Modelling the distribution of plant-associated microbes with species distribution models

Abstract

1 INTRODUCTION

Plants interact intimately with diverse microorganisms including bacteria, fungi, archaea, protists and viruses that inhabit both external and internal plant tissues (Berendsen et al., 2012; Fitzpatrick et al., 2020). These plant–microbe interactions comprise a range of symbiotic relationships including parasitism, commensalism, competition and mutualism, playing a pivotal role in shaping plant health and development (Pattnaik et al., 2021; Vandenkoornhuyse et al., 2015). While certain plant-associated microbial groups such as mycorrhizal fungi, phytopathogens and nitrogen-fixing bacteria have been extensively studied over the last century, it was not until recently that the remarkable taxonomic and functional diversity of the plant-associated microbiota has been revealed by the advent of high-throughput sequencing techniques. Over the past decades, there has been a growing consensus on the importance of plant-associated microbiota for plant diversity, productivity, biogeography and indeed nearly every aspect of plant ecology and evolution (Dastogeer et al., 2020; Delavaux et al., 2019; Hawkes et al., 2020). Moreover, plant-associated microbiota impact socio-economically important ecosystems, such as agroecosystems and forests, that are directly related to human well-being (Fisher et al., 2012; Thirkell et al., 2017). In this context, understanding the drivers of the diversity, distribution and function of plant-associated microbiota has been considered key questions in ecology and evolution, as well as in plant sciences, forestry and agronomy.

Anthropogenic environmental changes—including climate change, nutrient deposition, land-use change and the introduction of invasive species—influence the diversity and distribution of plant microbiota both directly (e.g. by altering temperature) and indirectly (e.g. through altering plant traits or host species distributions) (Trivedi et al., 2022). These impacts are expected to prompt a rapid shift in the composition and function of plant-associated microbiota, given the shorter generation time and higher sensitivity of microbes to stresses compared to plants and animals (Cavicchioli et al., 2019). The alteration of the plant microbiota, on the one hand, represents a potential catastrophic risk to ecosystem health and human well-being. For example, the increased impact of phytopathogens under climate change could potentially endanger food supplies for 10%–60% of the world's population and cause billions of dollars of economic damage annually (Bebber et al., 2014; Fisher et al., 2012). On the other hand, engineering the plant microbiota represents a promising approach to mitigate the negative effects of climate change on plants, for instance by enhancing plant resistance and acclimatisation to abiotic and biotic stresses (Angulo et al., 2022).

Despite the critical role of the plant microbiota in mediating the effect of global change on plants and ecosystems—either accelerating or mitigating depending on the context and the microorganisms that are present—they have seldom been the focus of climate change studies and are almost always overlooked in policy development. A major challenge that prevents us from harnessing the full power of plant-associated microbiota is our shallow understanding of the response of microbes to environmental change (Cavicchioli et al., 2019). Therefore, there is an urgent need to develop process-oriented understanding and predictive modelling of plant-associated microbial distribution at large geographic scales. While recent studies have initiated investigations into the global diversity and biogeography of plant microbiota, that is, the distribution of community diversity and composition (Li et al., 2023; van der Linde et al., 2018; Wang et al., 2023), we are lacking a general framework to predict the geographic distribution of individual plant-associated microbial taxa.

This article presents a conceptual framework to address the challenges associated with developing process-oriented predictive models for the distribution of plant-associated microbiota. We begin by reviewing the use of species distribution models (SDMs) and their extensions, such as joint SDMs, for free-living microbes and host-associated parasites. We argue that although these SDMs have offered insights into modelling the distribution of host-associated microbes, they are not readily applicable to plant microbiota for reasons that we detail below. We then identify the major limitations of current SDM frameworks in modelling plant-associated microbiota including (1) challenges in integrating host-related biotic factors into microbial SDMs, (2) difficulties in handling microbial sequencing data and (3) the evolutionary potential of microbes. To overcome these limitations, we first propose different strategies for incorporating host information into microbial SDMs. These range from simply incorporating host taxa as static ‘environmental’ variables into microbial SDMs, to increasingly complex models such as nested SDMs and joint SDMs of plant–microbe interactions, which are further demonstrated using a case study on plant leaf-associated bacteria. We then discuss potential solutions for the compositional and highly dimensional nature of microbial sequencing data, as well as the hierarchical data structure and the challenges in collecting standardized microbial datasets. Lastly, we extend our discussion to the potential influence of microbial niche evolution on SDMs, proposing possible avenues for further exploration. Overall, the aim of this article is to initiate new discussions on SDM-based frameworks for modelling the distribution of plant-associated microbiota.