Microbial community composition is linked to Sphagnum acclimation to warming
Bibliographic record
Abstract
Peatlands store about third of the terrestrial carbon (C) and exert long-term climate cooling. Dominant plant genera in acidic peatlands, Sphagnum mosses, are main contributors to net primary productivity. Through associative relationships with diverse microbial organisms (microbiome), Sphagnum mosses control major biogeochemical processes, namely uptake, storage and potential release of carbon and nitrogen. Climate warming is expected to negatively impact C accumulation in peatlands and alter nutrient cycling, however Sphagnum-dominated peatland resilience to climate warming may depend on Sphagnum-microbiome associations. The ability of the microbiome to rapidly acclimatize to warming may aid Sphagnum exposed to elevated temperatures through host-microbiome acquired thermotolerance. We investigated the role of the microbiome on Sphagnum’s ability to acclimate to elevated temperatures using a microbiome-transfer approach to test: a) whether the thermal origin of the microbiome influences acclimation of Sphagnum growth and b) if microbial benefits to Sphagnum growth depend on donor Sphagnum species. Using a full-factorial design, microbiomes were separated from Sphagnum “donor” species from four different peatlands across a wide range of thermal environments (11.4-27°C). The microbiomes were transferred onto germ-free “recipient” Sphagnum species in the laboratory and exposed to a range of experimental temperatures (8.5 – 26.5°C) for growth analysis over 4 weeks. Normalized growth rates were maximized for plants that received a microbiome from a matched “donor” and with a similar origin temperature (ΔTtreatment-origin: 0.3±0.9°C [±standard error], p = 0.73). For non-matched “donor-recipient” Sphagnum pairs, ΔTtreatment-origin was slightly negative with -4.1±2.1°C (p = 0.06). The largest growth rate of the “recipient” was measured when grown with a microbiome from a matching “donor” Sphagnum species and was 252% and 48% larger than the maximum growth rate of the germ-free Sphagnum and the non-matched “donor-recipient” Sphagnum pairs, respectively. Our results suggest that the composition of the Sphagnum microbiome plays a critical role in host plant temperature acclimation. We found that microbially-provided benefits to the host plant were most pronounced when: 1) the thermal origin of the microbiome is similar to experimental temperatures, and 2) when donor and recipient Sphagnum species are the same. Together, these results suggest that Sphagnum temperature acclimation can be modulated, in part, by microbial interactions and may potentially play a role in peatland resilience to climate warming.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".