Different Responses and Links of N:P Ratio Among Ecosystem Components Under Nutrient Addition in a Temperate Forest
Bibliographic record
Abstract
Abstract Nitrogen (N) and phosphorus (P) deposition have increased rapidly during the past decades, which likely changes soil N and P availability. These soil resource variations will further affect N, P concentration and N:P ratio in different ecosystem pools (i.e., soil, leaf, litter, root, and microbe). Various pools may show different stoichiometric responses to nutrient enrichment, with a further influence on ecosystem nutrient cycling. However, few studies have been conducted to fully examine the stoichiometric responses of different pools and their nutrient relationships in a given ecosystem. Here we established a 2‐year experiment of N (10 g m−2 year−1), P (10 g m−2 year−1), and combined N + P addition in a temperate forest of Changbai Mountain. We found significantly different N:P stoichiometric responses among various ecosystem components under P addition, with the leaves showing a higher response than litter and root while microbe behaving the lowest response. The responses of N:P ratio to N + P addition were similar with those under P addition in all pools. In most cases, N addition did not significantly affect N:P ratio. These results indicate that N:P ratio response was mainly determined by changes in P rather than N concentration in this temperate forest ecosystem. Moreover, we found tighter N:P stoichiometric correlations than elements among diverse ecosystem components under nutrient addition. Overall, our research reveals different responses and tight links of element stoichiometric variations among various ecosystem components in face of nutrient enrichment. It calls our attention to considering stoichiometric changes in the whole ecosystem beyond individual plant organ or microbial component.
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 | 0.001 | 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) | 0.000 | 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".