First evidence of nitrogen fixation associated with bryophytes from coastal Wabanaki–Acadian forests
Bibliographic record
Abstract
Associations between bryophytes and dinitrogen (N2)-fixing bacteria are a significant source of exogenous N in unmanaged boreal and possibly temperate ecosystems. However, the extent to which biological N2-fixation (BNF) applies to the boreal–temperate ecotone remains elusive. The current focus on common species limits our understanding of BNF at the community level. Our objective was to characterize the presence of cyanobacteria and BNF activity associated with bryophytes in the coastal forests of Fundy National Park (New Brunswick, Canada). In 2021, we harvested three liverwort and 11 moss species from two sites (71 samples) and measured environmental covariates (e.g., canopy composition and soil pH). We used stable isotope incubations with 15N2 gas in growth chambers to quantify potential BNF activity and used phycocyanin extractions as a cyanobacteria abundance proxy. Many species presented detectable BNF rates, which were similar to or higher than those of well-studied feather mosses. These included species that have rarely been found to contribute to BNF. While cyanobacteria were present on most samples, we found no positive association between abundance and BNF. Our findings are among the first records for bryophyte-associated BNF in the boreal–temperate ecotone of eastern Canada and offer insights into the potential role of this process in N cycling in coastal conifer-dominated forests.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".