Fertilizer regime impacts on abundance and diversity of soil fauna across a poplar plantation chronosequence in coastal Eastern China
Bibliographic record
Abstract
Soil fauna are critical for ecosystem function and sensitive to the changes of soil fertility. The effects of fertilization on soil fauna communities, however, remain poorly understood. We examined the effects of fertilization form and quantity on the abundance, diversity and composition of soil fauna across an age-sequence of poplar plantations (i.e., 4-, 9- and 20-yr-old) in the coastal region of eastern China. We found that the effects of fertilization on faunal abundance, diversity, and composition differed among stand ages. Organic fertilizers increased the total abundance of soil fauna, whereas low level inorganic fertilizers imparted increases only in the 4- and 9-yr-old stands. The number of faunal groups did not change with fertilization, but Shannon's and Margalef diversity indices increased under low level organic fertilization, and decreased under inorganic fertilization in the 9- and 20-yr-old stands. Community composition of soil fauna differed strongly with fertilization and stand age. The changes in soil fauna were strongly associated with the changes in microbial biomass carbon, dissolved organic carbon and nitrogen, and available phosphorus and potassium. Our findings suggest that the responses of soil fauna to fertilization may be mediated through the fertilization effects on soil nutrient availability.
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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 | 0.001 | 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".