Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Mañé Duarte, María de la Paz
Master of Science - MSc
University of British Columbia
Mycorrhizal fungi form symbiotic associations with plant roots, playing essential roles in ecosystem function. As obligate symbionts, understanding their ecology and biogeography requires knowledge of fungal-host associations. However, current knowledge of host compatibility remains inadequate due to extremely limited and taxonomically biased sampling, and methodological inconsistencies. The slow pace of new discoveries and data limitations hinder our ability to map and predict fungal-host relationships. Models for predicting potential associations between parasites and hosts have been applied successfully, but have yet to be considered for predicting potential mycorrhizal associations. In this thesis, I present a roadmap for predicting ectomycorrhizal (EM) fungi-host associations. I first review existing knowledge and key gaps, including challenges in sampling, taxonomic biases, and uncertainties in mycorrhizal classification. I then explore ecological, phylogenetic, and functional factors that shape fungal-host relationships, assessing the strengths and limitations of available data sources for these predictor themes. Additionally, I evaluate predictive modeling approaches, comparing their suitability for EM associations based on data requirements, performance, and interpretability. By outlining key considerations for method selection and data integration, this roadmap provides a foundation for more systematic and informed approaches to uncovering fungal-host associations. Ultimately, it could accelerate the discovery of novel EM partnerships, refine sampling strategies, and contribute to fungal conservation efforts in a changing environment.
A ROADMAP TO PREDICTING ASSOCIATIONS BETWEEN ECTOMYCORRHIZAL FUNGI AND HOST PLANTS by María de la Paz Mañé Duarte B.S., Universidad Autónoma de Yucatán, 2021 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) May 2025 © María de la Paz Mañé Duarte, 2025 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, the thesis entitled: A roadmap to predicting associations between ectomycorrhizal fungi and host plants submitted by María de la Paz Mañé Duarte in partial fulfilment of the requirements for the degree of Master of Science in Biology Examining Committee: Dr. Jason Pither, Irving K. Barber Faculty of Science, Biology Supervisor Dr. Melanie Jones, Irving K. Barber Faculty of Science, Biology Supervisory Committee Member Dr. Laura Grieneisen, Irving K. Barber Faculty of Science, Biology Supervisory Committee Member Dr. Michael Noonan, Irving K. Barber Faculty of Science, Biology Supervisory Committee Member Dr. Rebecca Tyson, Irving K. Barber Faculty of Science, Computer Science, Mathematics, Physics and Statistics University Examiner iii Abstract Mycorrhizal fungi form symbiotic associations with plant roots, playing essential roles in ecosystem function. As obligate symbionts, understanding their ecology and biogeography requires knowledge of fungal-host associations. However, current knowledge of host compatibility remains inadequate due to extremely limited and taxonomically biased sampling, and methodological inconsistencies. The slow pace of new discoveries and data limitations hinder our ability to map and predict fungal-host relationships. Models for predicting potential associations between parasites and hosts have been applied successfully, but have yet to be considered for predicting potential mycorrhizal associations. In this thesis, I present a roadmap for predicting ectomycorrhizal (EM) fungi-host associations. I first review existing knowledge and key gaps, including challenges in sampling, taxonomic biases, and uncertainties in mycorrhizal classification. I then explore ecological, phylogenetic, and functional factors that shape fungal-host relationships, assessing the strengths and limitations of available data sources for these predictor themes. Additionally, I evaluate predictive modeling approaches, comparing their suitability for EM associations based on data requirements, performance, and interpretability. By outlining key considerations for method selection and data integration, this roadmap provides a foundation for more systematic and informed approaches to uncovering fungal-host associations. Ultimately, it could accelerate the discovery of novel EM partnerships, refine sampling strategies, and contribute to fungal conservation efforts in a changing environment. iv Lay Summary Mycorrhizal fungi are soil fungi that form symbiotic associations with plants, benefiting both partners. Understanding these associations requires identifying which fungi pair with which hosts, but limited and biased sampling restricts current knowledge. Since discovering new associations is slow and data is scarce, predictive models could help uncover unexplored partnerships. Such models have been used successfully to predict parasite-host relationships, but have not been used to predict potential associations between mycorrhizal fungi and their plant hosts. This thesis outlines a roadmap to predicting fungal-host associations, summarizing existing knowledge and highlighting key gaps. Then, it examines factors influencing these associations and their data availability, evaluating the best methods for making accurate predictions. This framework could improve sampling strategies and support the identification of fungi in need of conservation. v Preface The primary goal of this thesis is to present a roadmap for predicting potential associations between ectomycorrhizal fungi and host plants, which fungi may form associations with which hosts. It does not include any data. Rather, it advances the field of mycorrhizal ecology by providing, in one comprehensive work, (i) a review of the literature and hypotheses relevant to predicting potential ectomycorrhizal associations, (ii) an overview of the data requirements along with potential sources, and identifying critical data gaps, and (iii) an evaluation of methods that have been used for similar purposes in disease and parasite ecology, and that hold promise for predicting potential ectomycorrhizal associations. All content is original, with the foundational ideas established by my advisor Dr. Jason Pither and myself, and further developed by me. Given that English is not my primary language, I used AI tools such as ChatGPT to improve the writing structure, vocabulary, and cohesion of the thesis. The thesis is structured into 3 Chapters and an Appendix, which provides summaries and additional details on variable selection, data sources, and methods comparison. Chapter 1 serves as the General Introduction, offering a brief overview of key topics to avoid redundancy with Chapter 2, which presents a Roadmap with a comprehensive literature review in manuscript format. Chapter 3 concludes the thesis with a synthesis of findings. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Abbreviations .................................................................................................................. xii List of Entity Abbreviations ...................................................................................................... xiv Glossary ...................................................................................................................................... xvi Acknowledgements .................................................................................................................... xxi Dedication .................................................................................................................................. xxii Chapter 1: Introduction ................................................................................................................1 1.1 General background on ectomycorrhizal fungi ............................................................... 1 1.2 Ectomycorrhizal associations.......................................................................................... 3 1.2.1 Evolutionary history and biogeography .................................................................. 3 1.2.2 Fungal host breadth, specificity, and preference .................................................... 4 1.2.3 Ecological impacts of EM associations .................................................................. 6 1.3 Barriers to understanding mycorrhizal associations ....................................................... 8 1.3.1 Identification of host mycorrhizal type ................................................................... 8 1.3.2 Quantification of EMF diversity ............................................................................. 9 1.3.3 Gaps in EMF-host associations data ..................................................................... 10 vii 1.4 Predicting potential ectomycorrhizal associations ........................................................ 11 1.5 Research objectives ....................................................................................................... 12 Chapter 2: Roadmap ...................................................................................................................14 2.1 Background ................................................................................................................... 14 2.2 Step 1: Assemble the response variable ........................................................................ 21 2.2.1 Associations matrix ............................................................................................... 21 2.2.2 Mycorrhizal associations data ............................................................................... 22 2.2.3 Data sources .......................................................................................................... 23 2.2.4 Limitations & Recommendations ......................................................................... 25 2.3 Step 2: Determine predictor variables........................................................................... 27 2.3.1 Biogeographic predictor theme ............................................................................. 32 2.3.1.1 Rationale ........................................................................................................... 32 2.3.1.2 Important variables in the literature .................................................................. 34 2.3.1.3 Data sources ...................................................................................................... 35 2.3.1.4 Implementation considerations ......................................................................... 36 2.3.2 Phylogenetic predictor theme ............................................................................... 37 2.3.2.1 Rationale ........................................................................................................... 37 2.3.2.2 Important variables in the literature .................................................................. 39 2.3.2.3 Data sources ...................................................................................................... 40 2.3.2.4 Implementation considerations ......................................................................... 41 2.3.3 Niche predictor theme â Abiotic ........................................................................... 41 2.3.3.1 Rationale ........................................................................................................... 41 2.3.3.2 Important variables in the literature .................................................................. 44 viii 2.3.3.3 Data sources ...................................................................................................... 45 2.3.3.4 Implementation considerations ......................................................................... 49 2.3.4 Niche predictor theme â Biotic ............................................................................. 49 2.3.4.1 Rationale ........................................................................................................... 49 2.3.4.2 Important variables in the literature .................................................................. 51 2.3.4.3 Data sources ...................................................................................................... 51 2.3.4.4 Variable selection based on data availability .................................................... 52 2.3.5 Host traits predictor theme .................................................................................... 52 2.3.5.1 Rationale ........................................................................................................... 52 2.3.5.2 Important variables according to literature ....................................................... 55 2.3.5.3 Data sources ...................................................................................................... 56 2.3.5.4 Implementation considerations ......................................................................... 58 2.3.6 Fungal traits predictor theme ................................................................................ 59 2.3.6.1 Rationale ........................................................................................................... 59 2.3.6.2 Important variables according to literature ....................................................... 63 2.3.6.3 Data sources ...................................................................................................... 63 2.3.6.4 Implementation considerations ......................................................................... 68 2.3.7 Limitations & Recommendations ......................................................................... 68 2.4 Step 3: Choose a method............................................................................................... 70 2.4.1 Network-based methods........................................................................................ 73 2.4.2 Trait-based methods .............................................................................................. 74 2.4.3 Comparison of methods and their data needs ....................................................... 75 2.4.4 Core elements of modelling .................................................................................. 78 ix 2.4.4.1 Associations matrix transformation .................................................................. 79 2.4.4.2 Training datasets ............................................................................................... 81 2.4.4.3 Data imputation ................................................................................................. 82 2.4.4.4 Model selection ................................................................................................. 83 2.4.4.5 Model validation ............................................................................................... 84 2.4.5 Promising methods for predicting potential EMF-host associations .................... 85 2.4.6 Limitations & Recommendations ......................................................................... 86 2.5 Hypothetical case study: North America north of Mexico ........................................... 88 2.6 Next steps ...................................................................................................................... 89 Chapter 3: Conclusion .................................................................................................................92 3.1 Review of objectives and major conclusions ................................................................ 92 3.2 Knowledge gaps in EMF research ................................................................................ 93 3.3 Limitations of the mycorrhizal predictive framework .................................................. 95 3.4 Recommendations for future research and conservation .............................................. 97 3.5 Significance and contribution of the thesis ................................................................... 98 Bibliography ...............................................................................................................................100 Appendices ..................................................................................................................................134 Appendix A: Variable selection and data sources .................................................................. 134 Appendix B: Method comparison ........................................................................................... 138 x List of Tables Table 1. Comparison of data needs and capacity to predict novel or unrecorded associations of the main models found in a subset of evaluated papers from the disease ecology literature for predicting parasite-host associations. Red: incompatible with EMF data or our aims. Blue: compatible with EMF data, but not suited to our aims. Green: compatible with EMF data and our aims. .................................................................................................... 77 Table A.1 Recommended variable selection and their corresponding data sources (73 variables, 5 sources). .......................................................................................................................... 134 Table A.2 Summary of the presented data sources showing their data content based on the predictor themes. Arranged in alphabetical order by general focus. .............................. 136 Table A.3 Overview of data sources, detailing their assembly year, update frequency, maintenance status, and versioning. The table also highlights whether each dataset supports community contributions, incorporates expert curation, and specifies the taxonomic framework used. ............................................................................................ 137 Table B.1 Comparison of methods from Step 3 of this roadmap with the studies they were derived from, detailing their specific applications and analytical approaches. .............. 138 xi List of Figures Figure 1. Schematic overview of the roadmap structure, outlining the key steps for predicting potential EMF-host associations. ...................................................................................... 21 Figure 2. Conceptual model of ecological and biological filters shaping ectomycorrhizal fungal host breadth. Each horizontal layer represents one of the proposed predictor themes, influencing the likelihood of an association between a fungus and a potential host. On the left, restrictive filters lead to a narrower host breadth and higher host specificity (specialist strategy), while on the right, more permissive filters allow for a broader host breadth and lower specificity (generalist strategy). The final outcome reflects the realized host range, shaped by both intrinsic (e.g., traits, phylogeny) and extrinsic (e.g., environmental and biotic) factors that govern EMF-host associations. ............................ 31 Figure 3. Example of the possible approaches to transforming the associations matrix. Other transformations are used in distinct network approaches but are beyond the scope of this roadmap............................................................................................................................. 80 xii List of Abbreviations AIC: Akaike information criterion. AM: Arbuscular mycorrhizal. AMF: Arbuscular mycorrhizal fungi. ASVs: Amplicon sequence variants. AUC: Area under the curve. BIC: Bayesian information criterion. BRT: Boosted regression trees. C/N: Carbon-to-nitrogen. EM: Ectomycorrhizal. EMF: Ectomycorrhizal fungi. ERM: Ericoid mycorrhizal. FM: Facultative mycorrhizal. GIS: Geographic information systems. GLM: Generalised linear model. GSPFF: Global spectrum of plant form and function. ISA: Indicator species analysis. ITS: Internal transcribed spacer. LES: Leaf economics spectrum. LMM: Linear mixed model. MANE: Mycorrhizal-associated nutrient economy. MAP: Mean annual precipitation. xiii MAT: Mean annual temperature. ML: Machine learning. NM: Non-mycorrhizal. OC: Organic carbon. OM: Obligate mycorrhizal. OTUs: Operational taxonomical units. ORM: Orchid mycorrhizal PCA: Principal component analysis. PCoA: Principal coordinate analysis. RES: Root economics space. RFE: Recursive feature elimination. RTD: Root tissue density. SH: Species hypothesis. SLA: Specific leaf area. SRL: Specific root length. xiv List of Entity Abbreviations BIEN: Botanical Information and Ecology Network. CHELSA: Climatologies at High resolution for the Earthâs Land Surface Areas. ECMWF: European Centre for Medium-Range Weather Forecasts. EOL: Encyclopedia of Life. ERA5: ECMWF Reanalysis v5. FAO: Food and Agriculture Organization. GBIF: Global Biodiversity Information Facility. GCC: Global Compositae Checklist. GNRS: Geographic Name Resolution Service. GRooT: Global Root Trait Database. GSMc: Global Soil Mycobiome consortium. ICN: International Code of Nomenclature for algae, fungi, and plants. IIASA: International Institute for Applied Systems Analysis. ILDIS: International Legume Database and Information Service. IPNI: International Plant Names Index. ISRIC: International Soil Reference and Information Centre. IUCN: International Union for Conservation of Nature. LCVP: Leipzig Catalogue of Vascular Plants. PLANTS: USDAâs Plant Database. NSR: Native Species Resolver. SOTER: Soil and Terrain database. xv SPUN: Society for the Protection of Underground Networks. TNRS: Taxonomic Name Resolution Service. TPL: The Plant List. WCSP: World Checklist of Selected Plant Families. WCVP: World Checklist of Vascular Plants. WFO: World Flora Online. WoSIS: World Soil Information Service. WP: World Plants database. xvi Glossary Association: In the literature, this term is used flexibly as a proxy for mycorrhizal symbiosis. Its specific definition depends on the type and resolution of the data used to infer a symbiosis. Unless otherwise noted (see below), this thesis considers an association as one that has been evidenced by an anatomically defined mycorrhiza (the âgold standardâ evidence). Other definitions can include, in decreasing order of stringency: (i) detection of mycorrhizal fungal DNA in host root samples, (ii) detection of mycorrhizal fungal DNA in soil adjacent to host roots, (iii) detection of mycorrhizal fungal DNA in composite soil samples near hosts, and (iv) the spatial co-occurrence of mycorrhizal fungi (e.g., sporocarps) and host plants within some predefined proximity. In sections pertaining to assembling an âassociations matrixâ or predicting potential associations, this thesis broadens the definition of association to include the detection of mycorrhizal fungal DNA in host root samples. Covariate: Measurable variable related to the dependent variable that can be used to explain or predict it. Here we use it as a synonym for âpredictorâ. Ecological specificity: The influence of biological and environmental factors on the ability of a host plant and mycorrhizal fungus to form a symbiosis in a given environment. False negatives: Cases where the model fails to predict an actual association (e.g., missing a real fungus-host pairing). This is a "Type II error" in binary classification. False positives: Cases where the model incorrectly predicts the presence of an association (e.g., predicting that a fungus and a host are associated when they are not). This is a "Type I error" in binary classification. xvii Feature: Synonym of attribute or characteristic. In this thesis, features can be divided into 4 categories: geographic, environmental, phylogenetic, or morphophysiological. Feature engineering: In network-based methods, feature engineering refers to the process of extracting informative characteristics from the network structure itselfâsuch as node degree, centrality, similarity indices (e.g., Jaccard, cosine), or shared partnersâso that these features can be used as predictors of unobserved or potential links (e.g., associations). These engineered features represent structural patterns that may indicate a higher likelihood of an interaction between nodes (e.g., fungi and hosts) and are often essential for training link prediction algorithms. Fungal community composition: The suite of fungal taxa present in a given sample, host, or defined geographic area. Fungal host breadth: Refers to the range of host species with which a fungal species has been shown to form mycorrhizal symbioses. Fungal host preference: Evidence of associations being observed among a subset of hosts more frequently than expected by an appropriate statistical null model. Fungal host specificity: Refers to the degree to which a mycobiont species is restricted in the taxonomic diversity of its phytobionts. As with plants, mycobionts with high specificity are often called âspecialistsâ while those with low specificity are called âgeneralistsâ. Host fungal breadth: Refers to the range of mycobionts with which a host species has been shown to form mycorrhizal symbioses. Host fungal community composition: The identities of fungal species associated with an individual phytobiont or a phytobiont species. Specific definition depends on the type of evidence used (e.g., anatomically defined mycorrhiza, DNA in roots). xviii Host fungal overlap: The ability of a given pair of phytobionts to share mycobionts or form symbioses with some of the same mycobiont species. Also known as âsharingâ. Host fungal specificity: Refers to the degree to which a phytobiont is restricted in the taxonomic diversity of its mycobionts. As with fungi, phytobionts with high specificity are often called âspecialistsâ while those with low specificity are called âgeneralistsâ. Host mycorrhizal type: A feature of the phytobiont, indicating the mycorrhizal types of fungi with which a host can form a symbiosis. It can be arbuscular (AM), ectomycorrhizal (EM), ericoid, orchid, or non-mycorrhizal (NM). Some hosts may exhibit dual mycorrhizal type (e.g., AM-EM). In-sample predictions: Within a predictive framework, this approach makes predictions only for hosts that exhibit at least 1 recorded association in the associations matrix (the âsampleâ). Model performance: A quantitative assessment of a model's effectiveness in generating accurate and reliable predictions, typically evaluated using metrics such as accuracy, precision, recall, F1-score, and other relevant statistical measures. It reflects the model's ability to generalize to new data and effectively capture underlying patterns. Mycobiont: Used in a mycorrhizal symbiosis context, emphasizing the fungal partner as one half of the symbiosis (the other being the phytobiont). Mycorrhizal compatibility: The ability of a mycobiont and a phytobiont to form an anatomically defined and functional mycorrhiza. If a mycorrhizal association has been demonstrated (even in a single, verified instance), then the two symbionts are deemed compatible. Compatibility is a necessary but not sufficient condition for an association to form (e.g., abiotic conditions must also be favourable). Mycorrhizal compatibility has been formally evaluated for a small fraction of potential fungus-host pairings. xix Mycorrhizal status: A feature of the host, reflecting the degree to which a host plant species requires a mycorrhizal symbiosis to grow and develop. The status of hosts can be obligate mycorrhizal (OM) (always mycorrhizal), facultative mycorrhizal (FM) (occasionally mycorrhizal), or non-mycorrhizal (NM). Mycorrhizal type: Feature of the fungus, indicating the mycorrhizal category to which a fungal species belongs, including arbuscular (AM), ectomycorrhizal (EM), orchid, or ericoid. Out-of-sample predictions: Within a predictive framework, this approach makes predictions for hosts that lack association records in the associations matrix (the âsampleâ). Typically, an associations (response) matrix will only include hosts with at least 1 recorded association, so additional hosts are considered âout-of-sampleâ. Phytobiont: Used in a mycorrhizal symbiosis context, emphasizing the plant partner as one half of the symbiosis (the other being the mycobiont). Potential associations: Associations that have not yet been observed but are possible and require further exploration. Predictive accuracy: A measure of the model's ability to correctly predict potential associations when they are present (true positives) and to correctly predict the absence of associations when none exist (true negatives). For example, if the model correctly predicts that Suillus brevipes forms mycorrhizal associations with Pinus ponderosa (true positive) and correctly predicts that it does not associate with Betula papyrifera (true negative), it contributes to high predictive accuracy. Conversely, if the model incorrectly predicts that Suillus brevipes associates with Betula papyrifera (false positive) or fails to predict its known association with Pinus ponderosa (false negative), predictive accuracy decreases. xx Predictor: In the present context, a synonym of feature and independent variable within a statistical model. Here we group predictors into 5 themes: geographic, phylogenetic, niche, host traits, and fungal traits. Test data: The subset of observations in the associations matrix used to validate the model predictions. Training data: The subset of observations in the associations matrix used to train the models. Trait: Morphological or physiological characteristic of a species. Unrecorded association: An association that has not been observed or confirmed through fieldwork and, thus, is not present in the available data. Defined as a 0 (absence) in the associations matrix. xxi Acknowledgements I would like to express my deepest gratitude to all those who supported me throughout this journey. First, to my supervisor Dr. Jason Pither, for his unwavering support, guidance, and endless patience. I also want to thank my committee members, Melanie Jones, Laura Grieneisen, and Michael Noonan, for their valuable feedback and insightful suggestions that shaped this work. A special thanks to my fellow lab members, who continuously pushed me to keep going and reminded me that suffering together is far more fun than suffering alone. I am truly grateful to my roommates and friends for encouraging me to take breaks, reminding me to eat, and insisting that I leave my house to enjoy some fresh air. To Ana, with whom I got to share ups and downs during this roller coaster ride called âMasterâs degreeâ. Your companionship made the long days much brighter. To my family and Draco, thank you for being a constant source of motivation, happiness, and love. I would never have finished this without your bad jokes, psychological hugs, and positivity. Mom, your wisdom and encouragement have always been a constant source of strength for me. And finally, Abu Mary Paz and Abu Susy, thank you for praying for my success so often that I could almost hear your echoes despite being miles apart. Your love and support mean the world to me. Tía Martha, thank you for believing in me till your last breath. xxii Dedication To my family, my greatest example of strength, love, and perseverance To my past self, because we can and we will1 Chapter 1: Introduction 1.1 General background on ectomycorrhizal fungi Mycorrhiza, derived from the Greek words âmycoâ (fungus) and ârhizaâ (root), describes a symbiotic relationship between terrestrial plants and specialized soil fungi (Freschet et al., 2021). In these mostly mutualistic symbioses, plants provide fungi with photosynthetically derived carbohydrates and lipids, while fungi deliver essential nutrients such as nitrogen (N) and phosphorus (P) and offer pathogen protection to their hosts (Brundrett, 2002; Smith & Read, 2008). These symbioses are fundamental to the structure, productivity, and diversity of plant communities, as evidence shows that plants forming mycorrhizal symbioses typically experience improved growth and survival rates (van der Heijden et al., 2015; Braghiere et al., 2021). Mycorrhizal fungi can be categorized into four main mycorrhizal types1: arbuscular mycorrhizal fungi (AMF), ectomycorrhizal fungi (EMF), orchid mycorrhizal (ORM) fungi, and ericoid mycorrhizal (ERM) fungi. AMF are estimated to associate with 72% of plant species, ORM fungi with 10%, EMF with 2%, and ERM fungi with 1.5% (Brundrett & Tedersoo, 2018). These mycorrhizal types differ in their structural adaptations and nutrient exchange strategies, comprising distinct ecological roles in plant communities and soil nutrient cycling (Phillips et al., 2013). Although most mycorrhizal fungi adhere to one of these categories, some do not fit neatly within a single type. For instance, some EMF also form specialized types of ectomycorrhiza-like associations including arbutoid, pyloroid, or monotropoid mycorrhizae, and even orchid mycorrhizae (Molina & Horton, 2015; Freschet et al., 2021). The same fungal species may shift 1 Italicized phrases are defined in the Glossary. 2 between these mycorrhizal types and ectomycorrhizal (EM) associations or even form multiple types simultaneously depending on the host plant (Smith & Read, 2008). Additionally, EMF and ERM fungi can live inside plant roots as endophytes without forming traditional mycorrhizal structures (Soudzilovskaia et al., 2020). With respect to hosts, host mycorrhizal type varies across vascular plants and is defined based on the mycorrhizal type a plant species associates with: arbuscular mycorrhizal (AM), EM, ERM, ORM, or non-mycorrhizal (NM). Host mycorrhizal status also varies considerably across plant lineages: it is estimated that 85% of plants are obligate mycorrhizal (OM), 7% are facultative mycorrhizal (FM), and 8% are NM (Brundrett & Tedersoo, 2018). The FM status is known only among AM hosts, with inconsistent NMâAM associations, indicating they can survive without mycobionts when necessary (Moora, 2014). Ectomycorrhizal fungi exhibit remarkable diversity, encompassing approximately 20,000 species across three major phyla: Ascomycota, Basidiomycota, and Zygomycota (Freschet et al., 2021). Compared to AMF, EMF form associations with a more limited range of plant taxa but are essential for the establishment and survival of shrubs and trees in temperate, boreal, and certain montane or tropical ecosystems (Soudzilovskaia et al., 2017). Their ability to access nitrogen from organic matter in nutrient-poor soils enables phytobionts to persist in environments where AMF associations are less favorable (Braghiere et al., 2021). In addition to facilitating plant survival, these fungi play a fundamental role in ecosystem processes such as nutrient cycling and carbon sequestration, contributing up to 39% of microbial biomass and 10â35% of respiration in boreal forest soils (Smith & Read, 2008; Högberg et al., 2010; Hawkins et al., 2023). The symbiotic structures formed by EMF include a Hartig net of hyphae enveloping the root cortical cells and a hyphal sheath, or mantle, covering the root tipsâboth of which are 3 characteristic features used to identify EM associations. Nutrient exchange occurs in fine roots, where fungal hyphae absorb essential minerals from the soil in return for carbon compounds supplied by the phytobiont (Smith & Read, 2008). Up to 80% of a plantâs N and P requirements can be fulfilled through these partnerships, which are often critical for host growth and survival (van der Heijden et al., 2015). By supporting tree species and enhancing forest productivity, EM symbioses contribute significantly to ecosystem functioning at a broad scale (Guo et al., 2008). 1.2 Ectomycorrhizal associations 1.2.1 Evolutionary history and biogeography The evolutionary history of EMF is deeply entwined with that of their host plant taxa, particularly tree taxa like the Pinaceae, which have been associated with EMF since the Late Jurassic, around 163â145 million years ago (LePage, 2003). Phylogenetic analyses suggest that key EMF genera and families likely originated during the Early Cretaceous, around 150â99 million years ago (Berbee & Taylor, 2010), evolving from saprotrophic fungi (Hibbett et al., 2000). This evolutionary shift toward mycorrhizal partnerships would have occurred after the emergence of these saprotrophic fungal genera and families, coinciding with the significant radiation of the Pinaceae taxa in the mid-Jurassic to Early Cretaceous, approximately 175â99 million years ago (Lin et al., 2010; Pickles et al., 2012). Moreover, certain plant families appear to foster richer EMF assemblages, reinforcing the co-evolutionary links between these fungi and their hosts (Brundrett & Tedersoo, 2018). There are an estimated 6,000 plant species that form EM associations, distributed across 145 genera and 26 families, with the majority being trees or shrubs (Brundrett, 2009). For the majority of EM hosts and fungi, the relationship seems to be largely obligatory. For instance, pines 4 appear unable to establish in non-native environments unless suitable EMF are present (Hatch, 1936). Based on the available evidence, EMF diversity is highest in boreal and temperate ecosystems, and lower in the tropics (Tedersoo et al., 2014; Abrego et al., 2024). However, this trend may be influenced by a sampling bias towards extra-tropical areas (Corrales et al., 2018). The observed increase in diversity towards the poles is likely linked to climatic conditions, with EMF thriving in nutrient-poor, acidic soils typical of colder and wetter climates (Soudzilovskaia et al., 2017). 1.2.2 Fungal host breadth, specificity, and preference Here, we define fungal host breadth as the range of plant species with which a fungus has been shown to form an association (c.f., Molina & Horton, 2015). This definition makes clear that a fungal taxonâs host breadth is based on observed associations, and thus represents its realized host breadth. The fundamental fungal host breadth is unknowable because it would be infeasible to fully quantify. Fungal host specificity describes the taxonomic diversity of host plants within a fungus's host breadth, ranging from highly specialized fungi that associate with specific plant taxa to generalists capable of colonizing a diversity of plant lineages (Dickie et al., 2013; Molina & Horton, 2015). For instance, based on the available evidence, Suillus species exhibit high specificity, forming associations restricted to particular plant genera (Bruns et al., 2002; Tedersoo & Smith, 2013), whereas generalist taxa like Cenococcum geophilum and Tomentella spp. associate with a wide range of hosts across multiple plant families (Tedersoo et al., 2010; Molina & Horton, 2015). However, demonstrating absolute host specificity in field studies is challenging, as environmental factors and fungal dispersal can obscure host-fungus relationships (Dickie, 5 2007). Consequently, the terms "specialist" and "generalist" have been widely used despite a lack of precise, quantitative thresholds, limiting comparability across studies (see dâEntremont & Kivlin, 2023). To address this, researchers such as Tedersoo et al. (2024) and WeiÃbecker et al. (2019) have attempted to quantify relative host specificity using indicator species analysis (ISA), a statistical approach that accounts for the non-random distribution of fungal taxa across host plants available in a defined region or context. The term ârelativeâ is crucial here, as the outcomes of ISAs necessarily depend on the breadth of host species and EMF being evaluated (c.f., Swenson et al., 2006). Additionally, the extent of host specificity appears to be shaped by a combination of fungal evolutionary history, host phylogeny, and environmental factors, as demonstrated in large-scale analyses of EMF communities (Sato et al., 2012; Põlme et al., 2013; van der Linde et al., 2018). A related but distinct concept is fungal host preference, which refers to the tendency of a fungus to associate with certain hosts more frequently than expected by chance, given the total range of hosts it is known to be compatible with (Molina & Horton, 2015). Host preference is strongly linked to ecological specificityâthe influence of environmental and biological factors on whether an association successfully establishes in nature. Despite advances in understanding fungal host associations, the mechanisms underlying host preference remain largely unresolved (Dickie & Moyersoen, 2008). While often shaped by similar factors, host specificity and host preference are distinct and should not be used interchangeably. More broadly, fungal host specificity, preference, and breadth may arise from overlapping biochemical, environmental, and evolutionary influences (El Amrani, 2024; Sato et al., 2024), and are typically not conserved beyond the level of fungal species (Tedersoo et al., 2024). 6 Analyses of fungal genomes and EM symbioses at a molecular level suggest that host breadth can be influenced by gene loss or acquisition related to nutrient acquisition (Hedh et al., 2009). However, only a limited number of ectomycorrhizal associations have been investigated at the molecular level, such as Laccaria bicolor with poplars and Hebeloma cylindrosporum with pines (Martin et al., 2016). The molecular processes governing the structural, anatomical, and physiological aspects of these symbioses remain largely unexplored. Beyond genetic influences, additional factors like environmental filtering may further shape EM associations, as soil properties, climate, and competitive interactions influence fungal distributions (El Amrani, 2024; Meng et al., 2023; van der Linde et al., 2018). Notably, a large-scale analysis by Sato et al. (2024) indicated that the distribution of compatible hosts remains one of the most significant drivers of EMF biogeography. 1.2.3 Ecological impacts of EM associations Roots of a single plant species can host multiple fungal species, with each species contributing differently to plant nutrition, pest resistance, and competitive interactions between plants. For example, distinct plantâfungal combinations may confer varying advantages, influencing competitive dynamics among coexisting plant species (van der Heijden et al., 2015). The ecological effects of fungal associations are further influenced by the high variability in their extraradical mycelium, which extends beyond the root into the soil (Smith & Read, 2008). This mycelium exhibits species-specific differences and directly influences nutrient cycling, carbon dynamics, and soil structure. Thus, different EMF species differ in their impacts on host performance and biogeochemical processes (Freschet et al., 2021). 7 Environmental stressors such as salinity, drought, and pollution can alter EM associations (van der Linde et al., 2018). Under stress, plants may shift to more stress-tolerant fungi. For example, the host Quercus mongolica in saline conditions may shift toward salinity-tolerant EMF such as Gomphidius viscidus, enhancing survival in salt-stressed soils (Bai et al., 2021). Similarly, Pinus species under drought conditions may form associations with Geopora pinyonesis, which aids in water and nutrient uptake, replacing EMF partners that are less drought-adapted (Gehring et al., 2017). Pollution, particularly heavy metals, may also influence EM associations: Betula hosts in contaminated areas formed symbioses with EMF such as Suillus luteus, which helps detoxify heavy metals like cadmium and lead (Chot & Reddy, 2022). These shifts highlight the role of environmental factors and fungal-host genotype interactions in shaping EMF-host associations (Courty et al., 2011; El Amrani, 2024). Climate change is increasingly impacting ecosystems, potentially disrupting symbioses between trees and their EMF partners. Comparatively little is known about how the structure and function of fungal communities are impacted by global change and anthropogenic pressures (Bazzicalupo et al., 2022). It is therefore difficult to predict how ecosystemsâand the processes that fungi underlieâwill change. Fungi appear particularly sensitive to climate change (Andrew et al., 2018) and atmospheric pollution, including nitrogen deposition (Allen & Allen, 2017). Changes in temperature and moisture availability can alter fungal fruiting body production and phenology, potentially leading to declines in EMF diversity and shifts in ecosystem functioning (Pickles et al., 2012). Additionally, anthropogenic disturbances, including land-use changes and climate-induced shifts, may further alter these patterns, affecting the distribution and abundance of compatible fungal partners for trees and other plants (Moeller et al., 2015). The complex interactions between climate, soil conditions, and EMF highlight the need for further research to 8 predict the resilience of EM symbioses and their effects on global biogeochemical cycles under future environmental conditions (Pickles et al., 2012). 1.3 Barriers to understanding mycorrhizal associations 1.3.1 Identification of host mycorrhizal type It is estimated that only around 5% of extant vascular plant species have had their mycorrhizal type examined (Brundrett, 2009). Because of this, the geographical distribution of mycorrhizal typesâincluding EMFâremains poorly understood, particularly in tropical regions. Nevertheless, substantial efforts to elucidate global distribution of mycorrhizal traits among studied plant species have been made (Meng et al., 2023). Records of known EM associations show bias towards fungal and plant species from temperate and boreal forests in North America and northern Europe, and are also unequally distributed among plant lineages (Soudzilovskaia et al., 2020). The mycorrhizal type of plant taxa is generally conserved among families or genera (Lofgren et al., 2018). Some hosts exhibit a dual mycorrhizal type (e.g., AM-EM or NM-AM), though records regarding dual types appear error-prone. Approximately 89 genera across 32 families have been reported as having an AM-EM dual mycorrhizal type (Teste et al., 2020). Brundrett (2009) found that published lists of mycorrhizal host plants had a ~10% apparent error rate in their mycorrhizal type determination. With alternative interpretations of mycorrhizae or mycorrhizal types in the literature (Brundrett, 2017), the lack of consensus has also generated an accumulation of errors propagated through the literature and data sources of mycorrhizal records (Brundrett & Tedersoo, 2019; Bueno et al., 2019). 9 1.3.2 Quantification of EMF diversity Despite the importance of EMF to forest ecosystems worldwide, substantial gaps remain in our understanding of their diversity, distribution, host breadth, and specificity. A primary reason for this is that sampling mycorrhizas is logistically demanding, particularly at greater soil depths and in substrates that are difficult to access (Taylor et al., 2014). Thus, the vast majority of mycorrhizal studies sample the top 0-15 cm of soil and roots, even though mycorrhizal symbioses can also occur far deeper in the soil (Pickles & Pither, 2014). Studies investigating EMF communities across soil horizons have identified distinct assemblages at varying depths (Rosling et al., 2003; Scattolin et al., 2008), highlighting the need for sampling beyond the organic horizon. To quantify EMF diversity, researchers employ various identification methods, including root-tip morphotype analysis and sequencing of the internal transcribed spacer (ITS) region (Tedersoo et al., 2010). Common sampling strategies include (i) root tip sampling, which directly examines colonized roots to identify mycobionts (e.g., Janowski & Leski, 2023); (ii) soil sampling for DNA analysis, which detects root-associated and free-living fungal structures, including resting stages such as spores and sclerotia, using high-throughput sequencing (e.g., Taylor, 2002); and (iii) sporocarp surveys, which identify fruiting bodies (e.g., Richard et al., 2005). However, sporocarp surveys often underestimate the diversity of resupinate taxa, hypogeous fungi, and species without apparent sexual stages, such as Cenococcum geophilum (Horton & Bruns, 2001; Richard et al., 2005). These methodological limitations make it challenging to accurately characterize EMF diversity and abundance belowground, posing a significant obstacle to quantifying their impacts on ecosystem processes (Eagar et al., 2023). However, efforts are underway to improve fungal sampling coverage globally using consistent methodologies, such as 10 those by the Society for the Protection of Underground Networks (SPUN, www.spun.earth) and the Global Soil Mycobiome consortium (GSMc, gsmc-fungi.github.io/). 1.3.3 Gaps in EMF-host associations data Despite growing interest in mycorrhizal ecology, our understanding of EMF-host associations remains limited, largely due to the logistical and financial demands of extensive mycorrhizal sampling (Sánchez-Ramírez et al., 2017; Smith & Read, 2008). Even for well-studied host taxa like Quercus and Pinus, many associated fungal taxa are known from only a single record, highlighting the need for broader geographic and taxonomic coverage (Põlme et al., 2013). The currently known associations mainly come from primary studies focusing on a few host species or a particular host species, genus or family and their fungal partners (e.g., Dickie & Reich, 2005; Smith et al., 2011; Hoeksema et al., 2018; Birch et al., 2021; Fruleux et al., 2022). Thus, reliable host association data are often unavailable for many mycorrhizal fungal species (Sánchez-Ramírez et al., 2017). Most EMF tend to associate with multiple host species across genera (Tedersoo et al., 2024). Similarly, mycorrhizal plants associate with multiple fungal species simultaneously, and the intensity of colonization by each fungal species can vary depending on the plantâs developmental stage and ecological factors (Soudzilovskaia et al., 2020; van Galen et al., 2023). This variation is often not quantified, biasing against detection of EMF species with lower colonization intensities. Inconsistent sampling intensity and methodology represent another source of uncertainty in detecting and accurately assessing EMF diversity and associations (Terry & Lewis, 2020). 11 Databases such as UNITE (Nilsson et al., 2019) provide valuable repositories of observed mycorrhizal associations, but include only a fraction of the total estimated EMF diversity and are often biased toward fungi associated with economically significant host taxa. These limitations underscore the need for standardized methodologies and strategic sampling efforts to flesh out the full extent of EMF diversity and EM associations. 1.4 Predicting potential ectomycorrhizal associations Identifying the factors that shape EMF-host associations remains a major challenge in fungal ecology. As noted in the preceding sections, knowledge gaps persist due to the logistical difficulties of sampling mycorrhizal associations across broad geographic and taxonomic scales (Sánchez-Ramírez et al., 2017). Thus, our knowledge of which EMF associate with which hosts (EMF-host pairings) remains limited. To address this knowledge gap, we need methods to predict potential associations, associations that have not yet been observed but are probable and warrant further exploration. In other realms of ecology, methods for predicting potential symbiotic associations have proven highly effective, even when working with datasets as sparse as those available for EMF. Methodologies from disease ecology that are used to predict potential associations between animal hosts and parasites offer promising frameworks for addressing the critical knowledge gap regarding EMF-host associations. Network- and trait-based models have been employed with great success to predict parasite-host interactions and potential parasite spillovers (associating with new host taxa), and to help focus sampling efforts (Dallas et al., 2017a; Stephens et al., 2019; Becker et al., 2022). Trait-based models, in particular, leverage host characteristics such as phylogeny, 12 biogeographical overlap, and ecological traits to predict potential associations, often with high accuracy (Farrell et al., 2022). To date, such predictive modeling approaches have not been applied to mycorrhizal fungi. Doing so could help identify likely but as yet undocumented EMF-host associations, which in turn could inform more strategic sampling efforts, and ultimately improve our understanding of EMF biodiversity and ecology. This thesis aims to develop a roadmap for predicting potential EMF-host associations, drawing inspiration from methods in other fields. 1.5 Research objectives The primary goal of this thesis is to provide a roadmap for researchers seeking to predict potential associations between EMF and their plant hosts. This aim is guided by the following three key objectives: (1) To provide an overview of variables that, based on a review of the literature, are relevant to predicting potential EM associations. o In addressing this objective, I categorize predictor variables into themes. I then assess the potential importance of these variables based on previous studies and empirical evidence. Lastly, I highlight gaps in current knowledge and discuss how missing or underexplored variables might impact predictive modeling. (2) For each of the variables described in Objective (1), identify sources of relevant data, and critically evaluate their strengths and limitations in relation to predicting potential EM associations. 13 o For this objective, I compile a list of available global databases and resources for each predictor theme; I evaluate data completeness and biases, pointing out data coverage and gaps; and I assess the applicability of existing data sources for large-scale versus fine-scale predictions of EM associations. (3) To identify and recommend predictive modeling approachesâprimarily from disease ecologyâthat appear both commonly used and accessible for application to EMF-host associations. o In this objective, I draw on a non-systematic review of the literature to explore methods used in disease ecology to predict parasite-host associations. Rather than conducting a comprehensive methodological review, I focused on approaches that are both frequently applied and anticipated to be accessible, including to mycorrhizal ecology researchers. I then evaluated the feasibility of adapting these methods to the EMF context, considering biological context and common data limitations. Based on this assessment, I recommend methods that are especially well-suited to predicting potential EMF-host associations under real-world ecological data constraints. 14 Chapter 2: Roadmap 2.1 Background Understanding what governs the diversity and composition of ecological communities is a fundamental goal in ecology. In the past century, knowledge of the drivers of terrestrial plant community diversity and composition has improved substantially (Cadotte, 2023). In contrast, the communities inhabiting the soil below the plants are considerably less well understood (Richard et al., 2005; Tedersoo et al., 2012). Although significant advancements in sequencing technologies have dramatically improved our ability to detect and identify soil organisms (Mikryukov et al., 2023), our understanding of the processes influencing their distribution and diversity remains limited. Mycorrhizas have been defined as symbiotic associations between terrestrial plants and specialized soil fungi, mostly involving a beneficial bidirectional exchange of nutrients and other services (Brundrett, 2002). Associating with the roots of their phytobionts, mycobionts can provide nitrogen, phosphorus, and varying degrees of pathogen protection to their phytobiont, which in turn provides photosynthetically derived carbon to the mycobiont (Smith & Read, 2008). Consequently, mycorrhizal fungi play an important role in the structure, productivity, and diversity of plant communities through their influence on their hostâs growth and survival (van der Heijden et al., 2015; Braghiere et al., 2021). The two most common types of mycorrhizal fungi are arbuscular mycorrhizal fungi (AMF) and ectomycorrhizal fungi (EMF). AMF are estimated to associate with ~80% of land plant taxa, especially herbaceous species, and are thus the most common mycorrhizal type globally (Brundrett, 2009). These fungi can be found in a very wide variety of ecosystems (Smith & Read, 15 2008; Soudzilovskaia et al., 2017), including grasslands and forests. Despite their broad distribution, AMF are thought to exhibit low diversity globally, with an estimated 400 to 1600 âspeciesâ or Operational Taxonomical Units (OTUs) (van der Heijden et al., 2015). EMF are much more diverse globally, comprising an estimated 20,000 species from 3 different divisions (Ascomycota, Basidiomycota, and Zygomycota) (Rinaldi et al., 2008). EMF exhibit high phylogenetic diversity, due in part to the EM type having repeatedly evolved independently around 78 times (Tedersoo & Smith, 2013). They are estimated to associate with 2-3% of plant taxa (van der Heijden et al., 2015)âpredominantly woody speciesâand are common in temperate and boreal forest ecosystems, but also in some montane and tropical or subtropical regions (Smith & Read, 2008; Soudzilovskaia et al., 2017). Being able to utilize nitrogen from soil organic matter, EMF allow their plant hosts to thrive in regions with lower soil fertility, unlike AMF, which tend to function as scavengers for soil nutrients (Braghiere et al., 2021; Chapman et al., 2006). Given that mycorrhizal fungi are obligate symbionts (Brundrett, 2002), a comprehensive understanding of the ecology and biogeography of mycorrhizal fungi requires knowledge of the host breadths of mycorrhizal fungi. However, very little is known about the factors governing fungal host breadth. This is partly because obtaining evidence of an association is extremely laborious in the field and is often not possible experimentally (Sánchez-Ramírez et al., 2017). As such, patterns in fungal-host associations and the biotic and abiotic factors influencing them are understudied (Molina & Horton, 2015). Here we focus on symbioses between plants and EMF, which, unlike the predominantly host-generalist AMF (Sánchez-Ramírez et al., 2017), exhibit a range of fungal host breadths, from generalist to specialist. For example, Cenococcum geophilum is known for its cosmopolitan distribution and large host breadth, while Rhizopogon and Suillus species appear to associate 16 almost exclusively with members of the Pinaceae family, especially from the Pinus and Pseudotsuga genera (Molina & Horton, 2015). Except for a few well-studied EMF lineages, host breadth is poorly characterized for the vast majority of EMF taxa (Tedersoo & Brundrett, 2017). Some notable exceptions include commercially important edible fungi or macrofungi in genera like Lactarius and Russula, but also highly invasive or widespread fungi like Amanita phalloides or Cenococcum geophilum (Sato et al., 2012; Lofgren et al., 2018). Equally relevant to improving our understanding of the ecology and biogeography of mycorrhizal fungi is knowledge about the fungal taxa that a host can associate with, also known as the host fungal breadth. For example, studies by Bruns et al. (2002) and Põlme et al. (2013) have focused on host plant taxa like Alnus, which exhibit high host fungal specificity. Conversely, plant taxa with lower fungal specificity, such as Pseudotsuga menziesii and Pinus, have been extensively studied and are known to form associations with hundreds or even thousands of fungal species (Molina & Horton, 2015; Tedersoo & Brundrett, 2017). Thus, examples like Alnus and Pinus appear to anchor the specialist and generalist ends of the fungal breadth continuum, respectively. It is important to note, however, that this host-focused research generally has limited capacity to inform about the fungal host breadths of the identified EMF. For example, although there is high confidence that Alnus exhibits a comparatively narrow fungal breadth, there is less confidence in estimates of the host breadths of the implicated EMF. As indicated by Põlme et al. (2013), many of the EMF taxa associated with Alnus have been recorded only once, which may reflect limited sampling rather than narrow host breadth. Fungal host breadth and host fungal breadth are hypothesized to be governed by a complex interplay of biotic and abiotic factors, as well as evolutionary and ecological processes acting across spatial and temporal scales (Tedersoo et al., 2012; van der Heijden et al., 2015). Molina and 17 Horton (2015) suggest that the specificity of mycorrhizal associations is primarily driven by features of both hosts and fungi, including, for example, phylogenetic relatedness. Genetic variation and evolutionary pressures to maintain or switch hosts may be significant in shaping fungal-host specificity (Tedersoo et al., 2012, 2024). However, broad-scale variation in climate, soil characteristics, and vegetation composition have been shown to influence the distributions and diversity of mycorrhizal fungi (Davison et al., 2021; Sato et al., 2012; van der Linde et al., 2018; VÄtrovský et al., 2019), and likely determines the regional pool of fungal taxa available for establishing associations. Locally, environmental factors such as soil composition, pH, moisture, and neighboring plant species, can influence the establishment of EM associations (Põlme et al., 2013; Molina & Horton, 2015). Although new EMF-host associations are being documented regularly (e.g., Lofgren et al., 2018; Soudzilovskaia et al., 2020), the pace of discovery remains slow, due in part to the logistical demands of sampling and identifying mycorrhizae(Smith & Read, 2008). Moreover, discoveries of novel EMF-host associations tend to be mostly opportunistic, rather than arising from strategic sampling efforts. The current total number of known EM associations represents a small and biased fraction of the total number of potential associations. This knowledge gap is a major impediment to obtaining a more thorough understanding of what governs the distribution, diversity, and functioning of EMF, from local to global scales (Rinaldi et al., 2008). Recently initiated projects that apply coordinated and standardized soil DNA sampling techniques (e.g., GSMc, GlobalFungi, SPUN) represent important advances, but are generally not designed to provide strong evidence of mycorrhizal associations. Given the substantial data limitations, we need new approaches for accurately predicting which potential associations are most probable between EMF taxa and hosts plants. 18 In recent years, a variety of methods have been developed to predict the probability of associations between organisms (Strydom et al., 2021; Becker et al., 2022). Especially relevant to the task at hand are methods developed in disease ecology to predict potential associations between animal hosts and parasites, and to predict the probability of spillover from known hosts to other hosts (Dallas et al., 2017a; Stephens et al., 2019). The logistical constraints associated with documenting the presence of an active or previous infection in wildlife have made narrowing the set of plausible sampling targets a priority for disease ecologists (Becker et al., 2022). This has clear parallels to the methodological challenges faced by mycorrhizal ecologists. In general, methods used to predict potential associations require sufficient data about known associations in order to âtrainâ models (Strydom et al., 2021). Association data are conventionally represented in a binary association matrix, with host taxa as rows and parasite (or fungal) taxa as columns. Each cell contains a 1 (indicating a known association) or a 0 (a presumed or confirmed absence of an association). As described in a later section, these data may be represented in different formats for the purpose of analyses. For predictors, models typically include variables or features describing properties of the hosts, including their similarity to other known host species with respect to geographic and environmental distributions, phylogenetic relatedness, and morphological or functional traits. These features have shown varying degrees of predictive power depending on the parasite and host taxa (Dallas et al., 2017b; Stephens et al., 2019). Example hypotheses for why these features have predictive value include that co-occurring species or species exhibiting similar realized environmental niches might have the same exposure to similar parasite species, or that closely related host species may be compatible with similar parasite species due to similar genetics or life histories (Farrell et al., 2022). Notably, most methods 19 exclusively leverage host features to predict the probability of an association because (i) host features have been demonstrated to be associated with parasite diversity and community similarity (Locke et al., 2014), and (ii) data about host features are more widely available compared to data about parasite features. As an example, Becker et al. (2022) compared the accuracy of various models trained with more than 60 host-related variables, including ecological traits, geographic range, and phylogenetic distance. With an associations matrix of 359 parasites by 710 mammalian hosts and a 1.1% fill of associations (cells containing known associations), the models explained up to 69% of the variation in host-parasite associations. Despite the limited data on associations and the exclusive use of host features as predictors, model validation showed a success rate between 77% and 100%. That is, among the hosts identified by the model as likely to harbor certain parasites, despite having no prior recorded associations, a high proportion were subsequently confirmed as true hosts. These validations were based on independent records, confirming that the modelâs predictions were often biologically accurate. Despite the demonstrated success of methods for predicting host-parasite associations, we are unaware of any efforts to apply those methods to predicting potential associations between mycorrhizal fungi and their plant hosts. We acknowledge that, given the enormous species diversity of both plant hosts and EMF, the size of the potential mycorrhizal association matrix is prohibitively large (~1.2 Ã 108 cells). However, in practice, matrix dimensions can be substantially reduced by focusing on a specific geographic region and its constituent plant and EMF taxa or by using coarser taxonomic resolution for either the hosts or EMF. The potential benefits that could come from such predictive models include (i) providing new insights into the factors that govern 20 fungal-host associations, and (ii) providing predictions that can guide future sampling efforts, which historically have been rather haphazard or biased taxonomically and geographically. Here, we present a roadmap for predicting potential EMF-host associations, and in doing so address three objectives. First, we provide an overview of variables relevant to predicting potential EM associations, and categorize them into thematic groups. We assess each variableâs potential importance for predicting potential associations, and identify knowledge gaps that may affect their utility. Second, we identify and evaluate data sources for each predictor variable, examine data completeness and biases, and in light of these, consider the variablesâ suitability for large- and fine-scale predictions. Finally, we review predictive modeling approaches from other ecological disciplines, particularly disease ecology, to assess their applicability to predicting potential EMF-host associations. After considering data limitations, we recommend methods best suited for advancing prediction efforts. To help frame the roadmap, we follow a structured approach outlined in Figure 1, which consists of three key steps. Step 1 involves assembling the response variable, which in this case is the binary associations matrix. Step 2 focuses on determining predictor variables, categorized into five key themes. Finally, Step 3 entails selecting an appropriate method for predicting potential EMF-host associations. This framework provides a clear and systematic approach to modeling EMF-host associations. 21 Figure 1. Schematic overview of the roadmap structure, outlining the key steps for predicting potential EMF-host associations. 2.2 Step 1: Assemble the response variable 2.2.1 Associations matrix Conventionally, studies from the disease ecology literature convey pair-wise interaction data using an associations matrix, as it provides an intuitive and standardized way to represent parasite-host (or EMF-host) relationships. This matrix serves as a common starting point for downstream data manipulation and is typically structured as an ð ð¥ ð binary matrix, where ð represents the number of host taxa and ð the number of parasite (or fungal) taxa. Each cell contains either a 1 (indicating a known association) or a 0 (indicating no recorded association), making it a useful framework for organizing and analyzing pair-wise interaction data (Strydom et al., 2021). Zeroes can either indicate a known absence of an association, where studies have explicitly tested for but failed to detect a given fungal-host relationship, or a presumed absence, which arises when an association has not been documented but may still exist due to incomplete sampling, taxonomic 22 uncertainties, or environmental constraints that prevent detection. Distinguishing between true and presumed absences is critical, as misclassification can introduce biases in predictive models, potentially underestimating or overestimating fungal host breadth (Dallas et al., 2017b; Strydom et al., 2021). In subsequent sections, we describe how these data structure can be reformatted for different analytical purposes. 2.2.2 Mycorrhizal associations data Following the definition from Molina and Horton (2015), mycorrhizal compatibility refers to the ability of a fungus and a plant to form an anatomically defined and functional mycorrhiza. If such a mycorrhizal association has been demonstratedâeven in a single, verified instanceâthen the two symbionts are considered compatible. Compatibility is therefore a necessary, but not sufficient, condition for an association to form, as environmental and ecological conditions may also be influential. The factors affecting compatibility are poorly understood (Lofgren et al., 2018). However, host lineages that, for example, have been confidently determined to be non-mycorrhizal or solely AM are, in practice, often considered incompatible with EMF (Moora, 2014; Brundrett & Tedersoo, 2018). Hence, many studies emphasize the distribution and abundance of compatible hosts as a key factor governing the diversity and distribution of EMF (Soudzilovskaia et al., 2015; Davison et al., 2021; El Amrani, 2024). Here, we follow the convention in the literature and use the term âassociationâ as a flexible proxy for âmycorrhizal symbiosisâ. Its specific meaning depends on the resolution and type of data available to infer a symbiosis. Unless otherwise noted, we henceforth broaden the definition of association to include cases in which mycorrhizal fungal DNA has been detected within host root samples. Advances in sequencing technologies have made this type of molecular evidence 23 more accessible, allowing researchers to infer potential associations even when anatomical confirmation is lacking. However, acquiring root DNA data remains resource-intensive and introduces some uncertainty, as it may capture fungal taxa not actively forming mycorrhizas with the hostâsuch as endophytes, saprotrophs, or transient EMF (Sánchez-Ramírez et al., 2017). Given the scarcity of anatomically defined symbiosis data, the use of root DNA as a proxy for EM association is widely accepted and often necessary. Importantly, the methods described here do not seek to predict mycorrhizal compatibility per se, but rather âunobserved potential realized associationsâ. The specific meaning of a predicted association depends on the data used to train the model. Within our predictive framework, we consider a pair of symbionts to be potentially associated when the model predicts a high probability of an association. In this context, a predicted â1â in the matrix implies that we would expect to detect the fungal DNA in that hostâs roots if further sampling were conducted. While some studies, such as Dallas et al. (2017c), classify predicted probabilities above 0.5 as potential associations (and thus, ones), using a higher threshold can increase confidence in predictions but may also increase rates of false negatives. 2.2.3 Data sources Among available sources of association data, the UNITE database (Nilsson et al., 2019) stands out as the most promising for large-scale modeling efforts, providing high-resolution molecular evidence of association, specifically the detection of fungal DNA in host root samples. While this represents the second-strongest tier of evidence after anatomically defined mycorrhizas, UNITE offers broader taxonomic and geographic coverage than most anatomical listings, which often lack accompanying DNA sequence data and therefore cannot be incorporated into UNITE. 24 This web-based platform provides comprehensive access to taxonomic, geographical, and ecological metadata for fungal molecular data while maintaining high-quality standards through regular exchanges with other databases. Despite these strengths, the volume of associations data in UNITE remains remarkably limited. As of May 2024, the database included records for approximately 768 EMF taxa (345 species) with at least one documented interaction involving one of the 273 host plant taxa (91 species) listed in its âinteracting taxaâ field. This represents only ~1.73% of the estimated global EMF diversity and ~1.52% of the total extant EMF host plants, based on the global estimates by van der Heijden et al. (2015). The database also exhibits significant biases. Among the ~20,400 georeferenced fungal sequence records, only 63 countries are represented, with over 50% of these records originating from North America and northern Europe. Additionally, the data are disproportionately skewed toward economically significant host taxa such as Pinus, Quercus, and Picea. Another potential source of association data is GenBank, which is one of the largest repositories of genetic sequences, including those of putative EMF taxa. While GenBank includes records that are also curated in UNITE, not all GenBank entries meet the same curation standards as those in UNITE. Some GenBank sequences include annotations on host plants, but these associations have not undergone formal curation, and details on sampling methods, isolation source, or association resolution are often missing (Sánchez-Ramírez et al., 2017). Thus, data from GenBank should be used with caution. Primary research studies utilizing DNA sequencing to detect and identify EMF are strongly encouraged to submit their sequencing data to databases such as UNITE, GenBank, or similar sources. However, this guideline is not consistently followed, making the primary research literature an additional but often underutilized source of association data (Foo et al., 2021). 25 Extracting and compiling such data manually can be highly labor-intensive, but emerging AI-assisted tools offer promising solutions for streamlining this process (e.g., Gougherty & Clipp, 2024). 2.2.4 Limitations & Recommendations The availability of high-resolution association data is extremely limited, introducing biases and uncertainties that reduce the reliability of the response matrix and, consequently, model predictions. Given these constraints, it may be necessary to use coarser-resolution data to populate the response matrix. Approaches to this include (i) using fungal taxa identified via metabarcoding DNA from soil samples and assuming those taxa could associate with nearby host trees (Mikryukov et al., 2023; Tedersoo et al., 2024), or (ii) leveraging occurrence records of EMF and host taxa, considering any records within a certain distance of each other as an association (Sugiyama & Sato, 2024). However, if such associations are to be used in the training data, it is important to emphasize that these sampling strategies are even less diagnostic of potential associations than mycorrhizal fungal DNA detected in host roots (Tedersoo et al., 2024). This approach will help fill the associations matrix, but it will come at the cost of inflating the number of false positives, and decreasing the accuracy of predictions. Primary research studies on EMF and their hosts are ultimately the sources of the data that populate databases such as UNITE and GenBank. It is therefore important to consider those studiesâ attributes when evaluating the utility of the resulting associations data. Studies that combine large geographic extents with limited sampling density (e.g., Tedersoo et al., 2012; Soudzilovskaia et al., 2015; VÄtrovský et al., 2019) may miss regional variations in fungal-host associations, which can shift across geographic gradients due to differences in host specificity or 26 preference (Wolfe & Pringle, 2012). For instance, widely distributed EMF may predominantly associate with one host group in Europe but a different host group in North America, even though both host groups are present in both regions. While such studies provide valuable insights into overall host breadth, they often fail to capture finer, regional patterns of host preference and specificity, introducing potential region-specific inaccuracies in the associations matrix. Moreover, while compatibility is necessary for an association, it is not sufficient for the formation of associations in natural settings, making these generalizations inherently uncertain. Studies using lower-resolution data further exacerbate these issues, as they tend to ignore geographic patterns of EMF-host associations and rely on broader assumptions, which likely increase error rates in predictions. While our primary focus is on predicting potential associations at the species (or species hypothesis, SH) level for both symbionts, predictions can also be made at higher taxonomic levels, such as genus or family, and may involve different taxonomic resolutions for hosts and fungi (e.g., species for hosts and genus for EMF). Although some fungal features are conserved at the genus level, like most functional traits and some patterns of host specificity (Põlme et al., 2020), fungal host breadth and preference appear to not be conserved above the species level (Tedersoo et al., 2024). Similarly, some studies aiming to identify variables that constrain fungal distribution have shown that analyses conducted at the species or SHs level explain more variation than those conducted at the family or genus level, obtaining more significant and better predictions (VÄtrovský et al., 2019). Thus, the taxonomic level also affects the resolution of the associations data and the predictive power for unrecorded ones. The associations matrices derived from higher-resolution data, such as those from the UNITE database, are small compared to the vast number of potential mycorrhizal associations 27 worldwide. Reducing the scope of predictions to regional scales could help mitigate noise and false negatives, especially given that the majority of available data are concentrated in northern Europe and North America. Thus, both regions represent promising case studies for refining mycorrhizal association predictions. Nevertheless, even if we were to limit the geographic scope to Canada and the conterminous USA (plus Alaska), the data in UNITE remain extremely limited. Of the estimated 878 tree species native to this region (Carrero et al., 2023), 265 (30%) are thought to be EM (using genus-level type assignments from FungalRoot, Soudzilovskaia et al., 2020). Of these, only 12% appear within the association metadata in UNITE, and half of these species belong to the Pinus or Quercus genera. This alarmingly small percentage shows us how few data our current databases hold regarding mycorrhizal associations. It is also important to reiterate that an unrecorded association (zero) in the matrix does not rule out the possibility of an association. Thus, false negatives and biases towards certain geographic regions and host species are important considerations. These data limitations, which are not unique to the UNITE database, underscore a pressing need to expand our knowledge of EM associations. Furthermore, many associations in the literature have not been entered into standardized databasesâa significant bottleneck. As mentioned above, manually curating and populating the associations matrix with additional data from the literature could improve predictive power and support the development of more comprehensive and reliable training and validation datasets. 2.3 Step 2: Determine predictor variables In parasite ecology, predictive models often prioritize host-related features over parasite ones as predictor variables. One key reason is the scarcity of parasite-specific data, combined with the comparative abundance of data about hosts. However, as more data on parasite traits become 28 available, their inclusion in predictive models is gaining attention (Dallas et al., 2017b). Commonly-used host-related predictor variables fall into three major categories: biogeographic, phylogenetic, and host traits (e.g., Stephens et al., 2019; Albery et al., 2020; Schatz & Park, 2021). These same themes hold promise for the challenge of predicting potential mycorrhizal associations. However, mycorrhizal symbioses introduce an added layer of complexity, as they are influenced not only by host features but also by environmental and ecological (niche) factors (van der Linde et al., 2018) While host-related variables alone may provide substantial predictive power for parasite-host associations, mycorrhizal fungi and the symbioses they form appear to be strongly influenced by abiotic conditions (VÄtrovský et al., 2019). Some studies highlight environmental factors as primary drivers of the global distributions of different mycorrhizal types (Steidinger et al., 2019; Davison et al., 2021; Mikryukov et al., 2023). Others suggest that biotic factorsâsuch as the identity and abundance of neighbouring host and fungal speciesâplay a role in determining the likelihood of an association (Molina & Horton, 2015; Lofgren et al., 2018; Singavarapu et al., 2024). Therefore, we include both biotic and abiotic niche variables in our roadmap. Several studies evaluating environmental factorsâsuch as climate and soil characteristicsâfound an influence on the geographic distribution and prevalence of mycorrhizal associations, revealing that patterns differ between mycorrhizal types (Soudzilovskaia et al., 2015; VÄtrovský et al., 2019; Meng et al., 2023) and among fungal taxa within the same mycorrhizal type (Twieg et al., 2009; Kivlin et al., 2021; Mansfield et al., 2023). These findings suggest that different mycorrhizal types respond to distinct environmental drivers and that high functional variability among fungi within a single mycorrhizal type may lead to differential responses to the same set of factors (Smith & Read, 2008; Tedersoo et al., 2024). Thus, we assume that factors influencing EMF diversity and community composition also play a role in structuring EMF-host 29 associations. While often studied in the context of distribution, richness, or assemblage variation, these variables are equally relevant for understanding fungal-host pairings. Most of the predictor variables proposed in this roadmap are grounded in literature and supported by strong rationales, although their true predictive utility will only become clear once the models are implemented. The process of building and testing these models will include opportunities to explore which predictors contribute most to accurate predictions. However, the primary goal of this roadmap is to provide a framework for identifying and deploying models with reasonable (or better) predictive accuracy. For mycorrhizal fungi, a key determinant of distribution is the availability of compatible (or potentially compatible) hosts (El Amrani, 2024). However, beyond host presence, the specific trait profiles of individual plant species may further influence fungal-host associations (Rutten & Allan, 2023). Features from both symbionts likely play a role in governing the likelihood of an association (Genre et al., 2020), but the relative contribution of these factors remains poorly understood (Lofgren et al., 2018). While some distribution models have incorporated broad vegetation measures as independent variables (e.g., Tedersoo et al., 2012; VÄtrovský et al., 2019), studies explicitly examining how host identity influences mycorrhizal type and fungal distribution are comparatively rare (van der Linde et al., 2018; El Amrani, 2024). The probability of an association is also driven by fungal host specificity and fungal host preference. The first term refers to the taxonomic breadth of hosts that a mycorrhizal fungus can associate with. The available evidence suggests that EMF exhibit a gradient from less specificityâalso called generalistsâwhich have no apparent constraint to a host taxonomic lineage, to higher specificityâreferred to as specialistsâwhich can only associate with a few host genera or families in natural settings (Lofgren et al., 2018). Additionally, host specificity can refer to the tendency of fungi associating with multiple host species within a higher taxonomic lineageâsuch as genus or 30 familyâto also form associations with other species within the same lineage. Specificity is a category assigned to fungi based on their (realised) host breadth, and is therefore influenced by the same factors that influence host breadth. Fungal host preference refers to fungi being more prone to form mycorrhizas with specific plant hosts among a mixture of compatible ones in natural settings than expected by chance. Although factors influencing host preference may also enhance predictive accuracy, there is currently limited understanding of its underlying mechanisms (van Galen et al., 2023) and thus, it is not considered further in this roadmap. The predictors proposed in this roadmap can be conceptualized as a series of filters that influence the likelihood of an EM association. These predictors are categorized into five predictor themes: biogeographic, phylogenetic, abiotic and biotic niche, host traits, and fungal traits. Figure 2 illustrates how these filters shape EMF host breadth, from narrow (high host specificity) to broad (low host specificity), depending on the extent to which each filter permits or restricts associations. 31 Figure 2. Conceptual model of how filters (hatched horizontal objects) influence the realized host breadth of an ectomycorrhizal fungal taxon. Each filter represents one of the proposed predictor themes, whose constituent variables could influence the likelihood of an association between the fungal taxon and a potential host taxon. On the left, restrictive filters lead to a narrower host breadth and higher host specificity (specialist strategy) for the fungal taxon, while on the right, more permissive filters allow for a broader host breadth and lower specificity (generalist strategy). The final outcome reflects the fungal taxonâs realized host breadth, shaped by both intrinsic (e.g., traits, phylogeny) and extrinsic (e.g., environmental and biotic) factors that govern EMF-host associations. Ecological specificity describes the phenomenon whereby abiotic and biotic conditions, which vary geographically, can influence which particular associations are realized within a given region. For example, in the scenarios depicted in the figure, and prior to filtering by the abiotic and biotic filters, the fungal taxa exhibit realized host breadths of 5 and 8 plant species (left and right, respectively). On the left, greater ecological specificity leads to a greater reduction in realized host breadth (from 5 to 2 host species), whereas on the right, the fungal taxon exhibits no evidence of ecological specificity. 32 In this section, we provide rationales for each of the five proposed predictor themes, which mainly implicate features of the hosts that are relevant (or potentially relevant) to mycorrhizal symbiosis. We then describe data sources for these variables and provide recommendations for their implementation. With respect to data sources, we focus exclusively on datasets that are openly accessible, some of which rely on community contributions for continued expansion. A summary of all datasets referenced in this roadmap can be found in Appendix A.2 and A.3. As we progress through Step 2, it is important to keep in mind that predictions regarding the likelihood of a potential association between a given host and fungal species (i.e., the prediction for a single cell in the association matrix) are generally informed by similarities (or differences) between the focal host species and other hosts in the matrix, particularly those with which the focal fungal species is known to associate. Thus, in addition to variables describing attributes specific to the focal host (e.g., geographic range size, average specific root length), many predictor variables are derived from pairwise comparisons with other hosts in the matrix (e.g., range overlap, phylogenetic distance). 2.3.1 Biogeographic predictor theme 2.3.1.1 Rationale In the parasite ecology literature, geographic overlap (or co-occurrence more generally) among compatible hosts serves as a strong predictor of parasite sharing and parasite community composition (Albery et al., 2020). In its most parsimonious form, the proposed mechanism is that increasing host co-occurrence increases encounter probability between hosts and parasites (Stephens et al., 2019). We assume this also applies to mycorrhizal plant species and their potential 33 fungal symbionts. While âencounterâ does not equate with âassociationâ, it nonetheless serves as an important, initial condition enabling a potential association. A more nuanced rationale for the importance of co-occurrence or geographic range overlap considers evolutionary/historical biogeographic processes. A study by Sugiyama & Sato (2024) suggests that range overlap and geographic similarity between host plants and their mycobionts account for some aspects of EM compatibility, arguing that differing biogeographic origins may contribute to incompatibility. This is consistent with evidence that the EM lifestyle evolved multiple times independently across different fungal lineages, rather than from a single ancestral EMF that diversified globally (Tedersoo & Brundrett, 2017). This pattern suggests that EM symbiosis arose in response to similar ecological pressures in different regions, leading to convergent evolution (Martin et al., 2016). As these fungi formed symbiotic relationships with various host plants, their diversification was shaped by the biogeographic history and evolutionary trajectories of their hosts (Tedersoo et al., 2010; Martin et al., 2016). Consequently, host species that frequently co-occur across space and time are more likely to exhibit greater host fungal overlap, or fungal sharing, reflecting both ecological opportunities for interaction and deeper evolutionary relationships. Additionally, EMF, which are estimated to form symbioses with approximately 60% of tree stems worldwide, are the predominant form of mycorrhizal fungi at high latitudes and elevations (Steidinger et al., 2019; VÄtrovský et al., 2019). Based on studies across diverse hosts, EM associations show an unimodal relationship with latitude and declining richness with altitude (Tedersoo et al., 2012; Põlme et al., 2013). Nonetheless, it is important to consider that some of these geographic patterns have also been associated with environmental conditions, indicating that 34 while geographic factors and environmental variables are linked, they each contribute to explaining different aspects of the variation in EM associations. 2.3.1.2 Important variables in the literature The geographic distribution and range size of both host plants and their fungal partners are variables that bear upon the degree of spatial overlap and the likelihood of an association due to their influence over encounter probabilities. Some authors predicting parasite sharing include mostly metrics of range overlap between hosts (Stephens et al., 2019; Albery et al., 2020) while studies predicting parasite host breadth include mainly separate variables for latitude, longitude, and range size or biogeographic region (Dallas et al., 2017b; Becker et al., 2022). Direct measures of geographic range overlap between hosts can come from raster data quantifying the overlapped cells or percentage of overlapped cells, stored in a pairwise host matrix, while indirect measures of range overlap tend to include single vector variables for each host (e.g., area of occupancy, latitude range, longitude range) (Dallas et al., 2017b; Albery et al., 2020). Predictive studies of parasite host breadth typically do not incorporate parasite geographic range data. However, including fungal range in mycorrhizal prediction models may be beneficial, as fungi can exhibit spatial distributions distinct from the full geographic range of all their host plants combined due to differing dispersal mechanisms, potentials, and environmental selective pressures (Põlme et al., 2013). Consequently, accounting for fungal range in these models could improve predictive accuracy by identifying potential mismatches between fungal and host distributions and providing a more comprehensive understanding of compatibility and symbiotic potential (Van Nuland et al., 2024). 35 2.3.1.3 Data sources The largest and most widely used database for accessing taxon occurrence records is the Global Biodiversity Information Facility (GBIF), a curated database of global scope, funded by the world's governments. Founded in 2001, GBIF has become the most comprehensive, widely accessed, user-friendly, open resource for all research involving geospatial data (CODATA et al., 2020). As of June 2024, this platform hosts 2.9 billion records submitted by 2,233 data publishers worldwide. A great advantage of GBIF is that all its data is accessible through the rgbif package (Chamberlain & Boettiger, 2017) from the R programming environment (R Core Team, 2021), which facilitates data accessibility and reproducible protocols. Other commonly used databases for geographic data include IUCN and BIEN. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species provides global comprehensive assessments for more than 157,100 species in several taxonomic groups, although only 83% of these include spatial data in range map format. These expert maps were created for conservation purposes, where recorded presences are more important than possible ones in equally suited habitats. The downloadable polygons are in Esri shapefile format (easily manipulated in the R programming environment) and have been widely used for studies that require range estimations (Herkt et al., 2017). However, the species' estimated ranges can sometimes be incomplete or can include inadequate areas for species that are not well-known or understudied (Higino et al., 2023). As such, these maps can be useful at a macroecological scale in the absence of more precise data but should be used with caution at finer-scale resolutions. The Botanical Information and Ecology Network (BIEN) database (Enquist et al., 2016) was created to help better understand the impact of climate change on plant diversity. This database hosts more than 100 million observations belonging to more than 380,000 plant species worldwide 36 and provides varied types of data that have undergone standardization and validation procedures to ensure their quality. Some of these include occurrence records, range maps, plot survey data, plant traits, and phylogenetic data, all of which can be easily visualized and downloaded through their website (biendata.org) or the rbien R package (Maitner et al., 2018). The website visualization tool only works for individual species while the R package allows for more flexibility and manipulation of multiple taxa or locations at a time. A user guide for the R package is provided on the website. BIEN also offers a range of tools and services like the Taxonomic Name Resolution Service (TNRS) (Boyle et al., 2013), for automatic standardization of plant scientific names; the Geographic Name Resolution Service (GNRS) (Boyle et al., 2022), doing the same as the TNRS but for geographic names and locations; and the Native Species Resolver (NSR), for the identification of native vs. introduced species in specific regions. Access to all of these can be found on their website. 2.3.1.4 Implementation considerations To be consistent with the majority of host-parasite studies, we recommend prioritizing indirect measures of host geographic range overlap, such as host plant geographic range size and ranges of latitudes, longitudes, and elevations (min and max), all of which are variables specific to each host. These variables should adequately capture spatial distribution and allow the model to account for range overlap. However, to maximize the potential predictive signal, we also suggest including a direct measure of range overlap between hosts. Geographic variables can be incorporated into predictive models in several ways. While some studies utilize point-based data such as latitude and longitude (Dallas et al., 2017b; Becker et al., 2022), others derive variables 37 from broader raster data created with occurrence records or range maps (Stephens et al., 2019; Albery et al., 2020). Here, we suggest deriving geographic variables directly from occurrence records rather than range maps to improve spatial resolution and accuracy, as occurrence data provide a more reliable representation of the hostâs known distribution. In contrast, fungal range data remains limited, sparse, and subject to sampling biases (e.g., primarily based on visible sporocarps) that could introduce considerable noise into predictive models. Thus, excluding fungal range information at this stage supports the framework's goal of maintaining robustnessâits resilience to noisy or biased input dataâand minimizing inaccuracies stemming from poorly represented fungal distributions. 2.3.2 Phylogenetic predictor theme 2.3.2.1 Rationale Compatibility between hosts and symbiotic partners, such as parasites or mycorrhizal fungi, is often influenced by host phylogenetic relationships. Evidence from parasite-host studies indicates that closely related hosts share more ecological features, which can increase compatibility with similar parasites and acquisition of parasites from one another (Schatz & Park, 2021). Host trait similarity, often tied to shared evolutionary history, has been shown to influence compatibility and increase the likelihood of parasites associating with closely phylogenetically related hosts (Elmasri et al., 2020). Using host phylogeny-derived variables is a common approach in studies investigating parasitic associations, as phylogeny can often serve as a proxy for unmeasured host traits relevant for parasite acquisition (Dallas et al., 2017b). Phylogenetic trees are a representation of the evolutionary relationships among species, which provide means to quantify co-evolutionary 38 history (Elmasri et al., 2020). Moreover, host phylogeny derived variables, such as phylogenetic distance or similarity, have proven to be a strong predictor in association models across species groups, providing more explanatory power than spatial overlap in some contexts, indicating that evolutionary history plays a prominent role in shaping many symbiotic systems (Albery et al., 2020). Similar patterns have been observed in EM associations, where phylogenetic similarity between host plants correlates with fungal community composition (Sugiyama & Sato, 2024). Hosts within the same genus or family tend to harbor taxonomically and functionally similar EMF, likely due to co-evolutionary processes and shared physiological traits that facilitate fungal recognition and compatibility (Matsuoka et al., 2020; Sugiyama & Sato, 2024). For example, studies on Alnus species have shown that host phylogeny is a primary driver of EMF community composition (Põlme et al., 2013). The evolutionary history of both EM plants and EMF plays a crucial role in determining the strength of mycorrhizal mutualism. Hoeksema et al. (2018) conducted a meta-analysis across 646 fungi-plant combinations and found that evolutionary history accounts for significant amount of variation in plant responses to mycorrhizal fungi. Notably, ectomycorrhizal associations are better explained by the multiple evolutionary origins of the ectomycorrhizal lifestyle in plants, although host phylogeny may have limited explanatory power among closely related hosts. Studies in temperate forests have found that closely related hosts often share similar fungal communities (Glassman et al., 2017), a pattern that has also been observed in tropical forests (Peay et al., 2015) despite their differing historical-biogeographic and evolutionary origins. This suggests that factors beyond phylogenetic lineageâsuch as specific host traitsâmay govern and better explain differences in fungal community composition between closely related hosts (Peay, 2016; 39 Li et al., 2021). Thus, while host phylogeny can offer a foundational layer for predicting potential EM associations, it may need to be complemented with detailed host trait data to capture finer-scale variations in EMF-host specificity, especially for studies focusing on closely related host species. In comparison, fungal phylogenies alone may not provide predictive utility for EMF-host associations, as host breadth and specificity do not appear to be conserved above the species level (Tedersoo et al., 2024). This suggests that factors such as nutrient preferences, environmental tolerances, and competition may play a stronger role than phylogenetic relatedness in shaping fungal distributions. 2.3.2.2 Important variables in the literature Incorporating host phylogeny into predictive analyses requires selecting an appropriate metric derived from the host phylogenetic tree and a pairwise host phylogenetic matrix. Phylogenetic distance between pairs of hosts remains the most widely used measure in this predictor theme, although some studies employ its complement, phylogenetic similarity, instead. For instance, some authors incorporate the first 3-5 phylogenetic eigenvectors of a principal coordinate analysis (PCoA) on a pairwise host phylogenetic distance matrix as the phylogenetic distance predictor variables (e.g., Dallas et al., 2017c). Alternatively, some studies have used mean phylogenetic distance or divergence time in million years calculated from the host phylogenetic distance matrix (e.g., Schatz & Park, 2023; Stephens et al., 2019), or accounted for phylogenetic distances through a correlation matrix of phylogenetic branch lengths (Wells et al., 2018). A similar approach has been applied using a host taxonomic dissimilarity matrix as a proxy for 40 phylogeny (Dallas et al., 2017b), though coarseness of this measure compared to true phylogenetic measures may limit predictive power. 2.3.2.3 Data sources Although a database hosting only phylogenetic information does not exist, some databases or studies also include openly available plant phylogenies that can be leveraged to calculate phylogenetic similarity or distance for the relevant plant hosts. An important resource for these data is the BIEN database mentioned above. Easily accessible from their R package, BIEN v2.0 includes 100 replicated phylogenies for almost all New World plant species in the database (more than 80,000 species) and a conservative one with the New World plant species for which sequences were available in GenBank (more than 65,000 species). Taxonomy for these trees is based on the World Flora Online (WFO), which replaces The Plant List (TPL), and the World Checklist of Vascular Plants (WCVP), which reconciles the names from the International Plant Names Index (IPNI) and the World Checklist of Selected Plant Families (WCSP). A recently developed resource that is gaining popularity in the scientific community is the V.PhyloMaker2 R package (Jin & Qian, 2022). This is a useful tool for generating tailored phylogenetic trees from an input list of vascular plant species at a fast speed. These phylogenies can be based on 1 of the 3 available mega-trees from different botanical nomenclature systemsâTPL, the World Plants database (WP), and the Leipzig Catalogue of Vascular Plants (LCVP)âeach with around 72,000-75,000 species. The authors also provide a user guide, an example of how to efficiently use the package, and the R scripts to reproduce it, all accessible from their GitHub repository (github.com/jinyizju/V.PhyloMaker2). Unlike the previously mentioned 41 phylogenetic resources with static downloadable mega-trees, this R package allows you readily create a tree that only includes your plants of interest, making the downstream phylogenetic calculations easier. This makes V.PhyloMaker2 an important tool to consider, especially for researchers with little experience in creating phylogenies. 2.3.2.4 Implementation considerations We suggest using phylogenetic distance, given that most available R packages calculate this measure (in millions of years), and to facilitate interpretation and comparison with the literature. The R package âpicanteâ (V.1.8.2) (Kembel et al., 2010) is a commonly used tool for this purpose, although there are many other resources available. Examples of generating this variable and additional phylogenetic metrics can be found in the work of Dallas et al. (2017a), Schatz & Park, (2021), and Becker et al. (2022) on phylogenetic distance, and Albery et al. (2020) on phylogenetic similarity. 2.3.3 Niche predictor theme â Abiotic 2.3.3.1 Rationale Mycorrhizal fungi can establish wherever a compatible host is present, which could be considered their fundamental niche. However, their actual distributions are shaped by environmental conditions that extend beyond host availability, imposing additional constraints on where they occur (Wolfe & Pringle, 2012; Meng et al., 2023). Thus, the realized niche of mycorrhizal fungi is not only defined by host availability but also by ecological specificityâfactors that influence whether a fungal species successfully forms symbioses with its plant hosts in a given environment (Molina & Horton, 2015; van der Linde et al., 2018). In this sense, the 42 realized niche can be seen as a reflection of this ecological specificity shaped by the ecological and environmental factors that influence successful symbioses, as hypothesized by Dickie (2007). Some authors have proposed that abiotic factors govern broad-scale distributions, while biotic interactions regulate local abundance (Molina & Horton, 2015; Davison et al., 2024). Additionally, Taylor et al., (2014) found that congeneric fungi can occupy divergent ecological niches. Predicting potential EM associations, therefore, requires accounting for both abiotic conditions and biotic interactions of hosts, potentially including the taxonomic and functional composition of the surrounding community (Lofgren et al., 2018; El Amrani, 2024). Abiotic factors play a critical role in shaping the global distribution and colonization intensity of mycorrhizal fungi. EMF are predominantly found in cooler, seasonally stable climates and nutrient-poor environments, particularly in high-latitude or high-altitude forests where decomposition rates are slower (Steidinger et al., 2019). Colonization intensity and abundance are closely linked to soil acidity, soil carbon-to-nitrogen (C/N) ratios, and precipitation seasonality, with acidic soils and relatively constant precipitation levels being particularly favorable (Soudzilovskaia et al., 2015). Tedersoo et al. (2022) highlighted precipitation as a key factor influencing EMF endemism, with temperate rainforests in the southern hemisphere showing particularly high numbers of endemic fungi. Such biomes, defined by distinctive climatic patterns, soil characteristics, and plant communities, are thought to harbor fungal populations that are highly specialized and uniquely adapted to their environments (McPolin et al., 2024). However, authors have found inconclusive or contrasting evidence on the true effect of precipitation on EMF richness. Tedersoo et al. (2012) and Meng et al. (2023) found that increasing precipitation can negatively affect EMF richness, possibly due to stress from low oxygen availability in water-saturated soils or competition among 43 functional guilds of soil microbes. Conversely, Põlme et al. (2013) found inconclusive evidence of a negative relationship between precipitation and EMF richness in Alnus-associated fungi, while Miyamoto et al. (2018) found that higher precipitation levels increased richness of some fungal taxa like Russulaceae. AMF niches seem to be more differentiated along resource axes, especially nutrient gradients, where limiting similarity mechanisms may be shaping community composition (Davison et al., 2024). As such, these are strongly influenced by soil characteristics such as C/N ratio, pH, textural properties, OC, total N, and ions like magnesium (Mg) and sodium (Na), which significantly shape their distribution (Soudzilovskaia et al., 2015; Bouffaud et al., 2017). AMF-dominant ecosystems are typically associated with higher soil fertility and warmer, more stable climates compared to EMF-dominant systems. However, incorporating insights from AMF niche properties could enhance predictive models of host-EMF associations, as AMF-soil relationships are better studied. Many edaphic variables found to influence AMF remain underexplored in EMF-focused studies, yet they could serve as valuable predictors for EMF distributions despite ecological differences between these fungal groups. Particular soil elements may also mediate host-fungal specificity in some cases. EM host species richness peaks in cooler forested regions, such as the eastern United States, eastern and southern Asia, and parts of Africa and Australia, where soils are characterized by high moisture, cation exchange capacity (CEC), organic carbon (OC), and phosphorus (P), but low cation content and bulk density (Meng et al., 2023). However, elevated nitrogen levels could inhibit EMF colonization, reflecting potential sensitivity to high nutrient availability (Molina & Horton, 2015; Soudzilovskaia et al., 2017). Additionally, the mycorrhizal-associated nutrient economy (MANE) hypothesis (Phillips et al., 2013) suggests that ecosystems dominated by EM plants exhibit distinct 44 carbon and nutrient cycling processes than AM-dominated ones, following the different mycelial structures and enzymatic capacities of their EMF partners. A study by Twieg et al. (2009) found that the abundance of some EMF taxa, such as Rhizopogon vinicolor and Cenoccocum geophilum, increased with organic P and P availability. Similarly, soil calcium (Ca) concentration was shown to be a key predictor in Alnus-specific EMF associations (Põlme et al., 2013). Nevertheless, studies focusing on general EMF distribution patterns have rarely examined such nutrients. This same situation applies to other edaphic components that are sometimes neglected in mycorrhizal studies, reducing comparability. While some soil elements have not shown strong patterns or correlations with fungal distribution at local scales, unexamined elements may still play a role in shaping EM associations under specific environmental conditions or across broader spatial gradients. Identifying these overlooked factors could refine predictive models by capturing previously unrecognized drivers of fungal-host associations. Thus, environmental drivers such as temperature, precipitation, soil pH, soil C/N ratio, and nutrient availability are essential for capturing the ecological niches and realized distributions of mycorrhizal fungi in predictive analyses (Mikryukov et al., 2023). 2.3.3.2 Important variables in the literature Some important environmental variables, such as precipitation and temperature, that have been widely used in the modeling of the geographic distributions of mycorrhizal fungi have been split into several separate measurements in climatic databases. For instance, most authors include mean annual temperature (MAT) in their models (Davison et al., 2021; Van Nuland et al., 2024). However, others have included temperature seasonality (Qin et al., 2023) or temperature of the coldest and warmest months (Soudzilovskaia et al., 2015; VÄtrovský et al., 2019) either as unique 45 or complementary temperature variables. This same situation applies to precipitation for which the most commonly used variables include mean annual precipitation (MAP), precipitation seasonality, and precipitation of the wettest and driest quarter or month (Soudzilovskaia et al., 2017; VÄtrovský et al., 2019; Qin et al., 2023; Van Nuland et al., 2024). Additional climatic variables that are sometimes included are evapotranspiration and isothermality (Meng et al., 2023; Mikryukov et al., 2023). The importance of these various climate variables relative to one another is currently unknown, in part because their inclusion as predictors is highly inconsistent among studies. These same limitations apply to edaphic variables as studies often include unique sets of variables, which precludes cross-study synthesis and interpretation. Nonetheless, soil pH, OC content, and C/N ratio, particularly at shallow depths (0â30 cm), are often considered important predictor variables (Põlme et al., 2013; Soudzilovskaia et al., 2017). Other occasionally cited variables include concentrations of key nutrients such as P, N, Mg, Na, Ca, and K (potassium), mainly for general fungi diversity or distribution studies (Soudzilovskaia et al., 2015; Davison et al., 2021). Additional variables included only in a handful of studies are land cover, stand age, or vegetation indices (Tedersoo et al., 2012; Meng et al., 2023; Mikryukov et al., 2023). 2.3.3.3 Data sources Environmental data related to mycorrhizal associations require both climate and soil information, meaning this predictor group must combine data from various sources. While regional databases with higher data resolution may be better options, the databases presented here can still serve as valuable sources for many of the necessary environmental variables. However, this should be considered based on the study scale, as some of these databases may exhibit significant discrepancies for certain variables at regional or more local levels (Bedia et al., 2013). 46 A commonly used database for climate data is WorldClim (Fick & Hijmans, 2017), currently in version 2.1. This database offers monthly climatic data for 19 bioclimatic variables from 1970 to 2000 at 4 spatial resolutions, between 30 seconds (1 km2) to 10 minutes (340 km2). Mixing data from ~60,000 weather stations (e.g., temperature, precipitation, wind speed, solar radiation, water vapor pressure) and some satellite-derived covariates (e.g., elevation, distance to the coast, surface temperature, cloud cover), WorldClim2 provides climate surface estimates for the entire world, based on predictions from the best-performing model for each region and variable. This database provides high-resolution historical and future climate data, along with historical monthly weather data. However, these datasets may lack precision in capturing local precipitation and temperature patterns, as noted by authors such as Bedia et al. (2013) and Bobrowski et al. (2021). Predictions based on these datasets rely heavily on a high-quality network of climate station data. Consequently, regions with a low density of weather stations may experience larger discrepancies between the predictions and observed data, particularly for variables like MAP. While these data are a valuable resource for historical and future climate data, which can be difficult to find elsewhere, they should be used cautiously at more local scales. The datasets use the WGS84 datum and are accessible through their website (worldclim.org). The Climatologies at High resolution for the Earthâs Land Surface Areas (CHELSA) database (Karger et al., 2017) is a downscaled model that provides precipitation and temperature estimates at a high resolution (30 seconds) for 1979â2013 and future scenarios. Regular updates and refinements ensure that these estimates are based on hourly climatic data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) from January 1940 to the present. The temperature variables come from the downscaling of atmospheric temperatures, while precipitation is based on satellite-derived cloud cover and frequency with 47 additional orographic predictors and a subsequent bias correction. Unlike other databasesâsuch as WorldClimâthat provide different types of climatic variables, CHELSA only provides estimates related to precipitation and temperature, although it offers both daily and monthly estimations of precipitation rates and outperforms WorldClimâs accuracy in monthly climatological means from 2003â2016 (Karger et al., 2021). These refined estimates could provide a better representation of small-scale (local) patterns of both precipitation and temperature. The data uses the WGS84 datum and can be downloaded from their website (chelsa-climate.org). Soil-related databases are mainly found via the International Soil Reference and Information Centre (ISRIC) and are available from the ISRIC website (data.isric.org/geonetwork /srv/spa/catalog.search#/home). One of the most commonly used datasets is SoilGrids 2.0 (Poggio et al., 2021), a product of international collaboration at the ISRIC. This database uses machine learning prediction methods for mapping the spatial distribution of soil properties worldwide at 250 m (medium) or 1 km (low) resolution for 6 standard depths. Based on more than 400 environmental covariates (e.g., geology, hydrology, vegetation, climate) and more than 240,000 soil profiles taken from the World Soil Information Service (WoSIS) database, the SoilGrids maps are one of the most detailed estimations of global soil distribution and include important metadata about uncertainty (e.g., 90% prediction intervals for particular variables). Nonetheless, predictions for regions like Central Asia, the Arctic regions, coastal areas, and deserts may present low accuracy (over- and underestimations) due to a very low density of soil profile observations (Poggio et al., 2021). The database does not consider soils below 2 m nor subaqueous and tidally exposed soils which can impact total stock predictions, like for soil organic carbon. Another important soil database is the Harmonized World Soil Database (HWSD), currently in its version 2.0 (HWSD2; FAO & IIASA, 2023). The first version was established in 48 2008 by the International Institute for Applied Systems Analysis (IIASA) and the Food and Agriculture Organization (FAO) in partnership with ISRIC for applications in agricultural assessments and biophysical models. The newest version of the database combines harmonized soil data (29 385 soil association mapping units) from 7 depth layers collected at distinct times and scales providing data on chemical, morphological, and physical properties of soils at ~1 km (30 arc-second) resolution. Reliability of the data is greater in regions in Soil and Terrain (SOTER) databases (available on the ISRIC website: isric.org/explore/soter) or national harmonized databases, and is lower in regions based on the Soil Map of the World, like North America, Australia, parts of West Africa and South Asia (Nachtergaele et al., 2023). These data come in the form of a GIS raster image file (in Esri BIL format) linked to an attribute table, which can be easily manipulated through any GIS software, R or Python. An official user guide for processing the database in R is also available through the ISRIC website. However, considering accuracy and resolution as the most important data qualities, Dai et al. (2019) consider SoilGrids as the highest-ranking option among the global soil databases. Note that this may vary among regions with better or lower coverage for each database. An alternative to databases containing extrapolated data, such as those described above, are datasets that have already undergone processing as part of previous studies and are made publicly available as part of publications. A useful, recent example is the large, publicly archived dataset provided by Meng et al. (2023), which encompasses 54 environmental variables that influence or are suspected to influence the mycorrhizal traits of plants, including mycorrhizal type and status. Following the taxonomy from TPL, this dataset includes data for 11,770 mycorrhizal host plant species. These authors joined data from various sources of climate data (CHELSA and WorldClim2), soil data (SoilGrids1km and the Global Soil Dataset for use in Earth System Models; 49 GSDE), and mycorrhizal host traits like mycorrhizal type and status. The resolutions and projections were matched for all the data from the distinct databases and parameter estimates were generated for all species with data gaps in the grid-cell records, making this dataset a ready-to-use source of environmental variables specifically selected for mycorrhizal associations. The dataset can be easily downloaded from their Dryad repository (doi.org/10.5061/dryad.x0k6djhq2). 2.3.3.4 Implementation considerations Thus far, we have provided biologically grounded rationales for select variables based on their demonstrated or hypothesized relevance. However, there remains considerable uncertainty regarding the predictive power of additional environmental factors. Correspondingly, Meng et al. (2023) opted for a more expansive approach in their predictive modeling of the global distribution of mycorrhizal types of plants, incorporating 54 environmental variables (Table A.1)âmany without specific biological justification. Despite this, several of these non-traditional predictors (e.g., sand, mean temperature of the coldest month, absolute depth to bedrock, silt) contributed meaningfully to the model, yielding strong explanatory and interpretative power. Given the variability in predictor sets across studies, which often hinders comparability, a comprehensive selection of variables ensures coverage of potentially relevant factors while enhancing predictive accuracy. Therefore, we prioritize model performance for this predictor group theme by incorporating biologically justified and exploratory variables. 2.3.4 Niche predictor theme â Biotic 2.3.4.1 Rationale Biotic niche variables are critical to modeling mycorrhizal associations, as they account for host community composition and interspecific interactions that can shape fungal diversity and host 50 compatibility. Empirical host-parasite research shows that biotic community structure may be as predictive of parasite associations as geographic variables and host-specific traits (Dallas et al., 2017b). Several studies suggest that neighbouring plant species can indirectly affect focal fungal-host relationships by altering soil chemistry through leaf and root litter, root exudates, and nutrient cycling, but also directly through mycorrhizal fungal spillover (Kernaghan et al., 2003; Li et al., 2021; Eagar et al., 2023). These neighbouring effects can amplify or reduce fungal diversity, particularly in forests where community composition shapes the ecological conditions that support or inhibit fungal colonization. For instance, Tedersoo et al. (2012) demonstrated that host community compositionâa measure of neighbouring hostsâsignificantly influences EMF diversity at local and regional scales, highlighting its importance as a potential predictor of EMF-host associations. This suggests that factors influencing diversity can serve as indirect predictors of potential mycorrhizal associations, particularly when direct fungus-host data are incomplete or unavailable. Fungal diversity and host compatibility are shaped by evolutionary and ecological interactions, including historical contingency, competition, and mutualistic co-adaptation. For instance, phylogenetic constraints can influence which fungi successfully associate with particular hosts, while environmental filtering and priority effects determine community assembly outcomes (Bruns et al., 2002; Peay et al., 2010). In diverse forests, neighbouring hosts may also mediate host specificity through shared mycelial networks that facilitate fungal colonization among compatible partners, although the extent of this influence remains uncertain (Molina & Horton, 2015; Sugiyama & Sato, 2021). Furthermore, the unique combinations of co-occurring species create 51 selective pressures and opportunities for adaptation, fostering distinct fungal assemblages that vary across forest compositions and environmental conditions (Põlme et al., 2013; El Amrani, 2024). 2.3.4.2 Important variables in the literature Although rare, a few predictive studies of parasite-host associations have attempted to incorporate biotic niche variables in the form of community composition measurements. Some studies have addressed this through a principal components analysis (PCA) of the associations matrix, specifically by including PCA components as predictor variables (Dallas et al., 2017b). This approach captures the parasite and host community structure from the associations matrix and reduces the information into a smaller set of explanatory variables (components). Phylogenetic data on both the neighboring hosts and their fungal communities may also serve as useful biotic predictors, as indicated by Davison et al. (2024) and Sugiyama & Sato (2024). This is because phylogenetically diverse host and fungal communities can expose fungi and other hosts to a wider array of potential partners. Additionally, Tedersoo et al. (2012) incorporated metrics such as the number of hosts sampled, the number of potential hosts present, and vegetation age, demonstrating their relevance in assessing EMF diversity and community composition. 2.3.4.3 Data sources All the variables in this predictor theme require some calculation from co-occurrence records (geographic data), phylogenetic trees, or the associations matrix. The data that feed into these calculations can be sourced from GBIF or BIEN, V.Phylomaker2 (for plant phylogenies only), and the UNITE database. While phylogenetic data for certain EMF species or lineages like 52 Russulaceae, Paxillaceae, and Sclerodermatineae exist (e.g., Rochet et al., 2011; Tedersoo et al., 2010; Tedersoo & Smith, 2013; Wilson et al., 2012), they are too limited in scope to be broadly applicable. As a result, global phylogenetic data for mycorrhizal fungi remain unavailable at this time (Mikryukov et al., 2023). 2.3.4.4 Variable selection based on data availability Based on key variables identified in the literature and the availability of relevant data, we recommend a selection of three predictors and one PCA for this predictor theme. Specifically, we propose including variables such as the total number of co-occurring plant taxa, the total number of unique EMF associated with these plant taxa, and the phylogenetic diversity of the co-occurring plant taxa. Additionally, incorporating two or more axes from the PCA of the association matrix is likely to capture additional unexplained variation in community composition, especially given the lack of phylogenetic data on the fungal partners. 2.3.5 Host traits predictor theme 2.3.5.1 Rationale Host traits are increasingly recognized as crucial determinants of symbiotic compatibility across diverse ecological systems. In parasite-host interactions, traits such as diet and habitat preferences have been shown to facilitate the acquisition of parasites by novel hosts that share ecological and morphological similarities with their established hosts (Dallas et al., 2017b; Schatz & Park, 2021). This concept can be extended to mycorrhizal fungi, whose associations with plants are influenced by aboveground and belowground host traits. Although phylogenetic predictors are inherently tied to host traits, their combined analysis offers valuable insights into the evolutionary 53 constraints of traits, including mycorrhizal symbiosis (Meng et al., 2023). For example, a recent study by Barceló et al. (2023) highlights a strong association between root morphological traits and mycorrhizal colonization, suggesting similar morphologies may result in similar fungal partner colonization. Aboveground traits, such as photosynthetic capacity and leaf nitrogen content, determine the carbon availability for mycobionts, while belowground traits govern the physical and chemical environment for fungal colonization (Bergmann et al., 2020; Carmona et al., 2021). These traits are not independent but form coordinated profiles that reflect broader ecological strategies. Plant traits align with two major functional frameworks: the Leaf Economics Spectrum (LES) for aboveground traits and the Root Economics Space (RES) for belowground traits (Carmona et al., 2021; Rutten & Allan, 2023). These frameworks encompass plant trade-offs in resource acquisition, storage, and conservation, providing a lens through which to understand the ecological and evolutionary constraints shaping host-fungal associations (Díaz et al., 2016). For mycorrhizal fungi, the interplay between plant traits and fungal functional capacities likely influences patterns of fungal-host compatibility (Bergmann et al., 2020; Comas et al., 2014). Aboveground plant traits, particularly those within the LES, may play a role in shaping ectomycorrhizal associations. The LES describes a continuum between "fast" and "slow" resource acquisition strategies, where fast-growing species invest in short-lived, nutrient-rich leaves optimized for rapid photosynthesis, and slow-growing species produce longer-lived, nutrient-conserving leaves (Wright et al., 2004; Díaz et al., 2016). Trees that associate with EMF typically exhibit traits associated with slow strategies, such as recalcitrant leaf litter and conservative nutrient cycling, which align with the nutrient-conserving enzymatic capacities of EMF (Eagar et al., 2023). These traits create belowground environments that favor EM symbiosis, as gradual 54 nutrient release supports fungal enzymatic activity over time. Additionally, the global spectrum of plant form and function (GSPFF) highlights the ecological importance of aboveground features such as leaf nitrogen content and specific leaf area (SLA), which influence photosynthate availability for mycobionts (Carmona et al., 2021). Belowground host traits provide the most direct link to ectomycorrhizal associations. Root traits such as root diameter, specific root length (SRL), root tissue density (RTD), and root nitrogen concentration represent key dimensions of the RES and are tightly linked to mycorrhizal colonization (Bergmann et al., 2020; Freschet et al., 2021). Most EM plants rely heavily on mycobionts for resource uptake, facilitating success in nutrient-poor environments (Guo et al., 2008). Nevertheless, tree species span a broader range of the RES than other plant species, reflecting the flexibility of EM fungi partnerships in supporting nutrient acquisition (Rutten & Allan, 2023). This allows EM-associated trees to occupy diverse ecological niches and adopt various nutrient acquisition strategiesâand root morphologiesâ, from heavy reliance on fungi (collaboration gradient) to near self-sufficiency (high SRL), which may vary between EM partnerships. Unlike AMF, which colonize cortical cells, EMF establish intercellular Hartig nets at root tips (fine roots), requiring specific root architectures for efficient colonization. Traits such as higher root branching intensity and larger root diametersâalthough thinner than AM host rootsâare strongly associated with EMF-host compatibility, as they increase the number of colonization sites and provide structural support for fungal mantles (Comas et al., 2014). Conversely, denser roots with higher RTD are less permeable and less conducive to fungal colonization, reflecting a "defense" strategy rather than a "collaboration" one (Rutten & Allan, 2023). Root exudates may further mediate EMF associations by acting as chemical signals that primarily attract fungal 55 partners while also influencing soil nutrient dynamics (Freschet et al., 2021). However, very few measures of this trait are available. One example is root phosphatase activity, which may exhibit contrasting relationships with mycorrhizal colonization depending on ecological context and successional stage (Liu et al., 2021; Han et al., 2022; Bi et al., 2023; Wong et al., 2024). Together, these belowground traits underscore the potential role of the RES in determining host specificity or compatibility and shaping the successful establishment of EM associations. 2.3.5.2 Important variables according to literature Studies investigating plant-soil feedbacks and mycorrhizal associations have highlighted several aboveground and belowground traits as key variables within the frameworks of the LES and the RES. Aboveground traits include adult plant height, diaspore mass, stem-specific density (SSD), leaf nitrogen concentration, leaf area, and SLAâall central to plant ecological strategies and resource acquisition (Díaz et al., 2016; Carmona et al., 2021). These traits are well-documented in the LES framework, capturing key dimensions of plant function and adaptation. For belowground traits, the RES framework emphasizes root diameter, SRL, RTD, and root nitrogen content as the most representative root traits for nutrient acquisition (Bergmann et al., 2020; Guerrero-Ramírez et al., 2021; Rutten & Allan, 2023). Additional traits, such as fine root biomass, length, and branching intensity, have been recognized as strongly associated with mycorrhizal colonization (Soudzilovskaia et al., 2017). Some studies also speculate on the importance of root cortex fraction, carbon cost of the fungus, and seedling age in influencing EMF compatibility (Molina & Horton, 2015; Bergmann et al., 2020). 56 2.3.5.3 Data sources Numerous databases containing plant trait data are available; however, here we highlight the most frequently cited resources for aboveground and belowground traits. The TRY database (Kattge et al., 2020) is the largest and most widely used database for plant traits. Currently in its version 6.0, this database hosts more than 15 million trait records for 2,660 traits of around 305,000 plant taxa (as of June 2024). However, as in all databases, there are data gaps: among-species coverage is excellent for a small subset of traits, but extremely poor for many others. Data volume and trait coverage continues to improve with ongoing contributions from the scientific community. More than 10,000 datasets have been contributed and integrated into the TRY database, following a rigorous workflow of data curation and harmonization for sufficient quality assurance. Taxonomic harmonization is done through the TNRS, which bases their taxonomic backbone on several plant taxonomy databases like TPL, Tropicos, the USDAâs Plant Database (PLANTS), Global Compositae Checklist (GCC), and International Legume Database and Information Service (ILDIS). The TRY Data Portal (www.try-db.org/TryWeb/dp.php) allows the user to search for specific plant traits, see the amount of data available for each, and request access to them. Although most of the data is publicly available, TRY requires users to create an account with them to make any data request or data upload, enabling interaction between users, contributors, and managers. The website also provides access to the TRY File Archive that hosts climate and soil data for some of TRY measurement sites, as well as an alternative to publish datasets and receive a DOI. Data contributed to the TRY database has also been used in efforts to compile other plant-related data lacking in the database. Some of these external databases include sPlot (vegetation plot data, Bruelheide et al., 2019), GIFT database (regional floras checklist and functional traits, Weigelt et 57 al., 2020), and the FRED database (root traits, Iversen et al., 2017), among others. These databases have also contributed to the TRY database in return. This database is currently the best openly-available source for plant traits, particularly aboveground ones. Several specialized root trait databases have become available as belowground traits gain more attention in research investigating plant-soil processes. One of these is the FRED 3.0 database (Iversen et al., 2017), which mobilized fine-root traits information from the literature and contributed its first versions to the TRY database. With more than 150,000 observations for around 330 root traits from more than 1400 data sources, FRED is the most populated and curated database for root morphophysiological traits. Nevertheless, the available data is unequally distributed among root traits, taxa, and geographic sources, only covering ~4,600 plant species out of the ~350,000 known vascular species worldwide (Iversen & McCormack, 2021). Apart from root variables, FRED also includes some data about soil features (e.g., P, N, C, Fe, K, Ca, Al content, pH), climate features (e.g., MAP, MAT), and aboveground plant traits, although the amount of data it offers for the latter is limited in comparison to the available in the TRY database. This database follows TPL taxonomy and is available for download from its website (roots.ornl.gov). Another source of belowground traits is the Global Root Traits (GRooT) database (Guerrero-Ramírez et al., 2021). This database consists of 2 standalone spreadsheets created to provide a ready-to-use dataset that facilitates the integration of root trait data into global analyses of plant form and function. In collaboration with FRED and TRY databases, Groot includes ~114,000 trait recordsâat the highest data resolution availableâfor 38 root traits considered most important for determining plant form and function at the species level. These data cover ~6,214 species across 254 families. Considering that root trait names and units have not been standardized across studies and datasets, a great advantage of Groot is that root trait names were standardized 58 across data sources and matched with the designated names in the handbook of root traits by Freschet et al. (2021). Moreover, data curation and quality checks were performed for each root trait by expert root ecologists, and plant taxonomic standardization was done through the TNRS, based on TPL, GCC, ILDIS, PLANTS, and Tropicos. The spreadsheets are available through their GitHub repository (groot-database.github.io/GRooT). 2.3.5.4 Implementation considerations The aboveground traits we recommend for this predictor theme are well-supported and have wide data coverage. These traits align with the LES and include diaspore mass, nitrogen content per unit leaf mass, adult plant height, leaf area, SLA, and SSD. An important consideration is inconsistencies in how certain plant traits are measured or defined, often leading to subtle but potentially meaningful differences. For instance, SLA, defined as leaf area per unit of dry mass, has four variations in the TRY database depending on whether the petiole is included, excluded, undefined, or whether the petiole, rachis, and midrib are excluded. Clearly specifying the chosen trait definition is a critical step for repeatability. When selecting traits, prioritizing measurements with broader data availability across species is generally the best approach, especially when no specific definition is required. The four traits comprising the root economics spaceâroot nitrogen content, root diameter, SRL, and RTDâare among the most widely measured and documented. Despite being at most indirect indicators of mycorrhizal colonization, Phillips et al. (2013) and El Amrani (2024) have suggested that plant nutrient acquisition strategies, which are influenced by these traits, may also shape fungal community structure. Thus, these root traits offer the most practical solution for belowground trait selection given their broader species-level data availability. Fine root traits, such 59 as root exudates, length, biomass, and branching intensity, may provide better insights into mycorrhizal associations, but are currently underrepresented in databases like TRY and FRED, with approximately 90% of available data covering fewer than 100 plant species. As such, the use of root economics space traits is recommended as a pragmatic choice for analyses where broad data coverage is essential. One additional consideration is that mycorrhizal colonization is restricted to the first three root orders (Guo et al., 2008), so data on root traits from higher orders are likely less relevant. Since most of these host trait variables have not yet been evaluated in mycorrhizal modeling, their actual variation among EM hosts remains uncertain. Some traits may be more directly relevant in different contexts, such as distinguishing the distributions of different mycorrhizal-type hosts, than for predicting potential EMF associations. However, we still recommend including them as potential predictors to explore their influence. Lastly, it is important to note that plant mycorrhizal traitsâincluding mycorrhizal type, status, flexibility, and dependency (Moora, 2014; Soudzilovskaia et al., 2020)âare not directly relevant to this work, as they primarily describe the mycorrhizal context of plant species rather than being traits that influence associations per se. However, dual mycorrhizal hosts (AM-EM) may exhibit distinct patterns of EMF-host associations, making mycorrhizal type flexibility a potentially relevant trait. That said, determining dual-type hosts is prone to high error rates due to conflicting literature (Teste et al., 2020), which may complicate its use in predictive models. 2.3.6 Fungal traits predictor theme 2.3.6.1 Rationale Recent studies on parasite-host associations have found that including parasite traits as variables may increase the accuracy of predictions, suggesting that parasite acquisition follows 60 consistent and predictable patterns influenced by the interplay between host and parasite traits (Schatz & Park, 2021). For instance, traits such as transmission mode, feeding behavior, and infected host tissue could be particularly influential factors in predicting these associations (Dallas et al., 2017b). These findings highlight the importance of considering both host and parasite traits when assessing association patternsâan approach that is equally relevant for mycorrhizal fungi. Like parasites, mycorrhizal fungi vary in key traits, such as exploration strategy, growth rate, and enzymatic capabilities, which likely influence their ability to form a mycorrhiza with particular hosts. As such, similar fungal trait patterns may result in a predictable similar host breadth. Ectomycorrhizal fungi exhibit a wide range of functional traits that influence their ecological roles, nutrient dynamics, and competitive interactions. Two classification approaches have been proposed to link EM hyphal morphologies to nutrient acquisition capacities: exploration types, which describe the extent of extraradical mycelial proliferation (Agerer, 2001), and foraging strategies, which characterize enzymatic capabilities (Koide et al., 2014). While exploration types seem to be mostly phylogenetically conserved, foraging strategies have shown variations within EMF genera (Tedersoo & Smith, 2013). These classifications provide valuable insights on their own, but may not fully capture the functional trade-offs that influence not only the composition but also the successional dynamics of EMF communities. For example, ruderal EMF species, which prioritize dispersal through stress-tolerant spores, tend to dominate in early successional environments but are eventually replaced by competitors that invest in vegetative spread and root colonization (Smith et al., 2018). This trade-off mirrors functional turnover in response to soil gradients, where habitat specialists exhibit enhanced nutrient acquisition traits that confer competitive advantages in stable environments (McPolin et al., 2024). 61 Ectomycorrhizal fungal traits not only influence dispersal and competitive dynamics but also play a crucial role in ecosystem functions, including organic matter decomposition and nutrient cycling. Mycorrhizal fungi occupy distinct trait spaces based on characteristics such as hyphal diameter, septation, host dependency, and nutrient exchange strategies, affecting both biogeochemical cycles and host interactions in unique ways (Smith & Read, 2008). These traits also shape the likelihood of successful colonization and persistence by determining spore viability, hyphal growth rate, and compatibility with host root exudates, which in turn affect fungal establishment and survival within plant roots. For example, fungi with fine hyphae and high foraging capacity may be more effective at colonizing resource-poor environments, while those with thick-walled spores may persist longer in the soil, facilitating delayed colonization (Janowski & Leski, 2022; Khalfallah et al., 2024). Given the substantial functional diversity among EMF species, studying variation in traitsâparticularly elemental stoichiometry, morphology, and biochemical mechanisms of mutualism stabilityâcan refine our understanding of fungal-host compatibility (Smith & Read, 2008). By incorporating fungal trait-based perspectives, predictive models of fungal-host associations can be improved, offering greater insight into how these interactions form and persist under different ecological conditions. Zanne et al. (2020) hypothesized that several fungal traits are central to understanding fungal ecological strategies, particularly in the context of their adaptive roles in symbiotic and environmental interactions. The six proposed traits, derived from prior research emphasizing their functional relevance and analogies to comparable plant traits, include spore size and number, maximum fruiting body size, specific hyphal length, extracellular enzyme activity, melanin content, and carbon-to-nitrogen-to-phosphorus (C:N:P) ratios. For instance, a trade-off exists between spore size and number, where a larger number of smaller spores enhances dispersal but 62 larger spores have higher survival rates (Aguilar-Trigueros et al., 2019). Fungal dispersal strategies may also be associated with host specificity, with species producing large, resistant spores often exhibiting broader host breadths (Janowski & Leski, 2022). Similarly, fungi with larger fruiting bodies produce larger spores and disperse them farther (Dressaire et al., 2016). This can also correlate with denser tissues, slower growth rates, and longer lifespans, with metabolic rates scaling with size (Aguilar-Trigueros et al., 2017). This trait, however, is difficult to apply to some EMF with inconspicuous, microscopic, or no fruiting bodies. Additionally, species that produce many fruiting bodies are not necessarily the ones most commonly found on roots (Horton & Bruns, 2001; Richard et al., 2005). Specific hyphal length, representing the ratio of hyphal length to biomass, is linked to faster fungal growth rates, shorter tissue lifespans, and more efficient water and nutrient uptake (Lovelock et al., 2004). In nutrient-poor environments, fungi often invest in extracellular enzyme production to extract scarce nutrients (Å najdr et al., 2011). However, hyphal growth and enzyme production in EMF appear to be subject to a trade-off between competitive advantage and functional efficiency, with an inverse correlation between the two (Moeller & Peay, 2016). Other traits are associated with tissue durability and nutrient efficiency. Fungi with higher melanin content are generally more resistant to decomposition, tend to have longer-lasting tissues, and may also be more tolerant of water stress (Fernandez & Koide, 2013; Fernandez et al., 2019). Nutrient allocation, reflected in the ratios of carbon, nitrogen, and phosphorus (C:N:P), also impacts growth strategies; species with higher nitrogen and phosphorus relative to carbon exhibit faster growth and greater returns on nutrient investment (Zhang & Elser, 2017). These traits also highlight the diverse strategies fungi employ to balance reproduction, growth, and nutrient acquisition. 63 2.3.6.2 Important variables according to literature Following the proposed traits by Zanne et al. (2020), spore size and number, maximum fruiting body size, specific hyphal length, extracellular enzyme activity, melanin content, and carbon-to-nitrogen-to-phosphorus (C:N:P) ratios, may be relevant variables accounting for the functional significance of fungi to their hosts. Additionally, some researchers have highlighted the potential importance of fungal traits related to dispersal, colonization, and establishment, including spore wall thickness, ornamentation, exploration strategy, foraging behavior, and propagule persistence (Clemmensen et al., 2015; Halbwachs et al., 2015; Molina & Horton, 2015; Andrew et al., 2016; Abrego et al., 2017; Halbwachs et al., 2017). It remains uncertain whether these functional differences translate into varying competitive abilities or other processes that influence differences in EMF-host associations (Moeller & Peay, 2016). Nevertheless, consistent with Meng et al. (2023), incorporating as many of the preceding traits as possibleâdespite uncertainties about their relevance to EMF-host associationsâremains a valuable approach for this predictor section. 2.3.6.3 Data sources As mycorrhizal fungal ecology research has surged over the past few decades, there have been several attempts at creating databases relevant to the field. Unfortunately, these efforts have yet to yield a centralized data repository like the TRY database for plants. Fungal data remains extremely sparse, and ongoing debates on the appropriate definitions, measures, and units for fungal traits make combining and harmonizing different datasets challenging. Databases that host publicly available fungal data include FunGuild, FunFun, FungalTraits, and GlobalFungi, 64 Additionally, we also include a description of MycoDB and FungalRoot, well-known plant-centered mycorrhizal databases that mycorrhizal ecologists may consider useful. The FunGuild database (Nguyen et al., 2016) is an online community-annotated database that aims to link fungal sequences with their functional groups and nutrient types at a genus level. It is based on a dataset published by Tedersoo et al. (2014) that compiled fungal guilds and cell types based on data at the genus and family level and currently includes over 13,000 fungal taxa (June 2024). This database has been widely used to analyze functions of fungal OTUs, although these functions have not been resolved for 59% of soil fungi and 20% of saprotrophs (Tanunchai et al., 2023). Nonetheless, FUNGuild offers dual functionality: it includes the database, and also a script for the Python 3 environment that assigns functional information to user-provided OTUs (e.g., obtained from sequencing of field samples). All this can be accessed through a GitHub repository (github.com/UMNFuN/FUNGuild), which includes clear instructions on how to use the bioinformatics pipeline and how to contribute data to the database. This database does not provide true fungal traits beyond guild, trophic mode (pathotroph, saprotroph, and symbiotroph), and growth form (e.g., agaricoid, corticioid, facultative yeast, hydnoid, microfungus). The FunFun database (Zanne et al., 2020) was created to supplement the data in FUNGuild with additional cellular, ecological, and biochemical traits at the species and genus levels (Põlme et al., 2020). These traits are distributed into 6 categories (morphological, genetic, life history, physiological, enzymatic, and stoichiometric) and encompass a total of 92 fungal traits. However, this database is quite limited as less than 800 records achieve 85% trait coverage. Containing a total of 5,866 fungal records across 3,104 unique species, trait data is only available for a comparatively small percentage of genera in the Ascomycota (7%), Basidiomycota (18%), Zygomycota (20%), and other (15%) fungal clades (Zanne et al., 2020). This database is hosted at 65 a GitHub repository (github.com/traitecoevo/fungaltraits) and is easily accessible through the R environment following the instructions in their portal. Do not be confused with the FunFun database by Krivonos et al. (2023), as this is a completely different database. Another potential data source is FungalTraits (Põlme et al., 2020). An important note to make is that here we use âfeatureâ for the characteristics they refer to as âtraitâ. Integrating data from NCBI, FUNGuild, and FunFun, this database has ecological information for almost 700,000 fungal ITS sequences summarized into more than 90,000 SHs at a 1% dissimilarity threshold. This database is a compilation of reannotated sequences for ~10,000 fungal and 151 Stramenopila genera that include 17 lifestyle-related features. This provides a higher resolution of fungal taxa and functional group features, allowing for an easier taxonomic and functional assignment for OTUs. Nevertheless, the taxonomic identification of fungal OTUs is resolved only to the level of genus. This database has the data arranged into 3 stand-alone spreadsheets or datasets (traits of genera, sequences, and SHs) and can be downloaded from the supplementary material of Põlme et al. (2020). A study by Tanunchai et al. (2023) demonstrated that FungalTraits outperforms FUNGuild in assigning functional traits to amplicon sequence variants (ASVs), yielding higher richness estimates and a more detailed representation of fungal functional community composition. This superiority is attributed to FungalTraitsâ broader coverage of endophytes, plant pathogens, and saprotrophs. However, extracting data for individual species or SHs from FungalTraits datasetsâparticularly the âtraits of species hypothesisâ datasetâcan be challenging. The extensive information contained within each âtraitâ cell often requires manual extraction and interpretation. Additionally, while some critical metadata, such as DNA isolation source, country, interacting taxon, and biome, are available for over 30% of the total records and identified SHs, the remaining 66 metadata are notably sparse. Although most individual sequence records are identified up to fungal genus, around 60% have an associated PlutoF (Abarenkov et al., 2010) ID, meaning they are available through the UNITE database. The GlobalFungi Database (VÄtrovský et al., 2020) is a rapidly growing database that compiles fungal sequences along with their taxonomy, occurrence points, and other fungal geographic and ecological features. Although this database does not include fungal traits, it is an important and easy-to-use resource similar to GBIF for fungi, allowing one to search individual sequences, SHs, species, genera, studies, and geographical areas and download associated records, sequences, and features referred to as âmetadataâ. Currently, the website does not permit searching by fungal guild. The current version of the database (5.0, November 2023) bases its taxonomy and SHs on UNITE version 9.0 (October 2022) and hosts almost 85,000 samples from 846 studies, covering ~144,500 SHs. Similarly, it hosts ~5 billion ITS sequences, both ITS1 and ITS2, the majority of which are distributed around Europe (33%), Asia (32%), and North America (20%). This database not only allows the user to filter records by biome, sample type, or sampling year, but also allows users to visualize the selected data with histograms by their sample type and biome, MAT and MAP (taken from the CHELSA database), pH, or location. All the resulting graphs and their metadata are downloadable (in text format). This tool can be very useful, for example, to visualize some inherent biases in the selected data. The GlobalFungi database is accessible through their webpage (globalfungi.com) and includes a YouTube tutorial on how to use the database and how to submit data from a new study. Additionally, as of 2023, a new database was assembled from this one for AMF samples only, called GlobalAMFungi (VÄtrovský et al., 2023). This one contains more than 8,400 samples from 100 studies, covering 1,429 molecular taxa. Records can 67 be searched by taxon, host plant, sequences, studies, or geographically, all easily accessible through their website (globalamfungi.com). MycoDB (Chaudhary et al., 2016) is a data source specific to mycorrhizal symbiosis. This standalone dataset focuses on plant productivity in response to AMF and EMF, containing 4,010 records from 438 publications. It describes the biotic and abiotic context of plant biomass response to mycorrhizal fungi, providing phylogenetic trees for all plants and fungi in the database. Although commonly referred to (e.g., Hoeksema et al., 2018), MycoDB doesnât provide any fungal data beyond some non-curated association records up to the fungal genus level. FungalRoot (Soudzilovskaia et al., 2020) is another important source of information. It is the largest empirical database of plant mycorrhizal traits, containing 36,303 species-by-site records for 14,870 plant species. The database is plant-focused and includes information at the genus level on mycorrhizal type, colonization intensity, and site soil conditions when available. However, it does not provide the identity of the fungal associates. Over 50% of the observations are linked to geographical coordinates, with 45% including mycorrhizal intensity data and 2.5% recording colonization frequency. Data were compiled from 1,775 sources, making FungalRoot a comprehensive resource for studying plant mycorrhizal associations from the plant perspective. The database is accessible through the PlutoF platform (https://plutof.ut.ee) for authenticated users, who can also contribute new data and provide feedback on existing entries. Additionally, the current version is available to download from GBIF, with each new version on PlutoF linked to the GBIF dataset. Contributions can be made through the "Mycorrhiza Intensity" workgroup in PlutoF, ensuring continuous updates and expansion of the database. While the full dataset requires access through PlutoF or GBIF, the recommended mycorrhizal status for plant genera can be quickly accessed in the supplementary materials. 68 2.3.6.4 Implementation considerations Data gaps in fungal morphophysiological traits present a major challenge in mycorrhizal research, as existing databases provide sparse and inconsistent species- and genus-level data, much of which pertains to fungal features (e.g., geographic, environmental) rather than fungal traits. While some fungal trait information is provided in the FunFun database, the limited data coverage across species would introduce high variability and noise if incorporated into ecological models like the one proposed in this roadmap. Thus, at this time, we recommend against incorporating fungal trait data in the predictors. 2.3.7 Limitations & Recommendations The total number of variables recommended in this roadmap across all predictor themes is approximately 73 (Table A.1). These variables were selected based on their relevance to EMF-host associations, data availability, and support from previous studies, ensuring a comprehensive approach to modeling these interactions. We acknowledge that many predictive studies limit the number of variables to minimize issues of overfitting and collinearity. However, given the significant uncertainty surrounding the factors influencing EMF-host associations, we argue that a comprehensive approachâincorporating a broad range of potentially relevant variablesâwill enhance predictive accuracy, as shown in Becker et al. (2022) and Meng et al. (2023). Since some variables within predictor themes may exhibit collinearity, one approach to addressing this issue is to perform a PCA. This tool reduces dimensionality while preserving variables that contribute to model variation, minimizing collinearity and maximizing predictive power. However, some methods are relatively robust to multicollinearity and can naturally downweigh redundant 69 predictors, reducing the necessity of performing a PCA. Additionally, PCA transformations can obscure the direct relationships between original predictors and mycorrhizal associations, potentially diminishing the interpretability of results and masking the influence of key variables. Many of the data sources listed here are well-curated, but they often require cleaning and adaptation to suit specific research needs. Compiling larger datasets demands extensive harmonization, as different sources store data in varied formats, use distinct definitions, or report variables in different units. Integrating data from multiple sources to fill gaps in a sparsely populated variable for certain species can be tedious or even impractical. Large databases also reflect sampling biases, with certain hosts, mycorrhizal types, regions, and time periods more extensively represented than others. Selecting the most relevant and least biased data for a given study is therefore essential. For an overview of the data sources presented here and their attributes, see Tables A.2 and A.3 in Appendix A. Broad-scale ecological databases, while invaluable for predictive modeling, often contain uncertainties and potential inaccuracies that can influence downstream analyses. Data quality issues such as misclassification, missing information, and regional sampling biases can introduce noise into predictive models, affecting the reliability of inferred patterns. For example, mycorrhizal type classifications may be subject to error, with some species inaccurately labeled as exclusively AM or EM despite the possibility of dual associations (Teste et al., 2020). Such inconsistencies can distort variable importance estimates and obscure ecological relationships, underscoring the need for careful validation and consideration of data uncertainty when selecting predictor variables (Brundrett & Tedersoo, 2019; Davison et al., 2021). Some studies suggest that the interacting effects of parasite and host traits shape patterns of associations (Schatz & Park, 2021). However, fungal trait data is currently too limited to be 70 useful for predictive models. Most databases emphasize general functional groupings or ecological metadata rather than specific morphophysiological traits, creating a substantial gap in data critical for modeling fungal responses and interactions. Moreover, existing fungal data is fragmented across multiple databases, each with limited and sparse coverage. This fragmentation underscores the growing need for a unified, centralized resource to support more reliable and comprehensive analyses in mycorrhizal ecology (Antunes et al., 2025). Given that fungal traits and evolutionary histories are important to understanding patterns of EMF-host associations (Brundrett & Tedersoo, 2018; Hoeksema et al., 2018), the absence of comprehensive fungal data highlights that filling those data gaps in the existing databases should be a priority in mycorrhizal ecology. Similarly, key root traits beyond those captured within the RES have been largely overlooked, resulting in significant data gaps that may constrain the predictive power of the host trait predictor theme. 2.4 Step 3: Choose a method After selecting and gathering the necessary data for the predictor variables, the next step is to select the method for predicting potential associations. Method suitability depends in part on the types and amounts of data available. This step does not aim to provide a systematic or exhaustive review of all possible modeling methods for this task. Instead, it summarizes the approaches that, through a non-structured assessment of the literature, were commonly used in contexts relevant to the present goals, and that were anticipated to be most accessible, including to mycorrhizal ecologists with limited modeling experience. Recent studies have found that host features alone hold sufficient information to adequately predict parasite distribution, diversity, and host geographic range or probability of interactions (Dallas et al., 2017b; Stephens et al., 2019). As such, most of the methods that aim to predict 71 parasite-host associations solely leverage host features like ecological traits (diet, morphology, physiology), phylogenetic relationships between hosts, and geographic ranges. Exceptions to this include analyses that consider multiple types of parasites simultaneously, such as bacteria, viruses, helminths, protozoa, arthropods, and fungi (Dallas et al., 2017a; Schatz & Park, 2021). However, this exclusion of parasite traits is mainly due to the scarcity of relevant data, which mirrors the situation for EMF. Methods can be used either in a descriptive or predictive way. Descriptive methods mainly aim to explain intrinsic patterns and relationships between the response variable and the predictors and to identify the predictors that best explain a set of observations (response variable). By contrast, predictive methods try to generate new probable data for the response matrix based on those inherent patterns in the predictor variables; they are less concerned with interpreting predictor importance. Depending on the method, the resulting predictions can be either in-sampleâonly considering hosts in the response matrix with at least 1 recorded associationâor out-of-sampleâalso including unsampled hosts with no recorded associations in the response matrix. Not all methods support out-of-sample predictions, so this is an important consideration when selecting a method. Here, we aim for a predictive framework, so we focused our attention on literature that used methods in a predictive context. In the disease ecology literature, recent studies have developed analytical methods that have proven successful at predicting novel parasite-host associations (Becker et al., 2022; Schatz & Park, 2021). These methods and models mostly fall into one of two general approaches: network-based and trait-based (Strydom et al., 2021; Becker et al., 2022). These will be explained in the following sections. Although many of these methods can be conceptually translated or adapted to address the EMF-host associations challenge, data availability is a limitation that will 72 reduce the number of methods suitable for our purposes. Thus, rather than offering a formal comparison or benchmark of all available methods, this Step describes major methodological categories, key stages in building a predictive model, and identifies a small set of recommended approaches based on available data and their practical applicability. Given our emphasis on predictive modeling, it is essential to clarify the meaning of key statistical terms used throughout this section. Predictive accuracy refers to the degree to which a model correctly identifies known or expected outcomesâin this case, EMF-host associations. It provides a measure of how well the model distinguishes true associations (true positives) from absences based on the patterns it has learned. Closely related, model performance is a broader term encompassing predictive accuracy and other evaluation metrics that reflect the modelâs overall ability to make reliable predictions across different datasets or conditions. Model robustness, on the other hand, describes the modelâs capacity to maintain high performance when faced with variation in input data quality, sampling intensity, or external perturbations such as missing values or noise. Predictive accuracy is typically quantified using statistical measures such as accuracy, sensitivity (true positive rate), specificity (true negative rate), and, most commonly in ecological prediction tasks, the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The AUC-ROC is especially useful for binary classification problems and reflects the model's ability to rank associations correctly. An AUC of 1.0 indicates perfect prediction, while an AUC of 0.5 indicates consistent with random assignment. When we say that a model achieves, for example, 70% predictive accuracy, this generally means that 70% of known associations (positives) and non-associations (negatives) were correctly identified based on the available data and a defined 73 threshold for classification. This value provides a general sense of model reliability, although its interpretation can depend on the balance of positives and negatives in the dataset. Because predictive accuracy can vary depending on the structure and quality of the training data, it is critical to evaluate it through model validation. Validation refers to the process of testing the modelâs performance on independent data or subsets of the data not used during training, helping to assess how well the model is likely to generalize to new data. Various validation techniques are available, and these are further discussed in Section 2.4.4.5 of this thesis. 2.4.1 Network-based methods Network-based approaches provide a framework for analyzing species interactions by representing them as networks, where nodes correspond to species and edges denote interactions (Dallas et al., 2017a). These methods are particularly well-suited for studying parasite-host and EMF-host associations, which are typically modeled as bipartite networksânetworks where interactions occur exclusively between two distinct sets of species rather than within a single group (Strydom et al., 2021). In such networks, hosts and fungi can associate with multiple partners, and network-based models attempt to predict unobserved interactions by leveraging the structure of recorded associations (Albery et al., 2020; Poisot et al., 2023). Unlike species pair-based approaches, network models prioritize community-level patterns over individual parasite-host interactions, aiming to capture emergent properties of the system. A key feature of network-based methods is their reliance on network properties such as degree (number of connections per species), centrality (influence within the network), nestedness (hierarchical structuring of interactions), and modularity (distinct subgroups within the network) to infer missing associations (Dehling, 2018). These methods assume that species with similar 74 interaction patterns are likely to share additional, yet-unobserved interactions. However, they generally incorporate only one or two network mechanisms as predictors (Strydom et al., 2021), which can oversimplify complex ecological factors shaping fungal-host associations. One major limitation of network-based approaches is their sensitivity to sampling biases (Dehling, 2018). Because these models rely on observed interactions to make predictions, incomplete or uneven sampling can lead to biased network structures and overgeneralized predictions. This is especially problematic when the association matrix is sparse, as it may underrepresent rare or under-sampled species, restricting reasonable predictive accuracy to species already well represented in the dataset; in-sample species (Becker et al., 2022). Despite these limitations, improvements in network-based modeling have been proposed. Ensemble modelingâcombining multiple network-based approachesâhas been suggested as a way to enhance predictive accuracy and model robustness, by increasing reliability across sparse or noisy datasets (Terry & Lewis, 2020; Strydom et al., 2021). Additionally, techniques such as link prediction algorithms can help identify likely missing interactions within the association matrix, mitigating data gaps and refining network-derived predictions (He et al., 2024). 2.4.2 Trait-based methods Trait-based approaches relate known associations to host features like morphophysiological, ecological, or phylogenetic characteristics to predict potential associations with unsampled hosts. This approach either matches parasites with a given host feature profile to rank the suitability of unknown hosts or finds complex patterns in associations based on host features. This approach helps correct sampling biases by leveraging measurable host features rather than relying solely on recorded associations, which are often incomplete or skewed toward 75 well-studied hosts. By incorporating feature data, models can infer likely associations even for unsampled hosts, leading to more accurate out-of-sample predictions (Stephens et al., 2019; Farrell et al., 2022). When association data are limitedâas in a sparse associations matrixâmethods that leverage host feature data can achieve greater predictive accuracy than those that donât. For example, a study by Becker et al. (2022) tested the accuracy of eight predictive methods for virus-host associations using a dataset with ~710 host bat species and ~359 viral species in which only a small percentage of the hosts had recorded associations (0.041% matrix fill). Using network-based and trait-based approaches (which included data on host phylogeny and over 60 ecological traits), they found that trait-based methods had a higher predictive accuracy in general than network-based ones. 2.4.3 Comparison of methods and their data needs Within the disease ecology literature, some of the most commonly used and successful methods for predicting parasite-host associations have demonstrated model performance typically exceeding 70%, depending on the specific parasite-host groups and the limitations of the available data (Becker et al., 2022). Certain parasite groups have been extensively studied and sampled, while others remain underrepresented (Schatz & Park, 2021). Methods have been adapted to accommodate these data limitations, particularly for parasite groups with limited availability of feature or associations data. Furthermore, for many of these parasite groups, the host traits that most influence the likelihood of interaction or spillover to other hosts are relatively well understood (e.g., FishPEST; Strona & Lafferty, 2012). Correspondingly, these methods often require fewer variables. 76 For EMF, the key factors influencing host breadth remain largely unknown. Thus, a sensible strategy would be to include a broad suite of variables to prioritize predictive accuracy. Another key challenge in mycorrhizal ecology is the limited availability of reliable association records. Therefore, methods must simultaneously accommodate limited response data alongside a large number of predictor variables. Additionally, the ability to generate robust out-of-sample predictions is crucial for accounting for unsampled hosts. These latter predictions are those that should be evaluated through targeted observational and experimental studies. A summary of the key characteristics of these methods, such as data requirements and their ability to predict unrecorded associations (Table 1), points to trait-based and hybrid approaches as the most suitable and capable of out-of-sample predictions. Most of the network-based models are only capable of predicting in-sample novel associations. As such, within the scope of our review, three methods emerged as most promising for our purposes: Boosted Regression Trees (BRT), Bayesian Additive Regression Trees (BART), and Two-Step Kernel Ridge Regression. Although the Neutral Phylogeographic method is another option capable of predicting out-of-sample associations and is compatible with the data available for EMF, it only trains models on phylogeny and range map data. Thus, this method is less well aligned with our goal of using variables from several predictor groups (Step 2). For a more in-depth comparison of the methods usage example, refer to Table B.1 in Appendix B. 77 Table 1. Comparison of data needs and capacity to predict novel or unrecorded associations of the main models found in a subset of evaluated papers from the disease ecology literature for predicting parasite-host associations. Red: incompatible with EMF data or our aims. Blue: compatible with EMF data, but not suited to our aims. Green: compatible with EMF data and our aims. Method Associations data requirement Number of variables used Predicts novel associations? NETWORK-BASED Abundance-based Coverage Estimator (ACE) Large unevenly-sampled dataset ~4 No k-Nearest Neighbours Limited records ~25-60 Yes (in-sample) Linear Filter Limited records ~1 Yes (in-sample) Plug-and-Play Limited records ~15-60 Yes (in-sample) Scaled Phylogeny Limited records ~2 Yes (in-sample) TRAIT-BASED Linear Mixed Model (LMM) Large well-populated dataset ~6 Yes (in-sample) Boosted Regression Trees (BRT) Limited records ~10-60 Yes (out-of-sample) Generalised Additive Mixed Model (GAMM) Large unevenly-sampled dataset ~5 Yes (in-sample) Logistic Hierarchical Regression (Bayesian) Large unevenly-sampled dataset ~9 Yes (in-sample) Bayesian Additive Regression Trees (BART) Limited records ~60 Yes (out-of-sample) Neutral Phylogeographic Limited records ~2 Yes (out-of-sample) HYBRID Two-Step Kernel Ridge Regression Limited records ~60 Yes (out-of-sample) 78 More information on the network-based methods mentioned here can be found in the work of Dallas et al. (2017a), Dallas et al. (2017c), Desjardins-Proulx et al. (2017), Elmasri et al. (2020), Farrell et al. (2022) and Poisot et al. (2023). Examples of the trait-based methods compared here can be reviewed in the work of Dallas et al. (2017b), Wells et al. (2018), Stephens et al. (2019), Albery et al. (2020), Carlson (2020), Schatz & Park (2021), and Becker et al. (2022). Within these trait-based and network-based approaches, machine learning (ML) is often leveraged as a technique to improve predictions (Strydom et al., 2021). ML can be introduced at any stage of the modelling process: data imputation, the method itself, model selection, or model validation, and can increase flexibility when modelling non-linear and interaction relationships (Wells et al., 2018). A common application of ML in predictive modeling is data imputation, either for filling gaps in predictor variable data or to infer associations when the associations matrix is highly sparse. Random forest, for example, is an ML algorithm commonly used to address these data gaps, as demonstrated in Dallas et al. (2017c) for predictor variable values and Dallas et al. (2017b) for missing associations. Some other examples of ML in predicting interaction networks are found in Strydom et al. (2021), and Becker et al. (2022). However, ML should be carefully approached as these techniques are considered âblack boxâ techniques, focusing on the input and output of a system or process without needing to understand its internal workings. In the particular case of data imputation, sensitivity analyses may be recommended. 2.4.4 Core elements of modelling To choose an appropriate method, one must consider the type of data, the amount of data in the response variable, the quantity of and possible correlation between predictor variables, and the type of models that accommodate these characteristics. Below we describe the general 79 elements and decisions to make during the modelling process after choosing a method suitable for the data and the purpose of the study. However, we first address the ways different modeling approaches handle the response data (the pairwise associations). 2.4.4.1 Associations matrix transformation Association matrices, which record interactions between two sets of entities (e.g., hosts and fungi), are typically transformed before being used in modeling. The transformation process depends on the type of method applied (Figure 3). Most trait-based methods convert the matrix into a structured data frame format suitable for regression or classification models. Each row represents a unique host-fungus pair, with the interaction presence/absence recorded as the response variable and the host features acting as predictor variables. Network-based methods restructure the matrix into a format that captures the network structure of interactions. The original association matrix is modified in different ways depending on the type of network analysis used. Some methods work directly with the bipartite network, representing it as an edge list that includes only observed associations. Others rely on a unipartite projection, transforming the matrix into a host-host (or fungus-fungus) structure, where hosts are linked based on the number of shared fungi. This, however, may vary among network-based methods and is beyond the scope of this work. 80 Figure 3. Example of the possible approaches to transforming the associations matrix. Other transformations are used in distinct network approaches but are beyond the scope of this roadmap. Beyond these transformations, network-based approaches can also derive additional network properties (feature engineering) that serve as predictor variables in modeling. Instead of restructuring the association matrix to facilitate a specific type of analysis, this approach extracts features such as degree centrality, shared associations, clustering coefficient, and modularity, among others. These computed properties characterize the structural role of nodes in the network and are particularly useful in machine learning applications, such as link prediction. By incorporating these features, models can infer missing interactions and assess the likelihood of new associations forming. Matrix transformation should always be a prior step to the train-test split of the data (see section 2.4.4.2) to ensure that the predictor variables are properly structured before model training. 81 If the dataset were split first, applying the transformation could lead to inconsistencies in feature representations across the training and testing subsets. However, certain network-based methods may calculate specific network properties, such as link prediction scores, after the dataset is split, particularly if these methods rely on observed interactions for feature engineering. 2.4.4.2 Training datasets After assembling and transforming the response matrix, these data are usually divided into training and test sets to reduce overfitting and for posterior accuracy evaluation of the model (Dallas et al., 2017b). The most common strategy for splitting is 60-80% of observations for the training data and 20-40% for the test data (which are picked randomly or selected based on some criteria). Percentages usually depend on the amount of associations data available, as sparser or smaller association matrices may require a larger proportion of records as training data for the model. Nevertheless, associations used in training should not be included in testing for proper validation. When determining how to partition data into training and test sets, it's crucial to consider the structure of the dataset. For data that do not exhibit evidence of non-independence, randomly splitting observations into training and testing subsets is appropriate. However, if the data exhibit evidence of non-independence (e.g., due to spatial, temporal, or phylogenetic structure) random splitting can lead to underestimation of predictive error (Roberts et al., 2017). In such cases, block cross-validation strategies, which stratify the subsetting of data, are more appropriate. For instance, if there is evidence of phylogenetic non-independence among hosts in the associations or predictor data, then the block cross-validation can be stratified across phylogenetic groupings of the hosts. 82 Implementing appropriate cross-validation techniques that reflect the data's inherent structure is essential for reliable model validation. Some studies conduct internal cross-validation, whereby the splitting of the data into training and testing sets is repeated many times, and the model is fit and tested each time to determine the uncertainty of the predictions associated with the way the data were split (Albery et al., 2020; Schatz & Park, 2023; Strydom et al., 2021). In cases where associations data were limited, some authors trained models on all available data rather than splitting it into training and test sets (Schatz & Park, 2021; Becker et al., 2022). This, however, precludes proper validation unless new data becomes available. We discuss this further in the following sections. 2.4.4.3 Data imputation It is common to find that data for certain species or specific features are limited or sparse. An example of this can be seen in the TRY database: despite being the largest plant traits database available (Kattge et al., 2020), only a few traits have data for most species, representing approximately 0.1% of the entire database matrix. This non-random nature of missing data and the sparseness of available records can limit the statistical power of models (Joswig et al., 2023). Because of this, some authors have approached this issue with an optional step in the modelling process: data imputation. This consists of gap-filling to reduce data missingness in the predictor variables and can be approached in various ways. Examples and further information on data imputation can be found in Wells et al. (2018) and Joswig et al. (2023). Although rare, data imputation has also been used to populate sparse associations matrices using background data (i.e. random set of possible hosts for a focal parasite) from a subset of hosts 83 that did not have any recorded association in the matrix (Dallas et al., 2017b). We do not describe this process here as it is not recommended for the present purposes. 2.4.4.4 Model selection Once the data have been appropriately processed and the method selected, the model is trained on the cleaned dataset, typically yielding multiple candidate models, each with a unique suite of predictors. At this stage, model selection is performed. It is essential to distinguish model selection from method selection, as the latter refers to the analytical approach, while the former identifies the optimal model among those generated. In predictive modeling, model selection is primarily aimed at optimizing generalization, predictive performance, and computational efficiency rather than identifying a minimal explanatory model. Feature selection techniques such as variable importance analysis, recursive feature elimination (RFE), and regularization (e.g., Lasso) help refine the model by removing redundant or weak predictors. However, rather than arbitrarily reducing predictors, some methods like BRTs can effectively manage a large number of predictors, as overfitting is less of a concern due to the algorithm's ability to focus on relevant interactions between predictors (Bühlmann & Hothorn, 2007; Elith et al., 2008). As long as model performance remains robust, there is no inherent need to reduce the number of predictors. One of the most common assessment techniques is calculating the area under the curve (AUC) for each candidate model, where a larger AUC value indicates a better-than-random performance. This metric ranges from 0 (inaccurate prediction) to 1 (perfect prediction), with the middle value (0.5) corresponding to a no-better-than-random prediction (Dallas et al., 2017b). Depending on the method, other techniques for model selection that avoid overfitting include information criteria such as Akaike information criterion (AIC), Bayesian information criterion 84 (BIC), or minimum description length, among others (Strydom et al., 2021). These techniques are particularly important in descriptive or explanatory modeling, where the goal is to identify the most parsimonious set of predictors that best explain underlying biological or ecological relationships. In predictive contexts, model selection is less about simplification and more about ensuring model efficiency and performance, with validation serving as the key step in assessing predictive accuracy. 2.4.4.5 Model validation Following the selection of the best-fitting model, the final step is model validation, with cross-validation being the standard approach for predictive models. This technique assesses predictive accuracy, which differs from model performance or fit evaluated during model selection. Predictive accuracy pertains to the model's ability to correctly predict interactions when they are present (true positives) and to correctly predict the absence of interactions when none exist (true negatives). Cross-validation can be implemented using various approaches, including predicted versus observed cross-validation (with or without repetitions), internal validation through iterative train-test splits, and k-fold cross-validation, among others. Further details on model validation methodologies can be found in Sato et al. (2012), Albery et al. (2020), Strydom et al. (2021), and Schatz & Park (2023). In cases where association data are limited and fully utilized for training, none of the previously mentioned cross-validation techniques are possible, except the leave-one-out cross-validationâa special case of k-fold cross-validation. Nevertheless, caution is advised when using leave-one-out cross-validation, as it may yield biased results in the presence of autocorrelated data (Roberts et al., 2017). Predictions can instead be validated on a more comprehensive matrix of 85 data that becomes available at a later date or through expanded data mining efforts (Becker et al., 2022). 2.4.5 Promising methods for predicting potential EMF-host associations Two-Step Kernel Ridge Regression, BART, and BRT emerged as promising and practical methods for predicting potential associations between plant hosts and EMF based on our review. Here we recommend BRT as the most accessible and practical option due to its capacity to accommodate a large number of diverse predictors, its efficacy in the face of sparse associations data, its ability to model complex, non-linear interactions, and its relative simplicity compared to more complex kernel or Bayesian approaches (Schatz & Park, 2021). Moreover, BRT is frequently used in ecological studies, and numerous published examples with accessible code and documentation make it easier to implement and adapt to similar research questions. It is important to acknowledge that there may be other statistical methods that are, in fact, more suited in a statistical sense to this modeling task. However, identifying the single most appropriate method was not the goal of this work. Instead, this review took a non-systematic approach aimed at identifying methods that appeared both widely used and user-friendly for general researchers, based on their prevalence in the literature and practical accessibility. A comprehensive or formal comparison of all relevant modeling methods lies beyond the scope of this roadmap. BRTs offer a powerful modeling approach that is particularly well-suited to ecological data with numerous predictor variables and complex relationships. It is thus widely used in the ecology literature. This method works by iteratively constructing weak decision trees, which split data into subsets based on decision rules derived from the input variables, ultimately mapping inputs to outputs in a tree-like structure of nodes and branches. Each decision tree is trained on the residuals 86 of the previous tree. This process, known as boosting, creates an ensemble of trees that are combined to generate the final model. In the context of our study, BRT models can incorporate geographic and niche covariates, host phylogeny and traits, and community composition variables to predict EMF-host associations. This method has been effectively applied in similar ecological contexts, such as predicting parasite-host interactions, where models trained on host traits, taxonomic distinctiveness, and geographic factors achieved predictive accuracies ranging from AUC = 0.69 to AUC = 0.94, depending on the data used (Dallas et al., 2017b). BRT is advantageous not only for its flexibility in handling diverse predictor types and missing data but also for its ability to model complex non-linear responses and interactions. Additionally, BRT models can be validated using internal cross-validation techniques. This method ensures robust model performance by fitting multiple models with different subsamples of the data, improving prediction stability and accuracy (Elith et al., 2008; Schatz & Park, 2023). However, a sufficient amount of association data is required for the dataset to be partitioned into training and test sets, which poses a challenge for this validation technique in the case of EM associations. Alternative cross-validation techniques may be more suitable for EMF at this time, like comparing predictions to associations documented in the literature, but not included in the model training. 2.4.6 Limitations & Recommendations Recognizing and addressing the limitations of available data is crucial when developing predictive models. This practice enhances transparency and reproducibility, enabling clearer interpretation and deeper insights into model results. Likewise, providing access to the code and 87 data used in modeling has become an increasingly standard approach, aligning with Open Science principles and fostering greater credibility and applicability. One major limitation is the lack of cross-validated, predictive models that demonstrate consistent accuracy across different datasets (Dallas et al., 2017b). Many published models are trained on limited or biased datasets, leading to overfitting or poor generalizability. To mitigate this, models should undergo rigorous cross-validation, preferably with independent test datasets or external validation using unseen data. Additionally, the implementation of standardized evaluation metrics and benchmarking against existing models can help assess predictive performance more reliably. Another challenge is the âcurse of dimensionalityâ, where an excessive number of predictor variables can degrade model performance, particularly when the associations matrix is sparse (Altman & Krzywinski, 2018). In high-dimensional spaces, data points become more spread out, reducing the density of observations within any given region of the predictor space. In the case of EMF-host associations, sparsity refers to the limited number of recorded associations ("ones") relative to the total host-fungal pairings, with known associations disproportionately concentrated in certain host lineages. If these well-sampled lineages do not share strong similarities with underrepresented hosts, the model may struggle to generalize patterns beyond the observed data. Thus, this sparsity weakens the ability of models to detect meaningful relationships, as the distance between observations increases, making it harder to differentiate signal from noise (Hastie et al., 2009). Additionally, many machine learning algorithms rely on distance-based metrics (e.g., nearest neighbors, kernel methods), which become less effective in high dimensions due to the concentration of distances. High-dimensional datasets increase computational demands, reduce statistical power, and introduce redundancy through collinearity, making it difficult to discern 88 meaningful patterns. Careful consideration of model complexity relative to the available data is essential to avoid overfitting. Nevertheless, from a predictive perspective, interpretability is less important than predictive performance. Large matrices with many predictors also pose significant computational challenges. As the number of predictors increases, so do the memory and processing power requirements, sometimes necessitating high-performance computing resources. This is particularly relevant for machine learning models, where computational demands can grow exponentially with the number of features (Berisha et al., 2021). By addressing these limitations, predictive modeling can become more robust, interpretable, and applicable across diverse ecological datasets. Future work should prioritize model validation, computational efficiency, and interpretability to ensure that predictive frameworks remain both scientifically rigorous and practically useful. 2.5 Hypothetical case study: North America north of Mexico To illustrate how this roadmap could be applied, consider a hypothetical case study focused on North America north of Mexico (i.e., the U.S. and Canada), where relatively high-quality EM association data are available in the UNITE database. A potential question for this case study could be âWhich unsampled tree species in North America are most likely to be found associating with Suillus americanus?â. In Step 1, the associations matrix would include as rows all tree species native to the region and known to be ectomycorrhizal (approximately 265 species). Many of these species will have exclusively zeroes in their respective rows, and all of these zeroes would be presumed absences. The columns would include all EMF taxa whose DNA was found in root samples (according to UNITE and/or GenBank) and assigned to the taxon Suillus americanus (using some sequence similarity/confidence threshold). Restricting the EMF data in this way 89 means that all columns (EMF taxa) will have at least one known association (1) in the matrix. Applying the appropriate filters (include interacting taxa subtaxa: yes, taxon rank: species (spe), country: Canada, United States), in UNITE (PlutoF) to pull the EMF records from Canada and the U.S. while excluding records that lack host association information, the resulting dataset contains 29 host species and 100 fungal species as of May 2024. In Step 2, predictor variables would be selected based on the five themes outlined earlier in the roadmapâgeographic, phylogenetic, niche, and host traitsâusing data drawn from publicly available databases (see Appendix A.1 for a full list of variables and data sources). In Step 3, one of the three recommended methods, such as BRTs, could be implemented to model the probability of potential association between all possible EMF-host pairs in the association matrix. The model could generate both in-sample predictions (filling in unrecorded associations between already sampled host taxa) and out-of-sample predictions (identifying likely partners among unsampled host taxa). This implementation would not only answer our focal question for the case study, concerning potential Suillus americanus hosts, but would also generate predictions for all other presumed zeroes in the matrix. These predictions could guide targeted sampling efforts in the region by highlighting plant taxa for which novel associations are most likely. The predictions could then be validated through literature review or targeted sampling campaigns, helping refine model performance and expand the known EMF-host associations in North America north of Mexico. 2.6 Next steps Some of the next steps that require immediate attention to advance the predictive framework for EMF-hosts associations are: a) mining the literature and existing databases to 90 populate the fungi-plant association matrix, b) highlighting key data gaps that require filling to enable reliable predictions, and c) testing one or two of the most promising methods (like BRTs) using available data. An essential next step for advancing the predictive framework for EM associations is the systematic mining of literature and databases to populate the fungi-host plants association matrix. Many known associations exist only in fragmented and inconsistent formats across various sources, and consolidating these into a standardized database would increase the predictive power of future models. This process involves curating data to resolve discrepancies, standardize taxonomy, and address dual-type host classifications. By organizing these data more effectively, researchers can reduce noise and improve the reliability of models built on these associations. Additionally, targeted sampling of undersampled host species is essential for capturing the full diversity of EM associations and improving model generalizability. Equally critical is identifying and addressing key gaps in fungal and root trait data. Current fungal-host association records are severely limited by the lack of information on traits that could influence host preference and specificity (Zanne et al., 2020; Antunes et al., 2025). Traits such as root architecture, fungal spore dimensions, and environmental tolerances, among others, remain poorly represented in existing datasets, making their inclusion in predictive models impractical. Addressing these gaps through targeted data collection and database updates is vital for understanding host breadth and refining predictions of novel associations. Expanding this knowledge base should be prioritized to enhance the resolution and accuracy of models. To test the feasibility of predictive modeling, implementing one or two promising methods, such as BRTs, is a logical starting point. BRTs are particularly suited for handling the limited and incomplete datasets available for mycorrhizal fungi. Despite these strengths, the scarcity of high-91 resolution association data makes splitting data into training and test sets challenging. To address this, models can be trained using all available data while supplementing with literature-mined records to fill gaps in the associations matrix. Cross-validation can then be performed using these enriched datasets to enhance model reliability. By combining these strategiesâliterature mining, gap-filling in fungal and root trait data, and testing predictive methods such as BRTsâwe can significantly accelerate the discovery of novel EM associations. Targeted sampling and data-driven approaches will not only improve our understanding of fungal-host associations but also contribute to the conservation and management of fungal-plant symbioses across diverse ecosystems. This roadmap outlines a structured path forward for mycorrhizal research, addressing existing challenges while paving the way for future advancements. 92 Chapter 3: Conclusion 3.1 Review of objectives and major conclusions The first objective of this roadmap aimed to identify key predictive variables relevant to modeling EMF-host associations, with a particular focus on plant host features, including those that remain underexplored in current studies. My findings suggest that EMF-host associations are likely to be influenced by variables belonging to five predictor themes: biogeographic, phylogenetic, niche, host traits, and fungal traits. While host-related features, such as geographic range size and root morphology, are expected to provide substantial predictive power for initial modeling efforts, incorporating fungal traits could account for additional variation and improve predictive accuracy. The second objective involved identifying global and widely used data sources for each predictor theme and assessing their scope, limitations, and applicability to EMF-host associations modeling. All major databases exhibit inherent biases, such as uneven sampling efforts and geographic disparities. However, data about hosts are comparatively common and readily available, including those describing biogeographic, phylogenetic, niche-related, and aboveground host traits. By contrast, belowground host traits and fungal traits remain rare, limiting their immediate integration into predictive models. Expanding datasets in these areas will be essential for improving future predictive accuracy. The third objective focused on evaluating ecological modeling methodologiesâparticularly those used in disease ecologyâto determine their suitability for predicting potential EMF-host associations. My findings suggest that, given data constraints and the complexity of EM symbioses, host trait-based approaches are the most appropriate for this task. Among potential 93 modeling techniques, BRT, Two-Step Kernel Ridge Regression, and BART emerged as promising candidates. However, BRTs stand out due to their relative simplicity, ability to capture nonlinear interactions despite limited data, and capacity to accommodate a large number of predictor variables. 3.2 Knowledge gaps in EMF research Despite the ecological significance of EMF, substantial gaps remain in understanding their diversity, biogeography, and host associations. A major challenge is the lack of comprehensive data across many regions and plant lineages, which limits our ability to generalize patterns of EMF-host associations and EMF biogeography. This roadmap addresses these gaps by focusing on predictive modeling of EMF-host associations, providing a framework for predicting likely fungal partners, particularly in regions and plant lineages with limited empirical data. By synthesizing potentially key predictive variables, this approach enhances our ability to infer likely EMF-host associations based on host and environmental features. In doing so, it serves as a practical tool to complement empirical research and improve our broader understanding of EMF distributions and host specificity, even in data-limited contexts. One of the primary challenges in studying EMF is the uneven and often limited nature of sampling across geographic regions. Large portions of the world remain poorly sampled, with notable geographic biases in EMF research. For example, although Canada has vast expanses of EM-dominated forests, it is strikingly underrepresented in global diversity studies (e.g., Soudzilovskaia et al., 2020; Tedersoo et al., 2012; VÄtrovský et al., 2019). A key issue is the limited and often biased nature of mycorrhizal data; economically important plant species are disproportionately studied, while many others have never been investigated. Furthermore, the dual 94 mycorrhizal nature of some plant species (e.g., AM-EM hosts) introduces additional uncertainty, as little is known about the environmental or genetic factors that govern mycorrhizal switching (Brundrett & Tedersoo, 2018). Compounding these issues is the presence of misdiagnosed mycorrhizal associations, where incorrect classification of plant hosts has led to errors being perpetuated in the literature (Bueno et al., 2019). These inaccuracies limit our ability to draw reliable conclusions on the distribution and ecological roles of EMF. As Brundrett (2009) points out, a lack of consensus on mycorrhizal classification and methodological inconsistencies contribute to conflicting data, emphasizing the need for clearer protocols in identifying and documenting mycorrhizal symbioses. A major shortcoming in mycorrhizal research is the reliance on traditional morphology- and culture-based identification methods, which are ineffective for the vast majority of fungal taxa that produce microscopic fruiting bodies or none at all (Horton & Bruns, 2001; Richard et al., 2005). Moreover, phylogeographic studies have revealed cryptic diversity within EMF lineages, highlighting the importance of high-throughput sequencing techniques for EMF identification (Sato et al., 2012; Tedersoo & Smith, 2013). Limited and biased sampling, along with inconsistent methodologies across studies, have led to gaps in taxonomic coverage, underrepresentation of certain habitats, and difficulty in comparing results across different datasets. This has hindered accurate assessments of fungal distribution, fungal-host associations, and conservation needs. Many fungal species remain undescribed, with estimates suggesting that up to 98% of fungi are still unknown, occupying unique ecological niches that remain unexplored (Taylor et al., 2014). Key gaps remain in our knowledge of the evolutionary and biochemical mechanisms underlying EMF-host associations. Poorly resolved phylogenetic relationships between EMF limit our understanding of host specificity and biogeographic patterns (Rochet et al., 2011). Additionally, 95 the molecular signaling pathways that regulate fungal colonization and host recognition remain largely unexplored (Martin et al., 2016). These uncertainties are especially concerning as climate change alters temperature, precipitation, and soil chemistry, potentially disrupting EM symbioses in unpredictable ways (Pickles et al., 2012; Bazzicalupo et al., 2022). Given these challenges, predictive modeling serves as a powerful tool to mitigate data limitations and improve our capacity to infer EMF-host associations. By leveraging available biogeographic, phylogenetic, environmental, and trait-based data, predictive approaches can generate testable hypotheses about fungal distributions and host specificity, guiding future empirical research. However, further methodological advancements and expanded datasetsâparticularly for host belowground traits and fungal traitsâmay be necessary to refine predictions and enhance their ecological applicability. 3.3 Limitations of the mycorrhizal predictive framework While the framework outlined in this roadmap provides a structured approach for modeling and predicting potential EMF-host associations, several limitations must be acknowledged. A major challenge lies in the availability and quality of data used to assemble association matrices. High-resolution datasets remain scarce, and existing databases suffer from inherent biases and gaps, particularly regarding fungal traits and belowground host traits. Large-scale studies on fungal distribution may overlook smaller-scale variations in host specificity and preference, leading to potential inaccuracies in local or regional predictions. However, there are inherent trade-offs between global and smaller-scale studies. In global studies, sampling intensity is often insufficient, resulting in many associations being missed. Conversely, restricting known associations to those georeferenced within a specific regionâcreating a more 96 manageable association matrixâmay exclude known hosts from other regions or continents that are also present in the area but lack recorded fungal associations. Additionally, fungal compatibility does not always guarantee successful mycorrhizal formation in natural settings, further complicating efforts to model these interactions. Given the fragmented nature of fungal data across multiple sources, the absence of a centralized database continues to hinder comprehensive analyses in mycorrhizal ecology. Another key limitation is the challenge of selecting appropriate predictor variables while minimizing issues of collinearity and data noise. Although this roadmap recommends approximately 73 variables across five predictor themes, evaluating the trade-offs of variable selection and data availability with predictive relevance is important. For some methods, such as BRT and BART, this may not be a problem (Elith et al., 2008). However, if the selected method is sensitive to variable collinearity, applying dimensionality reduction techniques like PCA can help minimize redundancy while retaining explanatory power. Discrepancies in taxonomic classifications and data curation methods across different databases introduce additional uncertainty, particularly when combining multiple datasets. Data imputation, while potentially beneficial for host traits, is not recommended for fungal associations due to the high risk of propagating errors. Beyond these technical considerations, the consequences of incorrect model predictions must also be considered. From a theoretical perspective, inaccurate predictions could reinforce biases in EMF research, misrepresenting patterns of host specificity or fungal biogeography. In applied contexts, incorrect associations could mis-direct sampling strategies or conservation efforts, particularly if the studies or restoration projects rely on predicted potential fungal partners to select focal host species (Hortal et al., 2015). The risks differ depending on the type of error: 97 false positives (incorrectly predicting an association) could lead to misguided sampling and management actions, whereas false negatives (failing to predict a true association) could result in overlooked ecological interactions (Fielding & Bell, 1997). Given these risks, predictive models should be interpreted cautiously, validated with independent datasets and empirical knowledge where possible, and continuously refined as new data become available. Lastly, adherence to Open-Science principlesâensuring transparency in data sources, code availability, and methodological documentationâis critical for improving reproducibility and fostering ongoing refinement of predictive models in mycorrhizal research. 3.4 Recommendations for future research and conservation To improve our understanding of EMF distribution and ecological roles, several key actions are needed. First, targeted fieldwork should prioritize underrepresented regions and host taxa to address geographic and taxonomic sampling biases, which may limit our ability to accurately model EMF-host associations. However, as Vargas et al. (2024) point out, data curation should complement fieldwork for augmenting biodiversity records, as herbaria and other repositories contain vast amounts of underutilized fungal data. A stepwise approach combining data curation with strategically planned field sampling would maximize research efficiency by filling data gaps in both spatial coverage and taxonomic representation. This will ultimately reduce sparsity and sampling biases in the EMF-host associations matrix. Second, a centralized, well-maintained database for mycorrhizal associationsâand fungal traitsâshould be developed to integrate scattered datasets and reduce inconsistencies that currently hinder large-scale modeling efforts. The lack of standardized metadata and definitions leads to mismatches between studies, making it difficult to compare or aggregate findings. 98 Establishing consistent methodologies for collecting and classifying mycorrhizal associations would improve data quality, allowing for more robust predictive models and better generalizability across regions and taxa. Finally, conservation efforts should incorporate fungal diversity into ecosystem management strategies, acknowledging that soil fungi are just as vulnerable to environmental change as plants and animals (Tedersoo et al., 2022). Despite their ecological importance, fungi are often overlooked in conservation planning due to limited knowledge about their diversity, distributions, and functional roles (Bazzicalupo et al., 2022). Addressing these gaps through more informed sampling efforts following this predictive framework will enhance our understanding of EM associations and inform conservation priorities. Understanding how fungi respond to climate change and land-use transformations is also critical given that shifts in host distributions or habitat conditions could disrupt key mycorrhizal networks, with cascading effects on plant communities and ecosystem functioning. 3.5 Significance and contribution of the thesis To my knowledge, this Roadmap represents a novel contribution to the field of mycorrhizal ecology: it is the first study to propose and set the basis for predicting potential EMF-host associations using a structured, integrative approach that synthesizes host traits, fungal traits, phylogenetic relationships, and biogeographic and environmental factors. Some of the concepts presented here have been previously explored in mycorrhizal ecology (e.g., knowledge gaps, fungal distribution predictors) whereas others (e.g., predictor variables and rationales for predicting potential mycorrhizal associations) have not. For the latter, my review of the disease ecology and other relevant literature highlighted their potential applicability to EMF-host 99 predictions. By outlining a framework for systematically evaluating predictor variables and model choices, this work provides a foundation for future studies to refine and expand predictive methodologies in mycorrhizal ecology. Given the logistical constraints of directly sampling EM hosts, predictive models offer a powerful alternative for estimating potential fungal-host associations. Statistical modeling approaches can help narrow the set of plausible fungal-host partnerships by identifying high-likelihood associations to advance the understanding of fungal host breadth at a faster rate. Additionally, soil fungi appear to be highly vulnerable to global change, with land cover transformation, low moisture, and high temperatures significantly altering fungal community composition (Tedersoo et al., 2022). 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Recommended variables Recommended sources Geographic predictor theme (5) Latitude range of host. Longitude range of host. GBIF Elevation range of host. Geographic range size. Range overlap Phylogenetic predictor theme (3-5) Phylogenetic distance (PCoA eigenvectors) V.PhyloMaker2 Abiotic niche predictor theme (54) Aridity index. Evapotranspiration. Meng et al. (2023) Isothermality. Mean diurnal range. Annual mean temperature. Temperature seasonality. Max temperature of the warmest month. Min temperature of the coldest month. Temperature annual range. Mean temperature of the wettest quarter. Mean temperature of the driest quarter. Mean temperature of the warmest quarter. Mean temperature of the coldest quarter. Annual precipitation. Precipitation of the wettest month. Precipitation of the driest month. Precipitation seasonality. Precipitation of the wettest quarter. Precipitation of the driest quarter. Precipitation of the warmest quarter. Precipitation of the coldest quarter. Exchangeable Na. Exchangeable Mg. Exchangeable Al. Exchangeable K. Exchangeable Ca. CaSO4. 135 Recommended variables Recommended sources CaCO3. Total C. Meng et al. (2023) Total N. Total S. Total P. Electrical conductivity. Exchangeable acidity. Base saturation. Soil organic carbon content. Cation exchange capacity. pH. Soil water capacity pF 2.0. Soil water capacity pF 2.3. Soil water capacity pF 2.5. Available water. Coarse. Clay. Bulk density. Sand. Silt. Depth to bedrock. Absolute depth to bedrock. Soil orders. Solar radiation. Water vapor pressure. Wind speed. Biotic niche predictor theme (4+) PCs of the associations matrix. Total number of unique co-occurring hosts. GBIF and V.PhyloMaker2 Total number of unique fungal symbionts associated with co-occurring hosts Phylogenetic diversity of co-occurring hosts. Host traits predictor theme (10) Aboveground Adult plant height (m). TRY Stem specific density (mg mmâ3). Leaf area (mm2). Specific leaf area (g mâ2). N content per unit leaf mass (mg gâ1). Diaspore mass (mg). Belowground Root nitrogen concentration (mg g-1). GRooT Root diameter (mm). Specific root length (m g-1). Root tissue density (g cm-3). Fungal traits predictor theme (0) None None 136 Table A.2 Summary of the presented data sources showing their data content based on the predictor themes. Arranged in alphabetical order by general focus. Data sources Geographic features Phylogenetic features Niche features Host traits Fungal features Abiotic Biotic Aboveground Belowground Mycorrhizal Traits OTUs Associations General databases CHELSA X GBIF X GenBank X HWSD2 X IUCN X SoilGrids 2.0 X WorldClim2 X Plant-focused data sources BIEN X X Bueno et al. X FRED X X X X FungalRoot X X GRooT X Meng et al. X X X MycoDB X TRY X X X V.PhyloMaker2 X Fungi-focused data sources FunFun X FungalTraits X FUNGuild X GlobalFungi X X X UNITE X X X 137 Table A.3 Overview of data sources, detailing their assembly year, update frequency, maintenance status, and versioning. The table also highlights whether each dataset supports community contributions, incorporates expert curation, and specifies the taxonomic framework used. Data sources Assembly year (last updated) Maintained or static Has had updates Current version Community growth Curated Taxonomy General databases CHELSA 2016(22) Maintained Yes v2.1 No Yes N/A GBIF 2008(~) Maintained Yes N/A Yes Yes GBIF Backbone Taxonomy GenBank 1982(~) Maintained Yes N/A Yes Yes NCBI Taxonomy HWSD2 2008(23) Static Yes v2.0 No Yes N/A IUCN ~2015(24) Maintained Yes N/A No Yes ICN SoilGrids 2.0 2017(20) Static Yes v2.0 No Yes N/A WorldClim2 2005(20) Static Yes v2.1 No Yes N/A Plant-focused data sources BIEN 2008(21) Maintained Yes v4.2 No Yes WFO & WCVP Bueno et al. 2018 Static No v1.0 No Yes Flora Europaea FRED 2017(21) Maintained Yes v3.0 Yes Yes TNRS* FungalRoot 2020(23) Static Yes v2.0 Yes Yes GBIF Backbone Taxonomy GRooT 2021 Static No v1.0 No Yes TNRS* Meng et al. 2023 Static No v1.0 No Yes TPL MycoDB 2017 Static No v1.0 No Yes Unknown for plants, Index Fungorum TRY 2007(22) Maintained Yes v6.0 Yes Yes TNRS* V.PhyloMaker2 2019(22) Static Yes v2.0 No N/A TPL, WP, LCVP Fungi-focused data sources FunFun 2018 Static** No v0.0.3 Yes Yes Index Fungorum FungalTraits 2020 Static No v1.0 No Yes TPL & EOL, Index Fungorum FUNGuild 2016 Static** No v1.0 Yes Yes Index Fungorum GlobalFungi 2019(23) Maintained Yes v5.0 Yes Yes Index Fungorum UNITE 2003(24) Maintained Yes v10.0 Yes Yes TPL, Index Fungorum ~ Constant updates. *TNRS includes TPL, GCC, ILDIS, PLANTS and Tropicos. **Assembled to be maintained. 138 Appendix B: Method comparison Table B.1 Comparison of Step 3 methods with their source studies, including applications and analytical approaches. Source Method Model selection Model validation Training dataset Predictive or descriptive Predicts novel associations Available code Network-based methods Dallas et al. (2017a) ACE MPE, APE, and MAPE Cross-validation of predicted vs. observed data Yes Predictive No Yes Dallas et al. (2017c) Plug and Play AUC Cross-validation of simulated dataset predictions vs. empirical dataset predictions Yes Predictive Yes (in-sample) Yes Desjardins-Proulx et al. (2017) k-Nearest Neighbours â Leave-one-out cross-validation No Predictive Yes (in-sample) Yes Stock et al. (2017) Linear Filter â Leave-one-out imputation No Descriptive Yes (in-sample) No Becker et al. (2022) k-Nearest Neighbours AUC (TPTSC) Cross-validation of predicted vs. newly documented data Yes Predictive Yes (in-sample) Yes Becker et al. (2022) Linear Filter AUC (TPTSC) Cross-validation of predicted vs. newly documented data Yes Predictive Yes (in-sample) Yes Becker et al. (2022) Plug and Play AUC (TPTSC) Cross-validation of predicted vs. newly documented data Yes Predictive Yes (in-sample) Yes Becker et al. (2022) Scaled Phylogeny AUC (TPTSC) Cross-validation of predicted vs. newly documented data Yes Predictive Yes (in-sample) Yes Farrell et al. (2022) Scaled Phylogeny AUC K-fold cross-validation Yes Predictive Yes (in-sample) Yes Poisot et al. (2023) Linear Filter + SVD â Internal cross-validation + bias and realism assessments No Predictive Yes (out-of-sample) Yes 139 Source Method Model selection Model validation Training dataset Predictive or descriptive Predicts novel associations Available code Trait-based methods Dallas et al. (2017b) BRT Intrinsic AUC on independent test data + null model comparison Yes Predictive Yes (in-sample) Yes Wells et al. (2018) Logistic Hierarchical Regression (Bayesian) â â No Descriptive No Yes Stephens et al. (2019) GAMM + Jaccard and Corrected Jaccard AIC scores â No Descriptive No No Albery et al. (2020) Neutral Phylogeographic (GAMM) Randomization of fixed and random effects Cross-validation of predicted vs. observed data x1000 times Yes Predictive Yes (in-sample) Yes Albery et al. (2020) LMM â Leave-one-out cross-validation No Descriptive Yes (in-sample) Yes Schatz & Park (2021) BRT Intrinsic Cross-validation of associations in non-native range of host vs. in native range No Descriptive Yes (in-sample) Yes Becker et al. (2022) BRT Intrinsic Cross-validation of predicted vs. newly documented data Yes Predictive Yes (out-of-sample) Yes Becker et al. (2022) BART Intrinsic Cross-validation of predicted vs. newly documented data Yes Predictive Yes (out-of-sample) Yes Becker et al. (2022) Neutral Phylogeographic AUC (TPTSC) Cross-validation of predicted vs. newly documented data Yes Predictive Yes (out-of-sample) Yes Schatz & Park (2023) BRT AUC, PDPs Internal cross-validation (iterative train/test splits) Yes Descriptive No Yes Hybrid methods Becker et al. (2022) Two-Step Kernel Ridge Regression Intrinsic Cross-validation of predicted vs. newly documented data No Predictive Yes (out-of-sample) Yes MPE: Mean Per cent Error. APE: Absolute Per cent Error. MAPE: Mean Absolute Percent Error. ACE: Abundance-based Coverage Estimator. AUC: Area under the curve. TPTSC: Training Prevalence-Test Sensitivity Curve. SVD: Singular Value Decomposition. BRT: Boosted Regression Trees. GAMM: Generalised Additive Mixed Model. AIC: Akaike Information Criterion. LMM: Linear Mixed Model. BART: Bayesian Additive Regression Trees. PDPs: Partial dependence plots.
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Biology
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Pither, Jason
A roadmap to predicting associations between ectomycorrhizal fungi and host plants
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