Tree species and earthworm effects on soil nutrient distribution and turnover in a northeastern United States common garden
Bibliographic record
Abstract
Differences in soil nutrients beneath different tree species are often attributed to the impacts of species-level patterns of nutrient uptake and litter chemistry. However, in naturally established forests it is difficult to isolate tree species' influence on soil development from differences in underlying soil properties that can affect tree species establishment. To discern the impacts of tree species on soil properties, we investigated how Norway spruce (Picea abies (L.) H. Karst.), red oak (Quercus rubra L.), and sugar maple (Acer saccharum Marshall) influence the distribution of carbon, nitrogen, and calcium in a 67-year-old common garden. We expected these species would produce foliar litter with contrasting chemistry, resulting in corresponding variation in organic matter (OM) turnover and nutrient accumulation in soils. Instead, we found that forest floor mean residence time correlated negatively with earthworm density and did not correlate with any measurement of litter chemistry. Red oak exhibited the fastest OM turnover and highest earthworm densities and Norway spruce showed the greatest OM accumulation and fewest earthworms. These findings suggest that future changes in earthworm invasion and forest tree species composition may have strong implications for ecosystem nutrient cycling and retention.
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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 | 0.001 |
| 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".