Quantification of Moss-Associated Cyanobacteria Using Phycocyanin Pigment Extraction
Bibliographic record
Abstract
In the boreal forest, cyanobacteria can establish associations with feather moss and realize the biological nitrogen fixation (BNF) reaction, consisting in the reduction of atmospheric dinitrogen into bioavailable ammonium. In this ecosystem, moss-associated cyanobacteria are the main contributors to BNF by contributing up to 50% of new N input. Current environmental changes driven by anthropogenic activities will likely affect cyanobacteria activity (i.e., BNF) and populations inhabiting mosses, leading to potential important consequences for the boreal forest. Several methods are available to efficiently measure BNF activity, but quantifying cyanobacteria biomass associated with moss is challenging because of the difficulty to separate bacteria colonies from the host plant. Attempts to separate cyanobacteria by shaking or sonicating in water were shown to be poorly efficient and repeatable. The techniques commonly used, microscopic counting and quantitative PCR (qPCR) are laborious and time-consuming. In aquatic and marine ecosystems, phycocyanin (PC), a photosynthesis pigment produced by cyanobacteria, is commonly used to monitor cyanobacteria biomass. In this study, we tested if PC extraction and quantification can be used to estimate cyanobacteria quantity inhabiting moss. We report that phycocyanin can be easily extracted from moss by freeze/thaw disturbance of cyanobacteria cells and can be quickly and efficiently measured by spectrofluorometry. We also report that phycocyanin extraction is efficient (high recovery), repeatable (relative SD < 13%) and that no significant matrix effects were observed. As for aquatic systems, the main limitation of cyanobacteria quantification using phycocyanin is the difference of cellular phycocyanin content between cyanobacteria strains, suggesting that quantification can be impacted by cyanobacteria community composition. Nonetheless, we conclude that phycocyanin extraction and quantification is an easy, rapid, and efficient tool to estimate moss-associated cyanobacteria number.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".