Effects of nutrient addition on vegetation and carbon cycling in an ombrotrophic bog
Bibliographic record
Abstract
Abstract We measured net ecosystem CO2exchange (NEE), plant biomass and growth, species composition, peat microclimate, and litter decomposition in a fertilization experiment at Mer Bleue Bog, Ottawa, Ontario. The bog is located in the zone with the highest atmospheric nitrogen deposition for Canada, estimated at 0.8–1.2 g N m−2 yr−1(wet deposition as NH4and NO3). To establish the effect of nutrient addition on this ecosystem, we fertilized the bog with six treatments involving the application of 1.6–6 g N m−2 yr−1(as NH4NO3), with and without P and K, in triplicate 3 m × 3 m plots. The initial 5–6 years have shown a loss of firstSphagnum, thenPolytrichummosses, and an increase in vascular plant biomass and leaf area index. Analyses of NEE, measuredin situwith climate‐controlled chambers, indicate that contrary to expectations, the treatments with the highest levels of nutrient addition showed lower rates of maximum NEE and gross photosynthesis, but little change in ecosystem respiration after 5 years. Although shrub biomass and leaf area increased in the high nutrient plots, loss of moss photosynthesis owing to nutrient toxicity, increased vascular plant shading and greater litter accumulation contributed to the lower levels of CO2uptake. Our study highlights the importance of long‐term experiments as we did not observe lower NEE until the fifth year of the experiment. However, this may be a transient response as the treatment plots continue to change. Higher levels of nutrients may cause changes in plant composition and productivity and decrease the ability of peatlands to sequester CO2from the atmosphere.
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".