Soil freezing and N deposition: transient vs multi‐year effects on plant productivity and relative species abundance
Bibliographic record
Abstract
Plant responses to increased atmospheric nitrogen (N) deposition must be considered in the context of a rapidly changing climate. Reductions in snow cover with climate warming can increase the exposure of herbaceous plants to freezing, but it is unclear how freezing damage may interact with increased N availability, and to what extent freezing effects may extend over multiple years. We explored potential interactions between freezing damage and N availability in the context of plant productivity and relative species abundance in a temperate old field using both snow removal and mesocosm experiments, and assessed the legacy effects of the freezing damage over 3 yr. As expected, N addition increased productivity and freezing damage decreased productivity, but these factors were nonadditive; N addition increased productivity disproportionately in the snow removal plots, whereas extreme freezing diminished N addition responses in the mesocosm experiment. Freezing altered relative species abundances, although only the most severe freezing treatments exhibited legacy effects on total productivity over multiple growing seasons. Our results emphasize that while both increased N deposition and freezing damage can have multi-year effects on herbaceous communities, the interactions between these global change factors are contingent on the intensities of the treatments.
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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".