​Understanding Phytobiomes

A phytobiome is commonly defined as a plant, its environment, and associated organisms and viruses, and taking a phytobiome research approach is an excellent way to look at plants as complex (eco)systems (Beans 2017; Young and Kinkel 2018). There is currently much work needed to address aspects of the vision of the Phytobiomes Roadmap (Young and Kinkel 2018) and extend beyond the foundations already laid. While phytobiome-related work happened well before the 21st century, recent advances in technology and computational power make it possible to research the phytobiome much more intensely and extensively than before. We present a new framework to improve understanding of phytobiomes, an actual classroom test using this model, and potential applications to plant pathology. In this article, the target audience for learners is introductory undergraduate or graduate students learning plant pathology at the university level for the first time. However, the ideas and framework could be adjusted for more advanced university learning and also for K-12 classrooms.

PEAI Model

"All models are wrong, but some are useful" (George Box, 1919–2013)

Phytobiome research is complex, and as more data becomes available, it is important to use frameworks to make sense of all the data in meaningful and thoughtful ways. Models can help phytobiome researchers and classroom learners achieve such an aim. In this article, we propose a framework to be explicit about phytobiomes—the plant(s), environment(s), associated organisms and/or viruses, and interaction(s) (PEAI) model (Table 1). The PEAI model includes scale (taxonomy, space, and time) (Ladau and Eloe-Fadrosh 2019), structure and part of whole, hypotheses and predictions, additional frameworks, and assumptions, as well as five T's (techniques, training, teamwork, technology, and tools). The model is simple at the outset, and learners using this model can add layers of complexity to suit the level of detail they need to understand holistically their phytobiome system(s) and subsequently compare then to other phytobiome systems.

Table 1. Plant(s), environment(s), associated organisms and/or viruses, and interaction(s) (PEAI) model table
Classroom test, scaffolded example (already filled in), which had the prompt "Take what you have learned, or will learn (see later in course!) and see Table 2, and fill in your own table."

Note: CEC is cation exchange capacity; GLoBI is Global Biological Interactions; five T's refers to techniques, training, teamwork, technology, and tools.​

First, the PEAI model framework includes plant(s) ("P"), which focuses on learners identifying the characteristics of a plant or multiple characteristics of multiple plants. The P component anchors the PEAI model and relates to the phytobiome as a biome, accounting for the fact that phytobiome landscapes have phenotypic plasticity, evolutionary taxonomy, and, over generations, adaptation by natural selection. Just as global biomes have a taxonomy that serves to help in understanding underlying drivers, such as effects of soil age on ecosystem structure and function across biomes (Delgado-Baquerizo et al. 2020), plants have a taxonomy grounded in their evolution and phylogenetic relationships. To help with synthesis, learners can indicate part(s) of the whole for the plant(s). We suggest learners report the structure, especially the habit of the plant (whatever number of descriptors are deemed useful), and four P parts of the whole to enable description and consistency. In other words, learners can provide the plant "habit" for level one structure (e.g., shrub or tree-like, succulent, grass, grass-like, nongrass herbaceous, vine, or liana). For level two structure, learners can report part(s) of the whole: 1) shoot or shoot-like parts (leaves, modified leaves); 2) root or root-like parts (roots, rhizomes, aerial roots); 3) dead or senescing parts (heartwood, bark); and 4) reproductive parts and propagules (flowers, fruits, sporangia, spores, seeds).

Second, the environment ("E") is the abiotic environment(s) associated with the phytobiome. E includes the commonly discussed broader scale environment, and even smaller scale, such as the environment on the scale of the plant landscape (Hunter 2016). We suggest learners report the same four E components to enable consistency: 1) lithosphere or solid (soil, tree); 2) hydrosphere or liquid (soil water, stem flow, precipitation [rain, mist, snow], hydroponics if indoors); 3) atmosphere or gas (wind); and 4) energy (light, heat). With these broad categories, additional information relating to structure, such as physical or chemical properties, can be provided as notes to supplement the characteristics.

Third, the associate(s) ("A") refers to the organism(s) and/or virus(es) 1) in; 2) on; as well as 3) interacting (regularly or transiently) with the plant(s). Scientists' understanding of associates is rapidly improving and growing and for now we suggest, until systematics and taxonomy are improved, that learners use the proposed categories. Learners can use three categories at level one: 1) prokaryotes; 2) eukaryotes; 3) nonliving sequenceable elements (e.g., viruses). Then, they can use eight categories at level two: A) bacteria; B) archaea; C) fungi; D) plants; E) animals; F) protists; G) viruses; and H) other associates. As our understanding moves from pattern to function, functional information can be incorporated with taxonomy, such as number of RNA sequences upregulated or downregulated with gene expression, as well as additional categories based on function, such as nitrogen fixers or fungal pathogens, as functional annotation with the taxonomy for all associates.

Fourth, the interaction ("I") refers to the plant(s)–environment(s)–associate(s) interaction(s). In this category, it is especially important for learners to think about implicitly assumed and explicitly measured interactions, as well as complex system properties (such as emergent properties) and predictions (e.g., will a system show an additive effect or a nonlinear interaction). When involving species interactions, we recommend inputting into and using descriptors from the Global Biotic Interactions (GloBI) repository, such as "pollinated by," "host of," etc. (Poelen et al. 2014; https://www.globalbioticinteractions.org). GloBI is an extensible, open-source service tailored to import, search, and export species-interaction data (Poelen et al. 2014). Using the same terminology and inputting information into such a repository allows for synthesis across phytobiome systems.

All the above parts have scales and structures. One of the key aspects of the PEAI model is to be more explicit to aid teaching and research. For example, as mentioned in Table 1, the taxonomic scale for P is species, bean plants (Phaseolus vulgaris L.). The plant structures of each bean plant are shoots at spatial and temporal scales of 0–5 cm and 0–10 min, respectively. Being explicit helps with understanding the types of conditions and the limitations for studies; for example, a study of the leaf microbiome will have different conditions and limitations than a study comparing microbiomes across different plant organs (roots vs. shoots). For spatial and temporal scales of all model components (P to I), we suggest thinking carefully about their relevant range (e.g., 0–1 cm, daily).

With so much data and literature available, it is crucial to draw on various disciplines to help with synthesis, especially to understand holistic systems such as phytobiomes. Our PEAI model can be used explicitly to help learners with hypothesis generation and testing, reading the literature, creating databases to catalog work, meta-analyses, and other forms of synthesis. The model helps learners critically evaluate their assumptions and clarify what is implicit or explicit in their learning, as well as their or others' research, note relevant frameworks useful for phytobiome(s), and work toward understanding phytobiomes as more than the sum of their parts. Learners will be well served by the five T's. Techniques that promote learning and holism are helpful for learning about the phytobiome (e.g., systems thinking diagramming). Training is useful in cutting-edge botany, molecular biology, and environmental data collection. Teamwork is promoted by open science, open data initiatives, and participation in classroom discussions, as well as extracurricular opportunities in diverse, mixed-career stage (early to late career) phytobiome working groups. Technology is relevant for learners who are proficient in high-performance computing for big data, data science, and data analysis and synthesis. Tools, depending on costs and availability, could include remote sensing, local environmental sensors, drones, and adaptable growth systems.

In Table 1, the last four columns are for hypothesis and prediction, discipline, assumptions and the five T's. These columns help learners note their specific hypotheses and predictions for the P, E, A, and I parts of the system under consideration. These four columns are important because they force learners to be explicit about what they are thinking about: what is going on in their systems to formulate testable research questions; the disciplines from which they are forming these ideas; the assumptions they are making relevant to their research questions; and the five T's relevant to these research questions.

Classroom Test

The PEAI table was used, by some of the authors, in fall 2022 as part of a class exercise in an upper-level undergraduate tree biology course at the University of Victoria, BC, Canada. The main focus of the classroom test was to foster student engagement and discussion of phytobiome research and to facilitate the creativity and critical thinking of the students. As the students in groups filled in and discussed the PEAI tables, instructors were circulating, providing feedback, asking questions, and gauging student learning.

In this course, the PEAI table and activity resulted in students creating hypotheses and investigating their underlying assumptions in an organized fashion. These learners were asked to take knowledge they had already obtained in the course and look at an example PEAI table (Table 1) to create hypothetical experiments and put the components into a blank PEAI table (Table 2). During this activity, students had to work their way through their ideas in a manner consistent with higher levels of Bloom's taxonomy as applied to biology (Crowe et al. 2008). Such learning can result in metacognition in the biological sciences, which moves learners beyond memorization to formulating their own novel ideas and synthesis applicable in the classroom and beyond as life-long learners.

Table 2. Plant(s), environment(s), associated organisms and/or viruses, and interaction(s) (PEAI) model table (blank)

Note: Five T's refers to techniques, training, teamwork, technology, and tools.​

The exercise was done in groups of students with feedback from themselves (introductory learners) and from the authors (biological sciences educators and researchers). Feedback was given using direct responses, as well as questioning by the authors. This meant constant discussion and scaffolded feedback as the students filled in the table in groups, creating a more authentic form of feedback conducive to higher-level thinking skills, such as reflective thinking skills important for 21st century learners.

Students engaged readily and quickly using the PEAI table and scaffolding created by the instructors. Students made decisions and then discussed these intelligently, as the PEAI framework lent itself very well to focused debate, both among one another in a group, as well as between instructor and students. Furthermore, students were asked to return to their decisions and reflect on them while adhering to the PEAI framework. The framework dispelled ambiguity given its clear structure. We noticed that students could more easily correct their interpretations and answers. The final test of the efficacy of the framework was whether students could complete the table within a designated period (30 min) during a 50-min class. This was accomplished. True intellectual success was measured by whether students were able to generate reasonable and testable hypotheses after having thoroughly discussed their completed table. We were very pleased with how the framework's hierarchy allowed all students, including the normally reticent, to puzzle their way through a phytobiome. The result was a better quality in their responses, i.e., intellectually superior answers compared with what we normally get using more standard question and answer methods.

Our university classroom observations were similar to when the PEAI model was used with the general public for phytobiomes exploration in a park. Again, these learners (this time adults, youths, and children), like the university students, were able to work together with facilitators to make observations and propose their own research questions. In this case, testing focused on the park as a study system and transect surveying of phytobiomes of understory plants and overstory trees. Both the university students and general public learners showed deep reflective thinking when using the PEAI model. Mohamad and Tasir (2023), in their study of reflective thinking by graduate students in an education course, stress that deep reflective thinking can be fostered under the right conditions, whereby learners are able to be conscious of their own thinking regarding what they know already, what is unknown, and how to reflect on these to solve novel problems.

Examples with Direct Applications to Plant Pathology

Three hypothetical examples using the PEAI framework that help illustrate applications to plant pathology follow: 1) scaffolding from reading a recent tomato bacterial speck disease research article as background for a lab course; 2) conducting citizen science research of beech leaf disease during a tree walk in a course; and 3) forest microbiome research as part of a directed studies course. All these examples, and others in plant pathology, could be implemented at the undergraduate or graduate level, with modifications to adjust for the level of mastery. In addition to the main text, we have included supplemental materials that provide three additional filled-out PEAI tables (Appendix).

Tomato Bacterial Speck Disease

Ehau-Taumaunu and Hockett (2023) conducted research in which they manipulated, via multiple approaches and transfers, the tomato phyllosphere microbiome and observed how it then impacted the presentation of bacterial speck disease. Before a lab session involving inoculating tomato leaves with bacteria and assessing disease, students as a preassessment activity could read this research article and fill in the PEAI model (Table 2). Then, in lab they could discuss their work with their peers, teaching assistants, and professors to increase their understanding of experimental design, research assumptions, and limitations. Those teaching the lab section also could draw a schematic to help the students visualize the parts and interactions. This approach of using the PEAI model along with reading, discussion, writing, and drawing helped those facilitating the lab with reflective practice and promoting universal design for learning (Super et al. 2021). Tomato is a plant commonly used in research, so students also could easily connect their understanding gleaned from using the PEAI model to assess and use other tomato literature in their lab reports after the lab session, such as recent work on the tomato phyllosphere regarding the involvement of priority effects in the assembly of microbial communities (Debray et al. 2023). Supplemental Table S1 (Appendix) is a completed table related to this example.

Beech Leaf Disease

To understand beech leaf disease, students going to areas with the disease could learn first-hand the importance of plant pathology detection and potentially contribute citizen science data during a class tree walk. Beech leaf disease is an emerging forest epidemic in the United States (Carta et al. 2020; Ewing et al. 2019; Kantor et al. 2022). Beech leaf disease is caused by the nematode Litylenchus crenatae subsp. mccannii (Anguinata) (Carta et al. 2020; Kantor et al. 2022). The disease has symptoms such as leaf interveinal darkening, as well as leaf shape alteration and thickening; mature trees can have thinner crowns and branch dieback; and trees can die within 7 years of beech leaf disease detection (Carta et al. 2020). At the start of the walk, the students could fill in the PEAI table (Table 2), printed on weather-proof paper, if necessary, to assist them in thinking about what they can directly and indirectly measure. They also could score disease and take photographs and when back in the classroom add more to the PEAI table and brainstorm ways that plant pathologists are dealing with this and other forest epidemics. Supplemental Table S2 (Appendix) is a completed table related to this example.

Forest Microbiome

With respect to researching the forest microbiome with a focus on current and future forest health, an upper-level honors undergraduate or introductory graduate course could use the PEAI model table to help track student understanding at the beginning, middle, and end of the course. Forest health worldwide is nearing a tipping point, especially with respect to nonnative pathogens and insects (Williams et al. 2023). Furthermore, climate change is predicted to favor fungal pathogens over mycorrhizal fungi (Baldrian et al. 2023). On the first day of the course, the participants could meet with the instructor and, without any other resources, take 10 min to attempt to fill in the PEAI table (Table 2). The exercise then could be revisited in the middle of the course as a formative assessment and at the end with a final report for summative assessment. Supplemental Table S3 (Appendix) is a completed table related to this example.

Conclusions

This focus article provides a new framework, the PEAI model, and a PEAI table to apply this model in multiple situations related to the phytobiome, which has many applications in plant pathology learning. The skills taught with this approach will not only help introductory learners and researchers, but also help set the foundation for more advanced learning and research.​

Acknowledgments

L. Super led the conceptualization and writing of this perspective, with feedback from all coauthors. L. Super and P. von Aderkas did the classroom test. L. Super thanks the National Science and Engineering Council of Canada (NSERC) for Ph.D. scholarship funding, CGS-D3 in Ecology and Evolution, and the University of British Columbia for other awards. We also thank Alexander Young for phytobiomes discussions. L. Super thanks Jessica Lowry and others at the Centre for Scholarly Communication for drafting and writing suggestions.​

Conflicts of interest

No authors had conflicts of interest.

Funding

No funding was provided for writing this article.​

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