Interspecific competition limits the realized niche of <i>Fraxinus nigra</i> along a waterlogging gradient
Bibliographic record
Abstract
Gradient studies of wetland forests have inferred that competition from upland tree species confines waterlogging-tolerant tree species to hydric environments. Little is known, however, about competition effects on individual-tree growth along stress gradients in wetland forests. We investigated tree growth and competition in mixed-species stands representing a waterlogging stress gradient in Fraxinus nigra Marsh. (black ash) forests in Minnesota, USA. Using competition indices, we examined how F. nigra basal area increment (BAI) responded to competition along the gradient and whether competition was size-asymmetric (as for light) or size-symmetric (as for soil resources). We modeled spatial distributions of F. nigra and associated tree species to assess how variation in species mixtures influenced competition. We found that although F. nigra BAI did not significantly differ with variations in site moisture, the importance of competition decreased as waterlogging stress increased. Competition across the gradient was primarily size-asymmetric (for light). Variation in species mixtures along the gradient was an important influence on competition. Some segregation of tree species occurred at all but the most upland site, where waterlogging stress was lowest and evidence of competition was greatest, confirming that competition from upland tree species confines F. nigra and potentially other waterlogging-tolerant species to hydric environments.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".