Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Loverock, Laura Rebecca
Master of Science - MSc
University of British Columbia
The French black truffle is the prized fruiting body of the ectomycorrhizal fungus Tuber melanosporum. International markets price the black truffle from 2000-4000 Euros per kilogram, leading to the cultivation of T. melanosporum on nearly every continent. Despite the high value and broad range of cultivated areas, little is known about how to optimize T. melanosporum growth, or mitigate potential ectomycorrhizal fungal competitors. This is particularly true in novel environments. I investigated the optimization of T. melanosporum biomass and assessed the ectomycorrhizal fungal community in roots and soil in relation to differing levels of irrigation in a T. melanosporum - Quercus robur orchard in Vancouver, Canada. I applied three irrigation treatments from June to September 2021 and quantified the abundance of T. melanosporum DNA in the soil and the composition of the surrounding soil and root ectomycorrhizal fungal community. During the study, which coincided with an extreme heatwave and moderate drought, irrigation treatments were maintained at high (- 30 kPa), moderate (- 80 kPa), or low levels (up to -260 kPa) of soil moisture. Soil samples were collected monthly at 50 cm and 100 cm from each tree for quantification of T. melanosporum DNA. The ectomycorrhizal fungal community was sampled in August from the soil and mycorrhizal root tips found 50 cm from the tree. DNA of T. melanosporum was quantified using droplet digital PCR (ddPCR), whereas composition of the ectomycorrhizal fungal communities was determined by PacBio Sequel II sequencing. Generalized Additive Models revealed that the DNA abundance of T. melanosporum was highest under low-irrigation trees during the hottest and driest weeks, while the lowest abundance of T. melanosporum was in the high irrigation treatment. Overall, the abundance of T. melanosporum decreased with increasing water content. Irrigation did not affect the fungal communities in roots or soil, but several dominant species were identified as potential competitors. To the best of my knowledge, this work represents the first North American trial involving T. melanosporum where a variable, such as irrigation, has been manipulated and is also the first application of DNA metabarcoding to a North American T. melanosporum orchard.
The effects of irrigation treatments on Tuber melanosporum extramatrical mycelium and the surrounding ectomycorrhizal fungal community in a British Columbia truffle orchard by Laura Rebecca Loverock B.Sc. (hons.), University of British Columbia Okanagan, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan Campus) May 2024 © Laura Loverock, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate studies for acceptance, a thesis entitled: The effects of irrigation treatments on Tuber melanosporum extramatrical mycelium and the surrounding ectomycorrhizal fungal community in a British Columbia truffle orchard. Submitted by Laura Rebecca Loverock in partial fulfilment of the requirements of the degree of Master of Science in Biology. Dr. Dan Durall, Faculty of Science, UBC Okanagan Co-Supervisor Dr. Kirsten Hannam, Agriculture and Agri-Food Canada Co-Supervisor Dr. Shannon Berch, Faculty of Science, UBC Vancouver Supervisory Committee Member Dr. Melanie Jones, Faculty of Science, UBC Okanagan Supervisory Committee Member iii Abstract The French black truffle is the prized fruiting body of the ectomycorrhizal fungus Tuber melanosporum. International markets price the black truffle from 2000-4000 Euros per kilogram, leading to the cultivation of T. melanosporum on nearly every continent. Despite the high value and broad range of cultivated areas, little is known about how to optimize T. melanosporum growth, or mitigate potential ectomycorrhizal fungal competitors. This is particularly true in novel environments. I investigated the optimization of T. melanosporum biomass and assessed the ectomycorrhizal fungal community in roots and soil in relation to differing levels of irrigation in a T. melanosporum - Quercus robur orchard in Vancouver, Canada. I applied three irrigation treatments from June to September 2021 and quantified the abundance of T. melanosporum DNA in the soil and the composition of the surrounding soil and root ectomycorrhizal fungal community. During the study, which coincided with an extreme heatwave and moderate drought, irrigation treatments were maintained at high (- 30 kPa), moderate (- 80 kPa), or low levels (up to -260 kPa) of soil moisture. Soil samples were collected monthly at 50 cm and 100 cm from each tree for quantification of T. melanosporum DNA. The ectomycorrhizal fungal community was sampled in August from the soil and mycorrhizal root tips found 50 cm from the tree. DNA of T. melanosporum was quantified using droplet digital PCR (ddPCR), whereas composition of the ectomycorrhizal fungal communities was determined by PacBio Sequel II sequencing. Generalized Additive Models revealed that the DNA abundance of T. melanosporum was highest under low-irrigation trees during the hottest and driest weeks, while the lowest abundance of T. melanosporum was in the high irrigation iv treatment. Overall, the abundance of T. melanosporum decreased with increasing water content. Irrigation did not affect the fungal communities in roots or soil, but several dominant species were identified as potential competitors. To the best of my knowledge, this work represents the first North American trial involving T. melanosporum where a variable, such as irrigation, has been manipulated and is also the first application of DNA metabarcoding to a North American T. melanosporum orchard. v Lay Summary The French black truffle (Tuber melanosporum) is cultivated in orchards with trees whose roots have been inoculated with this ectomycorrhizal fungus. However, recommendations for the optimization of truffle growth can be variable. This is particularly true in non-native growing areas. I applied three irrigation treatments to a Canadian black truffle – English oak (T. melanosporum – Quercus robur) orchard to investigate how the abundance of black truffle soil mycelium may respond. I also looked at the response of the surrounding ectomycorrhizal fungal community while identifying potential fungal competitors. I found that the soil under non-irrigated trees harbored the highest amount of truffle DNA during the hottest and driest months of study, and that overall, the abundance of truffle soil DNA decreased with increasing water content. I did not see any effect of irrigation treatment on the ectomycorrhizal fungal community, but identified several potential competitor species to the black truffle based on a high frequency of occurrence. vi Preface The experimental design was designed by me with contributions from Selena Carl, Dan Durall, Kirsten Hannam, Melanie Jones, Shannon Berch, Doug Campbell, and Carlos Colinas. Assistance in data collection for root tip collection and stem water potential was provided by Selena Carl and Dr. Thorsten Knipfer, respectively. Selena Carl was responsible for root tip processing, DNA extraction, root tip sample preparation for NGS, and prepared the root tip mock community. All figures and text were produced and written by me with contributions from Stefano Mezzini. The statistical analyses were performed by Stefano Mezzini and me. The overall research question was conceived by Shannon Berch and modified by my committee, Doug Campbell and me. vii Table of Contents Abstract…….………...…...……………………………………………………………………..iii Lay Summary..……...…..……………………………………………………………………….v Preface…………………………………………………………………………………………..vi Table of Contents.....…..………………………………………………………………………..ii List of Tables…..…..……………..…………………………………………………………….vii List of Figures.……..……………………..……………………………………………………viii Acknowledgements...………………….………………………………………………………..x Dedication..………….…………………………………………………………………………..xi 1.0 Introduction ............................................................................................................. 1 1.1 Mycorrhizal fungi ....................................................................................................... 1 1.1.2 Detection of Ectomycorrhizal fungi .................................................................. 3 1.2 Tuber melanosporum Biology ................................................................................... 7 1.2.1 Sexual reproduction ......................................................................................... 8 1.2.2 Lifecycle ........................................................................................................... 9 1.3 Production and cultivation of T. melanosporum ...................................................... 12 1.3.1 Hosts ............................................................................................................. 12 1.3.2 Climatic variables ........................................................................................... 13 1.3.3 Edaphic properties ......................................................................................... 14 1.3.4 Fungal competition ........................................................................................ 15 1.3.5 Water use ...................................................................................................... 17 1.3.6 Current state of production ............................................................................ 20 1.4 Objectives and hypothesis ...................................................................................... 21 viii 1.4.1 Objectives ...................................................................................................... 21 1.4.2 Hypotheses .................................................................................................... 22 2.0 The effect of irrigation on Tuber melanosporum extramatrical mycelium and on the soil and root ectomycorrhizal fungal community in a Canadian truffle orchard during an unprecedented heat wave and moderate drought……………...24 2.1 Abstract ................................................................................................................... 24 2.2 Introduction ............................................................................................................. 25 2.3 Materials and Methods ............................................................................................ 29 2.3.1 Study site ....................................................................................................... 29 2.3.1 Experimental design ...................................................................................... 32 2.3.2 Collection of soil samples .............................................................................. 36 2.3.3 Collection of root tips and processing ............................................................ 37 2.3.4 DNA extraction – soil and roots ..................................................................... 37 2.3.5 Ectomycorrhizal fungal community- DNA metabarcoding and bioinformatics 38 2.3.6 Statistical Analyses ........................................................................................ 40 2.4 Results .................................................................................................................... 43 2.5 Discussion ............................................................................................................... 50 2.6 Conclusion .............................................................................................................. 55 3.0 Conclusion ............................................................................................................. 56 3.1 Review of goals and hypotheses ............................................................................. 56 3.2 Comparison to current research .............................................................................. 57 3.3 Strength, limitations and future research directions................................................. 58 3.4 Applications in trufficulture ...................................................................................... 60 References .................................................................................................................... 62 ix Appendix: Supplementary Tables & Figures ................................................................. 90 x List of Tables 1.1: Comparison of NGS platforms…..………...…............................................................6 2.1: Site and soil characteristics………......................................................................... 19 2.2: Summary of experimental design parameters………..……………………………….25 A3: Irrigation schedule………..…………………………………………………………........83 A6: Comparison of BLAST and UNITE results………..……………………………………88 A7: GAM output: abundance of T. melanosporum………...…………………………........92 A10: List of ectomycorrhizal taxa found in roots and soil………..………………………..96 A11: GAM output: root ectomycorrhizal fungal community richness……………...……..98 A12: GAM output: soil ectomycorrhizal community fungal richness……………..………98 A13: GAM output: soil ectomycorrhizal fungal community composition…………………99 A14: GAM output: soil ectomycorrhizal fungal community composition………………...99 xi List of Figures 1.1: Hypothetical life cycle of T. melanosporum……….…..............................................11 2.1: Daily mean temperatures and precipitation………..................................................20 2.2: Experimental design................................................................................................22 2.3: Soil water potential readings..………………………………………….……………….24 2.4: T. melanosporum soil DNA abundance at 50 cm………..………….………………..34 2.5: Soil ectomycorrhizal fungi occupancy plots……………….………..…………………36 2.6: Root ectomycorrhizal fungi occupancy plots…………….…………………………….38 A1: Historical weather data………..………………………………………………...…….....79 A2: Midday stem water potential readings………..………………………………...………81 A4: Mock communities for ectomycorrhizal fungal community analysis………...………85 A5: GAM diagnostic plots………..…………………………………………………...………87 A8: Soil water content GAM………..………………………………………………...………93 A9: Partial effects and interaction terms of GAM………..…………………………………94 A15: Effect of irrigation on the ectomycorrhizal fungal communities……...…..………..100 xii Acknowledgements I would like to thank my co-supervisor Dr. Dan Durall for his guidance and unwavering support through all stages of research. I would also like to thank my co-supervisor Dr. Kirsten Hannam and committee members Dr. Melanie Jones and Dr. Shannon Berch for their immense help, funding, and patience regarding experimental design, data collection, and writing. This thesis would not have been possible without the contributions of Doug Campbell, which included but are not limited to experimental design, funding, field work, providing accommodations and general support throughout my thesis project. I am grateful for all members of the Durall and Jones labs for their support and comradery, in particular to Selena Carl for data collection of root tips and Olivia Rianhard for advice on Bioinformatics analysis. Additionally, I thank Dr. Thorsten Knipfer for training and providing lab space; Dr. Simone Castellarin for the provision of the water potential equipment; Shawn Kutcha for assistance with data interpretation; Dr. José Urbez-Torres and Dan O’Gorman for guidance on pest and disease identification; and Dr. Carlos Colinas for his helpful input on experimental design. Funding from a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant to MDJ is gratefully acknowledged. xiii Dedication This thesis is dedicated to my parents, Dave and Julie Loverock, for their support through my entire academic career. This thesis is also dedicated to my supervisor, Dan Durall, who has gone above and beyond the typical responsibilities of a graduate supervisor to ensure my success and completion of this project. Hopefully we can see the Jays take more wins in Seattle in 2024. 1 1.0 Introduction 1.1 Mycorrhizal fungi Mycorrhizal fungi are a diverse group of plant-root symbionts. First described by A. B. Frank (Trappe 2005), mycorrhizal fungi derive their name from the Greek words ‘mycos´ for ‘fungus’ and ‘rhiza' for ‘root’ (Trappe 2005; Parniske 2008). The oldest mycorrhizal associations date back 450 million years (Redecker et al. 2000) and likely facilitated the transition of ancient plants from water onto land (van der Heijden et al. 2015). These fungi are highly abundant, forming associations with 70-90% of tracheophytes (Brundrett and Tedersoo 2018). While different types of mycorrhizal associations exist, the general mechanism of the symbioses is the same: a fungal partner inhabits the root system of a plant partner and extends its hyphae outwards into the soil. The hyphal threads grow beyond the root uptake zone and can enter smaller pores in the soil (Allen 2007). The mycorrhizal fungus provides enhanced nutrient uptake for the plant, particularly for phosphorus, nitrogen, and potassium (Marschner and Dell 1994; Domínguez Núñez et al. 2006). The fungal partner receives carbon in the form of photosynthates (Le Tacon et al. 2013; Fellbaum et al. 2014). It is important to note that this relationship is not always mutualistic, existing as a spectrum from highly beneficial to parasitic (Klironomos 2003; Kokkoris et al. 2019). The four main classifications of mycorrhizal symbiosis are differentiated by the morphological structures where nutrients are exchanged (Peterson and Massicotte 2004; Brundrett and Tedersoo 2018). Endomycorrhizal fungi colonize the roots intracellularly and include the arbuscular mycorrhizal fungi (AMF), the ericoid mycorrhizal fungi (ErM), and the orchid mycorrhizal fungi (ORM). Arbuscular 2 mycorrhixal fungi are the most abundant mycorrhizal group and broadly associate with many plant lineages (van der Heijden et al. 2015). In contrast, both the ErM and ORM are highly specialized to their plant partner lineages (Bidartondo and Read 2008; Leopold 2016). Ectomycorrhizal fungi (EMF) associate with some gymnosperms and certain families or genera of flowering plants. This group does not associate with the plant cell membrane, instead EMF form an external fungal sheath over the plant root tip, with an internal apoplastic structure known as the Hartig net where nutrient exchange occurs (Marschner and Dell 1994). Collectively, this structure is referred to as the mycorrhiza (Trappe 2005). The ectomycorrhizal relationship represents a comparatively minor portion of the symbiosis; 2% of mycorrhizal plants are considered ectomycorrhizal symbionts compared with the 70% of plants that have arbuscular mycorrhizal symbionts (Parniske 2008; Brundrett and Tedersoo 2018). The ectomycorrhizal symbiosis has arisen independently at least 66 times within different orders of fungi (Tedersoo et al. 2010). Approximately 20,000-25,000 fungal species and 6000-7000 plant species are considered ectomycorrhizal (Tedersoo et al. 2010; Brundrett and Tedersoo 2018). A single tree is typically colonized by a multitude of different ectomycorrhizal fungi (Kennedy 2010; Delevich et al. 2021) and vice versa; an individual fungus can colonize different trees (Marjanović et al. 2020; Assad et al. 2021). In some systems, hyphal networks may form within tree stands (Beiler et al. 2010; Van Dorp et al. 2020). Most ectomycorrhizal fungi sexually reproduce to produce sporocarps, which contain billions of spores that will germinate into the next generation of offspring (Sánchez-García et al. 2020). 3 Ecologically and economically important tree genera Pinus, Quercus, and Pseudotsuga all require ectomycorrhizal associations for normal growth and development (Policelli et al. 2020; Anthony et al. 2022). The sporocarps of some ectomycorrhizal fungi are of economic interest as they are both edible and desirable to humans (Tedersoo et al. 2010; Iotti et al. 2012). Popular edible ectomycorrhizal species include chanterelles (Cantharellus spp.), porcinis (Boletus edulis), and truffles (Tuber spp.). 1.1.2 Detection of Ectomycorrhizal fungi Detection of ectomycorrhizal fungi is essential to survey diversity and to investigate responses to manipulated variables. Traditionally, visual assessment of mycorrhizae allows for differentiation into genera or species, a process known as morphotyping (Agerer 2006; Clasen et al. 2018). Ambiguous anatomical features and novel taxa can prevent taxonomic assignment through this visual process (Watrud et al. 2006). The time-intensive nature of morphotyping and high skill-level required also present challenges to many researchers (Clasen et al. 2018). In addition, morphotyping does not permit the detection and quantification of extramatrical hyphae in the soil. DNA barcoding refers to the process of species identification using a short, standardized region of the genome known as a ‘barcode’ (Valentini et al. 2009). The internal transcribed spacer (ITS) region within the nuclear ribosomal RNA cistron has become the standard DNA barcode for fungi (Schoch et al. 2012; Kõljalg et al. 2013). The ITS region provides a sufficient ‘barcode gap’, where the variation in the genetic sequence among species is greater than the variation within a species (Valentini et al. 2009; Schoch et al. 2012). 4 1.1.2.1 Individual Species Identification Individual fungal species can be identified using a series of molecular tools. Environmental samples containing fungal DNA are first subjected to the polymerase chain reaction (PCR). PCR targets a specific region of DNA to be exponentially copied for detection in a sample, quantification, or identification. The region to be copied is typically known as a DNA barcode as it will contain a unique region of the genome that can be used to identify individual groups or species. During PCR, the DNA barcode is exponentially copied through a series of thermal cycles. Once a sufficient amount of DNA has been produced, the resulting PCR product is sequenced and compared to online databases to confirm identity (Rådström et al. 2004; Zhu et al. 2020). Quantitative PCR (qPCR) is an advancement to PCR technology that allows for the quantity of the fungus to be assessed. Here, molecular probes are used to generate a fluorescent signal each time the PCR target is copied. The point in the thermal cycling where the fluorescent signal is greater than that of the background signal is known as the quantification cycle (Cq), which is inversely related to the amount of DNA in the sample (Orlando et al. 1998; Zhu et al. 2020). Using a known concentration of DNA (typically quantified using a spectrophotometer and diluted in series), the amount of DNA in the unknown sample can be determined by comparison to the standard curve (Orlando et al. 1998). Digital PCR uses the same fluorescent probes as qPCR, but PCR products are counted using molecular counting. A standard PCR reaction is partitioned into 15,000-20,000 microliter-sized droplets, where each droplet represents an individual PCR reaction. Droplets containing the target sequence (positives) will fluoresce when the 5 target is amplified while droplets without the target sequence will not fluoresce (negatives). The number of positive and negative droplets are counted and the relative fluorescence of each droplet is plotted, forming distinct clusters or ‘clouds’ of positive and negative droplets (Zhu et al. 2020). The partitioning of samples in ddPCR allows for higher sensitivity when compared to traditional qPCR in studies using both clinical (Link-Lenczowska et al. 2018; Poh et al. 2020) and environmental samples (Banks et al. 2021; Voegel et al. 2021). When comparing the reproducibility of ddPCR and qPCR, Wang et al. 2019 found ddPCR showed significantly lower between-run variation. Comparisons between ddPCR and qPCR on environmental samples have increased confidence in the new technology (Zhao et al. 2016). 1.1.2.2 Ectomycorrhizal Fungal Community Identification DNA metabarcoding provides taxonomic resolution from all individuals in a sample using metabarcoding. Sequences obtained from a sample are compared to a reference database for elucidation of species (Shokralla et al. 2012). Advanced computation methods are typically required to process the large breadth of sequence information obtained from a single sample. Different metabarcoding platforms have been available since 2005 (Lindahl et al. 2013) and the technology continues to improve in resolution, cost-effectiveness, and time required to process samples. Researchers using metabarcoding must consider which sequencing platform to choose. Illumina MiSeq has been frequently used in microbial ecology for several years (Quail et al. 2012; Sinclair et al. 2015), but is incapable of sequencing the full length of the ITS region and is typically used for either ITS1 of ITS2 subregions (Table 1) (Nilsson et al. 2019). The ITS region varies between 500 - 700 bases in length (Nilsson et al. 6 2019; Antil et al. 2023), with outliers up to nearly 1800 base pairs (Yang et al. 2018). Sequencing the full ITS region provides greater taxonomic resolution of the fungi compared ot sequencing ITS1 or ITS2 (Yang et al. 2018). The PacBio SMRT sequencing platform allows for greater taxonomic resolution of fungi (Tedersoo and Anslan 2019), since it has the capacity to sequence the entire ITS region (Rhoads and Au 2015). Table 1.1 Comparison of NGS platforms. Read length for Illumina refers to the two paired-end reads generated. Adapted from Gao et al. 2023. Platform Accuracy Read Length Throughput per run/Gb Run time (h) PacBio SMRT Sequel II >=99% ~15-18 kB 30 30 Illumina MiSeq ~99.6% 2x300 bp 15 4 - 55 Differentiation between base-calling errors and the natural intraspecific variation among sequences is essential to define a species. Metabarcoding produces sequences representing species or taxa, which are commonly defined by proxy using operational taxonomic units (OTUs). Operational taxonomic units are DNA sequences that have been clustered into a single ‘unit’ based on similarity levels, typically at least 97% similarity (Nilsson et al. 2011). A limitation of OTU clustering is that the presence of taxa with minor sequence variations is obscured as they cluster together. An alternative approach uses amplicon sequence variants (ASVs), which determines ‘sequence variants’ from the total number of unique sequences using Bayesian modeling algorithms (Callahan et al. 2016). In samples with naturally low species richness, such as agricultural soil (Song et al. 2019; Danielsen et al. 2021), using ASVs instead of 7 OTUs can provide more accurate accounts of the true fungal richness (Joos et al. 2020). Molecular methods are highly important to commercial producers of edible ectomycorrhizal fungi, as they need to monitor the persistence and abundance of their crop, as well as identify potential competitor species. Attempts to cultivate edible ectomycorrhizal fungi are generally met with poor success (Hall et al. 2003; Savoie and Largeteau 2011). One of the only ectomycorrhizal genera capable of successful commercial sporocarp production is Tuber, which produces subterranean sporocarps known as truffles (Reyna and Garcia-Barreda 2014; Oliach et al. 2021). 1.2 Tuber melanosporum Biology Tuber melanosporum Vittad. (Ascomycota, Pezizomycotina, Pezizomycetes, Pezizomycetidae, Pezizales, Tuberaceae), the French Black Truffle, is one of the most valued Tuber species (Rubini et al. 2014; Le Tacon et al. 2016). With unique organoleptic properties, T. melanosporum sporocarps are used in fine dining to elevate flavour and restaurant status. The outermost membrane of the sporocarp, the peridium (Moore-Landecker 1996), is characterized by a dark-brown to black colour and small pyramidal warts (Thomas and Büntgen 2017). A member of the Ascomycota, T. melanosporum produces meiotically-derived haploid spores in a structure known as an ascus (plural, asci) (Kendrick 2017), which resides in the dark fertile inner tissue called the gleba (De la Varga et al. 2017). Typically, members of Pezizales forcibly eject spores upon maturity via the opening of a small covering on the ascus, the operculum (Kendrick 2017). The hypogeous environment of Tuber has resulted in the loss of the operculate ascus and as such, this group no longer forcibly ejects spores (Moore- 8 Landecker 1996; Kendrick 2017). Many Tuber species have evolved an intense aroma which attracts animals and leads to the consumption of the sporocarps and passive dissemination of spores through feces (Kendrick 2017). Aroma development in T. melanosporum tends to peak at ascospore maturity, facilitating spore dispersal by animals (Le Tacon et al. 2013). 1.2.1 Sexual reproduction In order to produce the valuable sporocarp, sexual reproduction between two genetically distinct T. melanosporum individuals must occur (Moore and Novak Frazer 2002; Martin et al. 2010). Sexual reproduction is regulated by the mating-type (MAT) genes, which partly aid in mate recognition via complex pheromone pathways (Martin et al. 2010; Wilken et al. 2017). The MAT genes are specified by a single locus, indicating that T. melanosporum employs the bipolar mating strategy of most Pezizomycotina (Wilken et al. 2017). The genes at this locus are non-allelic, instead referred to as idiomorphs because they contain dissimilar sequences and code for different proteins (Metzenberg & Glass, 1990). The idiomorph named MAT1-1 codes for a group of proteins including an alpha box domain, while MAT1-2 contains a sequence for a high mobility group (HMG) protein (Martin et al. 2010). Historically, T. melanosporum was thought to be a homothallic species (Bertault et al. 1998). Homothallic fungi are capable of completing sexual reproduction within an isolated individual, sometimes referred to as “self-fertilization” (Moore et al. 2011). The haploid gleba and peridium of the truffle suggested that it was the product of selfing (Bertault et al. 1998). However, when sequencing DNA from asci, unique genetic material was found, suggesting the occurrence of reproduction with another individual in 9 a process known as ‘outcrossing’ (Riccioni et al. 2008). Sequencing of the T. melanosporum genome revealed the presence of an HMG-protein, indicative of the MAT1-1 idiomorph (Martin et al. 2010). Further screening of T. melanosporum sporocarps revealed that all sporocarps contained either the MAT1-1 or MAT1-2 idiomorph, leading Martin et al. (2010) to conclude that T. melanosporum uses a heterothallic mating strategy. 1.2.2 Lifecycle The lifecycle of T. melanosporum has not been observed to completion, though the theoretical lifecycle is generally agreed upon (Figure 1). One haploid partner forms an ectomycorrhizal association with a nearby tree and produces the ascogonia, the reproductive structure in which female gametes are formed, on hyphae extending through the soil (Rubini et al. 2011; De la Varga et al. 2017). This individual is known as the maternal partner as it contributes to the main structure of the sporocarp following fertilization (Murat et al. 2013; De La Varga et al. 2017; Selosse et al. 2017). The sporocarp body is therefore said to have a uniparental origin (Riccioni et al. 2008: Murat et al. 2013). The paternal partner forms the antheridia and male gametes, but the origin of this individual is still unknown. Analyses using microsatellite markers demonstrated that paternal genotypes showed higher genetic diversity and an abundance of small size genotypes, which could suggest annual turnover (De la Varga et al. 2017), i.e. a short-lived individual. Several hypotheses exist as to where the paternal partner originates from, including germinating ascospores, soil mycelium, ectomycorrhizae or possible conidia, though conidia have yet to be described in T. melanosporum (De la Varga et al. 2017). 10 Across its native range of North America, sporocarp development begins in May or June (Pacioni et al. 2014; Le Tacon et al. 2016) when they are known as truffle primordia. An increase in metabolic activity is seen during this stage of development (Pacioni et al. 2014). Unlike many other edible sporocarps, which are produced over a few days, truffles are slow growing and take many months to reach maturity (Kendrick 2017). An experiment using 13C pulse-labelling showed that the tree partner contributes carbon to the slowly developing truffle even after leaf senescence (Le Tacon et al. 2013). The authors suggest that hydrolysis of starch provides carbon to the fungus during tree dormancy (Le Tacon et al. 2013). This is in contrast to many other ectomycorrhizal fungi that produce sporocarps using carbon recently assimilated by the tree (Kaiser et al. 2015). Current knowledge of T. melanosporum ascospore development is primarily based on work from France and Italy (Le Tacon et al. 2013; Pacioni et al. 2014). Ascospore formation begins in late September and reaches maturity, marked by melanized spores and aroma development, from late October/November to January (Le Tacon et al. 2013; Pacioni et al. 2014). In a mature truffle, melanin can comprise up to 15% of the dried weight (Harki et al. 2006). A second peak in metabolic activity is reported during maturation from September to December (Pacioni et al. 2014). Harvesting of the truffles can occur from November until March in the northern hemisphere (Harki et al. 2006). 11 12 Figure 1.1 Hypothetical lifecycle of T. melanosporum. Sporocarp development begins in early spring with the birth of the truffle primordia following sexual reproduction between two individuals. The sporocarp slowly grows over the subsequent months, reaching full maturation in early winter. Sporocarps are harvested over the winter and can be found as late as March. Figure created using BioRender. 1.3 Production and cultivation of T. melanosporum The endemic range of T. melanosporum includes the Mediterranean basin, primarily Italy, France, and Spain (Reyna and Garcia-Barreda 2014), though successful orchards have been established around the globe (Linde and Selmes 2012; Guerin-Laguette et al. 2013; Berch and Bonito 2014; Čejka et al. 2020). Tuber melanosporum can be found in natural truffle beds in its native range, typically associating with trees in the Quercus and Corylus genera (Guerin-Laguette et al. 2013). Cultivation is achieved by establishing an orchard of seedlings that have been inoculated with T. melanosporum in greenhouses designed to reduce the abundance of other ectomycorrhizal fungal species (Alvarado and Manjon 2013; Fischer et al. 2017). Establishment of the fungus is confirmed microscopically or genetically before planting seedlings in the field. The period between orchard establishment and sporocarp production is generally 7-15 years (Suz et al. 2008; Reyna and Garcia-Barreda 2014; Oliach et al. 2022). 1.3.1 Hosts Quercus spp. and Corylus spp. are the most common partners in both cultivated orchards (Guerin-Laguette et al. 2013) and in natural beds (García-Montero et al. 2007c). Tuber melanosporum can also associate with plants in the genera Cistus (García-Montero et al. 2007b), Carya (Marozzi et al. 2017), Ostrya, Carpinus, Tilia, and 13 Cedrus (Jaillard et al., 2016). Interestingly, T. melanosporum has been found frequently and abundantly in the roots of herbaceous plants in proximity to the host tree (Plattner and Hall 1995; Schneider-Maunoury et al. 2018). It was thought that this occurrence might be the source of the elusive paternal genotype. However, microsatellite analysis of the herbaceous roots did not find the complementary genotype, leading the authors to suggest that T. melanosporum may exist on herbaceous roots as an endophyte (Schneider-Maunoury et al. 2018). 1.3.2 Climatic variables Both natural beds and cultivated orchards in the endemic range of T. melanosporum are generally characterized by warm temperatures and rapidly draining, calcareous soils (Valverde-Asenjo et al. 2009; Bruhn et al. 2013; Fischer et al. 2017). Precipitation in productive areas is variable, with one estimate for annual precipitation in naturally-occurring truffle sites ranging from 485-1500 mm year-1 (Fischer et al. 2017). Mean daily temperatures in productive areas generally range from 19-23 °C in the summer and from 1.6-5 °C in the winter (Hall et al. 2017). Low summer temperatures can slow the growth of the sporocarp (Le Tacon et al. 2013) and freezing temperatures can cause rot (S. Berch, personal comm). The successful cultivation of truffles in Australia indicates that T. melanosporum can tolerate higher temperatures than that of its native range (Fischer et al. 2017). There is currently no experimental data on the upper and lower temperature limits of T. melanosporum, and the aforementioned temperature ranges are based on observations from natural beds and orchards (Fischer et al. 2017). 14 1.3.3 Edaphic properties The pH of soils in T. melanosporum orchards typically ranges from 7.9-8.4 (Bonet et al. 2006; Suz et al. 2008; Zampieri et al. 2012; Parladé et al. 2013; Liu et al. 2014; Taschen et al. 2015; Piñuela et al. 2020). Production sites can be amended periodically with lime or pure calcium carbonate to maintain high soil pH (García-Montero et al. 2013). Sporocarp production occurs in alkaline soil (Jaillard et al. 2014; Taschen et al. 2015; Fischer et al. 2017). Productive areas contain higher amounts of active carbonate than surrounding areas where sporocarps are not found (Garcia-Montero et al. 2007a,b), though this may be site-dependent (Alonso Ponce et al. 2014). Tuber melanosporum allegedly prefers Ca2+ over other soil cations such as K+, Mg2+, and Na+ (García-Montero et al. 2007a; Fischer et al. 2017). It has been hypothesized that soils high in active carbonate could have a nutrient imbalance, leading to enhanced colonization of the roots by T. melanosporum (García-Montero et al. 2009). Soil stoniness, or the abundance of stones in the upper horizons, is generally accepted to have a positive effect on truffle production (García-Montero et al. 2007a; Le Tacon et al. 2013; Fischer et al. 2017), though this is not always seen in young trees (Suz et al. 2008). Stoniness increases the porosity and improves drainage in the soil, and has been correlated with the active carbonate concentration in areas with limestone parent materials (Garcia-Montero et al. 2007a). It should be noted that good drainage must be balanced with adequate water holding capacity to promote sporocarp production (Alonso Ponce et al. 2014). 15 Just as the soil can impact T. melanosporum, the presence of the fungus can also drive large changes in the soil. Associations with T. melanosporum are typically marked by a zone of absent or reduced vegetation surrounding a plant partner (Plattner & Hall, 1995). This area is known as a ‘burn’ or ‘brûlé’ and is generally the site of sporocarp production (Suz et al. 2008). Seed germination and fungal diversity are lower in the brûlé (Napoli et al. 2010; Mello et al. 2015; Taschen et al. 2020). While some Pseudomonas spp. increase the colonization of T. melanosporum on roots (Dominguez et al. 2012), the overall abundance of Pseudomonas in the soil is lower inside the brûlé than outside (Mello et al. 2013). The production of volatile organic compounds by T. melanosporum is likely the cause of the brûlé (Djurdjevic et al. 2016). Not all productive trees have a brûlé, suggesting that T. melanosporum individuals vary in their ability to produce the phytotoxic compounds (Splivallo et al. 2011). 1.3.4 Fungal competition An apparent contradiction exists where, despite evidence of being a competitive species (Napoli et al. 2010; Streiblová et al. 2012), T. melanosporum can be entirely displaced from a orchard by another Tuber spp. (Molinier et al. 2013). Notable Tuber contaminants across Europe include T. brumale (Ori et al. 2018), T. indicum (Janex-Favre et al. 1996; Murat et al. 2008), and T. aestivum (Molinier et al. 2013). Contaminants vary in economic value but are generally considered lower quality than T. melanosporum based on aromatic profile and sporocarp size (Culleré et al. 2013; Merenyi et al. 2016). Introduction of contaminants likely occurs through errors in identification and/or poor inoculum purity (Linde and Selmes 2012). European Tuber contaminants have appeared in T. melanosporum orchards in Australia (Linde and 16 Selmes 2012), New Zealand (Guerin-Laguette et al. 2013), the United States (Lemmond et al. 2023), and Canada (Berch and Bonito 2014). Orchards outside of Europe must also consider the local Tuber community, as native North American Tuber spp. have appeared in orchards (Berch and Bonito 2014). It is unknown how these local species impact cultivation of T. melanosporum (Berch and Bonito 2016). Competition is a concern for orchard owners due to the high financial and temporal investments associated with establishing a T. melanosporum orchard. Orchard management may mitigate against some competitors. Increasing inoculum quality to prevent the initial introduction of T. brumale has been suggested in T. melanosporum orchards (Linde and Selmes 2012; Ori et al. 2018). Higher levels of T. melanosporum colonization at planting increases its competitiveness (Mamoun and Olivier 1993a). Increasing soil pH to favour T. melanosporum has not been shown to be effective in reducing T. brumale (Valverde-Asenjo et al. 2009). Irrigation may promote T. melanosporum over T. aestivum (Piñuela et al. 2021) and T. brumale (Mamoun and Olivier 1993b) when compared to no-irrigation treatments, though additional factors like mulching may influence the positive effect (Piñuela et al. 2021). Further research surrounding essential agronomic inputs such as irrigation water and its impact on the ectomycorrhizal fungal community composition in T. melanosporum plantations is warranted. Monitoring of potential ectomycorrhizal fungal contaminants in T. melanosporum orchards can be accomplished through a variety of methods. Morphotyping of root tips followed by molecular confirmation has been used in surveys of T. melanosporum orchards (Belfiori et al. 2012; Berch and Bonito 2014). Advancements in DNA barcoding 17 technologies have allowed for rapid assessment of entire communities in complex environments like the soil (Shokralla et al. 2012). Recently, DNA metabarcoding has been used to quantify the ectomycorrhizal fungal community in white truffle (Tuber magnatum) beds in Serbia (Marjanović et al. 2020) and in native truffle beds in Thailand (Suwannarach et al. 2021), though neither of these studies were performed at sites where T. melanosporum occurred. The existence of T. melanosporum as a root endophyte in herbaceous vegetation has been determined using metabarcoding (Schneider-Maunoury et al. 2018, 2020). Bacterial communities have been assessed on T. melanosporum sporocarps (Benucci and Bonito 2016) and T. melanosporum inoculated seedlings (Zhang et al. 2020). There is a conspicuous lack of the application of metabarcoding directly to factors influencing T. melanosporum production. Assessing the ectomycorrhizal fungal community composition in an orchard is the first step to address downstream production questions, such as those surrounding competition and potential mitigation through orchard management. 1.3.5 Water use Water is the most important agronomic input for most truffle crops. While the sporocarps are vulnerable to climatic fluctuations (Le Tacon et al. 2014), the mycorrhizae seem to tolerate more extreme conditions (Domínguez Núñez et al. 2006; Otsing and Tedersoo 2015; Bajaj et al. 2021). In general, precipitation in the summer is positively correlated with sporocarp production across Europe (Büntgen et al. 2012; Baragatti et al. 2019; Thomas and Büntgen 2019), though this effect is dependent on the timing of precipitation. Annual harvest yields across France, Italy, and Spain have been positively correlated with precipitation totals from June to August (Büntgen et al. 2019). A similar 18 trend has been seen when examining harvests out of only Southern France (Baragatti et al. 2019). A slight negative effect of September precipitation has been shown in France (Baragatti et al. 2019). This trend expands to September, October, and November when including Italy and Spain (Büntgen et al. 2019). Drier regions, such as southern Spain, have a stronger dependency on summer precipitation compared to other endemic areas (Thomas and Büntgen 2019). Irrigation is used in many orchards to supplement precipitation (Fischer et al. 2017). Unlike natural precipitation, the amount of water applied (volume), occurrence of application (frequency), and timing of application (period) must be determined by the cultivator. There are a range of methods used to determine the volume of irrigation water to apply. One method is to irrigate based on replacing a proportion of the water lost due to evapotranspiration. Evapotranspiration (ET) refers to the combined loss of water due to evaporation from the soil surface and transpiration from the plant (Allen et al. 1998). Determining ET can be accomplished using different calculations (Allen et al. 1998), some of which have now been packaged in user-friendly applications (Petersen and van der Gulik 2009). Alternatively, soil water potential can be used to calculate irrigation volumes required. Soil water potential is classically defined as the amount of work required to transport, isothermically and reversibly, an infinitesimal quantity of water from a pool of pure water, at a specified pressure and elevation, to the system under condition (Livingston and Topp 1993). The differential between the water potential of the surrounding soil and the root xylem demonstrates the amount of work required to move water. In the simplest terms, soil water potential is a measure of how much water is available to plants. When irrigation is scheduled based on soil water potential, 19 irrigation is triggered when the soil water potential reaches a pre-determined lower limit and continues until the soil water potential reaches a pre-determined upper limit. Although several irrigation studies have been conducted in truffle orchards in Spain, optimal irrigation management in an orchard remains elusive. Irrigation regimes based on replacing 50% of the water lost to evapotranspiration have been shown to promote development of T. melanosporum mycorrhizae over high irrigation (100% ET replacement) or controls that did not receive any irrigation input during the spring (May-June) (Olivera et al. 2014a). Similar results are seen when irrigating to replace 50% of the water deficit (i.e., the amount of water falling as precipitation minus the amount of water lost to ET over a given period of time) compared to high irrigation levels (100% ET replacement) (Bonet et al. 2006; Olivera et al. 2011). In the case of no irrigation (i.e. experimental controls), T. melanosporum ectomycorrhizae growth was found to be comparable to the growth seen in moderate treatments, where 50% of the water deficit was replaced (Bonet et al. 2006). Another study found that T. melanosporum growth in unirrigated controls was significantly lower compared to the moderate irrigation treatments, where irrigation to replace 50% of evaporative losses was imposed (Olivera et al. 2011). As these independent studies occurred at the same site, this disparity is likely not due to environmental differences, and the question surrounding the risk of no irrigation remains. Piñuela et al. (2021) used soil water potential to determine when to irrigate Q. ilex trees in a T. melanosporum orchard that had been mulched with different fabric treatments. When irrigating at soil water potentials of -250 kPa (approximately every 2 weeks; July – September), they found a positive impact of irrigation on the 20 quantity of T. melanosporum soil mycelium compared to no irrigation (Piñuela et al. 2021). 1.3.6 Current state of production Tuber melanosporum sporocarp production in the Mediterranean basin as a whole has been relatively stable following a dramatic decline in the mid-20th century (Le Tacon et al. 2014; Reyna and Garcia-Barreda 2014). The decline was mainly due to a rapid rural-depopulation caused by the World Wars (Reyna and Garcia-Barreda 2014), leading to a reduction in the use of productive land (Le Tacon et al. 2014). Despite advancements in inoculation techniques creating a boom in orchard establishment in the 1970’s, the recovery of harvests to pre-20th century levels has not occurred (Le Tacon et al. 2014; Reyna and Garcia-Barreda 2014). Analyses of select regions across Italy, France, and Spain suggest annual harvests are declining (tons/year) (Büntgen et al. 2012; Baragatti et al. 2019). The sporocarps are thought to be particularly sensitive to climatic variations due to their long development period (Le Tacon et al. 2013, 2014). Reductions in precipitation and increasing temperatures across the Mediterranean (Mariotti et al. 2015; Zappa et al. 2015; Barredo et al. 2018; Lionello and Scarascia 2018) are thought to be large drivers of the decline (Thomas and Büntgen 2019). A complete collapse of the Mediterranean truffle industry within the century is possible (Thomas and Büntgen 2019). Expanding the cultivation of T. melanosporum sporocarps beyond the endemic range could potentially alleviate pressures from declining European harvests. It has been suggested that the optimum climatic conditions for truffle production may extend beyond the traditionally accepted Mediterranean climate (Thomas and Büntgen 2017; 21 Čejka et al. 2020). Sporocarps have been successfully produced in Australia (Linde and Selmes 2012), New Zealand (Guerin-Laguette et al. 2013), Chile (Reyna and Garcia-Barreda 2014), the United States, (Meadows et al. 2020), and Canada (Berch and Bonito 2014). In 2008, a plot of Holm oak (Quercus ilex) inoculated with T. melanosporum was established in England to monitor the response of its mycorrhiza to sub-optimal climactic conditions. In March of 2017, a black truffle of “excellent quality” was harvested from this site, marking the most northern occurrence of a T. melanosporum sporocarp to date (Thomas and Büntgen 2017). Mycorrhizae have been established and persist in orchards in the relatively extreme environments of Saudi Arabia and Estonia (Otsing and Tedersoo 2015; Bajaj et al. 2021). Western Canada represents a new frontier in T. melanosporum cultivation. British Columbia offers both Mediterranean and arid regions with its coastal and interior habitats. Cultivation has been successful on the coast (Berch and Bonito 2014) and orchards have been established in the interior Okanagan Valley. As this industry begins to develop locally, there is a need to complete basic research in this region to support current and future producers. Canadian truffle research is scant, with a complete lack of experimental studies surrounding cultivation of T. melanosporum in Canada. 1.4 Objectives and hypothesis 1.4.1 Objectives The overall objective for the proposed study was to investigate how irrigation impacts T. melanosporum soil mycelium and potential ectomycorrhizal fungal competitors in the surrounding soil. The abundance of T. melanosporum DNA found in the soil, as measured by ddPCR (droplet digital PCR) quantification of the ITS region, was used as 22 an index for the abundance of T. melanosporum soil mycelium. The ectomycorrhizal fungal community was assessed using DNA metabarcoding of soil samples and ectomycorrhizal root tip samples. The specific objective of this thesis was to apply different irrigation treatments over a single growing season and measure the effect on: 1. The abundance of T. melanosporum mycelium in the soil sampled from two distances from the trees over a period of 4 months. 2. The composition of fungal communities in the soil and on root tips sampled at a single time and distance from the trees. The study took place in an 9.5-year-old not-yet-productive Q. robur/T. melanosporum orchard near the south coast of British Columbia. Generalized additive models (GAMs) were used to quantify the effect of irrigation on T. melanosporum abundance and on the composition and richness of the ectomycorrhizal fungal community. Irrigation treatment (high, moderate, low), sampling period (June, July, August, September) and sampling distance (50 cm, 100 cm) were included as fixed effects. Random effects included tree height, tree ID and the interaction terms of (1) sampling distance and treatment, (2) sampling distance and tree height, and (3) sampling distance and tree ID. 1.4.2 Hypotheses Objective 1: Ha: Applying moderate volumes of water for irrigation will have a positive effect on T. melanosporum soil mycelium relative to applying low or high volumes of water during irrigation. This pattern has been shown in studies conducted in Spain (Bonet et al. 2006; Olivera et al. 2011, 2014). 23 Objective 2: Ha: There will be differences in the ectomycorrhizal fungal community composition of the soil among treatments. Treatments that promote T. melanosporum may see a lower abundance of other commerical Tuber spp. (Mamoun and Olivier 1993b; Piñuela et al. 2021), but it is unknown how native Tuber spp. may respond. Ectomycorrhizal fungus Cenococcum geophilium may increase in abundance in the low irrigation treatment due to its drought-resistant traits (Kerner et al. 2012; Fernandez et al. 2013). 24 2.0 The effect of irrigation on Tuber melanosporum extramatrical mycelium and on the soil and root ectomycorrhizal fungal community in a Canadian truffle orchard during an unprecedented heat wave and moderate drought. 2.1 Abstract The French black truffle is the prized fruiting body of the ectomycorrhizal fungus Tuber melanosporum, selling for 2000-4000 Euros per kilogram in international markets. Little is known about how to optimize T. melanosporum growth and mitigate ectomycorrhizal fungal competitors. We investigated the influence of three irrigation treatments on (1) the abundance of T. melanosporum extramatrical mycelium and (2) the soil and root ectomycorrhizal fungal community in a pre-production T. melanosporum-Quercus robur orchard located in Vancouver, British Columbia, Canada. During the study, which coincided with an extreme heatwave and moderate drought, irrigation treatments were characterized by high (- 30 kPa), moderate (- 80 kPa), or low (up to -250 kPa) soil moisture. Soil samples, 50 cm and 100 cm from the base of trees, were collected monthly from June to September 2021 to investigate T. melanosporum extramatrical mycelial abundance and distribution, as quantified by soil DNA abundance. The ectomycorrhizal fungal community was sampled in August from the soil and mycorrhizal root tips. Tuber melanosporum soil DNA abundance was quantified using droplet digital PCR (ddPCR), while ectomycorrhizal fungal community composition was determined by PacBio Sequel II sequencing. DNA abundance of T. melanosporum was lowest in soil collected from the high irrigation trees during the hottest and driest weeks of study (June 26 – August 7, 2021). Irrigation treatment did not substantially affect the ectomycorrhizal fungal community composition of roots or soil, but several dominant 25 ectomycorrhizal species were identified. To our knowledge, this is the first North American T. melanosporum study in which a management variable (irrigation) has been manipulated. 2.2 Introduction The ectomycorrhizal fungus Tuber melanosporum produces a highly valuable sporocarp known as the French black truffle that costs thousands of Euros per kilogram. Advancements in seedling inoculation (Donnini et al. 2014; Fischer et al. 2017; Marozzi et al. 2017) have allowed the cultivation of T. melanosporum to expand beyond the traditional growing region of the Mediterranean and into Australasia (Linde and Selmes 2012; Guerin-Laguette et al. 2013), North America (Berch and Bonito 2014; Meadows et al. 2020), and South America (Reyna and Garcia-Barreda 2014; Cejka et al. 2022). However, farmers incur substantial costs cultivating T. melanosporum sporocarps, and there is a lag period before seeing a return on this investment, since sporocarp harvest typically begins approximately 7-10 years post-establishment (Suz et al. 2008; Reyna and Garcia-Barreda 2014). Once in the production stage, individual trees can range from non-productive to highly productive from year to year, which affects yield size and yield stability (Suz et al. 2008; Zampieri et al. 2012; Chen et al. 2021). The causes of variation in production are poorly understood, but they include temperature, precipitation, soil pH, and competition from other ectomycorrhizal fungi (García-Montero et al. 2009; Molinier et al. 2013; Baragatti et al. 2019; Garcia-Barreda et al. 2020). Mycelial abundance may factor into overall productivity since the abundance of extramatrical T. melanosporum mycelium correlates with truffle productivity per tree 26 (Queralt et al. 2017), although this relationship is not always consistent (Suz et al. 2008). The abundance of T. melanosporum biomass can be managed through agronomic practices (Reyna-Domenech and García-Barreda 2008; Valverde-Asenjo et al. 2009). High rates of irrigation have been shown to suppress the abundance of T. melanosporum mycorrhizae (in the field: Bonet et al. 2006; Olivera et al. 2011, 2014; in the greenhouse: Mamoun and Olivier 1993), and irrigated orchards can contain a lower abundance of T. melanosporum extramatrical mycelium (Parladé et al. 2013), though irrigation in combination with cloth mulch may have a positive effect on T. melanosporum extramatrical mycelium (Piñuela et al. 2021). However, others have shown that a third ‘moderate’ irrigation treatment may promote T. melanosporum mycorrhizal growth the most compared to non-irrigated or high irrigation (Bonet et al. 2006; Olivera et al. 2011, 2014a). The relationship becomes additionally complex when one considers the ectomycorrhizal fungal community. High rates of irrigation appear to provide T. melanosporum with a competitive edge over T. brumale on Corylus seedlings in glasshouse studies (Mamoun and Olivier 1993a), but the use of irrigation to mitigate ectomycorrhizal fungal competitors has so far only been applied to seedlings (Mamoun and Olivier 1993a; Olivera et al. 2014b; Piñuela et al. 2021). To our knowledge, there are no studies that look at how irrigation can be used to manage ectomycorrhizal fungal competitors in T. melanosporum orchards near or having achieved production. Tuber melanosporum is considered a competitive species that reduces the ectomycorrhizal species richness on the roots of host plants and in the surrounding soil (Napoli et al. 2010; Mello et al. 2011; Belfiori et al. 2012) as it competes for space on 27 colonized roots (Mamoun and Olivier 1995; Olivera et al. 2011; Belfiori et al. 2012). However, competitors can still cause a displacement of T. melanosporum from the hosts’ roots and result in a reduced harvest (Molinier et al. 2013). Competitors to T. melanosporum can be introduced to the root system while establishing the symbiosis in seedling nurseries, including greenhouse contaminants such as Hebeloma (Mamoun and Olivier 1995; Alvarado and Manjon 2013), Cenococcum (Mamoun and Olivier 1995), and the Trichopheae woolhopia species complex (Mamoun and Olivier 1995; Marozzi et al. 2017), and poor-quality inoculum can introduce spores of the less desirable Tuber brumale (Linde and Selmes 2012) and Tuber indicum species (Murat et al. 2008). Once an orchard is established, endemic ectomycorrhizal fungi can also colonize the roots, as seen with Tuber aestivum in France (Molinier et al. 2013). DNA metabarcoding can be used to identify potential ectomycorrhizal fungal competitors both on the roots and in the soil. DNA metabarcoding offers a more accurate assessment of the true fungal diversity compared to traditional methods such as morphotyping (Nilsson et al. 2011), but metabarcoding of the root-associated ectomycorrhizal fungal community has so far been limited to natural truffle beds that did not contain T. melanosporum (Marjanović et al. 2020; Suwannarach et al. 2021). Oliach et al. (2022) used the PacBio sequencing platform on soils collected across a 20-year chronosequence of T. melanosporum orchards and found that while other ectomycorrhizal fungi persist on the roots across all orchard stages, displacement of T. melanosporum from the soil was not seen. However, these samples were collected randomly from a productive orchard, so this may not be true in an orchard that is not yet 28 producing truffles. There are currently no studies employing DNA metabarcoding on pre-productive orchards to assess ectomycorrhizal fungal competitors. While there are some surveys of T. melanosporum and the associated ectomycorrhizal fungal community in truffle orchards in North America (Berch and Bonito 2016; Meadows et al. 2020), most available literature regarding the cultivation of T. melanosporum originates from Europe. Due to the unpredictable nature and large upfront investments of truffle production, this expanding industry depends on additional research on T. melanosporum cultivation, particularly in non-native environments. Regions of British Columbia (BC), Canada, experience microclimates similar to both the native range of T. melanosporum and non-native areas where production has been successful. For example, truffle production has occurred on the coast of BC since 2013 (Berch and Bonito 2014) and orchards have been established in the interior Okanagan Valley, with the first production of T. melanosporum sporocarps reported in February 2023 (Mohr 2023). Despite local interest and pockets of successful cultivation, we are not aware of any field-based North American studies that investigated how agricultural management practices affect T. melanosporum or the greater ectomycorrhizal fungal community within black truffle orchards. Here, we investigate the effects of three irrigation treatments on the abundance of T. melanosporum extramatrical mycelium, as quantified by soil DNA abundance, in a pre-production Quercus robur orchard located in Vancouver, BC, Canada. DNA metabarcoding was applied to examine the influence of irrigation treatment on the composition of the ectomycorrhizal fungal community on tree roots and in the surrounding soil. A severe heatwave and moderate drought (defined as a 1-in 5-year 29 drought event) coincided with the study period and provided an opportunity to specifically measure the impact of irrigation treatments on T. melanosporum and the ectomycorrhizal fungal community under unusually high air temperatures and below-average precipitation. Based on previous work (Bonet et al. 2006; Olivera et al. 2011, 2014), we expected that irrigating to maintain a moderate level of soil moisture (-80 kPa) would result in the greatest abundance of T. melanosporum soil DNA compared with irrigation treatments that maintained a higher (-30 kPa) or lower (~ -265 kPa) soil water potential. Since Cenococcum geophilum has been reported in T. melanosporum orchards (Águeda et al. 2010; Suz et al. 2010; Garcia-Barreda and Reyna 2012; Sánchez et al. 2014), we expected the greatest abundance of C. geophilium in the lowest irrigation treatment due to its ability to withstand drought (Di Pietro et al. 2007; Kerner et al. 2012). 2.3 Materials and Methods 2.3.1 Study site In 2013, an orchard of 35 English oak (Quercus robur L.) trees inoculated with T. melanosporum was planted in Vancouver, BC, Canada. The trees were 1.5 years old at the time of planting and 9.5 years old at the time of the study (in 2021). A summary of site characteristics is presented in Table 2.1. The area is within the Coastal Western Hemlock, Very Dry Maritime biogeoclimactic zone and subzone (https://www.for.gov.bc.ca/hre/becweb/index.html), with frequently overcast skies and typically few temperature extremes. The field site was subject to the 2021 Pacific NorthWest heatwave, which began in late June and persisted for approximately 5 days, subsiding by July 1st (Fig. 2.1; Qian et al. 2022; White et al. 2023). Local weather data 30 indicates that the highest daily mean air temperature over this period was 32.4°C on June 29, 2021 (Fig. S.1). For comparison, the highest daily mean temperature in June from 1991 to 2020 was 18.9°C Fig. A1). A moderate drought (defined as one-in-five year event, Agriculture Canada: https://agriculture.canada.ca/en/agricultural-production/weather/canadian-drought-monitor) coincided with the heatwave and persisted until mid-September. Table 2.1. Site and soil characteristics of a 9.5-year-old Q. robur-T. melanosporum orchard subjected to three irrigation treatments from late June to late September 2021 in Vancouver, BC, Canada. Soil data are means of composite samples collected to a depth of 15 cm in each row of the orchard (n=7). Latitude 49.24970°N Longitude -123.2370°S Altitude (m) 71 Orientation (tree columns) N-S Soil N (%) 0.25 Soil C (%) 5.39 Available soil P (Bray P-1 extraction, mg/kg) 72.86 Soil pH 7.7 Soil texture Loamy sand Average annual precipitation (mm)) 1457 Average annual air temperature (°C) 14 31 Fig 2.1. Daily mean air temperature (°C) (left axis, red points) and daily total precipitation (mm) (right axis, blue bars) from June 21 to September 25, 2021 in Vancouver, BC. The black vertical dashed line indicates the date on which the experiment began, when all trees were irrigated to field capacity. Weather data were obtained from the Environment and Climate Change Canada weather station located at the Vancouver International Airport (climate ID: 1108395; https://climate.weather.gc.ca/historical_data/search_historic_data_e.html), which is located approximately 8 km southeast of the study site. 32 2.3.1 Experimental design Three irrigation treatments (low, moderate, and high) were assigned to 33 of the trees in the 35-tree orchard (n = 11 per treatment, Fig. 2.2a). Trees were variable in height, so each tree was assigned to one of three size classes based on tree height (short, medium, tall). Each tree was then allocated randomly to a treatment while ensuring that each group included an equal number of short, medium, and tall trees (Table 2; Fig. 2.2a). Irrigation water was applied using two 180° microsprayer nozzles (SKU#A47083, Rain Bird, Azusa, California, USA) installed at the base of each tree (Fig. 2.2b) so that they produced a circular wetting pattern with an approximate radius of 2 m. A total of seven soil water potential sensors (TEROS-21, Meter Group, Pullman, Washington, USA) were initially deployed on June 14th and 15th at representative trees within each treatment: three sensors were installed in the high irrigation treatment, two sensors were installed in the moderate irrigation treatment, and two sensors were installed in the low irrigation treatment (Fig. 2.2a). Sensors were buried at a depth of ~15 cm into the soil approximately 100 cm from the base of each tree, according to the manufacturer’s instructions. Four additional soil water potential sensors were deployed a month and a half later, on July 25th, 2021: one additional sensor was installed in the high irrigation treatment (total = 4), two additional sensors were installed in the moderate irrigation treatment (total = 4), and one additional sensor was installed in the low irrigation treatment (total = 3). 33 Fig 2.2. Details of irrigation treatments imposed from late June to late September 2021 on a 9.5 year-old Q. robur-T. melanosporum orchard in Vancouver, BC, Canada. A) Experimental design; each square represents one tree in the 35-tree orchard. The colour of each square corresponds to irrigation treatment (dark blue = high irrigation, medium blue = moderate irrigation, light blue = low irrigation, grey = not included in the experiment). Asterisks indicate trees equipped with soil water potential sensors. White asterisks represent sensors that were installed June 14/15, 2021. Orange asterisks represent additional sensors that were installed July 25th, 2021. B) The irrigation system installed at one tree, showing the distribution of water applied during irrigation. Irrigation treatments were maintained from late June to late September 2021. The irrigation frequency for each treatment was determined by a combination of soil water potential measurements and secondary measurements of stem water potential. All trees in the orchard were irrigated to field capacity on June 25, 2021 (Fig. 2.3). After this date, trees assigned to the high irrigation treatment were irrigated to maintain field ↑ N * * * * * * * * * * * B A 34 capacity (-20 kPa), while trees assigned to the moderate treatment were allowed to dry to a soil water potential of -80 kPa before being irrigated to field capacity (Table 2). Trees assigned to the low irrigation treatment were not irrigated after the first irrigation on June 25. Irrigation was controlled manually and allowed to run for 1-2 hours on irrigation days, wind permitting. Soil water potentials were monitored daily. To ensure that trees in the low irrigation treatment were not being subjected to potentially harmful water stress, stem water potential was measured weekly (July, August) or biweekly (September) (Fig. A2). Water stress was defined as a stem water potential below – 2 MPa, since at this level of stress, photosynthetic activity in Q. robur is reduced by half but trees can recover upon rewatering (Koller et al. 2013). Midday stem water potential measurements were taken with a pressure chamber (Model 615, Nu-Tech International, New Delhi, India) following the methods of Knipfer et al. (2020). In brief, two healthy leaves were randomly selected from the lower canopy of each tree and covered with aluminum foil bags before sunrise. Bagged leaves were excised at solar noon, sealed and transported on ice to the laboratory. Stem water potential measurements were recorded in the lab and calculated as mean values from the two leaves per tree. Though soil water potential sensors were installed to monitor soil moisture in all treatments, stem water potential was ultimately chosen as the irrigation trigger for the low irrigation treatment because stem water potential was expected to be a more reliable indicator of tree water stress and, consequently, fungal health (Table 2.2). Soil water potential in the moderate irrigation treatment group fell below the -80 kPa threshold on 2 days (July 22: -120 kPa and July 29: -130 kPa, Table A3) because windy conditions prevented irrigation. 35 Fig 2.3. Mean soil water potential measured at 15 cm depth in a 9.5-year-old Q. robur-T. melanosporum orchard subjected to three irrigation treatments from late June to late September 2021 in Vancouver, BC, Canada. Water potential sensors were installed 100 cm from the base of individual trees. From June 25, 2021, to July 25, 2021, a total of seven sensors were used to monitor soil moisture (high, moderate, and low irrigation treatments, n = 3, 2, 2, respectively) and from July 25 to the end of September, 10 sensors were used to monitor soil moisture (high, moderate and low irrigation treatments, n = 4, 3, 3, respectively). Each line represents the daily mean soil water potential for each irrigation treatment (dark blue = high irrigation, medium blue = moderate irrigation, light blue = low irrigation). Dashed vertical lines indicate the dates of soil sampling 36 Table 2.2. Summary of experimental design parameters. Treatment Point at which irrigation was initiated Monitoring method Sensor quantity Tall trees (> 2.5 m) Medium trees (1.5 – 2.5 m) Short trees (< 1.5 m) High - 20 kPa Soil water potential 4 5 4 2 Moderate - 80 kPa Soil water potential 4 5 4 2 Low - 2 MPa Stem water potential 3 5 4 2 2.3.2 Collection of soil samples Soil samples were collected once a month from June to September 2021 (Fig. 2.3). A 2 cm-diameter soil corer was used to collect soil to a depth of 15 cm. Soil cores were collected from the four cardinal directions at two distances (50 cm and 100 cm) from the trunk of each tree and pooled to produce one composite sample per tree at each sampling distance. Composite soil samples were immediately frozen at -20 °C (July, August, September) or frozen at -20 °C after storage at 4 °C for 8 days (June). Prior to DNA extraction, soil samples were thawed for one hour and homogenized, and a 0.25 g sub-sample was placed in a DNA extraction tube and stored at -20°C. The 0.25 g subsample remained frozen until DNA extraction began so as to limit the number of freeze-thaw cycles. The remainder of each sample was divided in two: one half was stored at -20 °C and the other half was used to determine gravimetric soil moisture content. 37 2.3.3 Collection of root tips and processing To determine the composition of the ectomycorrhizal fungal community, Q. robur root tips were sampled during the third week of August 2021. At each tree, a hand trowel was used to excavate three small holes equidistant from each other, 50 cm from the base of the trunk, and at 10-20 cm depth in the soil profile. Root samples were collected by locating a woody oak root in each excavated hole and following it until fine roots were reached. Fine roots were removed from the main lateral roots and stored on ice until being returned to the lab, where they were stored at 4 °C for no longer than 14 days. Equipment was sterilized with 70% isopropyl alcohol between trees. Root samples were soaked in water for 5 minutes to remove excess soil. Following soil removal, samples were examined under 40 x magnification. To ensure even sampling effort, as many mycorrhizal root tips as could be collected in 10 minutes were removed from each root sample. Mycorrhizal root tips were identified based on their swollen appearance, lack of root hairs, presence of a fungal mantle, or emanating hyphae. Root tips were then pooled by tree. Pooled root tips were freeze-dried, ground, and then stored at -20 °C for further processing. 2.3.4 DNA extraction – soil and roots DNA was extracted from 0.25 g of root or soil material using the DNeasy PowerLyzer PowerSoil Kit (Qiagen, Hilden, Germany) according to manufacturer’s instructions, with the following modifications: the first incubation period was extended by 5 minutes to enhance lysis of the fungal cell wall, and the volume of solution C4 was reduced from 1200 µL to 1000 µL to prevent cross contamination of samples from overfilled tubes. 38 The quality (A260/A280) and quantity (ng/µL) of extracted genomic DNA was assessed using a spectrophotometer (ND-1000, Thermo Fisher Scientific, Massachusetts, USA). Tuber melanosporum abundance in soil - Droplet digital PCR DNA quantification of T. melanosporum soil DNA was performed using PCR amplification of the ITS1 region with the Tmelfwd/Tmelrev primers and STQTmel Taqman MGB probe (Parladé et al. 2013). To the best of our knowledge, the relationship between hyphal nuclei density in Tuber spp. and soil moisture has not been conducted. As such, we are using DNA content as quantified by ddPCR as an index of extramatrical mycelium biomass. Reagents and cycling conditions were adapted for use with the droplet digital PCR (ddPCR) autoDG system (Bio-Rad Laboratories, CA, USA) as follows: each ddPCR reaction contained 2 µL template, 10 µL Supermix for probes (no dUTP) (Bio-Rad CAT# 1863024), 7 µL nuclease-free water, and 1 µL 20X primer/probe mixture. Cycling conditions were 98 °C for 10 min, followed by 40 cycles of 94°C for 1 min and 56°C for 2 min, ending with 98°C for 10 minutes and a hold at 4°C. 2.3.5 Ectomycorrhizal fungal community- DNA metabarcoding and bioinformatics DNA extracted from root samples and from the August soil samples collected at the 50 cm sampling distance were submitted for metabarcoding at Integrated Microbiome Research (Halifax, Nova Scotia, Canada). Sequencing of the full ITS region was performed using primers ITS1FKYO2/ITS4KYO1 (Toju et al. 2012) on a PacBio Sequel II system using four Single nucleotide, Real-Time (SMRT) cells (Pacific Biosciences, Menlo Park, CA, USA). 39 Raw reads were imported into R (Version 4.2.2; R Core Team, 2022) and run through the DADA2 pipeline (Callahan et al. 2016). One root sample (tree 5, moderate irrigation) did not have sufficient reads and was excluded from the analysis. Following primer removal, sequences were filtered and trimmed with the following constraints: minimum length of 200 base pairs (bp), maximum length of 1500 bp, maximum of two excepted errors per read, and a quality score of 3 (Oliach et al. 2022; Packard et al. 2023). Reads were then dereplicated and resolved to Amplicon Sequence Variants (ASVs). Chimeras were removed and taxonomy was assigned using the DADA2 native Bayesian classifier from Ribosomal Database Project (Wang et al. 2007) using UNITE v9.0 as the reference database delimited at the 97% level. Sequences from assigned taxa were queried against NCBI (BLASTn) with a minimum 98.5% similarity. Any disagreement between UNITE and BLAST was addressed as follows: if both databases matched to the same taxonomic resolution (e.g., species), the UNITE match was used because UNITE is a curated database (Table A6). The BLAST assignment was used in the case where it provided a higher taxonomic resolution. Following assignment of Species Hypotheses (SHs), sequences with the same taxonomic name but different SHs were compared. If sequences matched each other at > 98.5% similarity, the species and their respective abundance data were collapsed under one SH, henceforth referred to as ‘taxa’. Trophic mode was assigned using FUNGuild (Nguyen et al. 2016) or manual curation through published literature. Any sequences only assigned to Family or above were removed due to low likelihood of correct trophic mode assignment. Sequences 40 were filtered based on being ‘ectomycorrhizal’ and classified as either ‘probable’ or ‘highly probable’ by FUNGuild. Mock communities were used for both root and soil samples. These are artificial fungal communities (i.e. created in the lab) from genomic DNA of a known identity and quantity to serve as a positive control for DNA metabarcoding analyses. For the soil mock community, Tuber melanosporum, T. oregonense, T. aestivum, T. borchii, T. beyeleri, and Scleroderma bovista were included, while the root mock community comprised T. melanosporum, S. bovista, Lactarius deliciosus, Cenococcum geophilum, and Cortinarius anisatus. The identity of each taxon was confirmed using Sanger sequencing (Seqstudio Genetic Analyzer, Applied Biosystems, Foster City, CA, USA) of the ITS region using primer ITS1-F and ITS4. An equal concentration of DNA from each taxon was included for a final concentration of 24.5 µg/µL per taxon except for S. bovista in the soil mock community, which was 12 µg/µL due to limited genomic DNA. The mock communities were created in duplicate for both soil and root samples. Due to the high disagreement between expected and mock communities (Fig. A4), all community data were converted to absence/presence binary matrices. Contaminants seen in the mock communities were removed from the soil and root data sets either manually or by virtue of the filtering process (i.e., they were not designated as ectomycorrhizal by FUNGuild or manual curation). 2.3.6 Statistical Analyses 2.3.6.1 DNA Abundance Tuber melanosporum Raw data files were downloaded from the proprietary ddPCR software and imported into R (Version 4.2.2; R Core Team 2022). Two Generalized Additive Models (GAMs) were 41 run, both with a Tweedie family of distributions and a log link function, using the ‘mgcv’ package (Wood 2011). Model 1 had the following structure: Irrigation treatment (as discrete classes) and sampling distance (from tree trunk) were included as fixed effects. Sampling month was included as a smooth term and allowed to vary between irrigation treatments and tree IDs via two factor smooths (bs = ‘fs’). Random effects of tree height and of tree ID were included to account for random variation between trees. Interaction terms between random effects of (1) sampling distance and treatment, (2) sampling distance and tree height, and (3) sampling distance and tree ID were included to account for variations in the effect of (1) treatment levels, (2) tree heights, and (3) trees on the different sampling distances. Model 2 had the same structure as model 1 but with two exceptions. First, the fixed effect of irrigation treatment (a discrete parameter) was replaced with a smooth term of gravimetric soil water content (a continuous parameter). Second, the month term was absent in model 2 because the time series of soil water content already accounts for the temporal variation in water availability. Although model 2 does not directly reflect the initial study design, we chose to run it because it has greater interpretability, predictive power, and flexibility than model 1. Additionally, measurements of gravimetric soil moisture would be more accessible to T. melanosporum producers than soil water potential data, since gravimetric soil water content is a straightforward and easily measurable alternative to installing soil water potential sensors. The stem water potential data were not used in either model due to incomplete sampling of all treatment groups in the early stages of the experiment (Fig. A2). 42 The total number of data points used in model 1 was 260. Two outlier data points in the T. melanosporum abundance data were removed following inspection of model residuals (Fig. A5): one in the moderate treatment group and another from the high treatment group. Two additional points were removed due to technical errors in the ddPCR analysis. One of these two points (moderate irrigation treatment) did not reach the minimum threshold of generated droplets (11,000 droplets) required for quantification by the ddPCR software, while the second point (low irrigation treatment) reached the minimum droplet number (11,993), but no positive droplets were found. Equipment limitations prevented the rerunning of these samples, leading to their removal from the analysis. In model 2, three additional data points were removed due to missing soil samples (two samples from the low irrigation treatment, one from the moderate irrigation treatment) preventing the gravimetric water content analysis, resulting in a total of 257 data points. 2.3.6.2 Ectomycorrhizal fungal community richness We investigated the number of species in root and soil ectomycorrhizal fungal communities in response to irrigation treatment using two GAMs with Tweedie families of distributions, log link functions, and the same model structure as each other. Irrigation treatment was included as a fixed effect, while taxa (i.e. species identity based on Unite ID, see section 2.3.5), tree, tree height, and the taxa-treatment interaction were included as random effects. The total number of datapoints used in these models, which was dependent on the number of taxa obtained from the bioinformatics pipeline, was 726 and 448 for the soil and roots, respectively. 43 2.3.6.3 Ectomycorrhizal fungal community composition To estimate how irrigation affected the presence of ectomycorrhizal fungal taxa on the roots and soil, we used two GAMs with Binomial families of distributions and logit link functions. The effect of irrigation treatment was included as a fixed effect, while taxa, tree, tree height, and the taxa-treatment interaction were included as random effects. The total number of datapoints used in these models were 693 and 448 for the soil and roots, respectively. Changes in the communities were visualized with a non-metric multidimensional scaling (NMDS) ordination based on Jaccard’s dissimilarity. The members of the ectomycorrhizal fungal community among the treatments were visualized using occupancy plots, which show the occurrence frequency of each taxon as a proportion per replicate (tree root or soil sample). Dominant taxa were defined as those that appeared in at least 75% of the trees in at least one treatment. 2.4 Results Soil water potential Soil water potential varied among treatments and over time (Fig. 2.3). Soils under the high irrigation treatment were maintained between -11.2 kPa and -27.0 kPa, while soil under the moderate irrigation treatments were maintained between - 14.1 and -130.6 kPa. Soils under the low treatment saw a more dramatic range (-10 kPa to -266 kPa), with the decline in soil water potential coinciding with the period of regional drought. DNA Abundance of Tuber melanosporum Temporal trends in T. melanosporum DNA abundance varied among treatments and across sampling distances. Trees subject to the high irrigation treatment had lower DNA abundance and substantially different temporal trends than the other two treatments 44 (Model 1: month-treatment factor smooths, estimated p < 0.001, Fig. 2.4, Table A7). Unlike soils in the moderate and low treatments, the abundance of T. melanosporum DNA in soils under the high irrigation treatment remained relatively low and consistent over time. Differences among treatments decreased at the end of August. By September, natural rainfall homogenized water potentials among the treatments, and all treatments stabilized to similar mycelial abundances. Similar conclusions can be drawn from the overall effect of soil water content in model 2, since T. melanosporum DNA abundance decreased nonlinearly with soil water content (Fig. A8, Model 2: estimated p < 0.001). Overall, model 1 and model 2 explained 72.8% and 61.7% of the deviance, respectively. The abundance of T. melanosporum DNA in the soil also varied with sampling distance from the tree and with tree height. Approximately twice as much T. melanosporum DNA was found at 50 cm than at 100 cm from the base of the tree (Table A7, Fig. A9a; Model 1: p < 0.001; Model 2: p <0.001). The abundance of T. melanosporum at 50 cm and 100 cm were more similar in the high irrigation treatment than in the low and moderate treatments (Table A7, Fig. A9c, Model 1: p = 0.03), and there was no effect of tree size on sampling distance (Table A7, Fig. A9d, Model 1, p = 0.8). Still, across all irrigation treatments, soils with tall trees tended to contain a greater abundance of T. melanosporum DNA than soils adjacent to medium and short trees (Table A7, Fig. A9b, Model 1: p = 0.05, Model 2: p = 0.04). 45 Fig 2.4. Abundance of T. melanosporum DNA in soil (0-15 cm depth) collected a 50 cm and b 100 cm from 9.5 year-old Q. robur-T. melanosporum trees subjected to three irrigation treatments from late June to late September 2021 in Vancouver, BC, Canada (n=11). The relationship between T. melanosporum abundance and irrigation treatment was modeled using a Tweedie Generalized Additive Model (GAM). Lines represent the estimated means and the shaded portions indicate the 95% Bayesian credible intervals (assuming Gaussian residuals on the link scale) Ectomycorrhizal fungal community 46 In total, 22 and 14 ectomycorrhizal fungal taxa were recovered from soil and root tip samples, respectively (Table A10). Irrigation intensity did not measurably affect the ectomycorrhizal fungal community richness of root tips (Table A11, Fig. A15a, intercept: p < 0.001, moderate irrigation: p = 0.655, high irrigation p = 0.400) or soil (Table A12, Fig. A15b, intercept: p < 0.001, moderate irrigation: p = 0.591, high irrigation p = 0.680). There was also no substantial effect of irrigation on the ectomycorrhizal fungal community composition of root tips or soil (root tips: Table A13, Fig. A15c, intercept: p = 0.936, moderate irrigation: p = 0.656, high irrigation p = 0.503; soil: Table A14, Fig. A15d, intercept: p = 0.760, moderate irrigation: p = 0.774, high irrigation p = 0.434). Dominant taxa (occurring in over 75% of replicates in at least one treatment) were identified in both soil and root tip samples (Figs. 2.5, 2.6). Tuber melanosporum was found in 100% of root tip and soil samples. In addition, Scleroderma bovista (SH1650627.08FU), Scleroderma aerolatum (SH1526178.08FU), Scleroderma verrucosum (SH1526179.08FU), and an additional Scleroderma verrucosum (SH2727211.08FU) taxon were dominant on root tips across all treatments, with the highest occurrence seen in the moderate treatment (100%, 100%, 90%, and 90%, respectively). The first three Scleroderma taxa listed above were found to be dominant in the soil samples across all treatments, with the highest occurrence seen in the moderate treatments (91%, 100%, 82%, respectively). 47 Fig. 2.5 Occupancy plots of dominant ectomycorrhizal fungi detected in soil (10-20 cm depth) collected in August, 2021, 50 cm from 9.5 year-old Q. robur-T. melanosporum trees subjected to three irrigation treatments from late June to late September 2021 in Vancouver, BC, Canada. Bars represent frequency of occurrence of each taxon as a percentage of the replicates (trees) among irrigation treatments. Next-generation 48 sequencing of root tip and soil samples was performed using the PacBio sequencing platform and taxonomic information was obtained using the DADA2 bioinformatics pipeline 49 Fig. 2.6 Occupancy plots of dominant ectomycorrhizal fungi detected on mycorrhizal root tips collected in August, 2021, 50 cm from 9.5 year-old Q. robur-T. melanosporum trees subjected to three irrigation treatments from late June to late September 2021 in Vancouver, BC, Canada. Bars represent frequency of occurrence of each taxon as a percentage of the replicates (trees) among irrigation treatments. Next-generation 50 sequencing of root tip and soil samples was performed using the PacBio sequencing platform and taxonomic information was obtained using the DADA2 bioinformatics pipeline 2.5 Discussion To the best of our knowledge, this is the first experimental field trial and application of DNA metabarcoding in a T. melanosporum orchard in North America. While the positive relationship between mycelial abundance and sporocarp production is generally assumed (Suz et al. 2008; Parladé et al. 2013; Queralt et al. 2017), the relationship weakens or disappears in productive orchards, possibly due to a shift in resource allocation to fruitbody production over vegetative hyphae (Suz et al. 2008). Here, we used soil DNA as quantified by ddPCR as an index for mycelial abundance in a pre-production orchard. Quantitative PCR (qPCR) is standard in T. melanosporum quantification from the soil (Suz et al. 2006, 2008; Parladé et al. 2013), but ddPCR provides greater precision and repeatability in quantification of fungi from environmental samples (Wang et al. 2022b) and is less susceptible to PCR inhibitors (Wang et al. 2022a). Our work suggests that high irrigation suppresses T. melanosporum soil DNA, particularly during abnormally hot and arid conditions. Our results are consistent with the general notion that, in pre-production orchards, the abundance of T. melanosporum ectomycorrhizae and extramatrical mycelium is reduced under high irrigation inputs (Bonet et al. 2006; Olivera et al. 2011, 2014a; Piñuela et al. 2021). Unlike previous work, we homogenized the soil moisture level among treatments before commencing the study, meaning the low irrigation trees received an initial pulse of irrigation. This 51 initial watering could explain the lack of differences seen between the moderate and low treatments that was reported in previous work (Olivera et al. 2011, 2014a). Oxygen and water both occur within the soil pore space, creating an inverse relationship between the two molecules (Silver et al. 1999; Neira et al. 2015; Martinez-Arias et al. 2022). Ectomycorrhizal fungi are obligate aerobes (Vasilas et al. 2004), and saturated soils can have reduced ectomycorrhizal colonization levels (Lodge 1989; Thomas 2021). While I did not measure ectomycorrhizal colonization, a significant positive correlation between T. melanosporum ectomycorrhizal abundance and mycelial biomass has been previously reported (Queralt et al. 2017). Warmer soil temperatures seen in the upper soil horizons may also contribute to ectomycorrhizal abundance decreases (Lodge 1989), which could explain the particularly low abundance of T. melanosporum soil DNA seen here in June and July, the period of the study with the warmest air temperatures. Hydraulic redistribution has been reported in another oak species, Quercus agrifolia, where the root system moves water from deeper in the soil profile to the upper layers; it can then be accessed by ectomycorrhizal partners (Querejeta et al. 2003; Egerton-Warburton et al. 2007). Fungi under low-irrigation trees in our study may have benefited from a greater ability to engage in hydraulic redistribution, enhanced by the initial irrigation pulse. Despite the high heat and moderate drought, only a few of the trees within the low treatment crossed the predefined drought stress threshold of -2 MPa stem water potential, which hints at a drought tolerance mechanism that could be driven at least in part by hydraulic lift. 52 Our study coincided with a prolonged period of above-average temperatures, known colloquially as a ‘heat dome’ (Qian et al. 2022; White et al. 2023). Since such events are predicted to become increasingly frequent across BC (Hicke et al. 2022; Peña and Fine 2023), our results suggest that coastal regions of Southern British Columbia may be a favorable location for T. melanosporum production with the following caveat: although we have shown that pre-production orchards have relatively low water demands, summer precipitation is positively correlated with sporocarp production in mature, sporocarp-producing orchards (Büntgen et al. 2012; Thomas and Büntgen 2019; Garcia-Barreda and Camarero 2020). Therefore, a farmer in this region may need to prepare for a higher water demand once their orchard starts to produce truffles. We found 14 unique ectomycorrhizal fungal taxa on our 9.5-year old trees’ root tips. Given that previous studies in T. melanosporum-Q. ilex orchards in Spain showed that trees under 6 years old typically harbour ~15 ectomycorrhizal morphotypes on their root systems (Suz et al. 2010; Olivera et al. 2011), we hypothesize that richness may be relatively static after trees reach 6 years of age. Our soil richness estimates are comparable to those of Oliach et al. (2022), who used PacBio sequencing of the ITS2 region to investigate the soil ectomycorrhizal fungal community along a T. melanosporum – Q. ilex chronosequence of 3 to 20-year-old trees, and those of Meadows et al. (2020), who sequenced DNA from 14 root samples from truffle orchards in North Carolina. Thus, although this is the first application of DNA metabarcoding to a truffle orchard in North America (to the best of our knowledge), our species richness estimates are consistent with those in European truffle orchards. 53 The soil ectomycorrhizal fungal community composition within the study orchard was similar to those presented in previous literature, but, contrary to our expectations, we did not find evidence of Cenococcum geophilum in our study site. Unlike European orchards, where C. geophilum has been found (Águeda et al. 2010; Garcia-Barreda and Reyna 2012), it is not often reported in North American truffières (Meadows et al. 2020). Still, its absence in our temporally-limited samples does not demonstrate its absence in the soil. We also found examples of previously described taxa in European T. melanosporum orchards, including the Trichopheae woolhopia species complex (Belfiori et al. 2012; Sánchez et al. 2014; Otsing and Tedersoo 2015; Queralt et al. 2017), Tomentella (Suz et al. 2010), Suillus (Suz et al. 2010), and Hebeloma (Otsing and Tedersoo 2015). Two additional Tuber taxa were found in our soil samples, the Bianchetto truffle, T. borchii (SH1525755.08FU), and the Oregon or Western white truffle, T. oregonense (SH1542616.08FU). The nursery where the seedlings were originally sourced also inoculates Quercus seedlings with T. borchii, so it is likely that seedlings inoculated with T. borchii were erroneously added to the order of T. melanosporum inoculated seedlings (Berch and Bonito 2014). Tuber oregonense, which associates with Pseudotsuga menziesii (Benucci et al. 2016), might have been moved to our truffle orchard from adjacent Douglas-fir stands by small mammals, though we cannot confirm this. Aside from T. melanosporum, we found four dominant taxa that occurred in at least 75% of the samples in at least one treatment, all of which were members of the genus Scleroderma. Three taxa were found in both roots and soil: Scleroderma bovista (SH1650627.08FU), Scleroderma aerolatum (SH1526178.08FU), and Scleroderma 54 verrucosum (SH1526179.08FU), with an additional S. verrucosum genotype (SH2727211.08FU) occurring on root tips at lower frequencies. Scleroderma spp. show low host-specificity (Chen et al. 2006), associating with conifers (Villeneuve et al. 1991; Parladé et al. 1995, 1996) and broad-leaf trees (Mrak et al. 2017), including host species of T. melanosporum (Mamoun and Olivier 1995; Bzdyk et al. 2019). The high pH of soils in T. melanosporum orchards is also favorable for Scleroderma spp. (Ge et al. 2017), with some reporting that Scleroderma spp., like T. melanosporum, can also form brûlés (Hall et al. 2007). It is therefore unsurprising that Scleroderma spp. have been found in T. melanosporum orchards in Europe (Belfiori et al. 2012; Sánchez et al. 2014; Queralt et al. 2017) and the Southern United States (Bonito 2009; Ge et al. 2017; Meadows et al. 2020). However, we are the first to report the genus in a Canadian T. melanosporum orchard (Berch and Bonito 2016). The abundance of Scleroderma taxa seen here in both the roots and soil may cause concern, since ectomycorrhizal fungi compete with each other for nutrients in the soil and for space on the host root system (Koide et al. 2005; Kennedy et al. 2009; McBurney et al. 2017). While co-occurrence does not imply competition (Kennedy 2010), any non-target species may be considered as a potential competitor in agricultural systems where farmers are attempting to maximize production of a single species. The competitive interactions from other Tuber species in saplings has been investigated (Mamoun and Olivier 1993a; Piñuela et al. 2021), but research on other co-occurring ectomycorrhizal fungal taxa and how to mitigate their potential competitive effects is still lacking. Scleroderma bovista (SH1650627.08FU) and S. aerolatum (SH1526178.08FU) had the highest frequency (91% and 100%, respectively) in soils 55 collected from trees in the moderate irrigation treatment. On root tips, S. bovita (SH1650627.08FU), S. verrucosum (SH1526179.08FU), and S. verrucosum1 (SH2727211.08FU) were found in the highest frequency (91%, 100%, 100%) on trees subjected to moderate irrigation. While the lack of repeated measures over time prevented meaningful statistical analyses, we highlight these frequencies to encourage future work on the effects of single-pulse irrigation on the frequency and abundance of non-Tuber species in a pre-production orchard. 2.6 Conclusion Our study suggests that high irrigation may suppress the growth of T. melanosporum mycelia, as quantified by soil DNA, within coastal BC pre-productive truffle orchards. This trend was most pronounced during periods of unusual drought and heat. Irrigation regimens consisting of moderate, consistent water input or an initial pulse followed by no further water input was most favorable for T. melanosporum DNA abundance. Due to the expected rise in daily temperatures and more frequent heat waves and droughts, our work shows that orchards in coastal BC may be well-suited for T. melanosporum cultivation. In addition, we identified several species of Scleroderma as dominant ectomycorrhizal fungi on Q. robur roots in the orchard, and we encourage future work investigating the potential competitive interactions among Scleroderma spp. and T. melanosporum. 56 3.0 Conclusion 3.1 Review of goals and hypotheses For this thesis, I manipulated irrigation levels in a T. melanosporum-Q.robur orchard in Vancouver, BC, Canada and investigated the response of i) T. melanosporum extramatrical mycelium abundance as quantified using soil DNA, and ii) the ectomycorrhizal fungal community composition in the soil and on root tips. With respect to the first response variable, I had hypothesized that the moderate irrigation treatment would result in the highest abundance of T. melanosporum soil DNA. This hypothesis was based on literature from Spanish truffle orchards, where moderate irrigation had been found to promote the most T. melanosporum ectomycorrhizae compared to low or high irrigation treatments (Bonet et al. 2006; Olivera et al. 2011, 2014a). Ectomycorrhizae and soil mycelium of T. melanosporum are significantly, positively correlated (Queralt et al. 2017). Unlike the Spanish studies, I could not say that my moderate and low irrigation treatments were significantly different, only that the high irrigation treatment suppressed the soil mycelia, as quantified by soil DNA abundance. This effect was most pronounced in the month following the heatwave (July). The additional model (model 2: continuous water content data) provided further support, showing that overall, soil moisture was negatively correlated with T. melanosporum soil DNA. When investigating the ectomycorrhizal fungal community in the soil and on the roots, I had hypothesized that the drought-tolerant Cenococcum geophilum would be found in the highest abundance in the low irrigation treatment. Instead, I did not find C. geophilum in any sample from any treatment. This was unexpected as C. geophilum is 57 considered a cosmopolitan species (McPolin and Kranabetter 2020) which has been previously reported in T. melanosporum orchards (Belfiori et al. 2012; Guerin-Laguette et al. 2013). The root tip mock community used in the metabarcoding analysis was synthesized to contain 20% C. geophilum reads (Fig. A4), but it was found to be entirely missing from one replicate and represented less than 5% of the total reads of the second replicate (Fig. A4). The length of the ITS region in C. geophilum is around 630 base pairs (Courty et al. 2008) and is thus an appropriate length for the PacBio platform. The bias towards L. scrobiculatus and C. anisatus seen in the root tip mock communities could explain the relative decrease in C. geophilum in the mock communities, but this does not explain why C. geophilum was completely absent when using binary presence/absence data. The single sampling dates (one date each for root tips and soil collection) and subsequent lack of time series data is a reasonable explanation as to how the species may have been present but not identified during sampling. 3.2 Comparison to current research This work furthers current knowledge of T. melanosporum soil moisture preferences in the pre-production stage. It had been previously established that high irrigation levels will suppress T. melanosporum ectomycorrhizae in the field and in glasshouse studies (Mamoun and Olivier 1993b; Bonet et al. 2006; Olivera et al. 2011, 2014a). Piñuela et al. (2021) combined irrigation with cloth mulching treatments and found that irrigation in combination with mulch increased the abundance of T. melanosporum soil mycelium. The addition of mulch by Piñuela et al. (2021) makes a direct comparison difficult, but it can be said that in all previous irrigation literature, the 58 authors did not see the highest abundance of T. melanosporum in their non-irrigated treatments, whereas I did. I took an additional step of saturating the entire orchard before the experiment to homogenize soil moisture levels. This step was not described in previous literature, so it is possible that this initial watering penetrated deep into the soil profile where the trees were able to access moisture as the experiment progressed. As previously described, the trees in this study were also more mature (~10 years old compared to < 5 years old). This may explain differences seen among the studies, but importantly this work offers information on irrigation at a different life-stage of an orchard. Current research tends to focus on seedlings or saplings that are not producing, or occur in productive, mature orchards. This work brides the gap by working in a mid-stage orchard. 3.3 Strength, limitations and future research directions To the best of my knowledge, this was the first field trial investigating the effects of irrigation treatments on any commercial Tuber spp. in North America. The work done here will become part of a foundation of knowledge for the emerging truffle growing industry in British Columbia and can potentially be expanded to similar climatic regions. This study also represents the first application of PacBio sequencing in a truffle orchard. Oliach et al. (2022) used Illumina to sequence the ectomycorrhizal fungal community in soils in a T. melanosporum orchard in Spain. The PacBio sequencing platform offers longer sequencing reads (Xie et al. 2020) and better fungal taxonomic resolution compared to Illumina (Zhang et al. 2023). The ectomycorrhizal fungal community analyses done here add knowledge to previous surveys in North American truffle 59 orchards (Berch and Bonito 2016; Meadows et al. 2020) by identifying non-T. melanosporum ectomycorrhizal fungal species. In agronomic research, repeated field trials are typically deemed necessary to account for environmental variation from year to year (Loughin et al. 2007; Pagliari et al. 2022). This thesis was comprised of a single field season; this was partially due to logistic constraints, and also due to the time required for the DNA metabarcoding work. However, as the first North American field trial, the work done here serves as a basis for future research. Another limitation was that I did not record the volume of water applied. The water pressure available at the truffle orchard varied based on watering schedules of neighboring plots at the farm. We did not use pressure regulated sprayers due to high cost. Knowing the volume of water applied would increase the reproducibility to those who did not have access to soil water potential sensors. Still, the gravimetric water content data could be used as an indirect measure of total water volume applied. Finally, there were some equipment limitations with respect to the water potential sensors. Within the low irrigation treatments, sensors installed at different trees showed different readings, sometimes up to 100 kPa difference. This was not ideal in terms of reproducibility. However, because the low irrigation treatment trees were not irrigated for the duration of the study and we monitored the stem water potential to indicate when to irrigate, the sensor variation ultimately did not compromise the integrity of the study. Future work should consider the soil coarse fragment content prior to developing the experimental design. If soils are highly stony, the researcher could consider using evapotranspiration as a metric to trigger irrigation rather than soil water potential as measured using sensors. 60 This thesis has laid a strong framework for future research to continue. Primarily, the identification of Scleroderma in the truffle orchard offers enticing research potential. I found very high frequencies of multiple Scleroderma species in all treatments. Scleroderma has been described as both a potential competitor and as having a positive effect on T. melanosporum productivity (Hall et al. 2007; Meadows et al. 2020), but experimental evidence for either is lacking. Based on the frequency of Scleroderma spp. seen in this study, there may be potential to mitigate the abundance of this fungus in the truffle orchard using a no-irrigation strategy. Future work could use ddPCR to assess the abundance of the different Scleroderma species alongside that of T. melanosporum to see the dynamics in the irrigation responses. The study should also investigate if any Scleroderma species are truly competitors; does their occurrence negatively influence the abundance of T. melanosporum, or are they coexisting without detriment? As this work occurred in a pre-production orchard, there is opportunity to build a long-term research project at this orchard. Continuing the experimental treatments once the orchard begins producing truffles could help elucidate differences between the watering requirements of a pre-production orchard and that of a producing orchard. Differences seem to exist, but most available literature either occurs in pre-production orchards or producing orchards, but never a long-term study of both stages in one orchard. 3.4 Applications in trufficulture The information gained in this study will be used to provide truffle producers insight regarding water use optimization during the pre-production stage of an orchard. 61 Irrigation water demand in British Columbia is expected to increase (Neilsen et al. 2018), making the optimization of water use a shared priority between agronomists and researchers. The work done here yielded information that can potentially be used in the creation of an irrigation guideline for truffle producers in BC. Despite establishment of the commercial industry in the 1970s (Reyna and Garcia-Barreda 2014), Spanish researchers only recently published a ‘best practices’ guide for production of black truffles (Fischer et al. 2017). The work done in this thesis provides a foundation for the Canadian and North American truffle industries to grow and build upon, with the eventual goal of gaining enough insight to produce a comprehensive Best Practice guide for cultivation of black truffles in British Columbia. 62 References Agerer R (2006) Fungal relationships and structural identity of their ectomycorrhizae. Mycol Prog 5:67–107. https://doi.org/10.1007/s11557-006-0505-x Águeda B, Fernández-Toirán LM, De Miguel AM, Martínez-Peña F (2010) Ectomycorrhizal status of a mature productive black truffle plantation. For Syst 19:89. https://doi.org/10.5424/fs/2010191-01170 Allen MF (2007) Mycorrhizal fungi: Highways for water and nutrients in arid soils. Vadose Zo J 6:291–297. https://doi.org/10.2136/vzj2006.0068 Allen RG, Pereira LS, Raes D, Smith M (1998) FAO Irrigation and Drainage Paper No. 56 - Crop Evapotranspiration Alonso Ponce R, Ágreda T, Águeda B, et al (2014) Soil physical properties influence “black truffle” fructification in plantations. Mycorrhiza 24:55–64. https://doi.org/10.1007/s00572-014-0558-7 Alvarado P, Manjon JL (2013) A quantitative and molecular examination of Tuber melanosporum mycorrhizae in Quercus ilex seedlings from different suppliers in Spain. For Syst 22:159–169. https://doi.org/10.5424/fs/2013222-03559 Anthony MA, Crowther TW, van der Linde S, et al (2022) Forest tree growth is linked to mycorrhizal fungal composition and function across Europe. ISME J 16:1327–1336. https://doi.org/10.1038/s41396-021-01159-7 Antil S, Abraham JS, Sripoorna S, et al (2023) DNA barcoding, an effective tool for species identification: a review. Mol Biol Rep 50:761–775. 63 https://doi.org/10.1007/s11033-022-08015-7 Assad R, Reshi ZA, Rashid I, et al (2021) Metabarcoding of root-associated ectomycorrhizal fungi of Himalayan pindrow fir through morphotyping and Next Generation Sequencing. Trees, For People 6:100153. https://doi.org/10.1016/j.tfp.2021.100153 Bajaj SR, Marathe SJ, Grebenc T, et al (2021) First report of European truffle ectomycorrhiza in the semi-arid climate of Saudi Arabia. 3 Biotech 11:1–9. https://doi.org/10.1007/s13205-020-02559-w Banks JC, Kelly LT, Falleiros R, et al (2021) Detecting the pest fish , Gambusia affinis from environmental DNA in New Zealand : a comparison of methods Detecting the pest fi sh , Gambusia a ffi nis from environmental. New Zeal J Zool 0:1–15. https://doi.org/10.1080/03014223.2020.1858880 Baragatti M, Grollemund PM, Montpied P, et al (2019) Influence of annual climatic variations, climate changes, and sociological factors on the production of the Périgord black truffle (Tuber melanosporum Vittad.) from 1903–1904 to 1988–1989 in the Vaucluse (France). Mycorrhiza 29:113–125. https://doi.org/10.1007/s00572-018-0877-1 Barredo JI, Mauri A, Caudullo G, Dosio A (2018) Assessing shifts of Mediterranean and arid climates under RCP4.5 and RCP8.5 climate projections in Europe. Pure Appl Geophys 175:3955–3971. https://doi.org/10.1007/s00024-018-1853-6 Beiler KJ, Durall DM, Simard SW, et al (2010) Architecture of the wood-wide web: Rhizopogon spp. genets link multiple Douglas-fir cohorts. New Phytol 185:543–553. 64 https://doi.org/10.1111/j.1469-8137.2009.03069.x Belfiori B, Riccioni C, Tempesta S, et al (2012) Comparison of ectomycorrhizal communities in natural and cultivated Tuber melanosporum truffle grounds. FEMS Microbiol Ecol 81:547–561. https://doi.org/10.1111/j.1574-6941.2012.01379.x Benucci GMN, Bonito GM (2016) The truffle microbiome : species and geography effects on bacteria associated with fruiting bodies of hypogeous Pezizales. Microb Ecol 72:4–8. https://doi.org/10.1007/s00248-016-0755-3 Benucci GMN, Lefevre C, Bonito G (2016) Characterizing root-associated fungal communities and soils of Douglas-fir (Pseudotsuga menziesii) stands that naturally produce Oregon white truffles (Tuber oregonense and Tuber gibbosum). Mycorrhiza 26:367–376. https://doi.org/10.1007/s00572-015-0677-9 Berch SM, Bonito G (2014) Cultivation of mediterranean species of tuber (tuberaceae) in British Columbia, Canada. Mycorrhiza 24:473–479. https://doi.org/10.1007/s00572-014-0562-y Berch SM, Bonito G (2016) Truffle diversity (Tuber , Tuberaceae) in British Columbia. Mycorrhiza 587–594. https://doi.org/10.1007/s00572-016-0695-2 Bertault G, Raymond M, Berthomieu A, et al (1998) Trifling variation in truffles [5]. Nature 394:734. https://doi.org/10.1038/29428 Bidartondo MI, Read DJ (2008) Fungal specificity bottlenecks during orchid germination and development. Mol Ecol 17:3707–3716. https://doi.org/10.1111/j.1365-294X.2008.03848.x 65 Bonet JA, Fischer CR, Colinas C (2006) Cultivation on black truffle to promote reforestation and land-use stability. Agron Sustain Dev 26:69–76. https://doi.org/10.1051/agro Bonito G (2009) Fast DNA-based identification of the black truffle Tuber melanosporum with direct PCR and species-specific primers. FEMS Microbiol Lett 301:171–175. https://doi.org/10.1111/j.1574-6968.2009.01812.x Bruhn JN, Mihail JD, Pruett GE (2013) Truffle seedling production method has long-term consequences for tree growth and root colonization. Agrofor Syst 87:679–688. https://doi.org/10.1007/s10457-012-9588-3 Brundrett MC, Tedersoo L (2018) Evolutionary history of mycorrhizal symbioses and global host plant diversity. New Phytol 220:1108–1115. https://doi.org/10.1111/nph.14976 Büntgen U, Egli S, Camarero JJ, et al (2012) Drought-induced decline in Mediterranean truffle harvest. Nat Clim Chang 2:827–829. https://doi.org/10.1038/nclimate1733 Bzdyk RM, Olchowik J, Studnicki M, et al (2019) Ectomycorrhizal colonisation in declining oak stands on the Krotoszyn Plateau, Poland. Forests 10:. https://doi.org/10.3390/f10010030 Callahan BJ, McMurdie PJ, Rosen MJ, et al (2016) DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13:581–583. https://doi.org/10.1038/nmeth.3869 Cejka T, Isaac E, Oliach D, et al (2022) Risk and reward of the global truffle sector 66 under predicted climate change OPEN ACCESS Risk and reward of the global truffle sector under predicted climate change. Environ Res Lett 17: Čejka T, Trnka M, Krusic PJ, et al (2020) Predicted climate change will increase the truffle cultivation potential in central Europe. Sci Rep 10:1–10. https://doi.org/10.1038/s41598-020-76177-0 Chen J, De la Varga H, Todesco F, et al (2021) Frequency of the two mating types in the soil under productive and non-productive trees in five French orchards of the Périgord black truffle (Tuber melanosporum Vittad.). Mycorrhiza 31:361–369. https://doi.org/10.1007/s00572-020-01011-4 Chen YL, Kang LH, Malajczuk N, Dell B (2006) Selecting ectomycorrhizal fungi for inoculating plantations in south China: Effect of Scleroderma on colonization and growth of exotic Eucalyptus globulus, E. urophylla, Pinus elliottii, and P. radiata. Mycorrhiza 16:251–259. https://doi.org/10.1007/s00572-006-0039-8 Clasen BE, Silveira ADO, Baldoni DB, et al (2018) Characterization of ectomycorrhizal species through molecular biology tools and morphotyping. Sci Agric 75:246–254. https://doi.org/10.1590/1678-992x-2016-0419 Courty PE, Franc A, Pierrat JC, Garbaye J (2008) Temporal changes in the ectomycorrhizal community in two soil horizons of a temperate oak forest. Appl Environ Microbiol 74: 5792-5801. https:// 10.1128/AEM.01592-08 Culleré L, Ferreira V, Venturini ME, et al (2013) Potential aromatic compounds as markers to differentiate between Tuber melanosporum and Tuber indicum truffles. Food Chem 141:105–110. https://doi.org/10.1016/j.foodchem.2013.03.027 67 Danielsen JS, Morgado L, Mundra S, et al (2021) Establishment of spruce plantations in native birch forests reduces soil fungal diversity. FEMS Microbiol Ecol 97:1–9. https://doi.org/10.1093/femsec/fiab074 De la Varga H, Le Tacon F, Lagoguet M, et al (2017) Five years investigation of female and male genotypes in périgord black truffle (Tuber melanosporum Vittad.) revealed contrasted reproduction strategies. Environ Microbiol 19:2604–2615. https://doi.org/10.1111/1462-2920.13735 Delevich CA, Koch RA, Aime MC, Henkel TW (2021) Ectomycorrhizal fungal community assembly on seedlings of a Neotropical monodominant tree. Biotropica 53:1486–1497. https://doi.org/10.1111/btp.12989 Di Pietro M, Churin JL, Garbaye J (2007) Differential ability of ectomycorrhizas to survive drying. Mycorrhiza 17:547–550. https://doi.org/10.1007/s00572-007-0113-x Djurdjevic L, Gajic G, Jaric J, et al (2016) Truffles Allelopathy. Allelopath J 38:1–24 Dominguez JA, Martin A, Anriquez A, Albanesi A (2012) The combined effects of Pseudomonas fluorescens and Tuber melanosporum on the quality of Pinus halepensis seedlings. Mycorrhiza 22:429–436. https://doi.org/10.1007/s00572-011-0420-0 Domínguez Núñez JA, Serrano JS, Barreal JAR, González JAS de O (2006) The influence of mycorrhization with Tuber melanosporum in the afforestation of a Mediterranean site with Quercus ilex and Quercus faginea. For Ecol Manage 231:226–233. https://doi.org/10.1016/j.foreco.2006.05.052 68 Donnini D, Benucci GMN, Bencivenga M, Baciarelli-Falini L (2014) Quality assessment of truffle-inoculated seedlings in Italy: Proposing revised parameters for certification. For Syst 23:385–393. https://doi.org/10.5424/fs/2014232-05029 Egerton-warburton LM, Querejeta I, Allen MF (2007) Common mycorrhizal networks provide a potential pathway for the transfer of hydraulically lifted water between plants. 58:1473–1483. https://doi.org/10.1093/jxb/erm009 Fellbaum CR, Mensah JA, Cloos AJ, et al (2014) Fungal nutrient allocation in common mycorrhizal networks is regulated by the carbon source strength of individual host plants. New Phytol 203:646–656. https://doi.org/10.1111/nph.12827 Fischer CR, Oliach D, Bonet JA (2017) Best Practices for Cultivation of Truffles "Best Practices for Cultivation of Truffles. Forest Sciences Center of Catalonia, Solsona, Spain; Yasama Dair Vakif, Antalaya, Turkey. 1-68. ISBN: 978-84-697-8163-0 Gao L, Xu W, Xin T, Song J (2023) Application of third-generation sequencing to herbal genomics. Front Plant Sci 14:1–14. https://doi.org/10.3389/fpls.2023.1124536 Garcia-Barreda S, Camarero JJ (2020) Tree ring and water deficit indices as indicators of drought impact on black truffle production in Spain. For Ecol Manage 475:118438. https://doi.org/10.1016/j.foreco.2020.118438 Garcia-Barreda S, Marco P, Martín-Santafé M, et al (2020) Edaphic and temporal patterns of Tuber melanosporum fruitbody traits and effect of localised peat-based amendment. Sci Rep 10:1–9. https://doi.org/10.1038/s41598-020-61274-x Garcia-Barreda S, Reyna S (2012) Below-ground ectomycorrhizal community in natural 69 Tuber melanosporum truffle grounds and dynamics after canopy opening. Mycorrhiza 22:361–369. https://doi.org/10.1007/s00572-011-0410-2 García-Montero LG, Casermeiro MA, Hernando I, Hernando J (2007a) Effect of active carbonate, exchangeable calcium, and stoniness of soil on tuber melanosporum carpophore production. New Zeal J Crop Hortic Sci 35:139–146. https://doi.org/10.1080/01140670709510178 García-Montero LG, Casermeiro MÁ, Manjón JL, Hernando I (2007b) Impact of active soil carbonate and burn size on the capacity of the rockrose Cistus laurifolius to produce Tuber melanosporum carpophores in truffle culture. Mycol Res 111:734–739. https://doi.org/10.1016/j.mycres.2007.03.017 García-Montero LG, Manjón JL, Pascual C, García-Abril A (2007c) Ecological patterns of Tuber melanosporum and different Quercus Mediterranean forests: Quantitative production of truffles, burn sizes and soil studies. For Ecol Manage 242:288–296. https://doi.org/10.1016/j.foreco.2007.01.045 García-Montero LG, Quintana A, Valverde-Asenjo I, Díaz P (2009) Calcareous amendments in truffle culture: A soil nutrition hypothesis. Soil Biol Biochem 41:1227–1232. https://doi.org/10.1016/j.soilbio.2009.03.003 García-Montero LG, Valverde-Asenjo I, Grande-Ortíz MA, et al (2013) Impact of earthworm casts on soil pH and calcium carbonate in black truffle burns. Agrofor Syst 87:815–826. https://doi.org/10.1007/s10457-013-9598-9 Ge ZW, Brenneman T, Bonito G, Smith ME (2017) Soil pH and mineral nutrients strongly influence truffles and other ectomycorrhizal fungi associated with 70 commercial pecans (Carya illinoinensis). Plant Soil 418:493–505. https://doi.org/10.1007/s11104-017-3312-z Guerin-Laguette A, Cummings N, Hesom-Williams N, et al (2013) Mycorrhiza analyses in New Zealand truffières reveal frequent but variable persistence of Tuber melanosporum in co-existence with other truffle species. Mycorrhiza 23:87–98. https://doi.org/10.1007/s00572-012-0450-2 Hall IR, Frith A, Haslam W (2017) Climatic information for some areas where some edible ectomycorrhizal mushrooms are found Hall IR, Yun W, Amicucci A (2003) Cultivation of edible ectomycorrhizal mushrooms. Trends Biotechnol 21:433–438. https://doi.org/10.1016/S0167-7799(03)00204-X Harki E, Bouya D, Dargent R (2006) Maturation-associated alterations of the biochemical characteristics of the black truffle Tuber melanosporum Vitt. Food Chem 99:394–400. https://doi.org/10.1016/j.foodchem.2005.08.030 Hicke JA, Lucatello S, Dawson J, et al (2022) North America. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. IPCC Iotti M, Piattoni F, Zambonelli A (2012) Techniques for host plant inoculation with truffles and other edible ectomycorrhizal mushrooms. 145–161. https://doi.org/10.1007/978-3-642-33823-6_9 Janex-Favre M-C, Parguey-Leduc A, Sejalon-Delmas N, et al (1996) The ascocarp of Tuber indicum (Chinese truffle) recently introduced in France: preliminary study. Plant Biol Pathol 3319:517–521 71 Joos L, Beirinckx S, Haegeman A, et al (2020) Daring to be differential: metabarcoding analysis of soil and plant-related microbial communities using amplicon sequence variants and operational taxonomical units. BMC Genomics 21:1–17. https://doi.org/10.1186/s12864-020-07126-4 Kahmen A, Basler D, Hoch G, et al (2022) Root water uptake depth determines the hydraulic vulnerability of temperate European tree species during the extreme 2018 drought. Plant Biol 24:1224–1239. https://doi.org/10.1111/plb.13476 Kaiser C, Kilburn MR, Clode PL, et al (2015) Exploring the transfer of recent plant photosynthates to soil microbes: Mycorrhizal pathway vs direct root exudation. New Phytol 205:1537–1551. https://doi.org/10.1111/nph.13138 Kendrick B (2017) The Fifth Kingdom, 4th edn. Hackett Publishing Company, Indianapolis, Indiana Kennedy P (2010) Ectomycorrhizal fungi and interspecific competition: Species interactions, community structure, coexistence mechanisms, and future research directions. New Phytol 187:895–910. https://doi.org/10.1111/j.1469-8137.2010.03399.x Kennedy PG, Peay KG, Bruns TD (2009) Root tip competition among ectomycorrhizal fungi: Are priority effects a rule or an exception? Ecology 90:2098–2107. https://doi.org/10.1890/08-1291.1 Kerner R, Delgado-Eckert E, Del Castillo E, et al (2012) Comprehensive proteome analysis in Cenococcum geophilum Fr. as a tool to discover drought-related proteins. J Proteomics 75:3707–3719. https://doi.org/10.1016/j.jprot.2012.04.039 72 Klironomos J (2003) Variation in plant response to native and exotic arbuscular mycorrhizal fungi. Ecology 84:2292–2301. https://doi.org/10.1890/02-0413 Knipfer T, Bambach N, Isabel Hernandez M, et al (2020) Predicting stomatal closure and turgor loss in woody plants using predawn and midday water potential. Plant Physiol 184:881–894. https://doi.org/10.1104/pp.20.00500 Koide RT, Xu B, Sharda J, et al (2005) Evidence of species interactions within an ectomycorrhizal fungal community. New Phytol 165:305–316. https://doi.org/10.1111/j.1469-8137.2004.01216.x Kokkoris V, Hamel C, Hart MM (2019) Mycorrhizal response in crop versus wild plants. PLoS One 14:1–16. https://doi.org/10.1371/journal.pone.0221037 Kõljalg U, Nilsson RH, Abarenkov K, et al (2013) Towards a unified paradigm for sequence-based identification of fungi. Mol Ecol 22:5271–5277. https://doi.org/10.1111/mec.12481 Koller S, Holland V, Brüggemann W (2013) Effects of drought stress on the evergreen Quercus ilex L., the deciduous Q. robur L. and their hybrid Q. × turneri Willd. Photosynthetica 51:574–582. https://doi.org/10.1007/s11099-013-0058-6 Le Tacon F, Marçais B, Courvoisier M, et al (2014) Climatic variations explain annual fluctuations in French Périgord black truffle wholesale markets but do not explain the decrease in black truffle production over the last 48 years. Mycorrhiza 24:115–125. https://doi.org/10.1007/s00572-014-0568-5 Le Tacon F, Rubini A, Murat C, et al (2016) Certainties and uncertainties about the life 73 cycle of the Périgord black truffle (Tuber melanosporum Vittad.). Ann For Sci 73:105–117. https://doi.org/10.1007/s13595-015-0461-1 Le Tacon F, Zeller B, Plain C, et al (2013) Carbon transfer from the host to Tuber melanosporum mycorrhizas and ascocarps followed using a 13C pulse-labeling technique. PLoS One 8:e64626. https://doi.org/10.1371/journal.pone.0064626 Lemmond B, Sow A, Bonito G, Smith ME (2023) Accidental cultivation of the European truffle Tuber brumale in North American truffle orchards. Mycorrhiza 33:221–228. https://doi.org/10.1007/s00572-023-01114-8 Leopold DR (2016) Ericoid fungal diversity: Challenges and opportunities for mycorrhizal research. Fungal Ecol 24:114–123. https://doi.org/10.1016/j.funeco.2016.07.004 Lindahl BD, Nilsson RH, Tedersoo L, et al (2013) Fungal community analysis by high-throughput sequencing of amplified markers - a user’s guide. New Phytol 199:288–299. https://doi.org/10.1111/nph.12243 Linde CC, Selmes H (2012) Genetic diversity and mating type distribution of tuber melanosporum and their significance to truffle cultivation in artificially planted truffiéres in Australia. Appl Environ Microbiol 78:6534–6539. https://doi.org/10.1128/AEM.01558-12 Link-Lenczowska D, Pallisgaard N, Cordua S, et al (2018) A comparison of qPCR and ddPCR used for quantification of the JAK2 V617F allele burden in Ph negative MPNs. Ann Hematol 97:2299–2308. https://doi.org/10.1007/s00277-018-3451-1 74 Lionello P, Scarascia L (2018) The relation between climate change in the Mediterranean region and global warming. Reg Environ Chang 18:1481–1493. https://doi.org/10.1007/s10113-018-1290-1 Liu B, Fischer C, Bonet JA, et al (2014) Pattern of Tuber melanosporum extramatrical mycelium expansion over a 20-year chronosequence in Quercus ilex-truffle orchards. Mycorrhiza 24:47–54. https://doi.org/10.1007/s00572-014-0559-6 Loughin TM, Roediger MP, Miliken GA, Schmidt JP (2007) On the analysis of long-term experiments. J R Stat Soc 170:29–42. https://doi.org/10.1039/AN890150101c Mamoun M, Olivier JM (1993a) Competition between Tuber melanosporum and other ectomycorrhizal fungi, under two irrigation regimes II. Comparison of soils artificially infested with T. melanosporum and T. brumale. Plant Soil 149:219–225. https://doi.org/10.1007/bf00016612 Mamoun M, Olivier JM (1993b) Competition between Tuber melanosporum and other ectomycorrhizal fungi, under two irrigation regimes - I. Competition with Tuber brumale. Plant Soil 149:219–225. https://doi.org/10.1007/BF00016612 Mamoun M, Olivier JM (1995) Receptivity of cloned hazels to artificial ectomycorrhizal infection by Tuber melanosporum and symbiotic competitors. Mycorrhiza 6:15–19 Mariotti A, Pan Y, Zeng N, Alessandri A (2015) Long-term climate change in the Mediterranean region in the midst of decadal variability. Clim Dyn 44:1437–1456. https://doi.org/10.1007/s00382-015-2487-3 Marjanović Ž, Nawaz A, Stevanović K, et al (2020) Root-associated mycobiome 75 differentiate between habitats supporting production of different truffle species in serbian riparian forests. Microorganisms 8:1–27. https://doi.org/10.3390/microorganisms8091331 Marozzi G, Sánchez S, Benucci GMN, et al (2017) Mycorrhization of pecan (Carya illinoinensis) with black truffles: Tuber melanosporum and Tuber brumale. Mycorrhiza 27:303–309. https://doi.org/10.1007/s00572-016-0743-y Marschner H, Dell B (1994) Nutrient uptake in mycorrhizal symbiosis. Plant Soil 159:89–102. https://doi.org/10.1007/BF00000098 Martin F, Kohler A, Murat C, et al (2010) Périgord black truffle genome uncovers evolutionary origins and mechanisms of symbiosis. Nature 464:1033–1038. https://doi.org/10.1038/nature08867 McBurney KG, Cline ET, Bakker JD, Ettl GJ (2017) Ectomycorrhizal community composition and structure of a mature red alder (Alnus rubra) stand. Fungal Ecol 27:47–58. https://doi.org/10.1016/j.funeco.2017.02.006 McPolin MC, Kranabetter JM (2020) Influence of endemic versus cosmopolitan species on the local assembly of ectomycorrhizal fungal communities. New Phytol. https://doi.org/10.1111/nph.17015 Meadows I, Gaskill K, Stefanile L, et al (2020) Persistence of Tuber melanosporum in truffle orchards in North Carolina, USA. Mycorrhiza 30:705–711. https://doi.org/10.1007/s00572-020-00982-8 Mello A, Lumini E, Napoli C, et al (2015) Arbuscular mycorrhizal fungal diversity in the 76 Tuber melanosporum brûlé. Fungal Biol 119:518–527. https://doi.org/10.1016/j.funbio.2015.02.003 Mello A, Napoli C, Murat C, et al (2011) ITS-1 versus ITS-2 pyrosequencing: A comparison of fungal populations in truffle grounds. Mycologia 103:1184–1193. https://doi.org/10.3852/11-027 Merenyi Z, Varga T, Bratek Z (2016) Tuber bruamle: a controversial tuber species. In: Zombonelli A, Iotti M, Murat C (eds) True truffle (Tuber spp.) in the world. pp 49–62 Mohr R (2023) I’m a surgeon turned truffle farmer. Here’s how I became the first to harvest them in the Okanagan. In: Macleans. https://macleans.ca/society/truffle-farming-harvest-okanagan/. Accessed 21 Jun 2023 Molinier V, Bouffaud ML, Castel T, et al (2013) Monitoring the fate of a 30-year-old truffle orchard in Burgundy: From Tuber melanosporum to Tuber aestivum. Agrofor Syst 87:1439–1449. https://doi.org/10.1007/s10457-013-9649-2 Moore-Landecker E (1996) Fundementals of the Fungi, 4th edn. Prentice-Hall, Upper Saddle River, New Jersey Moore D, Novak Frazer L (2002) Essential Fungal Genetics. Springer-Verlag, New York Moore D, Robson G, Trinci A (2011) 21st Century Guidebook to fungi, 1st edn. Cambridge University Press, Cambridge Mrak T, Kühdorf K, Grebenc T, et al (2017) Scleroderma areolatum ectomycorrhiza on Fagus sylvatica L. Mycorrhiza 27:283–293. https://doi.org/10.1007/s00572-016-0748-6 77 Murat C, Zampieri E, Vizzini A, Bonfante P (2008) Is the Perigord black truffle threatened by an invasive species? We dreaded it and it has happened! New Phytol 178:699–702. https://doi.org/10.1111/j.1469-8137.2008.02468.x Napoli C, Mello A, Borra A, et al (2010) Tuber melanosporum, when dominant, affects fungal dynamics in truffle grounds. New Phytol 185:237–247. https://doi.org/10.1111/j.1469-8137.2009.03053.x Neilsen D, Bakker M, Van der Gulik T, et al (2018) Landscape based agricultural water demand modeling-A tool for water management decision making in British Columbia, Canada. Front Environ Sci 6:1–18. https://doi.org/10.3389/fenvs.2018.00074 Nguyen NH, Song Z, Bates ST, et al (2016) FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol 20:241–248. https://doi.org/10.1016/j.funeco.2015.06.006 Nilsson RH, Anslan S, Bahram M, et al (2019) Mycobiome diversity: high-throughput sequencing and identification of fungi. Nat Rev Microbiol 17:95–109. https://doi.org/10.1038/s41579-018-0116-y Nilsson RH, Tedersoo L, Lindahl BD, et al (2011) Towards standardization of the description and publication of next-generation sequencing datasets of fungal communities. New Phytol 191:314–318. https://doi.org/10.1111/j.1469-8137.2011.03755.x Oliach D, Castaño C, Fischer CR, et al (2022) Soil fungal community and mating type development of Tuber melanosporum in a 20-year chronosequence of black truffle 78 plantations. Soil Biol Biochem 165:. https://doi.org/10.1016/j.soilbio.2021.108510 Oliach D, Vidale E, Brenko A, et al (2021) Truffle market evolution: An application of the delphi method. Forests 12:1–15. https://doi.org/10.3390/f12091174 Olivera A, Bonet JA, Oliach D, Colinas C (2014a) Time and dose of irrigation impact Tuber melanosporum ectomycorrhiza proliferation and growth of Quercus ilex seedling hosts in young black truffle orchards. Mycorrhiza 24:73–78. https://doi.org/10.1007/s00572-013-0545-4 Olivera A, Bonet JA, Palacio L, et al (2014b) Weed control modifies Tuber melanosporum mycelial expansion in young oak plantations. Ann For Sci 71:495–504. https://doi.org/10.1007/s13595-014-0360-x Olivera A, Fischer CR, Bonet JA, et al (2011) Weed management and irrigation are key treatments in emerging black truffle (Tuber melanosporum) cultivation. New For 42:227–239. https://doi.org/10.1007/s11056-011-9249-9 Ori F, Leonardi P, Stagnini E, et al (2018) Is tuber brumale a threat to T. Melanosporum and T. aestivum plantations? IForest 11:775–780. https://doi.org/10.3832/ifor2785-011 Orlando C, Pinzani P, Pazzagli M (1998) Developments in quantitative PCR. Clin Chem Lab Med 36:255–269. https://doi.org/10.1515/CCLM.1998.045 Otsing E, Tedersoo L (2015) Temporal dynamics of ectomycorrhizal fungi and persistence of Tuber melanosporum in inoculated Quercus robur seedlings in North Europe. Mycorrhiza 25:61–66. https://doi.org/10.1007/s00572-014-0591-6 79 Pacioni G, Leonardi M, Di Carlo P, et al (2014) Instrumental monitoring of the birth and development of truffles in a Tuber melanosporum orchard. Mycorrhiza 24:65–72. https://doi.org/10.1007/s00572-014-0561-z Packard EE, Durall DM, Jones MD (2023) Successional changes in fungal communities occur a few weeks following wildfire in a mixed Douglas-fir-ponderosa pine forest. Fungal Ecol 63:101246. https://doi.org/10.1016/j.funeco.2023.101246 Pagliari P, Galindo FS, Strock J, Rosen C (2022) Use of repeated measures data analysis for field trials with annual and perennial crops. Plants 11:1–20. https://doi.org/10.3390/plants11131783 Parladé J, Álvarez IF, Pera J (1995) Ability of native ectomycorrhizal fungi from northern Spain to colonize Douglas-fir and other introduced conifers. Mycorrhiza 6:51–55. https://doi.org/10.1007/s005720050105 Parladé J, De la Varga H, De Miguel AM, et al (2013) Quantification of extraradical mycelium of Tuber melanosporum in soils from truffle orchards in northern Spain. Mycorrhiza 23:99–106. https://doi.org/10.1007/s00572-012-0454-y Parladé J, Pera J, Alvarez IF (1996) Inoculation of containerized Pseudotsuga menziesii and Pinus pinaster seedlings with spores of five species of ectomycorrhizal fungi. Mycorrhiza 6:237–245. https://doi.org/10.1007/s005720050131 Parniske M (2008) Arbuscular mycorrhiza: The mother of plant root endosymbioses. Nat Rev Microbiol 6:763–775. https://doi.org/10.1038/nrmicro1987 Peña MA, Fine I (2023) Future physical and biogeochemical ocean conditions under 80 climate change along the British Columbia continental margin. Atmos - Ocean 0:1–22. https://doi.org/10.1080/07055900.2023.2239186 Petersen A, Gulik TW Van Der (2009) Agriculture Sprinkler Irrigation Scheduling Calculator Users Guide. http://www.irrigationbc.com/assets/client/File/USER_GUIDE_Agric_Sprinkler_Irrig_Sched_Calc 2009.pdf. Accessed 10 Mar 2021 Peterson RL, Massicotte HB (2004) Exploring structural definitions of mycorrhizas, with emphasis on nutrient-exchange interfaces. Can J Bot 82:1074–1088. https://doi.org/10.1139/B04-071 Piñuela Y, Alday JG, Oliach D, et al (2020) Use of inoculator bacteria to promote Tuber melanosporum root colonization and growth on Quercus faginea saplings. Forests 11:. https://doi.org/10.3390/F11080792 Piñuela Y, Alday JG, Oliach D, et al (2021) White mulch and irrigation increase black truffle soil mycelium when competing with summer truffle in young truffle orchards. Mycorrhiza 31:371–382. https://doi.org/10.1007/s00572-020-01018-x Plattner I, Hall IR (1995) Parasitism of non-host plants by the mycorrhizal fungus Tuber melanosporum. Mycol Res 99:1367–1370. https://doi.org/10.1016/S0953-7562(09)81223-9 Poh TY, Nur A, Mohamed B, et al (2020) Evaluation of droplet digital Polymerase Chain Reaction (ddPCR) for the absolute quantification of Aspergillus species in the human airway. Int J Mol Sci 21:. https://doi.org/10.3390/ijms21093043 81 Policelli N, Horton TR, Hudon AT, et al (2020) Back to roots: The role of ectomycorrhizal fungi in boreal and temperate forest restoration. Front For Glob Chang 3:. https://doi.org/10.3389/ffgc.2020.00097 Qian Y, Hsu PC, Yuan J, et al (2022) Effects of subseasonal variation in the East Asian monsoon system on the summertime heat wave in Western North America in 2021. Geophys Res Lett 49:. https://doi.org/10.1029/2021GL097659 Quail MA, Smith M, Coupland P, et al (2012) A tale of three NGS sequencing platforms. BMC Genomics 13:13 Queralt M, Parladé J, Pera J, De Miguel AM (2017) Seasonal dynamics of extraradical mycelium and mycorrhizas in a black truffle (Tuber melanosporum) plantation. Mycorrhiza 27:565–576. https://doi.org/10.1007/s00572-017-0780-1 Querejeta JI, Egerton-Warburton LM, Allen MF (2003) Direct nocturnal water transfer from oaks to their mycorrhizal symbionts during severe soil drying. Oecologia 134:55–64. https://doi.org/10.1007/s00442-002-1078-2 Rådström P, Knutsson R, Wolffs P, et al (2004) Pre-PCR processing: Strategies to generate PCR-compatible samples. Appl Biochem Biotechnol - Part B Mol Biotechnol 26:133–146. https://doi.org/10.1385/MB:26:2:133 Redecker D, Kodner R, Graham LE (2000) Glomalean Fungi from the Ordovician Science 289:1920–1921 Reyna-Domenech S, García-Barreda S (2008) European black truffle: Its potential role in agroforestry development in the marginal lands of Mediterranean calcareous 82 mountains. Agrofor Eur 295–317. https://doi.org/10.1007/978-1-4020-8272-6_14 Reyna S, Garcia-Barreda S (2014) Black truffle cultivation: A global reality. For Syst 23:317–328. https://doi.org/10.5424/fs/2014232-04771 Rhoads A, Au KF (2015) PacBio Sequencing and Its Applications. Genomics, Proteomics Bioinforma 13:278–289. https://doi.org/10.1016/j.gpb.2015.08.002 Riccioni C, Belfiori B, Rubini A, et al (2008) Tuber melanosporum outcrosses: Analysis of the genetic diversity within and among its natural populations under this new scenario. New Phytol 180:466–478. https://doi.org/10.1111/j.1469-8137.2008.02560.x Rubini A, Belfiori B, Riccioni C, et al (2011) Isolation and characterization of MAT genes in the symbiotic ascomycete Tuber melanosporum. New Phytol 189:710–722. https://doi.org/10.1111/j.1469-8137.2010.03492.x Rubini A, Riccioni C, Belfiori B, Paolocci F (2014) Impact of the competition between mating types on the cultivation of Tuber melanosporum: Romeo and Juliet and the matter of space and time. Mycorrhiza 24:19–27. https://doi.org/10.1007/s00572-013-0551-6 Sánchez-García M, Ryberg M, Khan FK, et al (2020) Fruiting body form, not nutritional mode, is the major driver of diversification in mushroom-forming fungi. Proc Natl Acad Sci U S A 117:32528–32534. https://doi.org/10.1073/pnas.1922539117 Sánchez S, Ágreda T, Águeda B, et al (2014) Persistence and detection of black truffle ectomycorrhizas in plantations: Comparison between two field detection methods. 83 Mycorrhiza 24:39–46. https://doi.org/10.1007/s00572-014-0560-0 Savoie JM, Largeteau ML (2011) Production of edible mushrooms in forests: Trends in development of a mycosilviculture. Appl Microbiol Biotechnol 89:971–979. https://doi.org/10.1007/s00253-010-3022-4 Schneider-Maunoury L, Deveau A, Moreno M, et al (2020) Two ectomycorrhizal truffles, Tuber melanosporum and T. aestivum, endophytically colonise roots of non-ectomycorrhizal plants in natural environments. New Phytol 225:2542–2556. https://doi.org/10.1111/nph.16321 Schneider-Maunoury L, Leclercq S, Clément C, et al (2018) Is Tuber melanosporum colonizing the roots of herbaceous, non-ectomycorrhizal plants? Fungal Ecol 31:59–68. https://doi.org/10.1016/j.funeco.2017.10.004 Schoch CL, Seifert KA, Huhndorf S, et al (2012) Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proc Natl Acad Sci U S A 109:6241–6246. https://doi.org/10.1073/pnas.1117018109 Shokralla S, Spall JL, Gibson JF, Hajibabaei M (2012) Next-generation sequencing technologies for environmental DNA research. Mol Ecol 21:1794–1805. https://doi.org/10.1111/j.1365-294X.2012.05538.x Sinclair L, Osman OA, Bertilsson S, Eiler A (2015) Microbial community composition and diversity via 16S rRNA gene amplicons: Evaluating the illumina platform. PLoS One 10:1–18. https://doi.org/10.1371/journal.pone.0116955 Song H, Singh D, Tomlinson KW, et al (2019) Tropical forest conversion to rubber 84 plantation in southwest China results in lower fungal beta diversity and reduced network complexity. FEMS Microbiol Ecol 95:1–14. https://doi.org/10.1093/femsec/fiz092 Splivallo R, Ottonello S, Mello A, Karlovsky P (2011) Truffle volatiles: From chemical ecology to aroma biosynthesis. New Phytol 189:688–699. https://doi.org/10.1111/j.1469-8137.2010.03523.x Streiblová E, Gryndlerová H, Gryndler M (2012) Truffle brûlé: An efficient fungal life strategy. FEMS Microbiol Ecol 80:1–8. https://doi.org/10.1111/j.1574-6941.2011.01283.x Suwannarach N, Kumla J, In-On A, Lumyong S (2021) Soil metabarcoding offers a new tool for the investigation and hunting of truffles in northern Thailand. J Fungi 7:1–16. https://doi.org/10.3390/jof7040293 Suz LM, Martín MP, Colinas C (2006) Detection of Tuber melanosporum DNA in soil. FEMS Microbiol Lett 254:251–257. https://doi.org/10.1111/j.1574-6968.2005.00029.x Suz LM, Martín MP, Fischer CR, et al (2010) Can NPK fertilizers enhance seedling growth and mycorrhizal status of Tuber melanosporum-inoculated Quercus ilex seedlings? Mycorrhiza 20:349–360. https://doi.org/10.1007/s00572-009-0289-3 Suz LM, Martín MP, Oliach D, et al (2008) Mycelial abundance and other factors related to truffle productivity in Tuber melanosporum-Quercus ilex orchards. FEMS Microbiol Lett 285:72–78. https://doi.org/10.1111/j.1574-6968.2008.01213.x 85 Taschen E, Sauve M, Taudiere A, et al (2015) Whose truffle is this? Distribution patterns of ectomycorrhizal fungal diversity in Tuber melanosporum brûlés developed in multi-host Mediterranean plant communities. Environ Microbiol 17:2747–2761. https://doi.org/10.1111/1462-2920.12741 Taschen E, Sauve M, Vincent B, et al (2020) Insight into the truffle brûlé: tripartite interactions between the black truffle (Tuber melanosporum), holm oak (Quercus ilex) and arbuscular mycorrhizal plants. Plant Soil 446:577–594. https://doi.org/10.1007/s11104-019-04340-2 Tedersoo L, Anslan S (2019) Towards PacBio-based pan-eukaryote metabarcoding using full-length ITS sequences. Environ Microbiol Rep 11:659–668. https://doi.org/10.1111/1758-2229.12776 Tedersoo L, May TW, Smith ME (2010) Ectomycorrhizal lifestyle in fungi : global diversity , distribution , and evolution of phylogenetic lineages. Mycorrhiza 20:217–263. https://doi.org/10.1007/s00572-009-0274-x Thomas P, Büntgen U (2017) First harvest of Périgord black truffle in the UK as a result of climate change. Clim Res 74:67–70. https://doi.org/10.3354/cr01494 Thomas P, Büntgen U (2019) A risk assessment of Europe’s black truffle sector under predicted climate change. Sci Total Environ 655:27–34. https://doi.org/10.1016/j.scitotenv.2018.11.252 Toju H, Tanabe AS, Yamamoto S, Sato H (2012) High-coverage ITS primers for the DNA-based identification of ascomycetes and basidiomycetes in environmental samples. PLoS One 7:. https://doi.org/10.1371/journal.pone.0040863 86 Trappe JM (2005) A.B. Frank and mycorrhizae: The challenge to evolutionary and ecologic theory. Mycorrhiza 15:277–281. https://doi.org/10.1007/s00572-004-0330-5 Valentini A, Pompanon F, Taberlet P (2009) DNA barcoding for ecologists. Trends Ecol Evol 24:110–117. https://doi.org/10.1016/j.tree.2008.09.011 Valverde-Asenjo I, García-Montero LG, Quintana A, Velázquez J (2009) Calcareous amendments to soils to eradicate Tuber brumale from T. melanosporum cultivations: A multivariate statistical approach. Mycorrhiza 19:159–165. https://doi.org/10.1007/s00572-008-0224-z van der Heijden MGA, Martin FM, Selosse MA, Sanders IR (2015) Mycorrhizal ecology and evolution: The past, the present, and the future. New Phytol 205:1406–1423. https://doi.org/10.1111/nph.13288 Van Dorp CH, Simard SW, Durall DM (2020) Resilience of Rhizopogon-Douglas-fir mycorrhizal networks 25 years after selective logging. Mycorrhiza 30:467–474. https://doi.org/10.1007/s00572-020-00968-6 Villeneuve N, Le Tacon F, Bouchard D (1991) Survival of inoculated Laccaria bicolor in competition with native ectomycorrhizal fungi and effects on the growth of outplanted Douglasfir seedlings. Plant Soil 135:95–107. https://doi.org/10.1007/BF00014782 Voegel TM, Larrabee MM, Nelson LM (2021) Development of droplet digital PCR assays to quantify genes involved in nitrification and denitrification , comparison with quantitative real-time PCR and validation of assays in vineyard soil 1. 87 187:174–187 Wang C, Ding Q, Plant P, et al (2019) Droplet digital PCR improves urinary exosomal miRNA detection compared to real-time PCR. Clin Biochem 67:54–59. https://doi.org/10.1016/j.clinbiochem.2019.03.008 Wang D, Jiao X, Jia H, et al (2022a) Detection and quantification of Verticillium dahliae and V. longisporum by droplet digital PCR versus quantitative real-time PCR. Front Cell Infect Microbiol 12:1–11. https://doi.org/10.3389/fcimb.2022.995705 Wang D, Wang S, Du X, et al (2022b) ddPCR surpasses classical qPCR technology in quantitating bacteria and fungi in the environment. Mol Ecol Resour 22:2587–2598. https://doi.org/10.1111/1755-0998.13644 Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73:5261–5267. https://doi.org/10.1128/AEM.00062-07 Watrud LS, Martin K, Donegan KK, et al (2006) Comparison of taxonomic, colony morphotype and PCR-RFLP methods to characterize microfungal diversity. Mycologia 98:384–392. https://doi.org/10.1080/15572536.2006.11832673 White RH, Anderson S, Booth JF, et al (2023) The unprecedented Pacific Northwest heatwave of June 2021. Nat Commun 14:. https://doi.org/10.1038/s41467-023-36289-3 Wilken PM, Steenkamp ET, Wingfield MJ, et al (2017) Which MAT gene? Pezizomycotina (Ascomycota) mating-type gene nomenclature reconsidered. 88 Fungal Biol Rev 31:199–211. https://doi.org/10.1016/j.fbr.2017.05.003 Wood SN (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc Ser B Stat Methodol 73:3–36. https://doi.org/10.1111/j.1467-9868.2010.00749.x Xie H, Yang C, Sun Y, et al (2020) PacBio long reads improve metagenomic assemblies, gene catalogs, and genome binning. Front Genet 11:1–8. https://doi.org/10.3389/fgene.2020.516269 Yang RH, Su JH, Shang JJ, et al (2018) Evaluation of the ribosomal DNA internal transcribed spacer (ITS), specifically ITS1 and ITS2, for the analysis of fungal diversity by deep sequencing. PLoS One 13:1–17. https://doi.org/10.1371/journal.pone.0206428 Zampieri E, Rizzello R, Bonfante P, Mello A (2012) The detection of mating type genes of Tuber melanosporum in productive and non productive soils. Appl Soil Ecol 57:9–15. https://doi.org/10.1016/j.apsoil.2012.02.013 Zappa G, Hoskins BJ, Shepherd TG (2015) The dependence of wintertime Mediterranean precipitation on the atmospheric circulation response to climate change. Environ Res Lett 10:104012. https://doi.org/10.1088/1748-9326/10/10/104012 Zhang X, Li X, Ye L, et al (2020) Colonization by Tuber melanosporum and Tuber indicum a ff ects the growth of Pinus armandii and phoD alkaline phosphatase encoding bacterial community in the rhizosphere. Microbiol Res 239:126520. https://doi.org/10.1016/j.micres.2020.126520 89 Zhang ZF, Liu LR, Pan YP, et al (2023) Long-read assembled metagenomic approaches improve our understanding on metabolic potentials of microbial community in mangrove sediments. Microbiome 11:1–20. https://doi.org/10.1186/s40168-023-01630-x Zhao Y, Xia Q, Yin Y, Wang Z (2016) Comparison of Droplet Digital PCR and Quantitative PCR assays for quantitative detection of Xanthomonas citri Subsp. Citri. PLoS One 11:1–18. https://doi.org/10.1371/journal.pone.0159004 Zhu H, Zhang H, Xu Y, et al (2020) PCR past, present and future. Biotechniques 69:317–325. https://doi.org/10.2144/BTN-2020-0057 90 Supplementary Tables & Figures A1 Daily June air temperature means (a) and maximums (b) in Vancouver BC from 1991 - 2021. Each grey line represents mean daily air temperatures (a) or daily air temperature maximums (b) for a given year from 1991-2020, with the mean shown in black. The red line represents the daily average air temperatures (a) and daily maximum air temperature (b) of 2021. Two missing data points from June 2021 (15, 20) omitted from figure. Data obtained from Vancouver International Airport weather station 91 (climate ID: 1108395 https://climate.weather.gc.ca/historical_data/search_historic_data_e.html) 92 A2 Midday stem water potential readings. Two leaves from the lower canopy of each measurement tree were covered in aluminum foil before sunrise. Leaves were harvested at solar noon, bagged, and placed on ice for transport to the lab. Stem water potential readings were taken in the lab with a pressure chamber (Model 615, Nu-Tech International, New Delhi, India) and values from each leaf were averaged, giving on stem water potential value per tree. Red horizontal line denotes the irrigation initiation point for the low treatment trees (-2 MPa), where if most trees passed this threshold, 93 irrigation would be applied. Since less than half the low-irrigation trees crossed the threshold, all trees remained unirrigated for the duration of the study. Due to incomplete sampling from July 12 – August 13, all stem water potential readings were excluded from the statistical analysis. 94 A3 Irrigation schedule. Irrigation water was applied using two 180° microsprayer nozzles (SKU#A47083, Rain Bird, Azusa, California, USA) installed at the base of each tree to produce a circular wetting pattern with an approximate radius of 2 m. Irrigation system was manually turned on and run for approximately 2 hours on dates listed. Values represent the average soil water potential readings the day before irrigation (Before), the date of irrigation (During), and the date following irrigation (After). Soil water potential (kPa) Treatment Date Before During After High June 28 - 27.0 - 14.2 - 17.4 July 04 - 23.2 - 16.9 - 15.4 July 09 - 23.5 - 20.2 - 18.5 July 12 - 21.2 - 16.6 - 14.3 July 19 - 20.2 - 19.4 - 18.7 July 23 - 24.8 - 14.8 - 20.8 July 27 - 25.4 - 18.3 - 20.8 July 30 - 20.3 - 16.8 - 15.5 Aug 01 - 15.5 - 12.0 - 14.0 Aug 05 - 16.5 - 13.6 - 13.1 Aug 13 - 18.9 - 11.3 - 13.3 Aug 17 - 15.7 - 13.5 - 15.5 Aug 20 - 18.0 - 14.4 - 12.5 Aug 31 - 22.4 - 18.8 - 14.9 Sept 03 - 18.3 - 12.7 - 11.2 Sept 10 - 18.9 - 15.2 - 16.7 Moderate July 08 - 62.8 - 54.4 - 16.1 95 Soil water potential (kPa) Treatment Date Before During After Moderate July 22 - 120.8 - 46.0 - 17.9 July 29 - 130.6 - 124.9 - 16.1 Aug 16 - 53.6 - 47.7 - 26.55 Aug 24 - 65.7 - 36.4 - 16.0 Sept 05 - 33.7 - 14.1 - 17.8 96 97 Fig. A4 Expected and observed mock communities from a soil and b roots. DNA was extracted from sporocarps of roots tips and identity was confirmed by Sanger sequencing the ITS region. 98 A5 GAM diagnostic plots for (a) model 1 (irrigation treatment) and (b) model 2 (water content). a b 99 A6 Discrepancies between BLAST and UNITE databases used to query sequences against for root and soil samples. Green shading indicates the taxonomic assignment chosen Soil UNITE Genus (BLAST) Species (BLAST) Cercophora_sp g__Podospora s__dimorpha Tuberaceae_sp g__Tuber s__aestivum Preussia_sp g__Preussia s__terricola Didymosphaeriaceae_sp g__Paraphaeosphaeria Herpotrichiellaceae_sp g__Knufia s__tsunedae Trichoderma_sp g__Trichoderma s__hamatum Didymosphaeriaceae_sp1 g__Pseudocoleophoma Leucothecium_sp g__Leucothecium s__emdenii Scleroderma_sp g__Scleroderma s__verrucosum Trechispora_sp g__Trechispora s__hymenocystis Tomentella_sp g__Tomentella s__ellisii Phaeosphaeriaceae_sp g__Pyrenochaetopsis s__leptospora Lasiosphaeriaceae_sp g__Podospora s__decidua Cylindrosympodium_sp g__Cylindrosympodium s__variabile Glomeraceae_sp g__Septoglomus s__constrictum Diversisporaceae_sp g__Diversispora Glomeraceae_sp1 g__Septoglomus s__constrictum Crepidotus_sp g__Crepidotus s__mollis unidentified g__Absidia s__cylindrospora Polyporales_sp g__Ganoderma unidentified1 g__Periconia s__pseudodigitata Helotiales_sp g__Leohumicola Tremellomycetes_sp g__Solicoccozyma s__fuscescens 100 Soil UNITE Genus (BLAST) Species (BLAST) Pleosporales_sp g__Pyrenochaeta s__inflorescentiae Pleosporales_sp1 g__Keissleriella NA Microascales_sp g__Cirrenalia unidentified2 g__Pseudocoleophoma NA Fungi_sp g__Lophotrichus s__fimeti Fungi_sp1 g__Fusariella Botryosphaeriales_sp g__Pseudoarthrographis s__phlogis Pleosporales_sp2 g__Pseudocoleophoma NA unidentified3 g__Preussia s__flanaganii unidentified4 g__Penicillium s__paczoskii unidentified5 g__Preussia s__flanaganii Fungi_sp2 g__Nemania unidentified6 g__Penicillium s__roseopurpureum unidentified7 g__Gibellulopsis NA unidentified8 g__Solicoccozyma s__terricola unidentified9 g__Exophiala s__equina Fungi_sp3 g__Beauveria NA unidentified10 g__Backusella s__circina Serendipitaceae_sp g__Serendipita NA unidentified11 g__Metarhizium s__marquandii Claroideoglomeraceae_sp g__Claroideoglomus s__drummondii unidentified12 g__Verrucostoma s__freycinetiae Agaricomycetes_sp g__Rigidoporus s__sanguinolentus Fungi_sp4 g__Periconia Chaetothyriales_sp g__Trichomerium s__eucalypti Ascomycota_sp g__Clavatospora s__longibrachiata 101 Soil UNITE Genus (BLAST) Species (BLAST) Pleosporales_sp3 g__Pseudocoleophoma NA Pleosporales_sp4 g__Pyrenochaeta s__nobilis Fungi_sp5 g__Udeniozyma NA unidentified13 g__Pseudorobillarda s__phragmitis Fungi_sp6 g__Exophiala s__eucalyptorum Ascomycota_sp1 g__Microdochium NA unidentified14 g__Acremonium s__pilosum unidentified15 g__Penicillium s__decumbens Pleosporales_sp5 g__Pseudopithomyces s__rosae Pleosporales_sp6 g__Dendryphion s__europaeum unidentified16 g__Mortierella s__alpina Tremellomycetes_sp1 g__Solicoccozyma s__aeria unidentified17 g__Mortierella NA unidentified18 g__Pseudoophiobolus unidentified19 g__Phaeosphaeria s__poagena unidentified20 g__Mortierella s__humilis unidentified21 g__Saitozyma s__podzolica unidentified22 g__Mortierella NA Fungi_sp7 g__Lophotrichus s__fimeti unidentified23 g__Mortierella Hypocreales_sp g__Polyscytalum s__algarvense Fungi_sp8 g__Mortierella s__elongata Fungi_sp9 g__Entoloma s__undulatosporum Dothideomycetes_sp g__Pseudoophiobolus unidentified24 g__Clonostachys Veronaea_compacta g__Pseudophaeomoniella s__oleae 102 Trichocladium_opacum g__Pleotrichocladium s__opacum Talaromyces_duclauxii g__Penicillium NA Stagonospora_sp g__Phaeosphaeria s__poagena Byssoonygena_ceratinophila g__Coccidioides s__posadasii Truncatella_angustata g__Hyalotiella Pleospora_sp g__Neocamarosporium Root tips UNITE Genus (BLAST) Species (BLAST) Scleroderma_sp Scleroderma verrucosum Hebeloma aurantioumbrinum Hebeloma crustuliniforme Helvella_sp NA Unidentified Hyaloscypha finlandica Ascomycota_sp Cenococcum 103 A7 Summary tables with the coefficient estimates and associated standard errors, t statistics, and p values for the models investigating the effect of irrigation on the abundance of T. melanosporum extramatrical mycelium as discrete irrigation treatments (model 1) and continuous water content (model 2). Significance codes: *** < 0.001; ** < 0.01, * < 0.05. Model 1 Parametric coefficients: Estimate Std. Error t value Pr(>|t|) (intercept) 5.0823 0.3514 14.462 < 2e-16 *** moderate irrigation 0.1926 0.3759 0.512 0.609 high irrigation - 0.3442 0.3771 - 0.913 0.362 100 cm sampling point -0.8170 0.1445 - 5.655 5.03e-08 *** Approximate significance for smooth terms: edf Ref.df F p-value month-treatment 7.2990 9 9.604 <2e-16 *** month-tree 8.7688 129 0.091 0.1350 tree 26.3069 30 8.780 0.0123 * tree size 1.3752 2 138.109 0.0548 distance-treatment 1.4552 2 2.640 0.0300 * distance-tree size 0.1108 4 0.028 0.3863 Model 2 Parametric coefficients: Estimate Std. Error t value Pr(>|t|) (intercept) 5.06016 0.24409 20.730 < 2e-16 *** 100 cm sampling point -0.78065 0.08682 -8.992 < 2e-16 *** Approximate significance for smooth terms: edf Ref.df F p-value water content 2.3 2.641 10.054 1.47e-05** tree 27.40 32 6.701 0.0529 tree size 1.299 2 139.431 0.0398 * distance-water content 5.763e-01 7 0.127 0.2377 distance-tree size 5.312e-04 4 0 0.8496 104 A8 Change in T. melanosporum DNA abundance sampled 100 cm (top) and 50 cm (bottom) from the base of the tree along a gradient of water content. Predicted values obtained from a Generalized Additive Model with a Tweedie Distribution and log-link function. 105 A9 Summary of the partial effects and interaction effects included in the model. a Abundance of T. melanosporum soil DNA at 50 cm (light blue) and 100 cm (dark blue) sampling points. b Abundance of T. melanosporum soil DNA found in short (<1.5 m, left), medium (1.5 - 2.5m, middle), and tall (>2.5m, right) trees. c Effect of tree height and sampling distance on the abundance of T. melanosporum soil DNA. d Effect of irrigation on the abundance of T. melanosporum soil DNA at the different sampling distances. Soil was sampled at each tree from the four cardinal points at each distance 106 then pooled by distance. Abundance of T. melanosporum was quantified using droplet digital PCR (ddPCR) targeting the ITS1 region. 107 Table A10. Ectomycorrhizal fungal taxa detected in soil (10-20 cm depth) and on root tips collected in August, 2021, 50 cm from 9.5 year-old Q. robur-T. melanosporum trees subjected to three irrigation treatments from late June to late September 2021 in Vancouver, BC, Canada. PacBio sequencing of the full ITS region was performed on extracted DNA from soil and root tips. Bolded taxa are considered potential competitors of Tuber melanosporum (i.e., they appear in over 75% of replicate trees in any of the three treatment groups). 1 Assignment into Amplicon Sequence Variants (ASVs) and taxonomic groups was performed using the DADA2 pipeline. Soil Species Hypothesis11 Species Percentage of trees L M H SH1563427.08FU Tuber melanosporum 100 100 100 SH1650627.08FU Scleroderma bovista 73 91 64 SH1526178.08FU Scleroderma aerolatum 91 100 91 SH1526179.08FU Scleroderma verrucosum 82 82 45 SH2727211.08FU Scleroderma verrucosum1 18 27 27 SH1526187.08FU Scleroderma aerolatum1 0 9 0 SH1566870.08FU Helvella sp. 9 9 18 SH2290563.08FU Hebeloma aurantioumbrinum 27 18 9 SH1951394.08FU Hebeloma cavipes 27 18 27 SH1545221.08FU Cortinarius diasemospermus 0 9 0 SH1502249.08FU Tomentella ellisii 9 0 9 SH1502256.08FU Tomentella ellisii1 9 0 0 SH1502195.08FU Tomentella sublilacina 9 9 0 SH1548908.08FU Alpova concolor 9 9 9 SH1569017.08FU Paxillus ammoniavirescens 9 9 0 108 1 Assignment into Amplicon Sequence Variants (ASVs) and taxonomic groups was performed using the DADA2 pipeline. Soil Species Hypothesis11 Species Percentage of trees L M H SH2750819.08FU Entoloma undulatosporum 18 18 0 SH1630059.08FU Rhizopogon parksii 0 0 9 SH1555190.08FU Suillus caerulescens 0 0 9 SH1522364.08FU Gautieria monticola 0 0 9 SH1616481.08FU Scarcodon leucopus 0 0 9 SH1542616.08FU Tuber oregonense 0 9 0 SH1525755.08FU Tuber borchii 27 18 0 Root tips Species Hypothesis11 Species Percentage of trees L M H SH1563427.08FU Tuber melanosporum 100 100 100 SH1650627.08FU Scleroderma bovista 73 100 82 SH1526178.08FU Scleroderma aerolatum 100 100 82 SH1526179.08FU Scleroderma verrucosum 82 90 73 SH2727211.08FU Scleroderma verrucosum1 45 90 45 SH1566870.08FU Helvella_sp 55 40 36 SH2290563.08FU Hebeloma aurantioumbrinum 18 10 18 SH1951394.08FU Hebeloma cavipes 27 0 18 SH1545287.08FU Cortinarius anisatus 0 10 0 SH1547450.08FU Tarzetta cupularis 0 10 0 SH1545827.08FU Trichophaea woolhopeia 9 0 0 SH1544834.08FU Piloderma sp. 0 0 9 SH1625359.08FU Russula graminea 0 0 9 SH3236244.08FU Hyaloscypha finlandica 9 0 0 109 A11 Summary of the model for ectomycorrhizal fungal community richness on root tips. Significance codes: *** < 0.001; ** < 0.01, * < 0.05. Parametric coefficients: Estimate Std. Error z value Pr(>|z|) (intercept) 1.625 0.094 17.209 < 2e-16 *** moderate irrigation 0.061 0.137 0.477 0.655 high irrigation -0.113 0.134 -0.842 0.400 Approximate significance for smooth terms: edf Ref.df Chi.sq p-value taxa 8.005e-06 13 0 1 tree 24.68 29 136 < 2e-16 *** tree size 2.793e-04 2 0 0.366 taxa-treatment 3.193e-05 39 0 1 A12. Summary of the model for ectomycorrhizal fungal community richness in soil. Significance codes: *** < 0.001; ** < 0.01, * < 0.05. Parametric coefficients: Estimate Std. Error z value Pr(>|z|) (intercept) 1.541 0.195 7.921 < 2.36e-15 *** moderate irrigation 0.124 0.231 0.538 0.591 high irrigation -0.095 0.231 -0.412 0.680 Approximate significance for smooth terms: edf Ref.df Chi.sq p-value taxa 1.390e-06 21 0 1 tree 27.93 30 494.4 0.38 tree size 1.032 2 294.6 0.60 taxa-treatment 5.132e-05 63 0 1 110 A13 Summary of the model for ectomycorrhizal fungal community composition on root tips. Significance codes: *** < 0.001; ** < 0.01, * < 0.05. Parametric coefficients: Estimate Std. Error z value Pr(>|z|) (intercept) -0.6674 14.3677 -0.046 0.963 moderate irrigation 0.2667 0.5940 0.449 0.6563 high irrigation -0.3875 0.5785 -0.67 0.503 Approximate significance for smooth terms: edf Ref.df Chi.sq p-value taxa 12.91 13 116.952 < 2e-16 tree 12.04 29 21.58 0.0105 tree size 0.934 2 2.84 0.221 taxa-treatment 4.78 39 247.498 0.266 A14 Summary of the model for ectomycorrhizal fungal community composition in the soil. Significance codes: *** < 0.001; ** < 0.01, * < 0.05. Parametric coefficients: Estimate Std. Error z value Pr(>|z|) (intercept) -1.733 5.6714 -0.306 0.760 moderate irrigation 0.1303 0.4548 0.287 0.774 high irrigation -0.370 0.4725 -0.783 0.434 Approximate significance for smooth terms: edf Ref.df Chi.sq p-value taxa 19.89 20 123.48 < 2e-16 tree 13.12 30 53.72 < 2e-16 tree size 8.05e-04 2 0 0.483 taxa-treatment 4.41e-04 60 0 0.687 111 Fig. A15 The effect of irrigation on the ectomycorrhizal fungal community richness on a root tips and b in soil. Coloured points represent the ectomycorrhizal fungal community at each tree sampled within each irrigation treatment. Black dot represents estimates from a generalized additive model and bars represent 95% confidence intervals under 112 the assumption of Gaussian error on the link scale. The effect of irrigation on the ectomycorrhizal fungal community composition on c root tips and d in soil as represented by a non-metric multidimensional scaling ordination (NMDS) using Jaccard’s index.
Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Durall, Daniel
Hannam, Kirsten
The effects of irrigation treatments on Tuber melanosporum extramatrical mycelium and the surrounding ectomycorrhizal fungal community in a British Columbia truffle orchard
Text