The temperate forest phyllosphere and rhizosphere microbiome: a case study of sugar maple
Bibliographic record
Abstract
, Marshall) and its microbial communities are important for tree fitness, growth, and establishment. Despite recent progress in our understanding of the rhizosphere and phyllosphere microbial communities of sugar maple, many outstanding knowledge gaps remain. This review delves into the relationships between sugar maple and its microbes, as climate change alters plant species distributions. It highlights the multifaceted roles of key microbes, such as arbuscular mycorrhizal (AM) fungi and pathogens, in affecting the distribution and establishment of sugar maple in novel habitats. Furthermore, this review examines how microbial communities in different compartments contribute to tree fitness. Finally, it explores how microbial dispersal and altered species interactions under changing environmental conditions can affect sugar maple's ability to migrate beyond its current range, emphasizing the different scenarios associated with such shifts. In the rhizosphere, AM fungi are known for their roles in nutrient acquisition and improving stress tolerance. Yet, key questions remain about how these fungi interact with other microbes, how soil chemistry and climate change alter these interactions, and how the presence of beneficial microbes influences sugar maple's establishment. Additionally, the role of dark septate endophytes (DSE) in sugar maple's fitness remains underexplored, emphasizing the need for more research on their diversity and functions. In the phyllosphere, microbial communities are subject to shifts due to rising global change, with potential impacts on sugar maple's fitness. These changes may influence the tree's resistance to pathogens, tolerance to environmental stress, and overall health. Yet, our understanding of these interactions relies mostly on short-read sequencing methods targeting marker genes (e.g., 16S, ITS, 18S), which often fail to identify microbes at the species level. Limitations in molecular techniques and poor microbial reference databases hinder our ability to fully characterize tree-associated microbial diversity and functions. Future research should thus prioritize advanced molecular tools such as shotgun, hybrid, or long-read sequencing. Controlled experiments are also needed to establish causal links between sugar maple fitness and microbial communities, and to study whether microbial communities change throughout the tree's lifespan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".