New insights into the drivers of moss‐associated nitrogen fixation and cyanobacterial biomass in the eastern Canadian boreal forest
Bibliographic record
Abstract
Abstract Moss‐associated cyanobacteria nitrogen (N 2 ‐) fixation can contribute to support moss growth and constitutes a major source of new N in boreal forest ecosystems. The biomass of moss‐colonizing cyanobacteria and their N 2 ‐fixation are usually considered linearly correlated. Yet, recent evidence has shown that cyanobacterial biomass and N 2 ‐fixation can be decoupled, suggesting that they are not necessary affected by the same environmental and ecological drivers. Climate and nutrients were reported as affecting moss‐associated N 2 ‐fixation, with equivocal results, whereas drivers of moss cyanobacterial biomass remain unclear. In addition, these drivers are often determined through manipulative experiments (e.g. fertilization and incubation) and remain to be validated with complementary observational studies to help us better understand future impacts of global change on the moss–cyanobacteria symbiosis. We hypothesized that moss‐associated cyanobacterial biomass is controlled in situ by factors affecting bacterial growth, whereas N 2 ‐fixation is controlled by factors affecting enzymatic reactions. Using random forests, Spearman correlations and linear mixed‐effects models, we determined the main drivers of cyanobacterial biomass and N 2 ‐fixation of two feather moss species, which were collected over 3 years along a 1000‐km latitudinal transect in the eastern Canadian boreal forest. We found that temperature, precipitation and phosphorus were the main positive drivers of moss cyanobacterial biomass and that temperature and molybdenum were the main positive drivers of N 2 ‐fixation. Vanadium was a negative driver of N 2 ‐fixation, suggesting the use of alternative nitrogenases by cyanobacteria. Both cyanobacterial biomass and N 2 ‐fixation were strongly influenced by the moss species and were negatively correlated with moss C:N stoichiometry, highlighting the role of N 2 ‐fixation in moss N enrichment. Synthesis . We identified for the first time some environmental drivers of moss‐associated cyanobacterial biomass and showed that they contrast with the drivers of N 2 ‐fixation, which should be considered in further research and confirmed in other experimental settings. This is an important advance in our knowledge of moss–cyanobacteria associations, which would greatly help in better predicting the impacts of global change on this symbiosis and on nitrogen inputs in boreal forest ecosystems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".