Conifer epiphytic phyllosphere bacterial communities respond more strongly to rain exclusion and host species identity than to soil water content
Bibliographic record
Abstract
With global warming, the frequency and intensity of drought episodes are projected to increase worldwide, especially in the boreal forest. This represents a serious threat to the boreal forest ecosystem’s productivity and environmental services. It is thus crucial to better understand how drought or water limitation could affect boreal forest ecosystems functioning, and to be prepared to overcome damage caused by drought events. Studies suggest that microbes may mitigate the negative effects of drought or water shortage on plants. However, most of these studies focused on soil microbes and on agricultural ecosystems. Here, we used a rainout shelters and soil irrigation experimental design to study the response to rain exclusion and soil water content of epiphytic phyllosphere bacterial communities associated with four boreal conifer tree species. Our results showed only a weak response of phyllosphere bacterial communities to variation in soil water content. On the other hand, host tree species identity and rain exclusion were the main drivers of epiphytic phyllosphere bacterial communities’ structure and diversity. This suggests that fewer rain events, in the context of climate change, would impact boreal trees phyllosphere microbiome composition. • Rain exclusion has a strong influence on tree phyllosphere bacterial communities. • Tree host species identity is a driver of phyllosphere bacterial communities. • Soil water content has weak effects on tree phyllosphere bacterial communities. • Rainout shelters can contribute to alter phyllosphere bacterial communities. • Lower frequency of rain events can modify tree phyllosphere bacterial communities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".