Molybdenum threshold for ecosystem scale alternative vanadium nitrogenase activity in boreal forests
Bibliographic record
Abstract
Biological nitrogen fixation (BNF) by microorganisms associated with cryptogamic covers, such as cyanolichens and bryophytes, is a primary source of fixed nitrogen in pristine, high-latitude ecosystems. On land, low molybdenum (Mo) availability has been shown to limit BNF by the most common form of nitrogenase (Nase), which requires Mo in its active site. Vanadium (V) and iron-only Nases have been suggested as viable alternatives to countering Mo limitation of BNF; however, field data supporting this long-standing hypothesis have been lacking. Here, we elucidate the contribution of vanadium nitrogenase (V-Nase) to BNF by cyanolichens across a 600-km latitudinal transect in eastern boreal forests of North America. Widespread V-Nase activity was detected (∼15–50% of total BNF rates), with most of the activity found in the northern part of the transect. We observed a 3-fold increase of V-Nase contribution during the 20-wk growing season. By including the contribution of V-Nase to BNF, estimates of new N input by cyanolichens increase by up to 30%. We find that variability in V-based BNF is strongly related to Mo availability, and we identify a Mo threshold of ∼250 ng·g lichen −1 for the onset of V-based BNF. Our results provide compelling ecosystem-scale evidence for the use of the V-Nase as a surrogate enzyme that contributes to BNF when Mo is limiting. Given widespread findings of terrestrial Mo limitation, including the carbon-rich circumboreal belt where global change is most rapid, additional consideration of V-based BNF is required in experimental and modeling studies of terrestrial biogeochemistry.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".