A Metataxonomic Analysis of Maple Sap Microbial Communities Reveals New Insights Into Maple Syrup Complexity
Bibliographic record
Abstract
Maple syrup, an emblematic food product of Canada is produced from the concentration of sap collected from maple trees during spring. During this season, the trees come out of dormancy, which modifies sap composition. Meanwhile, microorganisms that contaminate sap as it is collected can also modify its composition. As these two factors can impact the quality of maple syrup, we aimed to better understand how microbial communities vary along dormancy release. We estimated the absolute abundance of bacteria and fungi in maple sap along a dormancy release index using high-throughput amplicon sequencing and digital droplet PCR (ddPCR). Several members were identified as indicators of maple sap composition, syrup organoleptic conformity and color, some of which are also hubs in the microbial association networks. We further explored bacterial communities by performing a predictive functional analysis, revealing various metabolic pathways correlated to dormancy release. Finally, we performed an experimental investigation of maple sap carrying capacity and limiting nutrients along dormancy release and found that maple sap composition variation influences its carrying capacity. Taken together, our results indicate that an increase in nitrogen supply in the form of allantoate combined with possible metabolite excretion could lead microbial communities towards different paths. Indeed, we observed a greater heterogeneity during late dormancy release which in turn could explain the variation in maple syrup quality. Further experimental investigation into the contribution of microbial, vegetal, environmental, technological, and processing factors to the final composition of maple syrup will be required to improve our understanding of this complex and flavorful food matrix and to develop quality control strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".