Long-term low-level nutrient additions significantly impact a low arctic mesic tundra plant community, but species responses differ from high-level fertilization: Implications for predicting climate warming impacts
Bibliographic record
Abstract
Arctic climate warming is expected to enhance the soil’s supply of growth-limiting nutrients, resulting in changes in plant community composition. Much of our understanding of nutrient influences on tundra plants is derived from experiments with very high levels of fertilization. Here, aboveground biomass of all species in a mesic birch hummock community, and shoot extension of Betula glandulosa, were measured in response to 11-19 years of factorial annual low-level nitrogen (N) and phosphorus (P) additions that simulate climatically realistic anticipated increases in soil fertility. Only the low N addition treatment had significant effects, enhancing the mosses and some infrequent vascular species’ biomass, reducing others, while the dominant shrubs were unaffected. Hence, overall community composition was altered, but in markedly different ways than classic high-level fertilization responses. By contrast, when measured in much larger sampling areas (9 m2), birch shrub new shoot extension was clearly stimulated by the separate low-level N and P additions, and even more so by their combination, indicating that its growth was NP co-limited. Overall, our results demonstrate that this tundra plant community is sensitive to low-level (climatically-realistic) increases in nutrient availability, and suggest that changes will be slow (multiple decades), and likely to favour species whose growth is primarily N-limited.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".