Open Collections

Abstract

Fungal endosymbionts of plants, including fungal pathogens and endophytes, can infect living plants and contribute to ecological dynamics, either directly via interactions with other microbes or indirectly via inducing physiological changes to the plant, which may affect its fitness. Fungal pathogens are recognized as being important in the process of non-native plant invasion. However, little is known about the role of endophytes. I review the literature to assess how fungal endophytes contribute to non-native plant invasion and conclude that fungal endophytes play diverse and context-dependent roles that are better understood by adopting an invasion stage-specific framework (Chapter 2). To understand how fungal endosymbionts impact host plants and their communities more broadly, it is critical to first understand their specific plant compatibilities, which are not well-characterized. Abundant host range data are present in the descriptive fields of some fungal biodiversity occurrence records, although these data are often unstandardized and difficult to access due to inconsistent, diverse, and/or unclear language and formatting used in describing the plant host–fungal interaction. I present a framework for cleaning and filtering occurrence data of fungi on plant hosts and construct a fungal endosymbiont-plant species interaction database (Chapter 3), which provides a baseline summary of recorded plant–fungi associations. Additional fungal associations may be present as genome bycatch in plants. I develop a bioinformatics pipeline to detect and taxonomically classify fungi from plant genomes (Chapter 4). I detect several previously unrecorded associations; nonetheless, the majority of fungal infections remain cryptic and novel host jumps may occur with species translocation. Link prediction methods allow for the identification of unknown or future endosymbiotic interactions among plants and fungi. I apply an affinity-based link prediction model on seed plants and fungal endosymbiont data sourced from biodiversity records (Chapter 5). This model is strongly informed by sampling bias and I recommend model improvement strategies that limit computational complexity. Improved plant–fungal interaction data will aid in understanding the physiological, ecological, and evolutionary factors governing fungal infection and spread across plant species. I recommend future sampling efforts targeting under-sampled geographical areas and an iterative process of link prediction and empirical evaluation.