Forestry, Faculty of
DSpace
UBCV
Zhang, Yiyuan
Master of Science - MSc
University of British Columbia
The Mountain pine beetle (MPB) has symbiotic relationships with Ophiostomatoid fungi (Ophiostomatales, Ascomycota), also known as blue stain fungi. The fungal symbionts associated with MPB contribute to the success of beetle survival, reproduction and outbreaks by providing a number of benefits to beetles. The multi-partite mutualism between fungi and MPB has remained remarkably stable over a long period of evolutionary time without exhibiting strong direct competition. We hypothesize that niche partitioning is a stabilizing mechanism which is important for the maintenance of coexistence and diversity of fungal species. The interspecific and intraspecific phenotypic characteristics of three fungal symbionts from a wide geographic and environmental range were investigated under different temperatures in vitro. The study found that three fungal species are differentially adapted to temperatures. The interspecific comparison indicates that Grosmannia clavigera and Leptographium longiclavatum were able to grow better at lower temperatures than Ophistoma montium, while Ophistoma montium displayed greater tolerance to extreme high temperatures. The intraspecific variation within species showed that isolates of the same species from different genetic groups have different temperature tolerances. Leptographium longiclavatum exhibited greater variability in response to temperature than Grosmannia clavigera and Ophistoma montium. The phenotypic data agrees with the genetic population structures of three fungi species. My research indicated that differential temperature tolerances of three fungal species may mediate competition among symbionts, thus influencing dynamics and compositions of the symbiosis in naturally attacked trees. Different growth responses to variable environmental conditions allow the competing species to capture resources at different times without expending energy on direct competition. By maintaining multipartite relationships with these fungi species, bark beetles are more likely to obtain benefits provided by different species of fungi under fluctuating environmental conditions. The complementarity of these nutritional symbionts could facilitate the colonization of new habitats and a broad ecological amplitude for MPB.
PHENOTYPIC CHARACTERIZATION OF MOUNTAIN PINE BEETLE FUNGAL SYMBIONTS UNDER A TEMPERATURE GRADIENT by YIYUAN ZHANG B.Sc., The University of British Columbia, 2014 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2019 © Yiyuan Zhang, 2019 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis/dissertation entitled: Phenotypic characterization of Mountain Pine Beetle fungal symbionts under a temperature gradient submitted by Yiyuan Zhang in partial fulfillment of the requirements for the degree of Master of Science in Forestry Examining Committee: Richard Hamelin Supervisor Yousry El-Kassaby Supervisory Committee Member Christopher P Chanway Supervisory Committee Member Nicolas Feau Additional Examiner Additional Supervisory Committee Members: Supervisory Committee Member Supervisory Committee Member iii Abstract The Mountain pine beetle (MPB) has symbiotic relationships with Ophiostomatoid fungi (Ophiostomatales, Ascomycota), also known as blue stain fungi. The fungal symbionts associated with MPB contribute to the success of beetle survival, reproduction and outbreaks by providing a number of benefits to beetles. The multi-partite mutualism between fungi and MPB has remained remarkably stable over a long period of evolutionary time without exhibiting strong direct competition. We hypothesize that niche partitioning is a stabilizing mechanism which is important for the maintenance of coexistence and diversity of fungal species. The interspecific and intraspecific phenotypic characteristics of three fungal symbionts from a wide geographic and environmental range were investigated under different temperatures in vitro. The study found that three fungal species are differentially adapted to temperatures. The interspecific comparison indicates that Grosmannia clavigera and Leptographium longiclavatum were able to grow better at lower temperatures than Ophistoma montium, while Ophistoma montium displayed greater tolerance to extreme high temperatures. The intraspecific variation within species showed that isolates of the same species from different genetic groups have different temperature tolerances. Leptographium longiclavatum exhibited greater variability in response to temperature than Grosmannia clavigera and Ophistoma montium. The phenotypic data agrees with the genetic population structures of three fungi species. My research indicated that differential temperature tolerances of three fungal species may mediate competition among symbionts, thus influencing dynamics and compositions of the symbiosis in naturally attacked trees. Different growth responses to variable environmental conditions allow the competing species to capture resources at different times without expending energy on direct competition. iv By maintaining multipartite relationships with these fungi species, bark beetles are more likely to obtain benefits provided by different species of fungi under fluctuating environmental conditions. The complementarity of these nutritional symbionts could facilitate the colonization of new habitats and a broad ecological amplitude for MPB. v Lay Summary The mountain pine beetle (MPB) is an aggressive mortality agent of pine forests in North America. Blue stain fungi form multipartite symbiotic associations with MPB and contribute to the success of beetles’ survival, reproduction and outbreaks. The three fungal species have coexisted over a long time without exhibiting strong competition, indicating that there is a mechanism that maintains the stability of the long-term symbiosis. We hypothesize that niche partitioning enables these fungi to co-occur in similar habitats. In this study, the growth rate of three fungal symbionts was compared under different temperatures in vitro. The results indicated that three fungal species are differentially adapted to temperatures. Different growth responses allow the competing species to capture resources at different times without expending energy on direct competition. The complementarity of these nutritional symbionts could provide benefits for MPB at variable environmental conditions and facilitate MPB to colonize broader ecological amplitude. vi Preface This thesis is an original intellectual product of the author, Y. Zhang. The sample collection was a collaborative work of D.I.O. Alayon. Experiment design, fungal inoculation, and temperature tolerance experiments were primarily done by me in the Forest Pathology lab at the University of British Columbia, Point Grey campus. I conducted Data analysis with the assistance from A. Capron who helped me with the codes in R. I was responsible for the manuscript composition. R. Hamelin provided the editorial support and supervised throughout the project. A version of the thesis has been published as part of Alayon, D.I.O., Tsui, C.K.M., Feau, N., Capron, A., Dhillon, B., Zhang, Y., Alamouti, S.M., Boone, C.K., Carroll, A.L., Cooke, J.E.K., Roe, A.D., Sperling, F.A.H., & Hamelin, R.C. (2017). Genetic and genomic evidence of niche partitioning and adaptive radiation in mountain pine beetle fungal symbionts. Molecular Ecology, 26, 2077-2091. I conducted the experiment of the section on “phenotypic characterization of fungal growth under a temperature gradient”. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ..................................................................................................................................x List of Figures .............................................................................................................................. xii List of Abbreviations ................................................................................................................. xiv Acknowledgements ......................................................................................................................xv Dedication ................................................................................................................................... xvi 1 Introduction .................................................................................................................................1 1.1 Literature review ............................................................................................................. 1 1.1.1 MPB epidemics ........................................................................................................... 1 1.1.1.1 Hosts and range ..................................................................................................... 1 1.1.1.2 Infestation and damage ......................................................................................... 3 1.1.1.3 Life history ............................................................................................................ 4 1.1.1.4 Climate change and range expansion .................................................................... 5 1.1.2 Beetle-fungal symbiosis .............................................................................................. 7 1.1.2.1 Diversity of fungal associates ............................................................................... 7 1.1.2.2 Benefits for beetles ............................................................................................... 8 1.1.2.2.1 Nutritional supplementation ........................................................................ 8 1.1.2.2.2 Overcoming tree defenses ......................................................................... 10 viii 1.1.2.3 Benefits for fungi ................................................................................................ 12 1.1.3 Ecological niche theory ............................................................................................. 12 1.1.4 Adaptation of fungi associates to temperature .......................................................... 14 1.1.5 Population genomics ................................................................................................. 16 1.2 Objectives ..................................................................................................................... 20 2 Materials and methods .............................................................................................................21 2.1 Fungal isolates .............................................................................................................. 21 2.2 Temperature tolerance experiments .............................................................................. 21 2.3 Data analysis ................................................................................................................. 27 3 Results ........................................................................................................................................28 3.1 Comparison of growth among species .......................................................................... 28 3.2 Comparison of growth among genetic groups within species ...................................... 31 3.2.1 Grosmannia clavigera .............................................................................................. 31 3.2.2 Leptographium longiclavatum ................................................................................. 32 3.2.3 Ophiostoma montium ............................................................................................... 34 4 Discussion ..................................................................................................................................37 5 Conclusion .................................................................................................................................43 References .....................................................................................................................................44 Appendices ....................................................................................................................................62 Appendix A: Information for isolates of G.clavigera used in temperature tolerance experiments ............................................................................................................................... 62 Appendix B: Information for isolates of L.longiclavatum used in temperature tolerance experiments ............................................................................................................................... 67 ix Appendix C: Information for isolates of O.montium used in temperature tolerance experiments ............................................................................................................................... 72 Appendix D: R script ................................................................................................................ 77 x List of Tables Table 1. ANOVA of the growth of G. clavigera, L. longiclavatum and O. montium at 5-30°C ....................................................................................................................................................... 29 Table 2. ANOVA of the growth of G. clavigera, L. longiclavatum and O. montium at 15-25°C ....................................................................................................................................................... 30 Table 3. Tukey multiple comparisons of means of the growth of G. clavigera, L. longiclavatum and O. montium at 15-25°C ......................................................................................................... 30 Table 4. ANOVA of the growth of G. clavigera, L. longiclavatum and O. montium at 30°C …....................................................................................................................................................... 30 Table 5. Tukey multiple comparisons of means of the growth of G. clavigera, L. longiclavatum and O. montium at 30°C ............................................................................................................... 30 Table 6. ANOVA of the growth of G. clavigera from three genetic groups at 15-25°C .......... 32 Table 7. Tukey multiple comparisons of means of the growth of G. clavigera from three genetic groups at 15-25°C ........................................................................................................................ 32 Table 8. ANOVA of the growth of G. clavigera from three genetic groups at 30°C ................. 32 Table 9. ANOVA of the growth of L. longiclavatum from three genetic groups at 15-25°C ... 33 Table 10. Tukey multiple comparisons of means of the growth of L. longiclavatum from three genetic groups at 15-25°C ............................................................................................................ 34 Table 11. ANOVA of the growth of L. longiclavatum from three genetic groups at 30°C ........ 34 Table 12. Tukey multiple comparisons of means of the growth of L. longiclavatum from three genetic groups at 30°C ................................................................................................................. 34 Table 13. ANOVA of the growth of O. montium from three genetic groups at 15-25°C .......... 35 xi Table 14. Tukey multiple comparisons of means of the growth of O. montium from three genetic groups at 15-25°C ........................................................................................................................ 36 Table 15. ANOVA of the growth of O. montium from three genetic groups at 30°C ................ 36 xii List of Figures Figure 1. Population structure of G. clavigera based on eight microsatellites using TESS (reproduced from Tsui et al. 2012). ............................................................................................. 17 Figure 2. Population structure of L. longiclavatum based on ten microsatellites using STRUCTURE (reproduced from Tsui et al., 2014). ................................................................... 18 Figure 3. Pairwise genetic differentiation (Nei’s unbiased FST-values) among genetic groups and populations of G. clavigera, L. longiclavatum and O. montium (reproduced from Alayon et al. 2017). ............................................................................................................................................ 19 Figure 4. Distribution map of the fungal samples used in this study, G. clavigera, L. longiclavatum and O. montium samples were grouped into “North” in red, “Central” in blue, “USA” in yellow for comparisons of growth characteristics ........................................................ 22 Figure 5. Example of a Petri dish containing a fungal culture. An agar plug was placed in the middle of the plate and growth was assessed by measuring the diameter once a day .................. 23 Figure 6. Photos of growth on Petri dish for isolates of G. clavigera, L. longiclavatum and O. montium used in temperature tolerance experiments .................................................................... 24 Figure 7. Area under the curve for 112 isolates of the three fungal symbionts (37 G. clavigera, 40 L. longiclavatum and 35 O. montium) at six temperatures (from 5 to 30 °C with steps of 5 °C) ....................................................................................................................................................... 29 Figure 8. Area under the curve for G. clavigera from three genetic groups at six temperatures (from 5 to 30 °C with steps of 5 °C) ............................................................................................. 31 Figure 9. Area under the curve for L. longiclavatum at six temperatures (from 5 to 30 °C with steps of 5 °C) ................................................................................................................................ 33 xiii Figure 10. Area under the curve for O. montium at six temperatures (from 5 to 30 °C with steps of 5 °C) ......................................................................................................................................... 34 xiv List of Abbreviations AB Alberta AFLP Amplified Fragment Length Polymorphism ANOVA Analysis of Variance AUC Area Under the Curve BC British Columbia DNA Deoxyribonucleic Acid Gc Grosmannia clavigera HSD Honestly Significant Different Ll Leptographium longiclavatum MEA Malt Extract Agar MPB Mountain Pine Beetle Om Ophistoma montium PCR Polymerase Chain Reaction rDNA Ribosomal Deoxyribonucleic Acid SNP Single nucleotide polymorphism xv Acknowledgements Firstly, I would like to express my sincere thanks and gratitude to my supervisor Dr. Richard Hamelin for his patience, motivation and support of my study and research. He enlightened me the first glance of research, encouraged me to continue my study when I was thinking about giving up because of health issues, and helped me to overcome numerous obstacles I have been facing through my research. Without his guidance it would not be possible to finish my research and thesis. Besides my advisor, I would like to thank the rest of my committee members: Dr. Yousry El-Kassaby, Dr. Christopher P Chanway, Dr. Nicolas Feau, Dr. Allan Carroll, for their valuable comments, advise and feedback, and also for the questions that widened my research from various perspectives. My sincere thanks also goes to Dr. Dario Alayon, Dr. Clement K. M. Tsui, and Dr. Arnaud Capron for their advice and help in my research. In addition, I would like to thank Padnimi Herath, Yueh Hesther, and Pia Smets, who gave me access to the laboratory and research facilities. Finally, I would like to express my very profound gratitude to my parents for their unfailing support and continuous encouragement throughout my years of studies and through the process of writing this thesis. xvi Dedication Perfection is the enemy of good. Perfection is a disguise of insecurity. As long as you are not dead, no mistake is a “big deal”.1 1. Introduction The mountain pine beetle has caused one of the worst forest pest outbreaks in modern recorded history, attacking and killing pines over 18.3 million hectares of forests in BC and over 6 million hectares in Alberta. It has been established that the beetle, in order to be successful, relies upon fungal symbionts for its attacking of the trees, for nutrition and for the maturation process. Much knowledge has been generated to better understand the interaction between the beetles and the fungi (Cullingham, et al. 2018). However, we still don’t understand why the beetles consistently requires fungal symbionts belonging to three different species and what their individual roles are. In this thesis I propose to investigate the niche adaptation of these fungal associates by examining their temperature profiles. 1.1. Literature review 1.1.1. MPB epidemics 1.1.1.1. Hosts and range The mountain pine beetle (MPB), Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae, Scolytinae), known as bark beetle (Gibson, Kegley & Bentz, 2009), is an indigenous species that coevolved with coniferous forest ecosystems in western North America (Bentz et al., 2010; Cullingham et al., 2011). MPB is an aggressive mortality agent of pine (Pinaceae) forests in western North America (Bentz et al., 2010; Safranyik & Carroll, 2006). It can successfully infest 22 species of Pinus L., including both native and introduced pine species in North America (Bentz et al., 2010; Rice & Langor, 2009; Safranyik et al., 2010). The major hosts of MPB include lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm), ponderosa pine (P. ponderosa, P. Laws. Ex C. Laws), whitebark pine (P. albicaulis Engelm.), 2 limber pine (P. flexilis James.), and western white pine (P. monticola Douglas ex D. Don) (Gibson et al., 2009; Safranyik & Carroll, 2006; Nealis & Peter, 2008; Shore et al., 2006; Natural Resources Canada, 2017). Among them, lodgepole pine is the principal host in British Columbia, Canada (Natural Resources Canada, 2017; Safranyik et al., 2010). Most recently, jack pine (P. banksiana Lamb.) has been attacked by MPB in expanded range (Natural Resources Canada, 2017). Some other eastern species of North American Pine are also susceptible to MPB, including eastern white pine (P. strobus L.), pitch pine (P. rigida Mill.) and red pine (P. resinosa Aiton) (Safranyik et al., 2010). Some non-pine hosts, such as firs (Abies Mill.), spruces (Picea A.Dietr.) and western larch, are infested occasionally when they are planted with pines, but MPB cannot reproduce successfully on non-pine hosts (Gibson et al., 2009; Natural Resources Canada, 2017; Beal, 1939; Evenden et al., 1943; Furniss & Schenk, 1969). However, successful brood development has been observed in interior hybrid spruce (P. engelmannii Parry ex Engelm.6 glauca (Moench) Voss) and black spruce (Picea mariana (Mill.) Britton, Stearns and Poggenb) during the extreme outbreaks in Canada since the 2000s (Huber et al., 2009). The range of mountain pine beetle generally follows its major host pine species throughout western North America (Gibson et al., 2009). MPB is widely distributed from Pacific Coast east to the Black Hills of South Dakota in USA, and from central British Columbia and western Alberta in Canada to northern Baja California in Mexico and southern Arizona in USA (Gibson et al., 2009; Safranyik et al. ,2010). It was also found in southwestern Saskatchewan, where the isolated population of pine surrounded by prairie in the Cypress Hills area was infested in the early 1980s (Natural Resources Canada, 2017). Since the 2000s, MPB populations have expanded the range beyond historical records, into east of the Rocky Mountains in northeastern British Columbia and northern Alberta (Natural Resources Canada, 2017). MPB 3 also has a wide range of elevation from near sea level in British Columbia to 11, 000 feet in southern California (Gibson et al., 2009). 1.1.1.2. Infestation and damage The successfully infested trees have reddish boring dust on the outer bark around beetle entrances as the first sign of attack (Natural Resources Canada, 2017). Attacked trees also produce “pitch tubes” which are cream to dark-red phloem boring dust mixed with resin. “Pitch tubes” can “pitch out” beetles and prevent beetles from inner bark (Gibson et al., 2009). The sapwood of attacked trees turns to blue by the fall in the year of attack (Natural Resources Canada, 2017). The beetles overcome tree defenses and kill trees by girdling the phloem (Gibson et al., 2009). After the attack, needles change from green to yellow, orange, red and then rusty brown at the end of the year of attack (Gibson et al., 2009). Grey trees with fading of upper crown or no needle in the same year is another sign of infestation (Gibson et al., 2009; Natural Resources Canada, 2017). The outbreaks and epidemics of MPB have caused massive mortality of lodgepole pine over large areas, resulting in enormous economic losses (Gibson et al., 2009; Safranyik et al., 2010). It is reported that MPB has killed around 53% of commercial lodgepople pine in British Columbia since the early 1990s, and has infested over 18 million ha of forest in British Columbia by 2012 (Natural resources Canada, 2017). By 2017, mountain pine beetle has damaged approximately 752 million cubic metres of merchantable pine, which takes up 58% of all pine volume in British Columbia (Natural resources Canada, 2017). The epidemics of mountain pine beetle have a significant impact on forest community compositions and successional pathways (Gibson et al., 2009). After the disturbance of beetles and subsequent wildfire, pioneer species (such as lodgepole pine) are replaced by late 4 successional tree species and plant associations (Veblen et al., 1991; Gibson et al., 2009). The extensive tree mortality reduces forest carbon uptake and increases emissions from dead trees, and thus converts the forest from a carbon sink to carbon source (Kurz et al., 2008). The reduction of thermal cover could affect the composition and distribution of wildlife species such as elk, deer and birds (Gibson et al., 2009). The widespread tree mortality associated of mountain pine beetle also has an influence in water yield and wildfire severity subsequently (Gibson et al., 2009). 1.1.1.3. Life history The typical life cycle of mountain pine beetle is one year in most of its range, but the time to complete a generation and the stage development rates vary depending on local temperatures (Eric, Jeffrey, & Polly, 2014; Natural Resources Canada, 2017). At high elevations and in cooler years, mountain pine beetles require one to two years to complete a life cycle (Gibson et al., 2009). Mountain pine beetles develop through the life stages of egg, larvae, pupae, and adult (Gibson et al., 2009; Logan & Bentz, 1999; Logan & Powell, 2001; Safranyik & Carroll, 2006). Female beetles mine into the inner bark and initiate attacks. Mountain pine beetles use a pheromone-mediated mass attack to overcome tree defences (Solheim & Krokene, 1998). Females produce highly effective aggregation pheromones to attract conspecifics of both sexes (Six and Paine 1998; Gibson et al., 2009). Anti-attractant pheromone is also produced to have an even spacing of attacks on tree bole (Raffa & Berryman, 1983). Long and vertical egg galleries are constructed after mating by the pair under the bark (Gibson et al., 2009; Solheim & Krokene, 1998). Tiny, pearl-white eggs are oviposited in small niches along both sides of egg galleries by females from late June to late August according to temperature (Paine, Raffa, & Harrington, 1997; Gibson et al., 2009; Six & Paine, 1998). During gallery construction, 5 mycangial fungi are inoculated into the phloem of host trees by beetles (Natural Resources Canada, 2017). After eclosion, the larvae feed on phloem and associated fungi, and excavate feeding galleries that extend outward at right angle from the egg gallery (Solheim & Krokene, 1998). Insects usually overwinter as larvae (Natural Resources Canada, 2017) and develop through four larval stages (instars) (Gibson et al., 2009; Six, 2013; Adams & Six, 2008). Larvae complete their development in the following spring and pupate in June or July in pupal chambers constructed at the ends of feeding galleries (Natural Resources Canada, 2017; Paine et al., 1997). Coincident with pupation, the fungi associates line the pupal chamber walls with a dense layer of conidiophores and conidia (asexual propagative spores) (Solheim & Krokene, 1998). The teneral (newly enclosed and sexually immature) adult beetles feed on tree phloem and dense fungal spore layers on the walls of pupal chamber for a period of days to weeks before emergence (Whitney, 1971; Six & Paine, 1997; Six & Paine, 1996). Mature adults emerge and disperse to attack new host trees in July or August (Gibson et al., 2009; Natural Resources Canada, 2017). 1.1.1.4. Climate change and range expansion Climate change has resulted in a rise of temperature in North America and more frequent extreme weather events such as greater duration and frequency of droughts (Seager et al., 2007). Because of anthropogenic climate change, previously climatically unfavourable environments may become climatically benign for MPB (Carroll et al., 2004). It has been demonstrated that there is an increase in the range of benign habitats by the examination of distribution of climatically suitable habitats in 10 year increments derived from climate normals (1921-1950 to 1971-2000) (Carroll et al., 2004). Hot summers facilitate beetle reproduction, and mild winter temperatures contribute to brood development and increase the overwinter survival of MPB (Safranyik & Carroll, 2006; Natural Resources Canada, 2017). The host trees are more 6 susceptible to MPB and associated fungi because of limited carbon assimilation and reduced defences caused by water deficit and drought stress (McDowell et al., 2008; Natural Resources Canada, 2017). Given the continuing warming in western North America, together with changes of precipitation patterns as well as high susceptibility of pine hosts, MPB has expanded well beyond its historic range recently, moving northward into northern British Columbia, eastward into north-central Alberta, and toward higher elevations (Carroll et al., 2004; Carroll et al., 2006; Bentz et al., 2010; Natural Resources Canada, 2017). Historically the MPB was only observed in southern regions of Alberta with two recorded outbreaks in 1940-1943 and 1985-1997 (Rice, Thormann, & Langor, 2008). In 1997, the MPB outbreak was observed in west-central Alberta with an increase of 2° latitude northward compared with its range in 1985 (Rice et al., 2008). In 2006, it was reported that mountain pine beetle spread northeastward into lodgepole ´ jack pine hybrid zone in western North Alberta (Rice & Langor, 2009), with a spread of 3° latitude northward and 5° longitude eastward since 1997 (Rice et al., 2008). Cullingham et al. (2011) have shown that the MPB can successfully attack natural jack pine stands at the leading edge of epidemics. Since jack pine (Pinus banksiana Lambert) is likely to be a compatible host, lodgepole ´ jack pine hybrid populations could provide a stepping-stone to mediate MPB range expansion into jack pine (Cullingham et al., 2011). Therefore, MPB could further expand eastward into boreal pine forests and become widespread across Canada, resulting in an ecological, economic and social disaster in the future (Carroll et al., 2006; Rice & Langor, 2009; Safranylk et al., 2010). 7 1.1.2. Beetle-fungal symbiosis 1.1.2.1. Diversity of fungal associates Symbiosis was first described as “nonparasitic interactions involving microbes” (Frank, 1877), and was further defined as “the living together of two differently named organisms” (Sapp, 1994), which is a ubiquitous phenomenon in nature (Feldhaar, 2011). Insects form symbiotic relationships with a variety of microorganisms to perform basic life functions and to exploit resources and previously unavailable ecological niches (Feldhaar, 2011; Aylward, Currie, & Suen, 2012; Six, 2013, Douglas, 2015). Bark beetles have symbiotic relationships with a vast array of microorganisms, including mites, bacteria, yeasts, and filamentous fungi (Paine et al., 1997; Six, 2003; Six & Klepzig, 2004). The symbioses between mountain pine beetle and fungi is diverse and widespread. Ophiostomatoid fungi (Ophiostomatales, Ascomycota), also known as blue stain fungi, are important bark beetle symbionts (Alayon et al., 2017). The interactions between beetles and blue stain fungi are complex, but their relationship has generally been considered mutualistic (Whitney 1982; Six & Paine 1998; Six, 2003; Adams & Six, 2007; Bleiker & Six, 2007; Six & Bentz, 2007). For D. ponderosea, there are three prevalent fungi consistently associated with the beetles and the pine trees: Grosmannia clavigera (Robinson-Jeffrey and Davidson) Zipfel, de Beer and Wingfield Wingfield [ º Ophiostoma clavigerum (Robinson-Jeffrey and Davidson) Harrington]; Leptographium longiclavatum Lee, Kim and Breuil, and Ophiostoma montium (Rumbold) von. Arx (Rumbold, 1941; Robinson, 1962; Whitney & Farris, 1970; Whitney, 1971; Six, 2003; Lee et al., 2005). However, studies have found a greater number of fungal species from several genera. Several fungal species have been isolated from D. ponderosae collected from sites in 8 British Columbia (Lee et al., 2006; Yamaoka et al., 1990; Rice et al., 2008; Six & Paine, 1999; Solheim & Krokene, 1998). Fungi are present in brood galleries, on larvae, pupae and adults of mountain pine beetle (Lee et al., 2006). The adults of mountain pine beetle feed on fungi and carry fungal spores in their mycangia, guts and on exoskeletons (Lee et al., 2006). Fungi can be passively disseminated on the exoskeleton of beetles (Six & Paine, 1997). There is also specific association between fungi and beetles which is called mycangium (Six & Paine, 1997). Mycangia are specialized integumental structures on the maxillary cardines of both sexes of adult beetles that evolved for the transmission and dissemination of fungi (Whitney & Farris, 1970). 1.1.2.2. Benefits for the beetles Bark beetles exploit highly specific niche which is the phloem layer of trees (Six, 2013). The woody tissues are poor nutrient resources and are low in particular nutrients and high in toxic defensive compounds (Six, 2013; Bleiker & Six, 2007; Six & Paine, 1997). Fungal symbionts contribute to the success of beetle survival, reproduction and outbreaks (Roe et al. 2011a). Fungal associates provide a number of benefits to mountain pine beetle, such as supplementing the diet with specific nutrients, overcoming host tree defenses and modifying environmental conditions for brood development (Bleiker and Six 2007; Lieutier et al., 2009; Six, 2003; Six & Paine, 1998). 1.1.2.2.1. Nutritional supplementation Both adults and larvae of mountain pine beetle gain nutritional benefits from fungal associates. Beetle larvae feed on phloem colonized with fungal hyphae (Adams & Six, 2007), and newly enclosed adults feed extensively on fungi in pupal chambers (Paine et al., 1997). Teneral adults consume fungal conidia that line pupal chambers to support reproduction 9 (Whitney, 1971; Six & Paine, 1998). Teneral adults that did not eat conidia were less likely to produce egg galleries nor lay eggs (Six & Paine, 1998). The phloem diet of bark beetles is relatively nutritionally poor, lacking in nitrogen and usable sterols (Six, 2013). Fungal symbionts are capable of improving the nutritional quality of host tissues through nitrogen supplementation, which is critical for the beetle’s development and survival (Paine et al., 1997; Ayres et al., 2000; Bleiker & Six, 2007; Six & Paine, 1998). Fungi can redistribute nitrogen from the sapwood to the phloem where beetle larvae live and feed, by transporting nitrogen from hyphae in sapwood to hyphae in phloem (Six, 2013; Bleiker & Six, 2007). Previous experiments suggested that nitrogen levels in phloem colonized by fungal associates were increased by 40% compared to control levels eight weeks after attack (Bleiker & Six, 2007). Beetles emerging with G. clavigera and/or O. montium were larger in size compared with beetles that do not carry any fungi (Bleiker & Six, 2007). The presence and abundance of G. clavigera and O. montium could provide vital nutrients that is inadequate concentrates in phloem, and thus influence the rate of beetle’s population dynamics and determine whether they could erupt into outbreaks (Harrington, 2005; Bleiker & Six, 2007). Not all fungi associates are equivalent in the ability of nitrogen concentration. G. clavigera is more efficient to concentrate nitrogen than O. montium (Bleiker & Six, 2007). The brood developing with G. clavigerum emerged earlier and more abundantly than those developing with O. montium (Six & Plaine, 1998). The difference may also explain why beetles carrying G. clavigera have greater size and survival rates than beetles developing with O. montium (Bleiker & Six, 2007; Goodsman et al., 2012; Six & Paine, 1998). Sterols play a critical role in growth, metamorphosis and reproduction of insects. Fungal symbionts provide relatively large quantities of ergosterol which is vital for successful oocyte 10 development, oviposition, larval development, and pupation of beetles (Norris & Baker, 1967; Norris et al., 1969; French & Roeper ,1975; Kok, 1979; Bentz & Six, 2006). However, insects cannot synthesize sterols and have to acquire them from dietary sources (Clayton, 1964, Richmond & Thomas, 1975, Svoboda et al., 1978). Woody plant tissues of trees infested by Dendroctonus bark beetles do not contain appropriate forms or adequate concentrations of sterols (Bentz & Six, 2006). Beetles appear to depend upon the ergosterol produced by fungal symbionts. Ergosterol was found in the mycelia of O. montium and G. clavigerum isolated from D. ponderosae mycangia (Bentz & Six, 2006). The level of ergosterol of phloem taken from infested trees colonized by D. ponderosae and its fungal symbionts was significantly higher than the one in phloem taken from uninfested trees (Bentz & Six, 2006). 1.1.2.2.2. Overcoming tree defenses Trees have effective defenses against beetle attacks, including preformed and induced resistance (Berryman, 1972). The preformed oleoresin and resin duct systems form the initial defenses (Paine et al., 1997). Induced components of resistance are elicited following invasion of inner bark tissues (Berryman, 1972; Reid, Whitney, & Watson, 1967; Shrimpton, 1973). Induction initiates a series of cellular and biochemical changes in host tree tissues that can inhibit fungal growth and intoxicate colonizing beetles, causing cellular necrosis, initiation of new impermeable cell layers, synthesis of new phenolic and monoterpene constituents, and changes in phenolic and phenylpropanoid chemistry (Mullick, 1977; Reid et al., 1967; Shrimpton, 1978; Shrimpton & Watson, 1971; Berryman & Ashraf, 1970; Paine & Stephen, 1988; Raffa & Berryman, 1983; Reid et al., 1967). It has been hypothesized that beetle-associated fungi play an important role in reducing tree resistance during colonization of beetles (Paine et al., 1997). Previous studies have shown 11 that fungi contribute to the death of host trees through combined and dynamic interactions between beetles and fungi, including mycelial penetration, translocation interruption and toxin release (Paine et al., 1997). G. clavigerum, L. longiclavatum and O. montium are pathogenic to lodgepole pine because they can disrupt water conduction in the sapwood to stop the supply of water and nutrients by occluding the vascular transport system (Nelson, 1934; Basham, 1970; Horntvedt et al., 1983; Webb & Franklin, 1978; Strobel & Sugawara, 1986; Yamaoka et al., 1995; Arango-Velez et al., 2011). After the inoculation by beetles, fungi rapidly invade and colonize sapwood. Fungal hyphae of blue stain fungi penetrate and destroy living ray parenchyma cells and then move into axial tracheids of infested lodgepople pine (Rumbold, 1941; Ballard et al., 1982; Ballard et al., 1984). Water conduction in xylem and phloem was prevented due to aspiration of tracheid tori or vascular plugging with resin (Nelson, 1934; Bramble, & Holst, 1940; Basham, 1970; Caird, 1935; Parmeter et al., 1989; Solheim, 1995; Craighead, 1928; Mathre, 1964; Reid et al., 1967; Berry man, 1969). The sap flow of infested lodgepole pines was reduced or stopped within 2-3 weeks after beetle attack (Suzuki et al., 1985; Miller et al., 1986; Yamoka, Swanson, & Hiratsuka, 1990). Fungi produce isocoumarin toxins which may affect water relations of trees (DeAngelis, Hodges, & Nebeker, 1986; Hemingway, McGraw, & Barras, 1977; Hodges et al.,1985; McGraw & Hemingway, 1977; Paine, 1984). Monoterpenes or other compounds released by trees following bark beetle attack could cause aspiration of bordered pits and tracheid cavitation, resulting in disruption of transpiration (Coutts, 1977; Kuroda, 1991). There are two phases of pathogenicity: the early phase of overcoming tree defense: 1) initial phloem colonization and 2) subsequent sapwood colonization resulting in mortality of 12 trees (Paine et al., 1997). Blue stain fungi exhaust the capacity of trees of defensive response by colonizing functional sapwood and restrict sap flow conduction, and provide a better environment for successful brood development of beetles (Yamaoka et al., 1990). Combined with the mechanical damage of the inner bark caused by tunneling action of the beetles, the threshold of tree resistance is exceeded (Paine et al., 1997). Trees can be killed since they can no longer defend themselves against beetle attack (Raffa, 1981; Berryman, 1982; Raffa & Berryman, 1983). Fungi play the role of facilitators and expediters of successful beetle attack through dynamic interactions of both beetles and responding trees (Paine et al., 1997). 1.1.2.3. Benefits for fungi In exchange, fungi also benefit from the association with beetles. Fungi are consistently transported and dispersed to relatively uncommon resources during attack process of beetles (Six & Plaine, 1997). Attacking beetles carry fungi and fungal spores on exoskeletons and in mycangia and disseminate them to new hosts (Bentz & Six, 2006). Fungi are inoculated into phloem and spread throughout sapwood of trees by beetles (Whitney & Farris, 1970; Bleiker et al., 2009; Bleiker & Six, 2007). The construction of egg galleries and/or subsequent lateral mining of larval galleries of beetles help fungi to gain access to sapwood tissues more quickly to acquire resources for growth and reproduction (Whiteney & Farris, 1970; Bleiker et al., 2009; Bentz & Six 2006; Berryman, 1972). 1.1.3. Ecological niche theory According to ecological niche theory, species that are ecologically similar in the same spatial region are seldom able to coexist indefinitely (Gause, 1934; Pianka, 1981). The species occupying similar niches will compete for common resources, resulting in reduction of fitness or competitive exclusion of other species (Gause, 1934; Pianka, 1981). Chesson (2000) indicated 13 that niche partitioning is a stabilizing mechanism which is important for species coexistence and maintenance of species diversity (Chesson, 2000). The biological and epidemiological differences in the requirements of related species for resources, space and/or time may engender niche differences or niche partitioning that enable them to coexist (Chesson 2000; Amarasekare 2003; Fitt et al., 2006). Evidence of niche differentiation has been well documented in closely related fungi (Gudelj et al., 2004; Fitt et al., 2006; Feau et al., 2012; Taylor et al., 2014). Feau et al. found temporal niche partitioning between two closely related plant pathogenic fungi (Feau et al., 2012). The different prevalence and abundance of two closely related fungal species suggested that they use different strategies for coexistence (Feau et al., 2012). Taylor (2014) also found that there are fine-scale variations of niches of closely related fungi from all major functional guilds and four phyla. Seasonal variations of two cucurbit powdery mildew species were observed and could be explained by the differences of ecological requirements and overwintering strategies of the two species (Pirondi et al., 2015). Empirical studies show that blue stain fungi associated with MPB exhibit exploitation competition (Solheim, 1995; Bleiker & Six, 2007; Bleiker & Six, 2009a,b). When these fungi colonize pine trees, they are highly competitive with one another for limited nutrients in the phloem (Bleiker & Six 2007, 2009a,b). As fungi grow from inoculation points, they encounter hetero or conspecifics of other fungal individuals (Moore, 2013). The growth halts when two fungi meet where some intermingling of hyphae occurs at the margin (Solheim, 1995; Bleiker & Six, 2007; Bleiker & Six, 2009a,b). The outcome of competition is determined by the rate of each species to capture shared resources (Moore, 2013). Such strong competition could lead to eventual exclusion of inferior fungi and the fixation for more competitive fungi over time (Moore, 2013; Alayon et al., 2017). However, the multipartite mutualism between these three 14 fungi and mountain pine beetle has remained remarkably stable over a long period of evolutionary time (Six & Paine, 1999; Six & Bentz, 2007), indicating that there is an environmental mechanism that maintains the stability of the long-term symbiosis between fungi and beetles (Moore, 2013). It is predicted that small niche differences between these fungi enable them to co-occur on the same substrate (Fitt et al. 2006; Alayon et al., 2017). The niche partitioning may be engendered by the subtle differences in biological or epidemiological features of each species (Fitt et al., 2006), resulting in a separation in space, time and use of resources (Chesson, 2000; Amarasekare, 2003; Fitt et al., 2006). 1.1.4. Adaptation of fungi symbionts to temperature Among biotic and abiotic factors that affect the growth of blue stain fungi, temperature is likely to play a strong role (Adams & Six 2007; Moore, 2013). Previous studies have examined growth rate responses of G. clavigera, L. longiclavatum and O. montium to different temperatures. It has been observed that these fungi differ in temperature tolerance (Six & Paine, 1997; Solheim & Krokene, 1998; Lee et al., 2005; Six & Bentz, 2007; Rice et al., 2008; Bleiker & Six, 2009b). G. clavigera and L. longiclavatum grow faster at cooler temperatures than O. montium, while O. montium grows faster at warmer temperatures than the other two fungi (Six & Paine, 1997; Solheim & Krokene, 1998; Rice et al., 2008, Bleiker & Six, 2009b). Solheim and Krokene (1998) demonstrated that G. clavigera grew faster than O. montium at temperatures below 25°C, whereas at higher temperatures, O. montium grew faster than G. clavigera (Solheim & Krokene, 1998). Six and Paine (1997) reported that the difference in growth rate of G. clavigera and O. montium is the most striking at extreme temperatures (Six & Paine, 1997). G. clavigerum grew poorly at 32°C compared to O. montium (Six & Paine, 1997). At 27 °C, G. clavigera has a significantly slower growth rate than O. montium (Six & Paine, 1997). Rice et 15 al. (2008) also demonstrated that three blue-stain fungi have different upper and lower limits. At -20°C, G. clavigera and L. longiclavatum survived, but O. montium died (Rice et al., 2008). At 30°C, growth of G. clavigera and L. longiclavatum was inhibited, but O. montium grew well (Rice et al., 2008). Each fungus has a different optimal growth temperature, with some overlap at intermediate temperatures (Hofstetter et al., 2006; Rice et al., 2008; Six & Paine, 1997). The optimum temperature was 22-25°C for G. clavigera and 25°C for O. montium (Solheim & Krokene, 1998). Rice et al. (2008) also found that G. clavigera and L. longiclavatum grew optimally at 20°C, while O. montium grew optimally at 25°C. At 25°C, the growth rate of L. longiclavatum was approximately half that of G. clavigerum on artificial media (Lee et al., 2005). The differential thermal tolerance may account for the temporally variable prevalence of growth of these fungi observed in natural attacked trees (Adams & Six, 2007). G. clavigera and O. montium exhibited generally opposing trends in prevalence in the study over year-long developmental periods of larvae and adults of D. ponderosae (Adams & Six, 2007). G. clavigera dominated the phloem adjacent to prewintering third- and postwintering fourth-instar larvae during winter and spring when temperatures were relatively lower, whereas O. montium were more likely to be isolated in the phloem adjacent to eggs, first-instar larvae, pupae, and teneral adults in summer when temperatures were relatively higher (Adams & Six, 2007). The variability in temperatures could also determine the relative prevalence of fungi with dispersing adult D. ponderosae (Six & Bentz, 2007). It has been observed that G. clavigera was isolated more often from the mycangia of dispersing beetles when temperatures were cool, while O. montium was isolated from beetles when temperatures were warm (Six & Bentz, 2007). 16 Previous research collectively shows that temperature tolerances of different species is correlated with the spatial and temporal distribution of MPB-associated fungi, with some fungi dominating during warm conditions and others dominating during cool conditions (Adams & Six, 2007; Moore, 2013). The differential thermal tolerances of fungi could translate to the variability of fungi prevalence over time by never allowing one fungus to fix with beetles (Six & Bentz, 2007; Six 2012; Moore, 2013). The patterns of fungal distribution may influence the dynamics of beetles as fungi could differentially affect the fitness of beetles through nutrient availability (Six, 2013; Moore, 2013). The coexistence and stability of multipartite symbiosis is important to MPB under complex environmental conditions (Moore, 2013). Under fluctuating temperatures in natural conditions, the shift in proportion of each fungus over time could also reduce the risk of MPB to be aposymbiotic without a fungal partner at extreme temperatures (Adams &Six, 2007; Six & Bentz, 2007).1.1.5. Population genomics The three fungal associates showed similar clustering patterns of broad-scale population structure (Roe et al., 2011b; Tsui et al. 2012, 2014; Lee et al., 2007; Alayon et al., 2017). For each species, there are differences in genetic diversity among populations (Roe et al., 2011b; Tsui et al. 2012, 2014; Lee et al., 2007; Alayon et al., 2017). Each species differentiated into northern and southern populations (Roe et al., 2011b). G. clavigera and L. longiclavatum have similar populations structuring, while O. montium is different from the other two species (Roe et al., 2011b). The population structure of G. clavigera has been widely investigated. Using amplified fragment length polymorphism (AFLP), Lee et al. (2007) provided evidence of two distinct groups in G. clavigerum collected from Canada and United States (Lee et al., 2007). Roe et al. 17 (2011b) also reported the differentiation of northern and southern populations of G. clavigerum using multilocus sequence (Roe et al. 2011b), which resembled the north-south population differentiation pattern of MPB (Samarasekera et al., 2012). Four major genetic clusters of G. clavigerum were identified using eight microsatellite markers of 19 populations in western North America (Tsui et al., 2012). The four clusters were named ‘NBC’ (Northern BC), ‘SBC’ (southern BC), ‘North’ (Epidemic populations in northern BC and AB), and ‘Rocky’ (the Rocky Mountains) (Fig. 1) (Tsui et al., 2012). Figure 1. Population structure of G. clavigera based on eight microsatellites using TESS (reproduced from Tsui et al. 2012). 18 Tsui et al. (2014) investigated the genetic structure of L. longiclavatum in populations established within the historic distribution range and in newly colonized regions (Tsui et al., 2014). Three populations of L. longiclavatum (“BC”, “North”, and “Rocky”) correspond to independent geographic locations (Tsui et al., 2014). The population structure of L. longiclavatum is similar to that of G. clavigera, and is congruent with that of MPB (Tsui et al., 2014; Roe et al., 2011b; Samarasekera et al., 2012; Cullingham et al. 2012). Figure 2. Population structure of L. longiclavatum based on ten microsatellites using STRUCTURE (reproduced from Tsui et al., 2014). Alayon et al. identified three main population clusters of three fungi based on SNP genotyping (Alayon et al., 2017). The pattern is more pronounced in L. longiclavatum and O.montium (Alayon et al., 2017). In addition, the three fungi species have different levels of genetic diversity among populations within species (Alayon et al., 2017). G. clavigera had the 19 lowest genetic diversity, while L. longiclavatum and O. montium have higher levels of genetic differentiation (Alayon et al., 2017). Figure 3. Pairwise genetic differentiation (Nei’s unbiased FST-values) among genetic groups and populations of G. clavigera, L. longiclavatum and O. montium (reproduced from Alayon et al. 2017). 20 1.2. Objectives Since fungal associates have an impact on beetle performance (Klepzig & Six, 2004; Bridges, 1983), understanding the phenotypic differences between the fungi associated with the beetle is important for the prediction of MPB expansion and modelling of MPB epidemics over a broad geographic and climatic range (Tsui et al., 2014). It is necessary to determine temperature tolerances of these fungi symbionts which could be indicators of potential pre-adaptation of MPB (Rice et al., 2008). However, most of the empirical studies focused on the comparison of G. clavigera and O. montium, little is known about growth rates of L. longiclavatum. In addition, the sampling size and the geographic distribution of sampling of previous studies are limited. To better understand the effects of temperature on blue stain fungi associated with MPB, I compared growth of a collection of samples of G. clavigera, O. montium and L. longiclavatum from a broad geographic and climatic range and from previously identified genetic populations. I conducted the growth experiments under a wide range of temperatures to determine if the fungi have developed different temperature profiles that could impact their niche specializations. The objectives of this study were to 1) assess the impact of fungal species and genetic populations on growth at different temperatures; 2) establish growth rate intraspecific variability for each species among the three populations with distinctive genetic composition; 3) estimate growth rate interspecific variability among the three species across the range of different temperature. 21 2. Materials and methods 2.1. Fungal isolates Fungal isolates of G. clavigera, L. longiclavatum and O. montium are deposited as live cultures at Forest Sciences Centre (FSC) at University of British Columbia (UBC). A total of 112 isolates (37 G. clavigera, 40 L. longiclavatum, 35 O. montium) were isolated from both MPB and the phloem of MPB-infested Pinus contorta in western and central North America. Different locations with distinctive geographic characteristics (latitude, longitude and elevation) were chosen (Appendix A, B, C). Fungal samples were collected from 29 locations in Canada and USA, including 14 locations shared among the three species (Figure 4). Fungal isolates were identified to species according to morphology and corroborated by PCR amplification of rDNA and other protein coding gene fragments using species-specific primers coupled with Sanger sequencing (Khadempour et al., 2010; Alayon et al., 2017). Pure fungal samples were maintained on 2% malt extract agar (MEA) and incubated at room temperature for use. 2.2. Temperature tolerance experiments The effect of temperature on the growth rate of three fungal species were tested at six temperatures (5°C, 10°C, 15°C, 20°C, 25°C, 30°C). The locations were selected according to three genetic clusters identified in previous studies (Roe et al., 2011b; Tsui et al., 2012, 2014; Alayon et al., 2017). Three locations were selected randomly for each genetic cluster for each species. All the fungal associates were obtained from beetles and/or larvae or phloem of the same host pine (Pinus contorta). Six single-spore isolates were chosen randomly and based on isolate availability and survival of the storage conditions for each group of each fungal species. In total, 48 fungal isolates (16 G. clavigera, 18 L. longiclavatum and 14 O. montium) were grown at six temperatures for the first part of experiments. The supplementary growth experiments were 22 conducted with another 64 fungal isolates (21 G. clavigera, 22 L. longiclavatum and 21 O. montium) at 15, 20 and 25°C. Figure 4. Distribution map of the fungal samples used in this study. G. clavigera, L. longiclavatum and O.montium samples were grouped into “North” in red, “Central” in blue, “USA” in yellow for comparisons of growth characteristics. All fungal isolates were kept as long-term storage at 4 °C. The fungal isolates were acclimatized on 25 mL MEA media at room temperature for 7 days prior to use. When the mycelial plugs were alive, a plug of active mycelium was taken at the leading edge of the Petri dish. The plug of mycelium was transferred to the center of a new Petri dish (100mm diameter) containing 25mL 2% MEA media. Plates inoculated with mycelial plugs were maintained in an 23 incubator in the dark at six temperatures (5, 10, 15, 20, 25, 30°C). Three replicates of each treatment were conducted for each fungal isolate. Two perpendicular lines were drawn on the bottom of the Petri dish. Because the fungi grew out in all directions from the inoculation point, the radius of each fungal colony in four directions was recorded under the microscope at the end of mycelium. The growth (in mm) was calculated as the radius of the colony subtracting the radius of the plug. The measurement was conducted at the same time every day until the colony filled the whole Petri dish. Figure 5. Example of a Petri dish containing a fungal culture. An agar plug was placed in the middle of the plate and growth was assessed by measuring the diameter once a day. 24 Species Population Grouping Isolates Name Day 3 Day 4 Day 5 Day 6 G. clavigera North TRIA292 G. clavigera Central TRIA110 G. clavigera USA TRIA776 25 L. longiclavatum North TRIA55 L. longiclavatum Central TRIA38 L. longiclavatum USA TRIA786 26 Figure 6. Photos of growth on Petri dish for isolates of G. clavigera, L. longiclavatum and O. montium used in temperature tolerance experiments O. montium North TRIA37 O. montium Central TRIA1081 O. montium USA TRIA825 27 2.3. Data analysis The area under the growth curve was calculated for all fungal isolates over 7 days. For temperatures 15, 20, and 25°C, the growth curve fit a linear regression model, so the area under the curve was calculated according to the linear regression model. For temperature 5, 10, and 30°C, the growth curve cannot fit a linear regression model, therefore, the area under the curve was calculated using trapeze method using the PRACMA package in R (Borchers 2016). Because many isolates had no growth at 30°C, and too many missing data will influence the significance level, the growth at 30°C was separated for data analysis. Multiple Linear Regression Model (ANOVA, maximum likelihood method) was used to test for the effects of species, temperature, and genetic groups and their interactions on area under the curve. For significant F-tests, Tukey-Kramer’s honestly significant different (HSD) tests were used to determine the significance level of different comparisons. 28 3. Results All three species exhibited variability in growth in response to the six temperatures (Figure 7). However, there were significant differences in growth response to temperature between the species (Tables 1 and 2; P<0.001). 3.1. Comparison of growth among species Growth curves for the three fungal species are shown in Figure 7. The majority of isolates of the three fungi grew between 5°C and 30°C (Figure 7). At 30°C, several G. clavigera isolates failed to grow. On average, the optimal growth occurred around 25°C for all three species, although there was some variability within species (Figure 7). The effect of temperature on the growth of three fungal species was significant at temperatures between 5-30° C (Table 1; P<0.001). There was a significant interaction between species and temperatures (Table 1; P<0.001), indicating that the fungi do not have the same performance consistency through temperature from 5° C to 30° C. The three fungi species have different responses to different temperatures. Between 15 and 25 °C, G. clavigera grew the fastest, followed by L. longiclavatum and O. montium (Figure 7, Table 3, Tukey’s HSD; P <0.001). At 30°C, the trend was reversed, with O. montium having the highest growth rate, followed by L. longiclavatum and G. clavigera (Figure 7, Table 5, Tukey’s HSD; P <0.001) . 29 Figure 7. Area under the curve for 112 isolates of the three fungal symbionts (37 G. clavigera, 40 L. longiclavatum and 35 O. montium) at six temperatures (from 5 to 30 °C with steps of 5 °C). Table 1. ANOVA of the growth1 of G. clavigera, L. longiclavatum and O. montium at 5-30°C Source of Variation DF Mean Square F Value Pr >F Species 2 83432 101.46 < 2e-16 Temperature 5 342251 416.20 < 2e-16 Species:Temperature 10 20666 25.13 < 2e-16 Residuals 1390 822 1Diameter was measured daily and the area under the growth curve was calculated for each sample 0501001502005 10 15 20 25 30Temperature (°C)Area Under The Growth Curve (mm² * day)speciesgcllom30 Table 2. ANOVA of the growth1 of G. clavigera, L. longiclavatum and O. montium at 15-25°C Source of Variation DF Mean Square F Value Pr >F Species 2 158911 186.50 < 2e-16 Temperature 2 174294 204.56 < 2e-16 Species:Temperature 4 646 0.76 0.553 Residuals 973 852 1Diameter was measured daily and the area under the growth curve was calculated for each sample Table 3. Tukey multiple comparisons of means of the growth1 of G. clavigera, L. longiclavatum and O. montium at 15-25°C Species diff lwr upr p adj ll-gc -23.50 -28.78 -18.21 0 om-gc -44.60 -50.03 -39.18 0 om-ll -21.11 -26.48 -15.74 0 1Diameter was measured daily and the area under the growth curve was calculated for each sample; gc, ll and om refer to G. clavigera, L. longiclavatum and O. montium Table 4. ANOVA of the growth1 of G. clavigera, L. longiclavatum and O. montium at 30°C Source of Variation DF Mean Square F Value Pr >F Species 2 28270 16.6 3.4e-07 Residuals 140 1703 1Diameter was measured daily and the area under the growth curve was calculated for each sample Table 5. Tukey multiple comparisons of means of the growth1 of G. clavigera, L. longiclavatum and O. montium at 30°C Species diff lwr upr p adj ll-gc 20.34 0.86 39.82 0.038 om-gc 50.10 29.45 70.76 0.000 om-ll 29.76 9.56 49.96 0.002 31 1Diameter was measured daily and the area under the growth curve was calculated for each sample; gc, ll and om refer to G. clavigera, L. longiclavatum and O. montium 3.2. Comparison of growth among genetic groups within species 3.2.1. Grosmannia clavigera G. clavigera isolates from the three genetic groups grew significantly differently at 15-25°C (Figure 8, Table 6; P < 0.001) . Isolates from the group “North” had the slowest growth rate in the comparison with the groups USA and Central (Figure 8, Table 7, Tukey’s HSD; P < 0.001). The comparison between the groups “Central” and “USA” was not significant (Figure 7, Table 7, Tukey’s HSD ; p>0.05). At 30°C, there was no significant difference in growth among the three genetic groups (Table 8). Figure 8. Area under the curve for G. clavigera from three genetic groups at six temperatures (from 5 to 30 °C with steps of 5 °C). 0501001502005 10 15 20 25 30Temperature (°C)Area Under The Growth Curve (mm² * day)Genetic GroupsCentralNorthUSAGrosmannia clavigera32 Table 6. ANOVA of the growth1 of G. clavigera from three genetic groups at 15-25°C Source of Variation DF Mean Square F Value Pr >F Temperature 2 64929 76.67 < 2e-16 Genetic Group 2 13077 15.44 3.96e-07 Temperature: Genetic Group 4 73 0.086 0.987 Residuals 320 847 1Diameter was measured daily and the area under the growth curve was calculated for each sample Table 7. Tukey multiple comparisons of means of the growth1 of G. clavigera from three genetic groups at 15-25°C Genetic Group diff lwr upr P adj North-Central -20.54 -29.95 -11.13 0.000 USA-Central -3.08 -12.25 6.09 0.709 USA-North 17.46 8.25 26.67 0.000 1Diameter was measured daily and the area under the growth curve was calculated for each sample Table 8. ANOVA of the growth1 of G. clavigera from three genetic groups at 30°C Source of Variation DF Mean Square F Value Pr >F Genetic Group 2 4259 2.947 0.063 Residuals 45 1445 1Diameter was measured daily and the area under the growth curve was calculated for each sample 3.2.2. Leptographium longiclavatum The growth of L. longiclavatum from the three genetic groups was significantly different from 15°C to 25°C (Figure 9, Table 9; P < 0.001). At 15-25°C, isolates from Group “Central” grew fastest, followed by those from “North” and “USA” (Table10, Tukey’s HSD; P<0.001). At 30°C, isolates from USA grew at the fastest speed (Table 12, Tukey’s HSD; P < 0.001), while isolates from “North” grew at the slowest speed (Table 12, Tukey’s HSD; P < 0.001). 33 Figure 9. Area under the curve for L. longiclavatum at six temperatures (from 5 to 30 °C with steps of 5 °C). Table 9. ANOVA of the growth1 of L. longiclavatum from three genetic groups at 15-25°C Source of Variation DF Mean Square F Value Pr >F Temperature 2 67157 89.33 < 2e-16 Genetic Group 2 28476 37.88 1.48e-15 Temperature: Genetic Group 4 883 1.17 0.322 Residuals 335 752 1Diameter was measured daily and the area under the growth curve was calculated for each sample Table 10. Tukey multiple comparisons of means of the growth1 of L. longiclavatum from three genetic groups at 15-25°C Genetic Group diff lwr upr p adj North-Central -19.84 -28.24 -11.43 0.000 USA-Central -31.25 -39.85 -22.66 0.000 USA-North -11.42 -20.01 -2.82 0.005 0501001502005 10 15 20 25 30Temperature (°C)Area Under The Growth Curve (mm² * day)Genetic GroupsCentralNorthUSALeptographium longiclavatum34 1Diameter was measured daily and the area under the growth curve was calculated for each sample Table 11. ANOVA of the growth1 of L. longiclavatum from three genetic groups at 30°C Source of Variation DF Mean Square F Value Pr >F Genetic Group 2 15538 10.22 0.000 Residuals 50 1520 1Diameter was measured daily and the area under the growth curve was calculated for each sample Table 12. Tukey multiple comparisons of means of the growth1 of L. longiclavatum from three genetic groups at 30°C Genetic Group diff lwr upr p adj North-Central -36.17 -67.56 -4.79 0.020 USA-Central 22.80 -9.05 54.64 0.205 USA-North 58.97 27.12 90.81 0.000 1Diameter was measured daily and the area under the growth curve was calculated for each sample 3.2.3. Ophiostoma montium Temperature also plays a significant role in the growth of O. montium (Table 13; P<0.001). Area under the curve of O. montium from three genetic groups differed significantly at 15-25°C (Figure 10, Table 13; P<0.001). Isolates from the group “Central” grew faster than those from the groups “USA” and “North” (Table 14, Tukey’s HSD; P<0.001), while isolates from the group “North” grew the slowest (Table 14, Tukey’s HSD; P<0.001). At 30°C, there was no significant difference among three groups (Table 15; P>0.05). 35 Figure 10. Area under the curve for O. montium at six temperatures (from 5 to 30 °C with steps of 5 °C). Table 13. ANOVA of the growth1 of O. montium from three genetic groups at 15-25°C Source of Variation DF Mean Square F Value Pr >F Temperature 2 43500 66.99 < 2e-16 Genetic Group 2 11746 18.09 3.83e-08 Temperature: Genetic Group 4 239 0.37 0.832 Residuals 300 649 1Diameter was measured daily and the area under the growth curve was calculated for each sample Table 14. Tukey multiple comparisons of means of the growth1 of O. montium from three genetic groups at 15-25°C Genetic Group diff lwr upr p adj North-Central -21.26 -29.97 -12.55 0.000 USA-Central -17.32 -25.92 -8.72 0.000 USA-North 3.94 -4.07 11.95 0.478 0501001502005 10 15 20 25 30Temperature (°C)Area Under The Growth Curve (mm² * day)Genetic GroupsCentralNorthUSAOphiostoma montium36 1Diameter was measured daily and the area under the growth curve was calculated for each sample Table 15. ANOVA of the growth1 of O. montium from three genetic groups at 30°C Source of Variation DF Mean Square F Value Pr >F Genetic Group 2 990.2 0.691 0.507 Residuals 39 1432.3 1Diameter was measured daily and the area under the growth curve was calculated for each sample 37 4. Discussion It has been suggested that fungi are highly competitive with one another (Wicklow, 1981; Rayner, 1991; Shearer, 1995; Carlile et al., 2001). The direct competitive interactions are predicted to result in the exclusion of inferior species over time (Doebeli & Knowlton, 1998). However, the multipartite symbiosis with MPB has remained stable over long periods of evolutionary time (Safranyik & Carroll, 2006; Six & Paine, 1998) without exhibiting strong direct competition (Solheim, 1995; Bleiker & Six, 2007; Bleiker & Six, 2009a; Bleiker & Six, 2009b). This long-term coexistence of three fungi with their host beetles indicates a mechanism that allows all species to remain in the system (Moore, 2013). My study investigated the interspecific and intraspecific phenotypic characteristics under different temperatures of the three fungi associated with the MPB (G. clavigera, L. longiclavatum and O. montium) from a wide geographic and environmental range in vitro. The study found that the three fungal species are differentially adapted to temperatures. G. clavigera and L. longiclavatum were able to grow better at lower temperatures than O. montium, while O. montium displayed greater tolerance to extreme high temperatures compared with the other two species (Fig. 6). The differential thermal tolerances may allow each species to grow at different growing seasons during the year. These results suggest that G. clavigera and L. longiclavatum are more adapted to cold winters, while O. montium is more adapted to hot summers. Temperature is also correlated with the relative prevalence and composition of fungal symbionts. It has been observed that G. clavigera was more often found in phloem adjacent to third and fourth instar larvae during early spring, late autumn and early winter when the temperatures are cooler in the field (Adams & Six, 2007). On the other hand, O. montium was more often isolated from phloem adjacent to pupae, teneral adults, and eggs in late spring and mid-to-late summer 38 when temperatures are relatively high and can reach 30°C (Adams & Six, 2007). At times when temperature is generally cooler, or at sites where conditions are generally cool, G. clavigera would be expected to dominate. In contrast, at times when temperature is relatively warmer, or at sites that are generally warm, O. montium would be prevalent (Moore & Six, 2015). Adam and Six (2007) indicated that the observation of temperature tolerance in the lab correlated with the distribution of fungi observed in the field. The temporally variable pattern of the two fungi observed in the field may be accounted for the difference of growth rates under variable temperatures (Adams & Six, 2007). It is possible that G. clavigera is more competitive in cold boreal climate, leading to a gradual prevalence of G. clavigera with increasing latitude. This could account for the shifting prevalence of G. clavigera and O. montium on attacked trees over time in the field (Adams & Six, 2007; Bleiker & Six, 2008). The intraspecific variation within species was also investigated. The results showed that isolates from the same species from different genetic groups have different temperature profiles. However, the patterns observed are not those expected based on the geographic source of the samples. We did not observe a gradient of increasing temperature tolerance from the northern to the southern populations, but there is a trend for each species. For example, within G. clavigera, the slowest growing isolates were from the “North” populations from 15°C to 25°C, while for L. longiclavatum, it is the southern USA population that grew the slowest for the mid-range temperatures. It has been reported that G. clavigera is less abundant in northern regions, possibly because the northern populations are less competitive at lower temperatures. There could also be a trade-off, with the northern populations of G. clavigera having reduced growth but increased resilience to extreme temperatures, in particular extreme cold temperatures. New experiments could be conducted to explore this hypothesis in the future. 39 Generally, there was a relatively homogeneous composition in temperature profiles among the G. clavigera populations. This phenotypic data of G. clavigera agrees with the genetic structure of G. clavigera that is a relatively even with a lot of admixture between genetic populations. The overall heterozygosity was the lowest in G. clavigera and the between-population genetic diversity was only 6.9% (Alayon et al. 2017). This may account for non-significant difference of temperature tolerances between “Central” and “USA” isolates at 15-25 °C. L. longiclavatum exhibited greater variability in response to temperature than G. clavigera and O. montium. But similarly, there was no gradient in temperature profiles associated with latitude. At 15–25°C, “Central” isolates grew fastest, and “USA” isolates had the slowest growth among L. longiclavatum populations. The significant differences of temperature tolerances among genetic populations could be explained by the genetic differences among groups. L. longiclavatum has the highest level of genetic differentiation among the groups with lots of genetic differentiations (FST = 0.15–0.34) compared to O. montium (FST = 0.11–0.20) and G. clavigera (FST = 0.04– 0.11) (Alayon et al., 2017). Tolerance to high temperatures could be important in shaping the populations of L. longiclavatum. At 30°C, “USA” isolates had the fastest growth while “North” isolates grew slowest. It has been suggested that L. longiclavatum is a cold weather fungus as it has been reported more frequently in the northern range of the beetle than in the southern range (Rice et al., 2008). The results I report in my thesis suggest that isolates of each species vary in their temperature profiles. It appears that there is variation that has either been acted by selection or that variation is present and that selection could take place in the future. In any case, this could lead to adaptation to temperature in different environments. Isolates with different temperature 40 growth profiles may be evolved to maximize their growth in a particular niche; in some cases, it could be that isolates from northern regions are more adapted to cooler temperatures, and isolates from southern parts more adapted at warmer temperatures (Rice et al., 2008). This phenotypic data agrees with the high level of genetic differentiation of L. longiclavatum that has been reported in L. longiclavatum and supports the hypothesis of adaptation to temperature of the fungi (Alayon et al., 2017). Temperature growth profiles varied among O. montium isolates as well. Isolates from the central regions grew faster at most temperatures than those from further north or south. The center of historic outbreaks of the MPB is situated in the regions genetically defined as ‘Central’ in previous work (Alayon et al., 2017; Tsui et al., 2012). The O. montium populations from more extreme regions (further north and south) that have more extreme temperatures grew slower at most temperatures. It could be that this fungus is more adapted to the historical niche, but that there is sufficient genetic diversity to generate populations that can tolerate more extreme temperatures. It is possible also that there is a trade-off between growth and survival as mentioned earlier. For example, O. montium grows better than the other species at this highest temperature. There could also be a temporal aspect that is important for the niche adaptation in temperature profiles. The unique phenotypic characteristics of these symbionts could explain the coexistence of competing fungi. By growing at different times, these fungi could avoid expending energy on direct competition (Six & Bentz, 2007; Six, 2012). As temperatures fluctuate over time, the relative proportion of fungi in a population could shift constantly under different conditions, such that no fungus is favored all the time and move to fixation with the host (Six & Bentz, 2007; Six, 2012). 41 The difference of temperature growth profiles may influence the behavior and the fitness of the beetles (Adams & Six, 2007), and could be an indicator of potential pre-adaptation to life as boreal pathogens (Rice et al., 2008). Because fungi have differential impacts on host beetles, the difference of fungal growth would translate to the effects on larval development of beetles and influence the population dynamics of beetles as a result (Moore & Six ,2015). It has been observed that the increase of O. montium would result in an increase of small adult female beetles (Bleiker & Six, 2008). It is possible that the fecundity of beetles would decrease at sites where conditions are generally warm. The redundancy of symbionts is critical to MPB when conditions vary through time and space (Six & Paine, 1999). By maintaining multipartite relationships, the bark beetles are more likely to obtain the benefits provided by different species of fungi under fluctuating environmental conditions, and thus reduce the risk of being left aposymbiotic (Alayon et al., 2017). The results have an implication for population dynamics of fungi and beetles during climate change. As temperatures increase, there could be an increase in the prevalence of O. montium and a decrease of G. clavigera , the fungus that is the least adapted to high temperatures (Adams & Six, 2008). The decline of G. clavigera may result in an increasing dependence of the beetles on O. montium. G. clavigera has been considered as a more aggressive than O. montium on most hosts (Reid et al., 1967; Owen et al., 1987; Yamaoka et al., 1990, 1995; Solheim, 1995; Solheim & Krokene, 1998). As such, I hypothesized that the loss of G. clavigera may reduce the capacity of MPB to develop a broader ecological amplitude (Moore & Six, 2015). The purpose of the study was to investigate the effects of temperature on fungal symbionts associated with the MPB. Previous studies use a smaller size of samples (Bleiker & Six, 2008; Rayner, 1991; Six, 2012; Solheim, 1995). In my study, a larger number of isolates from multiple 42 locations across the western coast of North America were selected to better understand the fungal response to temperature. The results of this study are consistent with those in previous studies but expand on previous studies by scale and by defining genetic groups. It is also worth noting that previous studies of the temperature impact of fungal associates of the MPB did not look at L. longiclavatum, a frequent and important fungal associate of the MPB. However, L. longiclavatum is morphologically very difficult to differentiate from G. clavigera. So, it is possible that previous studies have confounded these two species. By carefully characterizing the fungi genetically, I avoided this problem. However, there are still some factors that affect the accuracy of the results. First of all, the method of measurement of fungal growth needs to be more accurate. In this study, the size of the fungi colony was estimated according to the growth in four directions. Sometimes the fungi do not grow in perfect circle and thus the estimation may be inaccurate. A method that uses camera under microscope to capture the image of fungi colony and calculate the size by the number of pixels is recommended for future experiments. Secondly, the experiment was conducted on artificial media in Petri plates in the lab which is different from the natural substrates of live trees. The difference in nutrients, secondary chemistry, virulence, etc. may limit the applicability to natural trees (Six & Bentz,2007; Rice.et al., 2008). Therefore, a media for the growth of fungi that mimics natural conditions of trees is suggested in the future investigation. Observational studies tracking fungal growth within tree logs in the field is also necessary to fully investigate how temperature influence the fungal growth. 43 5. Conclusion Competitive interactions between fungi are thought to result in the loss of inferior species over time (Doebeli & Knowlton, 1998). However, the multi-partite mutualism between fungi and MPB has been remarkably stable over long periods of evolutionary time (Safranyik et al. 2010; Six & Paine, 1998). In this study, the temperature growth profiles of the three most important symbionts of the MPB were assessed in vitro. The results of my research indicated that the differential temperature growth profiles may mediate competition among the symbionts, thus influencing the dynamics and composition of the symbiosis in naturally-attacked trees. The different growth responses to variable environmental conditions may allow the competing species to capture resources at different times without exhibiting strong direct competition. These differences may determine the spatial and temporal distribution of these fungi associates (Rice et al., 2008), and maintain their long-term coexistence. By maintaining this multipartite relationship with these fungi species, MPB are more likely to obtain the benefits afforded by the fungi and reduce the risk of being left aposymbiotic (Alayon et al., 2017). The complementarity of these nutritional symbionts in the system could facilitate the colonization of new habitats and a broad ecological amplitude for the MPB. Experiments conducted in natural conditions with a large number of isolates from more locations from a broader range are suggested in order to have a more accurate estimation of temperature tolerances of MPB fungal associates in the future. 44 References Adams, A.S., & Six, D.L. 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Information for isolates of G. clavigera used in temperature tolerance experiments Species Population Grouping Isolates Name Location Host Date collected Latitude (°N) Longitude (°W) Elevation (m) G.clavigera North TRIA251 Fox Creek, AB, CA Pinus contorta 2010 54.3084 -116.6294 979.0 G.clavigera North TRIA292 Fox Creek, AB, CA Pinus contorta 2010 54.2625 -116.6088 873.0 G.clavigera North TRIA260 Grande Prairie, AB, CA Pinus contorta 2010 54.6990 -119.6836 1043.0 G.clavigera North TRIA261 Grande Prairie, AB, CA Pinus contorta 2010 54.8624 -119.8199 960.0 G.clavigera North TRIA268 Peace Area, AB, CA Pinus contorta 2010 56.6845 -118.1163 802.0 G.clavigera North ss266 Fairview, AB, CA Pinus contortaPinus banksiana(pcb) 2007 56.3059 -119.9641 744.0 63 G.clavigera North TRIA65 Tumbler Ridge, BC, CA Pinus contorta 2010 55.1790 -121.1124 1046.4 G.clavigera North TRIA66 Tumbler Ridge, BC, CA Pinus contorta 2010 55.1791 -121.1122 1045.9 G.clavigera North ss436 Kakwa, AB, CA Pinus contorta 2008 53.9960 -119.5280 1582.0 G.clavigera North TRIA129 Lesser Slave Valley, AB, CA Pinus contorta 2010 54.8194 -116.6917 760.0 G.clavigera North TRIA280 Terrace, BC, CA Pinus contorta 2010 54.4573 -128.5143 123.4 G.clavigera North TRIA296 Valemount, BC, CA Pinus contorta 2010 52.8607 -119.3500 824.0 G.clavigera Central TRIA120 Peachland, BC, CA Pinus contorta 2010 47.7971 -120.0575 495.0 G.clavigera Central TRIA210 Canmore, AB, CA Pinus contorta 2010 50.9358 -115.3373 1785.5 G.clavigera Central TRIA178 Kimberley, BC, CA Pinus contorta 2010 49.6950 -115.9578 1112.3 64 G.clavigera Central TRIA192 Kimberley, BC, CA Pinus contorta 2010 49.7793 -115.8506 1062.1 G.clavigera Central TRIA110 Peachland, BC, CA Pinus contorta 2010 49.7973 -120.0572 1279.5 G.clavigera Central TRIA177 Cascades, BC, CA Pinus contorta 2010 50.4324 -122.2530 1128.2 G.clavigera Central TRIA300 Cascades, BC, CA Pinus contorta 2010 50.4323 -122.2528 1135.1 G.clavigera Central ss484 Kootenay/Yoho, BC, CA Pinus contorta 2007 50.9130 -116.0380 1258.0 G.clavigera Central ss53 Kootenay/Yoho, BC, CA Pinus contorta 2007 50.9120 -116.0380 1285.0 G.clavigera Central ss446 Crowsnest Pass, AB, CA Pinus contorta 2008 49.6230 -114.4400 1482.0 G.clavigera Central ss361 Crowsnest Pass, AB, CA Pinus contorta 2008 49.6570 -114.5520 1511.0 G.clavigera Central ss216 Cypress Hills, AB, CA Pinus contorta 2007 49.5930 -110.0360 1316.0 65 G.clavigera Central ss209 Cypress Hills, AB, CA Pinus contorta 2007 49.5930 -110.0360 1316.0 G.clavigera USA TRIA440 Ft Collins, CO, USA Pinus contorta 2011 40.7620 -105.6140 2659.0 G.clavigera USA TRIA437 Ft Collins, CO, USA Pinus contorta 2011 40.7620 -105.6140 2659.0 G.clavigera USA TRIA883 Butt Mountains, CA, USA Pinus contorta 2011 41.9764 -120.2122 2069.0 G.clavigera USA TRIA890 Butt Mountains, CA, USA Pinus contorta 2011 41.9764 -120.2122 2069.0 G.clavigera USA TRIA776 Boise, ID, USA Pinus contorta 2011 44.2985 -115.2571 2134.0 G.clavigera USA TRIA911 Boise, ID, USA Pinus contorta 2011 44.2992 -115.2575 2134.0 G.clavigera USA TRIA560 Winema Forest, OR, USA Pinus contorta 2011 42.9464 -121.3992 2011.0 G.clavigera USA TRIA968 Winema Forest, OR, USA Pinus contorta 2011 42.9462 -121.3986 2025.0 G.clavigera USA TRIA594 Mount Rose Ski Resort, NV, USA Pinus contorta 2011 39.3324 -119.8874 2492.0 66 G.clavigera USA TRIA613 Mount Rose Ski Resort, NV, USA Pinus contorta 2011 39.3323 -119.8874 2489.0 G.clavigera USA TRIA563 Uintas, UT, USA Pinus contorta 2011 40.8721 -109.7927 2695.0 G.clavigera USA TRIA633 Grey's River Drainage, WY, USA Pinus contorta 2011 43.1315 -110.8663 1870.0 G.clavigera USA TRIA305 Pioneer Mountains, MT, USA Pinus contorta 2011 45.5342 -113.0802 2100.0 67 B. Information for isolates of L. longiclavatum used in temperature tolerance experiments Species Population Grouping Isolates Name Location Host Date collected Latitude (°N) Longitude (°W) Elevation (m) L.longiclavatum North TRIA58 Fox Creek, AB, CA Pinus contorta 2010 54.3089 -116.6319 892.2 L.longiclavatum North TRIA47 Grande Prairie, AB, CA Pinus contorta 2010 55.5171 -119.6520 931.8 L.longiclavatum North TRIA3 Grande Prairie, AB, CA Pinus contorta 2010 54.8610 -119.8203 888.0 L.longiclavatum North TRIA55 Peace Area, AB, CA Pinus contorta 2010 56.7068 -118.2749 874.0 L.longiclavatum North TRIA2 Peace Area, AB, CA Pinus contorta 2010 56.6235 -119.8970 894.0 L.longiclavatum North TRIA56 Peace Area, AB, CA Pinus contorta 2010 56.6225 -119.8981 897.6 68 L.longiclavatum North ss354 Fox Creek, AB, CA Pinus contortaPinus banksiana(pcb) 2008 54.7960 -116.6520 783.0 L.longiclavatum North ss428 Fox Creek, AB, CA Pinus contortaPinus banksiana(pcb) 2008 54.6380 -116.3470 922.0 L.longiclavatum North ss173 Tumbler Ridge, BC, CA Pinus contorta 2007 55.5380 -121.9850 909.0 L.longiclavatum North TRIA51 Tumbler Ridge, BC, CA Pinus contorta 2010 55.1790 -121.1119 1046.0 L.longiclavatum North ss329 Kakwa, AB, CA Pinus contorta 2008 53.9960 -119.5280 1392.0 L.longiclavatum North ss439 Kakwa, AB, CA Pinus contorta 2008 53.6410 -119.5000 1399.0 L.longiclavatum North ss508 Valemount, BC, CA Pinus contorta 2007 52.8530 -119.3830 1175.0 69 L.longiclavatum North TRIA52 Valemount, BC, CA Pinus contorta 2007/2008 52.8608 -119.3497 796.0 L.longiclavatum Central TRIA16 Canmore, AB, CA Pinus contorta 2010 50.9354 -115.3370 1718.0 L.longiclavatum Central TRIA19 Canmore, AB, CA Pinus contorta 2010 50.9355 -115.3370 1718.0 L.longiclavatum Central TRIA61 Kimberley, BC, CA Pinus contorta 2010 49.6951 -115.9580 1117.0 L.longiclavatum Central TRIA38 Kimberley, BC, CA Pinus contorta 2010 49.7784 -115.8480 1063.0 L.longiclavatum Central TRIA63 Peachland, BC, CA Pinus contorta 2010 49.7971 -120.0570 1280.5 L.longiclavatum Central TRIA25 Peachland, BC, CA Pinus contorta 2010 47.7971 -120.0575 648.0 L.longiclavatum Central ss491 Yoho, BC, CA Pinus contorta 2008 51.3340 -116.5420 1236.0 L.longiclavatum Central ss502 Yoho, BC, CA Pinus contorta 2007 51.3340 -116.5500 1217.0 70 L.longiclavatum Central ss505 Yoho, BC, CA Pinus contorta 2007 50.9120 -116.0410 1354.0 L.longiclavatum Central ss351 Crowsnest Pass, AB, CA Pinus contorta 2008 49.6570 -114.5520 1511.0 L.longiclavatum Central ss425 Crowsnest Pass, AB, CA Pinus contorta 2008 49.9860 -114.4950 1562.0 L.longiclavatum Central ss426 Crowsnest Pass, AB, CA Pinus contorta 2008 49.6580 -114.5660 1464.0 L.longiclavatum Central ss20 Cypress Hills, AB, CA Pinus contorta 2007 49.5930 -110.0360 1316.0 L.longiclavatum Central ss260 Cypress Hills, AB, CA Pinus contorta 2007 49.5930 -110.0360 1316.0 L.longiclavatum USA TRIA376 Ft Collins, CO, USA Pinus contorta 2011 40.7478 -105.6080 2703.0 L.longiclavatum USA TRIA378 Ft Collins, CO, USA Pinus contorta 2011 40.7478 -105.6080 2703.0 L.longiclavatum USA TRIA893 Butt Mountains, CA, USA Pinus contorta 2011 41.9762 -120.2117 2056.0 71 L.longiclavatum USA TRIA891 Butt Mountains, CA, USA Pinus contorta 2011 41.9762 -120.2117 2056.0 L.longiclavatum USA TRIA787 Boise, ID, USA Pinus contorta 2011 44.3028 -115.2570 2135.0 L.longiclavatum USA TRIA786 Boise, ID, USA Pinus contorta 2011 44.3028 -115.2570 2135.0 L.longiclavatum USA TRIA604 Winema Forest, OR, USA Pinus contorta 2011 42.9466 -121.3990 2019.0 L.longiclavatum USA TRIA978 Winema Forest, OR, USA Pinus contorta 2011 42.9466 -121.3990 2019.0 L.longiclavatum USA TRIA874 Mount Rose Ski Resort, NV, USA Pinus contorta 2011 39.3236 -119.8928 2567.0 L.longiclavatum USA TRIA879 Mount Rose Ski Resort, NV, USA Pinus contorta 2011 39.3236 -119.8928 2566.0 72 L.longiclavatum USA TRIA886 Mount Rose Ski Resort, NV, USA Pinus contorta 2011 39.3236 -119.8928 2566.0 L.longiclavatum USA TRIA532 Black Hills, SD, USA Pinus ponderosa 2011 43.9509 -103.6320 1718.0 C. Information for isolates of O. montium used in temperature tolerance experiments Species Population Grouping Isolates Name Location Host Date collected Latitude (°N) Longitude (°W) Elevation (m) O.montium North TRIA37 Fox Creek, AB, CA Pinus contorta 2010 54.2626 -116.6090 873.0 O.montium North TRIA1142 Grande Prairie, AB, CA Pinus contorta 2010 54.6985 -119.6841 1015.0 O.montium North TRIA18 Grande Prairie, AB, CA Pinus contorta 2010 54.8610 -119.8203 888.0 O.montium North TRIA1118 Peace Area, AB, CA Pinus contorta 2010 56.6845 -118.1163 799.7 73 O.montium North ss182 Fairview, AB, CA Pinus contorta 2007 56.1284 -118.5475 634.3 O.montium North ss304 Fairview, AB, CA Pinus contortaPinus banksiana(pcb) 2007 56.1646 -118.6504 653.0 O.montium North TRIA1094 Tumbler Ridge, BC, CA Pinus contorta 2010 55.1790 -121.1124 1046.5 O.montium North TRIA1126 Tumbler Ridge, BC, CA Pinus contorta 2010 55.1789 -121.1120 1046.8 O.montium North ss178 Kakwa, AB, CA Pinus contorta 2007 53.7751 -119.7674 1493.0 O.montium North ss185 Kakwa, AB, CA Pinus contorta 2007 53.8266 -119.6583 1542.0 O.montium North TRIA1078 Valemount, BC, CA Pinus contorta 2007/2008 52.8871 -119.2882 1111.6 O.montium North TRIA1123 Valemount, BC, CA Pinus contorta 2007/2008 52.8876 -119.2874 1133.5 74 O.montium Central TRIA1106 Canmore, AB, CA Pinus contorta 2010 50.9268 -115.1178 1505.0 O.montium Central TRIA1081 Kimberley, BC, CA Pinus contorta 2010 49.6938 -115.9645 1110.8 O.montium Central TRIA1086 Kimberley, BC, CA Pinus contorta 2010 49.7784 -115.8484 1063.0 O.montium Central TRIA1140 Peachland, BC, CA Pinus contorta 2010 49.7973 -120.0570 1299.0 O.montium Central TRIA1109 Cascades, BC, CA Pinus contorta 2010 50.4323 -122.2530 1092.0 O.montium Central ss282 Crowsnest Pass, AB, CA Pinus contorta 2007 49.6524 -114.5430 1514.2 O.montium Central ss370 Crowsnest Pass, AB, CA Pinus contorta 2008 50.113 -114.649 1824.0 O.montium Central ss452 Crowsnest Pass, AB, CA Pinus contorta 2008 49.9860 -114.4950 1562.0 O.montium Central ss309 Cypress Hills, AB, CA Pinus contorta 2007 49.5930 -110.0360 1316.0 75 O.montium Central ss301 Cypress Hills, AB, CA Pinus contorta 2007 49.5930 -110.0360 1316.0 O.montium USA TRIA523 Ft Collins, CO, USA Pinus contorta 2011 40.7476 -105.6070 2708.0 O.montium USA TRIA377 Ft Collins, CO, USA Pinus contorta 2011 40.7476 -105.6070 2708.0 O.montium USA TRIA442 Ft Collins, CO, USA Pinus contorta 2011 40.7621 -105.6140 2662.0 O.montium USA TRIA832 Boise, ID, USA Pinus contorta 2011 45.2610 -115.9018 1853.0 O.montium USA TRIA827 Boise, ID, USA Pinus contorta 2011 45.2609 -115.9023 1845.0 O.montium USA TRIA825 Boise, ID, USA Pinus contorta 2011 45.2739 -115.9090 1885.0 O.montium USA TRIA551 Winema Forest, OR, USA Pinus contorta 2011 42.94644 -121.39921 2011.0 76 O.montium USA TRIA611 Winema Forest, OR, USA Pinus contorta 2011 42.91651 -121.39907 2028.0 O.montium USA TRIA969 Winema Forest, OR, USA Pinus contorta 2011 42.94622 -121.3986 2025.0 O.montium USA TRIA627 Mount Rose Ski Resort, NV, USA Pinus contorta 2011 39.33252 -119.8874 2489.0 O.montium USA TRIA625 Mount Rose Ski Resort, NV, USA Pinus contorta 2011 39.3324 -119.88737 2492.0 O.montium USA TRIA384 Pioneer Mountains, MT, USA Pinus contorta 2011 45.5349 -113.0799 2121.0 O.montium USA TRIA320 Pioneer Mountains, MT, USA Pinus contorta 2011 45.5346 -113.0798 2110.0 77 D. R script library(pracma) data=read.csv(file="/Users/sophia/Desktop/R_Sophia /sophia_thesis.csv",sep=",", header = T) # ========= Linear Regression AUC ======================================== linearAUC = as.data.frame(matrix(data = NA, nrow = 1, ncol = 7)) for (degree in c(15, 20, 25)) { #, 5, 10 ,30)) { for (species in c("gc", "ll", "om")) { growthOfDegree = data[which(data$temp == degree & data$species == species), ] isolates = unique(growthOfDegree$iso) for (i in 1 : length(isolates)) { for (j in 1 : 3) { growth = data[which(data$iso == isolates[i] & data$temp == degree & data$rep == j & data$species == species & !is.na(data$r)), ] if (nrow(growth) == 0) { results = t(as.data.frame( c(as.character(isolates[i]), j, species, 78 as.numeric(degree), NA, NA, NA ))) linearAUC = rbind(linearAUC, as.data.frame(results)) next } fit=lm(as.numeric(growth$r)~growth$day) results = t(as.data.frame( c(as.character(isolates[i]), as.numeric(j), as.character(species), as.numeric(degree), as.numeric(fit$coef[2]), as.numeric(summary(fit)$r.squared), as.numeric(fit$coef[2] * 6 * 3) ))) linearAUC = rbind(linearAUC, as.data.frame(results)) } } } } 79 linearAUC = linearAUC[2 : nrow(linearAUC), ] colnames(linearAUC) = c("iso", "rep", "species", "temp", "coef", "R2", "AUC") linearAUC = merge(linearAUC, data[,1:13], by=c("iso", "rep", "temp", "species")) linearAUC = unique(linearAUC) linearAUC = linearAUC[,c(1:4,8:16,5:7)] #========== TrapZ AUC ================================================ trapzAUC = as.data.frame(matrix(data = NA, nrow = 1, ncol = 5)) for (degree in c(5, 10 ,30)) { for (species in c("gc", "ll", "om")) { growthOfDegree = data[which(data$temp == degree & data$species == species), ] isolates = unique(growthOfDegree$iso) for (i in 1 : length(isolates)) { for (j in 1 : 3) { growth = data[which(data$iso == isolates[i] & data$temp == degree & data$rep == j & data$species == species), ] results=t(as.data.frame( c(as.character(isolates[i]), as.numeric(j), as.character(species), 80 as.numeric(degree), trapz(growth$r, growth$day)))) trapzAUC = rbind(trapzAUC, as.data.frame(results)) } } } } trapzAUC = trapzAUC[2 : nrow(trapzAUC), ] colnames(trapzAUC) = c("iso", "rep", "species", "temp", "AUC") trapzAUC = merge(trapzAUC, data[,1:13], by=c("iso", "rep", "temp", "species")) trapzAUC = unique(trapzAUC) trapzAUC = trapzAUC[,c(1:4,6:14,5)] #========= Plot AUC =================================================== library(ggplot2) plotData = rbind(linearAUC[, !(names(linearAUC) %in% c("coef","R2"))], trapzAUC) plotData$AUC = as.numeric(plotData$AUC) plotData$temp = as.factor(plotData$temp) plotData$temp = factor(plotData$temp, levels(plotData$temp)[c(6, 1:5)]) plotData2 = aggregate(plotData$AUC, list(iso = plotData$iso, temp = plotData$temp, 81 species = plotData$species, exp = plotData$exp, gen_pop = plotData$gen_pop, geo_pop = plotData$geo_pop, loc = plotData$loc, date = plotData$date, host = plotData$host, lat = plotData$lat, lon = plotData$lon, ele = plotData$ele), mean) names(plotData2)[13] = 'AUC' plotData2$AUC = as.numeric(plotData2$AUC) plotData2$temp = as.factor(plotData2$temp) plotData2$temp = factor(plotData2$temp, levels(plotData2$temp)[c(2:6, 1)]) m=max(plotData2$AUC, na.rm=TRUE) ggplot(plotData2, aes(temp, AUC, color=species)) + geom_boxplot() + labs(x = 'Temperature (°C)', y = 'Area Under The Growth Curve (mm² * day)') + expand_limits(y = c(0,m))+ scale_x_discrete(limits=c("5","10","15","20","25","30")) 82 ggplot(plotData2[which(plotData2$species == "gc"),], aes(temp, AUC, color=gen_pop)) + geom_boxplot() + labs(x = 'Temperature (°C)', y = 'Area Under The Growth Curve (mm² * day)', color = 'Genetic Groups') + ggtitle('Grosmannia clavigera') + expand_limits(y = c(0,m))+ scale_x_discrete(limits=c("5","10","15","20","25","30")) ggplot(plotData2[which(plotData2$species == "ll"),], aes(temp, AUC, color=gen_pop)) + geom_boxplot() + labs(x = 'Temperature (°C)', y = 'Area Under The Growth Curve (mm² * day)', color = 'Genetic Groups') + ggtitle('Leptographium longiclavatum') + expand_limits(y = c(0,m))+ scale_x_discrete(limits=c("5","10","15","20","25","30")) ggplot(plotData2[which(plotData2$species == "om"),], aes(temp, AUC, color=gen_pop)) + geom_boxplot() + 83 labs(x = 'Temperature (°C)', y = 'Area Under The Growth Curve (mm² * day)', color = 'Genetic Groups') + ggtitle('Ophiostoma montium') + expand_limits(y = c(0,m))+ scale_x_discrete(limits=c("5","10","15","20","25","30")) #=================ANOVA============================================ fit1=aov(AUC ~ species*as.factor(temp), data=plotData) summary(fit1) fit2=aov(AUC ~ species*as.factor(temp), data=plotData[which(plotData$temp!=5 & plotData$temp!=10 & plotData$temp!=30),]) summary(fit2) TukeyHSD(fit2, "species") fit3=aov(AUC ~ species, data=plotData[which(plotData$temp==30),]) summary(fit3) TukeyHSD(fit3, "species") fit4=aov(AUC ~ temp*gen_pop, data=plotData[which(plotData$species=="gc"& (plotData$temp!=30)&(plotData$temp!=10)&(plotData$temp!=5)),]) summary(fit4) TukeyHSD(fit4, "gen_pop") fit5=aov(AUC ~ gen_pop, data=plotData[which(plotData$species=="gc"& (plotData$temp==30)),]) 84 summary(fit5) fit6=aov(AUC ~ temp*gen_pop, data=plotData[which(plotData$species=="ll"& (plotData$temp!=30)&(plotData$temp!=10)&(plotData$temp!=5)),], na.action=na.omit) summary(fit6) TukeyHSD(fit6, "gen_pop") fit7=aov(AUC ~ gen_pop, data=plotData[which(plotData$species=="ll"& (plotData$temp==30)),], na.action=na.omit) summary(fit7) TukeyHSD(fit7, "gen_pop") fit8=aov(AUC ~ temp*gen_pop, data=plotData[which(plotData$species=="om"& (plotData$temp!=30)&(plotData$temp!=10)&(plotData$temp!=5)),], na.action=na.omit) summary(fit8) TukeyHSD(fit8, "gen_pop") fit9=aov(AUC ~ gen_pop, data=plotData[which(plotData$species=="om"& (plotData$temp==30)),], na.action=na.omit) summary(fit9)
Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Phenotypic characterization of Mountain Pine Beetle fungal symbionts under a temperature gradient
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