Seedling growth responses to light and mineral N form are predicted by species ecologies and can help explain tree diversity
Bibliographic record
Abstract
Tree species distributions and diversity could be explained by rank changes in performance over multiple spatiotemporal resource gradients, i.e., resource partitioning. For 14 species planted in 45 harvest gap and closed canopy locations in a mesic northern hardwood forest community, Michigan, USA, we asked the following questions: (i) are species growth responses to light, nitrogen (N), or N form (ammonium vs. nitrate) related to their ecological distributions and phylogenies? and (ii) is there evidence of growth-based resource partitioning over measured resource gradients? Growth responses to the N form were consistent with both differences in uptake energy requirements between N forms and their availability through succession and across fertility gradients; height growth was negatively related to the species shade-tolerance score, especially in high light, i.e., shade-intolerant species responded to soil nitrate-N and shade-tolerant species responded to ammonium-N; fertile soil associated species responded to nitrate-N and infertile soil associated species to ammonium-N; and gymnosperms responded to ammonium-N and angiosperm responses varied. Modeled growth responses to resources showed only modest evidence for rank changes over resource gradients, with N contributing less to rank changes than light. Thus, growth responses to resources were accurately predicted by species ecology and (or) phylogeny; however, there was only modest support for the notion that growth-based resource partitioning underlies community-scale diversity in a northern hardwood forest.
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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 | 0.000 | 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".