Desiccation as a suitable alternative to cold-storage of phyllosphere samples for DNA-based microbial community analyses
Bibliographic record
Abstract
The study of microbial communities of the plant phyllosphere in remote locations using DNA-based approaches is limited by the challenges associated with their preservation in the field and during transportation. Freezing is a common DNA preservation strategy, but it may be unsuitable for leaf samples, or inaccessible in some locations. Other methods such as desiccation, ethanol or commercial preservatives are potential alternative DNA preservation methods for ambient temperature storage. In this study, we assessed the efficacy of desiccation (with silica gel packs), and of three preservation solutions (95% ethanol, RNAlater, LifeGuard) for the preservation of epiphytic phyllosphere communities of Populus tremuloides and Picea glauca at ambient indoor temperature (21 °C) for up to three weeks. We assessed effects on DNA concentration and quality and used metabarcoding to detect changes in bacterial and fungal communities between treatments over time. A secondary study was conducted on leaves of Populus grandidentata to further test the ability of the desiccation treatment to resolve differences between sampling sites. Silica gel packs were identified as effective ambient temperature preservative of phyllosphere bacterial and fungal communities. There were some changes in the communities compared to immediate extraction due to this treatment, but these changes did not affect the ability to distinguish tree species and sampling locations. Overall, our study supports the use of silica gel pack short term preservation at ambient temperature for phyllosphere samples intended for DNA-based microbial community analyses.
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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) | 0.001 | 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 | 0.001 |
| 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".