larval treatments.

180 All plants were removed from the treatment cages on day 15, and leaf samples from new, fully

181 expanded leaves were collected to analyse the physical and chemical traits in each treatment.

182 Tissues from both larval feeding and mechanical damage treatments were sampled for damaged

183 and undamaged leaves to evaluate local and systemic response, respectively. Plants were

184 placed back in the cages without any additional damage or larvae. Samples of new foliage

185 produced post-treatment were collected from all plants at day 22 to evaluate whether the plants

186 displayed a delayed response to damage.

187

188 Tree growth rate

189 Plant heights were measured before they were placed into the treatments at day one, and at day

190 15, when the treatments stopped, and then at day 22, at the end of the experiment. Growth rate

191 (cm day⁻¹) was calculated to determine differences in plant growth between treatments and

192 possible trade-offs between growth and any induced response. Growth rate between treatments

193 was analysed for each interval by one-way analyses of variance, and treatments were compared

Chapter 2 194 by Fishers Least Significant Difference (LSD) post-hoc test in SPSS (IBM Corp, V 24.0.0.0).

195 Spearman rank correlation (SPSS) was used to examine the relationship between tree growth

196 rate and larval mortality.

197

198 Physical response - Leaf toughness and trichomes density

199 New fully expanded leaves (≤ 1 g) were collected from 10 - 12 randomly selected plants of

200 each treatment at day 15 and from 12 randomly selected plants of each treatment at day 22.

201 Leaf samples were used to evaluate the physical response to damage. The leaves were digitally

202 scanned at a resolution of 300 dpi to measure leaf area using Image Analysis Software for Plant

203 Disease Quantification – ASSES 2.0 (Lamari 2008), transferred to paper envelopes and dried

204 at 70 °C before being weighed on a daily basis until leaf mass stabilized. Dry leaf mass was

205 measured, and the leaf toughness was determined as specific leaf weight (method after

206 Steinbauer 2001) by dividing the dry leaf mass by leaf area (mg cm⁻²).

207 Trichome densities were determined in four plants per treatment, using scanned leaf images

208 zoomed at 400%. Leaf trichomes were counted on three one cm² squares per leaf (Figure 2.1)

209 and averaged to provide one measurement per leaf.

210 Leaf toughness and leaf trichome density were analysed using one-way analyses of variance,

211 with significant treatments compared by Fishers Least Significant Difference (LSD) post-hoc

212 test in SPSS (IBM Corp, V 24.0.0.0). The correlation between physical traits and larval

213 mortality was tested using the Spearman rank correlation (SPSS).

Chapter 2

214 215 Figure 2.1 Example of undamaged/control, larval feeding and mechanical wounding Corymbia citriodora subsp. 216 variegata leaves and positions where the one cm² squares were placed to count the leaf trichomes. 217

218 Chemical response - Plant secondary metabolites

219 Plant secondary metabolites were extracted from new, fully expanded leaves (≤ 1 g) of 12

220 replicate plants collected at day 15 and day 22 from each treatment. The leaves were cut into

221 squares (≤ 1 cm2) and extracted with hexane (≥ 99%, RCI Labscan Limited) in the proportion

222 of 1:10 (g mL⁻¹) for 120 min, stirring for 1 min, six times within this period. The extract was

223 transferred into 1 mL vials and stored in the freezer (-20 °C) until analysis (method developed

224 by Li et al. 1997 and modified by Nahrung et al. 2009).

225 Samples (1 µL) were analysed using a gas chromatograph (GC) (Agilent 6890 Series) coupled

226 to a mass spectrometer (MS) (Agilent 5975) and fitted with a silica capillary column (Agilent,

227 Model HP5-MS, 30 m × 250 µm ID × 0.25 µm film thickness). Data were acquired under the

228 following GC conditions: inlet temperature 250 °C, carrier gas helium at 51 cm s⁻¹, split ratio

229 13:1, transfer-line temperature 280 °C, initial temperature 40 °C, initial time 2 min, rate 10 °C

230 min⁻¹, final temperature 260 °C, final time 6 min. The MS was held at 280 °C in the ion source,

231 with a scan rate of 4.45 scans s⁻¹.

232 Peaks that were present in blank hexane samples were discarded from the analysis in test

233 samples. Mass spectra of peaks from different samples with the same retention time were

Chapter 2 234 compared to ensure that the compounds were the same. Mass spectral fragmentation pattern

235 was used to assign each peak to a class of compounds. Compounds were grouped and analysed

236 by compound class - monoterpenes, sesquiterpenes, monounsaturated hydrocarbons,

237 oxygenated diterpenes, steroids, and long chain hydrocarbons.

238 Data were square-root transformed and analysed by nonparametric Bray-Curtis cluster

239 analysis. An analysis of similarity (ANOSIM) was conducted to determine whether secondary

240 metabolite levels in the treatments were significantly different from each other and a ‘similarity

241 percentages’ (SIMPER) analysis was used to determine the percentage of compound class

242 contribution to group dissimilarity. The differences detected between treatments were

243 graphically represented by non-metric multidimensional scaling ordination (nMDS). Each

244 point in the nMDS plot represents an individual plant, and clumped points correspond to

245 individuals with similar peak composition (presence and abundance). The software used for

246 the multivariate analysis was Primer 7 for Windows (V 7.0.13, Clarke and Gorley 2015). These

247 analytical procedures have been used successfully in previous studies to compare

248 chromatographic data statistically (e.g. Nahrung et al. 2009; Hayes et al. 2013). Differences in

249 mean relative area of each compound class between treatments were analysed by Kruskal-

250 Wallis test using SPSS. The correlation between the studied parameters (growth rate and

251 physical/chemical traits) and larval mortality was determined by Spearman rank correlation,

252 and the significant correlations were further analysed by linear regression using SPSS.

253

254 Results

255 Tree growth rate

256 Tree growth rate was not significantly different between treatments for any of the intervals

257 studied (Figure 2.2). Larval mortality was 87 ± 3%, the correlations between larval mortality

258 and growth rate during treatment (Spearman rank correlations: ρ = -0.14, n = 24, P = 0.515)

Chapter 2 259 and overall (Spearman rank correlations: ρ = -0.019, n = 24, P = 0.931) were not significant.

260 Total biomass of trees was not assessed in this study.

1.6 F2, 69 = 0.734 P = 0.484 1.4 F2, 69 = 0.660 P = 0.520 1.2 F2, 69 = 1.276 P = 0.286 1

0.8

0.6 s.e. s.e. growth rate (cm day⁻¹)

± 0.4

Mean 0.2

0 During treatment Post-treatment Overall 261 262 Figure 2.2 Mean ± standard error growth rate (cm day⁻¹) of Corymbia citriodora subsp. variegata subjected to 263 (black) undamaged controls, (grey) larval feeding by Paropsis atomaria and (white) mechanical wounding 264 treatments during treatment, post-treatment, and overall. Results of ANOVA comparing between damage 265 treatments are shown above for each period. 266

267 Physical response - Leaf toughness and trichomes density

268 Physical traits were not significantly different between treatments for damaged leaves or

269 undamaged leaves at day 15 (systemic effects), or at day 22 (delayed effects; Figure 2.3). There

270 was no significant relationship between physical traits and larval mortality (Spearman rank

271 correlations: Leaf toughness ρ = -0.114, n = 11, P = 0.661; Leaf trichomes cm⁻² ρ = -0.316, n

272 = 4, P = 0.684).

273

Chapter 2

(a) 6.0 F2, 32 = 1.609 P = 0.216 F2, 31= 1.400 5.0 P = 0.870 F2, 32 = 0.821 P = 0.449 4.0

3.0

s.e. leaf toughness s.e. leaf toughness (mg cm⁻²) 2.0 ±

1.0 Mean Mean

0.0 Local Systemic Delayed 274 (b) F2, 9 = 0.068 240 P = 0.935 F2, 9 = 0.230 F = 4.153 P = 0.799 200 2, 9 P = 0.053 160

120

80 s.e. leaf trichomes cm⁻² trichomes leaf s.e. ± 40 Mean Mean 0 Local Systemic Delayed 275 276 Figure 2.3 Mean ± standard error of (a) leaf toughness (mg cm⁻²) and (b) leaf trichomes cm⁻² determined for 277 Corymbia citriodora subsp. variegata local (damaged leaves), systemic (undamaged leaves on damaged plants) 278 and delayed (new leaves produced post-treatments) responses subjected to (black) undamaged controls, (grey) 279 larval feeding by Paropsis atomaria and (white) mechanical wounding treatments. Results of ANOVA comparing 280 between damage treatments are shown above for each period. 281 282

283 Chemical responses - Plant secondary metabolites

284 GC-MS analysis of hexane extracts of CCV leaves allowed the detection of 56 compounds

285 grouped in six classes, comprising five monoterpenes, 17 sesquiterpenes, nine

286 monounsaturated hydrocarbons, two oxygenated diterpenes, 15 steroids, and eight long chain

287 hydrocarbons. The plant chromatograms were also compared with authentic samples of methyl

288 salicylate and methyl jasmonate, however, we have no evidence for methyl salicylate, and the

289 presence of methyl jasmonate could not be unambiguously determined.

290 Damaged leaves at day 15 did not express significant differences between treatments

291 (ANOSIM: R = 0.035, P = 0.151; Figure 2.4a); thus, there was no local induced response to

Chapter 2 292 larval or mechanical damage. However, undamaged leaves from the same plants differed

293 significantly between treatments (ANOSIM: R = 0.277, P = 0.001; Figure 2.4b). Pairwise

294 comparisons demonstrated a significant difference between controls and larval feeding

295 treatments and controls and mechanical wounding treatments, indicating a systemic response

296 was induced when the plants were damaged. Larval and mechanical treatments did not differ

297 from each other. SIMPER analyses (Table 2.1) demonstrated that both damage treatments

298 expressed lower proportions of long chain hydrocarbons and higher proportions of

299 monounsaturated hydrocarbons and monoterpenes in comparison to undamaged controls.

300 An induced chemical response was also observed on new leaves collected at day 22, a week

301 after the treatments stopped (ANOSIM: R = 0.113, P = 0.002; Figure 2.4c). The larval feeding

302 treatment differed significantly from controls but not from the mechanical wounding treatment.

303 The response to larval feeding treatments at day 22 expressed the same pattern observed in the

304 systemic response, with a lower proportion of long chain hydrocarbons and a greater proportion

305 of monounsaturated hydrocarbons and monoterpenes in comparison to the control treatment

306 (Table 2.1).

307 The Kruskal-Wallis comparison of each compound class between the treatments on the studied

308 plant responses (local, systemic and delayed) reinforced the ANOSIM results. The compounds

309 found in damaged leaves were not significantly different between treatments (Figure 2.5a). In

310 contrast, compounds of undamaged leaves were significantly different between treatments

311 (Figure 2.5b), with a higher proportion of sesquiterpenes and a lower proportion of oxygenated

312 diterpenes in plants subjected to larval treatment in comparison to mechanical and control

313 treatments, and a higher proportion of monounsaturated hydrocarbons and lower proportion of

314 long chain hydrocarbons in both larval and mechanical treatments in comparison to control

315 treatment. Post-treatment leaves were only significantly different in plants that were subjected

316 to larval damage (Figure 2.5c), with a higher proportion of monounsaturated hydrocarbons and

Chapter 2 317 lower proportion of long chain hydrocarbons in comparison to control and mechanical

318 treatments.

319 Larval mortality was negatively correlated with the relative concentration of monounsaturated

320 hydrocarbons (Spearman rank correlation: ρ = -0.704, n = 12, P = 0.011; Fig. 2.6a), and

321 positively correlated with long chain hydrocarbons (Spearman rank correlation: ρ = 0.669, n =

322 12, P = 0.017; Fig. 2.6b), but not with other compound classes (Spearman rank correlations: ρ

323 = -0.035 – 0.113, n = 12, P = 0.727 – 0.931).

(a) (b)

324 325 Figure 2.4 Two-dimensional nMDS ordination of (c) hexane extracts of the Corymbia citriodora subsp. variegata subjected to (square) undamaged controls, (circle) larval feeding by Paropsis atomaria and (cross) mechanical wounding treatments to detect (a) local (damaged leaves), (b) systemic (undamaged leaves on damaged plants) and (c) delayed (new leaves produced post-treatments) responses. The plots are based on square root transformed abundances and a Bray-Curtis similarity matrix. Extracts from plants cluster separately. Ellipses are used for ease of interpretation only. 326

327

328 Table 2.1 Back-transformed mean ± standard errors relative area under the peak for compound classes used to 329 distinguish systemic response (damaged leaves) between C- undamaged controls, L- larval feeding by Paropsis 330 atomaria and M- mechanical wounding; and to distinguish delayed response (new leaves produced post- 331 treatments) between C and L. % Contribution % Contribution Mean % relative area Mean % relative area to group to group Systemic response Delayed response Substance Class dissimilarity dissimilarity C L M C × L C × M C L C × L Long chain 31.14 ± 4.34 10.05 ± 2.39 12.48 ± 3.12 35.1 32.8 46.06 ± 5.40 24.89 ± 3.47 29.47 hydrocarbon Monounsaturated 32.46 ± 3.98 54.81 ± 2.03 52.58 ± 3.79 25.1 25.9 21.96 ± 4.29 38.90 ± 3.54 29.02 hydrocarbon Monoterpene 7.34 ± 1.52 9.42 ± 0.99 12.43 ± 1.79 12.6 16.2 3.67 ± 0.77 7.30 ± 2.02 17.41

Chapter 2

Relative area (%) (a) 0.0 10.0 20.0 30.0 40.0 50.0 60.0

Monoterpene H2 = 2.869, n = 36, P = 0.238

Sesquiterpene H2 = 2.821, n = 36, P = 0.244

Monounsaturated Localresponse

- hydrocarbon H2 = 2.254, n = 36, P = 0.324 Oxigenated H = 2.533, n = 36, P = 0.282 diterpene 2

Steroid H2 = 0.547, n = 36, P = 0.761

H2 = 3.398, n = 36, P = 0.183 Compound class Compound Long chain hydrocarbon 332 (b) Relative area (%) 0.0 10.0 20.0 30.0 40.0 50.0 60.0

Monoterpene H2 = 5.389, n = 36, P = 0.068

a H2 = 9.284, n = 36, P = 0.01 Sesquiterpene b a H2 = 14.308, n = 36, P = 0.001 Monounsaturated a b hydrocarbon

Systemic response Systemic b - Oxigenated a b H2 = 13.419, n = 36, P = 0.001 diterpene a

Steroid H2 = 4.956, n = 36, P = 0.084

Long chain a b Compound class Compound hydrocarbon b H2 = 14.074, n = 36, P = 0.001 333 (c) Relative area (%) 0.0 10.0 20.0 30.0 40.0 50.0 60.0

H2 = 2.518, n = 36, P = 0.284 Monoterpene

H2 = 3.950, n = 36, P = 0.139 Sesquiterpene

H2 = 6.641, n = 36, P = 0.036 Monounsaturated a b Delayed response Delayed hydrocarbon a - Oxigenated H2 = 0.994, N= 36, P = 0.604 diterpene H2 = 2.324, n = 36, P = 0.313 Steroid H2 = 8.020, n = 36, P = 0.018 Long chain a

Compound class Compound b hydrocarbon a 334 335 Figure 2.5 Compound class and back-transformed means ± standard errors of relative area under the 336 chromatogram of components detected in hexane extracts detected in Corymbia citriodora subsp. variegata as (a) 337 local (damaged leaves), (b) systemic (undamaged leaves on damaged plants) and (c) delayed (new leaves produced 338 post-treatments) responses on plants subjected to (black) undamaged controls, (grey) larval feeding by Paropsis 339 atomaria and (white) mechanical wounding treatments. Different lowercase letters designate significant 340 differences in the overall profile (Kruskal-Wallis, P < 0.05). 341

Chapter 2

(a) 80 ρ = -0.704, n= 12, P = 0.011 (b) 80 ρ = 0.669, n= 12, P = 0.017 70 70 60 60 50 50 40 40 30 30 20 20 Relative area% Relative Relative area% Relative 10 10 0 Long hydrocarbon chain 0 Monounsaturated hydrocarbon hydrocarbon Monounsaturated 50 56 62 68 74 80 86 92 98 104 50 56 62 68 74 80 86 92 98 104 Larval Mortality (%) Larval Mortality (%) 342 343 Figure 2.6 Correlations between relative areas of (a) monounsaturated hydrocarbons and (b) long chain 344 hydrocarbons present in Corymbia citriodora subsp. variegata leaves and percentage of larval mortality. 345

346 Discussion

347 This study investigated the physical and chemical induced responses to P. atomaria damage

348 and mechanical wounding in CCV plants. Plant physical and chemical responses to damage

349 may be associated with plant defence mechanisms that may reduce the effects of insect

350 herbivory (Fürstenberg-Hägg et al. 2013; Mitchell et al. 2016; War et al. 2018). In this study,

351 of the attributes evaluated, CCV only expressed chemical responses to damage.

352 Plant growth rates were unaffected by the damage levels in the timeframes evaluated here, and

353 unrelated to the changes in plant secondary compounds that we observed. Indeed, eucalypts

354 have high regrowth capacity after defoliation by fire, drought, pathogen and herbivore damage

355 (Stone 2001), which may be associated with tolerance to external damage. Our results,

356 however, showed no correlation between larval mortality and tree growth rate. Many studies

357 report the costs of induced responses to insect damage, possibly reducing plant growth rates

358 (Feeny 1976; Coley et al. 1985; Björkman et al. 2008). However, a meta-analysis conducted

359 by King et al. (2006) found inconsistent patterns in trade-offs with plant defence mechanisms

360 and fitness parameters. Additionally, King et al. (2004) found that the accumulation of leaf oils

361 does not appear to impact negatively on growth in E. polybractea.

Chapter 2 362 Corymbia citriodora subsp. variegata had no physical response to either P. atomaria or

363 mechanical damage in terms of leaf toughness or leaf trichome density. Even though leaf

364 trichomes showed a trend towards higher levels on damaged leaves of plants subjected to larval

365 feeding (F2, 9 = 4.153, P = 0.053), we presume this was due to sample size or larval avoidance

366 of parts of the leaf with more trichomes rather than a plant response. This result contrasts to a

367 study of wild radishes, an annual plant with soft leaves (Agrawal 1999) which displayed

368 increased trichome density when subject to insect damage. The lack of physical changes on

369 CCV leaves may be related to P. atomaria feeding pattern, as it it consume the whole leaf

370 surface or scallop leaf edges (Carnegie et al. 2008), with no cellular interaction that would

371 cause modifications of leaf structures. In contrast, pathogens, mites and insects that interact

372 directly with individual cells (Sanchez-Serrano 2017) may be more likely to induce physical

373 response in eucalypts. For example, teratosphaeria leaf disease induced anatomical changes in

374 E. nitens and E. globulus (Smith 2007), and eriophyid mite feeding induced physical response

375 in CCV, increasing overall epidermal thickness and leaf toughness (Nahrung and Waugh

376 2012).

377 Considering the type of damage caused by P. atomaria larvae, constitutive traits would be more

378 likely to interfere with insect performance, as observed in E. viminalis and E. ovata, with leaf

379 toughness, thickness and strength, negatively influencing the performance of the phasmid

380 Extatosoma tiaratum (Malishev and Sanson 2015). In E. nitens leaves, physical properties

381 influenced the ability of larval paropsine beetles to initiate feeding (Nahrung et al. 2001). In

382 our study, leaf toughness and trichome density were not significantly correlated with larval

383 mortality. Leaf toughness may affect larval performance and reproductive success, however,

384 these parameters were not evaluated in this study.

385 Our finding that P. atomaria damage and mechanical wounding induces a chemical response

386 but not a physical response - is consistent with Koricheva et al. (2004), who suggested that the

Chapter 2 387 production of physical structures is irreversible and may integrate a response over several

388 years, while the production of secondary metabolites is more variable on a shorter timescale.

389 Eucalypt secondary metabolites are associated with allelopathic effects (Zhang et al. 2016;

390 Aleixo et al. 2016), attraction (Badenes-Perez 2014), plant interaction with pests and pathogens

391 (Naidoo et al. 2014) and repellent, insecticidal and fungicidal activities (Batish et al. 2008;

392 Barbosa et al. 2016). Moreover, the variation of terpene synthesis gene subfamilies involved

393 in secondary metabolite synthesis provides a selective advantage related with inducible

394 responses to biotic and abiotic stress (Külheim et al. 2015; Butler et al. 2018) supporting the

395 chemical induced response found in CCV.

396 We did not observe local chemical responses to P. atomaria feeding or mechanical damage, in

397 contrast with Anselmo-Moreira et al. (2019), who found both local and systemic response in

398 Tapirira guianensis parasitised by mistletoes. Local responses may not occur because of the

399 costs associated with allocating resources to leaves that are already damaged (McCall and

400 Fordyce, 2010), and it is likely that damaged leaves may transmit signals throughout the plant,

401 activating the expression or priming of induced responses in undamaged leaf tissues (Eyles et

402 al. 2010; Sanchez-Serrano 2017).

403 Interestingly, both damage treatments induced the same pattern of systemic response - reducing

404 the proportion of long chain hydrocarbons (heavier waxes) and increasing the proportion of

405 monounsaturated hydrocarbons (lighter waxes associated with plant hormones) and

406 monoterpenes - in comparison to the control treatment. Savatin et al. (2014) suggest that

407 mechanical damage induces similar plant responses to those induced by herbivores, as

408 wounding may activate plant signals involved with the biosynthesis of plant responses to

409 specific damage or general stress.

410 The increased levels of monoterpenes in larval and mechanical treatments may be associated

411 with a plant mechanism to reduce damage. Monoterpenes are known as feeding deterrents for

Chapter 2 412 a variety of generalist insects. For instance, monoterpene levels in Corymbia species and

413 hybrids potentially influenced host selection behaviour of P. atomaria (Nahrung et al. 2009).

414 Further, E. grandis suffering L. invasa infestation could be predicted by terpene levels,

415 including a combination of three monoterpenes (Naidoo et al. 2018).

416 The variation of waxes in larval and mechanical treatments may be associated with a plant

417 attempt to seal and heal the damaged tissue, protecting the plant from desiccation and

418 preventing infection of the exposed area (Müller and Riederer 2005; Savatin et al. 2014). Leaf

419 waxes comprise physical and chemical characteristics, playing an important role in insect-plant

420 interactions (Müller and Riederer 2005). Their formation can be regulated in response to both

421 developmental and environmental cues, and the high lipophilic composition of leaf waxes

422 forms a protective barrier against water and chemical loss (Wójcicka 2015). It also influences

423 insect adhesion and feeding on the leaf surface (Müller and Riederer 2005).

424 Delayed response was observed only on plants that received the larval feeding treatment,

425 exhibiting a reduced proportion of long chain hydrocarbons and increased the proportion of

426 monounsaturated hydrocarbons and monoterpenes in comparison to control and mechanical

427 treatments. Our results suggest that the response of CCV to herbivore damage may be

428 associated with a plant-insect interaction and not only with a general response to stress (e.g.

429 plant desiccation). The damage caused by herbivores has peculiar characteristics, as insect

430 secretions and saliva are known to elicit plant responses (Waterman et al. 2019). In addition,

431 the amount of cellular shearing and tearing, and the timing of leaf removal during insect feeding

432 is different from mechanical wounding and may activate different plant cues (Baldwin 1990).

433 Therefore, mechanical wounding may induce responses at certain levels but not sufficient to

434 trigger the full response activated by insects (Maffei et al. 2007; Savatin et al. 2014).

435 Larval mortality was positively correlated with heavy waxes and negatively correlated with

436 light waxes, which suggest that damaged plants appear to have increased the proportion of

Chapter 2 437 compounds - monounsaturated hydrocarbons and monoterpenes - that may have favoured

438 insect survival. However, it was not possible to infer if there was a casual effect of the plant

439 response and insect mortality. Perhaps, the variation of these compounds may be related to

440 physiological factors not included in this study, such as photosynthetic or nutrition levels,

441 attraction of natural enemies of herbivores (Kessler and Baldwin 2001), dispersion of damage

442 increasing herbivore movement (Karban et al. 1999), or may be an incidental response, with

443 no direct connection to plant fitness or insect performance (Agrawal 1999).

444 Although we have shown that CCV plants had a systemic and a delayed chemical response to

445 P. atomaria damage by varying the proportion of secondary metabolites, whether this is part

446 of a plant defence mechanism or influences insect performance remains unclear. Further studies

447 should focus on identifying the effects of secondary metabolite profiles on larval performance

448 to determine if the response is associated with a true plant defence mechanism. Moreover, it

449 would be interesting to identify which genes involved with terpene synthesis are expressed

450 after insect damage and mechanical wounding, to better understant the role of terpenes in plant

451 defences.

452

453 Conclusion

454 In this study, we found that CCV growth rate and physical structures (leaf trichomes and leaf

455 toughness) were not altered in response to mechanical damage or P. atomaria larval feeding.

456 On the other hand, plants had a chemical response to mechanical damage and larval feeding.

457 The response to mechanical damage was only evident immediately following damage, whereas

458 the response to P. atomaria larval feeding remained detectable two weeks after larval feeding

459 ceased, which may indicate an interaction between plant and larval oral secretions. We

460 identified six compound classes comprising monoterpenes, sesquiterpenes, monounsaturated

461 hydrocarbons, oxygenated diterpenes, steroids, and long chain hydrocarbons. The chemical

Chapter 2 462 response observed consisted of a reduced proportion of long chain hydrocarbons and a higher

463 proportion of monounsaturated hydrocarbons and monoterpenes in comparison to the control

464 (undamaged) treatment, and these substances were also correlated with larval mortality. Taken

465 together, these findings suggest that induced responses may be associated with CCV defence

466 to insect herbivory and provides a better understanding of plant-herbivore interactions, which

467 may assist on strategies of pest prevention and management in forestry plantations. Further

468 investigations on the effect of plant chemical response in insect performance and in identifying

469 the genes related to terpene synthesis that are expressed after insect damage and mechanical

470 wounding are needed.

471

472 Acknowledgements

473 We would like to thank Dr Manon Griffiths and Ngoc Hoan Le for assisting with the insect

474 collection for this study, and Dr Mervyn Shepherd (Southern Cross University) for the pre-

475 submission review of this manuscript. This work was supported by University of the Sunshine

476 Coast International Research Scholarship (USCIRS) awarded to FSB.

477

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Chapter 3

1 Chapter 3 Does disease severity impact on chemical and physical

2 foliar responses from two Corymbia citriodora subsp. variegata

3 pathogens?

5 Authors: Flávia Sarti Bonoraa,*, Helen F Nahrunga, Richard Andrew Hayesa, Geoff

6 Peggb, David John Leeᵃ

7 Affiliations: aForest Industries Research Centre, University of the Sunshine Coast - 90 Sippy

8 Downs Dr, Sippy Downs QLD 4556, Australia

9 bHorticulture and Forestry Science, Queensland Department of Agriculture and

10 Fisheries, Ecosciences Precinct, Dutton Park, Brisbane 4120, Australia

12 *Corresponding author: [email protected] +610426163647

13 Journal: Industrial Crops and Products (Elsevier)

14 Impact factor: 4.191

Chapter 3 16 Statement of Intellectual Contribution

18 I, Flávia Sarti Bonora declare that I have made substantial intellectual contribution (65%) to

19 the original research article ‘Does disease severity impact on chemical and physical foliar

20 responses from two Corymbia citriodora subsp. variegata pathogens?’ by F.S. Bonora, H.F.

21 Nahrung, R.A. Hayes, G. Pegg and D.J. Lee, published in the journal Industrial Crops and

22 Products (2020) 148:112288.

Chapter 3 25 Abstract

26 Eucalypt forests and industrial plantations are threatened by fungal pathogen outbreaks that

27 compromise timber and non-timber products and tree survival. Understanding host-pathogen

28 interactions may contribute to the development of disease management strategies and the

29 selection of resistant genotypes. Resistant and susceptible plants respond differently to disease

30 infection, presenting variations of phenotypic traits, such as leaf physical and chemical

31 parameters that may influence host-pathogen interactions. In this study, we evaluated physical

32 and chemical responses of Corymbia citriodora subsp. variegata to two fungal pathogens

33 Quambalaria pitereka (a co-evolved pathogen) and Austropuccinia psidii (an established

34 exotic pathogen), using the severity of infection as an indicator of plant

35 resistance/susceptibility. Our primary aim was to quantify differential plant responses between

36 uninoculated (controls), susceptible (severely infected) and resistant plants for each pathogen.

37 Growth rate, leaf toughness and foliar secondary metabolite profiles of control, resistant and

38 susceptible plants were compared 14 days after pathogen inoculations. Leaf secondary

39 metabolite profiles were analysed from uninfected regrowth of the same plants 90 days after

40 inoculation. Our results indicated that only susceptible plants elicited responses to pathogen

41 damage. Susceptible plants infected by Q. pitereka showed increased leaf toughness, and

42 susceptible plants infected by A. psidii had reduced growth rates and altered expression of

43 secondary metabolites in comparison to control and resistant plants. Austropuccinia psidii

44 infection led to a reduction in the proportion of monoterpenes and monounsaturated

45 hydrocarbons and an increase in long chain hydrocarbons. No differences in secondary

46 metabolite profiles were found between the treatments, 90 days after inoculation, suggesting

47 that differences observed were in response to severe infection and that leaf chemistry is not a

48 good predictive biomarker of susceptibility in C. citriodora subsp. variegata.

Chapter 3 50 Keywords

51 Quambalaria shoot blight; myrtle rust; secondary metabolites; leaf wax; plant response; leaf

52 toughness.

Chapter 3 53 Introduction

54 Fungal pathogens are a known problem in native and planted forests of eucalypts, threatening

55 tree survival and compromising timber and non-timber products and services (Wingfield et al.

56 2013). The complexity and consequences of disease outbreaks requires multiple methods of

57 tree protection, including traditional strategies such as silvicultural practices (Gonçalves et al.

58 2013), use of fungicides (Keane et al. 2000; Old et al. 2003) and biological control (Santiago

59 et al. 2015; Salla et al. 2016). All of these can be synergised with the selection of superior

60 genotypes resistant to diseases, through screening populations and breeding for resistance

61 (Naidoo et al. 2019). A better understanding of host-pathogen interactions and their

62 relationship to plant physiology may assist in the development of disease management

63 strategies.

64 Resistant plants respond differently to disease infection compared to susceptible plants,

65 producing specific anti-pathogen mechanisms including induced, hypersensitive and oxidative

66 responses that may influence plant chemical and physical parameters (Toyoda et al. 2002). For

67 instance, a moderately resistant clone of Eucalyptus grandis challenged by the stem canker

68 Chrysoporthe austroafricana deployed different defence responses in comparison to a

69 susceptible clone (Visser et al. 2015; Mangwanda et al. 2015). Resistant clones systemically

70 altered terpenoid levels, as measured in the leaves, during C. austroafricana infection,

71 presenting a higher concentration of monoterpenes and increasing the expression of terpenoid

72 biosynthesis genes (Visser et al. 2015). In addition, E. grandis plants resistant to C.

73 austroafricana had a reduced development of stem lesion and earlier expression of defence

74 response in comparison to susceptible plants (Mangwanda et al. 2015).

75 Leaf chemistry of eucalypts is associated with antifungal activities (Batish et al. 2008; Barbosa

76 et al. 2016), and compounds may act as biomarkers in terms of predicting plant susceptibility

77 to some pathogens (Hantao et al. 2013a, b; Potts et al. 2016; Yong et al. 2019b). The presence

Chapter 3 78 and chemical composition of epicuticular wax may also play a role in eucalypt susceptibility

79 to leaf diseases (Silva et al. 2017; dos Santos et al. 2019). For instance, specific leaf compounds

80 in eucalypt clones and hybrids were associated with plant resistance to Austropuccinia psidii

81 and Teratosphaeria nubilosa (Hantao et al. 2013a, b; Yong et al. 2019b). Differences in leaf

82 oil and wax chemistry were also related to higher resistance to A. psidii in the Eucalyptus

83 subgenus Symphyomyrtus in comparison to subgenus the Eucalyptus (Potts et al. 2016).

84 Fungal pathogens may also affect – and be affected by - tree physical parameters such as growth

85 and leaf anatomy. Leaf disease may negatively influence growth and plant architecture in

86 naturally infected eucalypt plantations and controlled experiments (Pegg et al. 2011c).

87 Eucalypts may also tolerate or increase growth rate to escape infection (Brawner et al. 2011;

88 Lan et al. 2011; Balmelli et al. 2013). In addition, leaf characteristics such as leaf toughness,

89 an indication of leaf strength and leaf structural anatomy (Steinbauer 2001; Smith et al. 2018),

90 may determine the capacity of fungal penetration and colonisation. For instance, leaf

91 characteristics influenced E. globulus resistance to Teratosphaeria fungal species, with

92 resistant families possessing tougher leaves, with a denser palisade layer and less intercellular

93 air space compared with susceptible families (Smith et al. 2018). Resistant provenances of E.

94 nitens also had a more tightly packed palisade mesophyll compared with susceptible

95 provenances (Smith et al. 2006).

96 Corymbia citriodora subsp. variegata (CCV) is an important species in Australian (Queensland

97 and New South Wales) native forests and industrial plantations (Lee et al. 2009). This study

98 focused on CCV physical and chemical responses to two fungal pathogens: Quambalaria

99 pitereka (Quambalaria shoot blight) and A. psidii (myrtle rust). The endemic fungal pathogen

100 Q. pitereka is the main threat to CCV, infecting new foliage and shoots, causing spotting,

101 necrosis and distortion, resulting in severe damage, loss of apical dominance and reduction of

102 tree fitness and productivity (Pegg et al. 2009, 2011a). Quambalaria pitereka affects Corymbia

Chapter 3 103 species in Queensland and New South Wales (Simpson 2000), and was reported in C. maculata

104 plantations in Western Australia (Paap et al. 2008) and in C. citriodora subsp. citriodora

105 plantations in China (Zhou et al. 2007). Defence mechanisms associated with resistance to Q.

106 pitereka include leaf anatomy and chemical composition (Butler et al. 2019).

107 Austropuccinia psidii is an exotic, highly invasive, fungal pathogen that was first detected in

108 Australia in 2010 (Carnegie et al. 2010). Commonly known as myrtle rust, this disease causes

109 significant damage to a wide range of Myrtaceae, affecting over 450 species globally (Carnegie

110 et al. 2016; Pegg et al. 2017; Soewarto et al. 2019), being very severe in naïve hosts such as

111 eucalypts (Makinson 2018). Although this pathogen is not currently causing significant damage

112 in Corymbia species and hybrids in the field in Australia (Carnegie 2015; Lee, pers. obs.),

113 glasshouse inoculation experiments demonstrated that Corymbia species and hybrids are

114 susceptible to A. psidii (Pegg et al. 2014). The pathogen attacks immature tissue, causing severe

115 necrosis, reduced growth and compromises growing shoots, with repeated infection resulting

116 in tree death (Pegg et al. 2014; Carnegie et al. 2016; Pegg et al. 2018), and thus represents a

117 real threat to native and plantation Corymbia forests. Eucalypt susceptibility to A. psidii

118 infection is variable, and multiple mechanisms seem to underlie plant responses to the pathogen

119 (dos Santos et al. 2019; Butler et al. 2019). Overall, resistant plants restrict fungal penetration

120 of the host and/or produce a hypersensitive reaction in response to the pathogen, with host cell

121 death surrounding the infected cell preventing the spread of the fungus, while cells of

122 susceptible plants are rapidly colonized (Xavier et al. 2001).

123 Resistance in Corymbia spp. to both Q. pitereka and A. psidii has been identified at a family

124 level, providing opportunities to improve disease resistance through breeding and selection

125 (Brawner et al. 2011; Lan et al. 2011; Pegg et al 2011a, b; Pegg et al. 2014). Studies have found

126 that specific genes are associated with CCV resistance to Q. pitereka (Butler et al. 2019), while

127 multiple genes are associated with resistance to A. psidii (Alves et al. 2012; Thumma et al.

Chapter 3 128 2013; Butler et al. 2016, 2019), however, the phenotypic traits influencing plant resistance

129 require further investigation. In this study, we tested whether pathogen-infected CCV differ in

130 the expression of their leaf physical and chemical parameters using the severity of infection as

131 an indicator of plant resistance/susceptibility. CCV plants were inoculated with Q. pitereka and

132 A. psidii separately with the effects on host physical and chemical properties assessed in

133 resistant and susceptible individuals.

134

135 Methodology

136 Plant material

137 Selecting from a broader population gives a greater chance of identifying a range of

138 properties/processes that might be driving resistance or susceptibility. Therefore, we selected

139 seeds from six open-pollinated CCV families across three provenances - Brooyar (26° 10' S,

140 152° 30' E, Altitude: 145 m a.s.l; Mean rainfall: 1143 mm year⁻¹), Woondum (26° 25' S, 152°

141 81' E, Altitude: 400 m a.s.l; Mean rainfall: 1536 mm year⁻¹) and Mt-McEuen (26° 14' S, 151°

142 39' E, Altitude: 300m a.s.l, Mean rainfall: 726 mm year⁻¹).

143 The seeds were sown in a glasshouse at the University of the Sunshine Coast in 8 × 5-celled

144 QNT trays (200 cc) under a misting regime: 6 times a day (3 × 10 min and 3 × 5 min). Potting

145 medium consisted of 50% pine bark fines (0–10 mm), 25% pine bark peat, 25% coarse perlite,

146 a mix of 8–9 month Osmocote Pro Low P (4 kg m⁻³), Agricultural lime (4 kg m⁻³); Gypsum (1

147 kg m⁻³), Micromax (1 kg m⁻³) and a granular wetting agent, Hydroflow (1 kg m⁻³). The seeds

148 germinated after two weeks and were transferred to 50% shade-cloth and watered twice a day

149 (2 × 30 min) using overhead sprinklers until roots were well established. Plants were re-potted

150 when two months old into 6 × 3 celled plant trays (600 cc) using the same potting medium,

151 maintained under the same conditions for one month and then transported to a shade-house at

152 the Ecosciences Precinct in Brisbane, under 10% shade-cloth, and hand-watered as required.

Chapter 3 153 Plants were trimmed and then fertilised with Seasol (Season International, Bayswater, Victoria,

154 Australia) and Nitrosol Concentrate Liquid Fertiliser NPK 10.5: 2.3: 6.8 (Amgrow Pty. Ltd.,

155 Lidcombe New South Wales) to stimulate new growth. Plants were acclimated for another

156 month before inoculation. The experiment was initiated when plants were four months old. A

157 total of 216 plants were randomly allocated to the three treatments: uninoculated controls and

158 plants inoculated with Q. pitereka or A. psidii. Prior to inoculation, the height of all plants was

159 measured from base to apical tip using a tape measure.

160

161 Inoculation

162 An isolate of Quambalaria pitereka was collected from infected Corymbia spp. at the Mary

163 Valley Research Facility at Traveston inoculated onto potato dextrose agar (PDA) and grown

164 for 2 to 3 weeks at 25°C in the dark to produce fungal cultures (method after Pegg et al. 2009).

165 Isolates of A. psidii were obtained from the mycological collection of the Department of

166 Agriculture and Fisheries (DAF, Queensland) at Ecosciences Precinct laboratories (method

167 after Pegg et al. 2014). A spore suspension of Q. pitereka was prepared by washing the cultures

168 with sterile distilled water to a concentration of 1 × 10⁵ spore mL⁻¹. A spore suspension of A.

169 psidii was prepared at the same concentration by adding the pathogen isolate in sterile distilled

170 water. Spore concentration was determined using a hemocytometer. The surfactant Tween 20

171 was added to each spore suspension at 0.1% to reduce clumping.

172 Inoculum was applied to CCV seedlings using a compressor driven spray gun (2.9 kPa; Iwata

173 Studio series 1/6 hp; Gravity spray gun RG3). Upper and lower leaf surfaces were sprayed with

174 the spore suspension as a fine mist, ensuring run-off was avoided (Pegg et al. 2009, 2014).

175 Uninoculated control plants were sprayed with sterile distilled water with Tween 20 added.

176 The plant trays were placed into solid plastic tubs and then onto a bench lined with a plastic

177 sheet. Immediately after inoculation, seedlings were covered with a plastic sheet for 24 h to

Chapter 3 178 maintain high humidity levels and leaf wetness in a controlled environment room set at 18 ±

179 2°C in the dark. Hot tap water (60°C) was applied to the lower plastic sheet immediately after

180 inoculation to ensure high humidity levels were achieved rapidly. After 24 h, the top plastic

181 sheet was removed, and plants returned to the shade-house and hand-watered as required and

182 development of disease symptoms monitored.

183

184 Disease assessment

185 To assess disease symptoms, the four most recently fully expanded leaves on each plant were

186 assessed 14 days after inoculation using a disease rating scale adapted from Junghans et al.

187 (2003) to evaluate Q. pitereka and A. psidii damage: 1 = no symptoms evident or presence of

188 clear/chlorotic flecking; 2 = presence of a hypersensitive reaction with fleck or necrosis; 3 =

189 presence of small pustules, <0.8 mm diameter, with one or two uredinia; 4 = medium-sized

190 pustules, 0.8–1.6 mm diameter with about 12 uredinia; 5 = large pustules, >1.6 mm diameter

191 (Pegg et al. 2014; Freeman et al. 2019).

192 Quambalaria pitereka and A. psidii resistant and susceptible seedlings were identifyed. Plants

193 rated 1-2 were considered resistant, and 4-5 considered susceptible, in a method similar to

194 Yong et al. (2019b). Dead plants were not included in the analyses or measurements. We used

195 a two-way contingency table to evaluate whether disease level (resistant and susceptible) was

196 associated with provenance (Brooyar, Woondum and Mt-McEuen). Fisher’s Exact Test

197 exhibited no significant association between disease level and provenance distribution (P =

198 0.155-0.167), with resistant and susceptible plants evenly distributed among provenances

199 (Table 3.1). Ten representative plants from each provenance were used as controls to ensure

200 that any underlying differences in plant traits were accounted for. Selected plants were used to

201 evaluate growth rate, leaf toughness and secondary metabolites. Because our primary aim was

202 to quantify differential plant responses among uninoculated controls, susceptible (severely

Chapter 3 203 infected) and resistant (low level of infection), each pathogen was analysed separately for these

204 treatment groups.

205

206 Table 3.1 Provenance range of uninoculated controls and resistant and susceptible plants inoculated with 207 Quambalaria pitereka and Austropuccinia psidii. Provenance Treatments Brooyar Woondum Mt-McEuen Total Uninoculated controls 10 10 10 30 Resistant 14 12 9 35 Quambalaria pitereka Susceptible 4 6 10 20 Resistant 5 3 1 9 Austropuccinia psidii Susceptible 12 14 18 44 208

209 Growth rate

210 To examine the impacts of A. psidii and Q. pitereka infection on growth, plant heights were

211 measured before inoculation and at 14 days post inoculation (dpi) when the treatments were

212 assessed. Growth rate (cm day⁻¹) was calculated to determine the influence of the severity of

213 disease on plant growth. Growth rate was analysed by one-way Analysis of Variance, and

214 treatments were compared by Fisher’s Least Significant Difference (LSD) post-hoc test in

215 SPSS (IBM Corp, V 24.0.0.0).

216

217 Sample collection

218 To examine leaf toughness, which is indicative of leaf strength (Steinbauer 2001), and

219 secondary metabolites profile, the first two to three most recent, fully expanded leaves were

220 collected 14 dpi from uninoculated controls, resistant (rating 1-2), and susceptible (rating 4-5)

221 plants to Q. pitereka and A. psidii (Figure 3.1).

222 Plants were then trimmed to remove infected leaves and shoots and maintained in the shade-

223 house. To determine if leaf physical and chemical changes detected at 14 dpi persist over time,

224 a second sampling was undertaken 90 dpi and secondary metabolite profiles of controls,

Chapter 3 225 resistant and susceptible plants compared. In both processes, samples were collected from each

226 seedling and pathogens were assessed separately.

227

228 229 Figure 3.1 Uninoculated controls, resistant and susceptible leaves from plants 14 days after inoculation with 230 Quambalaria pitereka (left) and Austropuccinia psidii (right). 231

232 Leaf Toughness

233 Leaf toughness was estimated by the specific leaf weight (leaf mass per area; method after

234 Steinbauer 2001; Smith et al. 2018) from control, resistant and susceptible leaf samples by

235 dividing the dry leaf mass by leaf area (mg cm⁻²). Leaf area was measured by digitally scanning

236 leaf material at a resolution of 300 dots per inch using Image Analysis Software for Plant

237 Disease Quantification – ASSESS 2.0 (Lamari 2008). Samples were transferred to paper

238 envelopes and dried at 70°C until leaf mass stabilized to obtain the dry leaf mass. Leaf

239 toughness was compared among uninoculated, resistant and susceptible plants using one-way

240 Analysis of Variance, and significance of differences were tested by Fisher’s Least Significant

241 Difference (LSD) post-hoc test in SPSS (IBM Corp, V 24.0.0.0).

242

Chapter 3 243 Secondary Metabolites Profile

244 Secondary metabolites were extracted from foliage of control, resistant and susceptible plants

245 at days 14 and 90. The leaves were cut into squares (≤ 1 cm²) and extracted with hexane (≥

246 99%, RCI Labscan Limited) in the proportion of 1:10 (g mL⁻¹) for 120 min, stirring for 1 min,

247 six times within this period. The extract was transferred into 1.8 mL vials and stored in the

248 freezer (-20°C) until analysis (method after Nahrung et al. 2009). Q. pitereka culture and A.

249 psidii urediniospores were also extracted in the proportion of 1:10 (g mL⁻¹), and their profiles

250 were compared with plant profiles to ensure there was no fungal material contributing to

251 infected plant profiles.

252 Samples (1 µL) were analysed using a gas chromatograph (GC) (Agilent 6890 Series) coupled

253 to a mass spectrometer (MS) (Agilent 5975) and fitted with a silica capillary column (Agilent,

254 Model HP5-MS, 30 m × 250 µm ID × 0.25 µm film thickness).

255 Measuremants were made under the following GC conditions: inlet temperature 250°C, carrier

256 gas helium at 51 cm s⁻¹, split ratio 13:1, transfer-line temperature 280°C, initial temperature

257 40°C, initial time 2 min, rate 10 °C min⁻¹, final temperature 260°C, final time 6 min. The MS

258 was held at 280°C in the ion source, with a scan rate of 4.45 scans s⁻¹.

259 Peaks that were present in blank hexane samples were discarded from the analysis in test

260 samples. Tentative identities were assigned to peaks using the National Institute of Standards

261 and Technology (NIST) mass spectral library. Mass spectra of peaks from different samples

262 with the same retention time were compared to ensure that the compounds were the same.

263 Compounds were grouped and analysed by compound class (determined from characteristic

264 fragmentation pattern of the mass spectra) - monoterpenes, sesquiterpenes, monounsaturated

265 hydrocarbons, steroids, and long chain hydrocarbons.

266 Statistical analyses were conducted using the software Primer 7 for Windows (V 7.0.13, Clarke

267 and Gorley 2015). Data were square-root transformed and analysed by nonparametric Bray-

Chapter 3 268 Curtis cluster analysis. Differences between the treatments were determined by analysis of

269 similarity (ANOSIM) and the percentage of compound contribution to group dissimilarity by

270 ‘similarity percentages’ (SIMPER) analysis. The differences detected between treatments were

271 graphically represented by non-metric multidimensional scaling ordination (nMDS). Each

272 point in the nMDS plot represents an individual plant, and clumped points correspond to

273 individuals with similar peak composition (presence and abundance). Differences in mean

274 relative area of each compound class between treatments were analysed by Kruskal-Wallis test

275 (SPSS) (IBM Corp, V 24.0.0.0).

276

277 Results

278 Growth rate and leaf toughness

279 Growth rate was not significantly different among resistant, susceptible and control plants for

280 Q. pitereka, but was significantly lower for susceptible plants inoculated with A. psidii. There

281 was a reduction of approximately 70% in the growth rate in comparison to uninoculated and

282 resistant plants (Figure 3.2a).

283 Leaves of Q. pitereka-susceptible plants were significantly tougher than uninoculated controls

284 and resistant plants, with leaf toughness being approximately 30% and 50% higher,

285 respectively. There was no statistical difference in leaf toughness of plants inoculated with A.

286 psidii, and controls (Figure 3.2b).

287

Chapter 3

F2, 30 = 2.708 (a) F2, 82 = 0.040 F2, 79 = 7.230 (b) 5.0 b P = 0.083 0.40 a a P = 0.961 P = 0.001 4.5 F2, 41 = 8.046 ) ²)

⁻¹ P = 0.001 0.35 ⁻ 4.0 a 0.30 3.5 a 0.25 3.0 0.20 2.5 b 2.0 0.15 1.5 s.e. growth s.e. growth rate (cm day 0.10 ±

s.e. leaf toughness (mg s.e. leaf (mg cm toughness 1.0 ± 0.05 0.5 Mean Mean

0.00 Mean 0.0 Quambalaria pitereka Austropuccinia psidii Quambalaria pitereka Austropuccinia psidii 288 289 Figure 3.2 Mean ± standard error (a) growth rate (cm day⁻¹) and (b) leaf toughness (mg cm⁻²) of controls (black), resistant 290 (grey) and susceptible (white) plants of Corymbia citriodora subsp. variegata inoculated with Quambalaria pitereka and 291 Austropuccinia psidii. Different lowercase letters within treatments designate significant differences in growth rate and 292 leaf toughness. 293

294 Secondary metabolites

295 Chromatographic analysis (GC-MS) of hexane extracts of CCV leaves detected 24 compounds

296 representing 91-93% of the total oil content across the inoculations with the two diseases and

297 the controls. The compounds grouped into five compound classes, comprising: two

298 monoterpenes; one sesquiterpene; five monounsaturated hydrocarbons; 10 steroids; and six

299 long chain hydrocarbons. No compounds were detected in hexane extracts of the spore

300 suspension references of either pathogen, so we are confident that our results are plant

301 compounds only and not affected by the presence of spores in severely infected samples.

302 The secondary metabolites analysed at day 14 indicated that plants inoculated with Q. pitereka

303 were not statistically different between treatments (ANOSIM R = -0.023, P = 0.704; Figure

304 3.3a). Plants inoculated with A. psidii were significantly different (ANOSIM R = 0.288, P =

305 0.001; Figure 3.3b), with susceptible plants presenting an altered secondary metabolite profile

306 in comparison to control and resistant plants. SIMPER analyses determined the compound

307 classes responsible for distinguishing between the treatments (Table 3.2). Half of the

308 compounds detected were responsible for 21% of dissimilarity between controls and

309 susceptible plants and 16% dissimilarity between resistant and susceptible plants. Susceptible

Chapter 3 310 plants had a reduced proportion of monoterpenes, monounsaturated hydrocarbons and a higher

311 proportion of long chain hydrocarbons.

312 The Kruskal-Wallis test comparison of each compound class between the treatments (control,

313 resistant and susceptible) for each pathogen reinforced the ANOSIM results. The compounds

314 found in plants inoculated with Q. pitereka were not significantly different between treatments

315 (Figure 3.4a), while plants inoculated with A. psidii were significantly different (Figure 3.4b).

316 Plants susceptible to A. psidii had a lower proportion of monoterpenes, sesquiterpene and

317 monounsaturated hydrocarbons, and a higher proportion of long chain hydrocarbons in

318 comparison to control and resistant plants.

319 Secondary metabolites were also analysed at day 90 to evaluate if plants with no sign of

320 infection. exhibited any indication of susceptibility from their oil profiles. Plants that were

321 previously rated resistant or susceptible were indistinguishable from each other and from

322 uninoculated controls for Q. pitereka (ANOSIM R = -0.008, P = 0.532) and A. psidii (ANOSIM

323 R = -0.075, P = 0.918).

Chapter 3

(a)

Quambalaria pitereka 324 (b)

Austropuccinia psidii 325 326 Figure 3.3 Two-dimensional nMDS ordination of extracts of Corymbia citriodora subsp. variegata leaves from 327 plants inoculated with (a) Quambalaria pitereka and (b) Austropuccinia psidii. The plots are based on square-root 328 transformed abundances and a Bray-Curtis similarity matrix. Extracts from A. psidii susceptible plants cluster 329 separately. Symbols: control (circle) resistant (cross) susceptible (square). Ellipses are used for ease of 330 interpretation only. 331

332 Table 3.2 Back-transformed mean ± s.e. relative percentage area of compound class used to distinguish 333 uninoculated controls from Austropuccinia psidii resistant and susceptible Corymbia citriodora subsp. variegata 334 plants. Mean % area % Contribution to group dissimilarity Compound class Control Resistant Susceptible Control × Resistant Control × Susceptible Monounsaturated hydrocarbon 27.10 ± 3.70 22.61 ± 7.14 9.06 ± 1.25 33.22 35.72 Long chain hydrocarbon 34.22 ± 4.08 44.16 ± 6.41 56.10 ± 2.47 28.90 22.73 Monoterpene 4.42 ± 0.82 2.74 ± 0.35 2.47 ± 1.00 16.90 17.62 335

Chapter 3

(a) Relative area (%) 0 10 20 30 40 50

H2 = 0.425 Monoterpene n = 40, P = 0.808

H2 = 2.805 Sesquiterpene n = 40, P = 0.246

Monounsaturated H2 = 2.977 hydrocarbon n = 40, P = 0.226

H2 = 2.173 Steroid n = 40, P = 0.337 H2 = 3.323 Quambalaria pitereka n = 40, P = 0.190 Long chain hydrocarbon

336 Compound class of plants with inoculated

(b) Relative area (%) 0 10 20 30 40 50

a H2 = 13.545 Monoterpene a n = 48, P = 0.001 b

a H2 = 25.794 Sesquiterpene a b n = 48, P < 0.001 Monounsaturated a H2 = 15.556 a n = 48, P < 0.001 hydrocarbon b

H2 = 4.071 Steroid n = 48, P = 0.131

Austropuccinia psidiiAustropuccinia H2 = 14.521 Long chain a n = 48, P = 0.001 a hydrocarbon b

337 Compound class of plants with inoculated 338 Figure 3.4 Back-transformed mean ± s.e. relative area of compound classes of controls (black), resistant (grey) 339 and susceptible (white) plants of Corymbia citriodora subsp. variegata inoculated with (a) Quambalaria pitereka 340 and (b) Austropuccinia psidii at day 14. Results of Kruskal-Wallis tests for each compound class are shown, with 341 different lowercase letters designating significant differences between treatments. 342

343 Discussion

344 Understanding variability in both host and host response to infection is important to optimise

345 control and management of pathogens in natural and planted forests. In this study, we evaluated

346 leaf physical and chemical responses to infection by two fungal pathogens, Q. pitereka and A.

347 psidii, comparing the differences of these parameters between resistant and susceptible plants

348 of CCV.

349 The ecological impacts of A. psidii are alarming, causing significant damage and plant

350 mortality (Carnegie et al. 2016; Pegg et al. 2017). Our results demonstrated a growth rate

Chapter 3 351 reduction in plants severely infected by A. psidii, compared to resistant and control plants, a

352 similar response to that was previously reported for Melaleuca quinquenervia, Leptospermum

353 laevigatum, Baeckea linifolia (Winzer et al. 2018) and Eucalyptus globulus (Balmelli et al.

354 2013). Quambalaria pitereka also reduces growth with increasing severity of infection

355 (Johnson et al. 2009; Lan et al. 2011). The fact that our study found no significant difference

356 in growth rate between controls and Q. pitereka treatments was, perhaps, associated with plant

357 mechanisms to compensate the loss of damaged tissues by stimulating regrowth (Mitchell et

358 al. 2016; Gong and Zhang 2014) and/or the short duration of the experiments.

359 The analyses of leaf physical and chemical parameters of uninoculated controls, resistant and

360 susceptible plants to Q. pitereka showed that leaf toughness was significantly higher only in

361 susceptible plants, while the secondary metabolites profile was not significantly different

362 between treatments. Quambalaria pitereka coevolved with Corymbia species, which means

363 that evolutionary changes in the host are likely to cause an evolutionary change in the pathogen

364 (Morgan and Koskella 2011; Freeman et al. 2019). Therefore, these results may be associated

365 with a specific host-pathogen interaction. Considering that Q. pitereka penetrates leaf through

366 stomata without specialized structures, growing only intercellularly and never entering the cells

367 directly (Pegg et al. 2009), it is unlikely that the leaf toughening would be a defence

368 mechanism. Additionally, the absence of plant response to Q. pitereka infection in terms of

369 secondary metabolites may be an adaptation of the pathogen to these compounds. Perhaps,

370 CCV susceptibility to Q. pitereka involves other physical structures, chemical compounds

371 and/or mechanisms that were not analysed in this study.

372 In contrast, the analyses of physical and chemical parameters between uninoculated controls,

373 resistant and susceptible plants to A. psidii show no difference of leaf toughness between

374 treatments, while secondary metabolite profiles of susceptible plants altered in comparison to

375 uninoculated controls and resistant plants. Austropuccinia psidii is an exotic pathogen, with a

Chapter 3 376 broad host range of approximately 350 species in 58 genera of the Myrtaceae in Australia and

377 over 450 species globally, the majority of which are considered naïve (Carnegie et al. 2016;

378 Pegg et al. 2017; Soewarto et al. 2019). The lack of host-specificity to infection may have led

379 to a different plant-pathogen interaction and consequently, different plant responses. Freeman

380 et al. (2019) associated CCV response to A. psidii to specific-pathogen recognition/resistance

381 mechanisms, as resistant genotypes display no symptoms, mild necrotic flecking or

382 hypersensitive responses. However, these responses have also been associated with non-host

383 interactions (Xavier et al. 2015; Yong et al. 2019b).

384 Secondary metabolite profile of susceptible CCV infected with A. psidii had a lower proportion

385 of monoterpenes and monounsaturated hydrocarbons (light waxes) and a higher proportion of

386 long-chain hydrocarbons (heavy waxes) in comparison to uninoculated controls and resistant

387 plants. Differences in terpene expression between susceptible and resistant plants to A. psidii

388 was also detected in hybrids of E. grandis × E. urophylla (Hantao et al. 2013b). A C. citriodora

389 subsp. citriodora × C. torelliana hybrid resistant to Calonectria quinqueseptatum also varied

390 in its constitutive chemical composition and monoterpene concentration in comparison to its

391 susceptible male parent C. citriodora subsp. citriodora (Varshney et al. 2012). In that hybrid,

392 the monoterpenes α-pinene, β-pinene and citronellal, found in both genotypes had antifungal

393 activities against C. quinqueseptatum. Differences in constitutive and induced terpene levels

394 were also found between resistant and susceptible clones of E. grandis, with resistant clones

395 containing five monoterpenes that were not found in susceptible clones and expressing higher

396 levels of p-cymene in comparison to susceptible plants when challenged by the stem canker

397 pathogen C. austroafricana (Visser et al. 2015).

398 Monoterpenes are largely associated with antifungal activities (Barbosa et al. 2016), and our

399 results indicate that plants severely infected with A. psidii have a reduced proportion of this

400 compound class. These plants had markedly lower levels of monoterpenes - approximately

Chapter 3 401 10% and 40% in comparison to resistant and uninoculated controls, respectively - possibly

402 suggesting these compounds were degraded during infection, which may relate to the high

403 susceptibility to A. psidii observed in CCV during this study.

404 In our study, A. psidii susceptible CCV plants had a reduced proportion of light waxes, and an

405 increased proportion of heavy waxes compared with resistant and uninoculated controls. The

406 opposite plant response was observed in CCV under Paropsis atomaria herbivory and

407 mechanical damage, where damaged plants had a lower proportion of long chain hydrocarbons,

408 and higher proportions of monounsaturated hydrocarbons - a compound class that appeared to

409 favour insect survival (Chapter 2). The variation of leaf waxes observed in susceptible CCV in

410 response to A. psidii may be associated with plant physiological functions, also playing a role

411 in plant-pathogen interactions, as the highly hydrophobic properties of waxes reduce leaf

412 wettability, influencing fungal adhesion, development and incubation time (Müller and

413 Riederer 2005; Smith et al. 2018). To overcome this barrier, some fungal pathogens are capable

414 of developing mechanisms to degrade waxes, allowing their adhesion and penetration of the

415 leaf surface (Müller and Riederer 2005). The high proportion of heavy waxes in severely

416 infected plants may also be related to a mechanism to seal and heal damaged tissues, to reduce

417 or prevent water loss, and possibly restrict symptom development (Koch et al. 2009; Savatin

418 et al. 2014). Moreover, the secondary metabolites that compose leaf waxes may work as

419 chemical signals for fungal pathogenicity and plant response activation (Müller and Riederer

420 2005).

421 Leaf wax quantity was found to be associated with lower levels of infection in older leaves of

422 susceptible clones of E. grandis inoculated with A. psidii in comparison to younger ones, with

423 a gradual reduction of germination, appressorium formation, and penetration (Xavier et al.

424 2015). Resistance levels and leaf age were also associated with wax quantity in hybrid E.

425 urophylla × E. grandis. Resistant hybrids and older leaves were waxier and supported lower A.

Chapter 3 426 psidii germination than susceptible hybrids and younger leaves (Silva et al. 2017). Conversely,

427 no difference in wax concentration was observed during comparisons of Teratosphaeria

428 resistant E. nitens and susceptible E. globulus, however, the stomata of resistant plants were

429 entirely hidden by epicuticular wax, which may have influenced pathogen penetration (Smith

430 et al. 2018).

431 In addition, of the 26 leaf wax compounds found in six eucalypts species, six compounds

432 occurred only in the A. psidii resistant E. urophylla, E. camaldulensis, E. urograndis and E.

433 robusta, while two occurred only in the susceptible E. grandis and E. phaeotricha. This

434 included hexadecanoic acid, a specific fatty acid considered to favour fungal germination (dos

435 Santos et al. 2019). Cuticular extract of susceptible E. grandis promoted a higher germination

436 rate of A. psidii in comparison to other eucalypt species (dos Santos et al. 2019), suggesting

437 that leaf wax may facilitate pathogen infection. In fact, hydrocarbons have been reported as A.

438 psidii urediniospore stimulants in Syzygium jambos (Tessmann and Dianese 2002). Plant

439 chemical responses are usually associated with an enhancement of plant resistance or defence

440 against pathogens, however, in CCV, at least with respect to the exotic pathogen A. psidii, the

441 response seems to be more complex.

442 Given the variable resistance to A. psidii observed in eucalypts (e.g. Silva et al. 2013; Pegg et

443 al. 2014; dos Santos et al. 2019) and their variability in leaf terpene levels (Boland et al. 1991;

444 Barbosa et al. 2016), several studies have tried to associate disease levels with leaf chemistry,

445 hoping to find biomarkers that would distinguish plant resistance and susceptibility to the

446 pathogen. For instance, 1,8-cineole and α-terpinyl acetate were reported to be associated with

447 A. psidii resistance in E. grandis × E. urophylla hybrids (Hantao et al. 2013b), and the higher

448 oil content, increasing levels of 1,8-cineole, β-pinene, limonene and pinocarvone and

449 decreasing levels of p-cymene, piperitone, α- and β-phellandrene and α-eudesmol were

450 associated with resistance in 17 eucalypts from the Eucalyptus subgenus Symphyomyrtus (Potts

Chapter 3 451 et al. 2016). On the other hand, E. grandis and E. obliqua had different terpene expressions

452 between species and within levels of A. psidii disease vulnerability (resistant, moderately

453 resistant and susceptible) (Yong et al. 2019a). The compounds associated with pathogen

454 resistance in E. grandis were β-pinene, δ-terpinene, cis-p-menth-2-en-1-ol, geraniol,

455 bicyclogermacrene and globulol, while the compounds associated with resistance in E. obliqua

456 were α-caryophyllene, δ-cadinene, caryophyllene oxide and longifolenaldehyde. Based on

457 these results Yong et al. (2019a) proposed that the terpenes associated with protection against

458 A. psidii were likely to vary within eucalypts and that terpenes may be species-specific

459 biomarkers for resistance to this pathogen. In our study, however, no difference was found

460 between previously infected (resistant or susceptible) and uninoculated control plants 90 days

461 after infection, suggesting that underlying CCV leaf chemistry is not a useful biomarker to

462 identify plant resistance to pathogen infection. Furthermore, it was more likely that differences

463 in the expression of secondary metabolites were an induced response to severe infection rather

464 than a constitutive mechanism or a characteristic of plant resistance or susceptibility.

465 This study has demonstrated that susceptible CCV had alterations in physical and chemical

466 parameters in response to pathogen infection. Susceptible plants infected by Q. pitereka

467 increased leaf toughness while the ones infected by A. psidii reduced plant growth and had

468 changes in the expression of secondary metabolites in comparison to uninoculated controls and

469 resistant plants. Further investigations on the effect of secondary metabolites on fungal spore

470 germination should be undertaken to better understand the nature of the response of CCV.

471 Conversely, resistant plants did not present any alteration in response to pathogen damage,

472 contrasting with several studies that found resistant plants had constitutive or induced traits in

473 response to pathogen damage (e.g. Smith et al. 2006, 2007; Visser et al. 2015; Mangwanda et

474 al. 2015; Silva et al. 2017; Smith et al. 2018). Perhaps, resistance may be associated with other

475 phenotypic traits, not included in this study.

Chapter 3 476 Molecular studies indicate A. psidii disease resistance is polygenic (Alves et al. 2012; Thumma

477 et al. 2013; Butler et al. 2016, 2019). For instance, quantitative trait loci (QTL) analysis has

478 found up to six QTL for A. psidii resistance (Ppr genes), with different control crossed families

479 displaying a range of segregation patterns and/or complex pattern of inheritance (Junghans et

480 al. 2003; Alves et al. 2012; Resende et al. 2017; Butler et al. 2019). The QTL resistance in C.

481 citriodora subsp. variegata × C. torelliana hybrid subjected to two strains of Q. pitereka were

482 disassociated with the QTL detected for A. psidii resistance, with little co-location between

483 QTL influencing resistance to the exotic and native pathogens (Butler et al. 2019). This

484 supports our findings, which suggest that Q. pitereka and A. psidii infection trigger different

485 phenotypic responses, thus, selecting for resistance to one pathogen may result in susceptibility

486 to the other. Based on these results further investigation of the phenotypic differences of plant

487 species to these pathogens is appropriate.

488

489 Conclusion

490 In conclusion, highly susceptible plants of Corymbia citriodora subsp. variegata differed from

491 resistant and uninoculated control plants in response to infection. Susceptible plants infected

492 by Q. pitereka increased leaf toughness, while severe infection of A. psidii reduced growth rate

493 and altered the expression of secondary metabolites. Plants resistant to either pathogen had the

494 same phenotypic expression as uninoculated controls. Our results also suggest that leaf

495 chemistry is not an efficient predictor of C. citriodora subsp. variegata susceptibility.

496 Differences between plant responses to co-evolved and exotic pathogen infections are worthy

497 of further investigation.

498

Chapter 3 499 Acknowledgements

500 This work was supported by University of the Sunshine Coast International Research

501 Scholarship (USCIRS) awarded to FSB. The authors thank Julia Woerner, Emily Lancaster,

502 Louise Shuey and Renata Grunennvaldt for assisting with glasshouse and laboratory work

503 associated with this study, and the Department of Agriculture and Fisheries (DAF) for

504 provision of facilities.

505

506 References

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Chapter 4

1 Chapter 4 Changes in leaf chemistry and anatomy of Corymbia

2 citriodora subsp. variegata (Myrtaceae) in response to native and

3 exotic pathogens.

5 Authors: Flávia Sarti Bonoraa, Helen F Nahrunga, Richard Andrew Hayesa, Tanya

6 Sharaschkinb, Geoff Peggc, David John Leea

7 Affiliations: aForest Industries Research Centre, University of the Sunshine Coast - 90 Sippy

8 Downs Dr, Sippy Downs QLD 4556, Australia

9 b Botanical Research, Art and Training, 54 Mill Road, Collinsvale, TAS 7012,

10 Australia

11 c Horticulture and Forestry Science, Queensland Department of Agriculture and

12 Fisheries, Ecosciences Precinct, 41 Boggo Road, Dutton Park, Brisbane QLD

13 4120, Australia

15 *Corresponding author: [email protected] +610426163647

16 Journal: Australasian Plant Pathology (Springer)

17 Impact factor: 1.106

Chapter 4 19 Statement of Intellectual Contribution

21 I, Flávia Sarti Bonora declare that I have made substantial intellectual contribution (60%) to

22 the original research article ‘Changes in leaf chemistry and anatomy of Corymbia citriodora

23 subsp. variegata (Myrtaceae) in response to native and exotic pathogens.’ by F.S. Bonora,

24 H.F. Nahrung, R.A. Hayes, T. Sharaschkin G. Pegg and D.J. Lee, submitted to the journal

25 Australasian Plant Pathology (Springer).

Chapter 4 28 Abstract

29 Corymbia citriodora subsp. variegata (CCV) is an economically and ecologically important

30 timber species, native to eastern Australia. It is highly susceptible to Quambalaria pitereka, a

31 coevolved endemic pathogen, and Austropuccinia psidii, an exotic invasive pathogen. Genes

32 associated with resistance to Q. pitereka are specific and uncorrelated with the genes associated

33 with resistance to A. psidii, suggesting different resistance mechanisms to each pathogen,

34 possibly associated with leaf phenotypic traits. This study examined leaf chemical and

35 anatomical differences in CCV between uninoculated plants and those inoculated with Q.

36 pitereka and A. psidii. The results demonstrate that the pathogens induce different responses in

37 CCV. Plants inoculated with A. psidii exhibited more obvious chemical and anatomical

38 changes than uninoculated controls and Q. pitereka-inoculated plants, such as deposition of

39 polyphenols and tannins in upper/lower epidermis, variation in the proportion of monoterpenes,

40 steroids, monounsaturated hydrocarbons and long chain hydrocarbons and higher leaf

41 toughness. In contrast, CCV response to Q. pitereka was more limited, only altering the

42 distribution of polyphenols and tannins in the leaves and possible accumulation of these

43 compounds and lignin in necrotic areas. These findings provide a better understanding of

44 factors underlying CCV responses to coevolved and exotic pathogens and add insights into

45 plant-pathogen interactions.

47 Keywords: Induced response; leaf anatomy; myrtle rust; Quambalaria shoot blight; leaf

48 chemistry; leaf histochemistry; spotted gum; secondary metabolites

Chapter 4 50 Introduction

51 Spotted gums (family Myrtaceae, section Maculatae, genus Corymbia) are an important part

52 of Australia’s native forest, and naturally distributed from northern Queensland to eastern

53 Victoria (Shepherd et al. 2008). Due to their desirable growth and form, adaptation to a wide

54 range of edaphic and climatic conditions, and excellent timber quality (Lee 2007) spotted gums

55 are important commercial taxa in Australia (Lee 2007; Lee et al. 2010), South Africa (Gardner

56 et al. 2007), Brazil (Alfenas et al. 2016; Cunha et al. 2019) and China (Zhou and Wingfield

57 2011; Chen et al. 2017).

58 The main limitation of spotted gums, as a commercial timber, is their susceptibility to

59 Quambalaria pitereka (Lee 2007), a coevolved pathogen that infects young shoots and stems,

60 causing leaf distortion, severe necrosis and loss of apical dominance, thereby compromising

61 tree form and productivity (Pegg et al. 2009). The pathogen has been introduced to areas

62 outside its natural range, affecting Corymbia calophylla in Western Australia (Ahrens et al.

63 2019) and C. citriodora plantations in China (Zhou and Wingfield 2011; Chen et al. 2017).

64 Further, spotted gums are highly susceptible to Austropuccinia psidii (syn. Puccinia psidii)

65 (Pegg et al. 2014), an exotic pathogen that was first detected in Australia in 2010 (Carnegie et

66 al. 2010). This pathogen threatens over 480 myrtaceous species from 69 genera (Soewarto et

67 al. 2019), infecting natural areas in Australia, and planted forests globally, causing enormous

68 economic and environmental loss (Carnegie 2015; Pegg et al. 2017; Pegg et. al 2018; Winzer

69 et al. 2018). Austropuccinia psidii infects young shoots and stems, flowers, fruits and coppice,

70 causing leaf spots, severe shoot and stem blight, necrosis and distortion that can lead to tree

71 dieback, with high levels of mortality in some species (Glen et al. 2007; Carnegie et al. 2016;

72 Pegg et al. 2017; Winzer et al. 2018).

73 Resistance to both of these pathogens in Corymbia citriodora subsp. variegata (CCV) is

74 independent and genetically uncorrelated (Freeman et al. 2019; Butler et al. 2019), with

Chapter 4 75 resistance regulated by specific R-genes and quantitative trait loci of large effect for Q. pitereka

76 (Butler et al. 2019), and by multiple genes for A. psidii (Alves et al. 2012; Thumma et al. 2013;

77 Butler et al. 2016, 2019). The geographical distribution of CCV resistance to Q. pitereka is

78 directly associated with pathogen-imposed selection, resulting from the ongoing

79 coevolutionary process between host and pathogen (Freeman et al. 2019). In contrast, there is

80 no pathogen-imposed selection shaping the geographical distribution of resistance to A. psidii

81 in CCV and other eucalypts (Pegg et al. 2014; Freeman et al. 2019; Yong et al. 2019a).

82 Despite the variable resistance to both pathogens in spotted gums, ranging from highly

83 susceptible to completely resistant (Lee 2007; Pegg et al. 2011, 2014; Freeman et al. 2019;

84 Butler et al. 2019), resistance mechanisms in CCV are pathogen-specific. Therefore, selecting

85 for resistance to Q. pitereka pathogen does not imply there will also be resistance to A. psidii

86 (Pegg et al. 2014; Freeman et al. 2019; Butler et al. 2019). This has been shown in CCV from

87 Woondum (26°25′ S; 152°81′ N), the most commonly used provenance in hardwood

88 plantations in Queensland (Freeman et al, 2019).

89 These pathogens also differ in their colonisation and nutrient acquisition processes, as Q.

90 pitereka enters leaves through stomata, without directly penetrating the leaf surface, and grows

91 only between cells without penetration structures such as appressoria or haustoria, obtaining

92 nutrients from host cell walls through interaction zones (Pegg et al. 2009). In contrast, A. psidii

93 directly penetrates leaf cuticle and epidermal cells through appressoria, growing within and

94 between cells, accessing host nutrients through haustoria structures (Xavier et al. 2001, 2015).

95 The differences in host interaction between the coevolved and exotic pathogens possibly

96 activate distinct defence mechanisms, involving leaf anatomy and leaf chemistry (Butler et al.

97 2019), however, very little is known regarding leaf phenotypic parameters in CCV during, or

98 as a result of pathogen infection. Leaf chemistry and anatomy may play a role in CCV

99 resistance, as shown in previous studies where plants severely infected with Q. pitereka have

Chapter 4 100 tougher leaves, and plants severely infected with A. psidii vary in monoterpene and wax

101 concentrations compared to resistant plants (Bonora et al. 2020; Chapter 3). Leaf wax

102 composition is strongly associated with susceptibility to A. psidii in Eucalyptus grandis and E.

103 phaeotricha (dos Santos et al. 2019). In addition, leaf anatomical and histochemical

104 parameters, such as leaf toughness, palisade parenchyma density and deposition of chemicals

105 in symptomatic tissues, have been found to influence Teratosphaeria leaf disease in E. grandis

106 and E. nitens. (Smith et al. 2006, 2007, 2018).

107 In this study, leaf chemical and anatomical parameters of CCV inoculated with Q. pitereka and

108 A. psidii were assessed, aiming to differentiate leaf phenotypic responses underlying each

109 pathogen infection. The parameters assessed were distribution of chemicals in the leaves,

110 overall leaf chemical composition and leaf anatomical structures, including cell size and

111 density, leaf thickness and toughness.

112

113 Material and Methods

114 Plant material

115 Seeds randomly selected from two open-pollinated CCV families from Woondum (26° 25' S,

116 152° 81' E, Altitude: 400 m a.s.l; Mean rainfall: 1536 mm year⁻¹) were sown in a glasshouse at

117 the University of the Sunshine Coast in 8 × 5-celled QNT trays (200 cc) and maintained under

118 mist six times a day (3 × 10 min and 3 × 5 min). Potting medium, consisting of 50% pine bark

119 fines (0–10 mm), 25% pine bark peat, 25% coarse perlite, a mix of 8–9 month Osmocote Pro

120 Low P (4 kg m⁻³), agricultural lime (4 kg m⁻³); gypsum (1 kg m⁻³), Micromax fertiliser (1 kg

121 m⁻³) and a granular wetting agent, Hydroflow (1 kg m⁻³) was used. The seeds germinated after

122 two weeks and were transferred under 50% shade-cloth and watered twice a day for 25 min

123 using overhead sprinklers until roots were well established. Plants were re-potted when two

124 months old into 6 × 3 celled plant trays (600 cc) using the same potting medium, maintained

Chapter 4 125 under the same conditions for one month and then transported to a shade-house at the

126 Ecosciences Precinct (Brisbane) under 10% shade-cloth, and hand watered as required.

127 Plants were trimmed and then fertilised with Seasol (Season International, Bayswater, Victoria,

128 Australia) and Nitrosol Concentrate Liquid Fertiliser NPK 10.5: 2.3: 6.8 (Amgrow Pty. Ltd.,

129 Lidcombe New South Wales) to stimulate new growth. Plants were acclimated for another

130 month before inoculation. The experiment was initiated when plants were four months old. A

131 total of 72 plants were randomly distributed between three treatments: uninoculated controls,

132 inoculated with Q. pitereka and inoculated with A. psidii.

133

134 Inoculation

135 Field-collected Quambalaria pitereka spores were inoculated onto potato dextrose agar (PDA)

136 and grown for 2 to 3 weeks at 25°C in the dark to produce fungal cultures (method after Pegg

137 et al. 2009). Austropuccinia psidii urediniospores were obtained from the mycological

138 collection of the Department of Agriculture and Fisheries (DAF, Queensland) at Ecosciences

139 Precinct laboratories (method after Pegg et al. 2014). Fungal material of each pathogen was

140 separately washed with sterile distilled water to form an inoculum suspension of 1×10⁵ spore

141 mL⁻¹. Spore concentration was determined using a hemocytometer. The surfactant Tween 20

142 was added to each spore suspension at 0.1% to reduce clumping.

143 CCV plants were placed into trays depending on the treatment group, and their upper and lower

144 leaf surfaces were sprayed with Q. pitereka or A. psidii spore suspension using a fine mist spray

145 (2.9 kPa), generated by a compressor driven spray gun (Iwata Studio series 1/6 hp; Gravity

146 spray gun RG3). Control plants were sprayed with sterile distilled water with Tween 20 added

147 as per spore suspensions and treated in the same way as inoculated seedlings. Plant trays were

148 placed onto a bench lined with a plastic sheet. Hot water (60 °C) was applied to the bench

149 immediately after inoculation to ensure high humidity levels were achieved rapidly. The bench

Chapter 4 150 was covered with a plastic sheet for 24 h to maintain high humidity levels and leaf wetness in

151 a controlled environment room 19 ± 1 °C in the dark. After 24 h, the top plastic sheets were

152 removed, and plants were transferred back to the shade-house and hand watered as required.

153 The first two to three most-recently fully expanded leaves of each plant were collected 14 days

154 post-inoculation.

155

156 Leaf histochemistry and anatomy

157 The histochemical and anatomical differences of CCV leaves from uninoculated controls, and

158 Q. pitereka- and A. psidii-inoculated plants were examined following the methodology of

159 Retamales and Scharaschkin (2014). Leaves presenting symptomatic and asymptomatic tissues

160 from four individual plants per treatment were cut into rectangles (≤ 1 cm²) and fixed in

161 formalin-acetic acid-alcohol (FAA) for 30 days. Slide preparation was conducted at the

162 Institute of Health and Biomedical Innovation at Queensland University of Technology (IHBI-

163 QUT). Samples were transferred to an Excelsior ES (Thermofisher Scientific) tissue processor

164 dehydrated through a graded ethanol series and infiltrated with paraffin wax.

165 Samples were embedded in paraffin wax using a Shandon Histocentre 3 (Thermofisher

166 Scientific). Transverse sections (5 μm thickness) were cut using a Leica RM2265 rotary

167 microtome. Samples were deparaffinized with xylene, and then gradually hydrated through a

168 decreasing alcoholic series (ethanol 100%, 90%, 70%, 50%, distilled water). Histochemical

169 staining of sections was performed using a 0.05% (w/v) solution of ruthenium red (Sigma-

170 Aldrich Co., St. Louis, Missouri, USA) in distilled water for one minute and then

171 counterstained with a 0.1% (w/v) solution of toluidine blue (Sigma-Aldrich Co., St. Louis,

172 Missouri, USA) in distilled water for 45 seconds. Sections were mounted using Pertex

173 mounting medium (Medite, Burgdorf, Germany).

Chapter 4 174 Images were obtained with a DP27 camera attached to an Olympus BX53 microscope using

175 cellSens software. Leaf compounds were tentatively identified based on the colour observed

176 from the staining process: blue/green = polyphenols, tannin, lignin, essential oils; pink =

177 mucilage; and purple = pectin (Soukup 2014; Retamales and Scharaschkin 2014). Images were

178 viewed and measurements taken using Adobe Photoshop.

179 Due to the difficulties in accurately isolating 1 cm² rectangles of symptomatic tissue, only one

180 sample exhibiting clear signs of infection symptoms was obtained for each of the inoculated

181 treatments (Q. pitereka and A. psidii). Differences in histochemistry and anatomy of

182 symptomatic tissues between inoculated leaves with Q. pitereka or A. psidii and uninoculated

183 controls are discussed, however, due to the small sample size, no statistical analyses were

184 conducted. The other parameters were assessed from asymptomatic tissues of each treatment

185 (four samples per treatment), including leaf thickness (μm), size of palisade parenchyma cells

186 (length and width; μm) and the number of palisade parenchyma cells per μm² were recorded

187 and compared between treatments. The relative percentages of various leaf structures (upper

188 and lower epidermis, palisade parenchyma, spongy parenchyma, air space and bundle sheath)

189 in relation to total leaf area was determined (area of structure/total leaf area × 100). Differences

190 in these traits between treatments were analysed by Kruskal-Wallis test using SPSS (IBM Corp,

191 V 24.0.0.0).

192

193 Leaf toughness

194 To determine differences in leaf physical properties as a result of pathogen infection, leaf

195 toughness, an indicator of leaf strength, was assessed on asymptomatic and symptomatic

196 tissues of uninoculated controls, and Q. pitereka- and A. psidii- inoculated plants. Leaf

197 toughness is estimated by the proportion of dry leaf mass per area, also known as specific leaf

198 weight (Steinbauer 2001; Smith et al. 2018). Leaves from 7 - 11 individual plants per treatment

Chapter 4 199 were digitally scanned at a resolution of 300 dpi, and leaf area was measured using Image

200 Analysis Software for Plant Disease Quantification – ASSES 2.0 (Lamari 2008). Leaves were

201 transferred to paper envelopes and dried at 70 °C until leaf mass stabilised (Steinbauer 2001),

202 and dry leaf mass was measured. Leaf toughness was determined by dividing the dry leaf mass

203 by leaf area (mg cm⁻²). Differences in leaf toughness among treatments was analysed by

204 Kruskal-Wallis test using SPSS (IBM Corp, V 24.0.0.0).

205

206 Secondary metabolites profile

207 To examine differences between plant chemical response to a native and an exotic pathogen,

208 leaf secondary metabolites were extracted from asymptomatic and symptomatic tissues of

209 uninoculated controls, and Q. pitereka- and A. psidii- inoculated plants. The leaves from 10 -

210 12 individual plants per treatment were cut into squares (≤ 1 cm²) and leaf secondary

211 metabolites extracted with hexane (≥ 99%, RCI Labscan Limited) in the proportion of 1:10 (g

212 mL⁻¹) for 120 min, stirring for 1 min, six times within this period. The extract was transferred

213 into 1.8 mL vials and stored at -20 °C until analysis (method after Nahrung et al. 2009). Q.

214 pitereka culture and A. psidii urediniospores were also extracted in the proportion of 1:10 (g

215 mL⁻¹), and their profiles were compared with plant profiles to ensure there was no fungal

216 material contributing to inoculated plant secondary metabolite profiles.

217 Samples (1 µL) were analysed using a gas chromatograph (GC) (Agilent 6890 Series) coupled

218 to a mass spectrometer (MS) (Agilent 5975) and fitted with a silica capillary column (Agilent,

219 Model HP5-MS, 30 m × 250 µm ID × 0.25 µm film thickness). Data were acquired under the

220 following GC conditions: inlet temperature 250 °C, carrier gas helium at 51 cm s⁻¹, split ratio

221 13:1, transfer-line temperature 280 °C, initial temperature 40 °C, initial time 2 min, rate 10 °C

222 min⁻¹, final temperature 260 °C, final time 6 min. The MS was held at 280 °C in the ion source,

223 with a scan rate of 4.45 scans s⁻¹.

Chapter 4 224 Peaks that were present in blank hexane samples were discarded from the analysis in test

225 samples. Tentative identities were assigned to peaks with respect to a Kovats Retention Index

226 analysis and the National Institute of Standards and Technology (NIST) mass spectral library.

227 Mass spectra of peaks from different samples with the same retention time were compared to

228 ensure that the compounds were the same.

229 Statistical analyses were conducted using the software Primer 7 for Windows (V 7.0.13, Clarke

230 and Gorley 2015). Data were square-root transformed and analysed by nonparametric Bray-

231 Curtis cluster analysis. Differences between the treatments were determined by analysis of

232 similarity (ANOSIM) and the percentage of compound contributing to group dissimilarity by

233 ‘similarity percentages’ (SIMPER) analysis. The differences detected between treatments were

234 graphically represented by non-metric multidimensional scaling ordination (nMDS). Each

235 point in the nMDS plot represent an individual plant, and clumped points correspond to

236 individuals with a similar chemical composition (presence and abundance). Differences in

237 mean relative area of each compound class among treatments were analysed by Kruskal-Wallis

238 test using SPSS.

239

240 Results

241 Leaf histochemistry

242 An overview of leaf anatomy and histochemistry reactions from all four samples from each of

243 uninoculated controls and asymptomatic tissues of leaves inoculated with Q. pitereka or A.

244 psidii is shown (Figure 4.1). In general, non-lignified primary cell walls stained pink,

245 associated with pectins (Soukup 2014; Retamales and Scharaschkin 2014). The contents of

246 epidermal and bundle sheath cells stained blue and dark blue, indicating the presence of

247 secondary metabolites such as polyphenols and tannins (Retamales and Scharaschkin 2014).

100

Chapter 4 248 Palisade parenchyma and spongy parenchyma cells stained pink-blue or pink-purple, indicating

249 the presence of pectins, mucilage, polyphenols and tannins.

250 The histochemical reactions in the control plants (Figure 4.1, Table 4.1a - d) resulted in a more

251 consistent staining pattern across the four samples. The distribution of tannins and polyphenols

252 in leaves that were not exposed to fungal pathogens were more concentrated in the upper

253 epidermis and bundle sheath (stained dark blue), and less concentrated in the lower epidermis

254 (stained light blue; Figure 4.1a - d; Table 4.1a - d).

255 The distribution of chemical compounds in leaves inoculated with Q. pitereka was inconsistent.

256 In the four samples analysed, two had a higher concentration of polyphenols and tannins in the

257 upper/lower epidermis and bundle sheath (Figure 4.1e, g; Table 4.1e, g). These compounds

258 were absent in one of the samples (Figure 4.1f; Table 4.1f) and the other sample had a lower

259 concentration of these compounds in the upper/lower epidermis (Figure 4.1h; Table 4.1h). The

260 distribution of polyphenols and tannins in leaves inoculated with A. psidii was concentrated in

261 upper/lower epidermis and bundle sheath, being less variable within samples in comparison to

262 Q. pitereka, and different from what was observed for uninoculated controls (Figure 4.1i - l;

263 Table 4.1i - l).

264 Symptomatic tissues in inoculated plants were observed in one leaf sample each of Q. pitereka-

265 and A. psidii-inoculated plants, displaying minor or severe symptoms (necrotic tissues). Tissues

266 with minor symptoms stained purple, indicating an accumulation of pectins (Figure 4.2e, f, i,

267 j), while necrotic tissues , which could easily be identifyed (Figure 4.2g, h, k, l) also stained

268 purple and dark blue, indicating an accumulation of pectins and lignin (Soukup 2014;

269 Retamales and Scharaschkin 2014). Different stages of pathogen infection were observed, for

270 example intercellular growth of Q. pitereka hyphae (Figure 4.2f, as observed in Pegg et al.

271 2009), cell death and collapsing in association with conidia and conidiophores (Figure 4.2g),

101

Chapter 4 272 intracellular growth of A. psidii hyphae (Figure 4.2i, j) and uredinia rupturing through lower

273 epidermis (Figure 4.2k).

274 Emergent oil glands, described by Carr and Carr (1970) and Ladiges (1984), were compared

275 between uninoculated controls and symptomatic tissues of inoculated leaves (Figure 4.3).

276 Necrotic tissues near the emergent oil glands presented an accumulation of pectin and lignin,

277 and leaf thickness was reduced in comparison to controls, but there were not enough samples

278 to conduct measurements. The interior of oil glands stained green-blue indicating the presence

279 of essential oils (Figure 4.3). Oil glands remained intact during pathogen infection (Figure 4.3b

280 - d).

102

Chapter 4

281 282 Figure 4.1 Comparison of leaf anatomy and histochemistry of asymptomatic tissues of inoculated and uninoculated leaves of Corymbia citriodora subsp. variegata. Top row 283 (a - d), uninoculated controls, middle row (e - h) inoculated with Quambalaria pitereka and lower row (i - l) inoculated with Austropuccinia psidii. Abbreviations used: UE = 284 upper epidermis; ↓↑ = stomata; PP = palisade parenchyma; BS = bundle sheath; A = airspace; SP = spongy parenchyma; LE = lower epidermis; Scale bar = 100 μm. Each 285 figure corresponds to one sample. Leaf material: 5 μm transverse sections double stained in ruthenium red and toluidine blue. 286

103

Chapter 4

287 288 Figure 4.2 Comparision of pectin and lignin accumulation in uninoculated controls and symptomatic tissues of 289 inoculated leaves of Corymbia citriodora subsp. variegata. Top row (a - d), uninoculated controls, middle row (e 290 - h) inoculated with Quambalaria pitereka and lower row (i - l) inoculated with Austropuccinia psidii. Inoculated 291 samples exhibited tissues displaying minor (e, f, i, j) and necrotic symptoms (g, h, k, l). An abundance of pectic 292 substances (purple) can be seen in symptomatic tissues of inoculated samples (e, f, i, j) showing intercellular 293 growth of Q. pitereka hyphae (f, ↑) and intracellular growth of A. psidii (i, j, ↑). Both pectic substances and lignin 294 (purple and dark blue) occurred in necrotic tissues (g, h, k, l), also showing Q. pitereka conidiophore and conidia 295 (g, ↑) and A. psidii uredinia (k, ↑). Scale bar = 100 μm. Within rows, each figure represents different areas of the 296 same sampled leaf. Leaf material: 5 μm transverse sections double stained in ruthenium red and toluidine blue. 297

104

Chapter 4

298 299 Figure 4.3 Leaf anatomy and histochemistry of Corymbia citriodora subsp. variegata showing emergent oil glands 300 of uninoculated controls (a), symptomatic tissue of leaf inoculated with Quambalaria pitereka (adaxial b, abaxial 301 c), and symptomatic tissue of leaf inoculated with Austropuccinia psidii (d). Emergent oil glands remained intact 302 during severe infection (b - d). The interior of the oil cavity stained in green-blue indicating the presence of 303 essential oils. Scale bar = 100 μm. Leaf material: 5 μm transverse sections double stained in ruthenium red and 304 toluidine blue. 305

105

Chapter 4 306 Table 4.1 Presence (+) or absence (-) of secondary metabolite compounds in each sample of each treatment 307 identified based on the staining reaction to ruthenium red and toluidine blue (Figure 4.1): uninoculated controls 308 (a - d), samples inoculated with Quambalaria pitereka (e - h) and samples inoculated with Austropuccinia psidii 309 (i - l). Blue/green = polyphenols, tannin, lignin, essential oils, lipids; pink = mucilage; purple = pectin. Quambalaria Austropuccinia Tissue Control Cell type Chemicals pitereka psidii system a b c d e f g h i j k l

Upper epidermal Polyphenols and tannins + + + + + - + + + + + + cells Mucilage - - - - - + - + + + + +

Lower epidermal Polyphenols and tannins + + + + + - + + + + + + Dermal cells Mucilage + + + + + + + + + + + +

Polyphenols and tannins - + + - + - + + + - + + Guard cells Mucilage + + + + + + + + + + + + Polyphenols, tannin + + + + + + + - + + + + Parenchyma palisade Mucilage + + + + + + + + + + + + Pectins + + - - + - - + + - + - Ground Polyphenols, tannin + + + + + - + - + + + + Spongy Mucilage + + + + + + + + + + + + parenchyma Pectins ------+ + - - - Lignin + + + + + + + + + + + + Vascular Bundle sheath cells Polyphenols and tannins + + + + + - + - + - + + Lignin + + Symptomatic tissues Polyphenols, tannin + + Figure 4.2: e-l; n.a. Special Figure 4.3: b-d Mucilage + + structures Pectic substances + +

Emergent oil gland Essential oils + + + Figure 4.3 Mucilage + + + 310

311 Secondary metabolite profiles

312 Chromatographic analysis (GC-MS) of hexane extracts of CCV leaves allowed the detection

313 of 24 compounds representing 92-96% of the total oil content depending on the treatment

314 (Table 4.2). The secondary metabolite profiles of plants inoculated with A. psidii was

315 statistically different compared to controls and Q. pitereka plants (ANOSIM R = 0.187, P =

316 0.002, Figure 4.4).

317 The Kruskal-Wallis test (Table 4.2) indicated significant differences between treatments in the

318 proportion of sesquiterpene 1 (H2 = 8.684, n = 34, P = 0.013), steroid 2 (H2 = 6.011, n = 34, P

319 = 0.050), steroid 4 (H2 = 6.187, n = 2, P = 0.045), steroid 8 (H2 = 7.250, n = 2, P = 0.027), long

320 chain hydrocarbon 3 (H2 = 7.836, n = 2, P = 0.020), long chain hydrocarbon 4 (H2 = 13.545, n

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Chapter 4

321 = 2, P = 0.001) and long chain hydrocarbon 6 (H2 = 6.528, n = 2, P = 0.038), but not for the

322 other compounds (H2 = 0.116-5.884, n = 34, P = 0.053-0.944). The SIMPER analyses (Table

323 4.3) detected a 23.56% dissimilarity between control and A. psidii plants, while there was a

324 20.15% dissimilarity between Q. pitereka and A. psidii.

325

326 Table 4.2 Comparison of leaf secondary metabolites of Corymbia citriodora subsp. variegata between treatments: 327 uninoculated controls, inoculated with Quambalaria pitereka and Austropuccinia psidii. For each compound the 328 back-transformed mean ± s.e. relative area are shown within rows, with different lowercase letters designating 329 significant differences between treatments, following Kruskal-Wallis test with pairwise comparisons. Kovats Control Quambalaria Austropuccinia Ret Time Compound Index n = 12 pitereka n = 12 psidii n = 10 4.67 933 Monoterpene 1 2.49 ± 0.36 2.66 ± 0.33 1.70 ± 0.40 5.38 976 Monoterpene 2 0.54 ± 0.07 0.54 ± 0.05 0.36 ± 0.09 13.11 1530 Sesquiterpene 1 0.53 ± 0.15 a 0.35 ± 0.09 ab 0.09 ± 0.08 b 14.07 1614 Monounsaturated hydrocarbon 1 10.98 ± 3.15 5.62 ± 1.47 2.84 ± 1.59 15.61 1757 Monounsaturated hydrocarbon 2 4.48 ± 0.84 4.19 ± 0.78 3.27 ± 0.57 16.52 1847 Monounsaturated hydrocarbon 3 1.13 ± 0.28 1.10 ± 0.18 0.93 ± 0.21 21.13 2364 Steroid 1 0.46 ± 0.09 0.39 ± 0.06 0.55 ± 0.06 21.26 2380 Steroid 2 0.64 ± 0.10 0.44 ± 0.07 0.66 ± 0.06 21.29 2384 Steroid 3 0.79 ± 0.12 0.52 ± 0.09 0.58 ± 0.07 21.65 2429 Steroid 4 3.27 ± 0.48 2.13 ± 0.27 2.98 ± 0.30 21.8 2448 Steroid 5 2.94 ± 0.43 1.86 ± 0.22 2.55 ± 0.28 22.13 2490 Steroid 6 0.75 ± 0.06 0.72 ± 0.06 0.86 ± 0.16 23.16 2631 Steroid 7 0.35 ± 0.11 0.35 ± 0.08 0.74 ± 0.19 23.6 2693 Steroid 8 0.22 ± 0.22 a 0.30 ± 0.16 ab 1.32 ± 0.40 b 23.62 2696 Steroid 9 2.66 ± 0.44 3.18 ± 0.28 1.67 ± 0.65 23.93 2739 Steroid 10 0.00 ± 0.00 0.48 ± 0.35 0.54 ± 0.54 24.34 2797 Steroid 11 0.69 ± 0.17 1.08 ± 0.15 0.77 ± 0.28 24.64 2835 Steroid 12 11.81 ± 1.54 13.42 ± 1.11 13.25 ± 1.51 25.1 2891 Long chain hydrocarbon 1 2.27 ± 0.49 2.01 ± 0.37 2.41 ± 0.67 25.17 2900 Long chain hydrocarbon 2 13.04 ± 1.26 14.47 ± 0.98 10.20 ± 1.38 26.58 3030 Long chain hydrocarbon 3 19.80 ± 2.14 a 22.64 ± 1.43 ab 28.15 ± 1.64 b 27.06 3066 Long chain hydrocarbon 4 0.00 ± 0.00 a 0.00 ± 0.00 a 1.14 ± 0.51 b 27.37 Long chain hydrocarbon 5 5.30 ± 0.70 7.00 ± 0.65 6.81 ± 1.04 29.52 Long chain hydrocarbon 6 6.43 ± 2.15a 8.33 ± 1.31ab 11.96 ± 1.77b 330

331

107

Chapter 4 332 Table 4.3 Comparison of compounds (back-transformed mean ± s.e. relative area) used to differentiate Corymbia 333 citriodora subsp. variegata inoculated with Austropuccinia psidii from controls and Quambalaria pitereka plants. % Contribution to group Mean % area dissimilarity RT Substance Control × A. psidii × Control Q. pitereka A. psidii A. psidii Q. pitereka 4.67 Monoterpene 1 2.49 ± 0.36 2.66 ± 0.33 1.70 ± 0.40 - 4.09 14.07 Monounsaturated hydrocarbon 1 10.98 ± 3.15 5.62 ± 1.47 2.84 ± 1.59 12.59 9.76 15.61 Monounsaturated hydrocarbon 2 4.48 ± 0.84 4.19 ± 0.78 3.27 ± 0.57 4.52 4.18 23.6 Steroid 8 0.22 ± 0.22 0.30 ± 0.16 1.32 ± 0.40 5.23 5.78 23.62 Steroid 9 2.66 ± 0.44 3.18 ± 0.28 1.67 ± 0.65 6.35 7.52 24.34 Steroid 11 0.69 ± 0.17 1.08 ± 0.15 0.77 ± 0.28 3.85 4.63 24.64 Steroid 12 11.81 ± 1.54 13.42 ± 1.11 13.25 ± 1.51 4.61 4.41 25.1 Long chain hydrocarbon 1 2.27 ± 0.49 2.01 ± 0.37 2.41 ± 0.67 4.03 4.58 25.17 Long chain hydrocarbon 2 13.04 ± 1.26 14.47 ± 0.98 10.20 ± 1.38 4.75 5.84 26.58 Long chain hydrocarbon 3 19.80 ± 2.14 22.64 ± 1.43 28.15 ± 1.64 6.44 5.15 27.06 Long chain hydrocarbon 4 0.00 ± 0.00 0.00 ± 0.00 1.14 ± 0.51 4.26 4.9 27.37 Long chain hydrocarbon 5 5.30 ± 0.70 7.00 ± 0.65 6.81 ± 1.04 4.02 4.38 29.52 Long chain hydrocarbon 6 6.43 ± 2.15 8.33 ± 1.31 11.96 ± 1.77 9.79 7.27 334

335 336 Figure 4.4 Two-dimensional nMDS ordination of the Corymbia citriodora subsp. variegata extracts inoculated 337 with Quambalaria pitereka and Austropuccinia psidii. The plots are based on square root transformed abundances 338 and a Bray-Curtis similarity matrix. Extracts from A. psidii treatments tend to cluster separately. Symbols: (circle) 339 control (cross) Q. pitereka (square) A. psidii. (ANOSIM, P < 0.05) 340

341 Leaf morphology

342 There was no significant difference in leaf thickness or the size and density of palisade

343 parenchyma cells between treatments (Kruskal-Wallis: H2 = 1.654 - 5.206, n = mean 12, P =

344 0.074 - 0.437; data shown in Appendix A - Figure 4.S1 and 4.S2). The proportions of leaf area

108

Chapter 4 345 occupied by different tissues or cell types (upper and lower epidermis, palisade and spongy

346 parenchyma, bundle sheath cells, airspaces) was not significantly different between the

347 treatments (Kruskal-Wallis: H2 = < 0.001 - 5.115, n = mean 12, P = 0.077 - 1.000; data shown

348 in Appendix A - Figure 4.S3). There was a significant difference in leaf toughness between

349 treatments (Figure 4.5) with greater leaf toughness in plants inoculated with A. psidii than

350 plants inoculated with Q. pitereka.

351

5.00 H2 = 13.528, n = 28 P = 0.001 b 4.50 4.00 ab 3.50 a 3.00 2.50 2.00 1.50 1.00

Leaf toughness (mg cm⁻²) (mg toughness Leaf 0.50 0.00 Control Quambalaria Austropuccinia 352 pitereka psidii 353 Figure 4.5 Comparison of leaf toughness (mg cm⁻²) mean ± standard errors of Corymbia citriodora subsp. 354 variegata between treatments: uninoculated controls, inoculated with Quambalaria pitereka and inoculated with 355 Austropuccinia psidii. Different lowercase letters designate significant differences in leaf toughness (Kruskal- 356 Wallis, P < 0.05). 357

358 Discussion

359 CCV resistance to Q. pitereka involves pathogen-specific response mechanisms arising from

360 ongoing coevolutionary processes underlying host-pathogen interaction, thus, pathogen

361 recognition occurs through specific R-genes leading to hypersensitive reaction (Freeman et al.

362 2019; Butler et al. 2019). In contrast, as a naïve host, CCV lacks adaptation to A. psidii,

363 showing indirect pathogen recognition and a nonspecific resistance mechanism (Butler et al.

364 2019). Due to the lack of genetic correlation underlying CCV resistance to these foliar

365 pathogens and the different mechanisms of host-pathogen recognition, changes in leaf

366 phenotypic parameters in response to these coevolved and exotic pathogens are likely to be

109

Chapter 4 367 distinct (Freeman et al. 2019; Butler et al. 2019). This study demonstrated that, as predicted,

368 CCV plants inoculated with Q. pitereka and A. psidii expressed different responses in terms of

369 leaf chemistry and leaf anatomy. These phenotypic changes may be associated with a plant

370 defence mechanism to hinder pathogen development, or they may simply represent a direct

371 consequence of pathogen infection.

372 Histochemical analysis suggested a greater variability in the distribution of polyphenols and

373 tannins with the leaves of plants inoculated with Q. pitereka than plants inoculated with A.

374 psidii and control plants. Plant polyphenols include several compound classes, such as tannins

375 and lignins, which play a role in plant defence against fungal pathogens (Lattanzio et al. 2006;

376 Witzell and Martín 2008). They can be pre-formed (constitutive) in healthy tissues of plants or

377 induced in response to infection, possibly reducing fungal penetration, spore germination and

378 protecting the plant against cell wall degradation (Lattanzio et al. 2006; Ganthaler et al. 2017).

379 Under pathogen attack, plants may accumulate polyphenols to symptomatic and asymptomatic

380 tissues (Pusztahelyi et al. 2015; Ganthaler et al. 2017). Plants may also oxidase pre-formed

381 polyphenols into antifungal compounds (Lattanzio et al. 2006) and/or activate phytochemicals

382 responsible for cell-wall lignification, enhancing mechanical barriers (Mandal and Mitra 2007;

383 Smith et al. 2006, 2007).

384 The enhancement of polyphenols and tannins concentration in upper/lower epidermis and

385 bundle sheath observed in the histochemical analyses of asymptomatic sections of leaves of A.

386 psidii inoculated samples (Figure 4.1 i - l) may have occurred in response to infection, as plants

387 attempted to prevent fungal penetration and/or spread throughout host tissue (Lattanzio et al.

388 2006). In contrast, the absence/reduction of these substances in some of asymptomatic sections

389 of leaves of the Q. pitereka inoculated samples (Figure 4.1 f, h) was perhaps associated with a

390 plant accumulation of polyphenols and tannins in symptomatic tissues (Pusztahelyi et al. 2015),

391 oxidation of polyphenols and tannins into antifungal compounds (Lattanzio et al. 2006),

110

Chapter 4 392 metabolization of these compounds by the pathogen (Hammerbacher et al. 2013) or with the

393 pathogen infection mechanism through stomata (Pegg et al. 2009).

394 Roots and shoots of E. grandis and E. globulus infected by Botrytis cinerea and/or treated with

395 the biological control agent Streptomyces sp. induced phenolic compounds and stimulated

396 enzymatic activities, which were related to the oxidation of polyphenols into antimicrobial

397 compounds, and associated with a hypersensitive response (Salla et al. 2016). Phenolic

398 compounds also varied in needles of Picea abies (Norway spruce) infected by the rust pathogen

399 Chrysomyxa rhododendri. In general, C. rhododendri infection increased the concentrations of

400 stilbenes, picein, shikimic acid, and flavonoids, which may have influenced the infection

401 intensity, possibly restricting and challenging fungal development (Ganthaler et al. 2017).

402 Although the effect of tannins in plant defence has mainly been tested on plant-herbivore

403 interactions, some studies report potential antifungal effects of tannins in plants (e.g. Scalbert

404 1991; Witzell and Martín 2008). For instance, the incidence of shoot blight caused by the

405 pathogen Venturia moreletii in Populus tremuloides was positively correlated with phenolic

406 glycosides and negatively correlated with constitutive foliar concentrations of condensed

407 tannins (Holeski et al. 2019). Tannins are water soluble polyphenols, with high levels of

408 toxicity, possibly inhibiting extracellular fungal enzymes and reducing leaf nutritional levels.

409 Their oxidation may be related to the formation of necrotic cells after pathogen infection

410 (Lattanzio et al. 2006).

411 Symptomatic tissues of leaves inoculated with Q. pitereka (Figure 4.2e - h) and A. psidii

412 (Figure 4.2i - l) compared with asymptomatic tissues of inoculated plants and uninoculated

413 controls indicated an accumulation of pectic substances (associated with hyphal growth),

414 lignin, polyphenols and tannins. Eucalyptus grandis and E. nitens infected by the fungal

415 pathogen Teratosphaeria spp. were also found to accumulate lignin and other polyphenols in

416 the necrotic tissues (Smith et al. 2007). During pathogen infection, pectins are one of the first

111

Chapter 4 417 compounds to suffer degradation or to increase/decrease their levels, which may signal the

418 presence of cell wall damage (Pogorelko et al. 2013). These signalling compounds elicit

419 biological responses related with constitutive and induced defences against pathogen infection,

420 such as accumulation of phenolics, production of compounds that inhibit pathogen enzymes

421 and activation of cell wall strengthening mechanisms through the deposition of lignin and other

422 compounds (Pogorelko et al. 2013; Miedes et al. 2014). The lignification of infected cells

423 possibly hinders the spread of pathogenic toxins and enzymes, their access to water and

424 nutrients of the host (Miedes et al. 2014) and reduces plant water loss (Smith et al. 2007).

425 As previously described by Carr and Carr (1970) and Ladiges (1984), juvenile leaves of CCV

426 exhibited emergent oil glands (Figure 4.3). In adult leaves of CCV, the oil glands are not

427 emergent, being distributed within mesophyll parenchyma (Nahrung et al. 2012), like oil glands

428 observed in other eucalypts (Smith et al. 2006, 2007; Young et al. 2019b). In this study, the

429 appearance of emergent oil glands did not vary between treatments, remaining intact during

430 pathogen infection, with no sign of compound release. The intact oil glands suggest that these

431 structures do not play a role as a physical or chemical barrier during Q. pitereka or A. psidii

432 infection. Oil glands store essential oils, releasing these compounds when plants are under

433 biotic and abiotic damage (Boland et al. 1991). Emergent oil glands occur along with leaf

434 trichomes, and due to the elongated characteristic, both structures may play similar roles in

435 plant defence, being a physical barrier against enemies, especially herbivores (Ladiges 1984).

436 However, trichome density (including trichomes and emergent oil glands) did not influence

437 Paropsis atomaria larval feeding in CCV, as larvae consumed the entire leaf surface, including

438 the compounds inside the oil glands (Chapter 2).

439 In this study, the relative abundance of 13 individual compounds (one monoterpene, two

440 monounsaturated hydrocarbons, four steroids and six long chain hydrocarbons) found in plants

441 inoculated with A. psidii were significantly different to uninoculated controls and plants

112

Chapter 4 442 inoculated with Q. pitereka. The fact that Q. pitereka is a coevolved pathogen and only grows

443 intercellularly (Pegg et al. 2009) may explain the lack of alteration in secondary metabolites of

444 plants inoculated with this pathogen. In contrast, A. psidii is an exotic pathogen that grows

445 inter and intracellularly (Hunt 1968; Xavier et al. 2001, 2015), which may have contributed to

446 the alteration of secondary metabolites, as CCV plants lack specific defence mechanisms for

447 this pathogen (Freeman et al 2019; Butler et al. 2019). Previous studies also found that CCV

448 severely infected by A. psidii altered leaf secondary metabolites in comparisson to controls and

449 resistant plants, increasing the proportion of long chain hydrocarbons and reducing relative

450 abundance of monoterpenes and monounsaturated hydrocarbons (Bonora et al. 2020; Chapter

451 3).

452 The lower relative abundance of one monoterpene and one sesquiterpene in plants inoculated

453 with A. psidii in comparison with the other treatments is possibly associated with a plant

454 response to the exotic pathogen infection. For instance, the genes controlling terpene syntheses

455 in CCV, E. grandis and E. globulus are numerous and highly conserved, which may reflect an

456 adaptation to environmental changes that allow eucalypts to quickly respond to stressors, such

457 as pathogen infection (Butler et al. 2019). Additionally, terpenes were reported as possible

458 biomarkers in determining susceptibility of eucalypts to A. psidii (Hantao et al. 2013, Potts et

459 al. 2016; Yong et al. 2019c). However, the compounds predicting A. psidii susceptibility vary

460 within eucalypts, and terpenes were suggested to be species-specific biomarkers for

461 susceptibility to this pathogen (Yong et al. 2019c).

462 In addition, the differences in relative abundance of leaf waxes (monounsaturated

463 hydrocarbons and long chain hydrocarbons) in plants inoculated with A. psidii in relation to

464 controls and Q. pitereka inoculated plants corroborates with the assumption that plants respond

465 differently to exotic and coevolved pathogens (Bonora et al. 2020; Chapter 3). Leaf waxes are

466 essential to plant survival, performing UV protection, water and gas regulation and protection

113

Chapter 4 467 to pests and pathogens (Gosney et al. 2016, 2017; dos Santos et al. 2019). The presence of

468 epicuticular waxes on leaves of E. urophylla × E. grandis, were associated with resistance to

469 A. psidii (Silva et al. 2017). However, these compounds in E. grandis and E. phaeotricha were

470 also found to stimulate fungal growth (dos Santos et al. 2019) and cuticular waxes in E. grandis

471 and E. globulus were not associated with a physical barrier to A. psidii (Yong et al. 2019b). As

472 CCV is highly susceptible to A. psidii (Pegg et al. 2014), the variation of waxes in response to

473 inoculation observed in this study may be stimulating pathogen growth. However, due to the

474 complexity of plant responses to this pathogen, further studies are necessary.

475 Steroids also form part of the cuticular wax composition of some eucalypts (dos Santos et al.

476 2019), therefore, the higher relative abundance of four steroids in plants inoculated with A.

477 psidii may be a response or a consequence of pathogen contact with leaf surface. Plants produce

478 a wide range of steroids, some of which are a vital component of cell membranes, with

479 metabolic and biological functions, that may include antifungal activities. In vitro tests with

480 the plant steroids glycoalkaloids and saponins, mainly from agricultural crops, found they

481 influenced fungal pathogen development, inhibiting or stimulating fungal growth (Roddick

482 1987). However, in plant metabolism, they are not crucial factors in the resistance response,

483 being more important in reproductive structures and only contributing to general defences in

484 plants (Roddick 1987).

485 Despite the lack of difference in the internal architecture of asymptomatic tissues of CCV

486 between treatments, leaf toughness was higher in leaves inoculated with A. psidii relative to

487 the control and Q. pitereka treatment. Plants inoculated with Q. pitereka did not differ in leaf

488 toughness in comparison to control plants. A previous study found greater leaf toughness in

489 plants severely infected with Q. pitereka in comparison to resistant and uninoculated plants

490 (Bonora et al. 2020; Chapter 3). Alteration in leaf structure was only observed in symptomatic

114

Chapter 4 491 tissues of inoculated leaves, with cell deformation and cell collapse due to pathogen infection

492 (Figure 4.2 e - l).

493 Increasing leaf toughness is possibly a result of a reduction in air space, increasing cell density

494 and leaf thickness, and/or cell lignification (Steinbauer 2001; Nahrung et al. 2012; Smith et al.

495 2006, 2007, 2018). However, none of these alterations occurred in leaves inoculated with A.

496 psidii. The greater leaf toughness observed in leaves inoculated with A. psidii could be a result

497 of the considerable cell collapse observed in symptomatic tissues of inoculated leaves, or due

498 to cell lignification that occurred in necrotic tissues (Fig 4.2i - l).

499 In conclusion, plant responses to Q. pitereka inoculation were more specific, involving only

500 the alteration of polyphenols and tannins throughout the leaf surface, while plant responses to

501 A. psidii infection were more general, involving alteration in a variety of chemical parameters

502 and increased leaf toughness. This result corroborates previous findings that CCV resistance to

503 Q. pitereka is regulated by specific resistance genes and quantitative trait loci of large effect

504 (Butler et al. 2019), while resistance to A. psidii is regulated by multiple genes (Alves et al.

505 2012; Thumma et al. 2013; Butler et al. 2016, 2019). These differences in genetic expression

506 of resistance genes are possibly moderating the specific and general phenotypic responses to

507 native and exotic pathogen, respectively. However, the variability in phenotypic parameters in

508 response to the exotic pathogen was not effective or related to plant resistance, as only 16% of

509 the plants were resistant to A. psidii, contrasting with 52% resistant to Q. pitereka. Additionally,

510 the responses observed here were possibly a direct consequence of pathogen infection and not

511 a plant defence mechanism. Altogether, these results improve the knowledge of plant-pathogen

512 interaction, which may explain the variability in CCV responses to coevolved and exotic

513 pathogens.

514

115

Chapter 4 515 Acknowledgements

516 Histochemical data reported in this paper were obtained at the Central Analytical Research

517 Facility operated by the Institute for Future Environments at Queensland University of

518 Technology. The authors thank Felicity Lawrence, Tamara Sijkova for assisting with

519 histochemical analyses at the Histology Laboratory-Central Analytical Research Facility,

520 operated by the Institute for Future Environments at Queensland University of Technology.

521 Many thanks to Julia Woerner, Emily Lancaster, Louise Shuey and Renata Grunennvaldt for

522 support with glasshouse and laboratory work associated with this study, and Andrew Manners

523 for access to compound microscope and assistance. We are grateful to the Department of

524 Agriculture and Fisheries for provision of facilities. This work was supported by University of

525 the Sunshine Coast International Research Scholarship and by the HDR Research Excellence

526 Scheme grant awarded to FSB. HFN was funded by an Advance Queensland Fellowship

527 through the Queensland Department of Innovation and Tourism Industry Development

528 supported by the University of the Sunshine Coast, Department of Agriculture and Fisheries,

529 Forest and Wood Products Australia, HQ Plantations Pty Ltd, Plant Health Australia and the

530 National Sirex Coordination Committee.

531

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Chapter 4 677 Scalbert A (1991). Antimicrobial properties of tannins. Phytochemistry. 30:3875-3883. 678 doi:https://doi.org/10.1016/0031-9422(91)83426-L. 679 Shepherd M, Kasem S, Ablett G, Ochieng J, Crawford A (2008). Genetic structuring in the 680 spotted gum complex (genus Corymbia, section Politaria). Australian systematic 681 botany. 21:15-25. doi:https://doi.org/10.1071/SB07028. 682 Silva RR, da Silva AC, Rodella RA, Serrao JE, Zanuncio JC, Furtado EL (2017). Pre- 683 infection stages of Austropuccinia psidii in the epidermis of Eucalyptus hybrid leaves 684 with different resistance levels. Forests. 8:12. doi:https://doi.org/10.3390/f8100362. 685 Smith AH, Pinkard EA, Hunter GC, Wingfield MJ, Mohammed CL (2006). Anatomical 686 variation and defence responses of juvenile Eucalyptus nitens leaves to 687 Mycosphaerella leaf disease. Australasian Plant Pathology. 35:725-731. 688 doi:https://doi.org/10.1071/ap06070. 689 Smith AH, Gill WM, Pinkard EA, Mohammed CL (2007). Anatomical and histochemical 690 defence responses induced in juvenile leaves of Eucalyptus globulus and Eucalyptus 691 nitens by Mycosphaerella infection. Forest Pathology. 37:361-373. 692 doi:https://doi.org/10.1111/j.1439-0329.2007.00502.x. 693 Smith AH, Potts BM, Ratkowsky DA, Pinkard EA, Mohammed CL (2018). Association of 694 Eucalyptus globulus leaf anatomy with susceptibility to Teratosphaeria leaf disease. 695 Forest Pathology. 48:10. doi:https://doi.org/10.1111/efp.12395. 696 Soewarto J, Giblin F, Carnegie AJ (2019). Austropuccinia psidii (myrtle rust) global host list. 697 Version 4. Australian Network for Plant Conservation. 698 http://www.anpc.asn.au/myrtle-rust. 699 Soukup A (2014). Selected simple methods of plant cell wall histochemistry and staining for 700 light microscopy. Methods in molecular biology (Clifton, NJ). 1080:25. 701 doi:https://doi.org/10.1007/978-1-62703-643-6_2. 702 Steinbauer MJ (2001). Specific leaf weight as an indicator of juvenile leaf toughness in 703 Tasmanian bluegum (Eucalyptus globulus ssp. globulus): implications for insect 704 defoliation. Australian Forestry. 64:32-37. 705 doi:https://doi.org/10.1080/00049158.2001.10676158. 706 Thumma B, Pegg GS, Warburton P, Brawner J, MacDonell P, Yang X, Southernton S (2013). 707 Molecular tagging of rust resistance genes in eucalypts. Plant Health Australia, 708 Canberra. 709 Winzer LF, Carnegie AJ, Pegg GS, Leishman MR (2018). Impacts of the invasive fungus 710 Austropuccinia psidii (myrtle rust) on three Australian Myrtaceae species of coastal 711 swamp woodland. Austral Ecology. 43:56-68. doi:https://doi.org/10.1111/aec.12534. 712 Witzell J, Martin J (2008). Phenolic metabolites in the resistance of northern forest trees to 713 pathogens - past experiences and future prospects. Canadian Journal Of Forest 714 Research-Revue Canadienne De Recherche Forestier. 38:2711-2727. 715 doi:https://doi.org/10.1139/X08-112. 716 Xavier AA, Alfenas AC, Matsuoka K, Hodges CS (2001). Infection of resistant and 717 susceptible Eucalyptus grandis genotypes by urediniospores of Puccinia psidii. 718 Australasian Plant Pathology. 30:277-281. doi:https://doi.org/10.1071/ap01038. 719 Xavier AA, da Silva AC, Guimaraes LMD, Matsuoka K, Hodges CS, Alfenas AC (2015). 720 Infection process of Puccinia psidii in Eucalyptus grandis leaves of different ages.

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Chapter 5 1 Chapter 5 Spotted gums and hybrids: impact of pests and diseases,

2 ontogeny and climate on tree performance

4 Authors: Flávia Sarti Bonoraa, Richard Andrew Hayesa, Helen F Nahrunga, David John

5 Leea

6 Affiliations: aForest Industries Research Centre, University of the Sunshine Coast - 90 Sippy

7 Downs Dr, Sippy Downs QLD 4556, Australia

9 *Corresponding author: [email protected] +610426163647

10 Journal: Forest Ecology and Management (Elsevier)

11 Impact factor: 3.126

122

Chapter 5 13 Statement of Intellectual Contribution

15 I, Flávia Sarti Bonora declare that I have made substantial intellectual contribution (65%) to

16 the original research manuscript ‘Spotted gums and hybrids: impact of pests and diseases,

17 ontogeny and climate on tree performance’ by F.S. Bonora, R.A. Hayes, H.F. Nahrung, and

18 D.J. Lee, published in the journal Forest Ecology and Management (in press).

123

Chapter 5 21 Abstract

22 The increased threat of pests and pathogens in plantations has strengthened the need to

23 understand the factors underlying their occurrence, such as climate and tree ontogeny, and the

24 effect of these pests and diseases on plant performance. Spotted gums (Corymbia citriodora

25 and C. henryi), C. torelliana, and their hybrids are important commercial plantation taxa,

26 because they are suited to a range of environments where traditional commercial eucalypts

27 grow poorly. They have desirable field performance and can produce a range of timber and

28 fibre products. This study investigated field performance, ontogenetic shifts from juvenile to

29 intermediate leaves, and damage by pests and pathogens in pure spotted gum taxa, including

30 C. citriodora subsp. citriodora (CCC), C. citriodora subsp. variegata (CCV) and C. henryi

31 (CH), C. torelliana (CT), and their hybrids over a two year period. The study revealed that

32 higher mean daily rainfall and maximum/minimum temperatures positively influenced tree

33 performance but were also associated with higher levels of defoliation by generalist insects.

34 Necrosis associated with Quambalaria pitereka was not correlated with climatic conditions in

35 most of the taxa. Growth rate and defoliation were positively correlated, while flushing and

36 damage parameters (defoliation and leaf necrosis) were often negatively correlated.

37 Ontogenetic shifts were variable between taxa, occurring earlier in pure spotted gum taxa than

38 in hybrids. The only environmental factor related to the transition from juvenile to intermediate

39 leaves was mean daily rainfall, suggesting that ontogenetic shifts may be associated with an

40 adaptation to water availability. Overall, CCC and CT × CCC displayed the best combination

41 of performance and resistance to pests and pathogens. Severe defoliation was associated with

42 reduced diameter at breast height over bark (DBH) in CCV and CH. Tree interactions with

43 damage and climate parameters were more consistent in the pure species than in the hybrids.

44 These results add important knowledge on plant interaction with biotic and abiotic stress,

124

Chapter 5 45 assisting with strategies of pests and pathogen management and selection of the most

46 appropriate taxa for pest and disease prone sites in southern Queensland.

48 Keywords: Corymbia, defoliation, necrosis, heteroblastic, ontogeny, growth rate, eucalypt

49 plantation

125

Chapter 5 51 Introduction

52 The worldwide expansion of eucalypt plantations has resulted in a demand for taxa suitable for

53 locations previously considered marginal for plantations. These plantations also need

54 tolerance/resistance to pests and pathogens, while preserving productivity and market

55 competitiveness (Gonçalves et al. 2013; Ferraz-Filho et al. 2014; Rhodes and Stephens 2015;

56 Payn et al. 2015). Spotted gums (genus Corymbia, section Maculatae) and hybrids between

57 spotted gums and Corymbia torelliana (CT) are promising for commercial plantations due to

58 their suitability to a vast range of site conditions, including shallow soils and dry environments

59 where traditional eucalypts grow poorly (Gardner et al. 2007; Lee 2007; Lee et al. 2009, 2010).

60 They also present desirable field performance, wood quality, and diverse utilization such as

61 timber, pulp and paper, biomass and charcoal production (Lee et al. 2009; 2010; Booth et al.

62 2014; Segura and Silva 2016; Loureiro et al. 2019; Peres et al. 2019). Hardwood plantations of

63 spotted gums and/or hybrids with CT have been developed in Australia (Lee 2007, Lee et al.

64 2010), South Africa (Gardner et al. 2007), Brazil (Alfenas et al. 2016; Cunha et al. 2019) and

65 Asia (Zhou and Wingfield 2011; Varshney et al. 2012; Chen et al. 2017).

66 Different levels of pest and pathogen susceptibility have been found within and between

67 spotted gums and hybrids (Lawson and McDonald 2005; Nahrung et al. 2009; Brawner et al.

68 2011; Pegg et al. 2011a, b; Pegg et al. 2014; Nahrung et al. 2014). For instance, susceptibility

69 to eriophyid mites (Rhombacus sp.) was higher in C. citriodora subsp. variegata (CCV) than

70 CT and CT × CCV (Lawson and McDonald 2005). CT was highly susceptible to red shouldered

71 leaf beetle (Monolepta australis) attack, while pure spotted gum taxa and hybrids were not

72 attacked (Lawson and McDonald 2005). Damage by the leaf beetle (Paropsis atomaria) was

73 higher in CCV than in CT, and hybrids had intermediate susceptibility (Nahrung et al. 2009).

74 In addition, a wide variation in resistance to the fungal pathogens Quambalaria pitereka and

126

Chapter 5 75 Austropuccinia psidii were found in spotted gum pure taxa and hybrids in glasshouse and field

76 studies (Brawner et al. 2011; Pegg et al. 2011a, b; Pegg et al. 2014).

77 Corymbia spp., like other eucalypts, are evergreen and predominantly heteroblastic, exhibiting

78 marked differences in leaf morphology, anatomy and chemistry during leaf development and

79 ontogeny (Steinbauer et al. 2002, 2004; Zotz et al. 2011). The ontogenetic stages of eucalypt

80 foliar development comprise seedling, juvenile, intermediate and mature/adult leaves

81 (Lawrence et al. 2003). Generally, the transition from seedling to adult leaves involves various

82 morphological changes ranging from ovate to lanceolate leaf shape, waxy to glabrous leaf

83 surface, opposite to alternate attachment, and changes of petiole attachment to the leaves

84 (Steinbauer et al. 2002, 2004; Lawrence et al. 2003).

85 The ontogenetic variation of leaf morphology and the timing of transition from juvenile to adult

86 stages may be associated with an adaptation to the different biotic and abiotic pressures acting

87 on plants as they grow and develop (Jordan et al. 2000; James and Bell 2001; Boege 2005a, b;

88 Boege and Maquis 2005; Zotz et al. 2011). For instance, water stress may influence leaf

89 ontogenetic shifts, as water requirements change during plant development, and changes in leaf

90 shape may be associated with water-use efficiency and hydraulic adaptation (James and Bell

91 2001; Zotz et al. 2011; Vlasveld et al. 2018; Lucani et al. 2019). In addition, the timing of

92 transition from juvenile to adult stages in E. globulus populations were associated with plant

93 growth and environmental conditions, with populations from warm and wet environments

94 having earlier transition and higher growth rates (Jordan et al. 2000). This pattern may also be

95 associated with higher biotic pressure, depending on environmental conditions and with plant

96 strategies/mechanisms, to minimize and escape damage (Jordan et al. 2000; Lan et al. 2011;

97 Brawner et al. 2011).

98 The differences in leaf morphology, defence traits and resource quality that occur during plant

99 development may influence plant interactions with pests and pathogens (Paine et al. 2011;

100 Lawrence 2003). The expression of leaf compounds with toxic effects, such as terpenes and

127

Chapter 5 101 cyanogenic glycosides, are genetically regulated during ontogenetic trajectories in eucalypts,

102 potentially affecting plant-herbivore interactions (Goodger et al. 2007, 2013; Borzak et al.

103 2015). In E. nitens, differences in leaf structure may have influenced Chrysophtharta agricola

104 (Coleoptera) feeding preference for adult leaves and oviposition on juvenile foliage (Nahrung

105 and Allen 2003; Lawrence et al. 2003). Juvenile leaves of E. globulus were more prone to

106 pathogen occurrence, hosting a wider range of fungal pathogens than adult leaves (Park et al.

107 2000; Sánchez Márquez et al. 2011). Furthermore, E. globulus trees with earlier transition from

108 juvenile to adult leaves were more resistant to leaf blight caused by Teratosphaeria nubilosa,

109 as this pathogen maily infects juvenile foliage (Balmelli et al. 2013b).

110 Young, fast-growing eucalypt plantations are marked by high availability of new, juvenile

111 leaves, which may favour pest and pathogen outbreaks, leading to significant impacts on tree

112 performance (Ohmart 1990; Lawrence et al. 2003; Paine et al. 2011; Borzak et al. 2015;

113 Gherlenda et al. 2016). High levels of artificial and insect defoliation, and necrosis caused by

114 Teratosphaeria spp. and Austropuccinia psidii infection compromised growth in newly

115 established plantations of E. globulus (Pinkard et al. 2006a b; Rapley et al. 2009; Balmeli et al.

116 2013a; Elek and Baker 2017). Severe T. nubilosa outbreaks in young plantations of E. globulus

117 had a significant impact on tree performance approximately one year after outbreak, however,

118 trees had recovered five years after the fungal attack (Smith et al. 2017). Similarly, severe

119 damage by Mnesampela privata (autumn gum moth, Lepidoptera: Geometridae) feeding and

120 artificial defoliation of E. globulus and E nitens resulted in significant reduction of growth in

121 the early years of plantation establishment (2 - 6 years), with tree growth rates recovering

122 several years after damage (Rapley et al. 2009; Elek and Baker 2017). Even though these

123 studies suggest trees can recover after pest and pathogen attack, damage may reduce timber

124 quality due to branching and loss of apical dominance (Pinkard et al. 2006a, b; Smith et al.

125 2017). Moreover, high levels of tree damage during early developmental stages may

128

Chapter 5 126 compromise eucalypt plantations grown on short rotations (Wingfield et al. 2008, 2013;

127 Gonçalves et al. 2013; Elek and Baker 2017).

128 Hence, understanding the effects of biotic and abiotic factors on tree performance over time is

129 important to determine the potential loss in productivity, develop management strategies (e.g.

130 selection of the most appropriate taxa and tree rotation) to ensure plantation success (Eyles et

131 al. 2013), and determine the consistence of the assessments in detecting patterns of variation

132 in these types of traits. Given the commercial interest in Corymbia taxa and hybrids, and their

133 adaptation and potential to be grown in plantations, in environments that are unsuitable for

134 many other eucalypts, this study investigated tree performance and damage by pests and

135 pathogens in pure spotted gum taxa, including CCV, C. citriodora subsp. citriodora (CCC) and

136 C. henryi (CH), CT and their hybrids during the first two years after planting. Assessments

137 included: growth rate, diameter, canopy flushing and tree survival, damage (pest and pathogen

138 severity) and ontogeny (leaf transition from seedling to juvenile and intermediate stages).

139 These were compared between taxa and correlated with climate data for the site.

140

141 Methods

142 Plant material and study site

143 A field trial was conducted between April 2017 and March 2019 at the Department of

144 Agriculture and Fisheries (DAF) Mary Valley Research Facility at Traveston (26°20' S 152°42'

145 E; Elevation 86 m) to determine: (a) differences in performance and damage by pests and

146 pathogens among seven Corymbia taxa over time; (b) the relationship of leaf ontogeny with

147 performance and pest and pathogen severity; (c) the impact of pests and pathogens on tree

148 performance; and (d) the relationship between climate on tree performance and pest and

149 pathogen severity.

129

Chapter 5 150 Seeds from open pollinated CCV, CCC, CH, CT and control crosses CT × CCV, CT × CCC

151 and CT × CH were selected for this study. Because there is wide variation of resistance to pests

152 and pathogens within provenances and families of spotted gums and hybrids (Brawner et al.

153 2011; Pegg et al. 2011a, b; Pegg et al. 2014), a broad range of families from a range of

154 provenances were used to represent each taxon (25 families across all taxa, Table 5.1). There

155 was a greater representation of CCV due to its commercial importance and to better link this

156 study to related glass house trials (Chapter 2 - 4), and a smaller representation of the hybrid

157 taxa due to availability of seed.

158 Seed of each family was sown in the glasshouse at the DAF facility at Gympie in 10 × 5-celled

159 QNT trays (300 cc) under mist 6 times a day (3 × 10 min and 3× 5 min). The seeds germinated

160 after two weeks and were transferred to 50% shadecloth and watered two times a day for 25

161 min (2 × 25 min) using overhead sprinklers for four months prior to field planting.

162 The field trial with seven treatments (= taxa, see Table 5.1) was established as a randomized

163 complete block design comprising 25 families in each block randomly allocated within each of

164 18 blocks (seven treatments (comprising 25 families) × 18 blocks = 450 trees). Before planting,

165 the field site was deep-ripped and mounded. Seedlings were planted with a spacing of 2 m

166 between trees and 5 m between rows. Each tree received 100g of a slow release fertilizer

167 (Osmocote Pro 5-6 month Controlled Release: N19% - P9% - K10% + 2MgO 2% + Trace

168 elements) six months after planting. The site was prone to weed occurrence. To enhance tree

169 survival, weed control was conducted by careful application of glyphosate (Roundup),

170 following manufacturer recommendations, along the planting lines until ten months after

171 planting, and by slashing the inter-rows and manually removing the weeds from around the

172 base of the trees when necessary.

173 Trees were assessed for performance, foliage ontogeny and damage parameters seven times

174 within 23 months at 2- to 4-month intervals (August and November 2017; February, April,

175 August and November 2018; March 2019). Climate data (daily rainfall, and daily maximum

130

Chapter 5 176 and minimum temperatures) of the site was obtained from the SILO Climate Database

177 (https://www.data.qld.gov.au/dataset/silo-climate-database, accessed on 23th of May 2019) for

178 each assessment interval: 26th April 2017 - 29th August 2017; 30th August 2017 - 13th November

179 2017; 14th November 2017 - 14th February 2018; 15th February 2018 - 27th April 2018; 28th

180 April 2018 - 8th August 2018; 9th August 2018 - 30th November 2018; 1st December 2018 - 11th

181 March 2019 (Figure 5.1). The thermal amplitude was approximately 12 °C during the 23

182 months, with the occurrence of six frost days (temperature < 2 °C; Binkley et al. 2017) during

183 July and August of 2018.

184

185 Table 5.1 List of studied taxa and hybrids. Eighteen plants of each family were used in the field trial, giving a 186 total of 36 – 108 trees per taxon. Taxon Provenance Families Provenance location Mt. Garnet x7484, x7491 18°00′ S, 145°11′ E Corymbia citriodora subsp. citriodora Yeppoon x7502, x7501 23°06′ S, 150°44′ E Brooyar x349, x416 26°10′ S, 152°30′ E C. citriodora subsp. variegata Woondum x517, x520 26°25′ S, 152°81′ E Mt-McEuan x3349, x3344 26°14′ S, 151°39′ E Lockyer x7512, x7507 27°28′ S, 152°17′ E C. henryi Nerang x4065, x4062 27°59′ S, 152°19′ E x169, x168, x1650, C. torelliana Gympie Landrace - x159, x1654 C. torelliana × C. citriodora subsp. citriodora - x1658, cc-002* - C. torelliana × C. citriodora subsp. variegata - x1660, x135 - C. torelliana × C. henryi - x194, x196 - 187 * cc-002 was a spontaneous hybrid with CT; seed was collected from a CCC tree at Gympie.The hybrid nature of 188 this material was verified via leaf chemistry. 189

190

131

Chapter 5 10 35

9 30 8 C 7 25 °

6 20 5 15 Temperature 4

3 Mean daily rainfall daily Mean (mm) 10 2 5 1

0 0

191 192 Figure 5.1 Climatic conditions at Traveston site during the experimental period: columns = mean ± s.e. daily 193 rainfall (mm); line = mean ± s.e. maximum temperature (°C); dashed line = mean ± s.e. minimum temperature 194 (°C). Data obtained from SILO database. 195

196 Assessment

197 The performance parameters assessed included:

198 1. Tree survival - number of trees alive at each assessment as a proportion of trees planted;

199 2. Tree growth rate - tree height was measured using a height pole, with daily growth rate

200 determined by dividing the difference in height between assessments by the number of days

201 in the measurement interval (cm day⁻¹);

202 3. Percentage of flushing foliage - visual estimate of the amount of young, expanding foliage

203 relative to the total crown;

204 4. Predominant foliage type (ontogeny) - visual assessment of seeding, juvenile and

205 intermediate stage foliage;

206 5. DBH - diameter at breast height over bark (only DBH > 0.5 cm) measured in the final

207 assessment.

208

209 The damage parameters assessed per individual tree were:

132

Chapter 5 210 1. Overall damage incidence - visual estimate of the percentage of crown damaged regardless

211 of cause;

212 2. Damage severity - visual estimate of the percentage of damage on each leaf affected,

213 regardless of cause;

214 3. Crown Damage Index (CDI) - calculated by multiplying incidence and severity and

215 dividing by 100 (Stone et al. 2003);

216 4. Defoliation score - visual estimate of defoliation intensity using Stone et al. (2003) rating

217 scale from 0 to 5 (0 = no damage; 1 = very low damage; 2= low damage; 3 = moderate

218 damage; 4 = high damage; 5= very high damage);

219 5. Necrosis score - visual estimate of necrosis using a rating scale from 0 to 5 (0 = no damage;

220 1 = very low damage; 2= low damage; 3 = moderate damage; 4 = high damage; 5= very

221 high damage (Stone et al. 2003).

222 The scores in damage parameters (4) and (5) were used to determine the prevalence (proportion

223 of trees) with low (scores 0 – 2) and high (scores 3 – 5) in each taxon, as well as to calculate

224 mean defoliation and necrosis damage scores.

225

226 Statistical analyses

227 Tree performance

228 The differences in performance parameters among taxa over the seven assessments were tested

229 by ‘Repeated measures’ analysis using residual maximum likelihood (REML) in GenStat V18,

230 with growth rate and flushing percentage as random terms and taxa and time as the fixed terms.

231 Flushing percentage was arcsine square-root transformed to meet the assumption of normality.

232 Differences in DBH between taxa at the end of the experimental period were determined using

233 REML Linear mixed model in GenStat V18, with DBH as a random term and taxon and block

234 as the fixed terms. The relationship of mean necrosis and mean defoliation between DBH

235 measures at the final assessment was analysed using Spearman rank correlations (SPSS - IBM

133

Chapter 5 236 Corp, V 24.0.0.0) for each taxon separately. Tree survival data were plotted in a Kaplan-Meier

237 survival curve to estimate the probability of tree survival within intervals, and curves were

238 compared at the end of the experimental period using two-way chi-square tests.

239

240 Leaf ontogeny

241 The proportion of plants with predominantly juvenile foliage was plotted over time and

242 compared using Chi-square tests (Bonferroni-adjusted, P = 0.002) in GenStat V18, to compare

243 between taxa for the experimental period. Relationships between climate, performance and

244 damage parameters with the proportion of juvenile-foliage dominant were examined using

245 Spearman rank correlations (SPSS - IBM Corp, V 24.0.0.0) for each taxon separately.

246

247 Pest and pathogen damage

248 The differences in damage parameters among taxa over the seven assessments were tested by

249 repeated measures analysis using residual maximum likelihood (REML; GenStat V18), with

250 CDI, defoliation and necrosis as random terms, and taxon and time as fixed terms. CDI was

251 arcsine square root transformed to meet the assumption of normality.

252 Prevalence of damage in each taxon were determined at assessment four (April 2018), when

253 trees had the highest damage levels, by allocating the visual scores for defoliation or necrosis

254 to high (scores of 3-5) or low (scores of 0-2) damage classes. The proportion of trees of each

255 taxon in each category was compared using two-way chi-square tests (Bonferroni-adjusted,

256 P=0.002).

257

258 Correlation matrix

259 To determine the influence of pest and pathogen damage on tree performance for each

260 assessment interval, correlations between defoliation, necrosis, growth rate and flushing

261 percentage were conducted separately for each taxon. Additionally, correlations among climate

134

Chapter 5 262 (mean daily rainfall, mean daily maximum/minimum temperature), performance (mean growth

263 rate and mean flushing percentage) and damage (mean defoliation and mean necrosis)

264 parameters were analysed for each taxon, to determine the strength of the relationship between

265 climate on performance and pest and pathogen damage. Correlations were conducted using the

266 Spearman correlation (SPSS - IBM Corp, V 24.0.0.0).

267

268 Results

269 Performance

270 The repeated measures REML analyses showed a significant difference in growth rate and

271 percentage flushing foliage between taxa over time, with significant taxon × time interactions

272 (Figure 5.2a, b), indicating that each taxon responds differently to the environment through

273 time (pairwise comparisons shown in Appendix B - Table 5.S1).

274 In general, higher growth rates were seen in CCC and CT × CCC, with some

275 overlap/intermediate growth observed in CCV and CH, while the poorest growth rate was

276 observed in CT, CT × CCV and CT × CH. During the hottest and wetter period (from November

277 17 to April 2018, Figure 5.1), all taxa except for CH exhibited similarly high growth rates

278 (Figure 5.2a).

279 Flushing percentage was significantly different between taxa over time (Figure 5.2b). CCC

280 showed the highest and CH the lowest flushing percentage in four assessments. In the

281 assessment of November 2017 all taxa had high flushing percentages.

282 DBH at the final assessment was significantly different between taxa (Figure 5.2c). The largest

283 DBH was seen in CT × CCC and the smallest in CT and CT × CCV, with intermediate DBH

284 in the other taxa. The Kaplan-Meier survival curves (Figure 5.2d) indicated that survival was

285 significantly lower in CT × CH than in the other taxa (pairwise comparisons: test statistic = 5.3

286 - 20.6, P < 0.001 - 0.02), with survival below 55%, while the other taxa ranged from 91 to 76%

135

Chapter 5 287 survival (test statistic = 0.18 - 3.75, P = 0.05 - 0.78). DBH was negatively correlated with the

288 overall defoliation severity in CCV (Spearman rank correlation, rho= -0.24, P = 0.04, n = 7)

289 and CH (Spearman rank correlations, rho= -0.40, P = 0.02, n = 7), but not for the other taxa

290 (Spearman rank correlations, rho= -0.41 - 0.11, P = 0.08 - 0.42, n = 7 each taxon) or with

291 necrosis (Spearman rank correlations, rho= -0.30 - 0.25, P = 0.06 - 0.17, n = 7 each taxon).

292

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(a) 0.9 (b) Repeated measures REML 100 Repeated measures REML 0.8 Taxa: F = 7, P < 0.001 Taxa: F6 = 17, P < 0.001 6 Time: F = 362, P < 0.001 0.7 Time: F6 = 119, P < 0.001 6 Taxa × Time: F = 3, P < 0.001 Taxa × Time: F36 = 4, P < 0.001 80 36 0.6 0.5 60 0.4

0.3 40 % Flushing 0.2 Growth rate (cm day⁻¹) 0.1 20 0.0 -0.1 0 Aug-17 Nov-17 Feb-18 Apr-18 Aug-18 Nov-18 Mar-19 293

(d) 1.0 (c) 3.5 a Linear mixed model REML F6 = 9.86, P < 0.001 a 3.0 0.9 a b bc a 2.5 a c cd 0.8 a 2.0 d a d 1.5 0.7 DBH (cm) Proporition surviving 1.0 0.6 0.5 b 0.0 0.5 CT × CCC CCC CT × CH CCV CH CT CT × CCV 0 20 40 60 80 100 294 Weeks since planting 295 Figure 5.2 Resutlts of performance assessments of each taxon. Comparison between taxon at each assessment interval of (a) growth rate and (b) back transformed flushing percentage. 296 Comparison of (c) diameter at hight breast over bark (DBH) ± s.e.taken at the end of the experiment (age 2), and (d) Plots of Kaplan-Meier curves to estimate taxon survival through 297 time. Symbols: (circle; CCC) Corymbia citriodora subsp. citriodora, (square, CCV) C. citriodora subsp. variegata, (triangle, CH) C. henryi, (×-cross, CT) C. torelliana, hybrids 298 (+-cross, CT × CCC) C. torelliana × C. citriodora subsp. citriodora, (diamond, CT × CCV) C. torelliana × C. citriodora subsp. variegata, (dash, CT × CH) C. torelliana × C. 299 henryi. For DBH and survival, taxa sharing the same letters are not significantly different at the final assessment. 300

137

Chapter 5 301 Ontogeny

302 All plants were in seedling foliage at the first assessment, transitioning through juvenile to

303 intermediate foliage faster in the pure spotted gum taxa (CCC, CCV and CH), which were

304 almost all in the intermediate stage from the fifth measure (August 2018) onwards. CT and CT

305 × CCV were ontogenetically distinct from the other taxa, with most plants retaining juvenile

306 foliage throughout the post-seedling period (Figure 5.3; pairwise comparison shown in

307 Appendix B - Table 5.S2).

308 For each taxon separately, the proportion of plants in juvenile foliage was unrelated to growth

309 rate or the severity of defoliation/necrosis, and the transition to intermediate foliage occurred

310 independently of these parameters (Spearman rank correlations, rho = -0.46 - -0.04, P = 0.29 -

311 0.94; n = 7 each taxon). The proportion of trees in juvenile foliage was positively correlated

312 with mean daily rainfall in CCC (Spearman rank correlations, rho= 0.86, P = 0.01, n = 7), CCV

313 (Spearman rank correlations, rho= 0.96, P < 0.001, n = 7), CT × CCC (Spearman rank

314 correlations, rho= 0.79, P = 0.04, n = 7) and CT × CH (Spearman rank correlations, rho= 0.79,

315 P = 0.04, n = 7), but not for the other taxa or climate parameters (Spearman rank correlations,

316 rho= -0.25 - 0.75, P = 0.05 - 0.59, n = 7 each taxon).

317

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1.0 a 0.9 a

0.8

0.7

0.6

0.5 b b foliage 0.4

0.3

0.2 c 0.1 Proportion trees with predominatly of juenile c c 0.0 318 Aug-17 Nov-17 Feb-18 Apr-18 Aug-18 Nov-18 Mar-19 319 Figure 5.3 Comparison of taxon with predominantly juvenile foliage and non-juvenile foliage over time between 320 taxa over time. Symbols: (circle) Corymbia citriodora subsp. citriodora, (square) C. citriodora subsp. variegata, 321 (triangle) C. henryi, (×-cross) C. torelliana, hybrids (+-cross) C. torelliana × C. citriodora subsp. citriodora, 322 (diamond) C. torelliana × C. citriodora subsp. variegata, (dash) C. torelliana × C. henryi. Taxa sharing the same 323 letters had similar proportions of juvenile foliage at the final assessment. 324

325 Damage

326 The repeated measures REML analyses indicated significant differences in CDI, necrosis and

327 defoliation between taxa over time, with significant taxon × time interactions (Figure 5.4 and

328 5.5a, b), indicating that taxa responded differently through time (pairwise comparison shown

329 in Appendix B - Table 5.S3). In August and November 2017, CDI was low in all taxa, but

330 damage levels diverged from the third assessment, with higher CDI occurring in CCV and CH

331 and lower CDI occurring in all other taxa. (Figure 5.4).

332 Defoliation was mainly caused by generalist insects such as caterpillars (Lepidoptera),

333 grasshoppers (Orthoptera) and beetles (Coleoptera). High defoliation scores began from

334 February 2018 onwards. In general, defoliation was lower in CCC and CT × CCC and higher

335 in CT and CH, however, there was no clear pattern, with, for example, CT had the lowest

336 defoliation in February 2018 and the highest in August 2018 (Figure 5a). The analyses of

337 prevalence of defoliation confirmed that CH and CCV had significantly more plants with high

338 levels of defoliation than the other taxa (Figure 5.5c).

139

Chapter 5 339 Necrosis was caused by the pathogen Quambalaria pitereka. High necrosis scores were

340 observed from April 2018 onwards. In general, CH and CCV had the highest necrosis scores

341 and were the first taxa to exhibit clear symptoms of Q. pitereka infection, followed by CCC

342 and CT × CCC. The analyses of necrosis prevalence confirmed this pattern (Figure 5.5d). The

343 other taxa had low or intermediate scores of necrosis. The pure spotted gums (CCC, CCV, CH)

344 were more susceptible to necrosis than CT and hybrid taxa, with about one-third of spotted

345 gum plants severely affected (scores 3-5) by necrosis, compared with CT (0) or hybrids (<5%).

48 Repeated measures REML Taxa: F6 = 11, P < 0.001 Time: F6 = 124, P < 0.001 Taxa × Time: F36 = 5, P < 0.001 38

18 Crown Damage Index (CDI)

-2 346 Aug-17 Nov-17 Feb-18 Apr-18 Aug-18 Nov-18 Mar-19 347 Figure 5.4 Comparison of crown damage index (CDI) between taxa over time. Symbols: (circle) Corymbia 348 citriodora subsp. citriodora, (square) C. citriodora subsp. variegata, (triangle) C. henryi, (×-cross) C. torelliana, 349 hybrids (+-cross) C. torelliana × C. citriodora subsp. citriodora, (diamond) C. torelliana × C. citriodora subsp. 350 variegata, (dash) C. torelliana × C. henryi. 351

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Chapter 5 352

Repeated measures REML 2.0 (a) 2.4 (b) Repeated measures REML Taxa: F6 = 6, P = 0.002 Taxa: F6 = 7, P < 0.001 Time: F6 = 109, P < 0.001 Time: F = 35, P < 0.001 2.1 1.7 6 Taxa × Time: F36 = 3, P < 0.001 Taxa × Time: F36 = 5, P < 0.001

1.8 1.4 5) -

1.5 5) - 1.1 1.2 0.8

0.9 Necrosis (0 Defoliation (0 Defoliation 0.5 0.6

0.3 0.2

0.0 -0.1 353 Aug-17 Nov-17 Feb-18 Apr-18 Aug-18 Nov-18 Mar-19 Aug-17 Nov-17 Feb-18 Apr-18 Aug-18 Nov-18 Mar-19

b a (c) 100 (d) 100

80 80 a ac b a a ab 60 60

a b 40 a 40 ab scores of necrosis scores scores of defoliation a bc 20 20 Percentage tres withof low high or Percentage tres withof low high or

0 0 354 CCC CCV CH CT CT × CCC CT × CCV CT × CH CCC CCV CH CT CT × CCC CT × CCV CT × CH 355 Figure 5.5 Results of damage parameters of each taxon. Comparison between taxon at each assessment interval of (a) mean defoliation and (b) mean necrosis scores. Comparison 356 of percentage of trees of each taxon with high (grey; scores 3-5) and low (black; scores 0-2) levels of (c) defoliation and (d) necrosis at assessment four (April 2018), when the 357 highest levels of damage occurred. Symbols: (circle; CCC) Corymbia citriodora subsp. citriodora, (square, CCV) C. citriodora subsp. variegata, (triangle, CH) C. henryi, (×cross, 358 CT) C. torelliana, hybrids (+cross, CT × CCC) C. torelliana × C. citriodora subsp. citriodora, (diamond, CT × CCV) C. torelliana × C. citriodora subsp. variegata, (dash, CT × 359 CH) C. torelliana × C. henryi. For prevalence of defoliation and necrosis, taxa sharing the same letters are not significantly different. 360

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361 Correlations

362 The correlations between performance, damage and climate parameters across all assessments

363 are shown (Figure 5.6; results of Spearman rank correlation shown in Appendix B - Table

364 5.S4). Growth rate was positively correlated with climate parameters (mean daily rainfall and

365 maximum/minimum temperatures) in all taxa, and flushing was positively correlated with

366 mean daily rainfall in all taxa and with maximum and minimum temperature in CCC and CT

367 × CCC. Growth rate and flushing were positively correlated for all taxa, except for CT and CT

368 × CH.

369 Defoliation was positively correlated with climate parameters, while necrosis was negatively

370 correlated with maximum temperature in CCV and with climate parameters in CT × CCV and

371 CT × CH. Growth rate was positively correlated with defoliation in all taxa and negatively

372 correlated with necrosis in CT × CCV. Flushing was negatively correlated with defoliation or

373 necrosis, except for CH and CT × CCC.

374 The relationships between performance and damage parameters for each taxon at each

375 assessment (Figure 5.7; results of Spearman rank correlation shown in Appendix B - Table

376 5.S5) were less consistent than the general patterns observed overall assessments (Figure 5.6).

377 In general, plant response to biotic stress are more variable and less pronounced in the hybrids

378 than in pure taxa.

379

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380 381 Figure 5.6 Result of Spearman rank correlations between performance and damage parameters for each taxon. 382 Correlations: (black) significantly positive; (grey) significantly negative; (white) not significant. Symbols: (MR) 383 mean daily rainfall; (Tmax) maximum temperature; (Tmin) minimum temperature; (G) growth rate; (F) flushing 384 percentage; (D) defoliation; (N) necrosis; (CCC) Corymbia citriodora subsp. citriodora, (CCV) C. citriodora 385 subsp. variegata, (CH) C. henryi, (CT) C. torelliana, hybrids (CT × CCC) C. torelliana × C. citriodora subsp. 386 citriodora, (CT × CCV) C. torelliana × C. citriodora subsp. variegata, (CT × CH) C. torelliana × C. henryi. 387

388 389 Figure 5.7 Result of Spearman rank correlations between performance and damage parameters for each taxon over 390 time. Correlations: (black) significantly positive; (grey) significantly negative; (white) not significant. Symbols: 391 (G) growth rate; (F) flushing percentage; (D) defoliation; (N) necrosis (CCC) Corymbia citriodora subsp. 392 citriodora, (CCV) C. citriodora subsp. variegata, (CH) C. henryi, (CT) C. torelliana, hybrids (CT × CCC) C. 393 torelliana × C. citriodora subsp. citriodora, (CT × CCV) C. torelliana × C. citriodora subsp. variegata, (CT × 394 CH) C. torelliana × C. henryi. 395

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396 Discussion

397 This experiment explored the correlations between rainfall and temperature, leaf ontogeny and

398 pests and disease on tree performance of seven Corymbia taxa. Rainfall and temperature are

399 key factors influencing tree performance and pest and pathogen outbreaks (Stape et al. 2004,

400 2008; Binkley et al. 2017). In this experiment high temperatures and rainfall coincided with

401 tree growth, as these parameters were positively correlated in all taxa and higher growth rates

402 corresponded with the summer period (November 2017 - April 2018), with overall higher

403 temperatures and rainfall. Spotted gums and hybrids demonstrate high plasticity to marginal

404 climatic conditions, such as high temperatures and low rainfall (Lee 2007; Smith et al. 2007;

405 Hung et al. 2016), which may have influenced the results observed in this study. The variability

406 in growth rates between taxa over time suggests that growth may be genetically regulated,

407 which is consistent with previous results for spotted gums and hybrids (Lee et al. 2010; Lan et

408 al. 2011; Brawner et al. 2011).

409 In Brazil and Uruguay, increased temperatures negatively influenced growth in Eucalyptus

410 clones, while higher precipitation positively increased growth in a study across 36 sites

411 (Binkley et al. 2017). In a study of 38 replicated taxa trials across tropical and subtropical

412 Australia, Brawner et al. (2013) found that CCC was relatively unresponsive to environmental

413 changes, whereas CCV was strongly influenced by minimum temperature with productivity

414 being inhibited by lower mean and absolute minimum temperature. Further, they found that

415 the productivity of CCV was better in areas where frost and pest and disease damage was low.

416 In this study, CCV was one of the most impacted by necrosis associated with Q. pitereka, and

417 by defoliation. This, along with several frosts at the site, may explain the poor growth of this

418 taxon here.

419 Production of new foliage (flushing) and mean daily rainfall was positively correlated in all

420 taxa, whereas maximum and minimum temperatures was only positively correlated with

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421 production of new foliage in CCC and CT × CCC. Trees had the highest percentage flushing

422 seven months after planting (Nov 2017) which also corresponded to the highest mean daily

423 rainfall period. Thus, the trees were possibly less stressed at this time in comparison to the

424 period immediately after planting (August - November 2017), had enough resources and it was

425 warm enough to promote growth.

426 Timing of leaf stage transition appears to be genetically regulated (Jordan et al. 1999; Hamilton

427 et al. 2011) and adapted to environmental factors, such as climate, soil, and pest and pathogen

428 stress (James and Bell 2001; Williams et al. 2004), which may be associated with patterns of

429 tree growth and fitness (Jordan et al. 2000). The ontogenetic shifts observed here were different

430 between taxa, as transition from juvenile to intermediate stage occurred completely in CCC,

431 CCV and CH, and partially in CT × CCC and CT × CH, while CT and CT × CCV had most of

432 the trees with juvenile leaves at the end of the experiment (see Figure 5.3). Performance and

433 damage parameters were unrelated to leaf ontogeny in the taxa that underwent phase change in

434 the time frame during this experiment. Tests with E. globulus across a wide geographic range

435 found that growth patterns were influenced by environmental conditions and timing of leaf

436 stage transition, as high growth rates were observed in trees from wet and warm environments

437 and with early phase transition to adult stages (Jones et al. 2000). However, other studies found

438 no correlation of leaf stage transition with growth parameters (Williams et al. 2004; Pinkard et

439 al. 2006a, b) or environmental conditions in E. globulus (Hamilton et al. 2011).

440 Early transition to adult leaves may reduce damage by insects and foliar pathogens that prefer

441 juvenile foliage (Jordan et al. 2000; Pinkard et al. 2006a, b; Hamilton et al. 2011). In E.

442 globulus, early transition from juvenile to adult leaves were associated with higher resistance

443 to Teratosphaeria nubilosa (Balmelli et al. 2013b). Here, pure spotted gum taxa had earlier

444 transition to intermediate stages than the hybrids, this however, was not associated with higher

445 resistance to pathogen infection, as necrosis scores were also higher in pure spotted gum taxa.

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Chapter 5

446 During this experiment, trees did not transition to adult stages, thus, further studies should be

447 conducted to detect if early transition to adult stages reduces pathogen damage in spotted gums

448 and hybrids.

449 Of the environmental factors evaluated here, mean daily rainfall positively influenced the

450 proportion of trees with juvenile foliage in CCC, CCV, CT × CCC and CT × CH, which may

451 be associated with water-use efficiency of young trees, suggesting that for some taxa, leaf stage

452 transition possibly occurs when water became a limiting factor. Competition for water may

453 increase as plants grow, and variations in leaf morphology observed in ontogenetic shifts may

454 be related to an adaptation to water availability (James and Bell 2001; Zotz et al. 2011; Vlasveld

455 et al. 2018). For instance, juvenile leaves of Angophora, Corymbia and Eucalyptus species had

456 lower density of veins in comparison to adult leaves, suggesting that juvenile leaves are

457 hydraulically conservative possibly due to less developed root systems in young plants

458 (Vlasveld et al. 2018). Additionally, juvenile leaves of E. globulus were more vulnerable to

459 drought than adult leaves, supporting this hypothesis (Lucani et al. 2019).

460 Climatic conditions, such as temperature and rainfall, influence pest and pathogen severity in

461 forest systems (Moreira et al. 2015; Paap et al. 2017; Pinkard et al. 2017). For instance,

462 temperature and rainfall influenced population dynamics of Glycaspis brimblecombei (red gum

463 lerp psyllid) on E. camaldulensis, with populations increasing during periods of low

464 temperature and rainfall and decreasing when temperature and rainfall were higher (Ferreira-

465 Filho et al. 2017). In addition, high temperatures and rainfall may favour pathogen

466 development, such as Austropuccinia psidii (myrtle rust) and Q. pitereka (Dickinson et al.

467 2004; Pegg et al. 2009, 2014; Freeman et al. 2019).

468 In this study, the highest levels of defoliation and necrosis coincided with the period of higher

469 rainfall and temperatures (February - April 2018), which may have influenced leaf nutritional

470 quality and insect preference. Greater defoliation was associated with higher rainfall and

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Chapter 5

471 temperature in all taxa. In contrast, rainfall and temperature was negatively correlated with

472 necrosis only in CT × CCV and CT × CH. These hybrids had low necrosis scores during the

473 experiment, which may explain this relationship. The pure spotted gums were highly

474 susceptible to Q. pitereka (the main cause of necrosis in this study), while the hybrids appear

475 to inherit resistance to this pathogen from the CT parent, which exhibited low levels of infection

476 in field conditions observed here and in previous studies (Lee 2007; Pegg et al. 2008; Lee et

477 al. 2009). Hence, this study supports the findings that hybridising CT with spotted gums

478 appears to reduce the impact of Q. pitereka (Lee 2007; Lee et al. 2009).

479 Pests and pathogens may negatively influence tree performance, reducing growth rate, flushing

480 and DBH, as removal/infection of leaf tissue can lead to a reduction of photosynthesis

481 depending on the severity of damage (Pinkard et al 2006a, b; Rapley et al. 2009; Balmelli et

482 al. 2013a; Eyles et al. 2013; Gong and Zahng 2014; Smith et al. 2017). To mitigate damage,

483 trees may increase growth rate and flushing to compensate or escape pest and pathogen damage

484 (Stone et al. 2001; Gong and Zahng 2014; Koch et al. 2016). The capacity of plants to regrow

485 after pest and pathogen occurrence will depend on the amount of damage, and tree performance

486 may be compromised if damage is too severe (Rapley et al. 2009; Elek and Baker 2017; Smith

487 et al. 2017).

488 In this study, despite the higher levels of damage during hot, wet season, growth rates were

489 also higher within this period. Additionally, growth was positively correlated with defoliation

490 in all taxa when considered across the whole experiment (Figure 5.6). When assessments were

491 analysed separately for each taxon, however, this relationship varied over time (see Figure 5.7).

492 In contrast, necrosis and growth rate were negatively correlated only in CT × CCV and were

493 rarely correlated with the studied taxa when assessments were considered separately. Flushing

494 was often negatively correlated with defoliation and necrosis, throughout the study period and

495 in the combined assessments. In another study with spotted gums and hybrids, arthropod

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Chapter 5

496 damage had no relationship with growth, even though tree growth was negatively impacted by

497 general crown damage (Nahrung et al. 2010). On the other hand, taller CCV trees were more

498 resistant or sustained lower levels of Q. pitereka infection than smaller trees, suggesting growth

499 parameters are associated with resistance/tolerance to this pathogen (Lan et al. 2011; Brawner

500 et al. 2011).

501 The correlation between performance parameters (growth rate and flushing) and defoliation

502 may be associated with mechanisms to compensate/tolerate or escape pest and pathogen

503 damage. Fast growing eucalypts are likely to tolerate high levels of defoliation because they

504 can regrow, compensating for the damage incurred (Stone et al. 2001). In this experiment, CCC

505 and CT × CCC had the highest growth rates but lower levels of damage. Tree flushing may

506 increase damage, as many insects and foliar pathogens prefer to feed on or infect new, soft and

507 nutritious leaves (Stone et al. 2001). The negative correlation between damage and flushing

508 observed here suggests that trees may outgrow damage. In many eucalypts, increased levels of

509 leaf production after damage was associated with higher tolerance to defoliation (Stone et al.

510 2001; Wills et al. 2004; Williams et al. 2004; Pinkard et al. 2003; Pinkard et al. 2006 a, b; Elek

511 and Baker 2017).

512 Studies suggest that impacts of pests and pathogens on eucalypts may only be significant if

513 plants are frequently and severely damaged (Wills et al. 2004; Alcon et al. 2008; Rapley et al.

514 2009; Balmelli et al. 2013a, b; Elek and Baker 2017; Smith et al. 2017). For instance, pest and

515 pathogen damage in eucalypts only significantly affected growth parameters when defoliation

516 exceeded 50 - 60% (Rapley et al. 2009; Elek and Baker 2017; Smith et al. 2017). In this study,

517 the effect of defoliation and necrosis on tree growth rate and flushing were inconsistent for

518 most taxa, although the overall defoliation severity in CCV and CH was negatively correlated

519 with DBH. Additionally, CCV and CH had the lowest DBH in comparison to the other taxa.

520 Artificial defoliation (Alcon et al. 2008; Elek and Baker 2017) and Mnesampela privata

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521 (autumn gum moth) feeding (Rapley et al. 2009) on E. nitens reduced DBH for 2 - 6 years after

522 damage. However, plants were able to recover from the damage, and by harvest, the defoliation

523 had no impact on timber volume production. In contrast, severe damage by pathogens, such as

524 T. nubilosa, A. psidii and Q. pitereka on young eucalypts had long-term negative effects on

525 tree performance, survival, productivity and timber quality (Milgate et al. 2005; Pegg et al.

526 2011a, b; Balmelli et al. 2013a, b).

527 Overall, relationships between performance and damage parameters were more consistent in

528 pure taxa than in hybrids. Hybridization can alter the expression of phenotypic traits in

529 comparison to parental species, influencing tree performance and interactions with biotic and

530 abiotic stress (Fritz et al. 1999; Nahrung et al. 2009, 2012; Hayes et al. 2013). The patterns of

531 hybridization in terms of resistance to pests and pathogens can range from no difference, more

532 resistant, more susceptible and intermediate resistance in comparison to their parental species

533 (Fritz et al 1999). Spotted gums and their hybrids with CT exhibited different patterns of

534 resistance to arthropods depending mostly upon site location and pest pressure rather than

535 genotype (Nahrung et al. 2010). Marked differences in the expression of leaf secondary

536 metabolites between these taxa may also explain the variability in pests and pathogens

537 (Nahrung et al 2009, 2012; Hayes et al. 2013). For instance, studies demonstrated that pests

538 and pathogens attack induce physical and chemical responses in CCV, and these responses

539 were specific to the damage agent (e.g. P. atomaria, Q. pitereka and A. psidii; Bonora et al.

540 2020; Chapters 2 - 4).

541 Studies also suggest that when properly selected, performance of spotted gum hybrids can be

542 significantly higher than the pure species, expressing good environmental plasticity, resistance

543 to pests and diseases, good form and desirable timber properties (Lee 2007; Lee et al. 2009,

544 2010). In this study, except for CT × CCV, performance parameters of hybrids were

545 intermediate or not different from pure spotted gums, especially CCC and CH, while damage

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546 was in general more severe in pure taxa, especially in CCV and CH than the hybrids. The

547 variable inheritance patterns observed in spotted gum hybrids may have contributed to the lack

548 of relationship between performance and damage parameters observed in this study.

549 In conclusion, performance in spotted gums, CT and their hybrids appeared to be influenced

550 by environmental factors such as rainfall, temperature and pest and disease occurrence. In

551 general, CCC and CT × CCC displayed the best combination of performance and

552 resistance/tolerance to damage in comparison to the other taxa at the study site. Severe

553 defoliation was associated with reduced DBH in CCV and CH. CCV is the most studied spotted

554 gum taxa and the focus of hardwood plantations in New South Wales and Queensland (e.g.

555 Dickinson et al. 2004; Lee 2007; Johnson et al. 2009; Lee et al. 2010; Lan et al. 2011). CCV

556 and CT × spotted gums hybrids may be associated with good field performance and reduced

557 pest and pathogen occurrence (Lee 2007; Lee et al. 2009, 2010). However, this pattern did not

558 occur in this study for CCV or most of the hybrids, possibly because the selected genotypes

559 were unsuited to the site, or because of poor specific and/or climatic conditions of the

560 experiment (Brawner et al. 2013). Warmer and wetter periods had a positive influence on

561 growth rate, flushing and defoliation in all taxa, but did not influence necrosis in most of the

562 taxa. Overall, performance parameters were often influenced by defoliation and necrosis.

563 However, these patterns were variable between assessments, highlighting that one field

564 assessment could not detect patterns of variation in these types of traits. Additionally, tree

565 interactions with damage parameters were more consistent in pure taxa than in hybrids, when

566 assessments were considered separately (see Figure 5.7). The ontogenetic transition from

567 juvenile to intermediate stage was variable between taxa, occurring earlier in pure taxa than in

568 hybrids. The main factor influencing ontogeny in this study was mean daily rainfall. These

569 results highlight the complexity of plant interaction with biotic and abiotic stressors and add

570 important knowledge on the long-term effects of pests and pathogens and environmental

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571 conditions in plant performance, assisting with management strategies including selection of

572 most appropriate taxa.

573

574 Acknowledgements

575 The authors thank Tracey Menzies, Anthony Burridge, John Oostenbrink, Julia Woerner and

576 Emily Lancaster for assisting with fieldwork at the Mary Valley Research Facility at Traveston.

577 We are grateful to the Department of Agriculture and Fisheries for provision of facilities. This

578 work was supported by University of the Sunshine Coast International Research Scholarship

579 awarded to FSB. HFN was funded by an Advance Queensland Fellowship through the

580 Queensland Department of Innovation and Tourism Industry Development supported by the

581 University of the Sunshine Coast, Department of Agriculture and Fisheries, Forest and Wood

582 Products Australia, HQ Plantations Pty Ltd, Plant Health Australia and the National Sirex

583 Coordination Committee.

584

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845 Stone C (2001). Reducing the impact of insect herbivory in eucalypt plantations through 846 management of extrinsic influences on tree vigour. Austral Ecology. 26:482-488. 847 doi:https://doi.org/10.1046/j.1442-9993.2001.01143.x. 848 Stone C, Matsuki M, Carnegie AJ (2003). Pest and disease assessment in young eucalypt 849 plantations: field manual for using the crown damage index. National Forest Inventory. 850 Bureau of Rural Sciences, Canberra. 851 Varshney VK, Pandey A, Thoss V, Kumar A, Ginwal HS (2012). Foliar chemical attributes 852 of the hybrid bred from Eucalyptus citriodora x E. torelliana and its parental taxa, 853 and implications for fungal resistance. Annals of Forest Research. 55:53-60. 854 Vlasveld C, rsquo, Leary B, Udovicic F, Burd M (2018). Leaf heteroblasty in eucalypts: 855 biogeographic evidence of ecological function. Australian Journal of Botany. 66:191- 856 201. doi:https://doi.org/10.1071/BT17134. 857 Williams DR, Potts BM, Smethurst PJ (2004). Phosphorus fertiliser can induce earlier 858 vegetative phase change in Eucalyptus nitens. Australian Journal of Botany. 52:281- 859 284. doi:https://doi.org/10.1071/BT03135. 860 Wills AJ, Burbidge TE, Abbott I (2004). Impact of repeated defoliation on jarrah (Eucalyptus 861 marginata) saplings. Australian Forestry. 67:194-198. 862 doi:https://doi.org/10.1080/00049158.2004.10674934. 863 Wingfield MJ, Slippers B, Hurley BP, Coutinho TA, Wingfield BD, Roux J (2008). Eucalypt 864 pests and diseases: growing threats to plantation productivity. Southern Forests. 865 70:139-144. doi:https://doi.org/10.2989/south.for.2008.70.2.9.537. 866 Wingfield MJ, Roux J, Slippers B, Hurley BP, Garnas J, Myburg AA, Wingfield BD (2013). 867 Established and new technologies reduce increasing pest and pathogen threats to 868 Eucalypt plantations. Forest Ecology and Management. 301:35-42. 869 doi:https://doi.org/10.1016/j.foreco.2012.09.002. 870 Zhou XD, Wingfield MJ (2011). Eucalypt diseases and their management in China. 871 Australasian Plant Pathology. 40:339-345. doi:https://doi.org/10.1007/s13313-011- 872 0053-y. 873 Zotz G, Wilhelm K, Becker A (2011). Heteroblasty-A Review. The Botanical Review. 874 77:109-151. doi:https://doi.org/10.1007/s12229-010-9062-8.

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1 Chapter 6 General discussion

2 In this thesis, aspects of plant interactions with pests and pathogens were investigated in

3 controlled systems and in a field trial, focusing on Corymbia spp. and hybrids, ecologically and

4 economically important eucalypt taxa. The controlled experiments investigated if the pest

5 Paropsis atomaria and mechanical damage induced physical and chemical responses in

6 Corymbia citriodora subsp. variegata (CCV) plants. Differences between plant responses to

7 Quambalaria pitereka and Austropuccinia psidii infection - a coevolved and an exotic

8 pathogen, respectively - were also investigated. A field experiment investigated how Corymbia

9 spp. and hybrids interact with pests and pathogens and with environmental conditions, and

10 whether the responses varied between taxa and through time. A synthesis of the results across

11 this study for the common taxon (CCV) is shown (Figure 6.1).

12 13 Figure 6.1Synthesis of the results across this study for Corymbia citriodora subsp. variegata. Symbols: (up arrow) 14 increased, (down arrow) reduced, (circle) altered distribution within leaf tissue, (dash) no significant effect, 15 (blank) not addressed in this study, (DBH) diameter at breast height over bark. 16

17 Paropsis atomaria larval feeding and mechanical wounding induced systemic responses in

18 CCV, altering leaf chemistry, reducing the relative abundance of long chain hydrocarbons

19 (heavier waxes) and increasing relative abundance of monounsaturated hydrocarbons (lighter

20 waxes associated with plant hormones) and monoterpenes. The response to mechanical damage

21 was only evident immediately following damage, whereas the response to P. atomaria larval

159

Chapter 6 22 feeding remained detectable after larval feeding ceased (Chapter 2). Neither mechanical

23 wounding nor larval feeding influenced leaf physical structures such as leaf toughness and

24 trichomes. Induced responses were also observed in CCV under Q. pitereka and A. psidii

25 infection, altering physical and chemical parameters (Bonora et al. 2020; Chapters 3 and 4).

26 In general, the distribution of polyphenols and tannins within leaf structures was altered in Q.

27 pitereka infected plants. In addition,leaves of Q. pitereka susceptible plants that displayed

28 severe infection symptoms were tougher than leaves of resistant plants that had minor infection

29 (Bonora et al. 2020; Chapters 3 and 4). CCV response to A. psidii infection increased leaf

30 toughness, altered the relative abundance of 13 individual leaf chemical compounds, including

31 one monoterpene, two monounsaturated hydrocarbons, four steroids and six long chain

32 hydrocarbons. Austropuccinia psidii infection also altered the distribution of polyphenols and

33 tannins within leaf structures (Bonora et al. 2020; Chapters 3 and 4). The differences in plant

34 response between these pathogens may be associated with mechanisms of host-pathogen

35 interactions, which may have activated the distinct defence mechanisms, involving leaf

36 anatomy and chemical composition observed here (Freeman et al. 2019; Butler et al. 2019).

37 The coevolved pathogen, Q. pitereka, is restricted to Corymbia species (Freeman et al. 2019)

38 and the interaction of CCV with this pathogen seems to activate specific plant responses

39 (Bonora et al. 2020; Chapter 3). In contrast, A. psidii is an exotic pathogen, with a broad host

40 range of 480 myrtaceous species from 69 genera (Soewarto et al. 2019), thus, CCV may lack

41 adaptation to A. psidii, showing indirect pathogen recognition and a nonspecific response to

42 infection (Butler et al. 2019).

43 The differences of leaf compounds observed in these experiments may be associated with plant

44 strategies to mitigate the negative effects of pests and pathogens (Bonora et al. 2020; Chapters

45 2, 3 and 4). For instance, leaf waxes have a variety of physical and chemical characteristics,

46 sealing the leaf surface and playing an important role in insect-plant interactions (Müller and

160

Chapter 6 47 Riederer 2005). Their formation can be regulated in response to both developmental and

48 environmental cues, and the high lipophilic composition of leaf waxes forms a protective

49 barrier against water and chemical loss, influencing pest and pathogen adhesion and feeding

50 (Müller and Riederer 2005; Wójcicka 2015). The variation of waxes observed here may be

51 associated with an attempt to seal and heal the damaged tissue, protecting the plant from

52 desiccation and preventing infection of the exposed area resulted from pathogen or larval

53 damage (Savatin et al. 2014). Moreover, the variability of polyphenols and tannins distribution

54 through leaf structures may be associated with pathogen recognition mechanisms, stimulating

55 or reducing fungal penetration, spore germination and possibly protecting the plant against cell

56 wall degradation (Lattanzio et al. 2006; Ganthaler et al. 2017).

57 The fact that the response was different between the P. atomaria feeding, mechanical wounding

58 and pathogen infection suggests CCV plants can activate specific metabolic pathways in

59 response to different types of damage (Chapters 2, 3 and 4). These results are in line with

60 studies suggesting that plants can recognize pests and pathogens through their interaction with

61 plant tissues, which may transmit signals throughout the plant, activating the expression or

62 priming of induced responses (Jones and Dangl 2006; Carr et al. 2019; Waterman et al. 2019).

63 Mechanical damage may also induce similar plant responses to those induced by insects, as

64 wounding may activate plant signals involved with the biosynthesis of plant responses to

65 specific damage or general stress (Savatin et al. 2014). However, as demonstrated here,

66 responses activated by mechanical wounding do not last as long as those to larval feeding,

67 which may indicate an interaction between plant and larval secretions and feeding pattern

68 (Baldwin 1990; Waterman et al. 2019). Even though the chemical and physical responses

69 potentially have a negative effect on pest and pathogen performance (Müller and Riederer

70 2005; Batish et al. 2008; Ganthaler et al. 2017), the experiments of this thesis were not designed

71 to detect the effect of induced responses on pest and pathogen performance. Therefore, further

161

Chapter 6 72 studies should be conducted to determine if the plant responses observed here are part of a

73 defence mechanisms and the efficiency of them in reducing pest and pathogen pressure.

74 The manipulation of plant-induced responses may benefit pest management in forestry

75 plantations, because they potentially reduce the negative effects of pests and pathogens,

76 possibly reducing the ecological impacts and costs associated with silvicultural practices and

77 use of pesticides and fungicides (Eyles et al. 2010; Naidoo et al. 2014, 2019). In annual,

78 herbaceous and short-lived perennials, the use of elicitors to activate induced responses

79 negatively affected pest and pathogen development and survival (Karban and Baldwin 1997;

80 Agrawal 1999; Traw and Dawson 2002; Boughton et al. 2005; Shavit et al. 2018; Block et al.

81 2019). The use of induced defences in management strategies of perennial trees is still under

82 development, as trees are long-lived organisms that experience a greater variety of biotic and

83 abiotic stress and the manipulation of induced responses are likely to be complex.

84 (Hammerschmidt 2006; Eyles et al. 2010).

85 Studies in plant defence suggest that investing metabolic resources in induced responses may

86 compromise plant growth (Feeny 1976; Coley et al. 1985; Björkman et al. 2008). In the

87 controlled experiments, CCV growth rate was not impacted either by P. atomaria nor by Q.

88 pitereka damage in the glasshouse experiments, however, severe A. psidii infection reduced

89 seedling growth by approximately 70% (Chapters 2 and 3). In the field trial, CCV was severely

90 affected by necrosis and defoliation associated with Q. pitereka. However, damage apparently

91 had no impact in height growth rate (cm day⁻¹) of CCV, as it was not significantly different

92 from other taxa that had lower levels of damage (Chapter 5). Nevertheless, the overall

93 defoliation observed in CCV had a negative effect on tree DBH and in the production of new

94 foliage.

95 In the field experiment, growth rate and defoliation was positively correlated in all studied taxa

96 (CCV, C. citriodora subsp. citriodora - CCC, C. henryi - CH, C. torelliana - CT, and their

162

Chapter 6 97 hybrids), while the production of new flushing leaves was negatively correlated with

98 defoliation (Chapter 5). These results may be associated with a plant mechanism to

99 compensate/tolerate or escape pest and pathogen damage, as fast-growing eucalypts are likely

100 to tolerate high levels of defoliation (Stone et al. 2001). Studies suggest that the negative effects

101 of pests and pathogens on tree performance may be significant only in the first years after the

102 outbreak, as trees can recover from damage with no detectable growth impacts in long term in

103 plantations grown on long rotations (Rapley et al. 2009; Elek and Baker 2017; Smith et al.

104 2017). However, it may negatively impact eucalypt plantations grown for short rotations

105 (Wingfield et al. 2008, 2013; Gonçalves et al. 2013; Elek and Baker 2017), and severe and

106 frequent outbreaks may affect performance permanently, not only influencing growth rate but

107 also compromising tree form, and thus the quality of timber products (Milgate et al. 2005;

108 Rapley et al. 2009; Pegg et al. 2011; Balmelli et al. 2013; Elek and Baker 2017; Smith et al.

109 2017).

110 In the field trial, the variation of growth rate, production of new foliage and defoliation were

111 associated with rainfall and temperature fluctuation. High temperatures and rainfall favoured

112 growth in spotted gums and hybrids, which have proven high plasticity in marginal

113 environments with high temperatures and low rainfall (Lee 2007; Smith 2007; Hung et al.

114 2016). The highest percentage flushing occurred seven months after planting (Nov 2017) which

115 also corresponded to the highest mean daily rainfall period. This is in line with the resource

116 availability hypothesis, as trees were possibly less stressed in comparison to the period

117 immediately after planting (August - November 2017), had sufficient resources and conditions

118 were warm enough to promote the production of new leaves (Coley et al. 1985; Endara and

119 Coley 2011). The higher levels of defoliation and necrosis also coincided with the period of

120 higher rainfall and temperatures, which may have influenced leaf quality and insect preference.

121 Additionally, high rainfall and temperatures may favour insect reproduction and growth, and

163

Chapter 6 122 reduce fungal infection cycle, increasing defoliation and necrosis. Necrosis scores were lower

123 in CT and the hybrids than in the other taxa, supporting the findings that hybridising CT with

124 spotted gums appears to reduce the impact of Q. pitereka (Lee 2007; Lee et al. 2009).

125 Ontogenetic shifts in leaves potentially alter the type and abundance of organisms hosted by

126 plants, which may be associated with the expression of different defence mechanisms in each

127 leaf stage (Hanley et al. 2007). Additionally, ontogenetic shifts are likely to influence patterns

128 of tree growth and fitness (Jordan et al. 2000). Here, transition from juvenile to intermediate

129 foliar stages was not correlated with performance and damage parameters. Nevertheless,

130 ontogenetic shifts were positively correlated with rainfall in most of the taxa, which may be

131 associated with water-use efficiency of young trees. This suggests that, for some taxa, leaf stage

132 transition possibly occurs when water became a limiting factor, as competition for water may

133 increase as plants grow, and variations in leaf morphology observed in ontogenetic shifts may

134 be related to an adaptation to lower water availability (James and Bell 2001; Zotz et al. 2011;

135 Vlasveld et al. 2018). In other species, early transition to adult stages is associated with low

136 levels of damage by insects and foliar pathogens that prefer juvenile foliage (Jordan et al. 2000;

137 Pinkard et al. 2006a, b; Hamilton et al. 2011). Thus, further studies should be undertaken to

138 detect if the transition to adult leaves has an influence on damage occurrence.

139 Most studies involving spotted gums focus on CCV because it is important to hardwood

140 plantations in New South Wales and Queensland due to its desirable performance parameters

141 (Dickinson et al. 2004; Lee 2007; Lee et al. 2010), including tolerance to borer attack (Lee et

142 al. 2010) and to Q. pitereka (Dickinson et al. 2004). CCV did not perform well in the field trial

143 of this thesis, showing high levels of defoliation and necrosis and presenting poor performance

144 in comparison with other taxa that are not as economically important as CCV to planted forests.

145 The provenances of CCV tested in this study, perform better in areas where frost and pest and

146 disease damage was low (Brawner et al. 2013). Therefore, the poor performance of CCV may

164

Chapter 6 147 have been related to high levels of pests and pathogens in the experiment site and the

148 occurrence of six frosts and the during the the study period, that may have impacted the growth

149 of the species. Overall, CCC and CT × CCC presented the best combination of performance

150 and resistance/tolerance to damage in comparison to the other taxa at this site.

151 The relationships between performance and damage parameters were more consistent in pure

152 taxa than in hybrids. Hybridization can alter the expression of phenotypic traits in comparison

153 to parental species, influencing tree performance and interactions with biotic and abiotic stress

154 (Fritz et al. 1999; Nahrung et al 2009, 2012; Hayes et al. 2013). In general, hybrid trees are

155 more susceptible to pests and pathogens than pure species (Whitham et al. 1994; Dungey et al.

156 2000; Potts and Dungey 2004; Nahrung et al. 2011; 2014). This may be due to their rapid

157 growth, which is often reached at the expense of defensive traits (Henery 2011). In this study,

158 however, most of the hybrids were less damaged by defoliation and necrosis than pure taxa,

159 which is in line with previous finding that spotted gum hybrids appear to have lower or similar

160 susceptibility to pests and pathogens than pure species (Dickinson et al. 2004; Lee 2007; Lee

161 et al. 2009; Nahrung et al. 2009). If properly selected, spotted gum hybrid performance can be

162 significantly higher than the pure species, expressing good environmental plasticity, resistance

163 to pests and diseases, good form and timber properties (Lee 2007; Lee et al. 2009, 2010).

164 This thesis increases the knowledge on induced defences in perennial trees and on plant

165 interaction with pests and pathogens in controlled and natural environments, demonstrating

166 that these interactions are complex, difficult to predict and highly variable depending on the

167 pest and pathogen encountered over time.

168

169 Future studies

170 This study provides important information for future studies testing the impact of induced

171 responses on plant fitness and pest and pathogen performance. Chapters 2 - 4 demonstrated

165

Chapter 6

172 that CCV induces different responses when challenged by a native insect pest and by a native

173 and an exotic pathogen, suggesting that plants may deploy different defence mechanisms

174 and/or activate different defence pathways depending on the source of damage agent.

175 Consequently, selecting for resistance to a specific damage agent may result in susceptibility

176 to other damage agents, which may have implications on strategies of pests and pathogens

177 management. Future studies should focus on the interactions of these plant responses on pests

178 and pathogens performance and determine the importance of the variable responses for the

179 selection of resistant germplasm to plant commercially. Linking induced responses with

180 resistance genes and identifying whether there is a genetic basis to plant responses should guide

181 selection for the multitude of pests and diseases that plantations may experience.The growing

182 threats of pests and pathogens in planted systems and the variation of plant responses

183 depending on damage agent found in this study indicated that selection for resistance may be

184 more effective if it integrates several components of plant interaction with pests and pathogens.

185 In addition, this study highlights the importance of continuous monitoring of forest plantations,

186 because one field assessment was not enough to determine the patterns of plant interaction with

187 biotic and abiotic stress. Thus, research arising from this study should also focus on testing

188 these taxa in different field locations over time to better describe effects of climate and pest

189 and pathogen in tree performance, and the patterns of interaction between these traits. The

190 results generated in this thesis benefit the development of pest and disease management,

191 contribute to strategies to select resistant genotypes and add important knowledge about plant

192 responses and interactions with pests and pathogens in natural and controlled conditions.

193

194 References

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Chapter 6 371 Waterman JM, Cazzonelli CI, Hartley SE, Johnson SN (2019). Simulated herbivory: the key 372 to disentangling plant defence responses. Trends in Ecology & Evolution. 373 doi:https://doi.org/10.1016/j.tree.2019.01.008. 374 Whitham TG, Morrow PA, Potts BM (1994). Plant hybrid zones as centers of biodiversity - 375 the herbivore community of 2 endemic Tasmanian eucalypts. Oecologia. 97:481-490. 376 doi:https://doi.org/10.1007/bf00325886. 377 Wingfield MJ, Slippers B, Hurley BP, Coutinho TA, Wingfield BD, Roux J (2008). Eucalypt 378 pests and diseases: growing threats to plantation productivity. Southern Forests. 379 70:139-144. doi:https://doi.org/10.2989/south.for.2008.70.2.9.537. 380 Wingfield MJ, Roux J, Slippers B, Hurley BP, Garnas J, Myburg AA, Wingfield BD (2013). 381 Established and new technologies reduce increasing pest and pathogen threats to 382 Eucalypt plantations. Forest Ecology and Management. 301:35-42. 383 doi:https://doi.org/10.1016/j.foreco.2012.09.002. 384 Wójcicka A (2015). Surface waxes as a plant defense barrier towards grain aphid. Acta 385 Biologica Cracoviensia Series Botanica. 57:95-103. doi:https://doi.org/10.1515/abcsb- 386 2015-0012. 387 Zotz G, Wilhelm K, Becker A (2011). Heteroblasty-A Review. The Botanical Review. 388 77:109-151. doi:https://doi.org/10.1007/s12229-010-9062-8. 389

171

Appendix A

1 Appendix A - Supplementary material for Chapter 4

140 H2 = 4.769 P = 0.092 120

100

80 m μ 60 H2 = 3.038 P = 0.219 40 H2 = 1.654 20 P = 0.437

0 Leaf thickness PP-Length PP-Width 3 4 Figure 4.S1 Comparison of leaf thickness, and length and width (μm) of palisade parenchyma (PP) in transverel 5 sections of Corymbia citriodora subsp. variegata between treatments (Kruskal-Wallis, P < 0.05; sample size = 6 12): uninoculated controls (black), inoculated with Quambalaria pitereka (grey) and inoculated with 7 Austropuccinia psidii (white). 8

0.30 H2 = 0.462 P = 0.794 0.25 m²

μ 0.20

H2 = 5.206 H2 = 3.562 0.15 P = 0.168 P = 0.074

0.10

0.05 number of of number cellsper

0.00 UE PP LE 10 11 Figure 4.S2 Comparison of the number of cells per μm² of upper epidermis (UE), palisade parenchyma (PP) and 12 lower epidermis (LE) in transverse sections of asymptomatic tissues of Corymbia citriodora subsp. variegata 13 between treatments (Kruskal-Wallis, P < 0.05; sample size = 12): uninoculated controls (black), inoculated with 14 Quambalaria pitereka (grey) and inoculated with Austropuccinia psidii (white). 15 16

172

Appendix A

35 H2 = 0.038 H2 < 0.001 P = 0.981 H2 = 0.038 P = 1.000 P = 0.981 30

H = 3.846 2 H = 5.115 H = 1.885 20 P = 0.146 2 2 P = 0.077 P = 0.390 15 Relative area Relative (%) 10

0 UE PP BS A SP LE 17 18 Figure 4.S3 Relative area of upper epidermis (UE), palisade parenchyma (PP), bundle sheath (BS), airspace (A), 19 spongy parenchyma (SP) and lower epidermis (LE) in transverse sections of asymptomatic tissues of Corymbia 20 citriodora subsp. variegata, compared between treatments (Kruskal-Wallis, P < 0.05; sample size = 12): 21 uninoculated controls (black), inoculated with Quambalaria pitereka (grey) and inoculated with Austropuccinia 22 psidii (white).

173

Appendix B

1 Appendix B - Supplementary material for Chapter 5

2 Table 5.S1 Results of REML analyses to detect the differences in growth rate mean ± s.e and percentage of 3 flushing mean ± s.e. between taxa over time. Data sorted by taxon name at each assessment date.

Assessment Taxa Growth rate (cm day⁻¹) Percentage of flushing

CCC 0.039 ± 0.007 abcdefg 32 ± 3 bcdefgh

CCV 0.019 ± 0.003 abc 34 ± 3 cefghi

CH 0.031 ± 0.006 abcdef 27 ± 3 bcde

Apr-Aug-17 CT 0.006 ± 0.002 a 54 ± 5 nopq

CT × CCC 0.018 ± 0.004 abcd 29 ± 5 bcdef

CT × CCV 0.011 ± 0.004 ab 38 ± 6 efghij

CT × CH 0.020 ± 0.008 abcd 42 ± 6 ghijkl

CCC 0.462 ± 0.034 twxyzABCD 95 ± 1 st

CCV 0.356 ± 0.020 rstuvwxyzA 93 ± 2 st

CH 0.326 ± 0.033 pqrstuv 89 ± 3 s

Aug-Nov-17 CT 0.123 ± 0.015 defghijkl 97 ± 2 t

CT × CCC 0.349 ± 0.041 pqrstuvwxyz 90 ± 4 st

CT × CCV 0.122 ± 0.017 cdefghijklmn 92 ± 3 st

CT × CH 0.188 ± 0.040 eghijklmnop 94 ± 4 st

CCC 0.567 ± 0.044 CD 43 ± 3 gijk

CCV 0.473 ± 0.036 zBCD 29 ± 3 bcd

CH 0.467 ± 0.054 twxyzABCD 16 ± 2 a

Nov-Feb-18 CT 0.329 ± 0.035 pqrstuvw 28 ± 3 b

CT × CCC 0.586 ± 0.057 DE 35 ± 3 bcdefghij

CT × CCV 0.269 ± 0.048 ijlmnopqr 29 ± 5 bc

CT × CH 0.396 ± 0.066 pqrstuvwxyzAB 31 ± 5 bcdefg 4

174

Appendix B 5 Table 5.S1 (Continued)

Assessment Taxa Growth rate (cm day⁻¹) Percentage of flushing

CCC 0.826 ± 0.076 F 50 ± 2 klmn

CCV 0.462 ± 0.042 tvwxyzABC 39 ± 2 fghij

CH 0.355 ± 0.044 prstuvwx 36 ± 3 cdefghij

Feb-Apr-18 CT 0.453 ± 0.036 tvwxyzABC 42 ± 2 ghijk

CT × CCC 0.738 ± 0.091 EF 51 ± 3 klmno

CT × CCV 0.300 ± 0.042 iopqrs 35 ± 3 bcdefghij

CT × CH 0.727 ± 0.118 EF 47 ± 5 gijklmn

CCC 0.296 ± 0.030 opqrstu 50 ± 3 klmn

CCV 0.169 ± 0.020 defghijk 44 ± 2 jklm

CH 0.105 ± 0.027 abcde 43 ± 4 gijk

Apr-Aug-18 CT 0.109 ± 0.017 bcdefghij 31 ± 2 bcdefghi

CT × CCC 0.252 ± 0.037 ijlmnopqrs 43 ± 3 ghijkl

CT × CCV 0.091 ± 0.017 abcdefgh 38 ± 4 fghijk

CT × CH 0.121 ± 0.034 abcdefghi 37 ± 5 cdefghijk

CCC 0.321 ± 0.024 opqrstu 68 ± 3 pr

CCV 0.363 ± 0.024 rstuvwxy 62 ± 2 opqr

CH 0.259 ± 0.028 hijklmnopq 59 ± 4 opqr

Aug-Nov-18 CT 0.283 ± 0.023 imopqrs 68 ± 3 pr

CT × CCC 0.409 ± 0.036 stuvwxyzABC 63 ± 4 nopqr

CT × CCV 0.250 ± 0.034 ijlmnopqrs 70 ± 4 pr

CT × CH 0.407 ± 0.062 qrstuvwxyzABC 67 ± 4 opqr

CCC 0.528 ± 0.042 BCD 69 ± 2 pqr

CCV 0.448 ± 0.043 txzABCD 55 ± 2 lno

CH 0.326 ± 0.056 prstuvw 49 ± 3 klmn

Nov-Mar-19 CT 0.173 ± 0.023 hijklmno 51 ± 2 klmno

CT × CCC 0.402 ± 0.058 rstuvwxyzABC 61 ± 3 nopqr

CT × CCV 0.138 ± 0.019 bcdefghijklm 49 ± 3 klmno

CT × CH 0.278 ± 0.057 ijlmnopqrst 56 ± 5 klmnop

Repeated measures REML F ndf ddf P F ndf ddf P

Taxon 16.8 6 18.3 <0.001 7.3 6 18.7 <0.001

Time 118.6 6 2709.7 <0.001 362.5 6 2699.3 <0.001

Taxon × Time 4.4 36 2709.8 <0.001 3.1 36 2699.6 <0.001 6 Symbols: (CCC) Corymbia citriodora subsp. citriodora, (CCV) C. citriodora subsp. variegata, (CH) C. henryi, (CT) C. torelliana, hybrids 7 (CT × CCC) C. torelliana × C. citriodora subsp. citriodora, (CT × CCV) C. torelliana × C. citriodora subsp. variegata, (CT × CH) C. 8 torelliana × C. henryi. 9

175

Appendix B 10 Table 5.S2 Results of two-way Chi square to detect the differences between taxa of the proportion of trees with 11 juvenile leaves at the end of the experiment (March 2019). CCC CCV CH CT CT × CCC CT × CCV CT × CH

Chi square

CCC 2.34 0.014 105.6 24.34 87.94 22.88

CCV 0.126 1.86 17.36 107.48 1.857 15.07

CH 0.9 0.173 82.29 21.92 99.68 82.29

CT <0.001 <0.001 <0.001 34.42 0.068 20.59 P value P CT × CCC <0.001 <0.001 <0.001 <0.001 24.86 0.024

CT × CCV <0.001 <0.001 <0.001 0.79 <0.001 21.17

CT × CH <0.001 <0.001 <0.001 <0.001 0.876 <0.001 12 Symbols: (CCC) Corymbia citriodora subsp. citriodora, (CCV) C. citriodora subsp. variegata, (CH) C. henryi, (CT) C. torelliana, hybrids 13 (CT × CCC) C. torelliana × C. citriodora subsp. citriodora, (CT × CCV) C. torelliana × C. citriodora subsp. variegata, (CT × CH) C. 14 torelliana × C. henryi. 15

16 Table 5.S3 Results of REML analyses to detect the differences in Crown Damage Index mean ± s.e, defoliation 17 mean ± s.e and necrosis ± s.e. between taxa over time. Data sorted by taxon name at each assessment date. Assessment Taxa Crown Damage Index (CDI) Defoliation (0 - 5) Necrosis (0 - 5)

CCC 0.48 ± 0.25 a 0.28 ± 0.05 a 0.10 ± 0.04 abcdefghij

CCV 0.51 ± 0.11 abcdef 0.45 ± 0.05 abcd 0.35 ± 0.05 abcdefghijklmnopqrstu

CH 0.92 ± 0.28 abcdefgh 0.56 ± 0.07 abcd 0.38 ± 0.06 abcdefghijklmnopqrstu

Apr-Aug-17 CT 0.61 ± 0.25 abc 0.36 ± 0.06 ab 0.06 ± 0.03 abcdef

CT × CCC 0.56 ± 0.23 abcde 0.44 ± 0.08 abcd 0.14 ± 0.06 abcdefghijk

CT × CCV 0.52 ± 0.25 ab 0.43 ± 0.08 abcd 0.09 ± 0.04 abcdefg

CT × CH 1.24 ± 0.68 abcdefg 0.56 ± 0.10 abcdef 0.09 ± 0.05 abcdefghi

CCC 0.40 ± 0.12 abcd 0.57 ± 0.07 abcde 0.11 ± 0.04 abcdefghij

CCV 3.83 ± 0.75 beghijklmno 0.87 ± 0.04 efghijk 0.20 ± 0.04 abcdefghijklmn

CH 3.77 ± 1.39 abcdefghijklm 0.90 ± 0.04 efghijkl 0.27 ± 0.06 abcdefghijklmnopqrs

Aug-Nov-17 CT 2.87 ± 1.16 abcdefghi 0.68 ± 0.06 cdefg 0.03 ± 0.02 abcd

CT × CCC 0.67 ± 0.28 abcde 0.71 ± 0.08 bcdefgh 0.06 ± 0.04 abcde

CT × CCV 3.36 ± 1.28 abcdefghij 0.65 ± 0.09 bcdefg 0.02 ± 0.02 abcd

CT × CH 3.52 ± 1.00 abcdefghijklmnop 0.85 ± 0.11 defghij 0.00 ± 0.00 ab

CCC 1.86 ± 0.53 abcdefghij 1.08 ± 0.06 hijklmnop 0.29 ± 0.09 abcdefghijklmnopqrst

CCV 9.94 ± 1.67 pqrstuvwxyzA 1.52 ± 0.08 ruwxyzABC 0.23 ± 0.07 abcdefghijklmnop

CH 10.38 ± 1.98 prstuvwxyzA 1.70 ± 0.13 xACDE 0.17 ± 0.06 abcdefghijkl

Nov-Feb-18 CT 2.38 ± 0.73 abcdefghijklm 1.08 ± 0.07 hijklmnopq 0.00 ± 0.00 a

CT × CCC 4.64 ± 2.38 abcdefghijklmnopqr 1.29 ± 0.10 jlmnopqrstuvwxyz 0.21 ± 0.11 abcdefghijklm

CT × CCV 4.98 ± 1.85 eghijklmnopqrs 1.15 ± 0.11 ijklmnopqrst 0.00 ± 0.00 a

CT × CH 5.12 ± 1.53 bdefghijklmnopqrst 1.54 ± 0.16 opqrstuvwxyzABC 0.00 ± 0.00 ab 18

176

Appendix B 19 Table 5.S3 (Continued) Assessment Taxa Crown Damage Index (CDI) Defoliation (0 - 5) Necrosis (0 - 5)

CCC 18.78 ± 3.33 ABCD 1.28 ± 0.09 jnopqrstuvwxyz 1.11 ± 0.22 vxzA

CCV 36.76 ± 3.15 E 2.03 ± 0.14 F 1.48 ± 0.20 ABCD

CH 46.74 ± 4.33 F 2.02 ± 0.19 DF 1.86 ± 0.28 D

Feb-Apr-18 CT 12.08 ± 2.93 prstuvwxyzA 1.41 ± 0.12 oprstuvwxyzABC 0.02 ± 0.02 abcd

CT × CCC 12.66 ± 3.30 stuvwxyzA 1.56 ± 0.16 rtuvwxyzABC 0.68 ± 0.18 bcghijlmnopqrstuvwx

CT × CCV 16.28 ± 3.30 vzABC 1.61 ± 0.19 xzABCD 0.00 ± 0.00 a

CT × CH 14.55 ± 3.43 vwxyzAB 1.45 ± 0.18 nopqrstuvwxyzABC 0.09 ± 0.09 abcdefgh

CCC 2.06 ± 0.91 abcdefgh 0.35 ± 0.07 abc 0.71 ± 0.17 hkmqsuvwxy

CCV 8.91 ± 1.76 klmnpqrstuvwxy 0.95 ± 0.12 ghijklm 1.00 ± 0.17 vxyz

CH 15.62 ± 3.22 tvxzA 1.31 ± 0.19 nopqrstuvwxyzAB 1.34 ± 0.26 zABC

Apr-Aug-18 CT 6.92 ± 1.98 gjklmnopqrstu 1.43 ± 0.15 rstuvwxyzABC 0.02 ± 0.02 abcd

CT × CCC 6.62 ± 2.83 abcdefghijklmnopqr 0.82 ± 0.13 defghi 0.58 ± 0.23 abcdfghijklmnopqrstuv

CT × CCV 10.09 ± 2.90 kmnopqrstuvwxyzA 1.09 ± 0.16 ijklmnopqrstu 0.00 ± 0.00 a

CT × CH 11.94 ± 3.44 npqrstuvwxyzA 1.29 ± 0.23 ijklmnopqrstuvwx 0.24 ± 0.15 abcdefghijklmnopq

CCC 3.06 ± 1.01 abcdefghijklm 1.08 ± 0.04 hijklmnop 0.20 ± 0.08 abcdefghijklmno

CCV 8.17 ± 1.27 npqrstuvwxyz 1.23 ± 0.07 jnopqrstuv 0.40 ± 0.11 abcdefghijklmnopqrstu

CH 11.93 ± 2.11 rtuvwxyzA 1.22 ± 0.11 jmnopqrstuvw 1.12 ± 0.22 vxyzAB

Aug-Nov-18 CT 2.06 ± 0.49 abcdefghijk 1.03 ± 0.06 hijklmn 0.02 ± 0.02 abcd

CT × CCC 3.42 ± 1.19 abcdefghijklmnopq 1.12 ± 0.06 hijklmnopqrs 0.24 ± 0.12 abcdefghijklmnopq

CT × CCV 2.60 ± 1.00 abcdefghijkl 1.00 ± 0.08 fghijklmn 0.00 ± 0.00 a

CT × CH 5.69 ± 2.43 bdefghijklmnopqrstuv 1.14 ± 0.10 ghijklmnopqr 0.19 ± 0.11 abcdefghijklm

CCC 5.03 ± 2.03 bcefghijklmn 1.26 ± 0.09 jmnopqrstuvwxy 0.26 ± 0.11 abcdefghijklmnopqr

CCV 13.70 ± 2.59 tvxzA 1.61 ± 0.12 xzABC 0.63 ± 0.15 chikmoqrstuvw

CH 25.46 ± 4.53 BD 1.47 ± 0.13 orstuvwxyzABC 1.55 ± 0.28 ACD

Nov-Mar-19 CT 5.38 ± 1.03 jklmnopqrstuvw 1.36 ± 0.08 nopqrstuvwxyzAB 0.03 ± 0.02 abcd

CT × CCC 5.90 ± 3.45 abcdefghijklmnopqr 1.06 ± 0.04 ghijklmno 0.30 ± 0.21 abcdefghijklmnopqrstu

CT × CCV 5.48 ± 1.18 gijklmnopqrstuvwx 1.29 ± 0.11 jmnopqrstuvwxyz 0.00 ± 0.00 a

CT × CH 5.46 ± 1.42 eghijklmnopqrstuv 1.33 ± 0.16 ijklmnopqrstuvwxyzA 0.00 ± 0.00 abc

Repeated measures REML F ndf ddf P F ndf ddf P F ndf ddf P

Taxon 10.7 6 18.2 <0.001 5.8 6 18 0.002 7.4 6 18.2 <0.001

Time 123.7 6 2698.4 <0.001 108.9 6 2698.2 <0.001 35.5 6 2698.3 <0.001

Taxon × Time 4.6 36 2698.6 <0.001 2.6 36 2698.5 <0.001 4.8 36 2698.4 <0.001 20 Symbols: (CCC) Corymbia citriodora subsp. citriodora, (CCV) C. citriodora subsp. variegata, (CH) C. henryi, (CT) C. torelliana, hybrids 21 (CT × CCC) C. torelliana × C. citriodora subsp. citriodora, (CT × CCV) C. torelliana × C. citriodora subsp. variegata, (CT × CH) C. 22 torelliana × C. henryi. 23

177

Appendix B 24 Table 5.S4 Results of Spearman correlations to detect the interaction between climate, performance and damage 25 parameters for the combined assessments. CCC CCV

MR Tmax Tmin GR F D N MR Tmax Tmin GR F D N

F value F value

MR 0.607 0.643 0.515 0.424 0.336 0.060 0.607 0.643 0.439 0.313 0.320 -0.038

Tmax <0.001 0.964 0.457 0.155 0.524 -0.012 <0.001 0.964 0.409 0.018 0.407 -0.094

Tmin <0.001 <0.001 0.485 0.171 0.541 0.027 <0.001 <0.001 0.401 0.042 0.424 -0.036

GR <0.001 <0.001 <0.001 0.243 0.347 0.064 <0.001 <0.001 <0.001 0.294 0.235 -0.014 461, P value P 461, value P 756,

F <0.001 0.001 <0.001 <0.001 -0.030 -0.169 <0.001 0.637 0.267 <0.001 -0.131 -0.073

D 504 - n= <0.001 <0.001 <0.001 <0.001 0.517 0.138 697 - n= <0.001 <0.001 <0.001 <0.001 0.001 -0.079

N 0.203 0.798 0.571 0.172 <0.001 0.003 0.311 0.013 0.341 0.711 0.053 0.037

CH CT

MR Tmax Tmin GR F D N MR Tmax Tmin GR F D N

F value F value

MR 0.607 0.643 0.387 0.267 0.258 -0.049 0.607 0.643 0.372 0.268 0.139 -0.050

Tmax <0.001 0.964 0.358 -0.051 0.335 -0.030 <0.001 0.964 0.364 -0.027 0.225 -0.035

Tmin <0.001 <0.001 0.347 0.001 0.335 0.033 <0.001 <0.001 0.389 0.007 0.247 -0.028

GR <0.001 <0.001 <0.001 0.207 0.155 0.075 <0.001 <0.001 <0.001 -0.024 0.348 -0.049 504, P value P 504, value P 504,

F <0.001 0.281 0.977 <0.001 -0.091 0.056 <0.001 0.576 0.887 0.620 -0.314 -0.034

D 448 - n= <0.001 <0.001 <0.001 0.001 0.055 -0.092 442 - n= 0.003 <0.001 <0.001 <0.001 <0.001 -0.039

N 0.303 0.524 0.486 0.114 0.233 0.051 0.293 0.457 0.554 0.302 0.471 0.413

CT × CCC CT × CCV

MR Tmax Tmin GR F D N MR Tmax Tmin GR F D N

F value F value

MR 0.607 0.643 0.455 0.439 0.274 0.006 0.607 0.643 0.332 0.261 0.165 -0.135

Tmax <0.001 0.964 0.404 0.160 0.358 -0.046 <0.001 0.964 0.297 -0.030 0.279 -0.117

Tmin <0.001 <0.001 0.408 0.205 0.371 0.001 <0.001 <0.001 0.316 -0.015 0.302 -0.117

GR <0.001 <0.001 <0.001 0.296 0.280 0.004 <0.001 <0.001 <0.001 0.121 0.212 -0.155 238, P value P 238, value P 378,

F <0.001 0.013 0.001 <0.001 -0.045 -0.122 <0.001 0.594 0.787 0.031 -0.212 -0.153

D 252 - n= <0.001 <0.001 <0.001 <0.001 0.489 0.105 316 - n= 0.003 <0.001 <0.001 <0.001 <0.001 0.014

N 0.922 0.483 0.983 0.950 0.061 0.107 0.017 0.038 0.038 0.006 0.006 0.803

CT × CH

MR Tmax Tmin GR F D N

F value

MR 0.607 0.643 0.393 0.360 0.213 -0.179

Tmax <0.001 0.964 0.410 0.057 0.285 -0.208

Tmin <0.001 <0.001 0.446 0.096 0.272 -0.194

GR <0.001 <0.001 <0.001 0.046 0.240 -0.007 252, P value P 252,

F <0.001 0.464 0.221 0.558 -0.252 -0.158

D 165 - n= 0.006 <0.001 <0.001 0.002 0.001 -0.071

N 0.021 0.007 0.012 0.932 0.043 0.366

26 Symbols: (MR) mean daily rainfall; (Tmax) maximum temperature; (Tmin) minimum temperature; (G) growth rate; (F) flushing percentage; (D) 27 defoliation; (N) necrosis; (CCC) Corymbia citriodora subsp. citriodora, (CCV) C. citriodora subsp. variegata, (CH) C. henryi, (CT) C. 28 torelliana, hybrids (CT × CCC) C. torelliana × C. citriodora subsp. citriodora, (CT × CCV) C. torelliana × C. citriodora subsp. variegata, 29 (CT × CH) C. torelliana × C. henryi. 30

178

Appendix B 31 Table 5.S5 Results of Spearman correlations to detect the interaction between performance and damage 32 parameters at each assessment interval.

Aug-17 Nov-17 Feb-18

G F D N G F D N G F D N

F value F value F value

G -0.001 0.307 0.179 -0.217 0.361 0.251 0.334 -0.030 -0.039

F 0.995 -0.109 -0.020 0.083 -0.282 -0.273 0.007 -0.524 -0.005

CCC D 0.009 0.365 0.318 0.003 0.023 -0.091 0.815 <0.001 0.104

N value P 71, n = 0.136 0.870 0.007 value P 65, n = 0.043 0.028 0.469 value P 64, n = 0.757 0.971 0.412

G 0.066 0.138 0.156 -0.078 0.241 0.261 0.443 0.021 0.165

F 0.495 -0.233 0.005 0.437 -0.013 -0.326 <0.001 -0.515 0.202

CCV D 0.155 0.015 0.182 0.015 0.896 0.038 0.840 <0.001 -0.088 n = 99, P value P 99, n = N value P 108, n = 0.106 0.959 0.060 value P 102, n = 0.008 0.001 0.701 0.102 0.045 0.384

G 0.057 0.060 0.467 -0.198 0.251 0.263 0.336 0.144 0.115

F 0.631 -0.179 0.214 0.109 0.064 -0.015 0.008 -0.355 0.223

CH D 0.618 0.133 -0.091 0.040 0.607 0.081 0.265 0.005 -0.173 n = 72, P value P 72, n = n = 67, P value P 67, n = value P 62, n = N <0.001 0.071 0.446 0.031 0.905 0.516 0.375 0.081 0.178

G 0.039 0.323 0.048 -0.177 0.382 -0.007 0.455 0.055 -

F 0.743 -0.260 -0.102 0.145 -0.157 -0.266 <0.001 -0.110 - CT D 0.006 0.027 -0.046 0.001 0.197 0.113 0.666 0.389 - n = 72, P value P 72, n = n = 69, P value P 69, n = value P 63, n = N 0.687 0.392 0.700 0.958 0.027 0.356 - - -

G 0.132 -0.067 -0.116 0.026 0.583 0.207 0.106 -0.150 -0.101

F 0.442 -0.135 -0.066 0.882 0.122 -0.087 0.552 -0.159 -0.159

D 0.696 0.431 0.126 <0.001 0.484 0.156 0.396 0.368 -0.201 CT ×CT CCC N value P 36, n = 0.499 0.702 0.465 value P 35, n = 0.232 0.618 0.372 value P 34, n = 0.570 0.368 0.255

G 0.056 0.178 -0.165 -0.031 0.331 -0.061 0.315 0.067 -

F 0.691 0.057 -0.109 0.831 -0.224 -0.349 0.033 -0.467 -

D 0.202 0.684 0.296 0.020 0.121 0.107 0.659 0.001 - CT ×CT CCV n = 53, P value P 53, n = n = 49, P value P 49, n = value P 46, n = N 0.238 0.436 0.032 0.676 0.014 0.464 - - -

G -0.211 0.029 -0.031 -0.331 0.321 - 0.228 0.212 -

F 0.254 -0.392 -0.349 0.106 -0.291 - 0.295 -0.345 -

D 0.877 0.029 0.271 0.118 0.158 - 0.332 0.107 - CT ×CT CH n = 31, P value P 31, n = n = 25, P value P 25, n = value P 23, n = N 0.870 0.054 0.141 ------33 Symbols: (G) growth rate; (F) flushing percentage; (D) defoliation; (N) necrosis (CCC) Corymbia citriodora subsp. citriodora, (CCV) C. 34 citriodora subsp. variegata, (CH) C. henryi, (CT) C. torelliana, hybrids (CT × CCC) C. torelliana × C. citriodora subsp. citriodora, (CT × 35 CCV) C. torelliana × C. citriodora subsp. variegata, (CT × CH) C. torelliana × C. henryi. 36

179

Appendix B 37 Table 5.S5 (Continued).

38 Apr-18 Aug-18 Nov-18 Mar-19

G F D N G F D N G F D N G F D N

F value F value F value F value

G 0.405 -0.222 0.049 0.235 0.120 -0.205 0.186 -0.066 -0.030 -0.068 -0.082 -0.119

F 0.001 -0.201 -0.163 0.062 0.061 -0.180 0.137 -0.291 -0.155 0.591 -0.462 -0.355

CCC D 0.077 0.111 0.172 0.344 0.633 0.051 0.599 0.019 0.390 0.515 <0.001 0.152

N value P 64, n = 0.702 0.197 0.174 value P 64, n = 0.104 0.154 0.687 value P 65, n = 0.811 0.217 0.001 value P 65, n = 0.345 0.004 0.228

G 0.407 -0.222 0.127 0.206 -0.042 0.111 0.121 0.258 -0.105 0.436 -0.198 -0.370

F <0.001 -0.182 0.250 0.043 -0.080 -0.162 0.244 -0.065 -0.206 0.000 -0.086 -0.323

CCV D 0.026 0.070 -0.211 0.686 0.435 -0.205 0.012 0.528 -0.186 0.054 0.407 -0.051 n = 97, P value P 97, n = value P 95, n = value P 95, n = N value P 100, n = 0.209 0.012 0.035 0.279 0.113 0.044 0.313 0.046 0.070 0.000 0.001 0.624

G 0.345 -0.248 0.383 0.130 0.091 0.017 0.247 0.033 -0.013 0.377 -0.064 -0.217

F 0.005 -0.117 0.249 0.319 0.230 0.052 0.059 0.074 -0.255 0.003 -0.171 -0.134

CH D 0.048 0.359 -0.248 0.483 0.074 -0.007 0.803 0.575 -0.358 0.626 0.189 -0.138

N value P 64, n = 0.002 0.048 0.048 value P 61, n = 0.896 0.688 0.955 value P 59, n = 0.920 0.051 0.005 value P 61, n = 0.092 0.303 0.288

G 0.285 0.016 0.017 0.181 0.004 0.099 -0.188 0.284 0.088 0.219 -0.025 -0.128

value F 0.023 -0.293 -0.165 0.175 -0.312 0.008 0.157 -0.369 0.064 0.096 -0.209 -0.129

CT D 0.903 0.020 0.193 0.973 0.017 -0.066 0.030 0.004 -0.006 0.853 0.112 -0.109

N value P 63, n = 0.892 0.196 0.129 value P 58, n = 0.460 0.951 0.620 value P 58, n = 0.510 0.631 0.963 P 59, n = 0.336 0.329 0.412

G 0.420 -0.074 0.078 0.576 -0.599 -0.065 0.146 -0.040 -0.009 0.277 0.007 -0.214

F 0.013 -0.216 -0.140 <0.001 -0.495 0.028 0.417 -0.329 -0.241 0.118 0.000 -0.061

D 0.677 0.219 0.244 <0.001 0.003 -0.073 0.827 0.061 0.146 value P 33, 0.970 1.000 -0.065 CT ×CT CCC N value P 34, n = 0.662 0.430 0.164 value P 33, n = 0.720 0.876 0.687 value P 33, n = 0.959 0.177 0.417 n = 0.231 0.735 0.721

G 0.291 -0.152 - 0.281 -0.266 - -0.015 -0.337 - -0.026 -0.001 -

F 0.058 -0.223 - 0.079 -0.088 - 0.924 -0.244 - value 0.870 -0.111 -

D 0.329 0.150 - 0.098 0.588 - 0.027 0.115 - 0.993 0.486 - CT ×CT CCV N value P 43, n = - - - value P 39, n = - - - value P 43, n = - - - P 42, n = - - -

G 0.133 0.088 -0.017 e -0.093 -0.328 0.039 -0.048 -0.283 0.312 0.352 0.144 -

F 0.555 0.141 -0.087 0.690 -0.113 -0.036 0.835 -0.484 -0.322 0.117 -0.260 -

D 0.696 0.532 -0.120 0.147 0.627 -0.275 0.213 0.026 -0.132 0.533 0.255 - CT ×CT CH

N value P 22, n = 0.939 0.700 0.594 valu P 21, n = 0.867 0.877 0.228 value P 21, n = 0.168 0.155 0.569 value P 21, n = - - - 39 Symbols: (G) growth rate; (F) flushing percentage; (D) defoliation; (N) necrosis (CCC) Corymbia citriodora subsp. citriodora, (CCV) C. citriodora subsp. variegata, (CH) C. henryi, (CT) C. torelliana, hybrids (CT × 40 CCC) C. torelliana × C. citriodora subsp. citriodora, (CT × CCV) C. torelliana × C. citriodora subsp. variegata, (CT × CH) C. torelliana × C. henryi. 180