Bryophyte species differ widely in their growth and N<sub>2</sub>-fixation responses to temperature
Bibliographic record
Abstract
Bryophytes are abundant in tundra ecosystems, where they affect carbon and nitrogen cycling through primary production and associations with N2-fixing bacteria. Bryophyte responses to climate warming are inconclusive, likely because species-specific responses are poorly understood. Here we investigated how warming affects the growth and nitrogenase activity of 10 tundra bryophyte species in two tundra landscapes. Collected bryophyte samples were grown in temperature-controlled growth chambers for 12 weeks at five temperatures from 3 to 18 °C. We measured growth, N concentration, δ15N, and δ13C after 3 months and nitrogenase activity after 5 and 12 weeks. Bryophyte growth and associated nitrogenase activity generally increased with temperature, but species differed in their optima. Bryophyte N concentration and δ15N indicated that, for some species, increased N2-fixation could not compensate for growth-induced N limitation. High landscape coverage and large positive warming effects on feather mosses and Sphagnum species highlight their competitive advantages, confirm earlier field observations, and contribute to the mechanistic understanding of differential bryophyte growth in response to warming. We suggest that indirect effects of climate change, such as surface drying and shrub expansion, are likely main threats to slow-growing bryophytes across the Arctic, with consequences for biodiversity and C balance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".