Spatial variability in soil nutrient availability in an oak-pine forest: potential effects of tree species
Bibliographic record
Abstract
Established species have been shown to affect soil nutrient availability, but the effects of "native invasive" species on soil nutrient availability are relatively unknown. Oak-dominated forests in the eastern deciduous forest are dynamic in their species composition, with increasing dominance of red maple (Acer rubrum L.) in the midstory and overstory. We hypothesized that higher quality red maple litter within a litter matrix dominated by oaks would accelerate N turnover, increase nutrient availability in the soil, and result in a thinner and less massive O horizon. We examined nutrient availability in soils under three overstory tree species (Quercus prinus L., A. rubrum, and Pinus echinata Mill. or Pinus rigida Mill.), under a shrub (Vaccinium spp.), and in locations without tree stems ("no tree"). Ex tract able nutrients (P, K, Mg, Ca) and total and available N were quantified in the O horizon and upper mineral soil at 0.5 m and 1.0 m from the base of individual trees or from the center of Vaccinium and no-tree locations. Despite low lignin concentration in red maple litter and low lignin/N ratio, the lowest N mineralization rates were found in red maple microsites; the highest N mineralization rates were found under oak. Extractable cations were generally highest under red maple and lowest under pines, and red maple had the highest levels of total N (but not NO3or NH4) in the upper mineral soil. Shifting species composition towards red maple and away from pines in these forests may alter nutrient cycling by increasing surface soil cation availability, but reducing soil N mineralization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".