Tree seedling growth, survival, and morphology in response to landscape-level variation in soil resource availability in northern Michigan
Bibliographic record
Abstract
To investigate causes of tree species distributions across soil resources in northern Michigan, we conducted a seedling transplant experiment with five species showing different site affinities: Acer saccharum Marsh. (sugar maple), Prunus serotina Ehrh. (black cherry), and Fraxinus americana L. (white ash), which are associated with high-fertility mesic moraine; Quercus rubra L. (red oak), associated with intermediate sites; and Quercus velutina Lam. (black oak), associated with low-fertility droughty outwash sites. Seedlings were planted in plots stratified across variation in light and soil nutrient and water availability. After one growing season, under 14%27% canopy openness, species tended to trade off between high survival on outwash versus high relative growth rate of root + stem mass (RGRrs) on moraine. The high survivorship of black and red oak on outwash was associated with greater root and whole-plant mass in comparison with sugar maple, white ash, and black cherry. High RGRrs on high-fertility moraine for these latter species was associated with high fine root area per unit whole-plant mass and plasticity to increase specific root area in response to increased soil resources. We did not detect a similar survivalgrowth trade-off for seedlings grown at lower light (3%10%) on intermediate versus high-fertility sites. Overall, these results suggest that species distributions across soil resource gradients can in part be explained by a trade-off between tolerance of low soil resources versus competitive ability (i.e., growth) under high soil resources.
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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".